investment performance of islamic versus conventional...
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INVESTMENT PERFORMANCE OF ISLAMIC
VERSUS CONVENTIONAL MUTUAL FUNDS:
EVIDENCE FROM MALAYSIA
FADILLAH MANSOR Bachelor of Shariah (honours) (major in economics) – University of Malaya
MBA (major in finance) – University of Malaya
A thesis submitted in total fulfilment of the requirements for the degree of
Doctor of Philosophy
Department of Finance La Trobe Business School
Faculty of Business, Economics and Law La Trobe University, Bundoora
Melbourne, Victoria 3086 Australia
November 2012
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STATEMENT OF AUTHORSHIP
Except where reference is made in the text of the thesis, this thesis contains no
material published elsewhere or extracted in whole or in part from a thesis submitted
for the award of any other degree or diploma.
No other person’s work has been used without due acknowledgement in the main text
of the thesis.
All research procedures which require ethical approval reported in this thesis were
approved by the relevant Ethics Committee.
This thesis has not been submitted for the award of any degree or diploma in any
other tertiary institution.
FADILLAH MANSOR
11 NOVEMBER 2012
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DEDICATION
This thesis is dedicated to my husband, Zainal, to my mother, Rakiah, to my late
father, Mansor, and especially to my children, Farah, Yasmin, Imran, Nadia and
Fareez, in recognition of their support, sacrifice, tears and endless love throughout my
life.
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ACKNOWLEDGEMENTS
Many people have been involved in this ‘sweet and sour’ PhD journey and to them I
express my sincere appreciation. I would like to give my special gratitude to my PhD
supervisors for their kindness and friendship that I can thank you enough. I am
extremely indebted and grateful to my principal supervisor, Associate Professor Dr
Ishaq Bhatti, for his invaluable guidance, assistance, motivation, advice and
continuous support during my PhD study. I would also like to record my sincere
thanks and deep gratitude to my second supervisor, Dr Hayat Khan, for his ideas,
invaluable assistance in the computation and analysis parts using EViews and Stata,
and his cooperation and help and throughout the supervision.
I am indebted to many other individuals who provided suggestions, assistance and
friendship at different stages throughout my PhD journey. Special thanks to Dr Robin
Luo, my ex co-supervisor, and La Trobe University grant 2008 for providing me
access to the data from the Morningstar database. I really appreciate his assistance and
kindness, especially in the data collection and early computation stage during my first
year of the study. I am also indebted to Professor Mohamed Ariff for significant input,
comments and genuine interest in my work.
I would also like express my sincere appreciation to Professor Michael Skully,
Professor Munawar Iqbal, Professor Andrew Worthington and participants of the 14th
Banking and Finance Conference 2009 at the University of Melbourne for suggestions
and comments at the beginning of this study. Thanks also go to Professor Zia Haqq,
Professor Robert Clift and participants of the 13th International Business Research
Conference 2010 in Melbourne for ideas and suggestions. I also thank Professor
Leighton Vaughan Williams and participants of the 6th International Money,
Investment and Risk 2011 at Nottingham, UK, for ideas and suggestions.
Appreciation also goes to Professor Abdullah Saeed, Professor Mervyn Lewis,
Professor Shamsher Mohamad and participants in Ethics in Financial Transactions
and Society: The Way Forward 2011 at Melbourne for their interest and helpful
comments. Thanks also go to Professor Ali, Professor Loredana Ureche Rangau and
participants in the Global Finance Conference 2012 at Chicago for useful comments
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and suggestions. I would also like to express my sincere thanks to Dr Laszlo Konya
and Dr John Shahnon for consultation and assistance with EViews, Stata and other
econometrics matters.
My PhD student colleagues whom I met along this journey are worthy of special
mention. Kak Hada, Lin, Sally, Kak Sham who are always there every time I need
help, Azwan, Wahida, Faridah, Azida, Fahmida, Shima, Azni, Nani, Rami, Mas, Wan
and many more, in particular my roommate, Nga, and my ex-roomates, Sabeha, Dr
Laura, Dr Tasha, Dr Ros and Dr Angela, thanks to all of you for invaluable
friendship. My grateful thanks also go to my neighbours, Tashi and Reme, for their
helping hands to me and my family during our hard times. Sincere appreciation to my
close friends back home, Yati, Kak Na and Zan, for prayers, assistance and never-
ending motivation. My appreciation also to Head of Department and staff of the
Department of Finance (formerly the School of Economics and Finance), La Trobe
University, for providing the necessary facilities and resources needed to work on my
thesis. Thanks also to the University of Malaya and the Ministry of Higher Education
Malaysia for granting a scholarship and study leave to pursue this study. Extended
thanks also go to Phillip Thomas and Annie Ryan for editing and proofreading my
thesis according to the Australian Standards for Editing Practice (Standards D and E).
Undertaking this PhD would have been very hard without the help, motivation, love
and never-ending support from my hubby, Zainal, and my lovely kids, Farah, Yasmin,
Imran, Nadia and Fareez. I owe my life to them, and deep gratitude for cheering me
up and giving my life meaning. Deep gratitude is also extended to my mum, brothers
and sisters, my big family and in-laws for their prayers, support and encouragement,
especially during my hard times. Last but not least, I thank Allah, the Almighty, for
giving me a good health, success and strength, spiritually and physically, to complete
this tougher task.
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TABLE OF CONTENTS
........................................................................................................ PAGE STATEMENT OF AUTHORSHIP ............................................................................ ii
DEDICATION ......................................................................................................... iii
ACKNOWLEDGEMENTS ...................................................................................... iv
TABLE OF CONTENTS .......................................................................................... vi
LIST OF ABBREVIATIONS ................................................................................... ix
LIST OF TABLES .................................................................................................. xii
LIST OF FIGURES ................................................................................................ xiv
LIST OF APPENDICES ......................................................................................... xiv
LIST OF CONFERENCE PRESENTATIONS ........................................................ xv
LIST OF PUBLICATIONS ................................................................................... xvii
ABSTRACT OF THESIS ..................................................................................... xviii
CHAPTER 1- INTRODUCTION .......................................................................... 1
1.1 Introduction ................................................................................................... 1
1.2 Issues and motivation of the thesis ................................................................. 4
1.3 Objectives of the study ................................................................................... 9
1.4 Contributions of the study ............................................................................ 10
1.5 Structure of the thesis ................................................................................... 13
CHAPTER 2 - LITERATURE REVIEW AND BACKGROUND OF ISLAMIC MUTUAL FUND INDUSTRY ............................................................................. 16
2.1 Introduction ................................................................................................ 16
2.2 Background of the Islamic mutual fund industry .......................................... 19
2.3 The development of the Malaysian mutual fund industry .............................. 23
2.4 IMFs versus CMFs ....................................................................................... 32
2.4.1 Salient features of IMFs .................................................................. 34
2.5 Islamic finance and Islamic investments ....................................................... 37
2.5.1 Fundamentals of Islamic finance ..................................................... 37
2.5.2 Islamic finance and the principle of Islamic investments ................. 40
2.5.3 Islamic finance and the impact of the global financial crisis ............ 42
2.6 Theoretical framework for mutual fund performance .................................... 46
2.6.1 Performance measurement against market benchmark .................... 46
2.6.2 Market timing expertise of fund managers ...................................... 49
2.6.3 Performance persistency ................................................................. 52
2.6.4 Empirical evidence on fees and fund attributes on performance ...... 54
2.6.5 Previous studies on ethical and Islamic funds .................................. 62
2.7 Summary ..................................................................................................... 78
CHAPTER 3 - RESEARCH METHODOLOGY ................................................ 79
3.1 Introduction ................................................................................................. 79
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3.2 Variables ...................................................................................................... 79
3.2.1 Dependent variables....................................................................... 79
3.2.2 Independent variables ..................................................................... 80
3.3 Hypotheses development .............................................................................. 81
3.4 Model specifications and the methodologies................................................. 83
3.4.1 Descriptive statistics and mean pair t-test ........................................ 83
3.4.2 Standard risk adjusted performance measures ................................. 84
3.4.3 The models ..................................................................................... 88
3.4.4 Time series regression analysis ....................................................... 95
3.4.5 Panel data regression analysis ........................................................ 96
3.5 Econometric estimation issues .................................................................... 101
3.5.1 Data stationary and test of normality ............................................. 101
3.5.2 Heteroskedasticity and positive serial correlation .......................... 102
3.5.3 Multicollinearity problem ............................................................. 102
3.6 Summary ................................................................................................... 103
CHAPTER 4 - RISK AND RETURN PERFORMANCE ANALYSIS ............ 105
4.1 Introduction ............................................................................................... 105
4.2 Issues and related studies ........................................................................... 106
4.3 The data and sample selection .................................................................... 109
4.3.1 Survivorship bias .......................................................................... 111
4.4 Results and discussions .............................................................................. 112
4.4.1 Descriptive statistics of IMFs and CMFs ...................................... 112
4.4.2 Test of normality .......................................................................... 115
4.4.3 Covariance and correlation analysis .............................................. 116
4.4.4 Risk-aversion analysis ................................................................. 118
4.4.5 Trend analysis ............................................................................. 121
4.4.6 Mean differences between IMFs and CMFs .................................. 123
4.4.7 Non-risk-adjusted performance of the funds and the crises ............ 126
4.4.8 Results for risk-adjusted return performance measurements .......... 129
4.4.9 CAPM performance analysis and the crises .................................. 132
4.5 Summary ................................................................................................... 135
CHAPTER 5 - MARKET TIMING EXPERTISE AND FUND SELECTIVITY SKILL: TIME SERIES DATA ANALYSIS ...................................................... 137
5.1 Introduction ............................................................................................... 137
5.2 Issues and the significance of the chapter ................................................... 138
5.3 Data sample ............................................................................................... 142
5.4 Results for and discussions of the market benchmark ................................. 144
5.4.1 Single CAPM performance analysis.............................................. 144
5.4.2 CAPM multiple benchmarks performance analysis ....................... 147
5.5 Results for and discussion of the market timing .......................................... 150
5.5.1 Market timing expertise and fund selectivity skill ......................... 150
5.5.2 Performance analysis on market timing and asset classes .............. 155
5.5.3 Correlation between market timing and fund selectivity skill ........ 159
5.5.4 Funds diversification .................................................................... 160
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5.6 Summary ................................................................................................... 162
CHAPTER 6 - MARKET TIMING EXPERTISE AND FUND SELECTIVITY SKILL: PANEL DATA ANALYSIS .................................................................. 164
6.1 Introduction ............................................................................................... 164
6.2 Data sample ............................................................................................... 164
6.3 Results and discussions on panel data ......................................................... 165
6.3.1 Single factor CAPM performance ................................................. 165
6.3.2 Market timing performance based on TM model ........................... 170
6.3.3 Multi-factor CAPM performance .................................................. 173
6.3.4 Market timing performance based on extended TM model ............ 176
6.4 Summary ................................................................................................... 179
CHAPTER 7 – FEES IMPACT AND FUND ATTRIBUTES ON EQUITY MUTUAL FUNDS PERFORMANCE ............................................................... 181
7.1 Introduction ............................................................................................... 181
7.2 Related literatures on fees and the fund attributes ....................................... 182
7.3 Data sample ............................................................................................... 185
7.3.1 Multicollinearity ........................................................................... 189
7.4 Results and performance analysis ............................................................... 192
7.4.1 Descriptive statistics on fund samples and fund attributes ............. 192
7.4.2 Single factor OLS regression ........................................................ 198
7.4.3 Market timing expertise and fund selectivity skill ......................... 200
7.4.4 Results of panel data using FEs and REs on TM model ................. 202
7.4.5 Fees and other fund attributes on single factor regression analysis 205
7.4.6 Fees and other fund attributes on multi-factor regression .............. 210
7.5 Summary ................................................................................................... 225
CHAPTER 8 - SUMMARY AND CONCLUSION ........................................... 228
8.1 Background of the thesis ............................................................................ 228
8.2 Summary of the thesis ................................................................................ 229
8.3 Key findings .............................................................................................. 238
8.3.1 Risk and returns performance ....................................................... 238
8.3.2 Expertise in market timing and fund selectivity skill ..................... 240
8.3.3 Fees impact and fund attributes on fund performance ................... 241
8.3.4 New improved model and extended literatures .............................. 242
8.4 Implications of this study ........................................................................... 246
8.4.1 Implications for policy-makers and regulators .............................. 248
8.4.2 Implications for fund management companies and fund managers 250
8.4.3 Implications for investors ............................................................. 251
8.4.4 Implications for researchers .......................................................... 253
8.5 Limitations ................................................................................................. 253
8.6 Suggestions for future research .................................................................. 254
APPENDICES ....................................................................................................... 256
REFERENCES ...................................................................................................... 269
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LIST OF ABBREVIATIONS
ADF Augmented Dickey-Fuller
Adj R2 adjusted returns
AEFs all equity funds
AIC Akaike Information Criteria
AFC Asian financial crisis
AMFs all mutual funds
AUM asset under management
APEC Asia-Pacific Economic Cooperation
AR Appraisal ratio
ASR adjusted Sharpe ratio
BNM Bank Negara Malaysia (Central Bank of Malaysia)
BPLM Breusch and Pagan LM test
CAGR compounded annual growth rate
CAPM capital asset pricing model
CDOs collateralised debt obligations
CDS credit default swaps
CEFs conventional equity funds
CIC Capital Issues Committee
CMFs conventional mutual funds
CMP capital market plan
CRSP Center for Study of Security Prices
CV coefficient of variation
CVAR coefficient variance decomposition
DEA data envelope analysis
DJIM Dow Jones Islamic Market
DJSI Dow Jones Sustainability Index
Diff. Different
dTYPE a dummy variable, written as 1 if the Islamic fund or 0 if the
conventional fund.
DW Durbin-Watson
ETFs exchange traded funds
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FEs fixed effects
FIMM Federation of Investment Managers Malaysia (formerly known
as Federation of Malaysian Unit Trust Managers [FMUTM])
GCC Gulf Cooperation Council
GDP gross domestic products
GFC global financial crisis
ICI investment company institute
ICM Islamic capital market
IEFs Islamic equity funds
IMFs Islamic mutual funds
JA Jensen alpha
JB Jarque-Bera
KLCI Bursa Malaysia Kuala Lumpur composite index
KLIBOR Kuala Lumpur interbank rate
KLSE Kuala Lumpur Stock Exchange or Bursa Malaysia
KLSE’s MC Bursa Malaysia market Capitalization
KLSE small-cap KLSE Malaysian small-cap index
KLSI Kuala Lumpur Syariah Index
Kt kurtosis
MF mutual fund
M2 Modigliani-Modigliani measure
MSCI Morgan Stanley Capital International World Index
MYR Malaysian Ringgit
NAVs net asset values
OLS ordinary least squares
PLS profit and loss sharing
REs random effects
rf risk free rate
ROC registrar of companies
SC Securities Commission of Malaysia
SEC US Securities and Exchange Commission
SFR single factor regression
SIRCA Securities Industry Research Centre of Asia-Pacific
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Sk skewness
SR Sharpe ratio
SRI socially responsible investment
Std. Dev standard deviation
TFP total factor productivity
TI Treynor index
TM model Treynor Mazuy model
TNA total net assets
UK United Kingdom
US United States of America
VIF variance inflation factor
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LIST OF TABLES
Table 1.1: Structural framework of the thesis ........................................................... 15
Table 2.1: The main differences between IMFs and CMFs ....................................... 33
Table 2.2: Main differences between Islamic finance and conventional finance ....... 39
Table 2.3: Summary of some previous empirical evidence on fund performance ...... 68
Table 4.2: The IMFs and CMFs monthly returns performance: summary statistics . 113
Table 4.3: Summary statistics of IMFs and CMFs non-risk-adjusted returns in relation
to the AFC and the GFC ........................................................................................ 115
Table 4.4: Covariance and correlation between IMFs, CMFs and the market portfolio
.............................................................................................................................. 117
Table 4.5: Overall average returns and standard deviations for Malaysian IMFs and
CMFs, January 1990 – April 2009: Regression results ........................................... 119
Table 4.6: Results of the unit root tests .................................................................. 123
Table 4.7: Results of mean t-test assuming equal variances for IMFs and CMFs .... 125
Table 4.8: Non-risk-adjusted return performance of the mutual funds .................... 127
Table 4.9: Fund performance based on risk-adjusted return measurements ............. 130
Table 4.10: CAPM performance analysis and the crises ......................................... 132
Table 5.1: CAPM analysis of the portfolios against the conventional and Islamic
benchmarks ........................................................................................................... 145
Table 5.2: CAPM performance analysis based on multiple benchmarks ................. 149
Table 5.3: Market timing expertise of IMFs and CMFs fund managers .................. 152
Table 5.4: Comparative market timing analysis for the TM and extended TM models
.............................................................................................................................. 154
Table 5.5: Comparative market timing analysis by asset class for TM and extended
TM models ............................................................................................................ 157
Table 5.6: Correlation between fund selectivity and market timing ........................ 159
Table 5.7: Diversification level of mutual funds ..................................................... 161
Table 6. 1(a): Single CAPM analysis using panel data REs GLS regressions ......... 167
Table 6. 1(b): Single CAPM analysis using panel FEs regressions ......................... 168
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Table 6. 2(a): Market timing expertise of IMFs and CMFs fund managers using GLS
REs regression. ...................................................................................................... 171
Table 6. 2(b): Market timing expertise of IMFs and CMFs fund managers using FEs
.............................................................................................................................. 172
Table 6. 3(a) Multi-factor CAPM analysis using panel data REs GLS (within)
regression .............................................................................................................. 174
Table 6. 3(b): Multi-factor CAPM analysis using panel FEs (within) regression .... 175
Table 6.4(a) Market timing analysis using panel data REs GLS (within) regression 177
Table 6. 4(b): Market timing analysis using panel FEs (within) regression ............. 178
Table 7. 2: Description on mutual fund samples and the fund attributes ................. 194
Table 7. 3:Descriptive statistics of IEFs, CEFs and AEFs relative to market and risk
free portfolios. ....................................................................................................... 195
Table 7.4: Mean test of statistical differences between IEFs, CEFs and AEFs,
comparative to market and risk free portfolios, based on panel data ....................... 197
Table 7. 5: Results of pooled OLS using single factor regression model ................. 198
Table 7. 6: Results of market timing ability based on pooled OLS panel analysis ... 201
Table 7. 7: Panel data regression results using FEs and GLS REs .......................... 203
Table 7. 8: Cross sectional analysis of returns versus fees and other fund attributes
based on single factor panel REs regression. .......................................................... 207
Table 7.9: Fees and fund attributes on gross returns of AEFs, 1990–2009. ............. 212
Table 7. 10: Fees and fund attributes on IEFs returns performance, 1990–2009 ..... 218
Table 7. 11: Fees and fund attributes on returns of CEFs, 1990–2009. ................... 220
Table 8. 1: Summary of the results ......................................................................... 236
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LIST OF FIGURES
Figure 2.1: Islamic funds by domicile of clients, 2007 and 2011 .............................. 21
Figure2.2: Assets of Islamic funds by geographic mandate, 2007 and 2011 ............. 22
Figure 2.3: Growth of the Malaysian mutual fund industry, 1992–2012 (February) .. 24
Figure 2.4: Growth in numbers of the Malaysian mutual fund industry, 1992–2012
(February)................................................................................................................ 29
Figure 2.5: Scatter plot of the ratio percentage of the annual NAV for IMFs and CMFs
portfolios to the total NAV of the industry and to KLSE market capitalisation, from
1993 to 2012. ........................................................................................................... 31
Figure 4.1: IMFs and CMFs graphs of normality, January 1990 to April 2009 ....... 116
Figure 4.2: The Scatter Plot for the Malaysian Islamic and Conventional mean returns
versus their average standard deviation (in percentage), January 1990 to April 2009.
.............................................................................................................................. 120
Figure 4.3: The relationship between the aggregate return performance of IMFs and
CMFs relative to the market portfolio .................................................................... 121
Figure 4.4: The trend pattern for the return of the IMFs and CMFs portfolios relative
to the market portfolio, January 1990 to April 2009 ............................................... 122
LIST OF APPENDICES
Appendix A Descriptive statistics of the Islamic and Conventional mutual funds in
Malaysia, 1992 to February 2012. .......................................................................... 256
Appendix B Year on year changes in the IMFs and CMFs, 1999-2012 .................. 258
Appendix C The differences between Islamic and Conventional financial products 259
Appendix D GFC and the major events .................................................................. 266
Appendix E CAPM analysis against Islamic and conventional benchmarks ........... 267
Appendix F TM model for market timing expertise of the fund managers .............. 268
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LIST OF CONFERENCE PRESENTATIONS
1. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “The Performance of the Islamic Mutual Funds: Malaysian Example”. Paper presented at the International Symposium of Islamic Banking and Finance at the InterContinental Melbourne, The Rialto, organised by La Trobe University, National Australia Bank (NAB) and the Muslim Community Corporation of Australia (MCCA) on 6 July 2009.
2. Mansor, Fadillah (2009). “Investment in the Islamic Mutual Funds: The Malaysian Performance”. Paper presented at Internal Workshop, organised by School of Economics and Finance, La Trobe University on 27 August 2009.
3. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “The Performance of Islamic
Mutual Funds: The Malaysian Case”. Paper presented at the 14th Banking and Finance Conference, organised by Financial Services Institute of Australasia (Finsia) and Melbourne Centre for Financial Studies at University of Melbourne, on 28–29 September 2009.
4. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “Islamic Unit Trusts
Development: Evidence from the Malaysian Unit Trusts Industry”. E-proceeding, paper presented at the 13th Annual Waikato Management School Student Research Conference, organised by University of Waikato at Hamilton, New Zealand, on 20 October 2009.
5. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “Developments in Islamic
Finance: Case Study on Islamic Mutual Funds”. Paper presented at the Islamic Finance Australia Conference 2010 at the Rendezvous Hotel, Melbourne, Australia on 9 June 2010.
6. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “The Performance of the Islamic
Mutual Funds in Malaysia: Risk and Return Analysis”. Paper presented at the 13th International Business Research Conference at the Novotel Hotel on Collins, Melbourne, Australia on 22–24 November 2010.
7. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “Investment Performance of the
Islamic Mutual Funds: A Case Study on the Selected Fund Managers’ Companies in Malaysia – A Proposal”, Paper presented at the Malaysian Students LTU Round Table, organised by La Trobe University Postgraduate Association on 23 November 2010.
8. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Islamic Mutual Fund
Performance: New evidence on Market Timing and Stock Selectivity”. Proceeding paper presented at the 2011 International Conference on Economics and Finance Research at Singapore on 26–28 February 2011.
9. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Islamic Mutual Fund
Performance: Evidence on Market Timing and Stock Selectivity”. Paper
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presented at the 2011 Second Foundation of Islamic Finance Conference at Kuala Lumpur, Malaysia, on 8–9 March 2011.
10. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “Islamic Mutual Fund
Performance for Emerging Market, during Bullish and Bearish: The Case of Malaysia”. Paper presented at the 2nd International Conference on Business and Economic Research at Langkawi, Malaysia, on 14–16 March 2011.
11. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Investment Performance of
the Islamic Mutual Funds in the Period of 1996–2009”. Paper presented at the 6th International Money, Investment and Risk at Nottingham Trent University, Nottingham, UK, on 3–5 April 2011.
12. Mansor, Fadillah, Bhatti, M. Ishaq and Abd Rahman, Nor Hadaliza (2011). “The
Performance of Islamic Mutual Funds: A Comparative Study”. Paper presented at the International Conference on Economics and Finance at Izmir, Turkey, on 15–17 April 2011.
13. Bhatti, M. Ishaq and Mansor, Fadillah (2011). “Impact of Fees on Ethics-based
and Conventional Funds: Do Ethics and Fees Make a Difference to Performance? Paper presented at Ethics in Financial Transactions & Society: The Way Forward at University of Melbourne, Australia, on 17–18 September 2011.
14. Mansor, Fadillah, Bhatti, M. Ishaq and Ariff, Mohamed (2012). “New Evidence
of the Impact of Fees on Mutual Fund Performance of Two Types of Funds”. Paper presented at the Global Finance Conference, 19th Annual Meeting, Chicago, IL, USA, on 23–25 May 2012.
15. Mansor, Fadillah and Bhatti, M. Ishaq (2012). “Islamic Mutual Funds
Performance: A Panel Analysis”. Paper presented at the 2nd Malaysian Postgraduate Conference (MCP2012), Bond University, Gold Coast, Queensland, on 7–9 July 2012.
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LIST OF PUBLICATIONS
Journals
1. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “Risk and Return Analysis on
Performance of the Islamic Mutual Funds: Evidence from Malaysia”, Global
Economy and Finance Journal, Vol. 4, No. 1, pp. 19-31.
2. Mansor, F. (2010). “Developments in Islamic Banking: The Case of Pakistan” – By M. Mansoor Khan and M. Ishaq Bhatti. Asian Politics & Policy, Vol. 2, No. 2, pp. 301–303. doi: 10.1111/j.1943-0787.2010.01194.x (Book Review).
3. Mansor, Fadillah. (2009). “The Islamic Finance Position and the Global Crisis”. Dialogue Asia-Pacific, No. 21, pp. 12-14.
Conference Proceedings
1. Mansor, Fadillah and Bhatti, M. Ishaq (2012). “Islamic Mutual Funds Performance: A Panel Analysis”. Proceedings of the 2nd Malaysian Postgraduate Conference (MPC2012), Bond University, Gold Coast, Queensland, Australia, 7–9 July 2012, pp.140-154.
2. Mansor, Fadillah, Bhatti, M. Ishaq and Ariff, Mohamed (2012). “New Evidence
of the Impact of Fees on Mutual Fund Performance of Two Types of Funds”. E-Proceedings of 2012 Global Finance Conference, 19th Annual Meeting, Chicago, IL, USA, 23–25 May 2012, p. 50.
3. Fadillah Mansor and Bhatti, M. Ishaq (2011), “The Islamic Mutual Fund
Performance: New Evidence on Market Timing and Stock Selectivity”. ISI Proceedings of 2011 International Conference on Economics and Finance Research, 26–28 February 2011, pp. 487-494.
4. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Islamic Mutual Fund
Performance: Evidence on Market Timing and Stock Selectivity”. E-Proceedings of Second Foundation of Islamic Finance Conference, Kuala Lumpur, Malaysia, 8–9 March 2011.
5. Fadillah Mansor and Bhatti, M. Ishaq (2011) “Islamic Mutual Fund Performance
for Emerging Market, during Bullish and Bearish: The case of Malaysia”. Proceedings of 2nd International Conference on Business and Economic Research, pp. 770-789.
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ABSTRACT OF THESIS
The main objective of this thesis is to investigate the performance of Islamic mutual
funds (IMFs) in comparison with the conventional mutual funds (CMFs). Risk and
return relationship is evaluated relative to market benchmark over a 20-year period
from 1990 to 2009. The data covers 479 mutual funds from the Malaysia, consisting
of 129 IMFs and 350 CMFs covering all asset classes (alternative, allocation, equity,
fixed income and money market funds). The mutual fund industry in Malaysia is
unique in the sense that IMFs account for 29 per cent of the world’s Islamic funds: the
growth worldwide in 2011 was also the highest. Several methods are employed in this
study. It starts with descriptive statistics and an analysis based on risk adjusted
performance and moves on to time series and panel data analysis. The analysis
focuses on risk and return performance, market timing and fund selectivity and the
impact of funds attributes including age and fees among other standard attributes. The
thesis uses Sharpe, Treynor and Jensen, single and multi-factor CAPM, quadratic
version of the single CAPM by Treynor and Mazuy for its analysis. The thesis finds
that the performance of IMFs is different from the CMFs peers. Unlike previous
studies, this thesis finds evidence of IMFs and CMFs outperforming market
benchmarks. In particular, results reveal that the IMFs performed better than CMFs
during the financial crisis and pre-crisis periods. In contrast, the CMFs outperformed
the IMFs during the post-crisis period. On average, IMFs are more sensitive to a
single market benchmark, while CMF performed better on multiple benchmarks.
Panel data analysis shows that IMFs managers outperformed CMFs and had superior
funds selectivity skills. There was however no evidence of market timing expertise of
the both fund managers. The impact of fees is the final evaluation. While focusing on
equity funds, the thesis finds that fee attributes had a significant impact on the
performance of equity funds. Overall, the findings reveal that fees charged have an
adverse impact on fund performance. A variety of fees were taken into account in the
analysis including management fee, expense ratio, trustee fee, and total load fee. Fees
had a hump-shaped, overall positive impact, on the performance of Islamic equity
funds (IEFs) and an inverted U-shaped, overall, negative impact on the performance
of conventional equity funds (CEFs). These factors could suggest that the fee
incentives are more important to the IEFs compared to that of CEFs, thus contributing
to the outperformance of the funds.
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CHAPTER 1- INTRODUCTION
1.1 Introduction
The rapid growth of investment in mutual funds globally and its significance to the
economic development of a country makes the study of mutual fund performance
important. In a high-tech and globally competitive financial market in the 21st century
where traders are trading funds and stocks using highly sophisticated powerful
computing aids such as iPads, tablets and/or cloud technology, the management of
portfolio funds and investment selections is both complex and versatile. Funds
managed can vary from ethics-based funds to faith-based funds with the margin
narrowing by a fraction of a cent over a million asset values in a second. The situation
makes global and domestic researchers interested in expanding their research area
from standard performance measures to more complicated measures such as
determinant factors and attributes using panel regressions. This expansion of interest
is increasing now with the introduction of various types of mutual funds, including
real estate investment trusts (REITs) and funds of funds.
The current scenario makes the study of mutual fund performance significant,
especially when it comes to new classes of asset such as Islamic mutual funds (IMFs).
Therefore, the study of the performance of IMFs compared to conventional mutual
funds in emerging markets such as Malaysia makes this thesis a timely contribution
that is of value to policy-makers and investors for understanding the benefits of
investing in this specific market.
The study of IMFs in Malaysia is significant as this is the only country that provides a
dual-system of Islamic and conventional financial markets within the same
infrastructure. Malaysia serves the Islamic mutual fund industry on a par with the
existing conventional mutual fund industry. Both Islamic and conventional funds are
in demand among investors who are looking for long-term investment with high
returns. Malaysia is also one of the countries in the emerging financial markets and a
founding member of Asia-Pacific Economic Cooperation (APEC), which was
Page | 2
established in 1989 to promote open trade and practical economic cooperation among
the Asia-Pacific economies (Worthington and Higgs 2004). Studying a country in the
emerging markets provides an opportunity to identify whether the characteristics and
behaviour of mutual funds (MFs) in less developed markets are similar to those in
developed and highly efficient markets (Bialkowski and Otten 2011).
The global demand for Islamic funds in particular was mostly driven by the steadily
increasing oil prices which peaked in 2007–2008, leaving Muslim investors in the
Gulf Cooperation Council (GCC) countries with excess liquidity to be invested. In a
growing population, the increasing numbers of Muslim clients who are looking to
invest in Islamic financial markets will increase their demand for investments in fund
portfolios. According to Pew Research Center’s Forum on Religion and Public Life,
the world’s Muslim population is expected to increase by about 35 per cent in the next
20 years, rising from 1.6 billion in 2010 to 2.2 billion by 2030. The figure will
represent 26.4 per cent of the world’s total projected population of 8.3 billion in 2030.
The Muslim population globally is forecast to grow at about twice the rate of the non-
Muslim population over the next two decades – an average annual growth rate of 1.5
per cent for Muslims compared with 0.7 per cent for non-Muslims (Pew-Research-
Center, 2011).
The issue here is the lack of research on the performance of Islamic funds. This thesis
attempts to fill this gap. The existing literature reveals that very few studies have
examined Islamic mutual funds (IMFs) or compared their performance with that of
conventional mutual funds (CMFs). The increasing demand for IMFs in particular and
the role of Islamic finance in the global market more generally are creating
opportunities for further exploration. Islamic finance is growing as a source of finance
not only for Muslim investors but also for other investors worldwide. The 2007–2008
global financial crisis (GFC) highlighted the importance of Islamic finance worldwide
and made the Islamic fund industry more popular in the international financial market.
At the same time, investment in mutual funds is becoming an increasingly arduous
task in the aftermath of the GFC. Due to the application of the Shariah principles of
Islamic finance, the Islamic fund industry is more resilient to recession. The Islamic
capital market is independent of the global financial market and therefore is insulated
Page | 3
from any external financial crisis. In addition to filling a gap in the finance literature
on Islamic funds, this study is important as it provides recent results on fund
performance, especially evidence from emerging markets.
There is also little empirical research on the relationship between fees charged by
fund managers and fund performance in an emerging market economy such as
Malaysia. This study adds new knowledge, particularly on the Islamic mutual fund
area, by investigating the relationship between fees and fund performance before and
after fees is incurred.
The investment performance of IMFs is also an interesting issue to investigate as an
alternative to CMFs and ethical mutual funds or socially responsible investment funds
(SRI). Several studies (such as Abderrezak, 2008; Abdullah et al. 2007; Elfakhani, M.
K. Hassan, and Y. Sidani, 2005; Elfakhani and Hassan 2005; Hayat and Kraeussl,
2011; Hoepner, Rammal, and Rezec, 2011; Kraeussl and Hayat, 2008) have examined
this issue. The major finding has been that IMFs perform better than CMFs only in a
bearish market. Most of these studies have employed standard performance
measurements and therefore the new research presented in this thesis enhances the
analytical methodologies to include other sophisticated econometrics and statistical
application methods.
Accordingly, this thesis builds on previous studies’ findings by examining the
performance of IMFs and CMFs. It is not limited to equity funds but includes more
diversified funds and more extensive data. This study considers a longer than usual
duration, a 20–year period from 1990 to 2009, and involves a larger number of mutual
funds in one country at one time, consisting of 535 mutual funds. Instead of using
weekly data that tend to suffer more fluctuations from market movements, the study
uses monthly and yearly returns data for analysis. The source of returns is the
Morningstar database and the returns provided are based on gross return and are net of
all expenses except the front and exit fees.
This study is conducted with the main objective of investigating investment
performance by examining mutual fund portfolios, concentrating on the returns
Page | 4
performance measurements of IMFs and CMFs. Another intention is to broaden the
evidence that the industry has improved with time, as mentioned by Elfakhani et al.
(2005), and to verify the evidence of outperformance or underperformance of the
Islamic funds and their conventional peers. This has been done by using recent data
and sophisticated statistical and econometric methods related to time series and panel
data regression analysis. The time series incorporates mean aggregate returns and the
panel data include individual fund returns. The models employed are various risk-
adjusted performance measures, standard performance based on CAPM single and
multiple benchmarks, the Treynor and Mazuy model (TM model), and also other
multiple regression models (see Section 3.4.3 for more detail).
The rest of this introductory chapter is structured as follows. Section 1.2 discusses the
related issues and motivation for conducting this study. Section 1.3 lists the objectives
of this study, and Section 1.4 explains the expected contributions of this thesis.
Finally, Section 1.5 describes the structure of the thesis.
1.2 Issues and motivation of the thesis
The main issue addressed in the study reported in this thesis is the underperformance
of mutual funds relative to the market benchmark. Therefore, the study investigated
the investment performance of mutual fund portfolios, concentrating on the
comparative performance of IMFs and CMFs. The study examined the returns
performance of IMF and CMF portfolios for a single economy – the mutual fund
domicile in Malaysia.
The major motivation for choosing a Malaysian dataset of mutual funds was to
accommodate a large sample and long period of study in which the Islamic and
conventional funds have operated within the same financial system, so that the
comparison can be rigorously analysed. Malaysia is strategically important since it
represents a single economic structure which includes Islamic and conventional funds
operating within the same financial market. A significant shortcoming of related
previous studies is their restricted data samples. In Malaysian mutual fund studies, the
largest samples have been 110 funds as examined by Taib and Isa (2007) and more
recently about 265 Islamic funds examined by Hoepner et al. (2011). This thesis
Page | 5
provides a larger sample of 479 funds consisting of 129 Islamic and 350 conventional
funds from the period of January 1990, when the Malaysian Islamic mutual fund
industry started, to April 2009.
The mutual fund industry has become increasingly popular in the last few decades
with the introduction of Islamic funds globally. This has created more awareness
among investors to include these investment funds in their portfolio selections in
order to achieve a personal preference or adequate portfolio diversification. Another
justification for choosing Malaysia is the significant growth of IMFs in that country,
which represents approximately two-thirds of the IMFs worldwide, according to the
2009 data.
Investment in mutual funds is part of an important strategy that contributes to the
development of the capital market in Malaysia. In Malaysia, Islamic fund
management activities began to grow rapidly in the 1990s. Now with more than 1000
Islamic funds worldwide, Malaysia has 167 of these funds as at May 2012. With the
establishment of the Islamic Financial Services Board centred in Malaysia in 2002
and with a significantly faster growth of the Islamic funds market in Malaysia than in
other countries, Malaysia is therefore a good case for examining the performance of
IMFs and CMFs.
This study is also motivated by the fact that Malaysia has the largest clientele of
Islamic funds in the global market and also in the emerging markets; thus, this study
is implemented in order to test whether the results are different from or similar to
those of studies which have focused on the MFs in developed markets such as the US
and the UK. About 29 per cent of Islamic funds worldwide are domiciled in Malaysia
(Eurekahedge, 2011). Most previous studies on fund performance are rooted in the US
and other developed countries, leaving the emerging markets virtually unexamined
and their huge potential1 completely unacknowledged. The gaps in the research
between these two kinds of markets are not only in the sample sizes, the database
1 The mutual fund industries in emerging markets are distinctive from those in developed markets in terms of growth, competitiveness, organisational structure and information availability (Suppa-aim, 2010).
Page | 6
providers and the models employed in the studies, but also in whether the most
important, advanced research on MFs in these developed countries could be extended
to markets in countries with emerging markets.
The studies in an emerging market such as Malaysia mainly focus on evaluation
methods and still use models such as the Sharpe, Treynor and Jensen measures and
the basic CAPM, all of which have been criticised for unsatisfactory explanations of
performance. It is still questionable whether evidence from the developed markets can
be applied to emerging markets. This study examines fund performance using recent
analysis such as panel data and sophisticated models involving various factors outside
the concept of market risk which can explain return performance and make the models
more informative and innovative. The concern is not only to evaluate performance but
also to determine whether factors such as fees and other funds’ attributes influence
fund performance.
Previous studies have agreed that there is no statistical difference in the performance
of Islamic and conventional equity funds in relation to the returns of their respective
market indices downturns (Elfakhani et al., 2005; Elfakhani and Hassan 2007). They
have concluded that the performance of funds does improve over time in that fund
managers became more expert in knowing how the market works. Elfakhani and
Hassan (2007) suggested that Islamic funds do not differ substantially from other
conventional funds, although Islamic funds do appear to perform slightly better.
Girard and Hassan (2005; 2008) also found that there is no performance difference
between Islamic and non-Islamic indices since the latter outperformed their
counterparts from 1996 to 2000 but underperformed from 2001 to 2005, and thus
similar reward, risk and diversification benefits exist for both Islamic and
conventional indices under their investigation. Abderrezak (2008) found that there is
no significant difference in performance between Islamic and ethical fund portfolios
and, in fact, both are unable to outperform the S&P 500 Index, a proxy for a
conventional portfolio.
Since Islamic and conventional funds have similar features concerning the subject of
mutual fund investments, then the results of this study are significant for identifying
Page | 7
whether they also disclose the same benefits in risk and return performance. The main
features of mutual fund investments are: (1) they provide the opportunity to individual
investors to invest in pooled stocks; (2) they provide the opportunity to individual
investors to reduce risks but invest in coverage of stocks using professional portfolio
management; and (3) they provide investors with professional investment
management and portfolio selection, thus reducing the workload of investors (Firth,
1977). Therefore, it is important to identify whether or not Islamic and conventional
funds provide similar benefits and advantages associated with their performance and
fund management and services. The conjecture of this study is that there is a
difference in the returns performance of these two funds since the Islamic funds differ
from the conventional ones in many respects, such as in the prohibition of interest and
gambling in their concept and operation. In the case of Malaysia, generally there is a
difference in terms of the growth rate of these two fund portfolios, as discussed in
Chapter 2.
In most cases, mutual funds bring diversified and professional benefits, and for these
considerations, they charge fees for expenses, namely management fees and expense
ratios, and also put in place some front-load fees and a redemption penalty. Most of
the time the fee charged is higher for investors and, in fact, it is not enough to trade
off with their returns from the investment. It is also arguable whether a higher fee is
associated with higher returns in relation to the market (see for instance, Carhart,
1997; Haslem, Baker, and Smith, 2008).
In the Malaysian market, although it possesses the largest percentage of funds, it
seems that previous studies have had mixed results. While Hayat and Kraeussl (2011)
noted that the IEFs in Malaysia underperform compared to the conventional and
Islamic benchmarks, as well as performing worse during a bearish market, Hoepner et
al. (2011) contended that the Malaysian IEFs significantly outperform the
international equity market index. However, these studies only focused on the
performance of IEFs. Previously, Abdullah et al. (2007) had contradicted these
findings by showing that CEFs perform better than IEFs during good economic times
and worse during bad economic times. Hence, these inconsistent results require
further examination.
Page | 8
The performance of IMFs and CMFs was evaluated by employing more recent and
more comprehensive data than other studies, and over a longer period of time, which
is crucial. Previous studies on Islamic funds investigated only one asset class, i.e.,
equity funds, but this thesis also provides evidence for MFs’ performance in various
asset classes within a portfolio, namely alternative, allocation, fixed income and
money market funds. The Malaysian data are appropriate due to encompassing a wide
range of data about MFs returns from various asset classes such as allocation,
alternative, fixed income and money market funds from 1990 to 2009. The data
consist of 479 fund returns over the 20-year period from January 1990 to April 2009.
The country has a long experience of managing Islamic and conventional funds side
by side in the industry. The data also represent the largest clientele of IMFs in the
global market. Furthermore, the application of data in a single economy can reduce
the bias which occurs when using cross-country data due to different national
characteristics and heterogeneity in Islamic fund performance (Hoepner et al. 2011).
Evidence from Malaysia regarding mutual fund performance is very limited, despite
the rapid growth of the industry in relation to the capital market. In comparison to
most studies on developed markets, the studies on Malaysia use only a very small
number of funds, cover a shorter period and calculate returns using hand-gathered net
asset values (NAVs) of the funds. Due to the timeframe limitations of the previous
Malaysian studies, their results could be biased. Moreover, the Islamic fund industry
only started in the 1990s and it is in the early stage of development where the industry
still lacks transparency, fund managers’ experience, limitations of product
diversification and effective fund portfolio management. With the current
development of the industry, there are external factors that can also contribute to the
final results. Thus, the findings reported in this thesis are important for providing
recent evidence on the returns performance of IMFs and CMFs, particularly following
the global financial crisis (GFC) and the liberalisation of foreign-exchange
administration rules by the Malaysian Central Bank in 2007, allowing fund managers
to invest up to 50 per cent of net asset values of their MF investments in foreign
markets. In 2008, this percentage was increasing to 100 per cent foreign ownership.
This means that Islamic fund management companies are allowed to have 100 per
Page | 9
cent foreign ownership and they are also permitted to invest 100 per cent of their
assets abroad (Securities-Commission-Malaysia, 2008).
The main and associated research questions in this thesis are as follows:
1. Do both IMFs and CMFs really underperform the market? Do these two
types of funds offer similar benefits to potential investors in risk, return
and fund diversification?
2. Is the performance of these funds sensitive to any single or multiple
benchmarks, performance measurements and any type of statistical or
econometric technique used in the analysis?
3. Do these funds really act differently in bearish and bullish markets?
4. Do Islamic and conventional funds managers offer similar advantages and
benefits to potential investors in market timing expertise and fund
selectivity skill?
5. Are there any differences with regard to the performance of Islamic funds
compared to their conventional counterparts, and what is the relevance of
certain funds attributes?
1.3 Objectives of the study
To address the various issues in the problem statements and the research questions
discussed in the previous section, the objectives of this study are as follows:
1. To examine the returns performance of IMFs and CMFs domiciled in
Malaysia. Specifically, the study analyses the returns performance of each
fund portfolio against its single market benchmark and also multiple market
benchmarks, using various standard performance measurements before and
after adjusting for risk-free rate returns.
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It has been hypothesised that the Shariah-compliant restriction applied to IMFs
could mean worse performing fund returns (Abdullah et al. 2007; Hayat and
Kraeussl 2011). However, this study contends that the returns performance of IMFs
is better than that of CMFs because the funds have grown much better (discussed
further in Chapter 2). The study also analyses the performance of each in different
sub-periods in a complete market cycle covering bullish and bearish markets.
Chapter 4 addresses this objective.
2. To analyse whether fund managers in Islamic and conventional mutual funds
have skills in fund selectivity and ability in market timing by using time series
and panel data analysis. These issues are addressed in Chapter 5 and Chapter 6
respectively.
3. To evaluate any differences in the return performance of IEFs and CEFs. With
this in mind, this study empirically explores whether fees charged on a mutual
fund have any impact on the returns performance of the fund. The study
analyses whether the fees have an adverse or beneficial impact on returns
performance and also identifies whether the fees are associated with the skills
of good fund managers. Chapter 7 discusses the impact of fees on fund
performance and provides evidence related to this objective.
4. To empirically discover the relationship between the returns performance of a
fund portfolio and fund attributes which consist of endogenous variables such
as risk (systematic risk and residual risk), type of fees, types of fund, return
factors and also the exogenous variables such as investment place of the funds
(either local or foreign market), age and size of the funds. This is done in order
to explain the differences between the Islamic and conventional equity funds,
if any. This theme is also covered in Chapter 7.
1.4 Contributions of the study
The study adds to the finance literature, particularly that related to fund management
and the Islamic mutual fund area. Since the findings of this study could raise the
Page | 11
awareness of people who are investing in mutual funds, the development and
enhancement of the industry could attract potential investors. The thesis contributes to
the growing body of literature on IMFs globally and the mutual fund industry in
Malaysia in particular. Specifically, an empirical analysis derived from time series
and panel data regression provides some information to investors regarding the
returns performance of MFs and their prospects, especially in Malaysia and other
countries that have similar fund characteristics.
Investment in mutual funds is a considerably lower risk than some other types of
investment, such as common stocks and hedge funds. The present study indicates that
the MF industry in Malaysia generally has beta values lower than 1, suggesting that
investment funds are less volatile and low in systematic risk compared to the market
return. In Malaysia, the level of awareness among investors regarding investment in
mutual funds is lower than their awareness regarding investment in common stocks.
As at 31 December 2003, the NAV of the mutual industry represented only 10.95 per
cent of the market shares, amounting to RM70.08 billion, while by February 2012, the
NAV of the industry had increased to 19.85 per cent, representing RM267.02 billion.
Fund investment is growing and therefore attracting more investors. However, this
figure is very small compared to the amounts in developed countries, which in the UK
and the US, for example, were about $US649,010 million and $US7,567,572 million
respectively at the end of 2005 (Ramos, 2009). However, the AUM outside the US
grew from 38 per cent to 54 per cent over the 10-year period from 1997 to 2007
(Ferreira, Keswani, Miguel, and Ramos, 2011). Research in this area provides
investors and market players with sufficient information regarding the performance of
mutual funds and their prospects, particularly on risk and returns trade-offs while
investing in mutual funds compared to normal stocks, based on the Malaysian market.
The evidence in this study would help market players and regulators to maximise their
profits or to achieve their investment objectives based on different market conditions.
Generally, although the movement of mutual funds is expected to follow the market
trend (Sharpe, 1966; Treynor and Mazuy 1966), it remains arguable whether mutual
funds perform better than the market benchmark (Benos and Jochec, 2011; Blake and
Timmermann, 1998; Carhart, 1997; Elton et al. 1993; Firth, 1977; Grinblatt and
Page | 12
Titman, 1994; Jensen, 1968; Malkiel, 1995). This study investigates the returns
performance of mutual funds based on different market trends, including bearish and
bullish market periods. Since the study covers a complete market cycle, the findings
of the study give investors a good basis for understanding the market behaviour of the
mutual funds, particularly in bearish and bullish market scenarios.
Other findings can provide market players with ideas of which type of mutual fund is
likely to perform better and is more correlated to market performance. The
comparative study between Islamic and conventional funds in this research also
provides investors with an alternative to diversify their investment portfolios covering
different asset classes. This exposure will enhance the effectiveness of portfolio
selection of their asset management.
The findings reported in this thesis add to the existing literatures, as they extend
previous evidence on the returns performance of Islamic funds compared to
conventional funds based on standard performance evaluation. These findings are also
related to other areas such as multiple benchmarks based on CAPM, TM model,
extended TM model, fees and other fund attributes that also contribute to the
performance of funds. The study also enhances the financial modelling applied to
Islamic mutual funds. By using panel data regression in addition to the standard
performance measurement, this constitutes an important step for encouraging more
Islamic mutual fund research in particular, extending the analysis based on various
types of financial modelling.
The emphasis on fees in this study could provide a better understanding for regulators
and fund management companies concerning how to encourage investors to invest in
mutual funds without sacrificing their investment profit for the fees. Fees or other
charges should be associated with higher returns and better fund performance, and
should not just be treated as a trade-off for the amount of investment funds. The
findings of the study inform investors of the Islamic funds industry and the
performance of the funds. Investors can understand the Shariah-compliant matters and
methods of investing in accordance with the Shariah principles, as well as the rules
and regulations relevant to Islamic finance principles.
Page | 13
With respect to the development of the Islamic fund industry, these findings can also
provide key investment information for fund managers and regulators who are
interested in managing Islamic fund portfolios or interested in enlarging their
portfolio management to include investments which comply with Shariah principles.
The Islamic funds could also serve as an investment vehicle available to Muslim
investors and other investors such as ethical or socially responsible investors, who can
maximise their portfolio selections and minimise their risk exposure (such as from
credit risk and interest rate risk) while considering this type of investment. In fact,
Islamic investment now is not just a religious matter for Muslims who want to avoid
riba in their trade transaction, but is also considered as a subset of global traditional
finance (Amin 2009).
Finally, the findings of this study add to the growing body of literature in mutual fund
performance and could contribute ideas to governments, market players and fund
management companies in establishing and enhancing their regulations and policies
pertaining to this industry. They could also inspire regulatory bodies in other
countries, particularly the Islamic countries, to supervise, implement and monitor
Islamic mutual fund investments in a way to provide further understanding of fund
characteristics that will lead to economic growth and better establishment of the fund
industry.
1.5 Structure of the thesis
The thesis has eight chapters. Additionally, Table 1.1 presents a diagram showing the
structural framework of the thesis. Chapter 1 provides an introduction. Chapter 2
contains a review of the literature on IMFs and CMFs, and a summary description of
the empirical evidence published in previous studies. The chapter also considers
studies related to the types of performance measures of mutual funds, market
benchmarking, market timing expertise, fund selectivity skill and fees, and the impact
of fees on fund performance.
Chapter 3 presents the research methodology and the sample selection of the data
used. Chapter 4 discusses the preliminary results and evaluates performance based on
Page | 14
basic descriptive statistics and risk-adjusted performance measures. Chapter 5
analyses the results based on the single-factor CAPM model and the TM model using
time series regression analysis. Chapter 6 incorporates a similar model to that
employed in Chapter 5 and then further extends the analyses by re-examining fund
performance by using other methods such as panel data regression analysis. Chapter 7
further extends the fund performance analysis and concentrates on evaluating the
performance of IEFs and CEFs, and the impact of fees and other fund attributes on the
performance of these equity funds. Lastly, Chapter 8 provides the conclusion,
summarising the most important findings, describing the implications and limitations
of this thesis, and providing suggestions for future research.
Page | 15
Table 1.1: Structural framework of the thesis
METHODS
TIME SERIES
PANEL DATA
RISK ADJUSTED PERFORMANCE MEASURES
MUTUAL FUNDS PERFORMANCE
RESEARCH QUESTIONS
OBJECTIVES FINDINGS
HYPOTHESES
SINGLE BENCHMARK
MULTIPLE BENCHMARKS
FUND ATTRIBUTES
MODEL
CAPM TM model
Multiple regressions
Multi-factor CAPM
Extended TM model
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CHAPTER 2 - LITERATURE REVIEW
AND BACKGROUND OF ISLAMIC
MUTUAL FUND INDUSTRY
2.1 Introduction
In the two decades from 1990 to 2009, the mutual fund (MF) industry played a
substantial role in the global financial market. Due to an interconnected, highly
correlated market, the performance of mutual funds has also been affected by minor
shock in small financial markets such as emerging markets and major shock in major
or developed markets. In other words, a crisis in one country has had contagious
effects in other countries, directly or indirectly. In addition, bearish and bullish
markets have also had a significant impact on the MF industry.
Hence, research in the MF industry is taking off due to its complex structure of fund
managers’ choices, trading timing selection, and closing and cloning in open-ended
funds. The closing and cloning strategy, for example, is an attractive strategy for fund
managers seeking to increase their management fees and the amount of funds they
manage (Chen et al. 2012).
This chapter discusses a number of empirical studies which have investigated the
performance of mutual funds, focusing on a variety of important issues. These issues
include measurements of performance, market efficiency with reference to the
relationship between stock markets and mutual funds, market benchmarking,
performance persistence, investment style, strategies for asset allocation, portfolio
management, market timing and fund selectivity, funds attributes and survivorship
bias (Blake and Timmermann, 1998; Brown and Goetzmann, 1995; Busse and Irvine,
2006; Carhart, 1997; Cuthbertson, Nitzsche, and O'Sullivan, 2008; Firth, 1977;
Grinblatt and Titman, 1993; Henriksson and Merton, 1981; Malkiel, 1995; Sharpe,
Page | 17
1966)2. All of these studies have focused on CMFs. Those focusing on IMFs and
other religious funds are fairly new and relatively few but growing in number (see for
example, Abderrezak, 2008; Abdullah et al. 2007; Ahmed, 2007; Elfakhani et al.,
2005; Elfakhani and Hassan 2005; Elfakhani and Hassan 2007; Hayat, 2006; Ismail
and Shakrani 2003). The scope of these IMFs studies is, however, limited as they used
standard performance evaluation based on Sharpe, Treynor and Jensen ratios and
basic CAPM in their investigation with benchmarking, persistency, and market timing
ability and fund selectivity skill.
The shortcoming of the CAPM is that it just evaluates single factor analysis based on
risk and return of a market portfolio, thus suggesting the incorporation of other
variables that also can explain the returns performance of the funds. As a result, more
models have been established in recent studies which are more efficient and more
informative in explaining fund performance relating to risk and return characteristics,
market timing expertise and fund selectivity skill, and also other aspects such as fund
style, strategy and fund attributes. The present study extends these standard
performance evaluations to include multi-factor CAPM, extended TM model and
multiple regression model based on time series and panel data analysis. This is to
ensure coverage of all the relevant issues: mutual fund performance in relation to the
market benchmark; the market timing expertise and fund selectivity skill of the fund
managers; fees and their impact on fund performance; and the relationship between
fund attributes and fund performance. The multiple regression models, for example,
enable evaluation of fees and other fund attributes and their relationship to the fund
performance. The study follows Bertin and Prather (2009) for multi-factor CAPM,
and Bello and Janjigian (1997) for the extended TM model.
There are many reasons for the importance of studying the performance of mutual
funds and why it has attracted a large number of researchers in the finance literature.
In the US, the increasing attention on mutual funds is particularly due to the
significant growth of institutional financial assets, the strict regulation of mutual funds
by the US Securities and Exchange Commission (SEC) and, most importantly, the
availability of databases and rating information from Morningstar Inc., Lipper Inc.
2 The details and significant of these previous studies are explained in Section 2.6.
Page | 18
and Wiesenberger Inc. (Gallagher, 2002). In the US, the studies on the performance of
mutual funds have grown significantly since the 1960s, particularly when Sharpe
(1964, 1965b) contributed towards an understanding of how investors could manage
risk and return in their investment portfolios using the Sharpe ratio.
With regard to Islamic funds, this subject has gathered momentum since the industry
began in the 1990s. This is due to the strong demand for Shariah-compliant products,
the continuing strength of the legal and regulatory framework of Islamic finance, the
demand from conventional investors, and the ability of the industry to develop
innovative financial instruments that meet investors’ needs (Hasan and Dridi, 2010).
The development of Islamic equity funds has also been driven by the increasing
capital value of Muslim investors to invest their funds in Shariah-compliant
investment products (Derigs and Marzban, 2009).
In this chapter, the scope of the discussion on the performance of mutual funds is
limited to the risk and return characteristics of funds, the standard measurements of
performance including the risk-adjusted return, and CAPM. Other relevant issues
focused on here include the performance of mutual funds in relation to the market
benchmark, and the market timing expertise and fund selectivity skill of fund
managers. The influence of fees and other fund attributes on fund performance is also
discussed in this literature review.
The chapter is structured as follows. Section 2.2 describes background details of the
Islamic fund industry and its development worldwide. Section 2.3 explains the
background of the mutual fund industry in Malaysia. Section 2.4 provides further
details about IMF and CMF fundamentals, particularly the salient features of IMFs.
Section 2.5 explains the fundamentals of Islamic finance and Islamic investments,
which are the basic concepts underlying the implementation of the IMFs, and relates
these to implications of the global financial crisis (GFC). The scenario of the GFC
and its implications are also explained in this section. Section 2.6 explains the
theoretical framework, issues and previous empirical studies on mutual fund
performance in the US, the UK and some countries in emerging markets, including
Malaysia. This section includes discussion on previous findings related to the
Page | 19
performance of IMFs. A summary of the empirical evidence on the performance of
mutual funds is also presented in Table 2.3. Section 2.7 concludes this chapter.
2.2 Background of the Islamic mutual fund industry
Islamic finance has grown tremendously over the decades since 1990. In the early
stage of Islamic funds in the global market, the number of the funds increased from
eight in 1991 to 95 with US$5 billion assets in 2000 (Elfakhani et al., 2005, pp. 1331-
1332), and the current global Islamic financial assets have been estimated to reach
US$750 billion and are expected to expand to US$1.6 trillion by the end of 2012.
While the Islamic financial industry is gaining momentum, the global financial
landscape on the other hand is facing a crisis of proportions in that financial assets fell
by US$16 trillion in 2008 – a significant break in the three-decade-long expansion of
global capital markets – thus presenting opportunities for a sector such as Islamic
finance, particularly when the Islamic capital market is set to play a bigger role in
driving global asset growth (Securities-Commission-Malaysia, 2009).
According to the UK Islamic Finance Secretariat (UKIFS), Islamic assets now
represents only one per cent of the global financial market. In 2011 they grew by
nearly 14 per cent to an estimated US$1,289 billion: an increase of about 150 per cent
from US$509 billion in 2006 (UK-Islamic-Finance-Secretariat, 2012). After the GFC
in 2007–2008, the Islamic fund industry remained at a plateau in 2009, with assets
under management (AUM) being worth $52 billion. The industry is fragmented, with
over 70 per cent of fund managers having AUM under US$100 million and less than
10 per cent with AUM, representing an excess of more than US$1 billion. The fees
associated with Islamic funds have also fallen. The average management fee fell to
1.15 per cent in Q1, 2010, due to an investor-driven market that forced fund managers
to reduce fees, on average, by about a quarter or 40 basis points since 2006. The
higher returns expected by investors have led to an investment pool available to the
Islamic fund managers that is estimated to be between US$360 and US$480 billion
(Ernst-&-Young (2010, pp. 6-28).
The establishment of the IMF industry globally is a recent phenomenon and is gaining
more attention as time passes. The global Islamic fund management industry
Page | 20
expanded by 7.6 per cent to US$58 billion in 2010 in AUM, which is 13 per cent
higher than in 2008. The AUM of this fund remained flat for three years (2008–2010)
after the global financial crisis (Ernst-&-Young, 2011). The growth of IMFs in
Malaysia, for instance, which functions in a similar pattern, is expected to increase
from a higher demand from local and foreign markets, as well as from increasing
levels of awareness and confidence among global investors. In Malaysia, Islamic
funds were operating in the 1970s through the introduction of Dana Amanah Bakti by
Asia Unit Trust Berhad in 1971. The growth of the industry in Malaysia has increased
significantly in relation to the global market. In February 2012, about 167 IMFs were
available in the Malaysian market, according to the SC.
The development of Islamic finance in the global market has made a substantial
contribution to the establishment of the industry. The growth rate of Islamic finance
has been maintained at 15 per cent annually (Bose and McGee, 2008). By the end of
2008, Shariah-compliant assets grew to around $US500 billion (Hassan 2008). The
trend also shows that Islamic finance is becoming an important part of the
international financial system. It is now recognised as an alternative channel of
financial intermediation not only within Islamic communities but also in Western
societies, with more conventional banking and finance systems offering Islamic
financial products.
Currently, the development of the Islamic financial system is occurring not only in the
Middle East and Southeast Asia (with Malaysia as the biggest hub), but it is also
appearing in continental Europe, the UK, the US, South Africa and other regions (see
Figure 2.1). With the higher demand for these funds in Malaysia, it is not surprising
that Malaysia now holds the largest percentage of IMF clientele in the world market.
As noted by Lewis (2009), Malaysia held about 24 per cent of funds by domicile of
clients in 2007 and the holding percentage rose to 29 per cent in 2011 (see Figure
2.1[b]). Although Malaysia is a tiny country in emerging markets, the country is
projected to become a global platform for the finance industry with its comprehensive
regulatory and governance framework, which includes the unique characteristics of
Islamic finance with stronger standards that could be seen as a global benchmark.
Page | 21
Thus the Islamic finance industry has more room to progress in practices in the
country (REDmoney, 2011).
Figure 2.1: Islamic funds by domicile of clients, 2007 and 2011 Figure 2.1(a): Islamic funds by domicile of clients in year 2007
24%
23%
8%
5%3%2%
2%
5%
18%
10%
Source: Eurekahedge Islamic Funds Database
Adapted from Lewis (2009).
Malaysia 24%
Saudi Arabia 23%
Kuwait 8%
Bahrain 5%
United States 3%
UAE 2%
Singapore 2%
Indonesia 5%
Figure 2.1(b): Islamic funds by domicile of clients in year 2011
The total AUM of IMF managers has steadily risen globally, especially the global
assets, as shown in Figure 2.2. Assets in 2007 according to geographic location were:
the Middle East and Africa 63 per cent; global market about 13 per cent; Asia-Pacific,
29%
20%11%
3%
4%
8%
4%
4%
3% 3%11%
Source: Eurekahedge (2011).
Malaysia 29%
Saudi Arabia 20%
Kuwait 11%
Bahrain 3%
United Kingdom 4%
UAE 8%
South Africa 4%
Indonesia 4%
Luxembourg 3%
Pakistan 3%
Others 11%
Page | 22
12 per cent; North America, 9 per cent; and Europe about 4 per cent, as shown in
Figure 2.2(a). By the end of July 2011, these global assets had more than doubled to
36.2 per cent and the emerging markets appeared to have 0.5 per cent of the assets
(see Figure 2.2[b]). Thus, greater internationalisation of the capital market is a critical
aspect of the strategy to strengthen Islamic finance worldwide and to position
Malaysia as a global ICM hub. The growth in Islamic financial products and
especially in the mutual fund industry will contribute to the continuing development
of the Islamic finance globally, in its diverse investment products.
Figure2.2: Assets of Islamic funds by geographic mandate, 2007 and 2011 Figure 2.2(a): Asset of Islamic funds in year 2007
62%13%
12%
9%
4%
Source: Eurekahedge Islamic Funds Database
Adapted from Lewis (2009)
Middle East/Africa 62%
Global 13%
Asia Pacific 12%
North America 9%
Europe 4%
Figure 2.2(b): Assets of Islamic funds in year 2011
Middle East/Africa
42.5%
Global 36.2%
Asia Pacific 12.6%
North America
7.9%
Europe 0.3%
Emerging
markets 0.5%
Source: Eurekahedge (2011).
Page | 23
2.3 The development of the Malaysian mutual fund industry
Compared to the mutual fund industry in the US and the UK, the Malaysian mutual
fund industry is fairly new, beginning in 1959 when the first mutual fund was
introduced by a company called Malayan Unit Trust Ltd. At that time industry
regulation involved several parties, namely, the Registrar of Companies, the Public
Trustee and Bank Negara Malaysia. During the first two decades (1959–1979),
investment in mutual funds was not popular due to the lack of public awareness.
Shortage of information about the funds and lack of marketing and advertising also
explained this situation.
When the Malaysian government intervened in the mutual fund industry in the period
1980–1990, the industry started to grow (Bala and Matthew, 2003). However, sales of
mutual funds were very limited due to the lack of public interest and lack of variety in
this new investment product. For example, from 1980 to 1990 only 18 funds were
introduced. This period also exhibited the entry of government participation in the
mutual funds industry with a committee called the Informal Committee for Unit Trust
to regulate the mutual funds industry. This committee consisted of representatives
from the Registrar of Companies (ROC), the Public Trustee of Malaysia, Bank
Negara Malaysia (BNM) and the Capital Issues Committee (CIC). The development
of the industry grew rapidly and consistently from 1990 to 1996 with the
establishment of a few new management companies and the launch of new fund
opportunities.
The introduction of guidelines on unit trusts and the enactment of the Securities
Commission Act 1993 generated greater public confidence in the mutual funds
industry and indirectly contributed towards its tremendous growth. During this period
the total NAV under management grew more than three-fold from RM15.72 billion
from the end of 1992 to RM59.96 billion by the close of 1996 (see Figure 2.3[a]).
This phase witnessed great product innovation, new investment products and
deregulation in the industry (Federation-of-Malaysian-Unit-Trust-Managers, 2004).
Page | 24
Figure 2.3: Growth of the Malaysian mutual fund industry, 1992–2012 (February)
13 1523 27 27
36 36 38 39 39 39 39 40 4034 34 37 39 36 36 38 38 39 39 39 39 40 40
0
15
30
45
60
75
90
105
120
135
150
165
180
195
210
225
240
255
270
15
.72
28
.13
35
.72
44
.13
59
.96
33
.57
38
.73
43
.26
43
.3
47
.35
53
.7
70
.08
87
.38
98
.49
12
1.7
6
16
9.4
1
13
4.4
1
19
1.7
1
22
6.8
1
24
9.4
6
26
7.0
2
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
RM
billi
on
Total NAV of the industry/Year
Figure 2.3 (a) Growth of fund managers and mutual fund industry
NAV IMFs NAV CMFs No, of Islamic Fund managers No. of Conventional Fund Managers
0
20
40
60
80
100
120
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
NA
V
Figure 2.3 (b) The ratio of the NAV for the IMFs and CMFs as part of the total NAV for the mutual fund industry
% NAV of IMFs to total
industry
% NAV of CMFs to total
industry
total industry
Page | 25
Moreover, the period of 1990–1996 witnessed expansion of the Islamic fund industry.
The availability of Shariah-compliant stocks boosted the development of this industry.
The NAV of the IMFs increased significantly from only RM 0.19 billion in 1993 to
approximately RM29.24 billion at the end of February 2012, as shown in Figure
2.3(a). The percentage of the IMF’s NAV to the total NAV of the mutual fund
industry is now about 2.17 per cent. Meanwhile, the percentage of NAV of the total
industry towards market capitalisation was about 19.85 per cent in the same period.
As shown in Figure 2.3(a), the total industry has maintained its upward trend – even
though the industry did suffer from the Asian financial crisis (AFC) during 1997–
1998 – with the net asset values to the Kuala Lumpur Stock Exchange (KLSE) market
capitalisation rising from 8.93 per cent in 1997 to 10.34 per cent in 1998 (see
Appendix A for details).
Interestingly, the main trend has been that IMFs portfolios have outdone CMFs, as
shown in Figure 2.3(b). The graph shows that the movement of IMFs is steadily
increasing and that of CMFs is decreasing. The graph also shows that there is a
positive relationship between the movement of the IMFs and the total industry
towards market movement, implying that the IMFs directly follow the market trend.
The decreasing movement of the CMFs could indicate that more investors, including
non-Muslim investors, are shifting their investments into IMFs. The results imply that
the portfolio of the IMFs is becoming increasingly important to the total industry. It
can be seen that both funds have parallel trends. The trend of IMFs mimics the market
trend, as the pattern is nearly similar to that of the total industry. The mutual fund
industry had become vital to the development of the Malaysian economy when the
government introduced the Capital Market Master Plan (CMP) in February 2001. The
CMP introduced a vision for the Malaysian capital market as one that should be
internationally competitive in all core areas necessary to support Malaysia’s basic
capital and investment needs, as well as its longer-term economic objectives. What
was wanted was a highly efficient conduit for the mobilisation and allocation of
funds, and for these to be supported by a strong and facilitative regulatory framework
that would enable the capital market to perform its functions effectively and provide a
high degree of confidence to its users (Securities-Commission-Malaysia, 2001, pp.1-
2).
Page | 26
Regarding making Malaysia an Islamic financial hub and the international Islamic
capital market centre, the CMP also identified the Islamic capital market (ICM) as a
key component in Malaysia’s capital market. This capital market currently plays an
important role in mobilising savings through the investment management industry,
and is not limited to the area of mobilising the effective Islamic funds. In fact, it
facilitates innovative products and services in the Islamic capital market and
strengthens the taxation, accounting and regulatory framework for the ICM.
The mutual fund industry in Malaysia began to gather momentum in the early 2000s,
when the industry recorded double digit growth of approximately 291 per cent within
seven years, growing in NAV from RM43.30 billion to RM169.41 billion between
2000 and 2007 (see Figure 2.3[a]). This mutual fund industry growth represented
15.32 per cent of the local market capitalisation in 2007. The NAV of the industry
was maintained at RM43.30 billion in the year 1999–2000, although in the meantime,
the KLSE market capitalisation suffered due to the crisis with the amount reducing
from RM552.69 billion in 1999 to RM444.35 billion in 2000. These figures show that
the mutual fund industry in terms of the NAV to the KLSE market capitalisation
increased from 7.83 per cent to 9.74 per cent in 2000 and grew to 15.32 per cent by
the close of 2007 (see Appendix A). This was particularly due to the government
playing an important role in the development of the mutual fund industry in Malaysia.
For example, even though there were only 31 government-linked funds in May 2001,
the NAV of the fund represented almost two-thirds of the total NAV of the fund
industry during this period (Bala and Matthew, 2003).
During the peak market cycle, it is evident that the NAV of CMFs increased from
RM112.59 billion in 2006 to RM152.55 billion in 2007. Meanwhile, the total NAV
for the Malaysian Islamic funds almost doubled in 2007, from RM9.17 billion to
RM16.86 billion (see Appendix A). This indicates a growth rate of 35.49 per cent for
CMFs, whereas the Islamic funds increased by 83.86 per cent. This figure,
furthermore, confirms this growth in terms of the NAV where the conventional fund
portfolio had a smaller growth rate compared to its Islamic counterpart, as shown in
Figure 2.3(b).
Page | 27
However, the strong growth was punctuated by the global financial crisis (GFC) in
2007–2008, which began with the infamous collapse of subprime loans in the US.
This spread to the property bubble, the global credit crunch and the banking crisis,
which greatly reduced share prices worldwide. As a result, the NAV of the Malaysian
mutual fund industry fell from RM169.41 billion in 2007 to RM134.41 billion in
2008. The KLSE market capitalisation dropped worryingly from RM1106.15 billion
to RM663.82 billion in the same period. Surprisingly, the percentage of the overall
industry NAV to market capitalisation grew to 20.25 per cent in 2008 from 15.32 per
cent in 2007 (see Appendix A). This could have been due to the new catalysts and the
removal of impediments by the SC and other regulations such as the liberalisation of
foreign-exchange administration rules by the BNM concerning mutual fund
investments in the foreign market. These allowed fund managers to invest up to 50
per cent of their net asset value in foreign currency after 2007. Furthermore, the
removal of impediments approved by the SC for overseas market investments in
March 2008 potentially boosted the growth of the global mutual fund domicile in
Malaysia. However, it declined to 19.18 per cent by December 2009 (further details in
Appendix A).
At the end of 2010, the NAV of the mutual fund industry in Malaysia rose to
RM226.8 billion from RM191.7 billion in 2009, representing a net growth of RM35.1
billion. Out of the total of 564 funds in the industry, the IMFs constituted 152 funds
(27%) while the CMFs represented 412 funds (73%). The value of the IMFs’ NAV
grew at about 22.6 per cent to RM26.6 billion (Federation-of-Investment-Managers-
Malaysia, 2010). However, the industry’s NAV in 2010 represented 17.8 per cent of
KLSE’s market capitalisation against 19.18 per cent in the previous year of.
The numbers of mutual funds in Malaysia are increasing tremendously. The number
of launched IMFs in 2007 was 128 funds compared to 95 funds in 2006. During the
same period, the number of CMFs grew to 367 funds in 2007 from 297 in 2006. The
figures have shown that in terms of growth, the IMFs have grown faster than the
conventional funds, with rates of 34.74 per cent and 23.60 per cent respectively
(Federation-of-Malaysian-Unit-Trust-Managers, 2009). Beginning with only two
funds in 1993, the net asset values (NAV) of the Islamic funds increased from
Page | 28
RM0.19 billion in 1993 to RM22.08 billion at the end of 2009. The updated figure in
Appendix A shows that the number of approved Islamic mutual funds grew from
eight funds in 1996 to 13 funds in 1999 and rose to 17 funds in 2000. By February
2012 there were 167 funds. With regard to asset values, at the end of February 2012,
the percentage of the NAV of the IMFs as part of the total industry was about 10.95
per cent (with units in circulation amounting to 61.96 billion units) with the CMFs
89.05 per cent. In relation to the KLSE market capitalisation, the IMF portfolio
contributed 2.17 per cent while the CMFs contributed 17.67 per cent (see Appendix
A).
Figure 2.4(a) shows a continuous increase in terms of the numbers of funds in both
portfolios from year to year. The figure further indicates growth in number of funds as
the IMFs reached 71 in 2004, up from five funds in 1995. By February 2012 there
were 167 funds. Although the number of funds is relatively small compared to the
CMFs, the number represents more than 20 per cent of the Islamic funds worldwide.
The growth in Islamic funds is estimated to be higher due to the establishment of new
funds from time to time. Moreover, subscriptions in new IMFs are very encouraging
(Mohd, 2007). Figure 2.4(a) also shows that there were about 150 IMFs relative to the
total of 565 funds in the Malaysian market at the end of 2009. This number rose to
605 funds in total comprising 167 IMFs and 438 CMFs by the end of February 2012.
In order to analyse the continuous growth of fund over a period of this study, Figure
2.4(b) is presented. This results when the data in Appendix A are simplified into year-
by-year changes as reported in Appendix B. Appendix B describes the comparative
ratio analysis of the IMFs and CMFs over the 13–year period from 1999 to 2012.3
Column 2 in Appendix B shows the number of fund managers managing the IMFs in
Malaysia and the percentage increase from year to year. It is evident that all fund
management companies in Malaysia have been managing IMFs in their portfolios
since 2004. Column 3 describes the funds that have been approved for the Islamic and
conventional counterparts, and notes that the percentage of the Islamic funds
approved rose from 12.15 per cent in 1999 to 26.54 per cent in 2009. The percentage 3 Although the IMF industry started in 1993, a comparative analysis cannot be done from that year due to some of the comparative data between these two portfolios not being available, as mentioned in Appendix B.
Page | 29
of the conventional funds approved declined from 87.85 per cent to 73.45 per cent in
the same period. Figure 2.4(b) displays both IMFs and CMFs funds forming an
overarching mutual funds industry. The legend total in the figure is the total number
of funds when the IMFs and CMFs are combined. The result is consistent with the
trend highlighted in Figure 2.3(b).
Figure 2.4: Growth in numbers of the Malaysian mutual fund industry, 1992–2012 (February)
5
15
25
35
45
55
65
75
85
95
92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12
Ra
tio
Figure 2.4(b)The ratio based on number of IMFs and CMFs for totalnumber of funds
IMFs
CMFs
0
100
200
300
400
500
600
700
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
Figure 2.4(a) The ratio of the numbers of the IMFs and CMFs fortotal number of mutual funds in Malaysia
IMFs
CMFs
Total Funds
Page | 30
Columns 4 and 5 of Appendix B report on the funds’ accounts and funds’ sizes in
terms of unit circulations for both portfolios. It is clearly seen from the table that the
account and the size of the IMFs kept growing from 1999 to February 2012 (from
2.33 per cent and 4.02 per cent to 12.80 per cent and 18.97 per cent respectively). In
contrast, the CMFs show a reduction in the percentages of account and size from
97.67 per cent and 95.98 per cent in 1999 to 87.14 per cent and 81.03 per cent
respectively in February 2012.
Columns 6 and 7 of Appendix B show the NAV of both portfolios as part of the total
mutual funds industry and the NAV of the IMFs, CMFs and total industry in relation
to the values of the market capitalisation (see Appendix B for details). The graph of
the NAV of the IMFs and CMFs portfolios relative to the NAV of the total industry is
previously exhibited in Figure 2.3.
Despite the encouraging growth of IMFs over a 10–year period from 0.25 per cent
(1999) to 2.21 per cent (2009), the market share of the IMFs industry remains very
small compared to the conventional mutual funds. In fact, the percentage of IMFs is
only 2.17 compared to CMFs which are about 17.67 per cent in relation to the market
share for the year 2012 (see Appendix A). Therefore, the question remains how well
Islamic funds are truly performing when compared to conventional funds. To
investigate this further by looking at the statistics, a scatter plot was developed from
the data in Appendix A. As described in Appendix A, there are two panels of data,
and the bottom line of the table (Panel A and Panel B) shows the percentage of the
NAV of IMFs and CMFs compared to firstly, the percentage of the total mutual fund
industry and secondly, the percentage of the KLSE market capitalisation. The
percentage shows an increasing value of IMFs but not CMFs. To further explain these
relationships, the study developed the trend based on scatter plot analysis for each
category, as presented in Figure 2.5.
Figure 2.5 describes the percentage ratio of the NAV for both Islamic and
conventional funds in relation to the total mutual fund industry, representing the
growth rate of each portfolio. For each point, the x-axis coordinate is the fund’s
annual NAV to total NAV and the y-axis coordinate is the fund’s NAV to KLSE
Page | 31
market capitalisation. Figure 2.5 shows a fairly strong linear relationship with a
positive slope for IMFs and a negative relationship with regard to the CMFs.
Whenever the NAV for Islamic portfolio increases, the NAV of the funds to KLSE
tends to increase. However, the conventional portfolio has a negative slope and this
demonstrates that as the NAV of the funds increases, the percentage of the funds to
KLSE decreases, regardless of the initial value of the NAV. It can be seen that the
growth rate of the IMFs is continuously increasing. However, the growth rate of the
CMFs portfolio seems to be decreasing over time.
The findings are consistent with the trend between IMFs and CMFs compared to the
total mutual funds industry as described in Figure 2.3. The results based on Figure 2.5
imply that the gaps between IMFs and CMFs are shown in Figure 2.3(b). The
negative relationship between IMFs and CMFs means that the gaps between IMFs and
CMFs were larger in 1993 but a little bit smaller in 2009. The smaller gaps mean that
IMFs and CMFs portfolios are converging as a proportion of the total mutual funds
industry.
Figure 2.5: Scatter plot of the ratio percentage of the annual NAV for IMFs and CMFs portfolios to the total NAV of the industry and to KLSE market capitalisation, from 1993 to 2012.
-0.5
0
0.5
1
1.5
2
2.5
3
0 10 20
IMF
s to
KLS
E M
C
IMFs to total industry
Figure 2.5(a) IMFs
% NAV of
IMFs to
Market
Capitalization
Linear (% NAV
of IMFs to
Market
Capitalization)
0
2
4
6
8
10
12
14
16
18
20
80 90 100
CM
Fs t
o K
LSE
MC
CMFs to total industry
Figure 2.5(b) CMFs
% NAV of
CMFs to
Market
Capitalization
Linear (% NAV
of CMFs to
Market
Capitalization)
Page | 32
2.4 IMFs versus CMFs The IMFs and CMFs operate in a parallel financial system as a whole. Unlike the
conventional financial system counterpart, the basic framework for an Islamic
financial system is a set of rules and laws, collectively referred to as Shariah
principles, governing economic, social, political and cultural aspects of Islamic
societies as way of life. This is because Shariah originated from the rules dictated by
the Quran (the Holy book of the muslims), the Sunnah (authentic traditions of the
prophet Muhammad PBUH) and Islamic jurisprudence. The basic differences between
Islamic and conventional mutual funds are illustrated in Appendix C. The detailed
differences between other Islamic and conventional investment products are also
described in the appendix.
A mutual fund is a common financial product available in financial market. There are
several types of mutual funds in the market: open-ended funds, closed-ended funds
and exchange traded funds (ETFs). Each type of fund has its own characteristics that
colour the risk and return to the portfolio investments. The IMFs and CMFs applied in
this thesis are open-ended funds. An open-ended fund is defined as a fund that is open
to public investment via the sale of shares. However, for the closed-ended mutual
fund, the investment opportunities are offered to a limited set of investors or simply
require investors to keep their shares or wait for a buyer. The funds can also be
classified into several types, basically based on the investment objective of the funds
and asset classes: equity funds; asset allocation funds; alternative funds; fixed income
or bond funds; and money market funds.
The CMFs in this study can be defined as a form of collective investment that allows
investors with similar investment objectives to pool their funds to be invested in a
portfolio of securities or other assets. The fund managers then invest the pooled funds
in the portfolio funds, which are assets classes such as cash, bonds, deposits, stocks,
commodities and others. The IMFs are operationally similar but differentiate
themselves from the CMFs in that they must conform to Shariah investment precepts.
In particular, the IMFs are managed by an investment company which initially raises
money from participants or investors to buy a diverse set of stocks, sukuks, and other
Page | 33
equity securities and to invest them in a group of assets with specific demands but
limited to Shariah-compliant products. The participants become shareholders and
receive an equity position on the underlying securities of the funds. The features of
IMFs and CMFs are compared in Table 2.1.
Table 2.1: The main differences between IMFs and CMFs Features IMFs
CMFs
The contract It is based on profit and loss sharing (PLS). Basically according to the Musharakah and Mudharabah principles. The Islamic financial system facilitates lending, borrowing and investments contracts based on risk-sharing basis or profit-loss sharing (Khan and Bhatti, 2008).
It is also a commercial-based contract, according to the lender and borrower contract. The investor is a lender who lends the money in order to get a high rate of return and the dividend.
Shariah- compliant
It is a legal requirement. The fund managers need to appoint the Shariah supervisory board.
It is not a legal requirement.
Investments Involvement is limited to certain activities which comply with Shariah principles and Islamic jurisprudence. The activities should not involve short selling and harmful investments, gambling, alcoholic beverages, non-halal products, cigarettes, prostitution, drugs, weapons and pornography. It is also not involved in interest-bearing deposits and interest-based banking and finance (Alhabshi, 1995; Bhatti 2009).
Involved in all activities, which can provide above the required rate of return.
Return Objectives
Profit maximisation is allowed but according to the Shariah principles and the Islamic jurisdiction.
Profit maximisation is always the fund’s objective without any restrictions.
Rate of Return It is based on profit rate. In Islamic equity financing, profit cannot be predetermined, but the proportion of the profit can be predetermined based on the capital ratio.
It is based on interest rate. It is predetermined and stated in the contract.
Riba element It is not allowed, according to Shariah law and a legal requirement. It is because riba rate is fixed and predetermined at the beginning of a contract.
It is accepted under the legal requirement.
Consequently, IMFs only offer investors the opportunity to invest in a diversified
portfolio of Shariah-compliant securities managed by professional fund managers
Page | 34
according to Shariah principles and guidelines. There is also a guideline for the
Islamic fund managers to appoint a Shariah advisory board or Shariah adviser for the
relevant funds. In Malaysia, this is regulated by the Guidelines of Unit Trust Funds
and supervised by the SC to ensure that their operations do comply with Shariah law.
The Shariah guidelines provide some important instructions on Islamic investing,
including asset allocation, portfolio screening, investment practices and income
distribution, and particularly on the enforcement of zakah (alms) as part of the
purification process (Girard and Hassan 2008).
More specifically, investment activities must follow certain criteria according to
Islamic law, such as the prohibition of riba (interest rate), the prohibition of maysir
(gambling), the involvement of only halal (legally permitted or permissible according
to Shariah law), and the activities and the obligation of zakah. Activities should also
not involve any short selling and harmful investments, alcoholic beverages, non-halal
products, cigarettes, prostitution, drugs, weapons and pornography. The investment
must also not involve interest-bearing deposits and interest-based banking and finance
(Alhabshi, 1995; Bhatti 2009). After fulfilling these criteria, all Islamic investment
products must be approved by the appointed Shariah supervisory board before they
are available on the market. This is to ensure that the main goal in developing Islamic
investments and their financial products is to obtain social-economic justice based on
abolishing the riba (interest rate) and other exploitative elements (Khan and Bhatti,
2008) as well as making profits.
2.4.1 Salient features of IMFs
The concept of Islamic mutual funds has its roots in the musharakah principle, an
Islamic investment vehicle wherein a syndicate of investors invests their capital in one
or more potential projects to share profits or possibly losses, also called profit and loss
sharing ventures. Common to this musharakah principle, the risks and rewards in
Islamic mutual funds are shared according to the equity participation (such as profit
and loss sharing) of each investor in the panel or the contract.
The list of Shariah funds excludes those companies whose major activities are
involved with interest-based banking and finance and conventional finance, gambling,
Page | 35
alcoholic beverages and non-halal products. According to (Ismail 1999), Islamic fund
management in Malaysia takes many forms, including in-house fund/portfolio
management, discretionary fund management by professional institutions and retail
Islamic unit trust. The Shariah guideline also ensures that those companies offering
Shariah funds undertake activities that are consistent with the mores of Islamic
investment and conform to Shariah principles.
Usually, in Islamic investments, investors have a range of choices when constructing
a financial portfolio. These include riba-free bank deposits; investments in Islamic
unit trusts and investment companies; private placements in Muslim businesses; and
investments in conventional institutions and businesses that undertake to deploy
funding from Islamic investors on a halal basis. Options regarded as haram include:
conventional bank savings and investment deposits; the purchase of interest yielding
bonds; and the acquisition of shares in companies involved in alcohol production or
distribution or in pork products. Participatory finance through musharakah as
previously discussed was one of the earliest forms of Islamic finance involving a
partnership between the provider of the capital and the user or entrepreneur (Wilson
1997, pp. 1331-1332).
According to Usmani (2007), the principles of Shariah governing IMFs or Islamic
investment funds should be subject to two basic conditions (pp. 203-204):
1. Instead of a fixed return tied up with their face value, they must carry a
pro-rata profit actually earned by the fund. Therefore, neither the
principal nor a rate of profit (tied to the principal) can be guaranteed.
The subscribers must enter into the fund with a clear understanding
that the return on their subscription is tied to the actual profit earned or
loss suffered by the fund. If the fund earns huge profits, the return on
their subscription will increase to that proportion. However, if the fund
suffers loss, they will have to share it also, unless the loss is caused by
negligence or mismanagement, in which case the management, and not
the fund, will be liable to compensate it.
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2. The amounts so pooled must be invested in a business acceptable to
Shariah principles. It means that not only the channels of investment
but also the terms agreed with the clients must conform to Islamic
principles.
The other feature is the presence of the Shariah board. Its primary mission is to ensure
that stocks selected are halal and remain so. The stocks invested in or scrutinised
must not involve companies engaged in any forbidden trade, incorporate an
unbearable amount of debt (debt-to-capital ratio > 33 %) or profit excessively from
interest income (non-operation interest income > 5 %). Considering these limits
requires constant attention and supervision of Islamic companies. Rebalancing the
portfolio of stocks is therefore done in close partnership with the fund managers.
Their role is to audit and monitor the firm, checking the company’s operations and
ensuring strict adherence to Islamic precepts (i.e., principles of Shariah and Fatwas).
They are also responsible for advising the fund managers on stock selection and for
inspecting closely the company’s stock activities (DeLorenzo, 2000).
Undoubtedly, the fund and its management also benefit as the services performed by
Shariah supervisors are directed towards the investors. Therefore, the supervisors
must take every possible step to ensure that the Islamic funds available in the market
represent halal investments for Muslim investors in particular. According to
DeLorenzo (2000), this is the vital challenge that needs to be resolved in order to
encourage Muslim people to participate in Islamic investment. This challenge is
related to the role and responsibility of the Shariah Supervisory Board to ensure that
all such income is free of impurities and completely halal according to Shariah law.
On behalf of the investors, again, it is the role of Shariah supervisors to ensure that the
purification process takes place according to Shariah principles and Islamic law. This
is unlike a Western company’s ethics, whose primary business and capital structure
are highly subjective and not easily quantified.
As a result of these efforts, the primary beneficiary is the Muslim investor who can
rest assured that his/her money is being utilised in accordance with the teachings of
Islam. In this circumstance, the responsibilities of a Shariah supervisor may be
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compared to those of an independent financial auditor (in the sense that regulatory
compliance is ensured). There is a further and far more vital aspect of the role of a
Shariah supervisor. In assuming responsibility for the Shariah compliance of a fund,
including its components and its management, the Shariah supervisor places himself
in a position to directly represent the religious interests of the investor. In discussing
the different aspects of Shariah supervision, it becomes clear that a Shariah supervisor
functions in different ways – as a consumer advocate with both religious and fiduciary
responsibilities.
Apart from Shariah supervision, the element that must be taken care of is the
purification process in the presence of zakah. In terms of the purification of the funds,
Muslims regularly purify their accounts by simply donating the interest earnings to
charity. However, the main concern here is the amounts of money earned by the
corporations in which the Islamic mutual funds have invested that are unacceptable
according to Shariah principles. These earnings must be quantified and then purified.
The sources of such earnings might include non-operating income from interest-
bearing investments or earnings from prohibited business activities that are beyond
the scope of a company's primary business. Whatever their source, the fact remains
that even Shariah-compliant equities will often yield small percentages of income that
are considered impure by Shariah standards, and which must then be purified
(DeLorenzo, 2000). IMF investment has much in common with modern forms of
investing such as ethical investment, socially responsible investment, faith-based
investment and green investment.
2.5 Islamic finance and Islamic investments
This section explains the principles of Islamic finance vis-à-vis conventional finance
and how these principles are related to Islamic investment and mutual funds. The
impact of global financial crisis in relation to Islamic finance is also discussed.
2.5.1 Fundamentals of Islamic finance
Islamic finance is derived from Islamic law (also known as Islamic jurisprudence or
Shariah law). The main sources of this law are al-Quran and al-Sunnah of the Prophet.
Hence, all activities in producing and accumulating wealth must be done according to
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this law. The concept of Islamic finance as well as Islamic investment is also derived
from similar sources. To complement their governance, the sources of law are also
derived from the opinions of Islamic scholars (ijtihad of ulama'). The objective of
Islamic finance is to combine economic growth with social justice and equity needs.
In an Islamic finance system, this law provides guidelines in the form of rules and
regulations that relate to the halal (permissible) and haram (forbidden). Therefore,
besides the written contract for business activities, the main principles governing the
philosophies behind the implementation of Islamic financial system are faith in
Tawheed (God), Ihsan (goodness) and amanah (trusteeship).
The major feature of Islamic finance is the prohibition of riba. Islamic finance also
prohibits gharar (uncertainty) and maysir (gambling) activities, and other activities
which are not allowed in Shariah law. Before Muhammad (PBUH) began to spread
the message of Islam in 610 CE, Arabia was in the midst of ayyam al-Jahilliyah, “the
Age of Ignorance”. He came to advocate not only submission to God but also ethical
responsibilities towards each other such as the prohibition of riba, which was seen as
a wicked financial entrapment of the needy. This prohibition is in the Holy Quran,
2:278, which is translated as “O ye who believe! Fear Allah, and give up what
remains of your demand for usury, if ye are indeed believers” (Bhatti 2007, p. 17). In
the early stages of Islamic history, Muslims therefore created a system that freed them
from interest, enabling them to be productive and develop their economy.
These features come from the same sources of al-Quran and al-Sunnah. They are
supported by other Shariah principles such as wealth generation, wealth distribution,
economic justice, work ethic, risk sharing, the mutual interest in contract, individuals’
and property rights and al-adl wa al-Ihsan (justice and beneficence). As a result, the
Islamic financial system is not limited to banking but also covers capital formation,
capital markets, equity markets and all other types of financial intermediation.
According to the Islamic Scholar Zarqa (1983), the Islamic financial system ensures
the optimal rate of capital formulation and its efficient utilisation leading to a
sustainable economic growth and fair opportunities for all. It is a value-based system,
which complies with Shariah principles and primarily aims to ensuring the moral and
material well-being of the individual and society. The philosophical foundation of an
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Islamic financial system goes beyond the factors of production and economic
behaviour. While the conventional financial system focuses primarily on economic
and financial transaction returns, the Islamic financial system puts its emphasis on the
ethical, moral, social and religious dimensions, to enhance equality and fairness for
the good of society as a whole (Zarqa, 1983). The main differences between Islamic
and conventional finance are summarised in Table 2.2.
Table 2.2: Main differences between Islamic finance and conventional finance Islamic Finance Conventional Finance
The contract It is a bilateral contract which is based on
certain assets (asset based). The relationship is between seller and buyer. Any trading assets not owned by the trader are considered void and banned under this contract.
It is a unilateral contract, which is based on certain debts. The relationship of the parties in the contract is between lender and borrower. The contract is considered valid as long as all the requirements of the contract are fulfilled.
Investment main objective
The main objective of the Islamic investment is to apply justice and community welfare.
Its primary objective is to maximise profit and to increase the shareholders’ wealth.
Shariah Supervisory Board
All Islamic financial products offered in the market must be approved by the Shariah Supervisory Board, to ensure that they comply with Shariah principles.
This requirement does not apply to conventional financial products.
Investment activities- Shariah-compliant products
Islamic finance limits its investments to Shariah-compliant products, and prohibits short selling and harmful investments, for example, investments involving cigarettes, prostitution, drugs and weaponry. All activities must conform to Islamic law. It consists of the following (Algaoud and Lewis, 2007, p. 38): riba is prohibited in all business transactions and investment undertaken on the basis of halal (legal, permitted) activities. Maysir (gambling) is prohibited and transactions should be free from gharar (speculation or unreasonable uncertainty). Zakah (alms) is to be paid by the bank to benefit society All activities should be in line with Islamic principles, with a special Shariah board to supervise and advise the bank on the propriety of transactions.
Conventional finance allows any investments generally without reference to any criteria or any cleansing screening process, as long as they could provide above average returns. It includes activities which involved gambling, speculation, short-selling and high risk (such as CDOs and swaps).
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Table 2.2 continued
How the system makes profit: profit rate or interest rate
In debt financing, Islamic finance uses the profit rate, which includes the profit margin in the selling price. The profit is fixed and it is determined at the beginning of the contract. The purchaser (borrower) needs to pay the whole selling price at the end of the contract time, without any extra cost. In equity financing, Islamic finance uses profit sharing or profit and loss sharing (PLS), which refers to mudharabah and musharakah respectively. Only the proportion of profit is determined during the contract, but the actual return is unknown, depending on the actual gain or loss from the project. It means that the profit is not fixed.
In debt financing, conventional finance uses the interest rate, which is excluded from the selling price. This interest rate is variable based on the current economic situation. At the end of the period, the borrower needs to pay to the lender the selling price and the nominal rate, which is the BLR and the actual rate. In equity financing, conventional finance also uses the interest rate to calculate the rate of return. The rate of return from a project is predetermined at the beginning of the contract. It is a fixed rate and not subject to the profit or loss obtained from the project.
2.5.2 Islamic finance and the principle of Islamic investments
Islamic finance is derived from the Shariah law, which can basically be divided into
two major themes: ibadah and muamalah.4 Muamalah refers to political, social and
economic activities (Ismail 1992).
Although the Islamic finance system considers all activities that are related to tasarruf
maliyyah (management of wealth) in the economy, the system is generally built on the
basis that all activities should be free from the element of riba and all investment
activities must be in line with the principles of Shariah, such as bay` (sale), bay’
bithaman ajil (deferred payment sale), ijarah (leasing), rahn (collateralised debt or
Islamic pawn broking), wakalah (agency or representative), wadi`ah (safe-keeping or
safe custody), ju`alah (wage, pay or reward), hiwalah (bill of exchange or
remittance), hibah (gift), al-qard hasan (benevolent loan) and the like, which have
been laid down in a specific contract or aqad. Specifically, Islamic finance should
comprise two important elements (Ab-Mumin, 1999):
4 Ibadah is concerned with the practicalities of a Muslim’s worship of Allah, whereas muamalah is concerned with human relationships.
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1. It should have the Islamic essence as a whole, which is not limited to its
labels. The application of the system should be Shariah-based and should
reflect its philosophy, values, ethics and the general objective. The main
objective of Islamic finance is to promote social justice and fairness through
economics and financial activities. Moreover, as indicated by (Lewis and
Algaoud 2001), the main objectives of the Islamic banking and finance can
be summarised as:
• The abolition of interest from all financial transactions and the reform
of all bank activities to accord with Islamic principles
• The achievement of an equitable distribution of income and wealth
• The promotion of economic development.
2. It should have the characteristics of a comprehensive and up-to date system
which is viable, competitive and comparable with the conventional financial
system.
Conclusively, a key principle of investment in Islamic finance is the prohibition of
riba. Riba means ‘interest’ in conventional economics terminology. Specifically, it
denotes the prohibition of payment and receipt of interest on deposits and loans. A
verse in al-Quran, al-Baqarah: 275, reveals: “But Allah [God] permitted trade, And
forbidden usury”. Therefore, Islamic investments limit their activities to investments
which comply with the Islamic finance principles derived from the Shariah law.
In the mid-1980s, as a result of the awareness of Muslim people to avoid usury5,
commercial activities were stimulated under the umbrella of interest-free schemes,
those without riba, and the term “Islamic financial system” became part of the market
parlance. However, describing the Islamic financial system simply as “interest-free”
does not provide a true picture of the system as a whole. The earlier reference to this
system as “Islamic banking”, which was first practised in 1963 and coincided with the
establishment of the Islamic Bank in Mit Ghamr, Egypt, is also not sufficient to
describe the system. However, the enhancements of banking activities in the Islamic
financial system have become milestones in the history of Islamic finance. The first
5 According to Shariah law, the word “interest” is the same as usury and refers to riba.
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benchmark was the establishment of the Islamic Development Bank in 1975 and the
most significant was the setting up of the Islamic Financial Services Board in 2002.
Overall, the system mainly aims to sustain economic growth in conjunction with
social justice needs to ensure that all activities conform to Islamic principles.
2.5.3 Islamic finance and the impact of the global financial crisis
Nobody denies that the current global financial crisis (GFC) requires a global
solution. It has been suggested that the most striking feature of the crisis is the
diversity of its impact. Different countries have been affected in different ways and
now face very different economic futures. The major financial shock that began the
crisis originated in the US when the sub-prime mortgage market collapsed and in turn
wrecked the derivatives and securitisation markets of commercial and investment
banks.
In the US, the shocks were caused by the virtual collapse of two keys industries that
rely heavily on finance and on confidence: housing and the automobile industry. The
final quarter of 2008 demonstrated that the motor vehicle output was 31 per cent
down when compared to the previous year, but other GDP factors were unchanged.
During the same period, housing investment declined by 19.5 per cent. The impact on
Britain was also immediate. The largest mortgage lender, Northern Rock, collapsed
and created major pressure on other British banks. The crisis spread due to the
implosion of housing bubbles and caused many countries to fall into recession or
nearly so. Too many countries had their banking and financial systems in utter
disarray; financial asset prices crashed and in some places real asset prices collapsed
as well (Highfill, 2008).
The global crisis in 2007–2008 is considered to have been the worst during the last
100 years (Ali, 2009). The International Monetary Fund forecast that the crisis would
be the first post-World War II demand-shock recession (Harding, 2009). Indeed, the
current global crisis has been compared to the Great Depression of the late 1920s.
Whether the current global slowdown can reach such proportions still remains to be
seen but, in reality, the world has seen how institutions underestimated the complexity
of financial products. They were unable to adequately measure tail risks and the pro-
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cyclical impact of fair value accounting, and failed to manage maturity mismatches,
which contributed to the financial crisis (Ghani, 2009). There are several reasons for
the impacts of the crisis on global finance, particularly on conventional banking and
finance. The reasons include the sub-prime mortgages, the incentive effects of
securitisation, a co-mingling in collateralised debt obligations (CDOs), monocline
insurers, insurers selling credit default swaps (CDS), US government sponsored
enterprises, and excessive leverage. The application of a new banking model which
involved “originate and sell” so that the banks could sell loans in the capital market
increased the risks associated with banking and finance products due to market
speculation (Amin 2009).
Ali (2009) further indicated that the root causes of the crisis included the debt culture,
moral failure and excessive speculation leading to poor or non-existent market
discipline, and inappropriate financial products. According to him, the size of the
CDS in 2007 alone was US$62.2 trillion and the size of the derivatives products more
than US$600 trillion. As a result, the conventional banks worldwide are now nursing
losses of more than US$400 billion from the credit crunch, particularly in subprime
mortgages, while the Islamic banks are virtually safe and sound. On the other hand,
the global crisis represents more than US$1trillion to the Islamic finance industry with
an opportunity to expand its demand beyond Muslim investors, particularly to ethical
and socially responsible investors. Nonetheless, Islamic finance has remained solid in
the face of the difficulties encountered by conventional finance due to the crisis.
The global crisis attracted world attention to Islamic financial products. Global
attention is changing from investment in conventional finance to investment in
Islamic financial products. This situation has arisen due to the development of a
modern finance that officially started in 1963, with Islamic financial institutions
growing much faster than conventional banks. It has witnessed growth from 176
banks with assets of US$148 billion in 1997 to 270 holding assets of around US$265
billion in 2001 (Hassan and Lewis 2007). During the crisis period, the total global
Islamic financial assets amounted to 40 per cent of the assets of the largest
conventional bank, with 300 market players globally (Zeti, 2008). In Malaysia the
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Islamic finance assets expanded by 22 per cent to RM$192 billion in 2008, and now
account for 15.0 per cent of the total assets in the industry (Ghani, 2009).
From the Islamic perspective, the root cause of the crisis was the fragility of the riba
system6. Thus the end result was the failure of a number of banks because of their
practices, generating uncertainty and a credit crunch. This led to a tightening of credit
to firms in other industries. The crisis may culminate in a severe recession leading to
the default of firms in the real estate sector as well as the financial sector (Ebrahim,
2008, pp. 111-112).
The Islamic finance system is not involved in activities that contain the element of
gharar – speculation, short-selling, and selling and buying debts. The system from the
beginning avoided excessive risk and instead promoted risk sharing in its principles
and some of its products. As a result, activities and products which involve greater
risk such as short-selling, some derivatives products and the CDOs are prohibited
according to Shariah principles. Thus, the crisis had a minimal impact on Islamic
finance because of it being so different from conventional finance. Western countries’
authorities intervened in the crisis by banning short-selling activities. The US
government temporarily banned short selling in 900 financial institutions and the UK
banned short selling in 34 financial institutions stocks (Securities-Commission-
Malaysia, 2008).
The subprime crisis in the US had a global impact on liquidity, causing many
conventional banks to tighten their lending criteria. It is suggested that syndicated
Islamic finance could become a mechanism for tapping alternative sources of liquidity
in the current credit environment. In 2007 there were approximately 28 syndicated
Islamic finance deals with a total value of US$15.2 billion. Due to the Shariah issues
associated with investing in subprime debt, many Islamic banks have stayed away
from this area and have been quite well insulated from the effects of the credit crunch.
Syndicated Islamic finance has strong growth potential now and for the foreseeable
future (Iqbal, 2007). On this theme, Iqbal (2007) emphasised two underlying forces
6 Riba system refers to the interest-based system which is practised in the conventional financial system but is banned in the Islamic financial system.
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that increase the appeal of Islamic finance. The first, paradoxically, is the fallout from
the subprime crisis, which has caused a tightening of global credit in conventional
financial markets. This has resulted in a need to tap new sources of liquidity, Islamic
finance being a prime option. The second is the growing requirement for larger and
more complex Islamic funding. For example, companies in Saudi Arabia are looking
to raise US$6.25 billion to finance a new phase of projects in the world’s top oil
exporter. Saudi Basic Industries Corp. (SABIC), Saudi Arabian Mining Co. and
Chevron Phillips Chemical Co. are all seeking cash for projects, primarily in
infrastructure. The size and similarity of these projects could make it hard for them to
compete for conventional funding, given the current market conditions. These projects
may therefore find alternative sources such as Islamic finance, which is appealing as it
is not severely affected by the credit crunch.
It is therefore crucial for Islamic finance to be recognized as a dual-system, which
works in parallel with the conventional financial system and at the same time could be
enhanced by its deeper knowledge and experience. As described by Amin (2009),
Islamic finance is a subset of conventional finance and it is compulsory for it to
complement the conventional system in order to conform with international rules and
regulations, despite its links to Shariah principles that cannot be compromised.
However, to move forward, the sustainability of Islamic finance in the world market
would require the following aspects to be executed (Akhtar 2007a):
1. Further deepening the efforts to enhance the legal and regulatory framework
of Islamic finance so that they are consistent with international practices.
2. Continued efforts to conform and align the structures and products with the
Shariah principles would help Muslims’ motivation to adopt this alternative
mechanism of financing, while attracting non-Muslims to explore the products
as well.
3. Recognising that Islamic finance has perpetuated and changed the dynamics of
cross-border private capital flows, this industry has the great potential to
augment the process of globalisation and financial integration, yet this requires
more cooperation and vigilance on the part of home and host regulators.
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4. Launching aggressive efforts to implement the evolving Islamic financial
regulatory and supervisory standards and capturing the different types of risks
associated with Islamic finance, while launching consumer protection
frameworks.
5. Promoting more financial diversification by encouraging financial innovation
and Islamic capital market development.
Hence, the key drivers for enhancing the competitiveness of Islamic finance include
the following (Akhtar 2007b):
1. Financial engineering and innovation
2. Global financial centres and their regulators’ support of the Islamic finance
industry
3. Standard governance, prudential regulation and supervisory guidance require
tweaking regulations to properly identify and assign proper weights for new
and different types of risks associated with the special and unique
characteristic of Islamic finance business.
4. Development and adoption of a simple, standard and cost-effective legal
framework for contracts associated with the new and hybrid products
5. Flexible and practical applications, enforcement of Shariah principles and
injunctions, and their acceptability by the public
2.6 Theoretical framework for mutual fund performance
This section elaborates theoretical framework used to assess the performance of
mutual funds. Important issues of focus for our analysis includes returns performance
against market benchmark, performance based on market timing and selectivity skills
and performance in relation to fees and selective fund attributes. A summary of the
empirical evidence of some previous studies on the mutual fund performance is
presented in Table 2.3 at the end of this section. Overall, the key relevant literatures
in this thesis can be explained into five subsections as the following.
2.6.1 Performance measurement against market benchmark
Many theories deal with the evaluation of mutual fund performance based on the
return performance of the funds in relation to their market benchmark at gross or after
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adjusted for the risk-free rate return. One of the popular performance evaluation
models is based on CAPM theory.7 The average outperformance or underperformance
of a fund in a portfolio or individually is determined by its high or low alpha, i.e., the
coefficient estimate obtained from the regression based on the CAPM. It is therefore
evident that potential investors will seek the higher alpha of the mutual funds since
the higher alpha means a higher return or abnormal return that they can obtain from
their investments. Bello (2005) validated the CAPM theory when he illustrated a
strong relationship between the mean return of mutual funds and risk. He also found
no significant linearity between return and volatility, measured by the standard
deviation, thus noting that the beta is absolute when measuring risk based on CAPM
theory.
Most of the evidence from the US and the UK indicates that, on average, return
performance of the mutual funds is not able to outperform the respective market
return portfolio (Benos and Jochec, 2011; Blake and Timmermann, 1998; Carhart,
1997; Elton et al. 1993; Firth, 1977; Grinblatt and Titman, 1994; Jensen, 1968;
Malkiel, 1995; Pollet and Wilson, 2008). For example, in the UK market, Firth
(1977) noted that none of the individual funds during 1965–1975 experienced
abnormal return performance, thus implying that, on average, UK fund managers are
not able to forecast the fund price so that it can perform better than a simple buy-and-
hold policy. Blake and Timmermann (1998) also noted similar evidence and it
appears that the funds underperformed approximately 1.8 per cent per annum on mean
excess return of the five-year UK government bond over the period 1972 to 1995.
Studies on mutual fund performance in Asia-Pacific countries such as Australia and
Malaysia reveal that the findings are in line with those in the US and the UK. The
study on Australian managed funds from 1983 to 1995 using conditional measures of
CAPM provided no abnormal returns (Sawicki and Ong, 2000). Previously, Robson
(1986) and Hallahan and Faff (1999) also reported inferior performance for overall
fund return against the respective market indices over the periods 1969 to 1978 and
1988 to 1997, respectively. Moreover, there is also evidence that, on average, active
7 Sharpe (1964) and Lintner (1965) are among the scholars who have led to the establishment and the
development of this CAPM model in measuring performance of mutual funds worldwide.
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Australian superannuation funds were unable to earn superior risk adjusted return in
relation to the relevant market benchmarks (Gallagher, 2002). There is also evidence
of the underperformance of Hong Kong mutual funds with reference to the market
benchmark (Abdel-Kader and Qing, 2007).
In Malaysia in particular, previous studies, except that of Chua (1985), have indicated
that, on average, the overall Malaysian mutual fund industry including IMFs performs
worse than the market return portfolio (see for example, Abdullah et al. 2007; Annuar,
Shamsher, and Hua, 1997; Aw, 1997; Low 2007; Shamsher and Annuar, 1995; Taib
and Isa, 2007). The most recent of the studies, (Low (2007), indicated that mutual
funds in Malaysia perform poorly in relation to the market benchmark, a proxy either
by the KLCI or the EMAS index. Taib and Isa (2007) further showed that, on average,
the Malaysian mutual funds industry underperforms compared to its market
benchmark and the portfolio for risk-free asset returns. Abdullah et al. (2007) also
stated that, on average, the whole Malaysian funds in their sample underperformed,
further noting that conventional funds perform better than Islamic funds during good
economic periods and worse during bad economic periods.
In the mid–1990s, Shamsher and Annuar (1995) examined the mutual funds’
performance from the 1980s to the early 1990s and found a contrasting result where
the return performance of mutual funds in Malaysia was below that of the market. Aw
(1997) also found 32 underperforming Malaysian mutual funds over the period from
1984 to 1996 in relation to the KLCI market benchmark. However, the only
outperformance evidence of Malaysian mutual funds indicated by Chua (1985) was
based on his study of the performance of 12 Malaysian funds managed by Amanah
Saham Mara Berhad and Asia Unit Trust Berhad from 1974 to 1984.
It is therefore believed that there has been a shift of fund performance over time in
Malaysia (Taib and Isa, 2007). This was confirmed in the study by Saad et al. (2010),
which found that the Islamic mutual fund companies in Malaysia are comparable to
their conventional counterparts. By using the data envelope analysis (DEA) approach,
they found that some of the Islamic mutual fund companies were above average in
terms of total factor productivity (TFP) performance. Recently, Hayat and Krauessl
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(2011) and Hoepner et al. (2011) found that most of the IEFs worldwide
underperform when set against the market benchmark. The underperformance consists
of 31 Malaysian IEFs included in this study, and such underperformance worsens
during a crisis period (Hayat and Kraeussl, 2011). In contrast, Hoepner et al. (2011)
found in their study that the Malaysian IEFs performed competitively well in relation
to the international equity market benchmarks. Their study from September 1990 to
April 2009 included data for 265 Islamic equity funds, of which 76 were from
Malaysia.
Another concern regarding the benchmark is the sensitivity of the choice of
benchmark (Grinblatt and Titman, 1994; Kothari and Warner, 2001). They
investigated performance using different benchmarks and confirmed that performance
is sensitive to the benchmark used, implying that the right choice of benchmark is
important. Kothari and Warner (2001) suggested that multi-factor models give a better
explanation of cross-section fund returns in the US. However, the evidence is contrast
to the finding of Low (2007), who indicated that using a different benchmark had no
impact on mutual fund performance in Malaysia.
2.6.2 Market timing expertise of fund managers
In the international market and the US market, a strategy of market timing ability is
becoming a common phenomenon and this concept is still relevant, with some mutual
funds providing evidence of negative or inferior market timing ability (Chang and
Lewellen, 1984; Chen et al. 1992; Henriksson, 1984), while others reveal positive or
superior market timing (see Bello and Janjigian 1997; Lee and Rahman, 1990;
Lehmann and Modest, 1987). Other important earlier studies in the US demonstrating
poor performance by MF managers are those by Chang and Lewellen (1984) and
Henriksson (1984). They provided evidence of negative market timing skills. Chang
and Lewellen (1984) applied parametric tests covering 67 MFs over the period
January 1971 to December 1979 and noted that fund managers were collectively
unable to outperform a passive investment strategy or to initiate market timing skills.
Henriksson (1984) on the other hand, applied parametric and non-parametric tests
methods to analyse the stock selectivity and the market timing of 116 MFs for the
period 1968 to 1980, and found no empirical evidence that MF managers could
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outperform the investment selection strategy or perform successfully on market
timing.
Kon (1983) and Henriksson (1984) also found a negative correlation between market
timing and stock selection skills. Kon (1983) studied 37 funds from January 1960 to
June 1976 and observed that fund managers as a group had no special information to
outperform on returns of the market portfolio. However, his study provided evidence
of significantly superior timing ability and selection skills performance at the
individual level. Kon (1983) reported that 14 out of 37 funds had overall positive
timing, yet none of them was statistically significant. Compared to the findings of
Kon (1983), Lehmann and Modest (1987) and Lee and Rahman (1990) found
evidence of positive selection abilities and superior market timing being executed by
fund managers at the individual funds level. Lehmann and Modest (1987) used the
arbitrage pricing theory model and found significant measurements of abnormal
market timing and stock selectivity performance among fund managers.
Admati et al. (1986) confirmed that the TM model developed by Treynor and Mazuy
(1966) was a valid measure of market timing ability, with Lee and Rahman (1990)
further detecting selection ability and market timing ability of a fund manager based
on monthly returns for 87 months from January 1977 to March 1984 for a sample of
93 funds. They reported that 14 funds out of 37 funds had overall positive timing, but
none of them was statistically significant. Furthermore, 10 funds had both significant
selection and timing skills, four funds had significant selection skill with no timing
skill, while five funds had significant timing skill with no selection skill. Lee and
Rahman (1990) also indicated that the test of market timing that ignores
heteroskedasticity rejects the null hypothesis of no market timing too often, when, in
fact, the null hypothesis is accepted after the heteroskedasticity correction. The
implication is that the correction decreases with the number of superior market timing
occurrences.
Ippolito (1989) studied fund performance for the period 1965 to 1984, using a sample
of 143 MFs, and found evidence of positive alpha within that period, implying that
there was a superior fund or stock selectivity performance among the fund managers.
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On the other hand, Chen et al. (1992) conducted a study on 93 MFs over a period of
87 months and indicated that a trade-off existed between market timing and security
selection ability. They provided evidence consistent with the findings of Chang and
Lewellen (1984), Henriksson (1984) and Kon (1983) that collectively fund managers
had no market timing ability for the period of January 1977 to March 1984.
Elton et al. (1993) also argued that no evidence was found that MF managers were
able to time the market successfully. They examined the overall portfolio performance
of MFs for the period 1965 to 1984 by adopting the original TM model. Their study
found that a fund manager had no selection ability and, specifically, they provided
contrasting evidence to Ippolito (1989) and concluded that fund managers
underperformed in passive portfolios, with funds consisting of higher fees and
turnover underperforming funds with lower fees and turnover in the portfolios.
Less than a decade later, Bello and Janjigian (1997) documented positive and
significant market timing abilities and security selection abilities for 633 funds from
1984 to 1994. They used the extended TM model by controlling the effects of non-
S&P 500 assets held in the fund portfolios. The evidence from the original TM model
failed to reveal a positive market timing ability. However, the extended TM model
revealed that on average there was a positive market timing ability and superior fund
selectivity skill in this period.
In the Malaysian market, the findings from most of the studies, excluding that of
Hayat (2006), have indicated a negative market timing ability of fund managers
(Abdullah et al. 2007; Ahmed, 2007; Annuar et al.1997; Elfakhani et al., 2005; Low,
2007). Annuar et al. (1997) provided evidence that, on average, mutual funds in
Malaysia had a positive selectivity performance but no market timing ability over the
period 1990–1995. Elfakhani et al. (2005) indicated similar results for the period
from January 1997 to August 2002. There was also poor selectivity performance of
the stock selection ability and market timing ability of the Islamic and conventional
fund managers in Malaysia for the period from January 1992 to December 2001
(Abdullah et al. 2007).
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Ahmed (2007) concluded that generally fund managers perform poorly in security
analysis and market timing. The study tested 60 individual funds from 1998 to 2004
and found that only two funds were superior in timing ability. Similarly, Low (2007)
indicated that fund managers had poor timing ability of over the five-year period from
1996 to 2000 regardless of the market benchmarks used. She found that, on average,
there was negative timing for the full sample of 40 funds, with some weak evidence of
positive timing at the individual level. She also revealed a significant negative
correlation between timing and fund selectivity, implying that good fund managers
with selectivity skills tend to be poor market timers. Hayat (2006), however, found a
relatively better market timing ability among IMF fund managers in Malaysia from
August 2001 to August 2006.
In other markets, Hallahan and Faff (1999) suggested that there was negative
selection performance and little evidence of market timing ability (8 out of 65 funds)
in the Australian market from 1988 to 1997 and that most of the individual funds
exhibited negative alpha. Imişiker and Özlale (2008) found weak evidence for
selection ability and some evidence for superior market timing quality in the Turkish
market, and remarked that experience had emerged as an important factor contributing
to superior market timing ability. Their study showed that out of the 49 mutual funds
in their samples, 20 and 22 funds were superior before and after correction for
heteroskedasticity. Thus, in contrast to the findings of Lee and Rahman (1990),
adjusting for heteroskedasticity increases the number of occurrences of superior
market timing ability. Abdel-Kader and Qing (2007) in their study on Hong Kong
actively managed mutual funds concluded that there was no selectivity and market
timing ability for a sample of 30 funds from August 1995 to July 2005.
2.6.3 Performance persistency
Many studies in the US have found that, on average, performance persistence exists in
mutual funds (Brown and Goetzmann, 1995; Busse and Irvine, 2006; Carhart, 1997;
Grinblatt and Titman, 1993; Henriksson and Merton, 1981; Malkiel, 1995; Sharpe,
1966). Sharpe (1966) discussed the relationship between the stock market and its
persistence in relation to the performance of mutual funds. He noted that the
Page | 53
implications of the capital market model on the performance mutual funds are
relatively straightforward and directly significant.8
Sharpe (1966) employed the average rate of return of a portfolio (��) and the actual
standard deviation of its rate of return (��) and defined performance persistence in the
scenario where all funds provide rates of return, giving��; �� values lying generally
along a straight line. In other words, performance persistence occurs where all funds
hold properly diversified portfolios and spend the appropriate amount on analysis and
administration. However, in the situation where some funds fail to diversify properly
or spend too much on research and administration, they persistently give rates of
return yielding inferior ��; �� values. Thus, performances are poorer and could be
expected to remain so (Sharpe, 1966, p. 122).
Hendricks et al. (1993) and Brown and Goetzmann (1995) provided evidence that
mutual funds in the US market perform in the short term. Carhart (1997) also
evaluated fund performance in the US and explained that performance persistence is
largely occurring in the worst performing funds, while in the emerging market
performance persistence largely occurs among the winner funds (Huij and Post,
2011). Blake and Timmermann (1998) indicated weak evidence for both top and
bottom performers in the period 1972–1995, while Cuthbertson et al. (2008) found
that the performance of past-winner funds did not exhibit persistence and past-loser
funds remained losers in the UK market over the period of study, 1975–2002,.
In the Hong Kong finance market, Abdel-Kader and Qing (2007) found that
performance persistence occurred among good and poor fund performers when a two-
year interval was used to define a short-term period. Meanwhile, Suppa-aim (2010)
found that there was a short-term persistence in mutual fund performance in Thailand
from June 2000 to August 2007. Similarly, Low and Ghazali (2007) discovered that
8 Sharpe (1966) in his study employed the ex post values of the average rate of return of a portfolio
(��) and the actual standard deviation of its rate of return (��) to predict future performance. Since the capital market model implies that the values for �� and �� for efficient portfolios should lie along a straight line, therefore, the higher values of �� are associated with higher values of�� . The model also indicates that a fund with a higher risk is expected to give a higher return. However, in this case, the values of �� and �� will not lie precisely along the straight line. For example, due to there being an element of risk in the stock market, this relationship of risk and return could still be visible and statistically significant, yet this needs further investigation.
Page | 54
there was a short-term relationship between Malaysian mutual funds and the stock
market in 1996–2000. This was because the price of mutual funds is related to the
stock market index, the KLCI, thus implying that fund managers respond to historical
performance and the movement of the stock market while determining their portfolio
selection. In contrast, Taib and Isa (2007) revealed that there was no persistence in
return performance in Malaysia over the period 1991–2001.
2.6.4 Empirical evidence on fees and fund attributes on performance
Fees are an important part of mutual funds and they are normally associated with the
returns performance of the funds. The fees are higher in normal funds than in index
funds. In other words, fees associated with active management funds are higher than
those with passive funds. From the investment point of view, the strategy of active
management funds seeks to create value and achieve alpha. As a result, fees are
higher and weighted towards better performance. On the other hand, the strategy of
passive management funds aims to track indices and to achieve beta. Therefore, the
fees of passive funds are normally lower than for the active management style and
weighted towards management (Ernst-&-Young, 2009). For fund managers, higher
fees could give more opportunities to create better returns. However, investors are
interested in funds associated with less expense since high expense funds have lower
net returns.
With reference to mutual fund investments, there are several types of fees and
expenses involved. These can be categorised into two types: load fees and operating
expense fees. The load funds normally charge both. And no-load funds charge only
expense fees without compulsory or load fees. Part of load fees is a sales load or a
sales charge, collected at the inception date of the fund being bought. Another part is
the exit fee or redemption fee, which is a deferred sales charge collected when the
mutual fund is redeemed. Some mutual funds charge only front-end fees but not back-
end or exit fees.
Operating expenses can be in the form of management fees, 12b-1 fees, trustee fees
and other fees. Management fees are investment management fees that are based on
an annual percentage of assets under management to help pay off the fund managers.
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The fees are calculated daily in the calculation of the NAV of the fund. These are paid
by all funds, load and no-load funds. There are also other fees such as 12b-1 fees,9
which are used to compensate brokers or to pay for advertising. In the Malaysian case,
the expenses fees consist of management fees, expense ratio, trustee fees and
switching fees, if any.
The findings from the conventional fund literature have indicated that fees do have an
adverse impact on investors. Most of the funds are not able to outperform their market
benchmarks; in fact, they perform even worse after deducting fee expenses from the
gross returns (see for example, Carhart, 1997; Haslem et al., 2008; Iannotta and
Navone, 2012; Malkiel, 1995).
Earlier studies highlighted the performance of mutual funds compared to the fees in
the US market. Malkiel (1995) showed that investors would have been better off
buying low-expenses index funds, since the funds did not achieve sufficient gross
returns to compensate for their management fees incurred during the 1971 to 1991
period. The funds underperformed their market benchmarks both after management
expenses and even gross of expenses excluding load fees. This is in line with the
study by Sharpe (1966), who noted that a higher Sharpe ratio (reward-to-volatility
ratio) is related to the fund performance with lower expenses.
Ippolito (1989), on the other hand, found that mutual fund returns were not related to
expense ratios and turnover during the period 1965 to 1984. He revealed that risk-
adjusted returns appeared to exhibit a negative correlation with expense ratios. He
further indicated that the risk-adjusted returns performance of the US mutual funds
(net of fees and expenses but including the load charges) are relatively comparable to
the returns performance of the index funds and adequate enough to compensate the
higher fees. He also argued that the relationship between mutual fund return and both
expense ratio and turnover in previous studies is associated with an active investment
management (Ippolito, 1993). Similarly, Elton et al. (1993) found that risk-adjusted
returns appear to exhibit a negative correlation with the expense ratios.
9 The 12b-1fees are popular in US mutual funds but currently are not imposed on Malaysian mutual funds.
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However, in contrast to Ippolito (1989), Elton et al. (1993) and Carhart (1997) argued
that high fees do not perform as well as the low fee funds, and therefore higher fees
are negatively correlated to fund performance. Elton et al. (1993) found that funds
with higher fees and turnover underperform compared to those with lower fees and
turnover. Carhart (1997) further explained that investment costs in mutual funds such
as expense ratios, transaction costs, turnover and load fees have a direct negative
impact on performance. Carhart (1997) also identified that persistence in expense
ratios is attributable to long-term persistence in mutual fund performance. He also
revealed that portfolio turnover and load fees are significantly and negatively related
to fund performance. More precisely, the expense ratios appear to reduce performance
to little more than one-for-one. Turnover reduces performance about 95 basis points
for every buy-and-sell transaction. The differences in costs per transaction account are
spread in the best and worst performing mutual funds.
Golec (1996) observed a similar relationship between fund performance and expenses
as found by Elton et al. (1993) and Carhart (1997). In fact, Golec found general
evidence that funds with low fees tend to perform better than those with high fees. He
also noted that funds with low administrative expenses perform relatively well. Older
and larger funds are associated with lower fees. Load fund is significantly negatively
correlated with management fees, implying that the fund trade-off is between lower
management fees and front fees. However, he indicated that funds with high
management fees do not necessarily imply poor performance but signal superior
investment skills leading to better performance. He therefore suggested that investors
should avoid funds with high operating expenses but not necessarily funds with high
management fees.
In the early 1990s, Chance and Ferris (1991) studied mutual fund distribution fees and
defined expense ratio as total expenses divided by total assets. They developed a
multiple regression model of the expense ratio as a function of six explanatory
variables: objective, growth and income, age, size, the existence of load charge and
the 12b-1 fees over each of the years from 1985 to 1988. The results – based on the
percentage of cross-sectional variation in expense ratios at about 42–50 per cent –
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revealed that both growth and income coefficients were negative and significant,
indicating that funds with growth or income as an objective have a lower expense
ratio than funds with the objective to maximise capital gains. Load variables were
negative and significant, except for the year 1986, implying that load funds have
lower expense ratios than no-load funds. However, the 12b-1 was positive for most of
the years and highly significant, suggesting that the 12b-1 fees increase the expense
ratio. Chordia (1996) further revealed that funds with load and redemption fees (exit
fees) hold less cash than those with no-load counterparts, therefore suggesting that
mutual funds dissuade redemptions through front and back-end load fees. On the
other hand, funds hold more cash when there is uncertainty about redemptions.
Indro et al. (1999) stated that fund size, which is based on net assets under
management, affects mutual fund performance. This is in line with the findings of
Chance and Ferris (1991), who stated that size is negative and highly significant in
relation to fund performance, reflecting the economies of scale associated with large
mutual funds. As a result, Indro et al. (1999) suggested that mutual funds must attain a
minimum size in order to achieve sufficient returns to justify their costs of acquiring
and trading on information. They also found that trading on information contributes
positive returns only for the value and blend categories of funds and not for growth
funds’ counterparts, which means that the size of net assets is important for growth
funds rather than for value and blend categories of funds.
Chen et al. (2004) analysed size and fund performance and investigated the work of
equity mutual funds in the US from 1962 to 1999. They wanted to check if
performance depended on the fund size measured by the log of the total net assets
under management. They provided evidence that performance declines as the fund
size increases. This inverse relationship between funds’ performance and size is
related to liquidity. They suggested that size and liquidity erode performance and this
is due to organisational diseconomies linked to hierarchy costs. They found that the
fund size erodes the performance in a much more pronounced way among small cap
stocks.
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Elton et al. (2003) extended the study on the impact of incentives fees and identified
that a key factor that affects performance is the size of expense ratios. An incentive
fee is used to compensate the fund managers and defined as a reward structure that
makes management compensation a function of investment performance in relation to
a benchmark. The funds that employ incentive fees emerge as having better alphas
(positive stock selection ability) since they charge lower expenses. However, the
funds on average are low risk compared to the market risk, as the beta of these
incentive-fee funds is less than one.
Haslem et al. (2008) studied the performance of mutual funds in the US market and
indicated that, on average, superior performance occurs among large funds that have
low expense ratios, low trading activity and no or low front-end loads. However, there
is no difference in performance of funds with respect to whether they have 12b-1 fees
or not. They also found evidence consistent with other studies that, on average,
actively managed mutual funds underperform their market benchmark after deducting
expenses. These expenses consist of management fees, 12b-1 fees and other fees,
excluding sales loads and fees directly charged to shareholder accounts and security
transaction costs. These refer to brokerage fees, bid-ask spreads and market impact
costs. Consistent with this study, Gil-Bazo and Ruiz-Verdú (2008) suggested that
better-quality funds are not expected to charge higher prices. They revealed that
worse-performing funds set fees that are greater than or equal to those set by better-
performing funds. As a result, they suggested that the funds should disclose the level
of fees charged in comparison to the average or median fees of other corresponding
funds in the same category to avoid overcharging unsophisticated investors.
Pollet and Wilson (2008) also noted that higher expenses (expense ratio) and total
load (combination of front-end and back-end loads) associated with funds are
significantly negative to the returns, but higher industry concentration is positively
significant to the marginal effect of a fund’s market capitalisation style. Large and
small funds diversify their portfolios in response to growth, but the diversification is
less pronounced for the large-cap funds and family funds with a large numbers of
siblings. They also found that greater diversification of the small-cap funds is
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associated with better performance, after controlling for fund size and fund family
size from 1975 to 2000 in the US.
Giambona and Golec (2009) evaluated the performance of 3696 retail equity funds
from 1962 to 2002, and indicated that larger incentive management fees lead to less
counter-cyclical or more pro-cyclical volatility among fund managers, since the
additional risk could earn them larger returns and fees. There is a positive relationship
between fees and volatility timing as the fund managers’ market volatility timing
strategies are partly driven by their compensation incentives.
Massa and Patgiri (2009) argued that if higher incentives compensation only increases
risk-taking and reduces the probability of survival in the US mutual fund industry,
then there should be no relationship between performance and incentives after
controlling the risk. Therefore they expected that there should be a positive
relationship between performance and incentives even after controlling the risk and
survival. Their findings supported this hypothesis and revealed that higher incentives
induce managers to take more risks and reduce the probability of funds’ survival.
Funds with higher incentives also deliver higher risk-adjusted returns and evidence of
persistence in performance, even after controlling for survival. Consequently, they
provide investors with a surplus. Massa and Patgiri suggested that incentives could be
a useful tool to motivate fund managers and increase welfare.
A recent study based on the US equity mutual funds found that there is insignificantly
negative relationship between past return performance and fees, a proxy for the
expense ratio net of 12b–1 fees. On average, the older funds tend to charge higher
expense ratios and funds with a higher degrees of risk charge more fees. The fees’
dispersion decreases with the fund size and age. Furthermore, the fees’ dispersion is
lower for the funds that charge market and distribution fees, namely, the 12b-1 fees
(Iannotta and Navone, 2012).
In market other than the US, studies on fees and fund performance have been quite
limited. Dahlquist, Engstrom, and Soderlind (2000) found that performance is
negatively correlated to fees, with higher fee funds tending to underperform relative
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to lower fee funds in the Swedish market from 1993 to 1997. There are some cases
when higher fee funds perform better than low fee funds but only at the gross level,
i.e., before fees are deducted. They basically found that actively managed equity
funds perform better than passively managed funds, indicated by the alpha in a linear
regression of fund returns on several benchmark assets, allowing for time-varying
betas. They also studied the relationship between fund performance and other fund
attributes and provided evidence that good performance occurs among small equity
funds, low fee funds, funds whose trading activity is high and, in some cases, funds
with a good history of business. They employed a cross-sectional analysis, revealing
that large equity funds tend to operate more poorly than small equity funds
In a study on Finnish mutual funds from 1993 to 2000, Korkeamaki and Smythe
(2004) noted that the funds’ fees decreased over time, suggesting that the market is
increasing in competitiveness. The fees are usually higher for older funds and the
funds that are managed by banks cannot be offset by their superior returns. However,
funds from larger families and funds from institutional investors have lower fees.
International equity funds also have lower fees than their domestic counterparts.
Geranio and Zanotti (2005) investigated the determinants of mutual fund fees in Italy
over the period 1999–2002 using 1958 funds sold on the local market. In their study,
total expense ratio is the annual percentage reduction in investor returns that would
result from operating costs even if the fund’s portfolio were to be held or not traded
during that period. The operating costs are annual costs, including management fee
and administration, custody, audit, legal and distribution fees. Geranio and Zanotti
(2005) further stated that funds which are larger in size, bigger in asset management
companies and domiciled abroad charge lower fees. However, the age of a fund does
not show a significant relationship with the total expense ratio. The total expense ratio
is also lower for load funds. The study also contended that funds sold exclusively by
financial advisors charge lower fees than those sold only by banks. More specifically,
funds sold by financial advisors charge higher redemption fees than funds sold by
banks, which contradicts the findings that funds distributed by banks charge
significantly higher front fees than funds distributed by financial advisors.
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In Australia, Gallagher (2003) noted that there is evidence of significantly higher
management fees charged by managers with larger Australian equities that benchmark
allocation exposures. The performance of Australian investment managers is also
significantly negatively related to the age of the institution. He also stated that
management fees or expenses are not related to the fund asset or fund size. Huij and
Post (2011), however, found that the winner funds in emerging markets covering 22
countries including Malaysia provide returns more than sufficient to cover fee
expenses. They suggested that emerging market funds generally exhibit better
performance than US funds.
Another study incorporating fees in the performance of mutual funds was done by
Babalos et al. (2009), who evaluated the performance of all Greek domestic equity
funds over the period 2000–2006. They used total expense ratio and defined as the
ratio of a fund’s total expenses over its average net assets for each year. The expenses
include the operational costs charged by equity mutual funds of management fees,
custodian and auditors’ fees, transaction costs and other costs that are linked to
research or customer support. However, they excluded front and back-end load fees.
Their study found that funds’ return performance is negatively related to expenses,
whereas investors’ flows are not directly affected by expenses. They concluded that
charging an expense ratio of 3 per cent was relatively stable over the period of the
study, comparatively more than twice as high as the expense ratio charged by US
equity funds. Funds affiliated with the dominant banking groups deliver a higher
return performance than other funds with similar expense ratios. They also concluded
that funds’ performance is positively related to their age and negatively related to
their size (Babalos et al., 2009).
In Malaysia, the most relevant research on fees and their relationship to fund
performance was by Low (2010). Low (2008) illustrated that fund expense ratio
determines the fund returns performance. Funds with high returns volatility are
associated with a low expense ratio. However, she found no evidence that fund
objective and fund age are related to the expense ratio. In the other study, Low (2010)
evaluated fund performance in relation to fund characteristics such as fund size, age,
expense ratio, turnover, beta and fund type, and found that, on average, the risk-
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adjusted returns of 65 Malaysian mutual funds were not significantly related to age
and fund size over the period 2000–2004. She further remarked that there is a
statistically negative correlation between the growth in fund size and fund
performance. Low (2008; 2010) further revealed that fund size is negatively
correlated with listed fund characteristics, namely, expense ratio, fund age, turnover
and beta of the fund.
To the best of my knowledge, no other study has investigated the comparative return
performance between Islamic and conventional funds both before excluding fees and
after excluding fees from the fund returns. In IMF-related studies, since no analysis
evaluates funds after fees, this investigation of fund performance in relation to fees
could provide new insights and new evidence for investors and regulators on how
fees react on Islamic funds in particular, and on Malaysian mutual funds in general.
2.6.5 Previous studies on ethical and Islamic funds
There is a conception that IMFs constitute a kind of investment which is close in
character to ethical funds. Furthermore, the studies on IMFs are still few. It is
therefore timely to evaluate the performance of Islamic funds based on studies on
ethical funds. In terms of empirical findings, results from the global studies on ethical
funds or socially responsible funds have demonstrated the same tone as the findings
on Islamic funds. There is no evidence of significant differences between ethical and
conventional mutual funds (see for example, Abderrezak, 2008; Bauer et al. 2007;
Bauer et al. 2005; Goldreyer, Ahmed, and Diltz, 1999).
Mueller (1991) analysed the ethical mutual funds in US over the period 1984 to 1988
and reported that investing in ethical mutual funds produced an average annualised
return of 1 per cent less than the return that could have been obtained from
comparable funds. For these religious investors, the opportunity cost of ethical
investing, in the form of foregone returns on investments, is estimated as an implicit
biblical tithe (Mueller, 1991, p. 121). Mueller (1994) also examined the performance
of the Amana Income Fund, an Islamic equity fund, in the US during 1987 to 1992,
and concluded that, on average, the fund and its peers in the equity income fund index
are less risky that the respective market index, a proxy by Vanguard Index 500 fund
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for the market benchmark. The Islamic fund provides, on average, only 0.6 per cent
per annum above the risk-free return (government money market fund index).
Meanwhile, its peers gain on average of 3.6 per cent more than the similar risk-free
rate.
Goldreyer et al. (1999) looked at a sample of 49 socially responsible funds and 20
random samples of conventional funds provided by Lipper Analytical Services. They
found no significant performance difference between the socially responsible funds
and the conventional ones over the period January 1981 to June 1997. Their
evaluation was based on risk-adjusted performance measurements and they applied
one-year treasury security rate as a proxy for returns of the riskless asset.
Bauer et al. (2005) found insignificant differences in risk-adjusted returns between
ethical and conventional equity funds over the period 1990 to 2001, using data from
Germany, the US and the UK markets. The authors reported evidence of statistically
insignificant differences in return performance between ethical and conventional
mutual funds after controlling factors including size, book-to-market and momentum.
Ethical funds exhibit clearly different investment styles from conventional funds
because the ethical funds are typically less exposed to market return variability. They
also tend to be more growth-oriented but less value-oriented. Compared to their
conventional peers, the UK and German ethical funds are heavily exposed to small
caps, while the US ethical funds, on the other hand, invest more in large caps.
Bauer et al. (2007) also studied ethical funds in Canada and noted that there is no
significant performance difference between ethical funds and their conventional peers.
In fact, Renneboog et al. (2008) found that socially responsible funds underperform in
most European and Asian markets but no evidence of cost of diversification emerged.
Recently, Gil-Bazo et al. (2010) found that there was no significant difference in fees
between socially responsible investment (SRI) and conventional funds in the US for
the period 1997 to 2005. They also provided evidence that SRI funds do better before
and after fees than conventional funds with the same characteristics.
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Other than the ethical fund studies, published research on IMFs is growing, and
recently gathered momentum in the global market as well as in the segmented market
(see for instance, Abderrezak, 2008; Abdullah et al. 2007; Ahmed, 2007; Elfakhani et
al., 2005; Elfakhani and Hassan 2005; Elfakhani and Hassan 2007; Hayat, 2006;
Hayat and Kraeussl, 2011; Hoepner et al. 2011; Ismail and Shakrani 2003). The first
listed study evaluated both Islamic and ethical funds in relation to the conventional
benchmark, the S&P 500 index, a proxy for a conventional portfolio. Abderrezak
(2008) revealed that Islamic and ethical funds perform similarly compared to their
conventional counterparts. He also found that there is no significant difference in
performance between both fund portfolios and, in fact, both are not able to outperform
the benchmarks while using Fama’s performance measures.
The last-listed study used the weekly price data of just 12 Islamic funds against the
relevant index to study the relationship between the fund risk as measured by beta and
returns for the period from 1 May 1999 to 31 July 2001. Ismail and Shakrani (2003)
reported that the adjusted-R² (adj R²) and standard error of the conditional relationship
are higher in down-markets than in up-markets. This would mean beta could be used
as a tool to explain cross-sectional differences in Islamic fund returns and as a
measure of market risk. This is consistent with the risk-return paradigm, and is
therefore a verification that the Islamic funds are behaving as if risk is the determinant
of returns. They suggested that beta could be used as a tool to measure risk, but they
did not address whether this type of fund yields lower or higher returns.
Girard and Hassan (2005) did a comparative study of Islamic versus non-Islamic
market indices. They examined the indices’ performance from 1996 to 2005 and
remarked that there was no performance difference between Islamic and non-Islamic
indices, because although the Islamic indices outperformed from 1996 to 2000, they
underperformed from 2001 to 2005. The similar reward to risk and diversification
benefits exist for both Islamic and conventional indices.
Elfakhani et al. (2005) and Elfakhani and Hassan (2007) used 46 global IMFs and
found no statistical difference in the performance of Islamic and conventional equity
funds in relation to the returns of respective market indices over the period of January
Page | 65
1997 to August 2002. They concluded that the performance of funds does improve
over time as the fund managers gain more experience and a sense of how the market
is operating. Elfakhani and Hassan (2007) also suggested that the funds do not differ
substantially from other conventional funds, although some Islamic funds appear to
perform better than others.
Elfakhani and Hassan (2007) also indicated that the Islamic portfolio may have
generated higher returns at lower risk over the full period from January 1997 to
August 2002. In addition, the major observation of the study was the strong
performance of Islamic mutual funds compared to both Islamic and conventional
benchmarks during the recession period. In fact, they suggested that there is no
statistically significant risk-adjusted abnormal reward or penalty associated with
investing in Shariah-compliant mutual funds. They therefore concluded that
conventional investors could consider Islamic funds in their portfolio selection,
especially during slow market periods, and it is investors’ duty to investigate the
various potential types of mutual funds in the market to suit their needs. This must be
done regardless of whether a fund is a conventional one or Islamic or an ethical or
socially responsible fund.
Abdullah et al. (2007) contradicted this finding by showing that conventional funds
perform better than Islamic funds during good economic periods and worse during
bad economic periods. Hayat and Kraeussl (2011) estimated the performance of 145
Islamic equity funds worldwide over the period from 2000 to 2009. They found that,
on average, the funds significantly underperformed the respective Islamic benchmark
by 1.71 per annum and the conventional market benchmark by 0.28 per annum. Hayat
and Kraeussl (2011) further revealed that IEFs perform worse over the benchmark
during the bearish market compared to the bullish market. This result is contradictory
to the finding of Abdullah et al. (2007), who found that IEFs perform better than the
CEFs counterparts during the bearish market. However, according to Hoepner et al.
(2011), the results of Hayat and Kraeussl (2011) are debatable as they replaced a
missing NAV with the average of previous and subsequent observations. Hoepner et
al. (2011) revealed that in their study most of the global Islamic funds
underperformed the relevant market benchmark from September 1990 to April 2009.
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In contrast, the 76 Malaysian Islamic funds out of 265 funds in their sample were, on
average, competitive with the international equity market benchmarks. Hoepner et al.
(2011) incorporated the most recent data from 1990 to 2009, but limited the
evaluation of performance to Islamic equity funds. Meanwhile, Abdullah et al. (2007)
investigated Islamic equity funds in comparison to conventional funds but employed
obsolete data from 1992 to 2001.
The study by Hassan et al. (2010) generated similar evidence to that of Girard and
Hassan (2005) in that there were no performance differences between Islamic and
conventional Malaysian unit trust funds from January 1996 to November 2005. The
study incorporated a sample of 80 equity funds including 30 Islamic funds. The study
found that Islamic unit trust funds are small cap oriented while their conventional
peers are value-focused. The study also singled out a significant long-term
relationship between Islamic and non-Islamic portfolios, suggesting that investors in
the Malaysian unit trust industry are benefiting from the international diversification
of financial risks. However, this poses a challenge, as these results are contradictory
to the findings of Hoepner et al. (2011), who suggested that Islamic funds display a
tilt towards growth and small cap stocks orientation. Moreover, these studies did not
examine either market timing or the impact of fees on the fund performance.
It is therefore important to note that most studies on IMFs in the Malaysian market
have provided evidence of the funds underperforming, and the findings portray a
similar pattern according to the evidence for global IMFs (see for instance,
Abderrezak, 2008; Abdullah et al. 2007; Elfakhani et al., 2005). One possible reason
for this is that the duration of the studies is quite similar. There is a contradictory
result regarding the Islamic funds in Malaysia due to the longer period of the study.
The findings of Abdullah et al. (2007), who indicated the superior performance of
Islamic funds in Malaysia specifically during the bearish market, have quite different
conclusions to those of Hoepner et al. (2011) that Islamic funds are compatible in
relation to the international equity market.
Hoepner et al. (2011) found that most of the IEFs worldwide underperform their
market benchmark, yet they also found that in comparison the Malaysian equity funds
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perform competitively against the international equity market benchmarks. They
analysed 265 Islamic equity funds worldwide including 76 from Malaysia. In contrast,
Kraeussl and Hayat (2008) and Hayat and Kraeussl (2011) found, on average, that
IEFs from a sample of 145 funds underperformed the market benchmark, whether
Islamic or conventional, and that underperformance worsens during a crisis or bearish
market. Hoepner et al. (2011) and Kraeussl and Hayat (2008) limited their studies to
the performance of Islamic equity funds (IEFs), with Hoepner et al. (2011) employing
an equally weighted average based on simple mean returns.
In the market there are many mutual fund categories other than equity funds –
allocation funds, alternative funds, fixed income funds and money market funds – and
they remain largely unexplored. Therefore, this thesis not only evaluates the
performance of equity funds but also investigates the diversified funds, which involve
all the cited categories.
Other relevant studies on the comparative performance of Islamic and conventional
funds by Abdullah et al. (2007) and Elfakhani et al. (2005) found that, on average,
neither IMFs nor CMFs outperform the market and IMFs perform better during a
bearish market, while CMFs perform better during a bullish market. This is in contrast
to two former studies: Elfakhani et al. (2005) employed diversified funds but
consistently, and Abdullah et al. (2007) employed equity funds only. However, the
number of funds involved in all the studies is very limited and the duration is
relatively short.
IMFs were introduced in the late 1990s due to the higher demand for Shariah-
compliant products and securities in the global market. Since then, investment in
IMFs has risen in the global market due to the development of Islamic finance
worldwide, which is illustrated by it becoming an important part of the international
financial system. Continuous demand for the Islamic funds industry has made it the
fastest growth area of the Islamic financial system. This growth is crucial because it
indirectly influences the continuing improvement of the global Islamic financial
market. In Malaysia, the industry has grown tremendously since the 1990s, with an
increase from two funds in 1992 to 150 funds at the end of December 2009.
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Table 2.3: Summary of some previous empirical evidence on fund performance
Author (Year)
Country Sources of Data/ No. of funds
Period of the Study/Data range/Approach
Inputs (Attributes/ Characteristics)
Outputs (Findings)
Sharpe (1966) US Weisenberger database (34 funds)
1954–1963/ Yearly/ Time series
• Sharpe ratio • Expenses • Conventional funds
• The higher Sharpe ratio is associated to the fund with lower expenses.
• This study does not include front and exit fees in the experiments.
Treynor and Mazuy (1966)
US Weisenberger database (57 funds)
1953–1962/ Yearly/ Time series
• Choice of funds/selectivity
• Market timing
• No evidence that the fund managers can outperform the market.
Merton (1981) US Simulated returns (growth of 1000) from market timing and protective strategies New York Stock Exchange ( for market return) and US T-bills (for riskless asset)
January 1927–December 1978/ Monthly/ Time series
• Modern capital market theory
• Forecasting skills • Microforecasting
(stock selectivity) • Macroforecasting
(market timing) • Equilibrium theory
• The equilibrium of price structure of management fees is no benefit in forecasting superior performance.
• The existence of different information among market participants plays an empirically insignificant role in the formation of equilibrium security prices.
Henriksson (1984)
US Standard & Poor’s stock price and
February 1968–June 1980/ Monthly/
• Parametrics and non-parametrics techniques based on multifactor
• Mutual fund managers are not able to follow an investment strategy that successfully times the market portfolio return.
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Weisenberger database (116 funds)
Time series CAPM • Market timing
Chang and Lewellen (1984)
US Center for Study of Security Prices (CRSP) (67 funds)
January 1971–December 1979/ Monthly/ Time series
• Market timing skill • Security selection
ability
• Neither skillful market timing nor security selection abilities are evident in fund return performance.
• The funds collectively unable to outperform a passive investment strategy.
Admatti et al. (1986)
- - Conceptual • Timing ability • Selectivity ability
• The TM model is a valid performance measure for timing and selectivity ability.
Grinblatt and Titman (1989)
US Hypothetical data from Grinblatt (1986–87), and Grinblatt and Titman (1988) (279 funds)
1975–1984; 1986; 1987/ Yearly/ Time series
• Selectivity factor • Price equilibrium • Portfolio performance • Conventional funds
• Links between performance measures and particular equilibrium models are not necessary. Therefore, the existence of different information among investors plays an insignificant role in price equilibrium.
Ippolito (1989)
US Weisenberger database (143 funds)
1965–1984/ Yearly/ Time series
• Expense ratio • Risk-adjusted returns • Conventional funds
• The returns after adjustment for expenses are comparable to the returns of index funds. The funds with higher fees are relatively well and sufficient to offset the higher charges.
• However, this study does not consider the front and exit load charges in the returns evaluation.
Lee and Rahman (1990)
US Center for Study of Security Prices (CRSP) (93 funds)
January 1977–March 1984/ Monthly/ Time series
• Selection ability • Market timing ability
• There is evidence of superior selectivity and timing ability of some fund managers of individual funds.
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Chen et al. (1992)
US Center for Study of Security Prices (CRSP) (93 funds)
January 1977–March 1984/ Monthly/ Time series
• Fund selectivity • Market timing • Fund objective • Expense ratio • Load fees
• The study finds selection performance for the fund sample.
• Collectively, the funds appear to possess no market timing ability over the period of the study, with the evidence suggesting that there is a trade-off between market timing and fund selection performance.
• Expense ratio is the dominant factor to explain timing ability of the mutual funds.
• Load funds on average seem to have no better ability in selecting individual securities than no-load funds.
Malkiel (1995)
US Lipper Analytic Services (239 funds)
1971–1991/ Yearly/ Time series
• "Hot hand" phenomenon
• Expense ratios • Survivorship bias • Performance persistence • Conventional funds
• The strong evidence in favour of a "hot hand" phenomenon in mutual funds, which achieved above average returns, would continue to enjoy superior performance.
• The existence of expense ratios that vary over the universe of funds tends to produce some persistence in returns.
• The fund with the lowest expense ratio is likely to outperform high expense funds persistently .
Golec (1996) US Morningstar database (530 funds)
1988–1990/ Yearly/ Time series
• Conventional funds • Expenses
• Funds with low fees tend to perform better than funds with high fees.
• Funds with low administrative expenses perform relatively well.
Chordia (1996)
US Investment Company Institute (397 funds)
January 1984–July 1993/ Monthly/ Time series
• Conventional funds • Load fees • Redemption fees
• Funds with load and redemption fees hold less cash than those with no-load counterparts. Therefore, mutual funds reduce redemptions through front and back-end load fees.
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Carhart (1997) US Micropal/Invest
ment Company Data, Inc. (ICDI)/ FundScope Magazine/ United Babson Reports/Wiesen-berger Investment Companies/The
Wall Street
Journal (1892 funds)
January 1962–December 1993/ Monthly/ Time series
• Persistence • Fund characteristics • Diversified equity funds • Load fees
• Funds are not able to outperform their market benchmark and they perform even worse after fees are deducted from the gross returns.
• Performance persistence is largely explained by the worst performing funds.
• Fees are negatively correlated with performance of fund returns.
• Expense ratios, portfolio turnover and load fees are negatively related to performance, with load funds substantially underperforming no-load funds.
• The study suggests three important rules-of-thumb for wealth-maximising mutual fund investors: (1) avoid funds with persistently poor performance; (2) funds with high returns last year have higher-than-average expected returns next year but not in years thereafter; and (3) the investment costs of expense ratios, transaction costs and load fees all have a direct, negative impact on performance
Bello and Janjigian (1997)
US Morningstar database (633 funds)
1984–1994/ Yearly/ Time series
• Market timing • Security selection • Extended TM model
• There were Positive and significant market timing abilities for all funds using the extended TM model, in which the results are sharply contrasted to the negative market timing abilities when using the original TM model.
• A significantly positively security selection skill and a negative correlation between market timing and this selectivity skill.
Annuar et al. (1997)
Malaysia
New Straits Times database
July 1990–August 1995/
• Fund performance • Selectivity skill
• These mutual funds did outperform the KLCI benchmark.
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(31 funds) Monthly/ Time series
• Market timing • TM model
• Mutual funds in Malaysia have a positive selectivity skill and a negative market timing ability.
• A positive correlation between selectivity and timing performance.
• The degree of diversification of the Malaysian mutual fund is below the market in general .
Black and Timmermann (1998)
UK Mocropal Ltd (2300 funds)
February 1972–June 1995/ Monthly/ Time series
• Persistence • Investment styles • Fund performance
• The average UK equity fund appears to underperform the market by around 1.8 per cent per annum based on a risk-adjusted basis.
• Evidence of persistence in performance among the best- and worst-performing funds in the UK market.
• The investment styles of the two groups of fund managers differ, with UK fund managers favouring asset allocation and market timing strategies, whereas their US counterparts favour quantitative (bottom up) stock selection.
Hallahan and Faff (1999)
Australia FPG research house (65 funds)
January 1988–September 1997/ Monthly/ Time series
• Market timing • Fund performance • Selectivity performance
• Australian mutual funds have a negative selection performance and little evidence of market timing ability over the study period.
Goldreyer et al. (1999)
Global Lipper Analytical Services (49 funds)
January 1981–June 1997/ Monthly/ Time series
• Socially-responsible investment funds
• Conventional funds
• The conventional funds appear to outperform SR funds in a larger number of circumstances.
Dahlquist et al. (2000)
Sweden TRUST database of
1993–1997/ Yearly/
• Past performance • Turnover
• Larger equity funds tend to perform less well than smaller equity funds with the exception that larger
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Findata Panel data regression
• Fund size • Fee structure
bond funds seem to perform better than smaller bond funds.
• Evidence suggests that actively managed equity funds perform better than more passively managed funds.
• Fund performance is negatively correlated to fees, that is, high fee funds seem not to perform as well as low fee funds before fees are deducted. Therefore, high fees may generate good performance but still not enough to cover the fees.
• A positive relation between lagged performance and current flows, and evidence of persistence in performance only for money market funds.
Ismail and Shakrani (2003)
Malaysia Malaysian Daily newspaper (12 Islamic funds)
May 1999–July 2001/ Weekly/ Time series
• Portfolio beta • Portfolio returns • Conventional funds
• The adjusted-R² and standard error of the conditional relationship is higher in down-markets than in up-markets.
• Beta has a role to play in explaining cross-sectional differences in Islamic Unit Trusts’ returns.
Bala and Matthew (2003)
Emerging market/ Malaysia
Malaysia-based (75 questionnaires)
2003/ Survey
• Emerging market • Past performance • Size of funds • Costs of transaction • Experienced fund
managers
• The three important factors which dominate the choice of mutual funds in emerging market are past performance consistency, size of funds and costs of transaction.
• The most important factor for investors is the final performance of the funds. This is followed by how the performance is achieved, either by experienced or educated fund managers.
Korkeamaki and Smythe (2004)
Finland Helsinki Exchanges Ltd. (150 funds)
1993–2000/ Yearly/ Timer series
• Conventional funds • Expense ratio
• This study on Finnish mutual funds shows that the funds’ fees decrease over time, suggesting that the market is increasingly in competitiveness.
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Girard and Hassan (2005)
Global Reuters and Datastream (DJIM and MSCI indices)
Jan 1996–Nov 2005/ Monthly/ Time series
• Dow Jones Islamic index
• Non-Islamic indices
• There is no difference between Islamic and non-Islamic indices. The Islamic indices outperform from 1996 to 2000 and underperform from 2001 to 2005 their conventional counterparts.
• Similar reward to risk and diversification benefits exist for both Islamic and conventional indices.
Taib and Isa (2007)
Malaysia The Star and The Edge Malaysia newspapers (110 funds)
January1990–December 2001/ Monthly/ Standard performance measures
• Performance measures • Persistency • Net asset values (NAV)
• No persistency in returns performance of the Malaysian mutual funds over the period of the study.
• On average, the performance of the Malaysian mutual funds falls below the market and risk free returns.
• The bond fund portfolio indicates superior performance rather than the market and equity funds.
Elfakhani et al. (2005)
Global Failaka Database (46 Islamic funds)
January 1997–August 2002/ Monthly/ Time series & pooled regression
• Islamic funds • Net asset values (NAV)
• The results of the Transformed Sharpe model showed that the performance of Islamic mutual funds compared to both benchmarks (S&P 500 Index and FTSE Islamic Indices) during the second period dominated by recession is better than that during the first (booming) sub-period, implying that the funds’ performance is improving with time.
• No statistically significant difference exists in fund performance compared to respective indices, suggesting that the behaviour of Islamic funds does not differ from that of conventional funds.
• No statistically significant risk-adjusted abnormal reward or penalty associated with investing in Islamic funds; thus conventional investors can consider Islamic mutual funds in their portfolio collection, especially during slow market conditions.
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Abdullah et al. (2007)
Malaysia Malaysian Daily Newspaper (65 funds including 14 Islamic funds)
January 1992– December 2001/ Monthly/ Time series
• Net asset values (NAV) • Islamic funds • Conventional funds • Diversification
• The Islamic funds performed better than the conventional funds during bearish economic trends.
• While, conventional funds showed better performance than Islamic funds during bullish economic conditions.
• The study implied that Islamic funds can be used as a hedging instrument during any financial meltdown or economic slowdown.
Renneboog et al. (2008)
17 countries (in Europe, North America and Asia-Pacific)
Standard & Poor’s Fund Service (Micropal); CRSP; Bloomberg and Datastream (432 SRI funds and 16,036 CEFs)
January 1991–December 2003/ Yearly/ Time series
• SRI funds • Conventional funds • Fund characteristics • Investment styles
• SRI fund has experienced an explosive growth around the world, reflecting the increasing awareness of investors to social, environmental, ethical and corporate governance issues.
• The SRI is expected to continue growth and relative importance as an asset allocation among investors.
Haslem et al. (2008)
US Morningstar database (1779 funds)
as at December 31, 2006
• Conventional funds • Expense ratio • Management fees • Load fees
• On average, superior performance occurs among large funds with low expense ratios, low trading activity and no or low front-end loads.
• The actively managed mutual fund underperforms its market benchmark after expenses.
Pollet and Wilson (2008)
US Center for Study of Security Prices (CRSP) and Thomson
1975–2000/ Yearly/ Time series
• Conventional funds • Total net asset values • Expenses • Diversification
• Higher expenses (expense ratio) and total load (combination of front and back-end loads) associated with the funds are significantly negative to the returns.
• Greater diversification for the small-cap fund is
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Financial (285 funds)
associated with better performance.
Giambona and Golec (2009)
US Center for Study of Security Prices (CRSP) (3696 funds)
1962–2002/ Yearly/ Time series
• Compensation incentives
• Fund managers • Volatility timing
strategies • Flow timing strategies • Conventional funds
• The propensity of fund managers to time conditional market volatility is partly driven by their compensation incentives.
• Larger incentive management fees lead to less counter-cyclical or more pro-cyclical volatility timing.
• The volatility timing and flow timing are negatively related.
Babalos et al. (2009)
Greek Association of Greek Institutional Investors (75 funds)
2000–2006/ Yearly/ Time series
• Conventional funds • Expense ratio • Management fees
• Evaluation of Greek equity funds performance using total expense ratio, including management fees, custodian and auditors’ fees, transaction costs and other costs that are related to research or customer support, but excluding front and back-end loads, finds that fund performance is negatively related to their expenses.
Bertin and Prather (2009)
Global Morningstar database (2541 funds, including 172 fund of funds (FOFs)
1996–2003/ Yearly/ Time series
• Fund of funds • Management structure • Fund performance • Sharpe ratio
• FOFs are cost effective for diversification, and their performance and characteristics are comparable relative to traditional equity mutual funds.
• FOFs invest in-family or identified team managers leads to superior fund performance to their unidentified team-managed counterparts.
Low (2010) Malaysia Fund’s prospectus and annual reports of the fund management
January 2000– December 2004/ Monthly/ Time series
• Expense ratio • Fund age • turnover
• On average, the risk-adjusted returns of the funds are not significantly related to age and fund size.
• Fund size is negatively correlated with expense ratio, fund age, turnover and beta of the funds.
• There is no evidence that fund size is related to fund
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companies (65 funds)
returns.
Hayat and Kraeussl (2011)
Global Bloomberg (145 Islamic funds)
January 2000–February 2009/ Weekly/ Time series
• Islamic equity funds • Market timing • Risk and return
characteristics
• On average, the Islamic funds underperform the Islamic and also the conventional benchmarks.
• Islamic funds perform even worse during the bearish market.
• There was negative market timing for the IEF fund managers over the period of the study.
Hoepner et al. (2011)
Global (20 countries including Malaysia)
Eurekahedge database (265 funds)
September 1990–April 2009/ Monthly/ Time series
• Islamic funds • Carhart model • Investment style
• National characteristics explain the heterogeneity in Islamic fund performance, with the Islamic funds from the six largest Islamic financial centres in our study (the GCC countries and Malaysia) performing competitively to international equity market benchmarks, but the Islamic fund portfolios from less developed Islamic financial services significantly underperform their benchmarks.
• The Islamic funds’ investment style globally is somewhat tilted towards growth stocks, with funds from predominantly Muslim economies also displaying a clear small cap preference.
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2.7 Summary
This chapter introduces the global Islamic fund industry globally and provides details
of the Malaysian mutual fund market have been discussed. The chapter discusses the
relevant literature and evidence related to the performance of mutual funds, focusing
on issues related to performance measurements, market timing and fund selectivity
skill, and also the relationship of fees to fund performance.
Studies on the Malaysian market have noted a shortage of recent findings on fund
performance with regard to IMFs and CMFs. Moreover, there is no study that studies
the impact of fees on the performance of IMFs. The thesis is an attempt to fill up this
gap, particularly in the Malaysian context.
The next chapter describes the variables and methodologies employed in this study
and provide details of the hypotheses and models employed for testing.
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CHAPTER 3 - RESEARCH
METHODOLOGY
3.1 Introduction
This chapter explains the relevant statistical tests and econometric methods employed
to examine the performance of IMFs compared to their CMFs counterparts. These
techniques are: (1) univariate testing based on pair mean t-test, (2) risk-adjusted
performance measures to conduct a risk and return analysis, and (3) regressions
analysis based on time series and panel data. The use of the first two techniques is
reported in Chapter 4 and the use of the third technique in Chapters 5, 6 and 7.
Chapter 5 focuses on time series data, while Chapters 6 and 7 use panel data in the
analysis. The results obtained from the models are summarised in the final chapter,
Chapter 8.
This chapter is structured as follows. Section 3.2 describes the variables employed in
the study, followed by a discussion of the main hypotheses of the thesis in Section
3.3. The method and model specifications are discussed in Section 3.4. Section 3.5
addresses the relevant econometric issues and Section 3.6 summarises the main points
in this chapter.
3.2 Variables
3.2.1 Dependent variables
In this study, the dependent variables refer to the mean returns of each mutual fund
portfolios. In Chapters 4 to 6, the dependent variable is divided into three main
portfolios, representing: (1) all mutual funds (AMFs), which refers to the full sample
of funds (479 mutual funds), (2) IMFs for the 129 Islamic funds in the sample and (3)
CMFs for the 350 conventional funds in the sample. The funds comprise all fund
types: alternative funds, allocation funds, equity funds, fixed income funds and money
market funds.
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Chapter 7 narrows the focus to fees and uses alternative measures of portfolio returns
including GROSS (returns before excluding all fees), ADJUSTED (returns net of all
expenses), ADJUSTED LOAD (returns net of load fees), and NET (returns after
excluding all fees), for each of the IEFs and CEFs. More explanation about the
variables is given in Section 7.2. This chapter focuses on equity funds because load
fees and other expenses are not relevant to other types of funds. In this case, the
sample data are the mean return of all 106 equity funds (AEFs), comprising the mean
return of 53 IEFs and the mean return of 53 CEFs.
3.2.2 Independent variables
The independent variables can be divided into three main categories: single
benchmarks, multiple benchmarks and fund attributes. The single benchmarks are the
Kuala Lumpur composite index (KLCI) and the Kuala Lumpur Syariah Index (KLSI)
market return index. For the multiple benchmarks, this study extends the independent
variables in single benchmarks to include other benchmarks, specifically Morgan
Stanley Capital International World index (MSCI), Dow Jones Islamic Market index
(DJIM), Kuala Lumpur Stock Exchange Malaysian small-cap index (KLSE small-
cap), the Malaysian fixed deposit rate for bond index and Kuala Lumpur interbank
rate (KLIBOR) for the money market index. All the indices are converted into
monthly rates of return to suit the monthly return data (see Chapters 5 and 6), and are
converted into a yearly return for Chapter 7.
Other independent variables in the category of fund attributes (mainly employed in
Chapter 7) are AGE, LNSIZE, dINVEST, ���, AlPHA, BETA, RESIDRISK,
��� �����, MGMTFEE, EXPENSE, TOTLOAD and TRUSTEE. These independent
or explanatory variables are specifically treated to include the exogenous and
endogenous variables. The endogenous variables are AlPHA, BETA, RESIDRISK,
��� �����, MGMTFEE, EXPENSE, TOTLOAD and TRUSTEE. The exogenous
variables comprise fund AGE, LNSIZE and investment style of the funds, namely
dINVEST and dTYPE. The explanation of each explanatory variable is as follows.
1. AGE is defined as fund age, measured in years, from the inception date to
2009 which is the end year.
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2. LNSIZE defines a natural logarithm of the total net asset (TNA) values of the
fund during the inception date, measured in RM (million).
3. dINVEST is an investment style dummy variable representing 1 if the fund is
invested in the domestic market and zero otherwise, i.e., if the fund is mainly
invested in the foreign market.
4. ���,a dummy variable of type of funds, is equal to 1 if the fund belongs
to Islamic funds and 0 if it belongs to conventional funds.
5. AlPHA is an intercept and is calculated for each of the funds (106 funds) with
yearly market adjusted return data using the CAPM and one-month Malaysian
t-bills used as a proxy for risk-free rate return; the natural logarithm of KLCI
price index is the market return portfolio.
6. BETA is the systematic risk and is calculated for each of the funds (106 funds)
using a similar method when calculating alpha.
7. RESIDRISK refers to residual risk or residual return standard deviation for
each of the funds (106 funds) and is calculated using a similar method when
calculating alpha and beta.
8. ��� ����� is the lagged one year return based on return of a fund i, net of
all expenses in year t-1.
9. MGMTFEE is a percentage of assets paid as a management fee. It is part of all
operating expenses.
10. EXPENSE is an expense ratio, i.e., a percentage of assets’ values spent on all
operating expenses but excluding management fees, trustee fees and load fees
such as sales charge and redemption fees.
11. TOTLOAD is the total load fee and it is a combination of FRONT fee (sales
charge) and EXIT fee (also known as back fee or redemption fee).
12. TRUSTEE is a trustee fee. A trustee fee is also known as a custody fee and is
part of the operating expenses.
3.3 Hypotheses development
This section explains the hypotheses structure for this thesis. Hypothesis testing is
part of statistical inference and is generally conducted in the process of making
judgements about population based on the sampling data observed (DeFusco,
McLeavey, Pinto, and Runkle, 2007). If the null hypotheses for those alternative
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hypotheses cannot be rejected, then the null hypotheses must be accepted. However, if
the null hypotheses can be rejected, then the alternative hypotheses will be accepted,
at one of the significance levels, based on 99 per cent, 95 per cent or 90 per cent
confidence intervals. In this case, the two-tailed hypotheses tests have been applied.
The first null hypothesis is to test the first objective of the thesis: whether there is any
difference in the return performance of IMFs and CMFs in relation to the market
benchmark (single and multiple).
Ha1: The performance of IMFs in terms of risk and return relative to market
benchmark is different from that of CMFs.
This hypothesis is presented in a series of alternative hypotheses as the following:
Ha1(i): The risk and return performance of IMFs differs from the CMFs counterparts.
Ha1(ii): The risk and return performance of IMFs differs from the market return.
Ha1(iii): The risk and return performance of CMFs differs from the market return.
The second hypothesis is to examine any differences in market timing expertise and
fund selectivity skill among IMFs and CMFs fund managers. This hypothesis refers to
the second and third objectives of this thesis.
Ha2: The performance of IMFs fund managers in relation to market timing
expertise and fund selectivity skills is different from that of CMFs fund
managers.
The third hypothesis of this study is to find any difference regarding fees and other
fund attributes on equity funds performance between the IEFs and CEFs portfolios.
Ha3: The difference in returns performance of the funds focusing on the IEFs
and CEFs can be explained by the impact of fees and other fund attributes.
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3.4 Model specifications and the methodologies
3.4.1 Descriptive statistics and mean pair t-test
The study conducts descriptive statistics on all the portfolio returns. The study also
performs a comparison test based on mean difference and median difference between
two groups of return data. The aim is to analyse the difference between returns
performance of each group. Two samples mean pair t-tests are conducted, firstly,
between Islamic and conventional portfolios and secondly, between each of the
portfolios and the market return.
This t-test assumes that the returns of the groups are independent and approximately
normally distributed. The Jacque-Bera (JB) test is conducted to test this normality. If
the JB test of each fund portfolio indicates that the test is not significant, we can then
conclude that the data in this time series analysis are normally distributed. The JB
statistic having a small value means that the actual values of skewness (Sk) and
kurtosis (Kt) must be relatively close to the values of 0 and 3, indicating that the data
have a normal distribution. Therefore this normality test would show that whether or
not both returns – Islamic and conventional portfolios – are normally distributed. The
JB test is calculated based on the formula shown below:
JB = Z��� +Z��� = �S� − 0!6/n %
�+�K� − 3
!24/n%�= *S�)�6/n +*K� − 3)�
24/n
(Eq. 3. 1)
The study also employs the non-parametric test, the Wilcox test, on sign rank test or
Mann-Whitney test to confirm the results from the t-test. This test is equivalent to the
normal t-test and is conducted where the t-test may not be reliable, and the JB shows
that the data are not normally distributed. The test statistic (�) converts the value to z-
score (+) using the formula below:
+ = � − ,*, + 1)/4!,*, + 1)*2, + 1)/24
(Eq. 3. 2)
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The trend analysis and scatter plot are also applied in order to identify the normality
of the data in the sample. When the data are normal, the linear regression model used
on the sample becomes more meaningful since the outcome of the study is not
influenced by the outliers that might exist in the raw data.
3.4.2 Standard risk adjusted performance measures
3.4.2.1 Sharpe ratio
There are two types of risks to consider in mutual fund investments. The first is the
risk that remains, which cannot be eliminated through diversification of a portfolio.
This is also called market risk because the risk is attributable to the market-wide risk.
It is measured by the beta of a fund or a portfolio that is also known as systematic
risk. The other risk is a non-systematic risk that can be eliminated through
diversification of a portfolio. Since this risk can be adjusted, it also called
diversifiable risk. It is calculated by deducting the systematic risk beta from the total
risk (standard deviation of a portfolio).
The Sharpe ratio (SR) has been used as one of the standard performance
measurements in mutual funds research to measure the risk and returns of a fund
portfolio. Many previous studies have employed this ratio to evaluate individual funds
or portfolio performance (Amin and Kat 2003; Bertin and Prather, 2009; Elfakhani et
al., 2005; Hodges, Taylor, and Yoder, 1997; Pilotte and Sterbenz, 2006; Sharpe, 1964,
1965a, 1966). As one of the risk adjusted performance measurements, SR is
considered to be a more precise return-risk measurement relative to risk-adjusted
measures on the list, due to its ability to recognise the existence of a risk-free return in
asset portfolios (Eling and Faust, 2010).
SR often refers to the return of an asset with zero risk. Zero risk implies zero standard
deviation. The investors or fund managers can choose this risk-free asset in their
portfolio as a combination in preference to a risky portfolio. Indirectly, the investors
or fund managers can also choose the level of absolute risk (as risk is measured by the
standard deviation of a risky portfolio) or expected return that they desire.
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The main function of SR in finance is to identify the performance of a fund portfolio
based on its return and risk. Although risk can also be measured by standard deviation
and coefficient of variation (CV), these two measurements have shortcomings in that
both cannot measure the return and risk of a portfolio independently. While standard
deviation is extensively used to measure risk, the CV on the other hand is used to
indicate the proportion of the return based on each unit of the standard deviation.
Therefore, SR can provide both benefits since it can evaluate and compare the
performance of a fund portfolio based on its return and risk, determined by an
appropriate risk-free asset (DeFusco et al., 2007, p. 116).
The application of SR in measuring the performance of mutual funds is considered
successful in determining the extent to which differences in performance persist over
time. In other words, this ratio can predict the differences in funds’ performance
(Sharpe, 1966). It is therefore expected to form reliable expectations about future
performance.
This study adopts the ex post SR introduced by Sharpe (1965a, 1966) in order to
examine the risk return trade-off of a fund’s portfolio. The larger the ratio is, the
better the performance. This is because the ratio is the reward per unit of variability or
standard deviation (Sharpe 1966, p.123). The historical data reflect the actual
performance of a fund’s portfolio. The formula is referred to as ex post SR based on
the historical data. It is calculated as follows:
.� = /0 −/120
(Eq. 3. 3)
Where /0 represents the mean returns to each of a portfolio, /1 , the mean returns to a
risk-free asset, and 20 , the standard deviation of returns on the portfolio. The average
return of one-month Malaysian t-bills rate is used as a proxy for the risk-free rate
asset.
Page | 86
The term /0 −/3 in SR is called the mean excess return on a portfolio, Islamic or
conventional. In this case, it measures reward in terms of mean excess return per unit
of risk, which is measured by the standard deviation of the /0 . The ratio of excess
return to standard deviation of a portfolio is obtained from any combination of
portfolio p and the risk-free asset that lies on a line with slope equal to the quantity
divided by standard deviation of return, 20. Thus, risk-averse investors prefer
portfolios with larger Sharpe ratios to the smaller ones (DeFusco et al., 2007). The SR
may be of the ex-ante and ex post types. The ex-ante SR refers to a portfolio going
forward based on the expectations for excess mean return, the risk-free return, and the
standard deviation of return (Elton and Edwin, 2007; Hodges et al., 1997, p. 74). In an
ex post SR, the historical data performance is used to measure the risk and return.
While using ex ante, estimating the data cannot be done and probably more rigorous
assumptions must be made in evaluating the performance. The ex post SR is more
accurate because the historical data reflect the actual performance of a fund’s
portfolio and the ex post SR is expected to lead to an accurate expectation of future
performance. Hence, ex post is employed, even though the actual result may diverge
considerably from predictions, but the result is necessary and adequate enough to be
used for the empirical test (Sharpe, 1965b).
3.4.2.2 Treynor index
Since there is no assurance that past performance is the best forecast of future
performance as can be predicted through the SR measure, the SR alone is not enough
and is complemented by other measures, for example, the Treynor index (Sharpe
1966).
The Treynor index (TI), which is also known as Treynor’s measure, gives excess
return per unit of risk, based on systematic risk (the beta of a portfolio) instead of total
risk (standard deviation of a portfolio). The beta of a portfolio is the standard
deviation of the portfolio divided by the standard deviation of the returns from the
market as a whole. This portfolio beta represents the systematic risk of a portfolio
against the relevant benchmark (Wilson 2010).
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The formula can be expressed as follows:
�4 = /0 −/150
(Eq. 3. 4)
where �4 refers to Treynor index; /0, average return on the portfolio, /1 , average
return of one-month Malaysian t-bills rate and 50 belongs to beta for the portfolio 6 .
50 is calculated as the following:
50 =20727�
(Eq. 3. 5)
3.4.2.3 Jensen alpha
The other risk-adjusted performance measure is Jensen alpha (also known as Jensen’s
measure). It measures average return on a portfolio over and above that predicted by
the CAPM, given the portfolio’s beta and the average market return (Bodie, Kane, and
Marcus, 2007). Jensen’s measure is the portfolio’s alpha value (80). The formula is
described below:
80 = /0 −9/1 +50:/7 −/1;< (Eq. 3. 6)
3.4.2.4 Appraisal ratio
The study also uses the appraisal ratio (AR), which is also referred to as the
information ratio. This AR divides the alpha of the portfolio (80) by the non-
systematic risk of the portfolio [σ:e?;]. It measures the abnormal return per unit of
risk that in principle σ:e?; could be diversified away by holding a diversified market
index portfolio (Bodie et al. 2002, p.813). Following Bodie et al. (2002), the
calculation is as follows:
�� = 802:A0;
(Eq. 3. 7)
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3.4.2.5 Modigliani-Modigliani measure
The study then applies the Modigliani-Modigliani measure (M2) proposed by
Modigliani and Modigliani (1997) as an alternative for measuring risk-adjusted
performance. The advantage of this procedure is that it measures a fund performance
in relation to the market in percentage term. The higher the M2 associated with a fund
portfolio, the higher the return of the fund at any level of risk. The formula used is the
following:
B2 = /̅0 − /̅120 D27
(Eq. 3. 8)
where /0, average return on the portfolio, /1, average return of one-month Malaysian
t-bills rate 20 is standard deviation of returns of a fund portfolio and 27 is standard
deviation of market excess returns.
3.4.2.6 Adjusted Sharpe ratio
To make further comparison, the study also adopts Adjusted Sharpe ratio (ASR) in the
performance measurement. The ASR is able to avoid bias in estimating the standard
deviation compared to using SR (Abdullah et al. 2007). This ASR is based on the
modification of the SR by adding the number of observations (OBS) in the model
developed by Jobson and Korkie (1981). Following Abdullah et al. (2007), the
calculation is based on the following formula:
�.� = .�DEF.EF. + 0.75
(Eq. 3. 9)
3.4.3 The models
3.4.3.1 Single and multi-factor CAPM
From risk-adjusted performance measures as previously discussed in Section 3.4.2,
the study then further enhances the methods of evaluating fund performance by using
the capital asset pricing model (CAPM). Many studies have examined risk-adjusted
Page | 89
performance measures such as SR and extended the analysis to include the CAPM
(see for example, Amin and Kat 2003; Bertin and Prather, 2009; Elfakhani et al.,
2005; Elton et al. 2003; Hodges et al., 1997; Pilotte and Sterbenz, 2006; Sharpe,
1964). The single CAPM is also widely used to measure the Islamic and conventional
fund performances in relation to the market benchmark (Abdullah et al. 2007;
Elfakhani et al., 2005; Hayat and Kraeussl, 2011; Hoepner et al., 2011).
The development of the CAPM in measuring performance of mutual funds was by
Sharpe (1964), Lintner (1965), Mossin (1966) and Jensen (1964; 1968). In this
analysis, the study employs the single and multi-factor CAPM. CAPM has been
acknowledged in many published studies (Busse, 1999; Carhart, 1997; Fama, 1972;
Giambona and Golec, 2009; Henriksson and Merton, 1981; Renneboog et al., 2008)
and is still a popular model.
Jensen (1968; 1969) used single CAPM to study the performance of MFs and
introduced the Jensen measure as the intercept from a regression of the excess return
(return minus the risk-free rate) of the managed portfolio on the excess return of a
benchmark portfolio. In his study, he indicated there is no evidence that good
subsequent performance follows good past performance (Jensen, 1969). A few years
later, Black, Jensen, and Scholes (1972) conducted a test of the CAPM by introducing
multi-factor benchmarks, which include many stocks indices as independent variables.
Other studies have also used the multi-factor CAPM (Goldreyer et al., 1999; Grinblatt
and Titman, 1993; Low 2007; Renneboog et al., 2008; Taib and Isa, 2007).
The CAPM has developed and many studies have used the multi-factor CAPM in
association with conditional performance evaluation (Busse, 1999; Carhart, 1997;
Fama, 1972; Fama and French, 1993; Ferson and Schadt, 1996; Giambona and Golec,
2009; Grinblatt and Titman, 1989; Henriksson and Merton, 1981; James and
Karceski, 2006; Jegadeesh and Titman, 1993; Merton, 1981). Other researchers have
applied both the single and multi-factor CAPM to assess how well mutual funds
perform, for example, Henriksson (1984), Chang and Lewellen (1984), Ismail and
Shakrani (2003), Girard and Hassan (2005) and Bauer et al. (2007). Bauer et al.
(2007), for example, found that the same evidence – either by using the single-factor
Page | 90
model or the multi-factor model that controls for returns associated with size, book-
to-market and stock price momentum of the investments – amounted to no significant
difference in terms of performance between ethical and conventional mutual funds.
In this thesis, the single factor market index proxy generated by the Bursa Malaysia
Kuala Lumpur Composite Index (KLCI) is used to represent a market return portfolio
for the IMFs and CMFs in the CAPM analysis. The KLCI is chosen as the market
benchmark due to Malaysia currently having approximately 825 stocks trading in
Bursa Malaysia Kuala Lumpur Stock Exchange (KLSE), 89 per cent of which are
Shariah-compliant securities, according to Bursa Malaysia as at 25 May 2012. In
October 2003, the number of Shariah-compliant securities in Malaysia was 722
securities or 81 per cent of the total listed securities on the KLSE compared to 684
securities or 80 per cent of the total listed securities in 2002 (Securities-Commission-
Malaysia, 2003). The percentage increased to 88 per cent of stocks listed on the KLSE
being Shariah-compliant, representing two-thirds of Malaysia’s market capitalisation,
as at the end of December 2010 (Bursa Malaysia 2010). Therefore, the KLCI is
considered relevant as a market portfolio for both fund portfolios, and this application
means that the study period can be extended from January 1990 to April 2009.
When a different single benchmark is analysed, the KLCI is used to represent a
market return portfolio for a single conventional benchmark. The Kuala Lumpur
Syariah Index (KLSI) is then employed as a proxy for the single Islamic benchmark.
For the different analysis of a single benchmark, the period under investigation is
shorter, from July 1999 to April 2009, since the KLSI did not begin until July 1999.
For the other benchmarks used as independent variables (as mentioned in Section
3.2.2) in multiple benchmarks analysis, the period of study depends on the inception
date of the related benchmarks. The MSCI and KLIBOR are from January 1990 to
April 2009. The DJIM is from January 1996 and the KLSE small-cap is from
December 1995. The data for all indices are from either Datastream or SIRCA.
Page | 91
Generally, calculations of a market return for each of the market indices employed in
this thesis use the formula as set out below:
/7� = ln*7�/7���) ∗ 100 or equivalently,
/7� = [ln*7�) − ln*7���)] ∗ 100
(Eq. 3. 10)
where, /7� the average market returns. 7� is price of stocks index, i.e. KLCI at time
M. The log price obtained is then time with 100% to get the market return in
percentage. The reason is to accommodate the returns of funds, which are in
percentage as well.
The basic CAPM is defined as the expected return of a fund portfolio after adjusting
for the risk-free interest rate, as below:
�:/0; = /1 +[�*/7) −/1<50
(Eq. 3. 11)
Since the study employs historical data based on past returns performance, the model
in Eq. 3.11 is modified as follows:
/0� = 80� +50�*/7�) +N0̅� (Eq. 3. 12)
The following equations are based on risk-adjusted return. To identify the gross return
of a fund portfolio as reported in Chapter 7, then all the returns /0�are not adjusted
for the /1� .To employ risk-adjusted return, the model in Eq. 3.12 is then transformed
to mean excess returns and written as follows:
/0� − /1� = 80� +50�:/7� −/1�; +N0̅� (Eq. 3. 13)
Page | 92
where /0� −/1� is the mean excess return on the fund portfolio, in the situation
where the average return of a portfolio above a risk-free rate, 80� is the excess risk-
adjusted return. This is also referred to as Jensen’s alpha, where 50� is the systematic
risk of the security, /7� −/1� is the market risk premium, and 80� and 50� are
coefficient estimates denoting return performance and systematic risk, respectively.
/1� is based on one-month Malaysian t-bills used as a proxy for the risk-free rate10.
The portfolios,/0� refer to Islamic, conventional and the stated fund portfolios, and
represent a dependent variable in the regression model./7� is an average market
return portfolio calculated as shown in Eq. 3.10. Furthermore, N0� is the error term
allowing for time-varying beta across the model, with usual assumptions on N0� ~�*0, P��).
Based on a theory of market efficiency, when CAPM is correctly specified and
securities are being correctly priced, the α is zero. If a security demonstrates superior
performance, then the α should be positive and statistically significant (Prather and
Middleton 2002). Building on this theme, the study incorporates the multi-factor
version of CAPM to evaluate the performance of the mutual funds. Similar to the
studies of Elton and Edwin (2007, p.659) and Bertin and Prather (2009)11, this study
incorporates three different equity market factors and one bond market factor as the
independent variables in examining the performance of the IMFs and the CMFs
against the multiple benchmarks.
The four-factors formula comprises: the large capitalisation stock index (KLCI), the
KLSE small-cap, a foreign stock index (MSCI world), and a bond index. The choice
of various benchmarks is important so that the model can capture the impact of a
fund’s differential holdings of large-cap, small-cap, foreign and bond investments
(Bertin and Prather, 2009, p.1366). For the variable /3�, two benchmarks are used in
which 1 refers to a conventional foreign benchmark, the MSCI world index, and 2 10 The calculation of the monthly risk-free rate is as follows: Rft =(1+R)1/12 - 1. In the case of yearly risk-free rate, the following formula is used: Rft =(1+R)1 - 1=R. 11 Bertin and Prather (2009), for example, employed multi-factor CAPM to identify the average alphas and coefficient estimates for the benchmarks and the average Sharpe ratios in their comparative studies on the fund of funds and traditional equity fund. The results indicated that neither group outperforms the overall market since the alpha estimates for both samples are negative.
Page | 93
refers to the Islamic foreign benchmark, the DJIM index. The modified multi-factor
CAPM based on Eq. 3.14 taking care of four-factors is expressed as follows:
/0� − /1� = 80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503:/3� −/1�;+50S:/S� −/1�; +N0̅�
(Eq. 3. 14)
where, /0� is the average return of a fund portfolio being evaluated, /1� is the average
return a risk free asset (one-month Malaysian t-bills), 50Tis the sensitivity to
benchmark j (j= L, S, F,B) with L is a large stock index (KLCI), S is a small stock
index (KLSE small-cap), F is a foreign stock index (MSCI or DJIM), B is a bond
index (Malaysian fixed deposits). /T� is the average return on the benchmark at period
t. N0̅�is the random error term, with assumptions that it is normally distributed,
~�*0, P��).
Finally, this study extends Bertin and Prather (2009) to include one more market
benchmark, namely, the KLIBOR rate, which serves as a proxy for the money market
index (/U�),. The equation is based on five-factors CAPM, as the following:
/0� − /1� =80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503 :/3� −/1�;+50S:/S� −/1�; + 50U:/U� −/1�; +N0̅�
(Eq. 3. 15)
where, m is a money market index (KLIBOR) and the rest are as previously explained
in Eq. 3.14.
3.4.3.2 TM model and the extended TM model
In order to evaluate the Islamic and conventional fund managers’ market timing
ability and fund selectivity skills, the study adopts the TM model. This was developed
by Treynor and Mazuy (1966) and is based on exponential growth of the market
benchmark in the CAPM model using quadratic regression. The regression model is
Page | 94
also applied to the time series and panel data analysis. According to Admati et al.
(1986), the TM model provides a valid measurement of market-timing performance
ability. Positive values of α and β are indicative of security selection skill and market-
timing skill for Islamic mutual funds managers. The model equation is as follows:
/0� − /1� =80� +50�:/7� −/1�; +V0�*/̅7� − /̅1�;� + N0̅� (Eq. 3. 16)
where 80� denotes the ability of portfolio fund managers to use effective skills
regarding stock selection and V0� denotes the market timing expertise of each fund
manager. /7� is a market benchmark, */̅7�)�is the quadratic term for a market
benchmark. The other variables are defined as previously mentioned.
If the 50� for all the funds value is less than 1, this implies that the fluctuation in the
stock market does not infinitely influence any specific fund per se. In other words, the
higher the beta of a fund portfolio, the higher the volatility of a fund compared to the
market.
The following regression is conducted in order to identify any differences concerning
the timing and selectivity skill of fund managers by adding the dummy variable
��� in all funds portfolio category. ��� is a dummy variable, written as 1 if it
is the Islamic fund or 0 if it is the conventional fund.
/0� − /1� = 80� + 50�:/7� −/1�; +V0�:/7� −/1�;² + ��� +N0̅� (Eq. 3. 17)
The TM model is also developed into a new model called the extended TM model by
adding the multiple benchmarks to the original TM model, following Bello and
Janjigian (1997).
Page | 95
The extended TM model is written as follows:
/0� − /1� = 80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503 :/3� −/1�;+50S:/S� −/1�; + 50U:/U� −/1�;+V0�*/̅7� − /̅1�;� + N0̅�
(Eq. 3. 18)
3.4.3.3 The different portfolio regression analysis
The differences between the IMFs and CMFs are presented in different portfolio
(Diff.) with the aim to identify if there are any differences between the performances
of IMFs and CMFs concerning risk and return characteristics (more detail on results
from this equation is in Section 5.4.1). It is based on the following:
[:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;] = 80� + 5̅0�:/̅7� − /̅1�; +N0̅�
(Eq. 3. 19)
where I and C are the mean excess return of the IMFs and CMFs respectively. The
term [:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;]is the excess return of the IMFs minus the excess
return of the CMFs. Eq. 3.19 can also be extended to diff. portfolio to include the
market timing variable as shown below:
[:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;] = 80� +5̅0�:/̅7� − /̅1�; + V̅0�:/̅7� − /̅1�;� +N0̅�
(Eq. 3. 20)
3.4.4 Time series regression analysis
The time series regression analysis is employed based on single and multi-factor
CAPM while evaluating the performance of IMFs and CMFs portfolios against the
single and multiple market benchmarks. The time series regression is also employed
using the TM model where the objective is to evaluate fund managers’ market timing
Page | 96
ability and fund selectivity skill. The results are reported in Chapter 5.The models
employed in this regression are those as previously discussed.
3.4.5 Panel data regression analysis
The study then revisits the research questions with panel data regression. Standard
panel data analyses applied are: (1) the Breusch and Pagan LM test for random
effects, which tests the appropriateness of the random effects model against OLS
pooled regression and (2) the Hausman test to compare the fixed effect model with
random effect, with (3) combinations of time-fixed effects. The significance of time-
fixed effects is also formally tested to see if time dummies are jointly significant or
not.
Whereas the CAPM and TM model estimates are presented in Chapter 6 (as discussed
in Section 3.4.3.1 and Section 3.4.3.2) using panel data, Chapter 7 focuses on two
main perspectives based on raw or non-risk-adjusted return and fund attributes for
equity funds. The first perspective estimates the raw returns performance using the
CAPM and TM model as in Chapter 6. The second perspective examines the raw
returns performance and the relationship with fund attributes (the fund attributes are
as mentioned in Section 3.2.2). Raw returns are used because the study is interested in
evaluating real return performance when associated with fees and other fund attributes
– which is also in the interest of investors who take into account only raw returns
while fund managers prefer to judge based on risk-adjusted return, as exhibited in
CAPM.
The gross return before market adjustment is written as follows:
/0� =80� +50�*/7�) +N0̅� (Eq. 3. 21)
where /0� is the return of the portfolio at time M and /7�. is the corresponding market
return. The intercept 80� measures the difference in fund managers’ performance
(positive or negative) and 50� the slope parameter, which quantifies return
performance of a fund portfolio and systematic risk at the same time. N0̅� is an error
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term. As indicated earlier, alternative measures of /0� used in the analysis (Chapter 7)
are GROSS, ADJUSTED, ADJUSTED LOAD and NET for each of the IEFs, CEFs
and AEFs portfolios.
In the case of the TM model before its adjustment for a risk-free rate of return, the
formula is written below:
/0� =80� +50�*/7) +V0� */7)² +N0̅� (Eq. 3. 22)
where the additional V0� indicates market timing expertise of the portfolio fund
managers, whereas other variables are as previously mentioned. 80� represents the
fund selectivity skill of fund managers.
The basic panel regression model is based on the ordinary panel least squares (OLS)
estimator and is expressed as:
Z�� = 8 +5��[�� + \�� i=1,...N; t=1, ...T
(Eq. 3. 23)
where Z�� , ]�� are N x 1, [ is N x k, and 5 is k x 1. 5^ is unknown constant coefficient
i and [�T^ is an observation of k explanatory variables for an Islamic and conventional
when j= 1, 2 respectively. \�� is \�� ~�*0, P��). In other words, the subscript i
denotes the cross-sectional dimension, whereas the subscript t denotes the time-series
dimension. Follow Baltagi (2005), the pooled OLS estimation, assuming one-way
error component model for the error term, is calculated based on:
Z�� = 8 +5�� [�� +\�� (Eq. 3. 24)
The single factor panel data regression is conducted in order to identify the impact of
each fund attribute and fund return performance,. It employs a cross-sectional single
Page | 98
panel regression, which is able to produce more reliable results and provide further
evidence on fund performance and the relationship with the fund attributes. The
equation is written as follows:
/0� = 8_ +50�:[̀0�; +N0̅� (Eq. 3. 25)
where N0̅� is a time-varying error term that can be written as N0̅� = a0 +b0��. The
a� denotes the unobservable factors that change over time or individual unobservable
effects of the individual mutual funds, and ]�� is the remainder error, which it is
assumed varies over time. /0� is the return for fund portfolio p in year t, 8 is an
intercept and [̀0� denotes a fund attribute, i.e., it refers to each of the endogenous and
exogenous variables applied in the study. The endogenous variables are alpha, beta,
residual risk, management fee and load fees, while the exogenous variables comprise
age, size and dummy of local or foreign investment (more detail is in Section 3.2.2).
The regression allows for fixed (year) effects, following Dahlquist et al. (2000) by
subtracting the mean of the return and the attribute during a year, represented by /0� and [̀0�respectively. The results from this regression are further discussed in Section
7.4.5. The equation yields:
/0� − /̅0� =50�:[0� −[̀0�; + *N0� −N�̅�) (Eq. 3. 26)
Then, averaging across all observations in Eq. 3.26 can also be written as:
/c0� = 50�[c0� + Nc0� (Eq. 3. 27)
where Nc0� is the error term that can be written as Nc0� = a� +]�� . The a� denotes the
unobservable effects of the individual mutual funds, and ]�� is the remainder
Page | 99
disturbance with the usual assumption that it is not correlated to the dependent
variable.
In the situation where unobserved effects a� are not correlated with the explanatory
variables due to the variables being truly random, the random effects are employed. In
the scenario where the unobserved effects a� are correlated with the explanatory
variables, the fixed effects panel data are used. The fixed or random effects are
determined by the Hausman test. If the test is significant, the fixed effects is chosen;
if not, then random effects is employed. The problems of serial correlation and
heteroskedasticity are corrected using the White cross-section standard error and
covariance test based on White (1980). Special cases of this equation are estimated for
AMFs, CMFs and IMFs as in Chapters 5 and 6 as the following:
/0� − /1� =80� + 50�:/7� −/1�; + V0� :/7� −/1�;� + d 50�:/�� −/1�;
�eQ,R,S,U,37Rf�,3gT�U+ 6a,AhAiiAjMk +N0̅�
(Eq. 3. 28)
where 80� denotes outperformance (positive sign) or underperformance (negative
sign) of the fund portfolio. 50� defines the superior fund selectivity skill of fund
managers if positive or otherwise if negative. This parameter indicates market risk
when V0� = 0. V0�, indicates superior market timing expertise of the fund managers if
positive or otherwise if negative. The rest of the variables are as previously defined in
Eq. 3.14. Results from this equation are explained in Chapters 5 and 6.
3.4.5.1 Fund attributes and panel data multi-factor regression
This study employs multiple regressions using panel data analysis to examine the
relationship between the return performance of funds and the fund attributes. The
analysis using this method is reported in Chapter 7. The fund return is gross, i.e., non-
adjusted for the market risk-free rate. This is because one of the aims is to examine
the real returns performance of funds. The analysis restricts the sample to funds that
have at least two years’ return data over the sample period. Each of the fund return
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portfolios is grouped into four groups: GROSS, ADJUSTED, ADJUSTED LOAD and
NET. The definitions of these groups and further explanations concerning the return
data are presented in Section 7.3.
The fund attributes are the explanatory variables in this analysis and are classified
them into two groups, namely the endogenous variables and the exogenous variables.
Whereby the exogenous variables are fund’s age, size, investment style (investment in
local or foreign markets) and also the types of the funds, the endogenous variables are
the risk, return and fees factors include alpha, beta, residual risk, management fees,
expense ratio, total load fees, and trustee fees. The dependent variable is the GROSS
return. The specific regression utilised is as follows:
/̅0� = l_ +l�0��m� +l�0�n�.4+� +lo0��4���.� +lp0����
+lq0��nr� + ls0���� ����� +lt0�F��� +lu0���.4v�4.w+lx0�BmB�y�� +l�_0��[��.� +l��0�d�E�nE�v
�
�e�
+ l��0��� .��� + 6a,AhAiiAjMk +N0̅� (Eq. 3. 29)
The discussions on the empirical results and findings of the regression equations are
in Section 7.4. Basically, there are three types of funds involved: all equity funds
(AEFs), Islamic equity funds (IEFs) and conventional equity funds (CEFs). The fund
attributes in this analysis are explanatory variables that involve endogenous and
exogenous variables. When the regression is conducted, these variables are controlled
while measuring the fund performance..
The quadratic regression is added to the Eq. 3.29 in order to clarify the relationship
between fees and fund returns. The equation is written as follows:
Page | 101
/̅0� = l_ +l�0��m� +l�0�n�.4+� +lo0��4���.� +lp0����+lq0��nr� + ls0���� ����� +lt0�F��� +lu0���.4v�4.w+lx0�BmB�y�� +l�_0��[��.� +l��0�d�E�nE�v
�
�e�
+ l��0��� .��� +l�o0�BmB�y��^2 +l�p0��[��.�^2+l�q0�d�E�nE�v
�
�e�^2 +l�s0��� .���^2 + 6a,AhAiiAjMk
+N0̅� (Eq. 3. 30)
Our expectation that there might be an evidence of non-linear relationship between
fees and fund returns, therefore, the quadratic panel regression for the fees factors is
developed. Details about the results from regression are discussed in Sections 7.4.7.1
and 7.4.7.2, Chapter 7.
3.5 Econometric estimation issues
A few econometric issues have been highlighted and taken care of while analysing
and estimating the data. Firstly, the study ensures that the funds’ sample of data used
is stationary and normally distributed. This is identified through the scatter plot of
graph in each data portfolio. Meanwhile, for comparative purposes, the study also
evaluates the data after they have been trimmed. The trimmed data are the data after
eliminating the years of crises in the period of the study (see more detail in Section
7.3). The reason for this treatment of the data is to overcome the outlier issues
associated with the extreme values of market return being too high or too low. The
trimmed data are solely employed in Chapter 7.
3.5.1 Data stationary and test of normality
The JB test is employed for the descriptive data in order to identify the normality of
the variables. Scatter plot analysis is also used for this purpose. If the data variables
are not normal, the study further employs the non-parametric tests, the Wilcox test
and the Mann Whitney test, to overcome these factors. Furthermore, the econometric
method is used to test whether the data employed are stationary or not. If the data are
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stationary, then they do not really suffer due to outliers. In other words, the linear
regression based on OLS is sufficient for the regression analysis. To test whether the
data are stationary or not, the unit root tests must be implemented. Normally, the
Augmented Dickey-Fuller (ADF) test is conducted for this reason. The test is based
on the following formulation:
∆D� = V + 5� +8D��� +∑ }��̂e� ∆D��� +b� (Eq. 3. 31)
where ∆ is the first-difference operator; D� is the time series variable tested for
stationary; � is a linear time trend; and b� is a covariance stationary random error.
The optimal choice of lag length removes autocorrelations in the error term. The
appropriate number of lagged differences ~ can be determined by Akaike Information
Criteria (AIC) based on Akaike (1970) and the critical value of the test developed by
MacKinnon (1996). The null hypothesis of unit root, |8| = 1 is tested against the
alternative of stationary, |8| < 1. In other words, failing to reject the null hypothesis
in the ADF test implies that the data are non-stationary.
3.5.2 Heteroskedasticity and positive serial correlation
The heteroskedasticity and positive serial correlation problems in the regression are
corrected using White’s (1980) test for the panel data. For the time series data, the
problems are corrected using both White’s (1980) and Newey and West’s (1987) tests.
The positive serial correlation or auto-correlation is identified when the Durbin-
Watson value in the regression is less than 2. The problem is also verified using the
Breusch-Godfrey Lagrange Multiplier (LM) test for serial correlation in the time
series data. If the heteroskedasticity and serial correlation problems exist in the data
regression, then these problems are corrected using the above mentioned tests.
3.5.3 Multicollinearity problem
In order to identify the presence of multicollinearity problems in the data, the study
detects them through: firstly, variance inflation factor (VIF) for the time series
analysis and secondly, either VIF or coefficient variance decomposition (CVAR) for
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the panel data analysis. If the VIF or CVAR values are more than five, this indicates
that there is a multicollinearity problem among the variables.
This test is important since the regression model with the multicollinearity problem
will lead to spurious regression where the outcome results are meaningless, since
there is a probability that few independent variables are highly significant to each
other. In this study, the diagnostic results indicate that multicollinearity is not a
problem in any of the regression models.
3.6 Summary
This chapter has explained the methodologies used to examine the performance of
479 Malaysian mutual funds, comprising 129 IMFs and 350 CMFs from 1990 to
2009. The dependent variable is represented by the average return of funds in each
portfolio, whereas the independent variables are segmented into single and multiple
benchmarks. These are applied as described in Chapters 4 to 6. In Chapter 7, the study
focuses on 106 equity funds, and the dependent variables are extended into four
groups including before and after fees, and the independent variables are also
extended to include multiple regression based on fund attributes.
This thesis has three overarching hypotheses. The objective related to the first
hypothesis is to identify any differences in fund return performance, i.e., IMFs and
CMFs, in relation to the market benchmark, either single or multiple benchmarks. The
objective related to the second hypothesis is to examine any differences in IMFs and
CMFs fund managers’ market timing expertise and fund selectivity skills. The third
hypothesis concerns any differences in the effects of fees on fund performance in
relation to the fund attributes of the IMFs and CMFs portfolios, focusing on equity
mutual funds.
Several methods are employed to evaluate mutual fund performance, as explained in
this chapter. Firstly, the study employs the descriptive and t-test analysis. Secondly,
the standard risk-adjusted performance measurements of SR, TI, JA, AR, M2 and
ASR are conducted. Finally, the study employs time series and panel data regression
analysis. With reference to regression analysis, fund performance is evaluated mainly
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using the CAPM model and the TM model, following Bertin and Prather (2009),
Treynor and Mazuy (1966) and Bello and Janjigian (1997) respectively. Then the
regression analysis is extended to a multiple panel regression to include the effects of
fees and other fund attributes on fund performance. Each of the portfolios is measured
against the independent variables.
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CHAPTER 4 - RISK AND RETURN
PERFORMANCE ANALYSIS
4.1 Introduction
Global Islamic funds have grown tremendously, with total assets of US$77 billion by
the end of July 2011. The Islamic Funds Index has grown at an annual rate of 38.9
percent (Eurekahedge, 2011) since its inception in the 1990s. In a broader perspective,
the Islamic banking and investment industry is regarded as one of the fastest growth
segments in the Islamic finance industry, with an annual growth rate of 15 per cent,
making it a worldwide phenomenon.
The increasing demand on the type of investment funds or mutual funds has generated
investors’ and financial analysts’ interest in risk and return performance
measurements for the Islamic finance industry. The growth of the fund has also made
managing risk and returns in portfolio management crucial for fund managers. This
chapter therefore addresses the following two issues: (1) to evaluate the overall
performance of IMFs and CMFs in relation to their market return benchmark using
risk adjusted performance measures and (2) to compare the performance of IMFs to
their conventional peers in different market conditions.
The chapter is organised as follows. The next section provides a review of the
literature on the risk and return characteristics of the MFs. Section 4.3 discusses the
data and sample selection. Section 4.4 provides results and discussions. The analysis
provides descriptive statistics of the IMFs and CMFs, the results of non-risk-adjusted
(gross) and risk-adjusted returns, and also the performance analysis related to the AFC
and the GFC. Section 4.5 concludes the chapter with a summary of the results.
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4.2 Issues and related studies
The Islamic fund industry began in the 1990s and the number of Islamic funds
worldwide increased from eight prior to 1992 to 95 in 2000, with a value of
US$5billion in assets by 2000 (Elfakhani et al. 2005). This increase indicates a record
in the compounded annual growth rate (CAGR) of 48.44 per cent for the first eight
years. The period from December 1997 to April 2008, for instance, indicates the
growth of these funds at an annualised rate of 26 per cent. By the end of April 2008,
the number of Islamic funds stood at 504 funds worldwide with total assets AUM at
US$33.9 billion (Shanmugam and Zahari 2009). Despite the rapid growth of the fund,
the Islamic fund industry is small compared to the whole mutual fund industry
operating in the global market. The global mutual fund industry doubled its size from
US$9.6 trillion in 1998 to US$18 trillion in 2005 with a growth rate of 9 per cent
annually (Ramos, 2009). At the end of the third quarter of 2011, the mutual fund
assets worldwide were US$23.13 trillion, according to the data from the Investment
Company Institute (Investment-Company-Institute, 2012). Although the Islamic funds
constitute only a small portion of the total global mutual fund assets, it is nonetheless
true that the Islamic fund industry is attractive and is of interest to many investors.
In Malaysia, the Islamic fund industry is one of the fastest growing in the country’s
capital market (Lewis 2009; Nathie, 2008), and this is reflected in the high growth of
the funds since the industry began in the 1990s. According to the Securities
Commission of Malaysia (SC), from 2003 to 2008, the NAV of the Islamic funds in
Malaysia grew at a CAGR of 26.3 per cent while the total industry in relation to the
market share recorded a growth rate of 11.4 per cent in the same period. The number
of approved funds in Malaysia has also risen tremendously from two funds in 1993 to
141 funds by the end of April 2009; there are 167 funds as at the end of February
2012. The total NAV of IMFs is now RM$29.24 billion (1993 at RM$0.19 billion)
and the CMFs is around RM$237.78 billion (1993 at RM$27.94 billion), with the
NAV of the total mutual fund industry contributing approximately 19.85 per cent to
the stock market as at the end of February 2012 (Securities-Commission-Malaysia,
2008, 2012). This figure reveals that the CAGR of IMFs is approximately 7.10 per
cent annually over a 19–year period, from December 1993 to February 2012.
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These IMFs, also known as Islamic unit trust funds, are steadily increasing as an
important selection asset in managed portfolios and have played a major role in the
development of the Islamic financial system in Malaysia. Due to their significance in
the current financial market, the superior performance of the IMFs could lead to the
continuing development of Islamic finance in the global market and particularly
Malaysia.12 Yet research on IMFs in Malaysia and worldwide has attracted little
attention in the finance literature. The lack of information in this area means that the
IMFs’ performance in the market in comparison to their conventional peers, the
CMFs, requires further investigation.
Studying the performance of Malaysian IMFs and CMFs is motivated by Malaysia
having 29 per cent of Islamic funds located there by the end of July 2011 (as
discussed in Section 2.2). The country is the most popular Islamic fund centre
worldwide and was acknowledged as a leading fund centre throughout the 2000s
(Eurekahedge, 2011). Malaysia successfully liberalised its Islamic financial system
through the introduction of an institution known as Pilgrimage Funds. This
environment is conducive for the growth of the Islamic mutual fund industry.
Furthermore, the motivation for this study is encouraged by the Islamic funds having
outperformed the Morgan Stanley Capital International World Index (MSCI) and
Dow Jones Sustainability Index (DJSI), and providing better downturn protection and
less volatile investment vehicles. The study by Eurekahedge noted that the MSCI and
DJSI declined by 41.12 per cent and 42.98 per cent respectively, but Islamic funds
dropped less severely by 28.53 per cent (Eurekahedge, 2011).13
The focus of this chapter on evaluating the risk and return performance of IMFs and
CMFs relative to their respective benchmarks is also inspired by the question of
whether the rapid growth of IMFs is associated with a higher return of the fund
portfolio or vice versa. One recent study demonstrated that IMFs perform worse over
the Islamic and conventional benchmark and the degree of underperformance was
even larger during the recent global financial crisis (Hayat and Kraeussl, 2011). In
12Islamic finance has continued to demonstrate its evolution and strong growth as assets have expanded by 22 per cent to RM192 billion in Malaysia for 2008 alone and now account for 15.0 per cent of the total assets in the Malaysian finance industry (Ghani, 2009). 13 See www.eurekahedge.com, September 2011, for details.
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contrast, another study stated that IMFs can perform better than CMFs only during
bearish market conditions (Abdullah et al. 2007). The inconsistency in findings might
be due to the studies incorporating different data, and also might be explained by the
fact that IMFs are not subject to the interest rate risk that erodes the return
performance of the CMFs during the bearish market, particularly during the Asian
financial crisis (AFC) in 1997–98. In fact, the greater impact of the crises was due to
the extreme event of the AFC rather than the GFC (Bhatti and Nguyen 2012).
Therefore, in this study, evaluating the performance of IMFs and CMFs using a
longer time period and incorporating more recent data is expected to reveal more
robust results.
It has been argued that the restriction of Islamic investments to Shariah-compliant
products led to the IMFs performing poorly compared to the whole market. Islamic
investments are required to follow strict Shariah screening criteria, whereas the
conventional funds counterparts are free to invest in investment activities without any
restrictions.14 The constraint from the Shariah criteria could create poorer
performance due to limitation on diversification and lack of opportunities to pursue
investments in high return profiles. According to Abdullah et al. (2007), these
regulations are some part of the contribution to the underperformance of the IMFs, but
this can be arguable. This is because following the Shariah principles means avoiding
uncertain high return investments related to highly risky and debt-ridden (M. Amin,
2009). However, these findings also generated a question about whether there are any
significant differences in the performances of these two fund types – the IMFs and
CMFs – particularly in their risk and return characteristics when related to the normal
market cycle and the bullish and bearish market scenarios.
Hassan et al. (2010) found that there were no convincing performance differences
between Malaysian IMFs and CMFs during the period January 1996 to November
14
To enable a mutual fund to be categorised as a permissible fund depends on the status of the authorised investments. Basically they must be free from any interest rate and must conform to Shariah principles. In Malaysia, the investments of the funds are subject to the rules and regulations of the conditions laid down by the Securities Commission of Malaysia (SC). SC under its Shariah Advisory Council is the government body responsible for monitoring, evaluating and approving the Islamic funds in Malaysia (Majlis-Amanah-Rakyat, 2002 ). This requirement is not applied to the conventional funds and their management is free to choose any authorised investments (Majlis-Amanah-Rakyat, 2002).
Page | 109
2005 and they concluded that there is similar reward for risk and diversification
benefits for these two fund portfolios. They also noted that both funds are not
correlated with the market portfolio, the KLCI. However, they found that there is a
statistically significant long-term relationship between Malaysian Islamic and
conventional funds. In this chapter, it is contended that there is a difference between
the return performances of the portfolios of these two funds, IMFs and CMFs. Where
there is no difference in the performance of these funds, questions arise about the fund
characteristics and the investment style differences between them. These matters will
be investigated in the analysis section in this chapter. In the next section, the data and
sample selection method is discussed.
4.3 The data and sample selection15
The source of the return data of all funds was the Morningstar database up to the end
of April 2009. There are 535 Malaysian open-ended mutual funds are available in the
Morningstar database from January 1990 to April 2009, as presented in Table 4.1.
These monthly returns are the net amounts after deducting all operating expenses, but
excluding the front and exit fees. These data returns are divided into 143 IMFs and
392 CMFs, falling into five broad categories which are based on the types of the
funds, namely, alternative, allocation, equity, fixed income and money market.
The data on market indices were obtained from the Securities Industry Research
Centre of Asia-Pacific (SIRCA) and from Datastream. The market indices include the
Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Morgan Stanley Capital
International World Index (MSCI) and the Dow Jones Islamic Market Index (DJIM).
For the risk-free rate return, this study employs the Malaysian t-bills. For the bond
index, the Malaysian fixed deposit rate of return is employed, while for the money
market index, the Kuala Lumpur Interbank Rate (KLIBOR) is used. Additionally, this
chapter’s analysis employs KLCI as a large stock market index and a single market
return benchmark for both IMFs and CMFs portfolios. This is particularly due to
many companies being registered with the Bursa Malaysia Kuala Lumpur Stock
Exchange (KLSE) Shariah-compliant list. In October 2003, the number of Shariah-
compliant securities in Malaysia was 722 securities or 81 per cent of the total listed
15 The data and sample selection described in this section are also employed in Chapters 5 and 6.
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securities on the KLSE, compared to 684 securities or 80 per cent of the total listed
securities in 2002 (Securities-Commission-Malaysia, 2003). Currently, about 88 per
cent of stocks listed in the KLSE are Shariah-compliant and they represent two-thirds
of the Malaysia’s market capitalisation (Bursa Malaysia 2010).
The sample of return data is then matched to a list of Malaysian mutual funds
obtained from SC. There are 554 approved mutual funds in Malaysia for the same
period, consisting of 141 IMFs and 413 CMFs. Thus, the sample in this study exceeds
the number of IMFs in the SC list with the two funds.16 These two funds originated
from the conventional funds based on information obtained from the fund prospectus,
and are converted to the Islamic fund category over the period of the study. They are
considered to be Islamic funds based on the current situation. Therefore, the total
IMFs is now 143 funds. In the conventional fund category, 413 CMFs are listed in the
SC as at the end of April 2009, and only 392 funds are available in the Morningstar
database. Therefore, 19 funds are missing from the list.
The study further restricts the final sample to those funds that have a minimum of 12–
month returns, thus limiting the total funds to 479, as reported in Panel B of Table 4.1.
The selection of a minimum of 12–month returns is adequate, following Bertin and
Prather (2009). As a result, 14 funds from the 143 IMFs are excluded from the
sample. As a result, 42 are also excluded from the list of 392 CMFs, so there are 350
CMFs in the final sample. The total number of funds excluded is 56, as shown in
Table 4.1. All the funds in the final sample are comprehensive, covering all fund type
categories. The final sample consists of 129 IMFs and 350 CMFs in Malaysia,
covering 232 monthly observations over the 20–year period from January 1990 to
April 2009.
Despite the restriction, the data in this study include the largest number of mutual
funds in Malaysia studied to date. According to Elfakhani et al. (2005), a 68–month
sampling period enables two distinctive market cycles to be covered. Even longer
historical performance data is likely to lead to more robust and conclusive results.
This study covers more than two complete market cycles and includes three
16 These two funds are Apex dana aslah (formerly known as Apex small cap) and Pacific dana dividen.
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expansions and three troughs over the duration of the study. The analysis utilises raw
returns before they are adjusted for the risk-free rate return and also risk-adjusted
return performance (i.e., the returns after being adjusted for the Malaysian t-bills, a
proxy for the market risk-free rate of return). Further details regarding the data and
sample selection are given in Table 4.1, for ready reference. To the best of my
knowledge of the Malaysian mutual fund literature, this is the largest sample that has
been studied.
Table 4.1: Sample selection in the study The table presents the full sample of Malaysian mutual funds, consisting of 143 IMFs and 392 CMFs available in the Morningstar database as at the end of April 2009. The 143 IMFs include two funds that changed their status from CMFs to IMFs during the period, and this study considers them to be categorized in the IMFs portfolio following the prospectus description.
Morningstar Excluded fund Total Funds SC List Panel A: Numbers of mutual fund in Malaysia IMFs 143 0 143 141 CMFs 392 19* 411 413 Total 535 19* 554 554 Panel B: Final sample selection IMFs 143 14** 129 CMFs 392 19*
23** 350
Total 535 56** 479
Notes: * denotes 19 funds obtained from 554–535 funds. These 19 funds were excluded due to non-available data in the Morningstar database.
**indicates the number of funds that do not have a minimum of 12–month return data.
4.3.1 Survivorship bias
With favour to the IMFs, the data is considered free from survivorship bias as the
study incorporates all the funds listed in the market. Survivorship bias occurs when
funds that stop reporting information or cease operation are purged from the database
and regarded as of no interest to investors (Fung and Hsieh, 2002, p.66). In the case of
the conventional funds, there is a survivorship bias in the data in that out of 413 funds,
Morningstar provides data for only 392 funds after excluding the two converted
funds. However, the impact of survivorship bias in this conventional fund category is
not a problem for comparison purposes as the Islamic funds are expected to perform
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better than the conventional funds. The disappearance of the non-surviving funds in
the conventional category will improve the overall performance of the conventional
funds compared to the Islamic funds.
4.4 Results and discussions
This section considers various econometric analyses from descriptive statistics to
single factor CAPM analysis using non-risk-adjusted returns and risk-adjusted returns.
The details are in the following subsections. The most important is the returns
performance, which is evaluated for different sub-periods: pre-crisis, during the AFC
and GFC crises and post-crisis.
4.4.1 Descriptive statistics of IMFs and CMFs
This sub-section describes the risk and return characteristics of the IMFs and the
CMFs for the period January 1990 to April 2009 using basic statistics. The IMFs
portfolio consists of 129 funds and the CMFs portfolio consists of 350 funds. The
study duration is divided into two sub-periods: sub-period 1 from January 1990 to
August 1999 and sub-period 2 from September 1999 to April 2009. The summary
statistics based on raw return performance (before being adjusted for the one-month
Malaysian t-bills) of the IMFs and CMFs for the period from January 1990 to April
2009 and between the two sub-periods are reported in Table 4.2.
Regarding overall performance for the period studied, on average IMFs earned a
higher monthly mean actual return percentage than CMFs: 0.98 per cent versus 0.63
per cent, as depicted in Table 4.2. Both IMFs and CMFs also obtained a mean return
higher than the market and the risk-free rate returns. The statistics results are
consistent in sub-period 1, whereas in sub-period 2 the CMFs portfolio appears to
have higher returns than the IMFs and the full sample consisting of all mutual funds
(AMFs). Building on this theme, in relation to the total risk which is based on the
standard deviation (std. dev.) of a portfolio, the IMFs portfolio is associated with
higher risk than the CMFs and AMFs portfolios in all periods.
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Table 4.2: The IMFs and CMFs monthly returns performance: summary statistics The table includes monthly average return performance of the full sample: 479 AMFs, 129 IMFs and 350 CMFs from the Malaysian market for the period January 1990 to April 2009. All returns are in percentages, based on gross return calculated using geometric mean return and net amounts from all expenses. The Kuala Lumpur Composite Index (KLCI) return is used as a proxy for a market return benchmark. The period is divided into two sub-periods: sub-period 1 from January 1990 to August 1999 and sub-period 2 from September 1999 to April 2009, to identify the impact of crises on fund return performance. Sub-period 1 includes the Asian financial crisis (AFC) in 1997–1998 and sub-period 2 includes the global financial crisis (GFC) in 2007–2008. The whole period consists of 232 observations, in which each of the sub-periods covers 116 observations. The mean returns of the IMFs and CMFs are higher than the market return and the risk-free rate of return, a proxy of the one-month Malaysian t-bill, implying that the return performance of the fund portfolios is relatively better than the market benchmark. Regarding overall performance, the results show that the IMFs’ return performance is higher than the CMFs. This means that the Islamic portfolio is more risky (as the standard deviation of the portfolio is higher) than the conventional portfolio. The Islamic and conventional returns are positively skewed, but the market returns demonstrate a negative skew (left tail). All portfolios show leptokurtic right tailed distribution since their kurtoses are greater than zero.
Overall period (Jan 1990 to April 2009) Sub-period 1 (Jan 1990 to Aug 1999) Sub-period 2 (Sept 1999 to April 2009)
Mean Return
Std. Dev. Skewness Kurtosis Mean Return
Std. Dev. Skewness Kurtosis Mean Return
Std. Dev. Skewness Kurtosis
AMFs 0.808 5.040 0.081 5.892 1.231 6.159 –0.057 4.864 0.385 3.568 0.044 3.969 IMFs 0.982 5.591 0.087 5.672 1.620 6.968 –0.124 4.373 0.345 3.664 0.108 4.683 CMFs 0.633 4.709 0.207 5.745 0.841 5.644 0.169 4.987 0.425 3.549 0.024 3.785 Market 0.244 8.092 –0.263 7.309 0.252 10.218 –0.244 5.589 0.237 5.207 –0.156 3.123 Risk free (rf)
0.365 0.153 0.495 1.817 0.494 0.112 –0.609 3.340 0.236 0.033 –0.194 3.019
Page | 114
Additionally, it is worth noting that the systematic risks of both IMFs and CMFs
portfolios, measured by the beta for each portfolio, are relatively lower than the
market risk, thus indicating that they are less volatile than the market (the lower the
beta, the less volatile a portfolio in relation to the market risk).
Table 4.2 shows that the IMFs are also associated with the higher std. dev. (5.59),
implying that the volatility and risk of the fund are also higher than CMFs’s total risk
at 4.71. These results suggest that the IMFs portfolio is more risky than the CMFs,
but provides substantially higher returns. The average monthly return of the KLCI, a
proxy for the market return portfolio, is lower (0.24%) but the risk is strongly higher
(8.09%) relative to both portfolios, indicating that the market fluctuation over time
and the volatility of the market return are substantially higher than the fund portfolio.
The low return is expected as the market did face at least two competing business
cycles (bullish and bearish markets) over this 20-year period.
The summary statistics in the form of sub-periods related to the crisis are also
presented in Table 4.3. The sub-periods are: pre-AFC (1990–1996), during AFC
(1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). More
explanation of the results relating to the crises is in Section 4.4.7 onwards. The results
show that the impact of the AFC has been greater than the impact of the GFC on the
mutual fund industry in Malaysia. The results support the evidence of a greater
impact of the AFC on the six stock indices in the Asia-Pacific, as recently discussed
by Bhatti and Nguyen (2012).
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Table 4.3: Summary statistics of IMFs and CMFs non-risk-adjusted returns in relation to the AFC and the GFC
Mean Return
Std. Dev.
Skewness Kurtosis Obs. Mean Return
Market rf AMFs Overall 0.808 5.040 0.081 5.892 232 0.244 0.365 Pre-crisis 1.749 4.834 –0.111 5.487 84 0.940 0.499 During AFC –1.216 8.703 0.235 3.214 24 –3.115 0.536 Post-crisis 0.727 4.246 0.932 6.270 96 0.652 0.234 During GFC
–0.006 3.234 –0.515 3.152 28 –0.361 0.267
IMFs Overall 0.982 5.591 0.087 5.672 232 0.244 0.365 Pre-crisis 2.298 5.627 –0.093 4.469 84 0.940 0.499 During AFC –1.173 9.690 0.179 3.157 24 –3.115 0.536 Post-crisis 0.621 4.459 0.939 6.618 96 0.652 0.234 During GFC
0.121 3.071 –0.355 2.783 28 –0.361 0.267
CMFs Overall 0.633 4.709 0.207 5.745 232 0.244 0.365 Pre-crisis 1.201 4.485 0.203 5.801 84 0.940 0.499 During AFC –1.259 7.808 0.297 3.238 24 –3.115 0.536 Post-crisis 0.833 4.101 0.927 5.915 96 0.652 0.234 During GFC –0.133 3.437 –0.659 3.571 28 –0.361 0.267
4.4.2 Test of normality
The graphs based on histogram and kernel density are presented in Figure 4.1. It can be
seen that the histogram graphs for both portfolios are approximately and normally
distributed. In comparison, the CMFs portfolio is fairly distributed relative to the IMFs.
Similar results are obtained for both IMFs and CMFs when the line and kennel density
graphs clarify the normal distribution. Consistently, the results support the results for
descriptive statistics, as reported in Table 4.2, which basically state that the CMFs
portfolio is less risky, suggesting that it is in fact more diversified, due to the standard
deviation and the mean returns being lower than those of the IMFs.
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Figure 4.1: IMFs and CMFs graphs of normality, January 1990 to April 2009
4.4.3 Covariance and correlation analysis
The covariance and correlation between IMFs and CMFs portfolios is investigated
relative to their market return portfolios. The covariance test is conducted to examine
the co-movement between the portfolio, while the correlation test aims to find if the
portfolio is dependent on them and the market. The study assumes that both portfolios
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are positively correlated and have dependence on their market portfolio. Table 4.4
reports the results of the analysis. They reveal that the covariance between IMFs and
CMFs for the whole period and the sub-periods are not equal to zero, using either raw
returns or risk adjusted returns. Therefore the null hypothesis can be rejected. The
results imply that these two portfolios are moving together to form an important part of
the total industry.
Table 4.4: Covariance and correlation between IMFs, CMFs and the market portfolio
Overall period Sub-period 1 Sub-period 2 IMFs CMFs IMFs CMFs IMFs CMFs Panel A: Raw Returns Covariance IMFs 31.125 48.131 13.307 CMFs 23.989 22.077 35.361 31.583 12.352 12.484 Market 37.224 33.599 57.745 50.371 16.693 16.824 Correlation IMFs 1 1 1 CMFs 0.915 1 0.907 1 0.958 1 Market 0.826 0.886 0.818 0.881 0.883 0.918
Panel B: Risk Adjusted Returns Covariance IMFs 31.073 48.297 13.333 CMFs 24.004 22.158 35.546 31.787 12.382 12.517 Market 37.288 33.729 57.969 50.467 16.730 16.863 Correlation IMFs 1 1 1 CMFs 0.915 1 0.907 1 0.958 1 Market 0.827 0.886 0.819 0.881 0.883 0.919
These results in Table 4.4 also show that both IMFs and CMFs portfolios are highly
correlated to each other. Both portfolios also have strong correlation with the KLCI (a
proxy for the market return), suggesting that these portfolios depend on the market
movement, with the conventional funds being slightly closer to reflecting the market
movement than the Islamic funds. The correlations between the IMFs and CMFs to
the market return portfolio are also relatively high in all periods.
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4.4.4 Risk-aversion analysis
In certain circumstances, the performance of the funds is based on the state of the
equilibrium of risk and return in the capital assets price. It can be predicted that
investors are risk averse and the activities of borrowing or lending funds happen at a
risk-free rate. This relationship, according to Sharpe (1965b), refers to the prediction
of expected returns from particular assets and their associated risks. Although the
actual results may diverge considerably from the predictions made by investors at the
time they purchase the assets, this forecasting is important for estimating the expected
value of distribution of the portfolios, linked to the market risk-free rate and the
standard deviation of the returns (Sharpe, 1965b). Consequently, forecasting the
future of mutual funds can be done by tracking the funds’ historical performance. In
many situations, this estimation is based on actual historical data (Sharpe, 1965b).
Finally, assumptions of the estimated values must be made, followed by doing the
empirical tests on the subject matter.
To investigate the relationship between risk and return of mutual fund performance, a
risk-aversion analysis, following Sharpe (1965b, p. 418), is conducted. The aim is to
estimate the relationship between the result obtained by regressing σ on /̅ and that
obtained by regressing /̅ on σ. The estimates for the pure rate of return and the risk
corresponding to the three lines are shown in Table 4.5. The table shows the
relationship between the mean return and the mean standard deviation of the IMFs
and CMFs portfolios based on average monthly returns from January 1990 to April
2009, the period to which the regression analysis was applied. Two types of
regressions were conducted and the results are shown in columns 4 and 5. Column 4
indicates (/̅ ) as a dependent variable and (σ) as an independent variable, and vice
versa for Column 5. Then the intermediate line is also as denoted in column 6. The α
and β for the intermediate line are obtained by adding the value of α and β from two
regression lines respectively and then dividing them into 2.
The results can be interpreted as follows. For the IMFs during this period, investors
required a monthly average rate of return of approximately 1.42 per cent on riskless
assets. Concerning the risk element, they required an additional 0.36 per cent of the
mean return per month for each 1 per cent of the standard deviation of the monthly
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return. On the other hand, for the CMFs during the same period, investors required a
monthly average rate of return of approximately 1.35 per cent on riskless assets.
Concerning the risk, the conventional portfolio required an additional 0.18 per cent of
the mean return per month for each 1 per cent of the standard deviation of the
monthly return. Thus, the results in Table 4.5 show that the IMFs portfolio is riskier
compared to the conventional counterparts, even though the portfolio performs
slightly better. This is because the investors acquire more return (IMFs about 1.42%
compared to CMFs about 1.35%) on the risk-free assets rate.
Table 4.5: Overall average returns and standard deviations for Malaysian IMFs and CMFs, January 1990 – April 2009: Regression results Portfolio (232 Obs)
Mean Return (/̅ )
Mean Standard deviation (σ)
Regression line : (σ) to (/̅ )
Regression line : (/̅ ) to (σ)
Intermediate line
α β α β α β IMFs
0.982
3.912
–0.843
0.465
3.673
0.245
1.415
0.355
CMFs
0.633
3.027
–0.300
0.308
2.999
0.045
1.350
0.177
To illustrate the results in Table 4.5 and to identify the trend movements of both IMFs
and CMFs portfolios, a scatter plot was constructed, as shown in Figure 4.2. The
study develops the scatter plot analysis and presented the graphs that are divided into
Figure 4.2(a) and Figure 4.2(b).
Figure 4.2(a) illustrates the scatter plot for both portfolios based on their aggregate
mean returns versus their standard deviations. It can be seen that both portfolios show
similar patterns and the scatter plot shows the diversity in both. However, in
comparison, the IMFs portfolio is less scattered and this implies that the portfolio is
less diversified. Even though most of the data in both portfolios were assembled at 0
point, it seems the IMFs have more outliers. It is therefore implied that the IMFs’
movements are more volatile than the CMFs.
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Figure 4.2: The Scatter Plot for the Malaysian Islamic and Conventional mean returns versus their average standard deviation (in percentage), January 1990 to April 2009. Figure 4.2 (a)
Figure 4.2 (b)
To validate Figure 4.2(a), the data in Figure 4.2(b) were plotted. The figure presents
the individual relationship of the average monthly return of each portfolio versus its
degree of precision. This degree of precision is measured by dividing one from the
standard deviation of each of the portfolio (1/std.dev.). Results in Figure 4.2(b)
clearly show that both portfolios are approximately normally distributed. However,
the CMFs are more diversified and more dispersed in relation to their IMFs
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counterparts. The figure also suggests that in terms of degree of precision, the CMFs
score higher than the IMFs because the standard deviations of the funds are lower and
closer to zero. Conversely, the figure implies that the degree of precision of the IMFs
is lower than that of the CMFs. The results are somehow robust when one looks at the
results in Table 4.2, as the mean return and the standard deviation of the IMFs are
higher than the mean return and the standard deviation of the CMFs.
To identify the relationships between both portfolios with their market portfolio, the
graph in Figure 4.3 displays the scatter plot for both portfolios versus their market
portfolios, i.e., KLCI return. The KLCI return is shown as a horizontal line because it
is considered to be an independent variable. The results in this figure highlight a
relationship between the IMFs and the CMFs and the market portfolio. The results
indicate that the IMFs are relatively more centred and follow the market trend more
closely than the CMFs. It also can be seen that the movement of the CMFs is more
dispersed than that of the IMFs.
Figure 4.3: The relationship between the aggregate return performance of IMFs and CMFs relative to the market portfolio
4.4.5 Trend analysis
Both graphs (raw return and risk adjusted return) in Figure 4.4 indicate a similar
pattern trend in that they are moving together and following the market trend.
However, it can be seen that CMFs portfolio is more volatile than the IMFs while
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using raw return but vice versa based on risk adjusted return. On the other hand, the
IMFs portfolio is more stable in regard to raw return performance but not for risk-
adjusted return performance. This outcome confirms the scatter plot (shown in Figure
4.2), where the IMFs are more diversified than the CMFs. However, both portfolios
seem to follow the market trend movement.
Figure 4.4: The trend pattern for the return of the IMFs and CMFs portfolios relative to the market portfolio, January 1990 to April 2009 Figure 4.4(a) Raw returns of IMFs and CMFs
Figure 4.4(b) Risk adjusted returns of IMFs and CMFs
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The trend analysis depicts the trend pattern data as fluctuating over time. To see the
robustness of the data, a unit root test is done in order to identify whether the data is
stationary or otherwise. Therefore, the data are tested using the Augmented Dickey
Fuller (ADF) test. The results are presented in Table 4.6. Failing to reject the null
hypothesis in the ADF test implies that the data are non-stationary.
Table 4.6: Results of the unit root tests
This table presents the results of the unit root tests. The asterisks ***, ** , * indicate significance at the 1%, 5% and 10% levels respectively. The critical values of the ADF statistics adopted from MacKinnon (1996) are: –3.47, –2.88 and –2.58 respectively for no trend; and are –4.02, –3.44 and –3.14 respectively. Phillip and Perron unit roots tests represent the same results for the no-trend level.
Table 4.6 reports the ADF test statistics for the IMFs, CMFs, total industry and the
market. The ADF test is conducted for all the portfolios without a trend and with a
linear trend level. The null hypothesis of the unit root for all portfolios, in a level
form with or without a trend, is rejected at all levels of significance, confirming that
the trend variables constitute a stationary series. In other words, each variable
represents the ∆� as a stationary series with no pattern trend in their time series of
data, suggesting that the data fluctuate all the time.
4.4.6 Mean differences between IMFs and CMFs
This sub-section aims to identify any significant difference in the risk and return
characteristics of the IMFs and the CMFs for the period January 1990 to April 2009
and between the two sub-periods: sub-period 1 from January 1990 to August 1999
and sub-period 2 from September 1999 to April 2009. Table 4.7 reports mean
Portfolio Level No trend Trend
IMFs ADF-0 lag –10.46*** –10.42***
ADF-1 lag –7.30*** –7.27***
CMFs ADF-0 lag –9.88*** –9.85***
ADF-1 lag –6.84*** –6.81***
ALL ADF-0 lag –10.17*** –10.14***
ADF-1 lag –7.05*** –7.02***
Market ADF-0 lag –10.37*** –10.36***
ADF-1 lag –7.03*** –7.04***
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difference in the returns performance of IMFs and CMFs. The analysis of these two
sub-periods is important for the purpose of identifying the differences in the funds’
performance during the earlier 10–year period. This is associated with the AFC while
the later 10–year period is linked to the GFC. The results reveal that there is
insignificant difference in the performance of a fund in sub-period 1 and sub-period 2.
Both sub-periods reveal the positive mean score, which implies that the IMFs have a
positive return of 1.62 per cent in sub-period 1 as well as a positive return of 0.35 per
cent in sub-period 2. The higher mean return of the IMFs in sub-period 1 than in sub-
period 2 could suggest that the Islamic funds’ return performance is less severe due to
the AFC rather than the GFC.
Results in Table 4.7 also reveal that there is no significant difference in the return
performance of the CMFs in both sub-periods. Similar to IMFs, the mean score for
the CMFs is higher in sub-period 1 than for sub-period 2, implying that the funds
perform better in sub-period 1. They are 0.84 and 0.43 per cent respectively. On
average, the IMFs portfolio outperforms better than the market return and the CMFs.
Interestingly, Table 4.7 denotes that the returns for both IMFs and CMFs are higher
than the market, suggesting that both portfolios performed well during the period of
analysis with the return of IMFs being higher than CMFs. This is indicated by the
mean score for the Islamic portfolio (0.98%) being higher than the mean score for the
conventional portfolio (0.63%). Results in the table validate the evidence presented in
Tables 4.1, 4.2 and 4.3. Indirectly, the results suggest that Islamic mutual funds in
Malaysia constitute a good long-term investment compared to their conventional
counterparts because the returns will be relatively higher. The findings could also
suggest that the Islamic funds are more strongly correlated to the market movement in
the sense that a market event could have a greater and quicker impact on IMFs than
on CMFs. Yet the insignificant difference between these two fund portfolios could
indicate that investors face a similar risk and reward penalty for these two types of
investment. Therefore, the lower mean score of the CMFs compared to their Islamic
counterparts in sub-periods 1 and 2 may imply that the conventional funds are more
affected in return underperformance relative to the market than the Islamic funds, due
to the AFC and GFC.
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Table 4.7: Results of mean t-test assuming equal variances for IMFs and CMFs The results show the performance of the IMFs and the CMFs for the whole period (January 1990 to April 2009), sub-period 1 (January 1990 to August 1999) and sub-period 2 (September 1999 to April 2009). Panels A, B and C show the results for these respective periods. The study tests the hypothesis that the return performance of each portfolio is not equal to each other either in the sub-periods or the total period. The null hypothesis is that there is no difference in the performance of the portfolios for the sub-periods using t-test, assuming equal variances. The results show t-test is an insignificant difference from zero in all the panels, thus the null hypothesis cannot be rejected. The results confirm an insignificant difference for both returns performance of IMFs and CMFs relative to the KLCI, the market return portfolio.
IMFs CMFs KLCI
Panel A: Overall period (232 observations)
Mean 0.982 0.633 0.244 Variance 31.260 22.184 65.478 Pooled Variance 48.369 43.831 Hypothesized Mean Difference 0 0 df 462 462 t Stat 1.143 0.633 P(T<=t) one-tail 0.127 0.264 t Critical one-tail 1.648 1.648 P(T<=t) two-tail 0.254 0.527 t Critical two-tail 1.965 1.965
Panel B: Sub-period 1 (116 observations)
Mean 1.620 0.841 0.252 Variance 48.549 31.857 104.414 Pooled Variance 76.482 68.136 Hypothesized Mean Difference 0 0 df 230 230 t Stat 1.191 0.544 P(T<=t) one-tail 0.117 0.294 t Critical one-tail 1.652 1.652 P(T<=t) two-tail 0.235 0.587 t Critical two-tail 1.970 1.970
Panel C: Sub-period 2 (116 observations)
Mean 0.345 0.425 0.237 Variance 13.422 12.616 27.110 Pooled Variance 20.266 19.863 Hypothesized Mean Difference 0 0 df 230 230 t Stat 0.183 0.323 P(T<=t) one-tail 0.427 0.374 t Critical one-tail 1.652 1.652 P(T<=t) two-tail 0.855 0.747 t Critical two-tail 1.970 1.970
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4.4.7 Non-risk-adjusted performance of the funds and the crises
This sub-section reports on the scrutiny of the relative raw return performance of
IMFs and CMFs in relation to the market before and during the AFC and GFC events.
It aims to identify any impacts of these crises in particular market cycles on mutual
fund performance. Therefore, the return before market risk-adjusted performance is
used, and the study period is sub-divided into four different periods based on bullish
and bearish market cycles. These four periods are: pre-AFC (1990–1996), during
AFC (1997–1998), post-AFC (1999–2006), and during GFC (2007–2009). The
results for the return performance of fund portfolios against the KLCI, a proxy for the
market return, are illustrated in Table 4.8.
During the pre-AFC, the IMFs portfolio obtains a higher monthly average return of
1.68 per cent in relation to the CMFs (0.63%) and the market (0.94%). On average,
the AMFs are also relatively higher than the market at 1.16 per cent. These results
imply that the Malaysian mutual funds do outperform the market counterparts based
on gross or non-adjusted return basis net of all expenses.
During the AFC (using a similar period to Abdullah et al. [2007]), the results indicate
that the market was badly affected and resulted in a negative return of monthly
average at 3.12 per cent. The CMFs portfolio also experienced negative return but
less than the market at 0.04 per cent. Surprisingly, the IMFs retained a positive return
but this is smaller than that for the pre-crisis at 0.32 per cent. The positive return of
IMFs could possibly be due to the portfolio not involving highly speculative
investments that require higher risks (Amin 2009). The other reason could be due to
the IMFs having expanded rapidly and their current performance being relatively
well-managed in comparison to the infancy stage. Regarding overall performance, the
results mean that the mutual funds portfolio performed better than the market during
the AFC. However, all results are statistically insignificant.
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Table 4.8: Non-risk-adjusted return performance of the mutual funds The table presents the non-risk-adjusted return performance of the IMFs and CMFs fund portfolios. The table includes alpha and beta estimates of a single regression for each of the four sub-periods. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006), and during GFC (2007–2009). The results reported are adjusted for heteroskedasticity and serial correlation problems using White’s (1980) and Newey and West’s (1987) correction models. The asterisks ***, **, * indicate that the coefficient estimates are different from zero at 1%, 5 % and 10% level respectively. The symbols +++, ++, + denote that the coefficient estimates are different across fund samples at 1%, 5 % and 10% level respectively.
Portfolio Pre-AFC During AFC
Post-AFC During GFC
Overall
Panel A: Return AMFs 1.156*** 0.140 0.308* 0.194 0.675*** (4.463) (0.156) (1.692) (0.747) (4.087) IMFs 1.684*** 0.319 0.194 0.310 0.843*** (4.288) (0.307) (0.898) (1.243) (4.022) CMFs 0.628*** –0.039 0.421** 0.077 0.507*** (3.126) (–0.049) (2.613) (0.267) (3.456) Market 0.940 –3.115 0.652 –0.361 0.244 RF 0.499 0.536 0.234 0.267 0.365 Pearson t-statistic
1.398 0.034 –0.342 0.292 0.728
Wilcox on/ Mann Whitney (p-value)
1.654+
(0.040) 0.093 0.378 0.156 0.779
Panel B: Systematic Risk (Beta) AMFs 0.632*** 0.435*** 0.643*** 0.552*** 0.543*** (13.447) (5.731) (14.520) (11.950) (1.835) IMFs 0.654*** 0.479*** 0.654*** 0.523*** 0.571*** (11.061) (5.708) (12.886) (12.524) (11.885) CMFs 0.610*** 0.392*** 0.631*** 0.581*** 0.515*** (14.970) (5.648) (16.120) (10.342) (11.425) Market 1.000 1.000 1.000 1.000 1.000
Panel C: F-test for equally variances (IMFs-CMFs) 1.574++ 1.540 1.182 1.252 1.410+++ (IMFs-Market) 1.443+ 3.141+++ 1.830+++ 2.968+++ 2.095+++ (CMFs-Market)
2.272+++ 4.837+++ 2.163+++ 2.371++ 2.953+++
Obs 84 24 96 28 232 With regard to the post-AFC period, results indicate a statistically significant positive
return of CMFs at 0.42 per cent on monthly average, higher than the IMFs at 0.19 per
cent. None of them is higher than the market (0.65%). Again, in contrast to the
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findings of Abdullah et al. (2007), this result reveals that on average, the return
performance of IMFs was higher during the pre-AFC than the return recorded during
the post-AFC stage. The contradictory results could possibly be due to a different
length of period in this study for the post-AFC phase from 1999 to 2006, rather than
1999 to 2001 asused by Abdullah et al. (2007).
These results indicate that the overall fund return is relatively higher than the risk-free
rate, which seems to contradict the findings of Taib and Isa (2007). However, this
study employs the one-month Malaysian t-bills, while Taib and Isa (2007) used
KLIBOR as the risk-free rate of return in their study. The duration of this study also
includes the crisis period of January 2007 to April 2009 for the GFC.
The results reveal that both IMFs and CMFs remain positive returns on this non-risk-
adjusted basis and the IMFs insignificantly outperformed their CMFs peers. However,
unlike the study by Hayat and Kraeussl (2011), who found that the IMFs had a
negative return performance over the market benchmark during the GFC, this study’s
finding is that the IMFs return performance is still positive and statistically
significant.
Panel B in Table 4.8 indicates the results of systematic risk compared to the market,
measured by the beta of a fund portfolio. All the portfolios regardless of the sub-
periods reveal significant results of betas less than 1, suggesting that, on average, all
the fund portfolios, IMFs and CMFs, are less volatile and less risky than the market.
Moreover, the risk of IMFs in all periods is relatively higher than CMFs except
during the GFC, suggesting that potentially the IMFs could provide higher returns
due to the fund’s higher risk.
The F-test results are also reported in Table 4.8. The F-test with null hypothesis of
equally variances in all sub-groups can be rejected, thus indicating that IMFs and
CMFs exhibit different risk exposures in pre-crisis and throughout the period of
study. The F-test also reveals that IMFs and CMFs exhibit significant different risks
relative to the market for the whole period and all sub-periods.
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4.4.8 Results for risk-adjusted return performance measurements
This sub-section reports the comparative performance of IMFs and CMFs over the
20–year period. The risk-adjusted performance measures are: Sharpe Ratio (SR),
Treynor Index (TI), Jensen Alpha (JA), Modigliani-Modigliani Measure (M2),
Appraisal Ratio (AR) and Adjusted Sharpe Ratio (ASR). Table 4.9 reports the results
of the risk-adjusted performance for the overall 20–year period and the periods of the
crises. The overall period is divided into four sub-periods: pre-AFC, during AFC,
post-AFC and during GFC. The results reveal that, on average, the IMFs portfolio
outperforms the CMFs peers and the overall sample, represented by AMFs, regardless
of type of performance measurements in all periods except for the post-AFC phase.
In the pre-AFC stage, the IMFs perform relatively better that the CMFs and the
AMFs in all types of risk-adjusted return measurements. In contrast, the results show
that the CMFs perform better than the IMFs and the AMFs for the post-AFC period.
These findings are completely different from the findings of Abdullah et al. (2007),
who contended that IMFs perform better during a post-crisis phase, whereas the
CMFs perform better in a pre-crisis period.
One possible reason for the difference in findings could be the larger numbers
employed in this study (about 479 funds, of which 129 are IMFs) than the sample
used by Abdullah et al. (about 65 funds, of which 14 are IMFs). Furthermore, the
duration of their study is generally shorter for both pre- and post-crisis periods than
the periods in this study.
With regard to the AFC period, the finding in this chapter is in line with the findings
of Abdullah et al. (2007) that IMFs perform better than CMFs in all types of
performance measurements. This study employs the same period as Abdullah et al.
for the AFC period. Both portfolios experience negative returns due to the crisis, with
the IMFs performing less poorly than the CMFs and the full sample, i.e., the AMFs.
In this crisis, the systematic risk and total risk of the IMFs are relatively higher than
those of the CMFs, suggesting the ability of these funds to obtain more abnormal
returns.
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Table 4.9: Fund performance based on risk-adjusted return measurements The table presents the results of risk-adjusted return performance using various types of measurements. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). The asterisks*** indicate that the Jarque Bera (JB) is statistically significant at 1% level, implying that the data is not normally distributed. For overall performance, these results show that the IMFs portfolio performs better than their CMFs counterparts.
Portfolio Pre-AFC During AFC
Post-AFC During GFC
Overall
Panel A: AMFs SR 0.259 –0.201 0.116 –0.084 0.088 TI 1.980 –4.015 0.766 –0.493 0.814 JA 0.972 –0.159 0.224 0.075 0.508 M2 1.748 –3.453 0.699 –0.446 0.711 AR 0.232 –0.019 0.062 0.028 0.113 ASR 0.257 –0.195 0.115 –0.081 0.087 Beta 0.631 0.436 0.643 0.553 0.543 Std.Dev 4.826 8.734 4.248 3.246 5.042 JB 21.443*** 0.272 54.433*** 1.242 82.903*** Panel B: IMFs SR 0.320 –0.176 0.087 –0.047 0.110 TI 2.754 –3.562 0.590 –0.277 1.082 JA 1.511 0.043 0.113 0.184 0.686 M2 2.161 –3.026 0.523 –0.250 0.895 AR 0.304 0.005 0.030 0.072 0.137 ASR 0.317 –0.171 0.086 –0.046 0.110 Beta 0.653 0.480 0.655 0.524 0.570 Std. Dev 5.617 9.720 4.463 3.083 5.586 JB 7.252*** 0.157 63.981*** 0.629 71.271*** Panel C: CMFs SR 0.157 –0.229 0.146 –0.116 0.057 TI 1.151 –4.568 0.948 –0.688 0.519 JA 0.433 –0.360 0.335 –0.035 0.330 M2 1.057 –3.940 0.880 –0.615 0.460 AR 0.112 –0.048 0.096 –0.012 0.079 ASR 0.155 –0.222 0.145 –0.112 0.057 Beta 0.609 0.393 0.631 0.582 0.516 Std.Dev 4.479 7.841 4.102 3.449 4.717 JB 28.626*** 0.415 45.896*** 2.378 74.395*** Market-adjusted return
0.441 –3.652 0.418 –0.628 –0.121
Risk free (rf) 0.499 0.536 0.234 0.267 0.365
Table 4.9 also examines the performance of IMFs and CMFs during the GFC, when it
is shown that IMFs perform better than CMFs. The impact of the crises on all funds’
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performance is more severe during the AFC than during the GFC. During both crises,
all performance measures reveal a negative return for all the portfolios except for the
JA and AR of the IMFs and the JA of the AMFs, demonstrating a positive return in
the GFC period. In other words, all the performance measures consistently represent a
negative return of the CMFs during both financial crises. Despite all the portfolios
being badly affected by these crises, IMFs can be seen as less affected than CMFs and
AMFs. This is evident in the smaller decline in returns in both crisis periods and a
positive return for IMFs and AMFs based on their JAs during the GFC. The results
also reveal that the IMFs portfolio is less volatile than the CMFs during the GFC. The
standard deviation of the portfolios is relatively lower at 3.08 than the standard
deviation of CMFs at 3.45 during the GFC.
The systematic risk of IMFs is also lower at 0.52 relative to CMFs at 0.58. The lower
systematic risk and the residual risk of the IMFs could be explained by the risk
exposure of this investment vehicle being lower because the investment is not
involved in the stock market. The stock market is associated with usury, gambling,
alcohol and the speculative investments that do not meet the criteria of the Shariah
screening process. It is evident that both IMFs and CMFs are relatively low-risk
investments regardless of the duration and all the betas are smaller than one. The
results for low risk (beta is smaller than 1) of the IMFs during the bull and bear
markets support the findings of Hayat and Kraeussl (2011). However, unlike their
findings, this study provides evidence that the IMFs portfolio does relatively
outperform the market not only in bullish markets but in bearish ones as well.
With reference to overall performance, the SR, TI and JA confirm that the IMFs
portfolio performs better than the CMFs. The SR estimates the return to risk trade-off
by dividing the average excess return of a fund portfolio over the sample period with
the standard deviation of returns within the same period. It is therefore evident that
the higher ratio indicates that a portfolio performing better Jensen alpha is an
intercept of the single factor CAPM model representing the outperformance of a
return portfolio in relation to the market, suggesting that the higher increment
provides a better excess return. The TI measurement is similar to the SR, except that
the performance of excess return in TI is related to systematic risk, but the SR is
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related to the residual risk or total risk of the fund return portfolio. Moreover, the
performance measures based on M2, AR and ASR indicate a similar pattern. The
outperformance of IMFs using ASR and M2 measures is consistent with what
Abdullah et al. (2007) found. However, the positive return of the full sample, i.e., the
AMFs, for the whole period of this study using various risk-adjusted returns is not in
line with the finding of Taib and Isa (2007), who found evidence of Malaysian mutual
funds underperforming based on a 110-fund sample for the period January 1990 to
December 2001.
4.4.9 CAPM performance analysis and the crises
To further investigate the return performance of the IMFs and CMFs for different
market conditions, the study empirically analyses the data based on CAPM
performance analysis. The crisis in 1997 refers to the Asian financial crisis (AFC)17 in
1997–1998. The global financial crisis (GFC)18 was in 2007–2009. The major events
that led to the GFC are demonstrated in Appendix D. The CAPM regression is
conducted on risk-adjusted return (mean excess return) of a portfolio as a dependent
variable for different market conditions. This is due to the fact that the overall results
might not be robust for the bear market. The excess returns refer to returns of a
portfolio over the one-month Malaysian t-bills, a proxy for risk-free rate portfolio.
The results are illustrated in Table 4.10.
Table 4.10: CAPM performance analysis and the crises The table presents the risk-adjusted return performance of the IMFs and the CMFs fund portfolios. Returns are in percentage and net of all expenses. The table includes alpha and
17 In Malaysia, the AFC had an impact when the Ringgit Malaysia (MYR) began to experience waves of speculative pressure following the depreciation of the Thai Baht on 2 July 1997. By the end of August 1998, the Ringgit had depreciated by 40% against the US dollar in relation to its level at the end of June 1997. The Kuala Lumpur Stock Exchange Composite Index (KLCI) fell by 79.3% from a high of 1271.57 points in February 1997 to a low of 262.70 points on 1 September 1998 (Bank Negara Malaysia 1999, p.560). In 1997, real GDP was 7.5% (10% in 1996), and it declined by 7.5% in 1998, the first negative growth in 13 years (Bank Negara Malaysia 1999). 18 The GFC crisis begin in early 2007 and the effect could be seen as in October 2007 in the US, when motor vehicle output was demonstrated to be 31% down in the final quarter of 2008, s compared to a year earlier. Within the same period, housing investment fell by 19.5%. The impact on Britain was also immediate. The largest mortgage leader, Northern Rock, collapsed and created major pressure on British banks. The crisis, therefore, was started off by the implosion of housing bubbles and caused many countries to go into recession or nearly so. Many countries had banking and financial systems in utter disarray; financial asset prices crashed; and in some places real asset prices as well (Highfill, 2008).
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beta estimates of a single regression for each of the four sub-periods. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). The results reported are adjusted for heteroskedasticity and serial correlation problems using White’s (1980) and Newey and West’s (1987) correction models. The one-month Malaysian t-bills and KLCI are used as proxies for risk-free rate and market return respectively. Standard errors are given in parentheses. The asterisks ***, **, * indicate that the coefficient estimates are different from zero at 1%, 5 % and 10% level respectively.
Portfolio Pre-AFC During AFC
Post-AFC During GFC
Overall
Panel A: All mutual funds (AMFs) in the sample α 0.972*** –0.159 0.224 0.075 0.508*** (0.253) (0.897) (0.180) (0.253) (0.161) β 0.631*** 0.436*** 0.643*** 0.553*** 0.543*** (0.047) (0.076) (0.044) (0.046) (0.046) Adj R² 0.776 0.728 0.832 0.810 0.761 Residual risk 4.826 8.734 4.248 3.246 5.042 Observations 84 24 96 28 232
Panel B: IMFs α 1.511*** 0.043 0.113 0.184 0.686*** (0.386) (1.040) (0.215) (0.246) (0.206) β 0.653*** 0.450*** 0.655*** 0.524*** 0.570*** (0.059) (0.084) (0.051) (0.041) (0.048) Adj R² 0.611 0.709 0.781 0.807 0.683 Residual risk 5.617 9.720 4.463 3.083 5.586 Observations 84 24 96 28 232
Panel C: CMFs α 0.433** –0.360 0.335** –0.035 0.330** (0.197) (0.794) (0.159) (0.280) (0.143) β 0.609*** 0.393*** 0.631*** 0.582*** 0.516*** (0.041) (0.069) (0.039) (0.056) (0.045) Adj R² 0.841 0.732 0.859 0.793 0.784 Residual risk 4.479 7.841 4.102 3.449 4.717 Observations 84 24 96 28 232 Results in Table 4.10 shows that beta values in Panel A, B, C, which represent the
systematic risk of AMFs, IMFs and CMFs, are relatively stable in different market
conditions. The overall beta of IMFs and CMFs is significantly smaller than 1,
implying that they are low-risk investments. The results support the findings of
Abdullah et al. (2007) and Hayat and Kraeussl (2011). The findings of this study
show that the overall beta for IMFs is 0.57 and for CMFs is 0.52, in which the value
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is greater than that found by Abdullah et al. (2007) at 0.25 and 0.38 for Islamic and
conventional funds respectively.
Unlike the findings of Hayat and Kraeussl (2011), the present findings reveal that
IMFs significantly outperform the market benchmark (indicated by positive alpha)
over the period of the study. IMFs also insignificantly outperform the market
benchmark during the AFC and GFC crises. The contradictory results here might be
due to Hayat and Kraeussl limiting their study to Islamic equity funds, whereas this
study includes diversified mutual funds consisting of alternative, allocation, equity,
fixed income and money market funds. The results reveal that the CMFs and the
AMFs are seemingly affected by the AFC crisis. However, the AMFs have positive
alpha during the GFC crisis, indicating that the Malaysian mutual funds in general
outperformed the market during the GFC period.
In the pre AFC period, all the portfolios have significantly positive alphas, suggesting
that they perform better than the market return benchmark. The IMFs achieve the
highest level of mean excess returns at 1.51 per cent compared to CMFs at 0.43 per
cent and AMFs at 0.97 per cent. Meanwhile, in the post-AFC period, results indicate
that all portfolios outperform the market benchmark; however, they are statistically
insignificant except for the CMFs. The results show that CMFs outperform the
market benchmark better than IMFs. The results also show that the returns
performance of all fund portfolios is higher in the pre-crisis period than in the post-
crisis period. Additionally, the IMFs perform better during the pre-crisis whereas
CMFs perform better during the post-crisis. This evidence is in contrast to the
findings of Abdullah et al. (2007) who found that both the Islamic and conventional
funds underperformed the market and that the average returns of these funds in post-
crisis were better than those documented during the pre-crisis period.
The results show that during the AFC, IMFs perform better than the overall fund and
the CMFs. The study provides evidence that IMFs outperform the market; however,
CMFs and AMFs underperform the market (indicated by negative alphas) during the
AFC. The CMFs also underperform during the GFC crisis; however, with less severe
impact than during the AFC. On the other hand, the IMFs perform better during the
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GFC than during the AFC period. The AMFs have positive alphas during the GFC
but underperform the market (with negative alpha) during the AFC, suggesting that
the Malaysian mutual fund performed better during the GFC than the AFC.
4.5 Summary
This chapter has investigated the performance of Islamic funds compared to
conventional ones. It has also evaluated the performance of both funds using monthly
average returns before and after adjusting for the market risk-free rate over the period
January 1990 to April 2009. It can be stated here that the performance measures
indicate that the IMFs portfolio performs relatively well compared to its CMFs
counterpart. The major outcome here is that both IMFs and CMFs perform better than
the market portfolio, which is the proxy used by the KLCI index. In particular, the
returns performance of the Islamic funds is slightly better than the returns
performance of the conventional funds, but there is no evidence of significant
difference between these two portfolios over the overall period, or between the two
sub-periods from January 1990 to August 1999 and from September 1999 to April
2009.
In view of the risk-adjusted returns performance, Islamic funds also perform better
than CMFs using various types of performance measures, namely Sharpe ratio (SR),
Adjusted Sharpe ratio (ASR),Treynor index (TI), Jensen alpha (JA), M2 measure and
Appraisal ratio (AR). Further investigation using these performance measures related
to before, after and during the crises also indicate that the returns performance of
IMFs is relatively less severe than that of CMFs, as shown in Table 4.9. On average,
the negative returns of both funds during the bearish markets suggest that both IMFs
and CMFs follow the market movement and were directly impacted on by these
crises.
The evidence here argues for the previous findings in the literature that Islamic funds
perform better during the bearish market and worse during the bullish market.
Overall, the IMFs perform better than the overall sample (the AMFs) and the CMFs
(see details in Table 4.10). The IMFs perform better than the CMFs during the pre-
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AFC period and during the AFC and GFC. In contrast, the CMFs portfolio performs
better than the IMFs during the post-AFC period.
Since the results reported in this chapter are contrary to the findings of previous
studies, detailed investigation is therefore required to explain the differences between
risk and return performance of these two types of funds. Moreover, the
outperformance and underperformance of both funds in different sub-periods could
mean that normal risk-adjusted performance measurements are not really meaningful,
thus requiring further investigation. The extended analysis using more reliable
methods such as the CAPM with different market benchmarks and the TM model
developed by Treynor and Mazuy (1966) could provide more rigorous findings on
these matters. These will be discussed in the next chapter.
The next chapter also examines fund performance using risk-adjusted return
performance based on the CAPM model with single and multiple benchmarks. The
performances of both IMFs and CMFs portfolios based on the ability of fund
managers in their timing expertise and fund selectivity skills are also assessed using
the TM model and the extended TM model, following Bello and Janjigian (1997), to
evaluate these abilities among the fund managers.
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CHAPTER 5 - MARKET TIMING
EXPERTISE AND FUND SELECTIVITY
SKILL: TIME SERIES DATA ANALYSIS
5.1 Introduction
The preliminary results reported in Chapter 4 were based on descriptive statistics and
risk-adjusted performance measures such as Sharpe ratio, Treynor index, Jensen alpha
and others, as introduced in Sub-section 3.4.2. As noted in Sub-section 3.4.2.3, Jensen
alpha is an intercept term which is derived from a single factor CAPM. This chapter
uses alternative benchmarks (Islamic and the conventional benchmarks) in single
factor CAPM performance analysis. The single factor CAPM is then expanded to
multi-factor CAPM by incorporating the multiple regression models adapted from
Bertin and Prather (2009). The CAPM regression is also broadened to a quadratic
regression, namely TM model, following Treynor and Mazuy (1966), and extended
TM model, following Bello and Janjigian (1997).
This chapter deals with the first and second hypotheses as previously stated in Section
3.3, and focuses on several specific objectives: (1) to identify whether these two
portfolios, the IMFs and the CMFs, differ significantly from each other in regard to
market benchmark using time series analysis, (2) to compare the returns performance
of the funds between IMFs and CMFs using a different Islamic and conventional
single market benchmark, (3) to specifically observe whether the IMFs are
significantly sensitive to either the Islamic benchmark or the conventional benchmark,
and (4) to evaluate fund managers’ performance on market timing expertise and fund
selectivity skill among the Malaysian fund managers in general, and more specifically
among IMFs and CMFs fund managers.
The structure of this chapter is as follows. Section 5.2 discusses the main issues and
the significance of the chapter. Section 5.3 further elaborates the data sample used
here (as described in Section 4.3). Section 5.4 provides results and discussions on
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firstly, the performance of the funds against the market benchmark. Secondly, results
based on an analysis of market timing and fund selectivity performance are described
in Section 5.5. Finally, Section 5.6 concludes the chapter.
5.2 Issues and the significance of the chapter
In view of the fact that this study compares the performance between two substantial
portfolios of mutual funds, the IMFs and the CMFs, in which the fundamentals of
these funds’ investment rationales differ totally, the outperformance of these two
portfolios is expected to be different when using single or multiple benchmarks. It is
also assumed that these two portfolios act differently in relation to different market
benchmarks since their fundamental concept and investment styles are different.
Previous studies have indicated that there is no difference in the performance between
Islamic and conventional benchmarks (Albaity and Ahmad, 2008; Elfakhani et al.,
2005; Girard and Hassan 2005; 2008; Hakim and Rashidian, 2004; Hassan et al.
2010). The available literature for Malaysia indicates that the KLCI and KLSI indices
act no differently in providing abnormal returns to the stock market (Albaity and
Ahmad, 2008). Therefore, it seems that using Islamic or conventional benchmarks
makes no difference when evaluating the performance of an Islamic fund. This
chapter re-investigates the issue.
It is evident that the return performance of mutual funds is directly responsive to stock
market performance (Low and Ghazali 2007). Thus, the analysis of the relationship
between fund and stock markets is of fundamental importance; in fact, the rise (fall)
of stock return could indirectly increase (decrease) the return performance of a mutual
fund. Moreover, currently about 20 per cent of the stock market shares in Malaysia
belong to the mutual fund industry. Therefore, knowledge of market efficiency and,
more specifically, market trends could reveal further insights when investigating
mutual funds’ performance, particularly with reference to their risk and return
characteristics. There is a lack of such evidence concerning the IMFs’ performance.
Since the right choice of benchmark is important (Grinblatt and Titman, 1994), it is
incorporated to conduct a multiple benchmark analysis based on the CAPM. Thus, the
standard CAPM is extended, following Bertin and Prather (2009), and relevant market
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benchmarks are included, ranging from large stock and small cap stock to foreign and
bond stock. The reason for following the Bertin and Prather model is to include
various benchmarks covering the asset class of the mutual funds. Next, the study
further extends this model to include the money market benchmark. This is because
the mutual funds in this study consist of diversified mutual funds including
alternative, allocation, equity, fixed income and money market. The purpose of
incorporating these market benchmarks is to examine whether the various mutual fund
categories are sensitive to any one or various benchmarks. Explanations about the
construction of the models were documented in Chapter 3.
The preliminary results reported in Chapter 4 also highlight the presence of a
statistical difference in fund risk and return characteristics for IMFs and CMFs. In this
chapter, not only a difference in the returns performance of the funds in relation to the
market benchmark is expected to be observed, but also differences in the expertise of
IMF and CMF fund managers on market timing and fund selectivity skill. A rationale
of this hypothesis was that Shariah screening criteria are foreseen as the restrictions
that may make it difficult the Islamic fund managers to avail timing opportunities but
at the same time induce the fund managers to carefully select. In other words, the
restrictions make the fund managers put more attention to selectivity outperformance
to offset the adverse impact of poorer timing ability, if there is a case.
Fund managers generally forecast on two distinct aspects of mutual funds
management: firstly, forecasts on the price movements of selected individual stocks
and secondly, forecasts on the price movement of the general stock market as a whole
(Merton, 1981). The former is usually associated with the analysis of the securities.
They are either undervalued or overvalued compared to their market benchmarks. The
fund managers endeavor to identify the fund or securities whose expected returns lie
significantly above the security market line in order to ensure above average returns.
In other words, the first forecasting relates to the fund managers’ stock selectivity
skill. In the second type of forecasting, the fund managers attempt to identify whether
the securities are undervalued or overvalued relative to the fixed-income securities or
bonds. This is because fund managers strategically change their investment portfolios
and asset classes to suit the bear and bull markets. For example, fund managers will
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switch to options such as fixed income securities and dividend funds during the bear
market and will make a staggered swap to equity funds when the market is in the
cycle of bottoming out or signals a bull market. The right forecast with regard to
timing when the equity fund will outperform or underperform bonds is a tough
challenge for the fund managers.
Hence, this chapter concentrates on measuring fund managers’ market timing
expertise and fund selectivity skills, and employs the TM model for measuring market
timing ability (Admati et al. 1986). Bello and Janjigian (1997) are also followed to
provide more benchmark indices in the TM model representing various types of
mutual funds in our sample. The model named as the extended TM model in Section
5.4 is used to analyse this further, using time series regression analysis.19
The existing literature has shown mixed results on fund managers’ market timing
expertise. In the US market, this ability is accepted as a common phenomenon with
some mutual funds providing evidence of negative market timing ability (for example,
Chang and Lewellen, 1984; Chen et al. 1992; Henriksson, 1984) and others revealing
positive market timing (see Bello and Janjigian 1997; Lee and Rahman, 1990;
Lehmann and Modest, 1987). In the Malaysian market, the findings from most
studies, with the exception of Hayat (2006), have illustrated a negative or inferior
market timing ability among fund managers (for example, Abdullah et al. 2007;
Ahmed, 2007; Annuar et al., 1997; Elfakhani et al., 2005; Hayat and Kraeussl, 2011).
Hayat (2006) indicated that about 29 per cent of Malaysian Islamic funds in the
sample had a positive market timing ability on an individual basis. In contrast, Hayat
and Kraeussl (2011) in their recent study found evidence of negative market timing
expertise among global Islamic equity fund managers. Their results, based on the TM
model, further indicated that Malaysian Islamic fund managers display inferior or no
market timing. As the findings are not consistent, further analysis in this area is
required.
The investigation in this study will provide a useful aid for market players, fund
managers and investors in particular, deepening their understanding of the efficiency
19 The issue based on panel data regression analysis l is discussedin Chapter 6.
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of asset allocations, portfolio diversification and portfolio selections in their
investment funds management. Furthermore, a reflection on the linear relationship
between superior forecasting skills in managing funds and the stock market’s
efficiency can offer useful insights to investors.20 In fact, the superiority (inferiority)
of fund managers’ strategies on market timing could reflect the inefficiency
(efficiency) of the market equilibrium. Superior market timing would directly
undermine the theory of the market being efficient (Henriksson and Merton, 1981).
Market efficiency means that the market players have access to complete information
reflecting the price value of the stock market. This implies that the fund values could
be equal to the values of the stock market. As a result, investment strategies such as
market timing and fund selectivity skills provide no benefits but are instead costly to
investors. However, in reality, the assumption of market efficiency based on perfect
information is not fulfilled; in most cases, some markets are efficient and others are
not. This situation explains a negative correlation between the expertise of fund
managers in timing the market and the theory of market efficiency, in that the more
efficient the particular market, the less the benefits of market timing. Hence, with the
assumption that the market is not fully efficient, an evaluation of fund managers’
market timing expertise requires further investigation.
If it is true that fund managers are not able to anticipate a rise or fall in the common
stocks market and adjust their portfolios based on market movements, it is necessary
to revise the accountabilities of investment management across the board. Indeed, this
suggests that fund managers do not actually have market timing expertise (Treynor
and Mazuy 1966). Therefore, the issue of market timing expertise – whether the fund
managers can really guess the market – remains important. It is the role of fund
managers to ensure that they can time the market correctly and thus provide higher
abnormal returns to their clientele.
20 The theory of CAPM proposes that there is a positive linear relationship between stock market return and systematic risk (measured by the beta). Bello (2005), for instance, proved this theory when he found a strong relationship between mean return of mutual funds and risk. He also found no significant linearity between return and volatility measured by the standard deviation, thus convincing his readers that the beta is absolute when measuring risk based on this CAPM.
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The analyses in this chapter are important since investors would gain an advantage in
knowing whether investments in Islamic funds could incur more risk or give more
returns than conventional funds. This is due not only to the fact that investment
activities in Islamic funds are governed by Shariah principles (Usmani, 2007), but
also to the fact that they are associated with other special risks such as insufficient
track record and low working capital (Hayat and Kraeussl 2011), which are not
present in their conventional counterparts. Since an investment in Islamic funds needs
to follow a Shariah screening process to ascertain that the investment is permissible, it
is arguable that such investment could provide more returns than the normal or
conventional fund. Therefore, the results of underperformance (outperformance) of
the IMFs investigated in this chapter indirectly suggest that the imposition of Shariah
screening of Islamic investments actually adds risk and extra costs to the potential
investors, or conversely.
5.3 Data sample
The analysis employs time series return data based on a sample of 479 mutual funds
domiciled in Malaysia between January 1990 and April 2009. These consist of 129
IMFs and 350 CMFs from various fund categories. Each of the fund portfolios is
divided into five categories based on their asset classes: allocation, alternative, equity,
fixed income and money market. January 1990 is chosen as the start of the period as it
was when the Islamic fund industry started to develop. April 2009 is the end of the
period since this is the most recent date for fund return data available in the
Morningstar database. Compared to previous studies, this study explores a longer
period and uses more extensive data concerning the Malaysian mutual fund industry
in relation to IMFs and CMFs.21
The monthly return of the data is calculated in percentages based on geometric mean
and net of all expenses but excluding front and exit fees. The returns in this analysis
are then adjusted for market risk-free return. The mean aggregate return is calculated
21 See, for example, Abdullah et al. (2007) and Taib and Isa (2007), who evaluated 65 and 110 mutual funds using Malaysian data from January 1992 to December 2001and from January 1991 to December 2001, respectively. A recent study by Hayat and Kraeussl (2011) evaluated the performance of 145 funds for 2000 to 2009. Another recent study by Hoepner et al. (2011) investigated 265 funds for September 1990 to April 2009. Unfortunately both studies limited their analysis to Islamic equity funds.
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based on simple return and also for the portfolio return. When necessary, the equally
weighted return is constructed for each of the stated categories and also for each asset
class category. The mean aggregate for IMFs is calculated based on the monthly
average of 129 funds while the mean aggregate for CMFs is based on the monthly
average of 350 funds. The other categories are top performer, middle performer and
bottom performer of the CMFs. These are generated based on 129 funds at the top,
middle and bottom of the mean return of all 350 CMFs. For these categories, only 129
funds were chosen to provide relatively matched pairs with the IMFs portfolio.
Other categories which are involved in the asset classes of the fund are also
constructed. For each of the asset classes (see Section 5.4.2 ), the mean return of each
is based on the number of funds included in each fund category. For the allocated
category, there are about 109 funds, 32 IMFs and 77 CMFs. For the alternative
category, there are about 31 funds, consisting of 8 IMFs and 23 CMFs. The equity
category consists of approximately 235 funds, of which 58 are IMFs and 177 are
CMFs. For the fixed income category, there are 64 funds consisting of 17 IMFs and
47 CMFs; and for the money market category, about 40 funds, of which 14 are IMFs
and 26 are CMFs.
In most cases, the KLCI is used as a market return benchmark for the IMFs and the
CMFs fund portfolios.22 However, for the benchmark analysis based on the single
CAPM model, the study employs the Islamic market index, known as the Kuala
Lumpur Syariah Index (KLSI), in order to examine the performance of the funds
against this particular benchmark. The funds’ performance is examined against the
Kuala Lumpur Composite Index (KLCI) market return as a proxy for the conventional
benchmark and then uses KLSI, a proxy for the Islamic benchmark, for the period
January 1990 to April 2009 (232 observations). The KLSI was launched in July 1999
and therefore investigating the performance of IMFs and CMFs using a different
benchmark is available for the period July 1999 to April 2009 (118 observations).
22 See Section 4.3.
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5.4 Results for and discussions of the market benchmark
5.4.1 Single CAPM performance analysis
This section examines the performance of all mutual funds in the sample (AMFs), the
IMFs and the CMFs in the Malaysian mutual funds, using the standard CAPM
regression model of the monthly excess return for January 1990 to April 2009. The
estimated alpha and beta coefficients are presented after correction for
heteroskedasticity and standard errors covariance, following White (1980). A non-
heteroskedasticity-adjusted regression analysis is also conducted (see results in
Appendix F) and since the results are similar, only the findings after the correction are
discussed. The outperformance (underperformance) alpha and high (low) beta are
obtained using two different market benchmarks, namely KLCI and KLSI, as proxies
for the conventional and Islamic market benchmarks correspondingly. The results are
presented in Table 5.1.
Table 5.1 presents the CAPM regression results for the time series of mean excess
return performance concerning the AMFs, IMFs, CMFs and other fund categories,
i.e., top performer, middle performer and bottom performer of the CMFs, against their
Islamic and conventional benchmarks. The corrections for heteroskedasticity and
serial correlation problems are managed using White’s (1980) and Newey and West’s
(1987) tests. However, the standard errors reported in parentheses are based on White
in order to verify the consistency of the report with the rest of the regression reports in
this thesis.
The results in Table 5.1 vividly illustrate that, on average, all the portfolios except the
middle and bottom performers perform strongly, and significantly outperform the
conventional benchmark. However, only IMFs underperform the market return when
the Islamic benchmark is applied. The adj. R2 obtained from the regressions is
relatively higher and it explains more than 60 per cent of the model’s variability.
Page | 145
Table 5.1: CAPM analysis of the portfolios against the conventional and Islamic benchmarks The table presents results on mean excess returns performance based on different market benchmarks after correction for heteroskedasticity. All returns are reported in percentages and net of all expenses. The overall sample period is based on monthly data from January 1990 to April 2009 and the full sample consists of 479 funds, made up of 129 IMFs and 350 CMFs. However, the period varies depending on the availability of the funds in a category. For example, the sample is from November 1994 to April 2009 for the bottom performer. For the Islamic benchmark, the period is from July 1999 to April 2009, due to the Islamic benchmark being launched in July 1999. The difference portfolio (Diff.) is constructed by subtracting the returns of CMFs from the returns of IMFs. Standard errors obtained from the cross-section of the estimated coefficients are reported in parentheses. The asterisks ***, **, * indicate significant levels at 1%, 5% and 10%, respectively. Obs is the number of observations.
According to the traditional interpretation of alpha as portfolio performance
measurement, a positive alpha higher than zero denotes outperformance of a fund
against the market return benchmark. The significant outperformance of AMFs as
shown in Table 5.1 suggests that, on average, Malaysian mutual funds outperformed
the market returns by 6.10 per cent per annum over the 20–year period of 1990–2009.
The IMFs and CMFs also performed better than the market as they gained abnormal
annual returns of 8.23 per cent and 3.96 per cent respectively within the same period.
These results clearly indicate that the Islamic funds perform better than their
conventional peers.
Conventional market benchmark Islamic market benchmark
α β Adj R2
Obs α β Adj R2
Obs
AMFs (N=479)
0.508*** (0.161)
0.543*** (0.046)
0.76 232 0.022 (0.159)
0.619*** (0.037)
0.76
118
IMFs (N=129)
0.686*** (0.206)
0.570*** (0.048)
0.68 232 –0.016 (0.181)
0.613*** (0.043)
0.71 118
CMFs (N=350)
0.330** (0.143)
0.516*** (0.045)
0.78 232 0.060 (0.150)
0.626 (0.034)
0.77 118
Top performer (129)
0.512*** (0.149)
0.499*** (0.046)
0.76 232 0.259* (0.152)
0.675*** (0.032)
0.81 118
Middle performer (129)
0.095 (0.144)
0.534*** (0.043)
0.79 232 –0.005 (0.151)
0.517*** (0.038)
0.71 118
Bottom performer (129)
–0.262 (0.336)
0.691*** (0.081)
0.63 232 –0.440* (0.234)
0.737*** (0.055)
0.68 118
Diff. 0.356**
(0.148) 0.054*** (0.016)
0.03 232 –0.076 (0.097)
–0.013 (0.026)
–0.00 118
Page | 146
Table 5.1 reveals the results for conventional fund performance in the categories of
top performer, middle performer and bottom performer. The outcome shows that fund
portfolios, namely top and middle performers, obtain abnormal returns relative to the
market benchmark; however, the latter is insignificant. For the bottom performer, as
expected, the returns performance drops to 0.26 per cent below the market returns;
however, the result is not significant. The outperformance of the top performer is
significantly higher than the overall performance of CMFs, but surprisingly its return
is slightly lower than the IMFs, with a decline of 2.09 per cent annually. Surprisingly,
using the Islamic benchmark, the IMFs portfolio experiences in its return performance
a drop of about 0.19 per cent per annum from 1999 to 2009. The short duration of the
Islamic benchmark could probably influence this result.
The difference portfolio (Diff.) is also constructed by subtracting the returns for
CMFs from the returns for IMFs, as shown in Table 5.1. The equation is explained in
Section 3.4.3.3. The aim is to identify if there is any difference between the
performance of IMFs and CMFs concerning risk and return characteristics. There is a
strong statistical significance regarding Diff. portfolio for alpha and beta, indicating
that there is a difference in the investment return style and systematic risk of the IMFs
and CMFs (see Table 5.1). These results imply importantly that there is a strong
statistically significant difference between systematic risk (beta) and return
characteristics (alpha) of IMFs and CMFs when the KLCI (a proxy for conventional
market benchmark) is employed. However, this significance disappears when the
explanatory variable, the Islamic benchmark, is employed.
Unlike previous studies which had indicated that there is no difference in IMFs and
CMFs risk and return characteristics in relation to the market benchmark (see for
example, Elfakhani et al.2005; Elfakhani and Hassan 2005; Hassan et al. 2010), the
findings of this study reveal a big difference. Other studies have claimed that there is
no difference between Islamic and conventional market indices (Albaity and Ahmad,
2008; Girard and Hassan 2005; 2008; Hakim and Rashidian, 2004). Therefore, the
results for the strong performance of alpha and beta in Table 5.1 would imply that it is
continually open to further investigation. In order to identify the robustness, further
Page | 147
investigation is undertaken to verify the finding using different approaches via panel
data analysis. This is explored in more detail in Chapter 6.
5.4.2 CAPM multiple benchmarks performance analysis
This section extends the single regression analysis to multi-regression CAPM to
evaluate fund performance after controlling factors related to the asset classes of the
funds. This means estimating alpha using the multi-factor benchmarks of the CAPM
model. Table 5.2 presents the results of the regression which include four different
models in each portfolio. The alpha estimate is an intercept term of the regression,
representing a measure of outperformance concerning a fund portfolio if it is a
positive value, and a measure of underperformance when the value is negative.
In this table, the regression analyses are conducted for each model for all portfolios.
Models 1 and 2 follow Bertin and Prather’s (2009) four-factor benchmarks to include
large, small, foreign and bond market benchmarks. Model 1 is modified to include the
conventional foreign benchmark and Model 2 incorporates the Islamic foreign
benchmark. As in the studies by Abdullah and Abdullah (2009), Bertin and Prather
(2009) and Cumby and Glen (1990), this study employs the MSCI World index to
represent the conventional foreign index benchmark. For the Islamic foreign index,
the analysis employs the DJIM Index. The latter began in February 1999, despite been
operational since 31 December 1995. The DJIM is one of the world pioneer indices
representing the Islamic benchmark.
The KLCI is used to represent a large stock index benchmark and the KLSE small-cap
index is used to represent a small stock index benchmark. Similar large and small
stock indices are used for the Islamic and conventional funds and take into account
the uniqueness of the Malaysian stock market in which more than 80 per cent of listed
stocks comply with Shariah law. Therefore, it is expected that using similar indices
for large and small stock indices for the IMFs and the CMFs portfolios will not matter
much. However, the overall sample period is reduced to December 1995 to April
2009, due to the fact that the KLSE small-cap stock benchmark has operated since
1995. For Models 2 and 4, the duration is shorter – from January 1996 to April 2009 –
due to the DJIM operating from 1996. For the bond index, this study uses the
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Malaysian fixed deposits as a bond benchmark. Models 3 and 4 are extensions of
Models 1 and 2, and include the money market benchmark in the models. The
KLIBOR is used as a proxy for the money market benchmark.
Looking at the overall performance (results are reported in Table 5.2), it can be seen
that all the portfolios have positive alpha estimates, implying that the excess return
performance of the IMFs, CMFs and AMFs are comparable to the overall market
benchmarks. The alphas of the CMFs seem to be higher than the Islamic counterparts
in all models, mirroring the outperformance of that portfolio in relation to the others.
However, none of the alphas is statistically significant. This finding seems not to be in
line with the evidence of single factor CAPM (as explained in Table 5.1), which
indicates that the magnitude of IMFs’ positive alpha in a single benchmark is
significantly higher than the CMFs counterparts.
The coefficient estimates for the betas of the multi-benchmark models in Table 5.2
show similar results to the single benchmark, stating that the large stock index is
significantly different from zero. This suggests that the benchmark exerts a strong
influence on the funds’ performance. Surprisingly, the coefficient estimates of the
conventional and Islamic foreign benchmarks are significantly different from zero for
the CMFs portfolio. This indicates that the returns’ performance is sensitive to these
benchmarks, as shown in Models 1 and 2 in columns 9 and 10. This finding is quite
similar to that of Ahmed (2007), who stated that the international market index adds a
premium to the overall performance of alphas in the Malaysian mutual funds. The
IMFs returns’ performance does not seem to be sensitive to any benchmark except for
the KLCI, a proxy for the large stock index benchmark. Hence, the multi-benchmark
model appears to be a better market benchmark for the CMFs. Nevertheless, the single
benchmark performs relatively well when evaluating the portfolio for the IMFs.
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Table 5.2: CAPM performance analysis based on multiple benchmarks This table presents coefficient estimates using AMFs, IMFs and CMFs monthly average return as the dependent variable. The sample period is from 1995M12 to 2009M04 for Models 1 and 3, and from 1996M01 to 2009M04 for Models 2 and 4. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected by using White’s (1980) and Newey-West’s (1987) correction tests. Standard errors based on White (1980) are given in parentheses. Variance inflation factor (VIF) to detect multicollinearity problems for each variable is also presented. The asterisks ***, **, * denote the significant level of the coefficient estimates that are different from zero at 1%, 5% and 10% levels respectively.
Variable AMFs IMFs CMFs Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 α 0.289
(0.518) 0.327
(0.538) 0.325
(0.506) 0.368
(0.526) 0.089
(0.584) 0.162
(0.606) 0.117
(0.572) 0.196
(0.594) 0.488
(0.487) 0.492
(0.508) 0.532
(0.472) 0.544
(0.493) VIF 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 βpL 0.492***
(0.056) 0.495*** (0.057)
0.501*** (0.051)
0.503*** (0.052)
0.527*** (0.059)
0.525*** (0.061)
0.533*** (0.056)
0.531*** (0.057)
0.457*** (0.053)
0.465*** (0.054)
0.468*** (0.048)
0.475*** (0.048)
VIF 1.464 1.503 1.455 1.488 1.440 1.493 1.466 1.497 1.457 1.467 1.415 1.430
βpS 0.012 (0.036)
0.009 (0.037)
0.014 (0.036)
0.012 (0.037)
0.004 (0.039)
0.001 (0.040)
0.006 (0.039)
0.003 (0.040)
0.019 (0.035)
0.018 (0.035)
0.022 (0.035)
0.021 (0.035)
VIF 2.223 1.955 2.000 1.821 2.067 1.875 1.957 1.803 2.307 1.969 2.036 1.831
BpFMSCI 0.074 (0.053)
-
0.063 (0.057)
-
0.050 (0.055)
-
0.042 (0.061)
-
0.097* (0.054)
-
0.084 (0.056)
-
VIF 1.831 - 2.539 - 1.747 - 2.499 - 1.785 - 2.315 -
BpFDJIM -
0.066 (0.040)
-
0.059 (0.042)
-
0.061 (0.045)
-
0.056 (0.048)
-
0.070* (0.038)
-
0.062 (0.039)
VIF - 1.666 - 1.932 - 1.631 - 1.899 - 1.571 - 1.783
BpB –0.006 (0.178)
–0.017 (0.185)
–0.092 (0.155)
–0.107 (0.163)
0.047 (0.200)
0.025 (0.207)
–0.019 (0.174)
–0.041 (0.181)
–0.058 (0.167)
–0.060 (0.174)
–0.164 (0.149)
–0.172 (0.158)
VIF 1.342 1.461 1.154 1.299 1.223 1.335 1.088 1.198 1.400 1.475 1.210 1.358
BpM - -
6.663 (6.345)
6.835 (6.097)
- -
5.153 (6.983)
5.099 (6.772)
- - 8.173 (5.861)
8.571 (5.588)
VIF - - 1.959 1.554 - - 1.786 1.418 - - 2.097 1.681
Adj R2 0.77 0.77 0.77 0.77 0.73 0.73 0.73 0.73 0.77 0.77 0.78 0.78 Obs 161 160 161 160 161 160 161 160 161 160 161 160
Page | 150
5.5 Results for and discussion of the market timing
5.5.1 Market timing expertise and fund selectivity skill
This section examines the performance on return (alpha) and risk (beta) as previously
discussed, allowing for the time varying systematic risk. This is done by using the
quadratic regression model known as the TM model, developed by Treynor and
Mazuy (1966). The regression procedure is similar to the one applied in the single
CAPM model with the addition of a squared-market return variable (details explained
in Chapter 3). The heteroskedasticity and serial correlation problems in the time series
regression (when existing) are corrected using White’s (1980) and Newey and West’s
(1987) procedures.
A comparison is made by using KLCI for the period 1990 to 2009 and KLSI for the
shorter period 1999 to 2009. For this reason, the study employs KLCI as a proxy for
the market benchmark in all portfolios. This is due to the fact that the Islamic
benchmark, the KLSI, is too recent (launched in July 1999). Evidence indicates that
about 88 per cent of the stocks in Malaysia are Shariah-compliant, representing two-
thirds of Malaysia’s market capitalisation (Bursa Malaysia 2010). A previous study
also showed that there is no difference in how KLCI and the KLSI perform (Albaity
and Ahmad, 2008). Therefore, we can consider the KLCI as a representative proxy for
the market benchmark for the Islamic fund as well.
Table 5.3 reports the results of the regression on the average alpha and the coefficient
estimates of the TM model for all the fund portfolios. The table indicates a positive
alpha estimate with all the other portfolios, with the exception of the middle and
bottom performers, implying that the fund managers of these portfolios have actively
managed the funds and done well in selecting funds in relation to the market
benchmark. Most of the alphas are statistically significant except for the CMFs and
the middle performer, indicating that both IMFs and CMFs fund managers in
Malaysia had superior fund selectivity skills over the period 1990–2009.
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The finding of positive selectivity skill is in contrast to most findings in the Malaysian
market, such as the results elicited by Ahmed (2007), Abdullah et al. (2007),
Elfakhani et al. (2005) and Hayat and Kraeussl (2011), except Annuar et al. (1997).
Such contradictory findings are possibly related to the crisis period which caused
outliers in the data which were not treated in the sample. This may be due to the
shorter duration of the study or to the limited sample of Islamic mutual funds.23
Table 5.3 also shows that the coefficient estimates of theta θ for all the portfolios are
positive but not significantly different, with the exception of the bottom performer,
implying that the fund managers have tried to time the market but their activities end
up with perverse or no market timing. The higher Adj. R2 in this return performance
could suggest that the model used is appropriate. The evidence of no market timing is
consistent with the previous findings of Annuar et al. (1997) and Ahmed (2007), who
revealed that the Malaysian mutual funds performed relatively poorly in terms of
market timing during 1998–2004 and 1990–1995.
On the other hand, our finding is in contrast to the finding of Hayat (2006), who
contended that the Malaysian Islamic fund does have a positive market timing on an
individual basis. The results of no market timing here also contradict those of
Lehmann and Modest (1987), Bello and Janjigian (1997), Ippolito (1989) and Lee and
Rahman (1990), but are consistent with those of Chen et al. (1992), Kon (1983),
Henriksson (1984), Chang and Lewellen (1984) and Elton et al. (1993) in the
developed market. At the same time, our finding is also consistent with Abdel-Kader
and Qing (2007) and Suppa-aim (2010), but is a little different from that of Imisiker
and Ozlale (2008) for the emerging market.
23
Elfakhani et al. (2005) also reported that Malaysian Islamic funds had negative market timing and fund selectivity skills over the period between 1997 and 2002, and Hayat and Kraeussl (2011) confirmed the same results for the period 2000–2009. The evidence is consistent with the findings of Lehmann and Modest (1987), Bello and Janjigian (1997), Ippolito (1989) and Lee and Rahman (1990), but it contrasts with those of Chen et al. (1992), Kon (1983), Henriksson (1984), Chang and Lewellen (1984) and Elton et al. (1993) in the international market.
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Table 5.3: Market timing expertise of IMFs and CMFs fund managers The table presents the results of market timing expertise and stock selectivity skill of IMFs and CMFs Fund Managers using the TM model. The KLCI is used as a proxy for the market return. The returns are based on mean aggregate and mean equally weighted, and reported in percentages. The returns are net from all expenses and adjusted for the risk-free rate using Malaysian t-bills as a proxy. The period of study is from January 1990 to April 2009. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors based on White (1980) are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. N represents the total number of funds in each portfolio and Obs is the number of observations.
Table 5.3 also compares the IMFs portfolio to the overall performance of the 129 top
performers, 129 middle performers and 129 bottom performers of the CMFs.
Unexpectedly, the study posits that the performance of the IMFs is relatively better
than the top performer of the CMFs. This is probably due to the fact that the CMFs’
top performer was more strongly affected during the crisis relative to the IMFs’
counterparts.
The Diff. portfolio is also examined in Table 5.3 by subtracting the CMFs return from
the IMFs return. The portfolio is added to the analysis in Table 5.3 in order to identify
the style differential between these two portfolios in relation to their market timing
ability and fund selectivity skill. The coefficient estimate of the Diff. portfolio denotes
that there is a statistically significant difference in the mean excess return
performance of the IMFs and CMFs, implying that fund managers of both are
Portfolio α β θ Adj R2 Obs
AMFs (N=479)
0.330** (0.186)
0.550 *** (0.042)
0.003 (0.003)
0.77 232
IMFs (N=129)
0.534** (0.231)
0.576*** (0.044)
0.002 (0.003)
0.69 232
CMFs (N=350)
0.127 (0.169)
0.524*** (0.042)
0.003 (0.003)
0.80 232
Top performer (N=129)
0.338* (0.181)
0.506*** (0.045)
0.003 (0.003)
0.77 232
Middle performer (N=129)
–0.135 (0.159)
0.543*** (0.037)
0.004 (0.003)
0.81 232
Bottom performer (N=129)
–0.712** (0.326)
0.712*** (0.066)
0.007* (0.003)
0.65 174
Diff.
0.407* (0.224)
0.064*** (0.016)
–0.001 (0.001)
0.13 231
Page | 153
different in their fund selectivity skill. However, they are no different in their market
timing expertise. The reason might be that Islamic funds focus on selecting portfolios
consisting of Shariah-compliant investments. Therefore, there is expected to be a
difference in the fund selectivity skill.
The beta coefficient estimates of both IMFs and CMFs in Table 5.3 are also positive
but lower than 1, implying that both funds are less risky than the market portfolio.
This strongly significant finding with regard to the β values of both portfolios
indicates that none has a volatility level greater than the market, thus implying that
mutual funds in Malaysia are relatively more stable and more diversified. In fact, such
funds are not greatly influenced by market conditions. The fact that both portfolios are
less volatile suggests that this is a good indicator to potential investors when choosing
mutual funds, as this portfolio investment could provide diversification and stable
returns. On the other hand, less volatility also implies that the fund regulatory bodies
are concerned about introducing new funds in a riskier category in order to cater for
some demands from aggressive or risk-seeking investors.
To deepen the investigation, the study also compares the market timing expertise of
fund managers, using both the TM and extended TM models. The results are reported
in Table 5.4. Results in the table illustrate that IMFs and CMFs have no market timing
expertise but the results are insignificant. However, both portfolios have positively
insignificant fund selectivity skills. More specifically, the coefficient estimate of
alpha is strongly significant for the IMFs using the TM model and for the CMFs using
the extended TM model. The results provide consistency with the findings previously
discussed in Section 5.4, i.e., that single and multi-benchmarks are better for IMFs
and CMFs respectively.
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Table 5.4: Comparative market timing analysis for the TM and extended TM models The dependent variable in each regression is the fund’s mean excess monthly return. The overall sample period is from January 1990 to April 2009. However, the extended model sample period is reduced to January 1996 to April 2009 due to the fact that the KLSE small-cap benchmark was only launched in 1995. The foreign benchmark is MSCI for the all funds and CMFs portfolios, whereas for the IMFs portfolio, the DJIM is employed. All the returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected by using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors are given in parentheses below the coefficient estimates. VIF is also presented in italic form. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively.
Variable TM Model Extended TM Model
AMFs IMFs CMFs AMFs IMFs CMFs Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF α 0.330*
(0.186) 0.000 0.534**
(0.231) 0.000 0.127
(0.169) 0.000 0.619
(0.460) 0.000 0.411
(0.542) 0.000 0.829**
(0.416) 0.000
β 0.550*** (0.042)
1.012 0.576*** (0.046)
1.000 0.524*** (0.042)
1.061 0.499*** (0.053)
1.760 0.532*** (0.057)
1.737 0.466*** (0.050)
1.719
θ 0.003 (0.003)
1.012 0.002 (0.003)
1.000 0.003 (0.003)
1.061 0.003 (0.003)
1.427 0.003 (0.003)
1.259 0.003 (0.003)
1.677
BPS - - - - - - –0.002 (0.026)
1.316 –0.006 (0.029)
1.368 0.010 (0.024)
1.296
BPF - - - - - - 0.089 (0.046)
1.997 0.068 (0.053)
2.090 0.110*** (0.044)
1.809
BPB - - - - - - –0.205 (0.151)
1.211 –0.132 (0.176)
1.179 –0.277** (0.140)
1.254
BPm - - - - - - 3.161 (5.413)
1.775 1.671 (5.992)
1.580 4.651 (5.066)
2.023
Mean var.
0.442 0.617 0.268 0.103 0.0085 0.122
Residual 5.042 5.586 4.717 4.963 5.313 4.704 ����� 0.77 0.69 0.80 0.78 0.74 0.79 N (Obs) 479 (232) 129 (232) 350 (232) 479 (161) 129 (161 350 (161)
Page | 155
5.5.2 Performance analysis on market timing and asset classes
Table 5.5 shows the performance analysis based on various asset classes of the fund
portfolios using both the TM and extended TM models. The asset classes are divided
into five categories: allocation, alternative, equity, fixed income (fix_income) and
money market (m_market). Panel A reports the results of the full sample for all
mutual funds (AMFs), whereas Panel B and Panel C show the results of IMFs and
CMFs respectively. It can be seen that the equity category has a positive and
significant alpha in the extended TM model, which would suggest that the multi-
benchmark model is the best choice for evaluating the Malaysian equity fund
performance.
In Panel B of Table 5.5, the results indicate that the allocation fund has negative
selectivity and slightly positive timing. The results are consistent with the previous
studies reported by Kon (1983), Henriksson (1984) and Chen et al. (1992), showing
the existence of a trade-off between stock selection and market timing ability for the
allocation of funds involved in both activities. The alternative has a statistically
insignificant negative selectivity and timing ability. The equity fund type has a
positive selectivity and significant positive timing, whereas the money market has
significant positive selectivity but insignificant positive timing ability. Furthermore,
the fixed income category has statistically significant positive selectivity and
insignificant negative market timing ability.
Panel C in Table 5.5 shows the asset classes for the CMFs portfolio. The allocation
and equity funds have insignificant positive fund selectivity skill and market timing
expertise. The alternative fund has insignificant positive selectivity skill but
statistically significant negative market timing. The fixed-income fund has
insignificantly no market timing expertise and positively fund selectivity skill. In
contrast, the money market fund has significantly positively fund selectivity skill but
no market timing. Moreover, the alphas of the money market category for both IMFs
and CMFs portfolios seem to be strongly significant and different from zero,
suggesting that this category outperforms the market, using the standard TM model.
Page | 156
However, the alphas become negative when applied to the extended TM model;
specifically, the alpha of the CMFs money market has a strong statistical significance.
To sum up, the results indicate that some categories in IMFs and CMFs outperform
and underperform the market benchmarks in the models. This finding supports
strongly the theory of the right choice of benchmark – that different categories of
funds require different benchmarks. In this case for example, only the equity,
alternative and allocation categories of the IMFs obtained higher alphas using the
extended TM model, whereas it is more suitable to use this model for all CMFs
categories. Another important contribution of the analysis is that the outperformance
of all the portfolios could be considered more reliable when the extended TM model
is employed rather than the normal TM model.
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Table 5.5: Comparative market timing analysis by asset class for TM and extended TM models The table presents the results of investment type category based on TM and extended TM models. The dependent variable in each regression is the fund’s mean excess monthly return in each of the investment type categories. The overall sample period is from January 1990 to April 2009. For the extended TM model, the duration is varied depending on the establishment of the fund category and the market benchmark. For allocation, it is from 1995M12 to 2009M04. For the alternative, the sample is from 2004M09 to 2009M04. For equity and money market, the period is from 1996M01 to 2009M04. Furthermore, for fixed income it is from 1995M12 to 2009M04. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors are given in parentheses. VIF is presented in italic form. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively.
Asset Class
TM Model Extended TM Model Allocation Alternative Equity Fix_income M_market Allocation Alternative Equity Fix_income M_market
Panel A: AMFs α 0.220
(0.199) 0.053 (0.072)
0.148 (0.202)
0.140 (0.140)
0.945*** (0.302)
0.473 (0.451)
0.721 (0.801)
1.126** (0.566)
0.134 (0.213)
–1.009 (1.020)
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 β 0.509***
(0.053) 0.081*** (0.017)
0.618*** (0.046)
0.151*** (0.038)
0.244*** (0.035)
0.398*** (0.048)
0.053* (0.029)
0.559*** (0.062)
0.032** (0.012)
0.233*** (0.047)
1.166 1.169 1.011 1.302 1.009 1.770 3.108 1.744 3.157 2.060 θ 0.003
(0.004) –0.004 (0.002)
0.004 (0.003)
0.001 (0.002)
0.001 (0.002)
0.002 (0.003)
–0.003 (0.003)
0.005 (0.003)
–0.001 (0.002)
0.001 (0.002)
1.166 1.169 1.011 1.302 1.009 1.734 1.631 1.797 3.972 1.607 Mean var. 0.323 –0.012 0.307 0.175 0.985 0.208 –0.012 0.071 0.170 0.506 Residual 4.856 0.616 5.596 2.627 4.640 4.238 0.616 5.825 0.800 3.864 ����� 0.71 0.35 0.79 0.21 0.17 0.72 0.33 0.79 0.11 0.23 N (Obs) 109 (232) 31 (56) 235
(232) 64 (232) 40 (232) 109 (161) 31 (56) 235 (160) 64 (161) 40 (160)
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Table 5.5 continued
Asset Class
TM Model Extended TM Model Allocation Alternative Equity Fix_income M_market Allocation Alternative Equity Fix_income M_market
Panel B : IMFs α –0.049
(0.193) –0.035 (0.061)
0.361 (0.242)
0.159* (0.090)
1.605*** (0.616)
0.375 (0.547)
0.574 (0.622)
0.799 (0.656)
0.014 (1.068)
–1.490 (2.159)
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 β 0.542***
(0.039) 0.023 (0.018)
0.608*** (0.046)
–0.002 (0.012)
0.511*** (0.072)
0.530*** (0.041)
0.025 (0.021)
0.561*** (0.061)
–0.007 (0.014)
0.466*** (0.093)
1.157 1.011 1.00 1.067 1.007 1.408 1.694 1.521 1.489 1.703 θ 0.002
(0.002) –0.002 (0.002)
0.003 (0.003)
–0.001 (0.002)
0.002 (0.004)
0.002 (0.003)
–0.001 (0.002)
0.004 (0.003)
–0.001 (0.002)
0.002 (0.005)
1.157 1.011 1.00 1.023 1.007 1.600 1.303 1.315 1.549 1.531 Mean var. –0.103 –0.064 0.458 0.129 1.677 –0.103 –0.066 0.019 0.129 0.812 Residual 5.201 0.458 5.847 0.550 9.479 5.201 0.4622 5.770 0.550 8.032 ����� 0.79 0.03 0.70 0.19 0.19 0.79 0.10 0.74 0.20 0.26 N (Obs) 32 (155) 8 (56) 58 (232) 17 (103) 14 (231) 32 (155) 8 (55) 58 (159) 17 (102) 14 (231) Panel C : CMFs α 0.246
(0.205) 0.067 (0.097)
0.086 (0.203)
0.142 (0.140)
0.222*** (0.037)
0.558 (0.465)
1.002 (0.906)
1.120** (0.519)
0.141 (0.213)
–0.219*** (0.045)
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
β 0.501*** (0.055)
0.103*** (0.020)
0.623*** (0.047)
0.153*** (0.038)
–0.001 (0.003)
0.385*** (0.049)
0.062* (0.034)
0.568*** (0.060)
0.032** (0.012)
0.001 (0.003)
1.151 1.199 1.055 1.298 1.038 1.772 3.295 1.685 3.154 2.274
θ 0.002 (0.004)
–0.005** (0.002)
0.004 (0.003)
0.001 (0.002)
0.000*** (0.000)
0.001 (0.003)
–0.004 (0.003)
0.005 (0.004)
–0.000 (0.001)
0.000 (0.000)
1.151 1.199 1.055 1.298 1.038 1.665 1.667 1.626 3.997 2.106
Mean var. 0.347 –0.028 0.264 0.177 0.251 0.242 –0.028 0.104 0.172 0.168 Residual 4.837 0.783 5.636 2.629 0.638 4.208 0.783 5.889 0.811 0.262 ����� 0.69 0.35 0.79 0.21 0.04 0.70 0.35 0.79 0.13 0.57 N (Obs) 77 (232) 23 (53) 177 (232) 47 (232) 26 (231) 109 (161) 31 (53) 177(161) 47(161) 40 (161)
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5.5.3 Correlation between market timing and fund selectivity skill
This section reports on the correlation test conducted for each fund type and for the
overall performance of both portfolios, i.e. the Islamic and conventional mutual funds.
The aim is to identify the existence (or otherwise) of a correlation between selectivity
and market timing ability performance based on the TM model. Results show
evidence of a substantial negative mild correlation between timing and selectivity of
the Islamic and conventional mutual funds for January 1990 to April 2009. The details
of the results are presented in Table 5.6 below.
Table 5.6: Correlation between fund selectivity and market timing The table presents results for the correlation coefficient among IMFs, CMFs and AMFs fund managers between market timing and fund selectivity skill. The asterisk *** denotes that the coefficient estimates are significant at 1% level.
Asset Class Correlation Coefficient
AMFs IMFs CMFs
Allocation
–0.126***
–0.154***
–0.126***
Alternative –0.103 –0.103 –0.110 Equity –0.126*** –0.126*** –0.126*** Fixed–income –0.126*** –0.091 –0.126*** Money market –0.126*** –0.126*** –0.126*** Overall –0.126*** –0.126*** –0.126***
Table 5.6 indicates the presence of a negative correlation between selectivity and
market timing performance among Islamic and conventional fund managers in
Malaysia over the period of the study from January 1990 to April 2009. Overall, there
is a negative mild correlation between selection and timing performance of all types
of portfolios: AMFs, IMFs and CMFs. The evidence for a negative correlation
regarding the IMFs is consistent with previous findings by Kon (1983) and
Henriksson (1984), but different from those of Lee and Rahman (1990) and Annuar et
al. (1997), both of which found that all Malaysian mutual funds between 1990 and
1995 appeared to show a positive correlation between selectivity and timing
performance at 0.53.
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5.5.4 Funds diversification
This section further examines the degree of diversification of the mutual funds when
IMFs and CMFs are compared. Diversification is measured by estimating the R2 of all
the portfolios. Most previous studies measured the degree of diversification using the
coefficient of determination, R2, which is calculated on the basis of the degree of
diversification of a fund in relation to the market portfolio’s diversification. It
normally ranges between 0 and 1. Since the TM model is based on a multivariate
analysis in this study, the usage of Adj. R2 is preferred to the R2. 24 Table 5.7 presents
the summary of the Adj. R2 while using the TM model and extended TM model.
The results in Table 5.7 illustrate that the CMFs generally have a better degree of
diversification than the IMFs. This evidence is consistent with the results of Abdullah
et al. (2007), particularly in relation to the fixed income asset classes of funds. The
level of diversification of IMFs is smaller when compared to the full sample (AMFs)
using both models.
In terms of details, Table 5.7 reports that the diversification level of funds is relatively
higher with more than 50 per cent, suggesting that the larger excess return of the
mutual funds in all portfolios is explained by the models. By extending the TM model
to the extended TM model, the explanatory power of the model for the AMFs and
IMFs is slightly improved. Nevertheless it does not have much effect on the CMFs.
In contrast to Abdullah et al. (2007), who indicated the presence of a low
diversification level among funds between 1992 and 2001, the results of this study
show that Islamic and conventional mutual funds in Malaysia have a reasonably high
level of diversification. This means that the ability of managers of both funds to
diversify their investment in funds is relatively higher. Our results are also contrary to
those of Annuar et al. (1997). They evaluated fund performance for the period 1990–
1995 and noted that the degree of diversification was relatively poor and below their
expectations. They explained this as being due to the regulatory constraints imposed
by the SC, the lack of advertising and lack of fund managers’ expertise. 24
This is because the econometric procedure for computing R2 never decreases when extra variables are added to the regression. It could, however, increase, even though the variables do not add explanatory power to the model. Therefore to mitigate this caveat, the Adj. R2 is applied.
Page | 161
Table 5.7: Diversification level of mutual funds The table presents results of the correlation coefficient for IMFs, CMFs, and AMFs fund managers between market timing and fund selectivity skill.
Asset Class Adj R2
AMFs IMFs CMFs Panel A: TM Model Allocation
0.71
0.79
0.69
Alternative 0.35 0.03 0.35 Equity 0.79 0.70 0.79 Fixed-income 0.21 0.19 0.21 Money market Overall
0.17 0.77
0.19 0.69
0.04 0.80
Panel A: Extended TM Model Allocation
0.72
0.79
0.70
Alternative 0.33 0.10 0.35 Equity 0.79 0.74 0.79 Fixed-income 0.11 0.20 0.13 Money market Overall
0.23 0.78
0.26 0.74
0.57 0.79
On the other hand, the findings are consistent with most mutual fund studies on the
Malaysian market (Ahmed, 2007; Elfakhani et al., 2005; Hayat and Kraeussl, 2011).
Hayat and Krauessl (2011) indicated that Islamic funds are well-diversified as the
percentage of R2 of the portfolio is relatively high, about 75 per cent over the period
2000–2009. Elfakhani et al. (2005) also found that, on average, the Islamic mutual
fund portfolio was diversified by about 68 per cent over the period from January 1997
to August 2002. Ahmed (2007) indicated further that the percentage range of
diversification of the Malaysian mutual funds rose from 8 per cent to 95 per cent over
the period 1998–2004.
On an asset class basis, the results suggest that allocation and equity funds are well
diversified, as confirmed by an R2 with values between 69 to 79 per cent. On the other
hand, fixed income and money market funds are relatively less diversified using both
of the models. The results also suggest that the IMFs portfolio is better diversified in
allocation funds. In the meantime, the CMFs portfolio performs better in equity funds.
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5.6 Summary
This chapter has addressed the question of whether IMFs and CMFs outperform the
market benchmark or not when using the single factor CAPM model, when compared
to the Islamic and conventional benchmark. This chapter also investigated whether
IMFs and CMFs perform better in relation to multiple benchmarks. Both IMFs and
CMFs portfolios significantly outperform the conventional market benchmark, with
results indicating that the IMFs portfolio performs better than the CMFs. Furthermore,
results for multi-benchmarks indicate that the CMFs perform better than the IMFs,
although none of the alphas is statistically significantly different from zero. The
inconsistency in these results needs further examination of fund performance.
Therefore, the next chapter employs panel data regression analysis to explore fund
performance with the aim of producing more robust results.
The most important evidence arising from this chapter is the impact of different single
and multi-benchmarks and how they affect the performance of the alphas for the fund
portfolios. In other words, what exactly is the right choice of market benchmark for
the IMFs and CMFs? The findings provide evidence that IMFs perform relatively
better when using a single benchmark, and CMFs perform better when using multi-
factor benchmarks analysis. The implication is that fund managers can apply relevant
benchmarks to particular mutual funds. At the same time, the choice of benchmark is
a focal factor in determining fund performance.
Further investigation was done in order to identify whether IMFs and CMFs do have
market timing expertise and fund selectivity skill using the standard TM and extended
TM models. As expected, while using the TM version the results show that both IMFs
and CMFs outperform the market return benchmark. Consistently, the IMFs portfolio
performs better than the CMFs on fund selectivity skill using mean aggregate return.
However, in terms of market timing expertise, both portfolios have perverse or no
market timing since the estimated coefficient of each portfolio is relatively small
despite being positive. These results remain after the extended TM model has been
used. The result showing inferior market timing expertise suggests that fund
managers’ talents in timing the market have no impact on fund returns or adversely
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affect the returns performance of the mutual funds. The extended TM model also
reveals that the outperformance of IMFs and CMFs portfolios in terms of their asset
classes are improving compared to the standard TM model. A more detailed
exploration of these issues is addressed in Chapter 6.
On average, IMFs and CMFs in Malaysia are considered well-diversified portfolios,
hence individual investors could benefit from including a mutual fund in their
investment vehicles. The time series analysis also indicates that, on average, there is a
negative correlation between fund selectivity skill and market timing expertise in the
Malaysian market. Therefore, investors could consider this trade-off while making a
decision whether to invest in any type of mutual funds. Finally, the significant
contribution reported in this chapter is that the study extends the multi-factor CAPM
and extended TM models to include a money market component. The proposed model
is more reliable due to the diversity of the asset classes or categories of mutual funds
employed in this study.
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CHAPTER 6 - MARKET TIMING
EXPERTISE AND FUND SELECTIVITY
SKILL: PANEL DATA ANALYSIS
6.1 Introduction
Following the time series analysis reported in Chapter 5, this chapter reports on the
performance of funds using a relatively sophisticated and recent econometric method
of panel data analysis instead of time series. This is done in order to compare the
results reported in this chapter with those in Chapter 5 by replicating models similar
to those applied in Chapter 5 using panel data regressions.
One of the benefits of using panel data regression is that it reduces survivorship bias
and is able to accommodate funds with different inception dates. In the context of this
thesis, this technique has the advantage of controlling for fund-specific and time
related variations in a variety of ways. The findings of such analysis are expected to
be relatively more rigorous as the technique works with actual returns without
resorting to calculating the simple mean of the portfolio returns. This increases the
sample size, which comes at an efficiency gain.
The rest of the chapter is structured as follows. Section 6.2 briefly explains the data
sample and is followed by the presentation of results and discussion in Section 6.3.
Section 6.4 provides a summary of the chapter.
6.2 Data sample
The analysis in this chapter uses the same dataset as that reported in Chapters 4 and 5:
129 IMFs and 350 CMFs, giving a total of 479 AMFs from various categories, these
being allocation, alternative, equity, fixed income and money market asset classes,
over the same period from January 1990 to April 2009. These funds have different
inceptions dates. The panel structure in this case is therefore unbalanced with a
maximum of 31,614 observations in total.
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6.3 Results and discussions on panel data
Table 6.1 to Table 6.4 present the results based on special cases of the following
regression equation, refer to Eq. 3.28 (see Chapter 3, p. 99). Each table contains two
sub-tables: (a) and (b). Tables suffixed with (a) report REs GLS panel regressions and
those suffixed with (b) report FEs panel regressions: (1) the Hausman test to compare
the fixed effects model with random effects and (2) combinations of time-fixed effects
where appropriate. The significance of time-fixed effects is formally tested to see
whether time dummies are jointly significant or not. Problems related to serial
correlation and cross-sectional heteroskedasticity are also accounted for. Regressions
without time-fixed effects are labelled Model (1) and those with time FEs are labelled
Model (2). While comparing these results across IMFs and CMFs, the alphas in FE
regressions cannot be directly compared because every cross-section has its own alpha
(α) which is not reported. This comparison is, however, possible in REs estimation.
6.3.1 Single factor CAPM performance
The results of the single CAPM are reported in Table 6.1(a) using random effects
(REs) and generalised least squares (GLS), and in Table 6.1(b) using fixed effects
(FEs) panel data regression. These results show the performance for each of the
portfolios (i.e., IMFs, CMFs and AMFs) against Islamic and conventional
benchmarks.
Table 6.1 depicts the results of the single factor CAPM using REs (as shown in Table
6.1[a]) and using FEs (as shown in Table 6.1[b]) without and with time-fixed effects.
The results in both tables show the total variability of adj. R2 ranging from 42 to 59
per cent of the returns, implying that the amount of percentage is explained by the
models. The Hausman tests in Panel B and in Model (2) of Panel A denote that the
tests are not significant, implying that the REs model is appropriated as opposed to
the FEs model. The Hausman tests also indicate that the AMFs and CMFs are
appropriated with the FEs as shown in Model (1) before the time FEs are added.
Table 6.1(a) shows that all the alphas are positively significant using the REs model
with time FEs (as shown in Model 2 in Panel A), suggesting that all the portfolios
Page | 166
outperform the KLCI, a proxy for the conventional market benchmark, with the IMFs
performing better relative to the CMFs and the overall funds, AMFs. Model 2,
however, shows that the CMFs outperform the IMFs counterparts in relation to the
Islamic benchmark.
On average, the IMFs portfolio significantly outperforms the conventional market
benchmark by 17.51 per cent per annum over the period 1990–2009. The evidence of
the outperformance of IMFs compared to the market benchmark is generally
consistent with the findings of Hayat and Kraeussl (2011). They found that, on
average, 51 Islamic equity funds in Malaysia outperformed the KLSI by 0.73 per cent
per annum over the period 2000–2009. Surprisingly, the results also show that the
IMFs insignificantly outperform the market (when using the Islamic benchmark) by
1.09 per cent per annum from 1999 to 2009. The KLSI serving as a proxy for the
Islamic benchmark in Malaysia was launched in August 1999.
Page | 167
Table 6. 1(a): Single CAPM analysis using panel data REs GLS regressions The table presents results of returns performance of the portfolios compared to the conventional and Islamic market benchmarks. All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills, a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total number of funds (cross-section) is 479 funds (129 IMFs and 350 CMFs). However, for the Islamic benchmark the period is from August 1999 to April 2009 because it began only in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is an initial for the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively. Regression model (1): REs only, and model (2) REs with time FEs.
Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) Diff. (N=31,614) α 0.100***
(0.025) 1.117* (0.573)
0.209*** (0.080)
1.459*** (0.045)
0.052* (0.030)
0.935 (0.638)
0.077*** (0.028)
1.090* (0.571)
β 0.532*** (0.017)
0.458*** (0.059)
0.540*** (0.024)
0.368*** (0.044)
0.529*** (0.022)
0.497*** (0.067)
0.532*** (0.017)
0.458*** (0.059)
dTYPE - - - - - - 0.090 (0.061)
0.121*** (0.055)
Rho 0.009 0.022 0.000 0.000 0.016 0.031 0.009 0.021 Adj. R2 0.456 0.572 0.417 0.545 0.471 0.593 0.456 0.572 Hausman test 0.001 1.000 0.656 1.000 0.001 1.000 0.001 1.000 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000 Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) Diff. (N=31,614) α –0.045*
(0.023) 0.399*** (0.148)
0.011 (0.034)
0.091 (0.241)
–0.077** (0.031)
0.512*** (0.182)
–0.070** (0.030)
0.370** (0.148)
β 0.557*** (0.017)
0.481*** (0.025)
0.523*** (0.030)
0.451*** (0.046)
0.569*** (0.021)
0.492*** (0.029)
0.557*** (0.017)
0.481*** (0.025)
dTYPE -
- - - - - 0.095** (0.045)
0.107** (0.047)
Rho 0.020 0.031 0.000 0.003 0.027 0.036 0.019 0.030 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 0.443 0.550 Hausman test 0.328 0.680 0.242 0.834 0.110 0.261 0.329 0.639 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000
Page | 168
Table 6. 1(b): Single CAPM analysis using panel FEs regressions The table presents results of returns performance of the portfolios compared to the conventional and Islamic market benchmarks. All returns are net of all expenses and reported in percentage. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills, a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total number of funds is 479 funds (129 IMFs and 350 CMFs). However, for the Islamic benchmark the period is from August 1999 to April 2009 because it began only in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is an initial for the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively. Regression model (1): FEs only, and model (2) FEs with time FEs.
Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) Diff. (N=31,614) α 0.158***
(0.003) 0.803 (0.579)
0.209*** (0.080)
1.602*** (0.135)
0.139*** (0.005)
1.241** (0.574)
0.158*** (0.003)
0.803 (0.579)
β 0.531*** (0.017)
0.784*** (0.083)
0.539*** (0.024)
0.791*** (0.078)
0.528*** (0.022)
0.209*** (0.020)
0.531*** (0.017)
0.784*** (0.083)
dTYPE - - - - - - (omitted) (omitted) Rho 0.036 0.044 0.018 0.022 0.044 0.054 0.036 0.044 Adj. R2 0.456 0.572 0.417 0.545 0.471 0.593 0.456 0.572 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000 Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) Diff. (N=31,614) α –0.010***
(0.001) 0.197 (0.228)
0.011*** (0.002)
0.656 (0.441)
–0.017** (0.001)
0.194 (0.328)
–0.010*** (0.001)
0.197 (0.228)
θ 0.557*** (0.017)
0.829*** (0.041)
0.524*** (0.030)
0.952*** (0.052)
0.568*** (0.021)
0.818*** (0.043)
0.557*** (0.017)
0.829*** (0.041)
dTYPE - - - - - - (omitted) (omitted) Rho 0.046 0.056 0.023 0.028 0.052 0.065 0.046 0.056 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 0.443 0.550 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000
Page | 169
Systematic risk, which is estimated by the beta in the regression when V0� = 0,
generally shows that the IMFs portfolio has the highest beta compared to the CMFs
and AMFs. However, both IMFs and CMFs indicate betas less than 1, suggesting that
both funds are less volatile and less risky than the market portfolio. The average beta
of IMFs between 0.36 and 0.54 in Table 6.1(a) is smaller in this study than the beta
estimate in the study by Hayat and Kraeussl (2011), which was 0.75 in relation to the
Malaysian Islamic equity funds.
The systematic risk of the CMFs is greater than that of the IMFs, but more so in the
case of the conventional benchmark as opposed to the Islamic benchmark. The
findings, in contrast to previous studies, indicate that there is no difference in the risk
and return characteristics of IMFs and CMFs (for example, Elfakhani et al. 2005;
Hassan et al. 2010). Traditionally, it has been argued that IMFs are more risky
because Islamic funds are associated with some specific risks that are not present in
the conventional counterparts. Examples of these risks are inconsistency with Shariah
scholars’ judgements, the lack of a track record, and high exposure to companies with
poorly leveraged and low amount of working capital (Hayat and Kraeussl, 2011). The
evidence here, however, suggests the opposite, that IMFs are less risky. This can be
explained with the help of a counter argument, which is that these restrictions induce
managers to resort to more careful decision making which leads to reduction in risk-
taking.
In this regression, the difference portfolio (Diff.) is also constructed by adding
dummy variable dTYPE to the model of AMFs in order to identify the significant
difference between the IMFs and CMFs portfolios. If the fund belongs to the IMFs,
the value is 1 and if it belongs to the CMFs, the value is 0. The positive sign shows
that the IMFs perform better than the CMFs and vice versa for the negative sign.
Consistent with the results for the time series (as previously discussed in Section
5.3.1), the results of the panel data show that there is a significant difference that is
consistent with our conclusions from the results of CMFs and IMFs shown in Table
6.1(a), as discussed above.
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The analysis of single CAPM is extended to each of the asset classes and the results
show consistency with the previous results when using time series analysis. The
results are not reported here but are available upon request.
6.3.2 Market timing performance based on TM model
This section examines the performance of the market timing and fund selectivity skill
of IMFs and CMFs fund managers over the 20-year period from 1990 to 2009 relative
to the conventional and Islamic market benchmarks. Table 6.2(a) reports results based
on REs and Table 6.2(b) reports results based on FEs regressions.
The TM model is used, which contends that any significant positive beta provides
evidence that the fund managers of the fund portfolios demonstrate superior fund
selectivity skill and any positive theta exhibits superior market timing expertise. The
results show strongly significant outperformance of IMFs managers’ fund selectivity
skill using either REs or FEs regressions, with the exception of RE estimates with
Islamic foreign benchmark, where the opposite is true. The results are consistent with
the results for the time series data shown in Table 5.3, which shows the significant
superior fund selectivity skill of the fund managers.
Surprisingly, the alpha of IMFs now shows negatively significant in relation to
conventional market benchmark when applied to Model (2) using REs regression.
However, the alpha is positively significant when the FEs regression is applied, which
is irrelevant. Table 6.2(a) indicates that all alphas are negative, with the exception of
IMFs in Model (2), in relation to the Islamic market benchmark.
Moreover, the results show that all fund managers demonstrate inferior market timing
expertise as it is economically insignificant because the values of theta are very small,
with the higher percentage at 0.003 per cent. The result of perverse or no market
timing in all the portfolios is also reliable with regard to the previous finding using
time series data. Interestingly, IMFs managers demonstrate inferior market timing
with regard to the conventional benchmark and CMFs managers demonstrate inferior
market timing with regard to the Islamic foreign benchmark.
Page | 171
Table 6. 2(a): Market timing expertise of IMFs and CMFs fund managers using GLS REs regression. The table presents the market timing expertise and stock selectivity skill of Islamic and conventional fund managers based on the Treynor Mazuy model. The table presents results of mean excess returns performance of the portfolios using panel data pooled regression after correction for heteroskedasticity standard errors and consistent covariance estimator, using the method of White (1980). All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills as a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total numbers of funds (N) is 479 (AMFs), including 129 IMFs and 350 CMFs. However, the period varies depending on the availability of the data in a category. For the Islamic benchmark, the period is from August 1999 to April 2009 due to this benchmark being launched in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.
Model(1) Model(2) Model(1) Model(2) Model(1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) α 0.003
(0.025) 1.170** (0.559)
0.099 (0.076)
–1.859** (0.734)
–0.041 (0.029)
0.999 (0.638)
β 0.538*** (0.018)
0.401*** (0.043)
0.546*** (0.024)
3.960*** (0.795)
0.536*** (0.022)
0.427*** (0.049)
θ 0.003*** (0.0002)
0.005*** (0.001)
0.003*** (0.0003)
-0.294*** (0.065)
0.003*** (0.0003)
0.006*** (0.002)
Rho 0.009 0.022 0.000 0.000 0.016 0.031 Adj. R2 0.460 0.572 0.421 0.545 0.476 0.593 Hausman test
0.0003 1.000 0.656 –14.98# 0.0003 1.000
Test for time FEs
- 0.000 - 0.000 - 0.000
Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) α –0.012
(0.021) –0.157** (0.068)
–0.003 (0.041)
0.227** (0.090)
–0.027 (0.026)
–0.297*** (0.182)
β 0.556*** (0.017)
0.886*** (0.095)
0.524*** (0.030)
0.351*** (0.116)
0.567*** (0.021)
1.082*** (0.121)
θ –0.001* (0.0007)
-0.030*** (0.008)
0.0005 (0.001)
0.007 (0.010)
–0.002** (0.0008)
–0.043*** (0.009)
Rho 0.019 0.031 0.000 0.003 0.026 0.036 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 Hausman test
0.044 0.023 0.433 1.000 0.028 1.000
Test for time FEs
- 0.000 - 0.000 - 0.000
Page | 172
Table 6. 2(b): Market timing expertise of IMFs and CMFs fund managers using FEs The table presents the market timing expertise and stock selectivity skill of Islamic and conventional fund managers based on the Treynor Mazuy model. The table presents results of mean excess returns performance of the portfolios using panel data pooled regression after correction for heteroskedasticity standard errors and consistent covariance estimator using the method of White (1980). All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills as a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total numbers of funds (N) is 479 (AMFs), including 129 IMFs and 350 CMFs. However, for the Islamic benchmark, the period is from August 1999 to April 2009 due to this benchmark being launched in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.
Model (1) Model (2) Model (1)
Model (2)
Model (1)
Model (2)
Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) α 0.047***
(0.007) 1.344** (0.533)
0.101*** (0.013)
2.098*** (0.191)
0.027*** (0.009)
1.221** (0.593)
β 0.537*** (0.018)
0.246*** (0.023)
0.545*** (0.024)
0.298*** (0.045)
0.535*** (0.022)
0.228*** (0.026)
θ 0.003*** (0.0002)
0.0006 (0.0005)
0.003*** (0.0004)
0.0006 (0.0005)
0.003*** (0.0002)
0.0005*** (0.0006)
Rho 0.036 0.044 0.018 0.022 0.043 0.054 Adj. R2 0.443 0.550 0.421 0.545 0.476 0.593 Test for time FEs
- 0.000 - 0.000 - 0.000
Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) α 0.017
(0.016) 0.324 (0.337)
–0.001 (0.029)
1.241* (0.647)
–0.024 (0.020)
0.339*** (0.328)
β 0.556*** (0.017)
0.705*** (0.032)
0.525*** (0.030)
0.721*** (0.049)
0.567*** (0.021)
0.676*** (0.039)
θ –0.001 (0.0006)
–0.015*** (0.004)
0.0005 (0.0012)
0.005 (0.006)
–0.002** (0.0008)
–0.013*** (0.004)
Rho 0.046 0.056 0.023 0.028 0.052 0.065 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 Test for time FEs
- 0.000 - 0.000 - 0.000
Page | 173
6.3.3 Multi-factor CAPM performance
Similar to the previous chapter, this chapter extends the CAPM analysis on multiple
benchmarks. Tables 6.3(a) and (b) present the results of the panel regressions.
With reference to overall performance, the results in Table 6.3(a) as shown in Model
(2) highlight the underperformance of all the portfolios with conventional as well as
Islamic foreign benchmarks, Since the time FEs tests are significant, the results in
Model (2) are reliable, thus imply that all portfolios relatively underperform the multi-
factor market benchmarks, with the IMFs relatively better than the CMFs
counterparts. This finding is in contrast with the previous results when the time series
analysis was applied.
The coefficient estimate results for the betas of the multi-benchmark models show no
significant differences between IMFs and CMFs betas in most cases. CMFs, however,
seem to be less risky when using a fixed-effect model with a conventional foreign
benchmark. This conclusion is different from the single factor model but consistent
with the results of Hayat and Kraeussl (2011), as discussed above.
Page | 174
Table 6. 3(a) Multi-factor CAPM analysis using panel data REs GLS (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the full sample (AMFs), IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: without Islamic foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.116***
(0.022) -3.323*** (0.310)
0.101*** (0.039)
-2.726*** (0.584)
0.115*** (0.027)
-3.538*** (0.367)
BpL 0.484*** (0.020)
0.320*** (0.020)
0.518*** (0.029)
0.322*** (0.038)
0.473*** (0.024)
0.320*** (0.024)
BpS 0.001 (0.003)
0.117*** (0.016)
-0.007 (0.005)
0.098*** (0.029)
0.004 (0.003)
0.124*** (0.019)
BpFmsci 0.094*** (0.011)
0.638*** (0.032)
0.034** (0.017)
0.540*** (0.054)
0.115*** (0.014)
0.674*** (0.040)
BPB 1.811** (0.714)
-350.311*** (19.243)
-2.506 (1.575)
-322.278*** (35.835)
3.192*** (0.776)
-360.312*** (22.902)
BPm 2.189** (0.857)
107.548*** (12.380)
7.792*** (2.520)
77.762*** (17.099)
0.485 (0.736)
118.401*** (15.728)
Rho 0.008 0.022 0.000 0.000 0.013 0.029 Adj. R2 0.467 0.577 0.442 0.566 0.478 0.589 Hausman test
0.001 –146.80# 0.757 1.000 0.017 –119.18#
Test for time FEs
- 0.000 - 0.000 - 0.000
Panel B: with Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.132***
(0.022) –2.487*** (0.261)
0.110*** (0.039)
–2.055*** (0.392)
0.135*** (0.027)
–2.651*** (0.327)
BpL 0.481*** (0.020)
0.284*** (0.017)
0.515*** (0.029)
0.292*** (0.027)
0.470*** (0.024)
0.281*** (0.021)
BpS 0.003 (0.003)
0.112*** (0.016)
–0.006 (0.005)
0.094*** (0.027)
0.006** (0.003)
0.119*** (0.019)
BpFmsci 0.142*** (0.017)
0.543*** (0.030)
0.081** (0.032)
0.463*** (0.065)
0.191*** (0.020)
0.572*** (0.033)
BPFdjim –0.070*** (0.009)
–0.073*** (0.025)
–0.049** (0.022)
–0.058 (0.051)
–0.078*** (0.009)
–0.077*** (0.029)
BPB 1.353* (0.724)
–242.744*** (26.464)
–2.863* (1.615)
–236.032*** (56.265)
2.695*** (0.785)
–246.065*** (29.847)
BPm 2.830*** (0.869)
110.189*** (12.808)
8.247*** (2.602)
79.879*** (18.361)
1.193 (0.740)
121.206*** (16.174)
Rho 0.008 0.022 0.000 0.000 0.013 0.029
Adj. R2 0.470 0.579 0.442 0.566 0.482 0.591
Hausman test
0.011 –43.69# 0.855 1.000 0.043 –37.88#
Test for time FEs
- 0.000 - 0.000 - 0.000
Note: Regression model (1): REs only, model (2) REs with time FEs, and model (2) simple pooled OLS with time FEs. # results report chi2<0, model fitted on these data fails to meet the assumptions of the Hausman test. The problem is fixed by putting the additional command ‘hausman fixed random, sigmamore or sigmaless’. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.
Page | 175
Table 6. 3(b): Multi-factor CAPM analysis using panel FEs (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the AMFs, IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All portfolio returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses.
Model (1)
Model (2) Model (1) Model (2)
Model (1)
Model (2)
Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.138***
(0.014) –2.051*** (0.358)
0.104*** (0.032)
–2.813*** (0.949)
0.147*** (0.015)
–1.899*** (0.375)
BpL 0.483*** (0.020)
0.328*** (0.025)
0.518*** (0.029)
0.433*** (0.056)
0.472*** (0.024)
0.297*** (0.027)
BpS 0.002 (0.003)
0.158*** (0.015)
–0.006 (0.005)
0.163*** (0.023)
0.004 (0.003)
0.157*** (0.018)
BpFmsci 0.094*** (0.011)
–0.198*** (0.055)
0.034** (0.017)
–0.430** (0.166)
0.115*** (0.014)
–0.138*** (0.051)
BPFdjim - - - - - - BPB 0.823
(0.793) (omitted) –3.148**
(1.573) (omitted) 2.204**
(0.873) (omitted)
BPm 2.803*** (0.883)
(omitted) 8.261*** (2.625)
(omitted) 1.077 (0.755)
(omitted)
Rho 0.036 0.046 0.018 0.024 0.042 0.054 Adj. R2 0.467 0.577 0.442 0.566 0.478 0.589 Test for time FEs
- 0.000 - 0.000 - 0.000
Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.152***
(0.014) 0.506** (0.228)
0.112*** (0.032)
0.993* (0.508)
0.164*** (0.015)
–0.614*** (0.180)
BpL 0.480*** (0.020)
0.230*** (0.024)
0.515*** (0.029)
0.287*** (0.053)
0.469*** (0.024)
0.249*** (0.031)
BpS 0.003 (0.002)
0.167*** (0.014)
–0.006 (0.005)
0.182*** (0.022)
0.006** (0.003)
0.139*** (0.014)
BpFmsci 0.161*** (0.017)
0.466*** (0.050)
0.082** (0.032)
0.437*** (0.133)
0.190*** (0.020)
0.564*** (0.060)
BPFdjim –0.069*** (0.009)
–0.317*** (0.040)
–0.049** (0.022)
–0.278*** (0.090)
–0.077*** (0.009)
–0.750*** (0.096)
BPB 0.427 (0.803)
(omitted) –3.452** (1.628)
(omitted) 1.776** (0.881)
(omitted)
BPm 3.399*** (0.897)
(omitted) 8.685*** (2.714)
(omitted) 1.737** (0.763)
(omitted)
Rho 0.036 0.047 0.018 0.024 0.042 0.055 Adj. R2 0.470 0.579 0.442 0.566 0.482 0.591 Test for time FEs
- 0.000 - 0.000 - 0.000
Note: Regression model (1): FEs only, model (2) FEs with time FEs, and model (2) simple pooled OLS with time FEs. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. The omitted variables are due to multicollinearity problems between the variables and time (period) FEs.
Page | 176
6.3.4 Market timing performance based on extended TM model
Tables 6.4(a) and (b) report the comparative results of the TM model and extended TM
model regarding the IMFs and CMFs fund managers’ fund selectivity skill and market timing
expertise relative to the conventional and Islamic foreign benchmarks.
Comparing the results in Table 6.4(a) across IMFs and CMFs, CMFs outperform in terms of
α, without time fixed effects. However, when we add time FEs to the model, IMFs
outperform CMFS. Both α’s are, however, negative which implies underperformance relative
to the market. Since time FEs are significant, we conclude that IMFs outperform CMFs in
terms of α but both underperform the market benchmark when we include conventional
foreign benchmark (MSCI) in the regressors. Both outperform the market when we replace
MSCI with an Islamic foreign benchmark (DJIM), and CMFs now do better than IMFs. The
two seemingly conflicting results are, however, not directly comparable with each other as
they are two different models in the sense that one includes MSCI and the other includes
DJIM. The reason both are not in one regression at the same time is to compare the results
with the single factor TM model.
With regard to market timing, no economically significant (although statistically significant)
evidence is found in favour of superior market timing among fund managers when IMFs are
compared with CMFs. Both, however, exhibit inferior market timing abilities when we
replace MSCI with DJIM. Islamic managers are found to have better fund selectivity skills
when we use DJIM instead of MSCI as the foreign benchmark and there is no other
difference relative to each other.
Consistent with the results of the time series (see Table 5.4), the panel data results show that
IMFs and CMFs fund managers are positively superior in fund selectivity skill in relation to
the large stock market index, in this case, the KLCI index.
Page | 177
Table 6.4(a) Market timing analysis using panel data REs GLS (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each of the portfolios. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.055**
(0.022) –3.086*** (0.267)
0.041 (0.040)
–2.536*** (0.467)
0.054** (0.027)
–3.287*** (0.323)
θ 0.003*** (0.000)
0.002*** (0.001)
0.003*** (0.000)
0.002 (0.002)
0.003*** (0.000)
0.003*** (0.001)
BpL 0.486*** (0.020)
0.303*** (0.018)
0.520*** (0.029)
0.308*** (0.030)
0.476*** (0.025)
0.302*** (0.022)
BpS –0.007** (0.003)
0.112*** (0.016)
–0.015*** (0.004)
0.094*** (0.027)
–0.004 (0.003)
0.119*** (0.019)
BpFmsci 0.120*** (0.011)
0.587*** (0.026)
0.051*** (0.017)
0.499*** (0.050)
0.130*** (0.014)
0.620*** (0.031)
BPFdjim - - - - - - BPB –0.764
(0.741) –320.647*** (13.679)
–5.131*** (1.617)
–298.494*** (25.716)
0.621 (0.814)
–328.806*** (16.208)
BPm 1.209 (0.853)
101.452*** (11.607)
6.582** (2.569)
72.874*** (14.676)
–0.405 (0.714)
111.926*** (14.928)
Rho 0.008 0.022 0.000 0.000 0.013 0.029
Adj. R2 0.472 0.579 0.449 0.577 0.482 0.591
Hausman test 0.001 1.000# 0.799 1.000# 0.005 1.000# Test for time FEs
- 0.000 - 0.000 - 0.000
Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α –0.002
(0.022) 4.821*** (0.433)
0.011 (0.037)
4.180*** (0.850)
–0.015 (0.027)
5.057*** (0.505)
θ 0.003*** (0.000)
–0.030*** (0.002)
0.003*** (0.000)
–0.026*** (0.004)
0.002*** (0.000)
–0.032*** (0.002)
BpL 0.511*** (0.019)
0.044** (0.018)
0.534*** (0.028)
0.088*** (0.031)
0.504*** (0.024)
0.028 (0.021)
BpS 0.000 (0.003)
0.114*** (0.016)
–0.011** (0.005)
0.096*** (0.030)
0.004 (0.003)
0.121*** (0.020)
BpFmsci - - - - - - BPFdjim 0.055***
(0.007) –0.957*** (0.043)
0.020* (0.011)
–0.813*** (0.081)
0.068*** (0.008)
–1.020*** (0.050)
BPB 1.707** (0.823)
707.365*** (36.039)
–3.812** (1.723)
574.701*** (68.760)
3.470*** (0.911)
755.989*** (42.247)
BPm 0.598 (0.877)
216.747*** (12.532)
6.299** (2.582)
170.806*** (16.473)
–1.115 (0.757)
233.590*** (15.990)
Rho 0.008 0.022 0.000 0.000 0.013 0.029
Adj. R2 0.467 0.581 0.448 0.577 0.476 0.593
Hausman test 0.000 1.000# 0.787 1.000# 0.000 1.000# Test for time FEs
- 0.000 - 0.000 - 0.000
Note: Regression model (1): REs only and model (2) REs with time FEs. Number of results report chi2<0. The model fitted on these data fails to meet the assumptions of the Hausman test. The problem is fixed by putting the additional command ‘hausman fixed random, sigmamore or sigmaless’.
Page | 178
Table 6. 4(b): Market timing analysis using panel FEs (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the AMFs, IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All portfolio returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.074***
(0.013) –5.314*** (0.475)
0.040 (0.028)
–5.455*** (0.755)
0.084*** (0.028)
–0.215*** (0.048)
θ 0.003*** (0.000)
0.003*** (0.000)
0.003*** (0.000)
0.002*** (0.002)
0.003*** (0.000)
0.003*** (0.001)
BpL 0.485*** (0.020)
0.346*** (0.026)
0.519*** (0.029)
0.448*** (0.054)
0.475*** (0.025)
0.316*** (0.029)
BpS –0.007** (0.003)
0.095*** (0.010)
–0.016*** (0.004)
0.111*** (0.018)
–0.004 (0.003)
0.001*** (0.012)
BpFmsci 0.120*** (0.011)
–0.272*** (0.051)
0.051*** (0.017)
–0.490*** (0.148)
0.130*** (0.014)
–0.215*** (0.048)
BPFdjim - - - - - - BPB –1.787**
(0.793) (omitted) –5.548***
(1.513) (omitted) –0.446
(0.888) (omitted)
BPm 1.840** (0.877)
(omitted) 6.975** (2.687)
(omitted) 0.217 (0.731)
(omitted)
Rho 0.037 0.046 0.019 0.024 0.043 0.054
Adj. R2 0.472 0.577 0.447 0.566 0.483 0.589
Test for time FEs - 0.000 - 0.000 - 0.000 Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.017
(0.011) –1.171*** (0.348)
0.010 (0.028)
–0.195 (1.248)
0.016 (0.012)
–1.469*** (0.253)
θ 0.003*** (0.000)
0.003*** (0.000)
0.003*** (0.000)
0.004*** (0.001)
0.003*** (0.000)
0.003*** (0.000)
BpL 0.510*** (0.019)
0.246*** (0.024)
0.533*** (0.028)
0.274*** (0.060)
0.503*** (0.024)
0.235*** (0.025)
BpS –0.000 (0.003)
–0.031** (0.013)
–0.012** (0.005)
–0.073 (0.005)
0.004 (0.003)
–0.019** (0.009)
BpFmsci - - - - - - BPFdjim 0.056***
(0.007) 0.049 (0.033)
0.020* (0.011)
0.167 (0.138)
0.068*** (0.008)
0.015 (0.013)
BPB 0.617 (0.839)
(omitted) –4.202*** (1.557)
(omitted) 2.307** (0.943)
(omitted)
BPm 1.244 (0.895)
(omitted) 6.667** (2.697)
(omitted) –0.464 (0.765)
(omitted)
Rho 0.037 0.047 0.019 0.024 0.042 0.055
Adj. R2 0.467 0.579 0.445 0.566 0.477 0.591
Test for time FEs - 0.000 - 0.000 - 0.000 Note: Regression model (1): FEs only, model (2) FEs with time FEs, and model (2) simple pooled OLS with time FEs.. The omitted variables are due to multicollinearity problems between the variables and time (period) FEs.
Page | 179
It is not possible to conclude from the analysis whether or not FEs are more
appropriate than REs, as the Hausman test is inconclusive for our preferred model,
which is the model with time FEs. Most of the Hausman test decides in favour of FEs.
Table 6.4(b) reports results from a fixed-effect model, which shows that both CMFs
and IMFs do not have economically superior or inferior market timing among fund
managers. IMFs fund managers, however, show some evidence of better fund
selectivity skill. This is not a surprising result as Islamic investment is mostly about
sharing risk, which induces the fund managers to take their project selection more
seriously. This seriousness results in better fund selectivity. This result is consistent
with the result of the time series analysis reported in Chapter 5.
6.4 Summary
This chapter has mainly aimed to evaluate and compare the risk-adjusted return
performance of IMFs and CMFs relative to CAPM single and multi-factor
benchmarks using panel data analysis. This chapter has also aimed to compare the
results with the findings based on time series analysis as previously discussed in
Chapter 5. In summary, the results show that panel data analysis provides more
rigorous results but they are still in line with the time series.
The results based on single and multi-factor benchmarks show consistency with the
time series analysis in that, on average, alphas of both IMFs and CMFs outperform
the KLCI market return benchmark. The results demonstrate that all the portfolios are
more sensitive to the KLCI, a proxy for the large stock index, as there is a strong
coefficient estimation of this index in all models.
The findings in this chapter have revealed a significant superior fund selectivity skill
among the IMFs and CMFs fund managers using TM and extended TM models. The
findings also report that the IMFs and CMFs fund managers exhibit similarly inferior
market timing expertise using TM model and the extended TM model.
In conclusion, the empirical results from panel data FEs or REs show consistency
with the previous empirical analysis based on time series, in that: (1) both IMFs and
CMFs outperform the single conventional market benchmark, and (2) the IMFs and
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CMFs fund managers are superior in fund selectivity skill but inferior in market
timing ability, with the IMFs fund managers slightly better than the CMFs. In
contrast, the results of IMFs and CMFs in term of negative and positive alphas are
mixed when applied to the Islamic single benchmark.
The limitation of the results reported in this chapter is that the performance
investigation is limited to identifying whether the funds are outperformed or
underperformed. Further issues arise on what determinant factors impact on fund
performance and what the reason is for the differences between the returns
performance of IMFs and CMFs, particularly when different approaches such as time
series and panel data are implemented. To examine this, Chapter 7 addresses the
determinant factors in terms of fund attributes and fees that contribute to the
performance of the fund portfolios and their relationship to mutual fund performance,
specifically focusing on equity mutual funds.
Next, Chapter 7 further investigates the impact of fees on fund performance by
evaluating one of the asset classes in mutual funds as previously mentioned, namely
equity funds. The equity fund category is also associated with load fee, with higher
fees charges on the investment funds due to the expectation of having higher payback
in return. The fees are expected to have an adverse impact on fund returns, which
could imply that higher fees reduce fund returns, thus making a significant difference
between the returns performance of funds before and after excluding fees. Ironically,
investors assume that higher fees give better returns performance and they are willing
to pay more fees for the fund managers who can provide higher investment returns.
Hence, this evaluation could be useful for actively managed investors and market
players. These issues are analysed in detail in Chapter 7.
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CHAPTER 7 – FEES IMPACT AND
FUND ATTRIBUTES ON EQUITY
MUTUAL FUNDS PERFORMANCE
7.1 Introduction
In the previous chapters, we found some evidence indicating that IMFs managers
outperformed CMFs and demonstrated relatively superior funds selectivity skills, with
no significant difference in market timing. This is more or less true in the case of
equity fund as well. This chapter narrows down the focus to equity funds and
investigate whether or not the differences in performance could be explained by fees
and other fund attributes. These attributes include age, size, investment styles, alpha,
beta, residual risk, expense ratio, management fee and load fee as defined in Section
3.2.2 of Chapter 3.
The chapter uses yearly return rather than monthly return (as examined in Chapters 4
to 6) as fees are usually charged on yearly basis. The chapter uses an unbalanced
panel with a maximum of 106 equity funds (cross-sections) with time series
observation ranging from a minimum of 2 to a maximum of 20. Consistent with our
analysis in the previous chapters, this chapter aims to (1) investigate the performance
of IEFs and CEFs in relation to their market benchmarks, (2) examine performance of
IEFs and CEFs fund managers in terms of market timing expertise and fund
selectivity skill, and (3) examine the relationship between fund’s return, fees and the
fund’s attributes.
The rest of the chapter is organised as follows. Section 7.2 discusses the relevant
literatures on fees and other fund attributes. Section 7.3 explains the sample of the
data. Section 7.4 provides results and discussion of the findings. This section
describes the statistical descriptions of the fund samples, discusses results of fund
performance based on single factor regressions and also the performance on market
timing expertise and fund selectivity skill. The main part examined in this section is
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the impact of fees and other fund attributes on the equity fund performance. Finally,
section 7.5 presents a summary of the chapter.
7.2 Related literatures on fees and the fund attributes
In view of the extensive discussions in existing finance literature regarding the
performance of equity mutual funds, the findings reported in this chapter are relatively
important. Generally speaking, the literature discusses relationship between the
performance of fund portfolios, the relevant market benchmarks and the different
characteristics of these funds and reports mixed results. For instance, most studies in
the US reveal that equity mutual funds are not able to outperform the corresponding
market benchmarks (Benos and Jochec, 2011; Carhart, 1997; Elton et al. 1993;
Grinblatt and Titman, 1994; Jensen, 1968; Malkiel, 1995). Similarly, in the UK, Firth
(1977), and Blake and Timmermann (1998) find that equity funds underperformed the
market return over a period of 1965–1975 and 1972–1995 respectively. Blake and
Timmermann (1998) also report that on average, UK equity fund risk adjusted return
underperformed by approximately 1.8 per cent per annum. An exception is the study
by Ippolito (1989), which finds that excess returns performance net of all expenses for
equity funds exceeds that of index funds in the market. He further reports that the US
mutual fund return performance is relatively adequate to compensate for the higher
fees associated with the returns over the period 1965–1984.
Similarly, previous studies on mutual fund performance in Asia-Pacific countries
including Australia and Malaysia, mostly find that mutual funds have generally
underperformed the market benchmark. A study on Australian managed funds
between 1983 and 1995 using conditional measures of CAPM provides no abnormal
returns (Sawicki and Ong, 2000). Previously, Robson (1986), and Hallahan and Faff
(1999) also report inferior performance on the overall Australian fund returns against
respective market indices over a period of 1969–1978 and 1988–1997 respectively.
There is also evidence that on average active Australian superannuation funds are
unable to earn superior risk adjusted returns in relation to the relevant market
benchmarks (Gallagher, 2002).
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Similarly, most studies on Malaysian equity mutual funds, with the exception of
Chua (1985), note that the returns are not able to outperform the Kuala Lumpur
Composite Index (KLCI) market returns (see for example, Abdullah et al. 2007;
Annuar et al., 1997; Aw, 1997; Shamsher and Annuar, 1995; Taib and Isa, 2007). In
fact, these returns are lower than the risk free rate of return (Taib and Isa, 2007).
With respect to Islamic and conventional funds, evidence indicates that the IMFs
significantly underperform the market benchmark, as well as their conventional
counterparts (Abderrezak, 2008; Abdullah et al. 2007; Elfakhani and Hassan 2005;
Kraeussl and Hayat, 2008). Abdullah et al. (2007) for instance finds that both Islamic
and conventional funds in Malaysia underperform the market benchmark and that
Islamic funds perform better during bearish market. Conventional funds on the other
hand perform better during bullish market. Hoepner et al. (2011) similarly finds that
most of the IMFs worldwide underperform market benchmark, however, at the same
time Malaysian equity funds perform at par with the international equity market
benchmarks (Their study includes data on 265 IEFs, 76 of which are from Malaysia,
from September 1990 to April 2009). As for the religious mutual funds, findings
indicate that Australian ethical funds on average underperform the market by
approximately 1.5 per cent per annum (Tippet, 2001). The religious funds in the US
also underperform the market and conventional mutual funds over a period of
January 1994-September 2010 (Ferruz, Muñoz, and Vargas, 2012).
The underperformance of these mutual funds gives the investors an impression that
investing in these funds is associated with losses and financial penalty. In this regard,
Grinblatt and Titman (1994) suggest that the right choice of benchmark is particularly
important for performance evaluations. Therefore, in this chapter, the study analyses
the performance of fund portfolios allowing for different benchmarks to identify
whether the IEFs and CEFs are sensitive to any particular benchmarks.
Moreover, the performance of the mutual funds is not only related to market
benchmarking but also to other fund characteristics, including the imposition of fees.
Malkiel (1995) for example, indicates that in terms of aggregate returns, funds are not
able to outperform their market benchmarks, not only after management fees, but also
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in gross, before the expenses. The discussion about fees and mutual fund performance
is quite encouraging especially in the area of fees impact and how the different kinds
of fees could influence fund performance. Most studies based on the US equity
mutual funds report that there is an insignificant negative relationship between fund
return performance and fees (Carhart, 1997; Elton et al. 1993; Gil-Bazo and Ruiz-
Verdú, 2008; Haslem et al., 2008; Iannotta and Navone, 2012; Pollet and Wilson,
2008). The superior performance of mutual funds occurs mostly among large funds
with low expenses, low trading activity and no or low front-exit fees (Haslem et al.,
2008).
However, to this point there is no existing study that discusses fees and their relative
impact on Islamic and conventional mutual funds’ performance. In Malaysia for
instance, there are no previous published findings about the relationship between fees
and Islamic fund performance. The most relevant references are the ethical fund
studies. Gil-Bazo et al. (2010) investigate fund performance in the US over a period
of 1997–2005, between the ethical or socially responsible investment (SRI) funds and
the conventional funds and find that returns performance of US SRI funds before and
after fees is better than that of conventional funds with similar characteristics. They
also find no significant differences in fees between SRI and conventional funds apart
from the finding that the SRI funds from the same fund management companies are
cheaper than their conventional counterparts.
Fees are considered important for investors, as well as, for the fund management
companies. Although higher fees could decrease the returns on investment but at the
same time can also increase the fund managers’ revenue. Therefore, for investors, fees
incurred in the mutual fund investments are the price paid for the investment services
with the expectation that the expected return from the investment will be higher and
more than enough to offset the fees, while for the management companies, they
actually generate income (Khorana, Servaes, and Tufano, 2009). Nevertheless, the
previous findings indicate that the fees have an adverse impact on the investors since
most of the funds are not able to outperform their market benchmarks, not only on an
after fees expenses basis but also in gross (see for example, Carhart, 1997; Haslem et
al., 2008; Iannotta and Navone, 2012; Malkiel, 1995).
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Therefore, the study on comparative performance on Islamic and conventional equity
funds is largely to be explored but it gains little attention in the literature. The
popularity of these funds is gaining momentum due to the high growth of these funds
and the Islamic financial markets in the global financial industry (Hasan and Dridi,
2010). Hence, it is reasonable to further investigate the performance of Islamic funds
in order to identify whether or not these funds offer similar advantages as
conventional funds, and provide significant benefits to investors especially with
respect to fees and fund managers’ expertise, and risk and return trade-off
comparative to their conventional peers. In that case, this chapter aims to investigate
any difference between returns performance of the Malaysian equity funds, across
Islamic and conventional funds and their relationship with the market benchmarks.
The findings from this chapter are also important as they attempt to clarify several
issues such as why investors are interested in mutual funds if they predominantly
acknowledge that the funds give no abnormal returns or give lesser returns as
compared to the market. In particular, Benos and Jochec (2011) conclude that small
investors in the domestic US equity funds cannot make profits from the funds and that
large investors can only earn positive abnormal returns by rebalancing their fund
portfolios annually.
7.3 Data sample
The study examines the performance of 106 equity funds in Malaysia, including 53
IEFs and 53 CEFs over a period of 1990 to 2009 using single factor CAPM and
extends to the Treynor and Mazuy (1966) model to evaluate the selectivity skill and
market timing expertise among the Islamic and conventional fund managers. Most
previous studies compare portfolio returns based on gross returns or net returns after
excluding all expenses fees. Little attention is given to returns performance after
excluding load fees or returns after excluding all fees (the load fees plus all expenses
fees). Therefore, this study aims to extend the former two categories to include the
latter two categories.
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Finally, the analysis on the fund’s performance in this chapter is based on returns
before excluding all fees (GROSS returns) and returns after excluding all fees (NET
returns). The study also employs return net of all expenses (ADJUSTED returns) and
return net of load fees (ADJUSTED LOAD returns). The returns obtained from the
Morningstar database is net of all expenses (except front and exit fees) corresponding
to the ADJUSTED return category in this chapter. These returns are calculated based
on multiplicative methods using geometric mean and all expenses are deducted except
the front and exit fees such as sales charge and redemption fees. Since Morningstar
does not provide information about fees; the data about fees for all equity funds in the
sample are gathered directly from the related fund management companies and
prospectus of the funds.
For Malaysia, previous studies use gross returns and also returns adjusted for market
risk (market risk adjusted return). Moreover, they also use hand-gathered data,
collected from newspapers and the related fund management companies. The gross
returns here mean the raw returns before deducting any fees, and are calculated from
the NAV of a fund after adjusting for dividend payments. This complex process may
encounter problems if it is not properly managed.
In this study, the analysis is done using returns which are not adjusted for the risk free
rate of return. Since the study concerned a fund manager’s performance, the
application of gross return by adding back all the fees to net returns is appropriate
(Shukla and Van Inwegen, 1995). This is also to ensure that investors get information
on real value of their investment. Therefore, besides the GROSS category, this chapter
also incorporates other groups of returns, namely ADJUSTED, ADJUSTED LOAD
and NET.
Since there is no comparable study in Islamic mutual fund literature that emphasizes
on the relationship between fees and the returns performance of the funds, this chapter
therefore attempts to fill the gaps, employing four categories of returns, namely
GROSS, ADJUSTED, ADJUSTED LOAD and NET returns as dependent variables.
The data is more comprehensive and the outcomes of this study are important in order
to provide the investors estimates of real value of expected returns on investments.
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The process of selecting the equity mutual funds is based on the list of IEFs available
in Malaysia as of April 2009. Our sample is drawn from the 535 Malaysian mutual
funds included in the Morningstar database. We identify 143 Islamic mutual funds
falling into one of the five broad categories basically based on the fund types:
alternative, allocation, equity, fixed income and money market.
The data is matched with the list obtained from the SC. There are 554 approved
mutual funds in Malaysia as of April 2009, consisting of 141 IMFs and 413 CMFs.
The study considers all the IMFs in the list. Thus the IMFs list is free from
survivorship bias. The study also finds that there are two funds from the CMFs list
that are converting to Islamic fund operations within the period of this study based on
information from their prospectus. Therefore the study includes both in the IMFs
category ending up with 143 funds.
The study however, restricts the final sample to include only the IMFs that have a
minimum of 12 months returns. Based on this criterion 14 funds are excluded from
the sample of 143 funds, reducing the number to 129 funds from five broad categories
as previously mentioned. Out of the 129 IMFs, 58 are included in the IEFs category.
The study further restricts the number of IEFs to 53 since 5 of them are excluded as
they are state-funds and do not provide fees information.
For the conventional funds category, on the other hand, there are 413 CMFs in
Malaysia as of April 2009. We exclude 2 funds that are included in Islamic mutual
fund category, as mentioned above, and 19 funds due to unavailable data in the
Morningstar database. Again, we only include 349 CMFs having a minimum of 12
months returns out of the 392 CMFs covering all categories over a similar period.
There are 176 CEFs out of final 349 CMFs and we choose the top 53 from them based
on the highest average returns over the period of the study. As a result, the final
selected sample consists of 106 equity mutual funds, including 53 IEFs and 53 CEFs.
The full sample of 106 funds is categorized as all equity mutual funds (AEFs). Since
this chapter focuses on annual returns, we obtain annual data from the Morningstar
database for these selected funds. The yearly return from 1990 to 2008 is available for
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each of the funds. In order to have the annual returns for 2009, the monthly returns
data for each of the selected equity funds in the sample is used (similar to the monthly
data return as previously employed in Chapters 4 and 5 of this thesis). We convert the
monthly data on returns using the geometric means to obtain the annual returns.
The resulting dataset includes annual returns data for 106 equity mutual funds in
Malaysia from 1990 to 2009. The returns were net of all expenses except front and
exit fees. From the data, other portfolio returns are calculated, namely GROSS,
ADJUSTED, ADJUSTED LOAD, and NET returns. In order to develop these
portfolio returns, other information about all expenses and compulsory fees, including
front fees or sale charges fees and exit fees or redemption fees, were obtained from
fund management companies’ website and from the prospectus of these funds. (The
details about the fees structure of the equity mutual funds involved in this sample are
given in Table 7.2 of this chapter). It can be seen that on average, the management
fees of Islamic equity mutual funds in Malaysia are higher than the global market, and
also slightly higher than the conventional counterparts (about 1.57 per cent25 as
compared to 1.53 per cent).
The GROSS portfolio returns mean returns including all fees, generated by adding
back all the fund expenses (all expenses and load fees) to net returns. The
ADJUSTED portfolio returns are the returns net from all expenses fees (This is
exactly the data obtained from the Morningstar database). The ADJUSTED LOAD
portfolio returns are the returns net from compulsory fees or load fees, normally
consisting of front fee or sales charge and also exit fee or redemption fee. Meanwhile,
the NET portfolio returns are the returns after excluding all fees. This dataset is
hereafter called original data.
Other dataset employed in this study, namely trimmed data is obtained after
controlling for the outliers. This process involves excluding periods of crisis in the
mutual fund returns. The crisis periods that have been excluded are 1997, 1998 and
2008. These years correspond to the Asian financial crisis (AFC) and global financial
25 Ernst and Young (2009) reported that on average, the management fee for the active Islamic equity fund is about 1.4 per cent.
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crisis (GFC), respectively. These crises had a significant impact on the KLCI, a proxy
for the Malaysian market returns. Since mutual funds’ performance is sensitive to
market movements, the true performance of the mutual fund cannot be examined if
the data is not trimmed.
In this chapter, we use gross or raw returns before adjusting for the risk free rate of
return. This is to give equity mutual fund investors insights about the real expected
returns performance of the mutual fund investments. Moreover, raw returns are more
informative for interpreting the important events in the economic cycles as compared
to the risk adjusted or market adjusted returns (Dann, 1981). The study hypothesises
that there is a difference between returns performance of Islamic and conventional
equity funds. Furthermore, the imposition of fees on equity mutual funds could have
adverse impacts on the equity fund performance.
7.3.1 Multicollinearity
Since the regressions in this analysis involve numerous independent variables, the
correlation matrix is reported for the explanatory variables in order to identify
potential high correlations or multicollinearity issues among the explanatory
variables. Table 7.1 presents this information.
From the table, it can be seen that multicollinearity does not appear to be a severe
issue as most correlation coefficients are below 0.50. To mitigate this issue, for the
variables that have high correlations, a separate regression analysis is conducted to
exclude the variables from the relevant model. Furthermore, for front fee and exit fee
variables, we combine them to form a new variable called TOTLOAD. Where
necessary, we alternate between TOTLOAD or front and exit fees while ensuring that
the high correlation variables are dropped while running the relevant regression
models.
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Table 7. 1: Correlation matrix of the explanatory variables Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 Panel A: AEFs Exogenous 1-AGE 1 –0.372 0.248 –0.031 0.344 –0.063 0.087 0.112 –0.046 –0.016 0.348 0.659 2-LNSIZE 1 0.028 –0.135 –0.281 0.014 –0.132 0.019 0.131 –0.205 –0.181 –0.353 3-dINVEST 1 0.036 0.238 –0.070 0.106 0.053 –0.111 –0.021 –0.011 0.103 4- dTYPE 1 –0.256 0.029 –0.190 –0.039 0.184 0.017 0.055 0.124 Endogenous Return 5- ALPHA 1 –0.074 0.108 0.544 –0.097 0.006 0.222 0.102 6-RETURNt-1 1 –0.014 –0.018 0.009 –0.010 –0.008 –0.020 Risk 7- BETA 1 0.131 –0.046 –0.075 –0.131 –0.155 8- RESIDRISK 1 0.103 –0.061 0.223 –0.075 Fees 9-MGMTFEE 1 0.220 0.130 –0.021 10-EXPENSE 1 0.014 0.166 11-TOTLOAD 1 0.074 12-TRUSTEE 1 Panel B: IEFs Exogenous 1-AGE 1 –0.479 0.329 0.392 –0.096 –0.096 0.068 –0.025 0.045 0.356 0.360 –0.061 0.687 2-LNSIZE 1 –0.115 –0.554 0.047 –0.151 –0.302 0.197 –0.126 –0.228 –0.225 –0.025 –0.399 3-dINVEST 1 0.273 –0.137 0.149 –0.005 –0.231 –0.188 0.060 0.073 –0.143 0.175 Endogenous Return 4- ALPHA 1 –0.106 0.517 0.603 –0.079 0.159 0.286 0.292 –0.078 0.254 5-RETURNt-1 1 –0.078 –0.007 –0.009 0.007 0.006 0.001 0.061 –0.037 Risk 6- BETA 1 0.840 0.168 0.105 0.116 0.124 –0.087 0.017 7- RESIDRISK 1 0.274 0.193 0.213 0.214 –0.020 0.028 Fees 8-MGMTFEE 1 0.347 0.180 0.171 0.095 –0.088 9-EXPENSE 1 0.146 0.155 0.098 0.044 10-TOTLOAD 1 0.996 0.007 0.053 11-FRONT 1 –0.087 0.053 12-EXIT 1 –0.004 13-TRUSTEE 1
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Table 7.1 continued Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 Panel C: CEFs Exogenous 1-AGE 1 –0.288 0.181 0.341 –0.037 –0.034 0.157 –0.053 –0.056 0.346 0.342 0.083 –0.653 2-LNSIZE 1 0.144 0.084 –0.006 0.382 0.374 0.129 –0.266 –0.130 –0.101 –0.106 –0.290 3-dINVEST 1 0.253 –0.021 0.197 0.108 –0.012 0.081 –0.063 –0.024 –0.179 0.056 Endogenous Return 4- ALPHA 1 –0.030 0.383 0.540 –0.050 –0.259 0.309 0.326 –0.179 –0.069 5-RETURNt-1 1 –0.025 –0.026 0.013 –0.021 –0.021 –0.035 0.074 –0.014 Risk 6- BETA 1 0.753 0.020 –0.024 0.075 0.137 –0.335 –0.109 7- RESIDRISK 1 –0.045 –0.264 0.241 0.278 –0.260 –0.115 Fees 8-MGMTFEE 1 0.146 0.077 0.004 0.348 –0.011 9-EXPENSE 1 –0.071 –0.002 –0.329 0.300 10-TOTLOAD 1 0.981 –0.208 0.080 11-FRONT 1 –0.392 0.072 12-EXIT 1 0.012 13-TRUSTEE
1
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7.4 Results and performance analysis
7.4.1 Descriptive statistics on fund samples and fund attributes
Table 7.2 illustrates the summary of statistics for the total net assets (TNA), returns,
risk and the fees structure. All the variables reported in the table are calculated as 20–
year annualised averages, based on panel data. For example, the average TNA for the
total sample is RM$600.53 million. On average, IEFs are the smallest, with net assets
of RM$589.00 million as compared to CEFs that are the largest with RM$697.00
million.
Overall, all equity mutual funds (AEFs) achieved 15.77 per cent average annual
returns before fees over the 20–year period as shown in Table 7.2, approximately 510
basis points (bps) higher than the market return of the KLCI at 10.67 per cent.26
However, on average, annual returns after fees indicate that mutual funds
underperformed the market return at 6.54 per cent, approximately 413 bps lower than
the KLCI. The descriptive statistics mainly denote that CEFs’ portfolio was the best
performer over the period. However, the higher returns of the CEFs are accompanied
with a higher systematic risk (beta) and a higher unsystematic risk (residual risk).
With respect to fees structure of the portfolios, as shown by average expense ratios,
IEFs invested relatively more in research relative to CEFs. The average mutual funds
expense ratio of 1.73 per cent is higher than that in the US, where it is about 1.31 per
cent (see Indro et al., 1999, p.77). The low expense ratio of the US mutual funds
might suggest the presence of economies of scale in that industry.
In addition, higher operating expenses could lead to low performance of a fund.
Although the percentage is very small, they have the adverse impact of reducing fund
returns. The reason is that investors must allocate some of their initial investment or
profits from the investment to pay for the fees. These expenses could be in the form of
management fees, expenses ratio or trustee fee. In contrast to that, investors pay one-
26 The average KLCI market return based on trimmed data is approximately 10.67 per cent per annum over the 1990-2009 as shown next in Panel B of Table 7.3. The percentage is higher as compared to the findings of Taib and Isa (2007) who report that on average market return of the Malaysian stock market is about -0.96 per annum over the period of 1990 to 2001.
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off front and exit fees, operating expenses need to be paid overtime that most
unsophisticated investors are not aware about the impact of fees.
Furthermore, investors’ insensitivity to management fee may reflect the lack of salient
information about the fee. Since the fee is paid over time, and is calculated as a
fraction of the value of the investment, it is rarely translated into dollar terms.
Furthermore, investors seem to be more sensitive to load fees as compared to
operating expenses (Barber, Odean, and Lu, 2005; Gil-Bazo and Ruiz-Verdú, 2008).
Consequently, most of the unsophisticated investors do not realise how much
expenses were deducted from their investments in terms of fees.
Further description on the data is conducted. Table 7.3 presents the descriptive
statistics from the panel data, for AEFs, IEFs, and CEFs relative to the market and
risk free portfolios. Table 7.3 provides more rigorous results but apparently
contradicts with the findings of the time series analysis as mentioned in Chapter 5 of
the thesis. The mean returns performance of IEFs is relatively lower than the CEFs.
This suggests that CEFs perform better than IEFs while using panel data regression
analysis.
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Table 7. 2: Description on mutual fund samples and the fund attributes
Fund size: TNA (RM millions)
Age
20–year average return (%)
KLCI as a proxy for market
Fees structure
Gross Adjusted Adjusted Load
Net alpha beta Residual risk (%)
Mgmt. Fee
Expense ratio
Total load
Trustee fee
AEFs: N=843 Mean 600.53 14.730 15.766 12.423 9.887 6.544 5.424 1.081 26.675 1.537 1.728 5.878 0.079 Std. Dev 500.01 11.944 28.847 28.847 28.837 28.839 5.308 1.252 6.518 0.157 0.345 0.600 0.014 Median 500.00 10.042 16.644 13.304 10.953 7.782 5.414 0.848 27.215 1.500 1.650 6.000 0.070 IEFs: N=342 Mean 589.00 13.983 14.613 11.227 8.688 5.302 3.770 0.792 26.320 1.568 1.738 5.925 0.080 Std. Dev 456.00 12.852 29.697 29.684 29.655 29.643 7.244 0.196 7.678 0.143 0.316 0.624 0.015 Median 450.00 8.017 17.387 14.298 11.472 8.042 3.758 0.787 24.201 1.500 1.670 6.000 0.080 CEFs: N=501 Mean 697.00 15.239 16.552 13.239 10.706 7.392 6.728 0.840 26.917 1.515 1.721 5.847 0.077 Std. Dev 525.00 11.267 28.255 28.263 28.266 28.275 2.508 0.148 5.585 0.163 0.364 0.581 0.013 Median 500.00 11.028 16.515 13.275 10.644 6.895 6.426 0.848 28.320 1.500 1.640 5.500 0.070
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Table 7. 3:Descriptive statistics of IEFs, CEFs and AEFs relative to market and risk free portfolios. Fund portfolio N Mean Max Min Std. Dev JB
Panel A: Original data IEFs GROSS 342 14.613 154.111 –58.794 29.697 8.859** ADJUSTED 342 11.227 150.631 –62.064 29.684 8.608** ADJUSTED
LOAD 342 8.688 147.611 –63.794 29.655 8.307**
NET 342 5.302 144.131 –67.064 29.643 8.059** Market 1060 2.832 69.387 –73.360 29.889 92.394*** rf 1060 4.345 7.700 1.920 1.882 111.581*** CEFs GROSS 501 16.552 109.432 –54.382 28.255 0.705 ADJUSTED 501 13.239 106.272 –57.932 28.263 0.747 ADJUSTED
LOAD 501 10.706 103.932 –60.632 28.266 0.695
NET 501 7.392 100.772 –64.182 28.275 0.738 Market 1060 2.832 69.387 –73.360 29.889 92.394*** rf 1060 4.345 7.700 1.920 1.882 111.581*** AEFs GROSS 843 15.766 154.111 –58.794 28.847 7.089** ADJUSTED 843 12.423 150.631 –62.064 28.847 6.972** ADJUSTED
LOAD 843 9.887 147.611 –63.794 28.837 6.688**
NET 843 6.544 144.131 –67.064 28.839 6.578** Market 2120 2.832 69.387 –73.360 29.881 184.789*** rf 2120 4.345 7.700 1.920 1.881 223.161*** Panel B: Trimmed data IEFs GROSS 276 24.037 154.111 –25.996 23.266 131.926*** ADJUSTED 276 20.655 150.631 –29.886 23.245 130.754*** ADJUSTED
LOAD 276 18.107 147.611 –32.496 23.223 128.720***
NET 276 14.725 144.131 –36.386 23.204 127.464*** Market 901 10.669 69.387 –28.296 21.967 105.033*** rf 901 4.220 7.700 1.920 1.908 103.280*** CEFs GROSS 419 24.071 109.432 –21.871 22.950 37.030*** ADJUSTED 419 20.746 106.272 –24.781 22.980 37.133*** ADJUSTED
LOAD 419 18.219 103.932 –28.371 22.973 36.420***
NET 419 14.895 100.772 –31.281 23.003 36.565*** Market 901 10.669 69.387 –28.296 21.967 105.033*** rf 901 4.220 7.700 1.920 1.908 103.280*** AEFs GROSS 695 24.057 154.111 –25.996 23.059 132.991*** ADJUSTED 695 20.710 150.631 –29.886 23.069 131.608*** ADJUSTED
LOAD 695 18.175 147.611 –32.496 23.056 129.479***
NET 695 14.827 144.131 –36.386 23.066 128.155*** Market 1802 10.669 69.387 –28.296 21.962 210.065*** rf 1802 4.220 7.700 1.920 1.907 206.560***
However, standard deviation of the IEFs portfolio is relatively larger than the CEFs
portfolio (see Panel A, Table 7.3). This evidence validates findings in time series data
as previously discussed in Chapter 5 (that Islamic funds are more risky than the
Page | 196
conventional funds). The finding is also consistent with Mansor and Bhatti (2011)
who find that Islamic funds have relatively higher risk using F-test and Bartlett test
for variances. They also find that Islamic funds are more volatile thus providing
higher expected risk in relation to their conventional peers. Therefore, the findings
here directly signify that conventional funds perform better than Islamic funds using
panel data analysis, with higher returns and lower risk.
The JB tests for each of the portfolios also indicate that the data is not normally
distributed (see Panel B, Table 7.3). In such situations normal linear regression is not
enough to provide comprehensive results, and panel data regression analysis is robust
to overcome the weaknesses. Panel data regressions are adjusted for
heteroskedasticity and cross-sectional standard errors, and covariance, and for serial
correlation when the Durbin-Watson (DW) is less than 2.
Additionally, Table 7.4 presents t-tests for statistical differences using equal variance
independent samples t-test. The results of the table compute mean differences t-test
and indicate that there is no significant difference between Islamic and conventional
funds. These results show a highly significant difference at one per cent level between
mean returns of all the portfolios and the market returns. In contrast to time series
data, panel data analysis indicates a significant difference between net returns of all
portfolios with the market. There is also a significant difference between all equity
funds portfolio and the risk free returns using trimmed data (Panel B). Since this
study covers a duration of 20–year, trimmed data is employed for controlling outliers
arising due to the AFC in 1997/1998 and the GFC in 2007/2008, to provide more
rigorous results.
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Table 7.4: Mean test of statistical differences between IEFs, CEFs and AEFs, comparative to market and risk free portfolios, based on panel data Table presents t-tests using equal variance to evaluate whether Islamic and conventional equity fund portfolios perform significantly different from each other. These t-tests are also conducted to compare the IEFs, CEFs portfolios, market portfolio and risk free (rf) portfolio. The results are based on average annualised returns over the period 1990 to 2009. The asterisks ***, **, * indicate the statistical significance based on a two-tail test at 1%, 5% and 10% respectively. Portfolio IEFs-
CEFs IEFs- market
CEFs-market
AEFs-market
AEFs- rf
Panel A: Original data GROSS Mean
difference 1.939 11.781 13.720 12.933 11.421
t-statistic –0.958 6.348*** 8.615*** 10.734*** 18.137*** ADJUSTED Mean
difference 2.013 8.394 10.407 9.590 8.078
t-statistic –0.995 4.524*** 6.534*** 7.960*** 12.828*** ADJUSTED LOAD
Mean difference
2.017 5.856 7.873 7.055 5.542
t-statistic –0.997 3.157*** 4.943*** 5.856*** 8.804*** NET Mean
difference 2.090 2.470 4.560 3.712 2.199
t-statistic –1.033 1.331 2.863*** 3.081*** 3.494*** Panel B: Trimmed data GROSS Mean
difference 0.034 13.367 13.402 13.388 19.837
t-statistic –0.019 8.722*** 10.171*** 13.462*** 36.211*** ADJUSTED Mean
difference 0.092 9.985 10.077 10.040 16.490
t-statistic –0.051 6.516*** 7.644*** 10.095*** 30.088*** ADJUSTED LOAD
Mean difference
0.112 7.438 7.550 7.505 13.955
t-statistic –0.063 4.855*** 5.728*** 7.547*** 25.476*** NET Mean
difference 0.170 4.056 4.225 4.158 10.607
t-statistic –0.045 2.648*** 3.204*** 4.181*** 19.356***
To further examine the above mentioned results, the study proceeds with the single
factor panel data regression and TM model panel data regression analysis in the
following sections (the results of single factor regression are reported in Section 7.4.2,
while Section 7.4.3 discusses the findings on the market timing expertise of fund
managers and their selectivity skill).
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7.4.2 Single factor OLS regression
Table 7.5 presents findings of the single factor regression (SFR) model by using panel
ordinary least squares (OLS) regression using similar models that have been discussed
in Chapter 3 of this thesis. Results show that all the portfolios outperform the market
benchmark. These results also provide evidence that IEFs seem to underperform the
CEFs.
Table 7. 5: Results of pooled OLS using single factor regression model Table reports the results (in percentage) of regressions based on single factor regression, with 53 cross-sections included for IEFs and CEFs, and 106 cross-sections for AEFs portfolio, from 1990 to 2009.The results are after adjusted for heteroskedasticity and serial correlation problems. N is the number of observations. The asterisks ***, **, * indicate the statistical significance based on two-tail test at 1%, 5% and 10% respectively. Standard errors are given in parentheses. Fund portfolio Original data Trimmed data
α β ��gT� α β ��gT�
IEFs GROSS 13.468***
(3.257) 0.875*** (0.052)
0.690 10.585** (5.086)
1.047*** (0.147)
0.452
Original data (N=342)
ADJUSTED 10.082*** (3.256)
0.875*** (0.052)
0.691 7.208 (5.086)
1.047*** (0.147)
0.452
Trimmed data (N=276)
ADJUSTED LOAD
7.544** (3.262)
0.875*** (0.052)
0.691 4.663 (5.097)
1.047*** (0.147)
0.453
NET 4.157 (3.262)
0.875*** (0.052)
0.692 1.286 (5.097)
1.046*** (0.147)
0.453
CEFs GROSS 14.529*** (2.692)
0.888*** (0.055)
0.755 11.925*** (3.526)
1.052*** (0.088)
0.608
Original data (N=501)
ADJUSTED 11.217*** (2.698)
0.888*** (0.055)
0.754 8.591** (3.533)
1.053*** (0.088)
0.607
Trimmed data (N=419)
ADJUSTED LOAD
8.683*** (2.699)
0.888*** (0.055)
0.754 6.073* (3.532)
1.052*** (0.087)
0.606
NET 5.371** (2.706)
0.883*** (0.052)
0.752 2.739 (3.539)
1.053*** (0.087)
0.606
AEFs GROSS 14.101*** (2.880)
0.883*** (0.052)
0.727 11.408*** (4.037)
1.049*** (0.103)
0.545
Original data (N=843)
ADJUSTED 10.759*** (2.883)
0.883*** (0.052)
0.727 8.056** (4.040)
1.049*** (0.103)
0.544
Trimmed data (N=695)
ADJUSTED LOAD
8.224*** (2.885)
0.883*** (0.052)
0.727 5.528 (4.042)
1.048*** (0.102)
0.545
NET 4.881* (2.888)
0.883*** (0.052)
0.727 2.177 (4.046)
1.048*** (0.103)
0.544
The returns performance difference however varies across funds categories. It can
also be seen that the impact of fees on mean returns performance of AEFs, IEFs and
CEFs becomes more severe in NET as expected. Therefore, it seems that the
imposition of fees, either load fees or all expenses fees, would have a direct impact in
Page | 199
reducing the fund returns performance as is also supported by the various previous
studies (Babalos et al., 2009; Carhart, 1997; Haslem et al., 2008; Iannotta and
Navone, 2012; Indro et al., 1999; Malkiel, 1995; Pollet and Wilson, 2008).The main
findings in the above mentioned studies also indicate that the returns performance of
funds after fees is relatively lower than the market return and is not even able to offset
the fees. However, the present study shows that even though the imposition of fees
reduces returns performance of all portfolios, they are still superior to the market
returns performance before and after deducting fees. This is consistent with the report
of Ippolito (1989).
The results exhibit in Table 7.5 are after correcting for heteroskedasticity and cross-
sectional standard errors and they show that the returns for the gross category of the
IEFs are moderately good, at 10.59 per cent per annum. However, the returns
performance diminishes for the other categories after deducting the fees. The returns
performance of the ADJUSTED category (net of all expenses) decreases to 7.21 per
cent per annum albeit being insignificant. Similarly, the performance of ADJUSTED
LOAD category (after deducting the load fees) is insignificant but lower than the
ADJUSTED category at 4.66 per cent per annum.
Returns with fees (GROSS) performs better than returns after fees (NET) suggesting
that the fees may generate better fund performance, good enough to cover the cost of
fees. However, after deducting all fees (NET), equity investors are only able to get
1.29 per cent profit per annum for the Islamic funds and 2.74 per cent per annum for
the conventional funds.
The results in the table also indicate that the performance of Islamic and conventional
funds is better than the market although the returns are considerably low for long term
investments over a 20–year period. The NET returns performance (after deducting all
fees) diminishes for both Islamic and conventional equity fund portfolios, suggesting
that the profits that investors gain from mutual fund investments are largely used to
offset the fees.
Page | 200
7.4.3 Market timing expertise and fund selectivity skill
The findings from OLS regressions for market timing expertise and fund selectivity
among fund managers are reported in Table 7.6. Fund selectivity skills of the fund
managers (measured by the alpha (α)) have a significantly positive effect based on
both original and trimmed data before correction for heteroskedasticity (the results are
available upon request). The results are less clear cut after the correction as shown in
Table 7.6.
These results are more or less in line with results discussed in Chapters 5 and 6. The
results reveal that there is no significant impact of market timing expertise on fund
performance of IEFs, CEFs (measured by the theta θ) when using original data, after
correction for heteroskedasticity, implying perverse or inferior market timing
expertise among Islamic and conventional fund managers. Moreover, after correction
for heteroskedasticity is made, all results of thetas show insignificantly perverse or no
market timing expertise at 0.00 per cent. The findings are consistent with Low (2007)
who reports that Malaysian fund managers are poor in their timing ability over the
period 1996 to 2000, which contributes to the negative overall fund performance, thus
leading to no economic benefits accruing to fund managers involved in market timing
activities and that the fund managers should consider reducing the imposition of
relevant fees to offset the low fund performance.
In summary, the results indicate that all the portfolios outperform the market
benchmark. There is also evidence that IEFs underperform the CEFs. This finding is
in line with many previous studies on the topic. The results however are in contrast
with the findings of Gil-Bazo et al. (2010) who state that SRI perform better than the
conventional funds.
Page | 201
Table 7. 6: Results of market timing ability based on pooled OLS panel analysis This table reports the results of panel data regressions based on the TM model over a sample period from 1990 to 2009 for annual returns performance of 106 AEFs (53 IEFs and 53 CEFs). The results are based on 53 cross-sections for IEFs and CEFs and 106 cross-sections for AEFs portfolio, over the same period. The difference of alpha (α) and (θ) from zero indicates that there is superior in fund selectivity skill and market timing expertise among the fund managers. The asterisks ***,**,* denote statistical significance at 1%, 5% and 10% respectively. Standard errors are given in parentheses. The standard errors allow corrections for heteroskedasticity and serial correlation follow White (1980).
Fund portfolio Original data Trimmed data α β θ ��gT� α β θ ��gT� IEFs GROSS 10.526**
(4.831) 0.958*** (0.050)
0.004* (0.002)
0.701 10.409** (5.165)
0.916*** (0.166)
0.005 (0.004)
0.459
Original data (n=342)
ADJUSTED 7.153 (4.830)
0.958*** (0.050)
0.004* (0.002)
0.702 7.034 (5.164)
0.917*** (0.166)
0.005 (0.147)
0.459
Trimmed data (n=276)
ADJUSTED LOAD
4.618 (4.842)
0.957*** (0.049)
0.004* (0.002)
0.703 4.490 (5.177)
0.918*** (0.165)
0.005 (0.004)
0.460
NET 1.245 (4.841)
0.957*** (0.049)
0.004* (0.002)
0.703 1.115 (5.176)
0.918*** (0.166)
0.005 (0.004)
0.460
CEFs GROSS 11.861*** (3.517)
0.954*** (0.036)
0.003*** (0.001)
0.770 11.614*** (3.706)
0.971*** (0.136)
0.003 (0.003)
0.612
Original data (n=501)
ADJUSTED 8.530** (3.524)
0.954*** (0.036)
0.003** (0.001)
0.769 8.277** (3.713)
0.971*** (0.136)
0.003 (0.003)
0.611
Trimmed data( n=419)
ADJUSTED LOAD
6.027* (3.528)
0.953*** (0.036)
0.003** (0.001)
0.769 5.769 (3.715)
0.973*** (0.136)
0.003 (0.003)
0.610
NET 2.696 (3.534)
0.953*** (0.036)
0.003** (0.001)
0.768 2.432 (3.722)
0.973*** (0.136)
0.003 (0.003)
0.610
AEFs GROSS 11.337*** (3.957)
0.955*** (0.035)
0.003** (0.001)
0.741 11.123*** (4.184)
0.950*** (0.139)
0.004 (0.003)
0.551
Original data (n=843)
ADJUSTED 7.987** (3.960)
0.955*** (0.035)
0.003** (0.001)
0.741 7.771* (4.187)
0.950*** (0.139)
0.004 (0.003)
0.551
Trimmed data (n=695)
ADJUSTED LOAD
5.473 (3.966)
0.954*** (0.035)
0.003** (0.001)
0.741 5.249 (4.192)
0.951*** (0.139)
0.004 (0.003)
0.550
NET 2.123 (3.969)
0.955*** (0.035)
0.003** (0.001)
0.741 1.897 (4.195)
0.951*** (0.139)
0.004 (0.003)
0.550
Page | 202
7.4.4 Results of panel data using FEs and REs on TM model
The study also extends the analysis to include panel least squares regressions,
allowing for cross-sectional fixed effects (FEs) and also using panel generalised least
squares (GLS) using random effects (REs). These regressions are carried out to
examine the robustness of the results on panel regressions concerning market timing
expertise among fund managers, as previously described in Table 7.6.
Table 7.7 reports the results of these FEs and REs panel regressions. All the
regressions are also corrected for heteroskedasticity and serial correlation using
method of White (1980).The findings of the GLS using trimmed data are consistent
with the previous results as shown in Table 7.6 that there is perverse or no market
timing expertise among fund managers for both Islamic and conventional ones. These
also suggest that the previous regressions based on pooled regression are already
adequate to deal with the questions on hand.
The Hausman test (r�) is also presented in Panel B, Table 7.7. Results of Hausman
test (r�) suggests that the tests are not significant for the IEFs portfolio, implying that
FE model is not preferred over the REs. Results in Table 7.8 also show inconsistent
findings that there is a statistically significant difference on market timing expertise
between Islamic and all equity fund portfolios using panel regression with fixed
effects. These findings indicate that the CEFs and AEFs portfolios are not sensitive to
random effects while allowing for the cross-sectional fixed effects, but the same is not
true for the IMFs portfolio when the trimmed data is used.
Page | 203
Table 7. 7: Panel data regression results using FEs and GLS REs Fund portfolio Original data Trimmed data α β θ ����� α β θ �����
Panel A: OLS fixed effects with correction for heteroskedasticity IEFs GROSS 10.778**
(4.697) 0.971*** (0.056)
0.003 (0.002)
0.726 10.692** (5.191)
0.852*** (0.179)
0.006* (0.003)
0.536
ADJUSTED 7.391 (4.697)
0.971*** (0.056)
0.003 (0.002)
0.726 7.310 (5.191)
0.852*** (0.179)
0.006* (0.003)
0.535
ADJUSTED LOAD
4.853 (4.697)
0.971*** (0.056)
0.003 (0.002)
0.725 4.763 (5.191)
0.852*** (0.179)
0.006* (0.003)
0.534
NET 1.466 (4.697)
0.971*** (0.056)
0.003 (0.002)
0.725 1.381 (5.191)
0.852*** (0.179)
0.006* (0.003)
0.534
CEFs GROSS 11.779*** (3.416)
0.964*** (0.040)
0.003*** (0.001)
0.769 11.656*** (3.696)
0.908*** (0.140)
0.005 (0.003)
0.618
ADJUSTED 8.466** (3.416)
0.964*** (0.040)
0.003*** (0.001)
0.769 8.332** (3.696)
0.908*** (0.140)
0.005 (0.003)
0.619
ADJUSTED LOAD
5.932* (3.416)
0.964*** (0.040)
0.003*** (0.001)
0.769 5.805 (3.696)
0.908*** (0.140)
0.005 (0.003)
0.619
NET 2.619 (3.416)
0.964*** (0.040)
0.003*** (0.001)
0.769 2.481 (3.696)
0.908*** (0.140)
0.005 (0.003)
0.620
AEFs GROSS 11.367*** (3.855)
0.967*** (0.041)
0.003** (0.001)
0.752 11.244*** (4.169)
0.891*** (0.143)
0.005* (0.003)
0.587
ADJUSTED 8.024** (3.855)
0.967*** (0.041)
0.003** (0.001)
0.752 7.897* (4.169)
0.891*** (0.143)
0.005* (0.003)
0.588
ADJUSTED LOAD
5.488 (3.855)
0.967*** (0.041)
0.003** (0.001)
0.752 5.362 (4.169)
0.891*** (0.143)
0.005* (0.003)
0.587
NET 2.145 (3.855)
0.967*** (0.041)
0.003** (0.001)
0.752 2.014 (4.169)
0.891*** (0.143)
0.005* (0.003)
0.588
Page | 204
Table 7.7 continued
Fund portfolio Original data Trimmed data α β θ ����� r� α β θ ����� r� Panel B: GLS random effects with correction for heteroskedasticity IEFs GROSS 10.993*
(5.790) 0.957*** (0.050)
0.003* (0.002)
0.719 0.011 8.955
11.695 (7.378)
0.889*** (0.167)
0.005 (0.003)
0.490 0.157 3.700
ADJUSTED 7.602 (5.759)
0.957*** (0.050)
0.003* (0.002)
0.720 0.013 8.754
8.302 (7.350)
0.889*** (0.167)
0.005 (0.003)
0.490 0.154 3.730
ADJUSTED LOAD
5.035 (5.711)
0.957*** (0.050)
0.003* (0.002)
0.719 0.010 9.152
5.736 (7.333)
0.890*** (0.167)
0.005 (0.003)
0.490 0.148 3.823
NET 1.644 (5.683)
0.957*** (0.050)
0.003* (0.002)
0.719 0.011 8.941
2.344 (7.308)
0.890*** (0.167)
0.005 (0.003)
0.490 0.146 3.855
CEFs GROSS 11.861*** (3.517)
0.954*** (0.036)
0.003*** (0.001)
0.770 0.000 15.693
11.614*** (3.706)
0.971*** (0.136)
0.003 (0.003)
0.612 0.000 17.371
ADJUSTED 8.530** (3.524)
0.954*** (0.036)
0.003*** (0.001)
0.769 0.000 16.424
8.277** (3.713)
0.971*** (0.136)
0.003 (0.003)
0.611 0.000 17.192
ADJUSTED LOAD
6.027* (3.528)
0.953*** (0.036)
0.003** (0.001)
0.769 0.000 17.678
5.769 (3.715)
0.973*** (0.136)
0.003 (0.003)
0.610 0.000 18.571
NET 2.696 (3.534)
0.953*** (0.036)
0.003** (0.001)
0.768 0.000 18.393
2.432 (3.722)
0.973*** (0.136)
0.003 (0.003)
0.610 0.000 18.348
AEFs GROSS 11.547*** (4.389)
0.956*** (0.035)
0.003** (0.001)
0.747 0.000 17.183
11.723** (5.276)
0.933*** (0.137)
0.004 (0.003)
0.564 0.001 13.536
ADJUSTED 8.199* (4.399)
0.956*** (0.035)
0.003** (0.001)
0.747 0.000 17.513
8.378 (5.296)
0.933*** (0.137)
0.004 (0.003)
0.564 0.001 13.512
ADJUSTED LOAD
5.678 (4.381)
0.955*** (0.035)
0.003** (0.001)
0.746 0.000 18.154
5.852 (5.277)
0.934*** (0.136)
0.004 (0.003)
0.563 0.001 14.167
NET 2.332 (4.395)
0.955*** (0.036)
0.003** (0.001)
0.746 0.000 18.446
2.508 (5.299)
0.934*** (0.137)
0.004 (0.003)
0.563 0.001 14.115
Note: Standard errors are given in parentheses. Chi.sq. stats are in italic. The asterisks ***, **, * denote statistical significance at the 1%, 5% and 10% levels respectively.
Page | 205
7.4.5 Fees and other fund attributes on single factor regression analysis
This section extends the analysis to fund attributes and fees variables and reports the
cross-sectional analysis using single panel REs regression with time period fixed
period based on 20–year data to include size, age, a dummy variable for local or
foreign fund, management fee, expense ratio, and load fee. This section conjectures
that these explanatory variables have some impact on the performance of the fund
portfolios.
The regression analysis are conducted based on each types of returns categories, using
both original and trimmed data, with N represents the number of observations in each
of the regressions. The single panel regressions estimate the coefficients where the
standard errors allow for heteroskedasticity and serial correlation based on the work
of White (1980). These regressions also allow for the time fixed effects (year), as
employed by Dahlquist et al. (2000). Table 7.8 presents results of the regressions. The
similar results obtained in IEFs, CEFs and AEFs either using original data or trimmed
data (as shown in Panel A and Panel B of Table 7.8 respectively).
The results in the table indicate that there is no relationship between returns
performance and the size of the fund, where the fund size refers to the total net asset
values (TNA) of the funds (in logs) at the inception date. For both original and
trimmed data, the results reveal that there is no statistically significant difference
between the size and fund performance of IEFs, CEFs and AEFs over the period of
1990–2009. This contradicts with the findings of Indro et al. (1999) who argue that
size has an impact on the mutual fund performance.
Results in Table 7.8 also indicate that age has negative but insignificant effect on IEFs
and AEFs. There is a negative relationship between performance and the size of the
fund. Further, size has a larger impact on the performance of CEFs, particularly in the
categories of net (after deducting all fees) and adjusted load (after deducting front and
exit fees) returns.
Page | 206
The regression coefficient estimates, where returns are regressed on a constant and
age, approximately equal –0.12 for adjusted load and net returns. Even though these
are not significant, these slope coefficients for CEFs are quite large as compared to
the IEFs, implying that the older the fund, the less it performs relative to the market
because the fund is already achieved the economics of skill.
The management fees have a negative impact on the Islamic equity funds (IEFs)
performance both with original and trimmed data. The results support the findings of
Dahlquist et al. (2000) that management fee has a strong negative relation to the
performance of the funds. The coefficient estimate for the IEFs is less than minus five
(–5.06) based on original data, and even more severe for the trimmed data (–7.70).
The higher negative impact of management fee to IEFs fund performance could imply
that IEF fund managers charge investors higher management fees than their
conventional counterparts.
For the AEFs, the regression coefficient is –2.153 (see Panel B, Table 7.8) suggesting
that the fund managers offset more than double the direct effect of the fees to the fund
performance. This evidence implies that the effect of management fee on gross mutual
fund performance in Malaysia is about two to one on a yearly basis. Although the
adverse impact is larger in magnitude, this finding is also consistent with (Dahlquist et
al., 2000) who provide evidence that management fee weakens the US fund
performance one for one per annum.
However, the results also indicate that the management fee has a positive impact on
the conventional equity funds (CEFs) performance and this is highly significant while
using original data. For example, the regression coefficient estimate is about 5.08 and
4.82 for the gross and adjusted load returns after deducting load fee, (the front and
exit fee but not the expense fees) respectively. However, the results are insignificant
when applying with the trimmed data.
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Table 7. 8: Cross sectional analysis of returns versus fees and other fund attributes based on single factor panel REs regression. This table reports the regression results based on original data (Panel A) and trimmed data (Panel B), allowing for fixed period (year) effects, and are adjusted for heteroskedasticity and serial correlations as in White’s (1980) test. Standard errors are given in parentheses.
Attributes Coefficient estimates N SIZE AGE dINVEST MGMT FEE EXPENSE TOTLOAD
Panel A: Original data IEFs GROSS 342 0.000 –0.028 1.930*** –5.062 5.739*** 3.148*** (0.000) (0.082) (0.706) (4.533) (1.864) (1.152) ADJUSTED 342 0.000 –0.029 2.205*** –6.792 4.591** 3.035*** (0.000) (0.082) (0.710) (4.541) (1.868) (1.151) ADJUSTED 342 0.000 –0.048 1.795** –5.794 5.461*** 2.148* LOAD (0.000) (0.080) (0.714) (4.539) (1.850) (1.152) NET 342 0.000 –0.049 2.070*** –7.523 4.313** 2.035* (0.000) (0.080) (0.720) (4.548) (1.854) (1.151) CEFs GROSS 501 0.000 –0.098 –0.718 5.076*** –2.525** –0.768 (0.000) (0.074) (1.721) (1.682) (1.100) (1.120) ADJUSTED 501 0.000 –0.098 –0.845 3.934** –3.565*** –0.781 (0.000) (0.074) (1.760) (1.760) (1.079) (1.140) ADJUSTED 501 0.000 –0.116 –0.647 4.824*** –2.504** –1.768 LOAD (0.000) (0.000) (1.794) (1.712) (1.161) (1.120) NET 501 0.000 –0.117 –0.774 3.682* –3.544*** –1.781 (0.000) (0.074) (1.833) (1.801) (1.139) (1.140) AEFs GROSS 843 0.000 –0.052 0.840 1.121 0.438 0.798 (0.000) (0.071) (0.744) (2.489) (0.961) (0.964) ADJUSTED 843 0.000 –0.053 0.895 –0.234 –0.643 0.735 (0.000) (0.071) (0.751) (2.594) (0.945) (0.979) ADJUSTED 843 0.000 –0.071 0.837 0.667 0.345 –0.202 LOAD (0.000) (0.070) (0.799) (2.556) (1.007) (0.964) NET 843 0.000 –0.072 0.892 –0.687 –0.736 –0.265 (0.000) (0.070) (0.806) (2.667) (0.990) (0.979)
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Table 7.8 continued
Attributes Coefficient estimates N SIZE AGE dINVEST MGMT FEE EXPENSE TOTLOAD
Panel B: Trimmed data IEFs GROSS 276 0.000 –0.058 2.394*** –7.703 5.604** 2.678** (0.000) (0.089) (0.785) (5.589) (2.368) (1.189) ADJUSTED 276 0.000 –0.058 2.680*** –9.424* 4.452* 2.572** (0.000) (0.089) (0.788) (5.601) (2.376) (1.188) ADJUSTED 276 0.000 –0.077 2.276*** –8.458 5.342** 1.678 LOAD (0.000) (0.087) (0.788) (5.593) (2.351) (1.189) NET 276 0.000 –0.079 2.563*** –10.179* 4.191* 1.572 (0.000) (0.087) (0.793) (5.606) (2.359) (1.188) CEFs GROSS 419 0.000 –0.087 –0.096 2.841 –2.002 –0.339 (0.000) (0.087) (2.068) (1.804) (1.347) (1.369) ADJUSTED 419 0.000 –0.086 –0.177 1.467 –3.070** –0.318 (0.000) (0.087) (2.110) (1.989) (1.326) (1.391) ADJUSTED 419 0.000 –0.105 0.021 2.513 –1.894 –1.339 LOAD (0.000) (0.087) (2.144) (1.858) (1.409) (1.369) NET 419 0.000 –0.104 –0.060 1.139 –2.963** –1.318 (0.000) (0.087) (2.185) (2.072) (1.386) (1.391) AEFs GROSS 695 0.000 –0.058 1.384 –2.153 0.692 0.825 (0.000) (0.084) (0.866) (3.038) (1.181) (1.144) ADJUSTED 695 0.000 –0.058 1.457* –3.671 –0.407 0.784 (0.000) (0.084) (0.874) (3.191) (1.163) (1.161) ADJUSTED 695 0.000 –0.077 1.415 –2.706 0.664 –0.175 LOAD (0.000) (0.083) (0.926) (3.126) (1.234) (1.144) NET 695 0.000 –0.077 1.488 –4.225 –0.435 –0.216 (0.000) (0.082) (0.935) (3.290) (1.215) (1.161)
Page | 209
In line with Ippolito (1989), for the IEFs portfolio in Table 7.8 there is a strong
positive relationship between the expense ratio and the fund performance. On the
other hand, for all CEFs expense ratio has a significantly negative effect on returns
performance. The same negative impact is also observed for adjusted returns and net
returns for the AEFs portfolio, but the results are insignificant.
The negative correlation between fund returns and expense ratio is in line with the
findings of Elton et al. (1993) and Indro et al. (1999) but does not support the
findings of Ippolito (1989). The significant negative coefficients for CEFs adjusted
returns (net after all expenses fee but not the load fee) suggest that the conventional
equity mutual fund, on average, overinvest in information (Indro et al., 1999).
Similar findings are reported for the impact of load fee. There is a positive but
insignificant relationship between load fee and the IEFs performance. However, the
relationship is negative for the CEFs.
Lastly, the results are mixed for the relationship between fund performance and
expense ratio and load fee with respect to the AEFs portfolio. Based on adjusted
returns, AEFs performance is negatively related to expense ratio but positively related
to load fees. Although the results are not significant, the adverse impact is larger for
the expense ratio as compared to load fees. The net returns performance of AEFs (see
Panel A, Table 7.8) indicates that expense ratio has a larger impact on the returns
performance (–0.74) relative to the load fee that has an impact of about –0.27.
However, the impact is less severe when using trimmed data (Panel B, Table 7.8). In
conclusion, the higher expense ratio and load fees of the IEFs could probably be
associated with low returns performance of the fund portfolio (Pollet and Wilson,
2008).
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7.4.6 Fees and other fund attributes on multi-factor regression
The main objective of this section is to examine the relationship of fund attributes on
fund performance by employing the multi-factor panel REs with time fixed effects.
The specific regression utilised is as in the Eq. 3.30 (see p. 101). The study then
extends the analysis in order to identify the linear relationship between fund
performance and the fund attributes. Our expectation that there is a non-linear
relationship exist between the fees factors. These fund attributes are the explanatory
variables in this analysis and are classified them into two groups, namely the
endogenous variables and the exogenous variables. The endogenous variables include
risk and fees factor namely alpha, beta, residual risk, management fees, expense ratio,
total load fees, and trustee fees. Whereas the exogenous variables are fund’s age, size,
investment style (investment in local or foreign markets) and also the types of the
funds (see details in Section 3.4.5, Chapter 3).
The main issue addressed in this section is not only to investigate the fund
performance and the impact of fees on fund manager’s performance but also to
identify whether the linear relationship holds between fees and fund returns, and
between fund performance and other fund attributes. Since EViews that have been
employed in the previous analysis in this chapter has a limitation to do cross-sectional
FEs and period FEs at the same time due to multicollinearity problems exist (in which
singular matrix appear when the regression is conducted using EViews), therefore this
section employs Stata 11. This section compared on panel analysis based on FEs and
REs with GROSS as dependent variable and the independent variables are set of
attributes and fees that have been previously defined in Chapter 3 of this thesis.
The regressions of fund performance on fund attributes in this section are conducted
by using panel REs (reg. 1) and REs with for fixed effects (reg. 2). The results are
robust for cross-sectional standard errors and heteroskedasticity. The BPLM test for
random effects is also conducted, whereby if the test is not significant, then the pooled
OLS is conducted with time FEs as exhibit in reg. (3). Details about the results from
regression are discussed in the following sub-sections. Section 7.4.7.1 provides results
and discussion on performance of AEFs, meanwhile, the discussion on the findings on
comparative performance between IEFs and CEFs are presented in Section 7.4.7.2.
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7.4.7.1 Performance of AEFs
The results of AEFs performance are exhibited in Table 7.9. The regressions of fund
performance on fund attributes is conducted using panel REs only (Reg. 1) and REs
with for fixed effects (Reg. 2).The BPLM test for random effects is conducted and if
the test is significant, it shows that REs panel is an appropriate model as opposed to
the pooled OLS estimation. When the BPLM is not significant, then the pooled OLS
with time dummies (Reg. 3) is employed. These dummies are however suppressed
and not reported. The regression based on FE cannot be done due to multicollinearity
problems as previously mentioned.
All of the exogenous variables are explained in Model 1, which contains of control
variables, return and risk factors. Model 2 contain the fees factors, which is the focus
factor in this analysis. Model 3 include all the variables but exclude the squared terms
of the fees factor as in Eq. 3.29 in Chapter 3. And, lastly Model 4 explains all the
variables including the square terms of the fee factors as mentioned in Eq. 3.30. The
dependent variable is the gross return of the portfolio.
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Table 7.9: Fees and fund attributes on gross returns of AEFs, 1990–2009. Model 1 (N=843) Model 2 (N=843)
Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2)
Intercept 17.985** (7.692)
18.281** (7.073)
24.291** (8.389)
20.445 (46.652)
25.472 (46.154)
AGE –0.178*** (0.018)
–0.173*** (0.039)
–0.173*** (0.029)
- -
LNSIZE –0.398 (0.399)
–0.756** (0.357)
–0.756* (0.394)
- -
dINVEST 0.097 (0.881)
–0.450 (0.942)
–0.450 (0.800)
- -
dTYPE 0.590 (0.497)
0.396 (0.469)
0.396 (0.741)
- -
ALPHA 1.008*** (0.102)
0.887*** (0.087)
0.887*** (0.129)
- -
RETURNt-1 0.515*** (0.005)
0.537*** (0.015)
0.537*** (0.025)
- -
BETA –0.210 (0.311)
–0.267*** (0.183)
–0.267 (0.183)
- -
RESIDRISK –0.024 (0.069)
–0.013 (0.068)
–0.013 (0.068)
- -
MGMTFEE - - - –59.678*** (15.664)
–66.254*** (16.065)
MGMTFEE^2 - - - 15.533*** (3.662)
17.596*** (3.815)
EXPENSE - - - 17.166*** (6.487)
16.398** (7.724)
EXPENSE^2 - - - –3.723** (1.523)
–3.429* (1.843)
TOTLOAD - - - 12.222 (14.150)
5.856 (15.369)
TOTLOAD^2 - - - –0.907 (1.172)
–0.389 (1.294)
TRUSTEE - - - –19.188 (65.747)
12.313 (53.832)
TRUSTEE^2 - - - 106.303 (407.016)
–104.898 (331.590)
Adj. R2 0.710 0.937 0.710 0.095 0.855
Rho 0.000 0.000 - 0.000 0.048 BPLM 0.000 0.774 - 0.000 0.000 Test for time FEs
- 0.000 - - 0.000
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Table 7.9 continued
Model 3 (N=737#) Model 4 (N=737#)
Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2) Reg.(3)
Intercept –1.449 (9.901)
3.376 (9.762)
9.691 (11.581)
11.362 (24.782)
26.771 (24.155)
33.350 (26.554)
AGE –0.159*** (0.037)
–0.248*** (0.057)
–0.248*** (0.039)
–0.163*** (0.038)
–0.253*** (0.057)
–0.253*** (0.040)
LNSIZE –0.131 (0.419)
–0.675* (0.385)
–0.675 (0.438)
0.191 (0.352)
–0.319 (0.309)
–0.319 (0.387)
dINVEST 0.323 (0.919)
–0.165 (1.020)
–0.165 (0.808)
0.397 (0.917)
–0.117 (0.978)
–0.117 (0.795)
dTYPE 0.398 (0.505)
0.081 (0.490)
0.081 (0.766)
0.774 (0.471)
0.482 (0.442)
0.482 (0.738)
ALPHA 1.025*** (0.093)
0.907*** (0.083)
0.907*** (0.130)
0.991*** (0.086)
0.876*** (0.078)
0.876*** (0.129)
RETURNt-1 0.515*** (0.005)
0.539*** (0.014)
0.539*** (0.025)
0.514*** (0.005)
0.542*** (0.014)
0.542*** (0.024)
BETA 0.082 (0.334)
–0.058 (0.211)
–0.058 (0.195)
0.194 (0.302)
0.070 (0.181)
0.070 (0.192)
RESIDRISK –0.038 (0.072)
–0.029 (0.067)
–0.029 (0.068)
–0.002 (0.065)
0.009 (0.062)
0.009 (0.066)
MGMTFEE 0.405 (4.233)
2.760 (4.232)
2.760 (4.048)
–45.232*** (8.025)
–40.603*** (8.603)
–40.603*** (8.747)
MGMTFEE^2 - - - 12.033*** (1.728)
11.404*** (1.936)
11.404*** (1.954)
EXPENSE 0.416 (1.329)
–0.475 (1.389)
–0.475 (1.180)
8.326*** (2.917)
5.878* (3.422)
5.878* (3.187)
EXPENSE^2 - - - –1.568** (0.762)
–1.149 (0.843)
–1.149 (0.829)
TOTLOAD 1.280** (0.613)
0.839 (0.547)
0.840 (0.586)
5.105 (8.593)
0.962 (8.403)
0.962 (9.779)
TOTLOAD^2 - - - –0.281 (0.721)
0.040 (0.705)
0.040 (0.837)
TRUSTEE 7.999** (3.143)
7.337** (3.145)
7.337*** (2.436)
6.766 (32.157)
1.575 (28.816)
1.575 (26.580)
TRUSTEE^2 - - - 10.421 (200.192)
37.315 (183.484)
37.315 (164.687)
Adj. R2 0.734 0.943 0.937 0.710 0.710 0.937
Rho 0.00 0.00 - 0.000 0.000 - BPLM 0.000 0.626 - 0.000 0.544 - Test for time FEs
- 0.000 - - 0.000 -
Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.
Page | 214
The analysis aims to explain the relationship in term of the impact of fees and fund
attributes on fund return performance. Model 1 regresses the fund return on control
variables consist of exogenous variable fund age (AGE), fund size (LNSIZE) and
investment style (dINVEST), return factors, namely ALPHA and lagged return
(RETURNt-1 ) and risk factors, such as BETA and RESIDRISK, and also includes a
dummy variable for the type of fund (dTYPE). The results indicate that dTYPE has no
significant impact on fund performance, suggesting that there is no significant
difference between these two types of funds, the IEFs and CEFs.
Results show that there is a negatively significant relationship between age and fund
returns. There is also positive and significant relationship between ALPHA and lagged
return (RETURNt-1) on fund returns performance suggesting that the alpha and past
performance of the funds could contribute to higher fund returns performance. In the
model, risk is reflected by both, fund’s systematic risk (beta) and also the
unsystematic risk (residual risk), however there is a significantly negative relationship
only between systematic risk (BETA) and fund performance
The results yield that fund size (LNSIZE) now has a negative significant relationship
with fund performance based on regression in Model 3 using REs with time FEs. the
negative relationship between fund size and fund performance that we find here could
imply that the Malaysian mutual funds are small-cap rather than large-cap oriented.
The evidence is consistent with the findings on Swedish markets where there is a
negative impact on fund performance due to fund size and fee (Dahlquist et al., 2000)
and Grinblatt and Titman (1994), thus supporting the idea that a larger fund size is
associated with cost disadvantages that could lead to reduced returns performance
(Indro et al., 1999). Most importantly, this finding is consistent with Low (2010) who
reports a negative relationship between size and fund returns for the Malaysian mutual
funds suggesting that as the funds grow in size, they become less efficient in their
operations management that leads to less returns performance. However, it is not in
line with the findings of Chen et al. (1992) and Otten and Bams (2002) who find that
size is positively related to fund performance, suggesting that the larger funds perform
better. But the BPLM is insignificant, thus results could be dubious, but OLS
estimation with time FEs also provides similar results.
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Model 2 regresses all the fees variables, Results indicate that that there is a significant
relationship between management fees and expense ratio on fund performance,
indicate that there is no significant relationship between the other fees, namely total
load and trustee fee and the fund performance.
Model 3 also reports results from the regression which include all the variables but
excluding the quadratic term of fees factors. Results show consistency with the
previous model (Models 1 and 2) with exception that now the total load and trustee
fee are positively significant on fund performance. Model 4 then includes all the
variables and the quadratic term for the fees factors. The results obtained are in line
with the results in Models 1 and 2 from the table.
In Model 4, result reveals the expense ratio is positively significant to fund returns.
The positive expense ratio could suggest that the fund managers’ earnings could
sufficiently support the cost of investment and, this is in line with the finding of Chen
et al. (1992) who study the US market over the period 1977 to 1984. This finding in
line with the finding of Ippolito (1989) and Droms and Walker (1996), but contradicts
with the finding of Elton et al. (1993), Golec (1996), and Prather et al. (2004).
Our expectation that there is a non-linear relationship between fees factors and the
fund returns. Therefore the quadratic term of fee factors are included in the model.
We expect there is a negative relationship between the fees factors and their square.
Results in Table 7.9 show that an increase in expense ratio will stimulate the efforts of
fund managers to increase more profit on fund returns. The marginal impact of each
percentage paid to fund managers in the form of expense ratio is however could lead
to diminishing impact resulting a hum-shape relationship which is captured by a
positive sign on coefficient estimates on the expense and a negative sign on its square,
which implied a positive impact on the fund performance. Based on results in Table
7.9, this situation did not hold for the management fees.
The results in Model 4 also indicate that there is a strongly negative relationship
between age and fund returns, but the alpha and lagged returns show strongly positive
relationship with the fund returns. Therefore, results suggest that longer age of the
Page | 216
fund could possibly reduce the performance of the fund returns, the table also reports
that alphas and RETURNt-1are statistically positively significant across all the
regressions. In line with our expectation, there is evidence that return factors and fees
are related to each other.
The evidence of negative relationship between fund age on the fund performance
across the model could also suggest that young funds may have potentially performed
better than older funds. this finding is in line with the finding of Otten and Bams
(2002) in the European markets, and Blake and Timmerman (1998) in the UK market.
However, this finding is not consistent with the previous findings of Ferreira et al.
(2011), Chen et al.(2004) and Prather et al.(2004) in the US market who indicate that
there is no relationship between age and fund performance.
In addition, the findings indicate that there is a strong negative effect of fund age and
management fee as shown in Model 4. However, the expense ratio is positively
significant on fund performance. The finding is in contrast with the findings of
Geranio and Zanotti (2005) who report no significant effect of age and expense ratio
in Italian funds industry. However, it is in line with the evidence of Malhotra and
McLeod (1997) and Tufano and Sevick (1997) from the US market and Korkeamaki
and Smythe (2004) based on Finnish markets.
The variables ALPHA and the lagged returns (RETURNt-1) show a positive and
significant relationship could explain the outperformance of the funds that can be
explained by the positive alphas. The evidence that fund performance is statistically
significantly correlated with RETURNt-1; returns performance of the past year
suggests that there is a short term persistency in mutual fund performance. This result
is inconsistent with the finding of Taib and Isa (2007) who find that there is no
persistency in Malaysian mutual funds over the period of 1991-2001. However, this
result is in line with the findings of Low and Ghazali (2007) who found that there is a
short run relationship between Malaysian mutual funds and the stock market in 1996–
2000. The result is also consistent with the evidences on developed market provided
by Hendricks, Patel, and Zeckhauser (1993) and Brown and Goetzmann (1995), and
Page | 217
also that from emerging markets (see for example, Suppa-aim, 2010), all of which
indicates a short-term persistency in mutual fund performance.
Results also indicate a positive relationship between lagged return (RETURNt-1) and
the expense ratio (EXPENSE), suggesting that the higher returns is compensate with
higher expenses. This evidence supports the finding of Korkeamaki and Smythe
(2004) who hypothesise that returns influence the expenses, and as a result the high
return funds charge higher expenses. However, this finding is contradict with the
finding of Gil-Bazo and Ruiz-Verdú (2008) who claim that worse-performing funds
set fees that are greater or equal to those set by better performing funds, and the
finding of Bechmann and Rangvid (2007) that the funds with higher expenses tend to
be low on returns performance.
7.4.7.2 Comparative performance between IEFs and CEFs
The results for the IEFs performance are presented in Table 7.10, and the CEFs
performance in Table 7.11 with a similar panel regression technique as previously
employed in Table 7.9.
Page | 218
Table 7. 10: Fees and fund attributes on IEFs returns performance, 1990–2009 Model 1 (N=342) Model 2 (N=342)
Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2) Reg.(3)
Intercept 7.841 (9.522)
12.340 (12.095)
20.779* (12.396)
146.688 (179.002)
146.688 (406.799)
–44.774 (143.102)
AGE –0.047 (0.033)
–0.200** (0.094)
–0.200*** (0.052)
- - -
LNSIZE 0.033 (0.448)
–0.336 (0.577)
–0.336 (0.555)
- - -
dINVEST –0.781 (1.132)
–1.627 (1.162)
–1.627 (1.269)
- - -
ALPHA 1.106*** (0.086)
0.972*** (0.103)
0.972*** (0.145)
- - -
RETURNt-1 0.519*** (0.008)
0.526*** (0.024)
0.526*** (0.044)
- - -
BETA 7.669 (4.901)
0.469 (6.383)
0.469 (6.469)
- - -
RESIDRISK –0.193* (0.111)
–0.038 (0.139)
–0.038 (0.128)
- - -
MGMTFEE - - - –243.285 (197.149)
–243.285 (408.220)
–16.983 (156.479)
MGMTFEE^2 - - - 68.269 (56.012)
68.269 (119.257)
1.712 (44.419)
EXPENSE - - - 11.447 (10.079)
11.447 (20.039)
17.895 (11.138)
EXPENSE^2 - - - –1.763 (3.143)
–1.763 (5.838)
–3.484 (3.278)
TOTLOAD - - - 23.356 (39.545)
23.356 (71.731)
4.777 (34.589)
TOTLOAD^2 - - - –1.682 (3.272)
–1.682 (6.111)
–0.180 (2.881)
TRUSTEE - - - –36.410 (117.974)
–36.410 (171.895)
91.877 (82.680)
TRUSTEE^2 - - - 245.032 (756.047)
245.032 (1048.332)
–578.311 (513.634)
Adj. R2 0.711 0.917 0.711 0.094 0.013 0.836
Rho 0.000 0.000 - 0.000 - 0.019 BPLM 0.000 0.083 - 0.214 - 0.000 Test for time FEs
- 0.000 - - - 0.000
Page | 219
Table 7.10 continued
Model 3 (N=289#) Model 4 (N=289#)
Reg.(1) Reg.(2) Reg.(1) Reg.(2)
Intercept –4.366 (9.344)
–7.426 (12.010)
–137.928** (60.704)
–237.621*** (86.640)
AGE –0.091 (0.040)
–0.284*** (0.104)
–0.127 (0.058)
–0.303** (0.117)
LNSIZE 0.290 (0.336)
–0.067 (0.466)
0.215 (0.416)
–0.311 (0.654)
dINVEST –0.242 (0.949)
–1.151 (1.337)
0.653 (1.297)
–0.418 (1.277)
ALPHA 1.036*** (0.058)
0.896*** (0.102)
1.055*** (0.056)
0.904*** (0.109)
RETURNt-1 0.517*** (0.008)
0.528*** (0.022)
0.516*** (0.008)
0.525*** (0.021)
BETA 7.242* (4.245)
–0.499 (5.925)
6.859* (4.139)
–0.664 (5.705)
RESIDRISK –0.163* (0.093)
0.016 (0.108)
–0.169* (0.091)
0.020 (0.100)
MGMTFEE –5.837** (2.887)
–6.053 (4.460)
153.221*** (54.310)
245.340*** (81.556)
MGMTFEE^2 - - –45.812*** (15.553)
–72.885*** (23.830)
EXPENSE 3.000*** (0.833)
2.727*** (0.847)
10.262*** (3.948)
11.738*** (9.104)
EXPENSE^2 - - –2.008* (1.164)
–2.580** (1.256)
TOTLOAD 2.464*** (0.598)
2.467*** (0.815)
–1.355 (13.797)
0.315 (17.369)
TOTLOAD^2 - - 0.293 (1.186)
0.128 (1.461)
TRUSTEE 1.837 (2.624)
5.441* (2.915)
1.024 (37.863)
60.610* (31.964)
TRUSTEE^2 - - 13.334 (241.291)
–338.491* (202.369)
Adj. R2 0.735 0.929 0.711 0.917
Rho 0.00 0.00 0.000 0.006 BPLM 0.000 0.029 0.000 0.000 Test for time FEs - 0.000 - 0.000 Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.
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Table 7. 11: Fees and fund attributes on returns of CEFs, 1990–2009. Model 1 (N=501) Model 2 (N=501)
Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2) Reg.(3)
Intercept 12.075 (11.477)
13.140 (8.751)
19.234* (9.911)
48.169 (34.242)
53.118 (39.425)
53.118 (42.087)
AGE –0.039 (0.036)
–0.136*** (0.027)
–0.136*** (0.042)
- - -
LNSIZE –0.208 (0.633)
–0.624 (0.480)
–0.624 (0.513)
- - -
dINVEST –1.003 (0.869)
–0.399 (1.103)
–0.399 (1.025)
- - -
ALPHA 0.932*** (0.154)
0.876*** (0.134)
0.876*** (0.177)
- - -
RETURNt-1 0.515*** (0.006)
0.550*** (0.021)
0.550*** (0.031)
- - -
BETA 11.743*** (4.469)
4.985 (3.982)
4.985 (4.179)
- - -
RESIDRISK –0.292 (0.119)
–0.152 (0.092)
–0.152 (0.125)
- - -
MGMTFEE - - - –18.878 (15.382)
–26.084 (18.070)
–26.084 (19.976)
MGMTFEE^2 - - - 5.895 (3.706)
7.860* (4.398)
7.860* (4.979)
EXPENSE - - - 8.618 (6.298)
2.760 (8.223)
2.760 (8.142)
EXPENSE^2 - - - –2.222 (1.355)
–0.718 (1.752)
–0.718 (1.770)
TOTLOAD –2.788 (11.736)
0.138 (13.370)
0.138 (15.746)
TOTLOAD^2 0.208 (0.990)
–0.033 (1.128)
–0.033 (1.354)
TRUSTEE –36.182 (47.933)
–67.268 (45.908)
–67.268 (53.965)
TRUSTEE^2 202.423 (290.573)
380.161 (281.885)
380.161 (328.042)
Adj. R2 0.708 0.950 0.869 0.003 0.885 0.885
Rho 0.000 0.000 0.000 0.000 BPLM 0.000 0.801 0.000 0.537 Test for time FEs
- 0.000 - 0.000
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Table 7.11 continued
Model 3 (N=448#) Model 4 (N=448#)
Reg.(1) Reg.(2) Reg.(1) Reg.(2) Reg.(3)
Intercept 7.284 (14.971)
11.709 (14.081)
7.176 (35.067)
43.390 (29.938)
51.114 (36.115)
AGE –0.169*** (0.054)
–0.254*** (0.051)
–0.152** (0.060)
–0.250*** (0.053)
–0.250*** (0.059)
LNSIZE –0.564 (0.666)
–1.193** (0.552)
–0.176 (0.706)
–1.005* (0.542)
–1.005* (0.542)
dINVEST –0.446 (0.904)
0.219 (1.009)
–0.434 (0.868)
0.239 (0.840)
0.239 (1.043)
ALPHA 1.078*** (0.154)
0.981*** (0.138)
1.054*** (0.147)
0.948*** (0.142)
0.948*** (0.197)
RETURNt-1 0.514*** (0.006)
0.559*** (0.020)
0.514*** (0.006)
0.562*** (0.021)
0.562*** (0.030)
BETA 10.680*** (4.111)
4.516 (3.407)
12.765*** (4.329)
7.108** (3.281)
7.108* (4.208)
RESIDRISK –0.238 (0.145)
–0.099 (0.103)
–0.229 (0.140)
–0.092 (0.102)
–0.092 (0.139)
MGMTFEE 4.101 (2.871)
6.459** (3.093)
–34.698** (13.598)
–26.577** (10.527)
–26.577* (15.236)
MGMTFEE^2 - - 9.540*** (3.054)
8.008*** (2.454)
8.008** (3.561)
EXPENSE –2.366** (1.035)
–3.078** (1.200)
1.262 (4.559)
–4.429 (4.362)
–4.429 (5.740)
EXPENSE^2 - - –0.285 (1.044)
0.867 (0.978)
0.867 (1.298)
TOTLOAD –0.187 (0.731)
–0.449 (0.555)
9.978 (10.491)
5.561 (7.922)
5.561 (12.348)
TOTLOAD^2 - - –0.806 (0.882)
–0.457 (0.665)
–0.457 (1.045)
TRUSTEE 14.061*** (3.916)
12.189*** (3.340)
–8.754 (42.681)
–51.990 (32.830)
–51.990 (39.148)
TRUSTEE^2 - - 123.187 (267.316)
376.971* (208.092)
376.971* (243.109)
Adj. R2 0.734 0.956 0.734 0.956 0.954
Rho 0.000 0.000 0.000 0.000 - BPLM 0.000 0.000 0.000 0.175 - Test for time FEs
- 0.000 - 0.000 -
Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.
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Model 1 reveals no significant relationship between the exogenous variables, namely
fund size and dINVEST on IEFs returns performance. Model 1 indicates that fund age
has a significant negative relationship with the IEFs returns as in reg. (2) in the model.
The results analysis show insignificantly negative effect of the fund size relative to the
IEFs fund returns using REs with time FEs across the models, which is in line with
the findings in the US market, which provides no evidence of a significant
relationship between fund performance and fund size, see for example, (Droms and
Walker, 1996; Grinblatt and Titman, 1994). In contrast, the results are not consistent
with the findings of (Dahlquist et al., 2000) on Swedish mutual funds. Results for the
CEFs performance also reveal that there is no significant relationship between fund
size and dINVEST on returns performance of the CEFs.
Results further indicate that age has significantly negative impact on both IEFs and
CEFs but more on the IEFs than the CEFs counterparts, suggesting that young funds
may have potentially performed better than older funds. This is in contrast with
previous findings that indicate that there is no relationship between age and fund
performance mostly in the US market (Chen et al. 2004; Ferreira et al., 2011; Prather
et al. 2004). However, this finding is in line with the finding of Otten and Bams
(2002) who reveal a negative relationship between fund performance and age and
expense ratio in the European markets, and a weak evidence in the UK market that
young funds (during their first year of existence) have performed better (Blake and
Timmermann, 1998).
Size has causing negative significant impact on CEFs, but it does not matter on IEFs
performance. The significant negative effect of fund size on the CEFs fund
performance supports the findings of Grinblatt and Titman (1994) and Low (2010).
The evidence is consistent with the findings on Swedish markets where there is a
negative impact on fund performance due to fund size and fee (Dahlquist et al., 2000).
The finding is also similar to Ferreira et al. (2011) who argue that smaller funds in the
US market perform better than the larger funds thus supporting the idea that a larger
fund size is associated with cost disadvantages that could lead to reduced returns
performance (Indro et al., 1999). Model 3 in regression with time FEs also reveals
that fund size and age have a significantly negative relationship with fund
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performance And lastly, the dINVEST factor also does not have any impact on both
portfolios. The evidence of dINVEST is in line with Abdullah and Abdullah (2009)
who find that there is no significant difference between internationally invested funds
and domestically diversified funds in Malaysia over a period of 2004 to 2008.
The risk factors include beta and residual risk. The betas for both portfolio indicate
risk is rewarding or it means risk –taking contributed to the CEFs returns but risk-
taking did not head on returns of IEFs, which is the finding is consistent with the
previous results that the IEFs is appear to have higher risk, but the beta gives no
impact to the returns. Residual risk however gives no impact on the both portfolios.
With regards to endogenous return factors, alpha and lagged return (RETURNt-1 ),
both variables give more a less similar impact on the both IEFs and CEFs The
findings show that alpha give a positive impact on the returns performance of IEFs
and CEFs across the models, suggesting that there is a short run relationship between
these variables and that historical performance is important for measuring fund
performance. There is also no significant difference between these portfolios on the
lagged return (RETURNt-1 ). A positive significant impact of variables ALPHA and the
lagged returns (RETURNt-1) could explain the outperformance of the funds that can be
explained by the positive alphas
Results also reveal that returns performance of CEFs have a statistically significant
relationship with beta and one year lagged returns, implying that the systematic risk
and past returns performance play important roles to explain the variation in the fund
returns. On the other hand, results based on REs in Models 3 and 4 also indicate a
significant relationship between systematic risk (BETA) and IEFs fund performance.
The models also show a significant positive effect of lagged return and beta on fund
performance, implying that the systematic risk and past returns performance play
important roles to explain the variation in the IEFs fund returns, but slightly lower the
CEFs.. The significant beta is in line with Indro, et al (1999) who find that systematic
risk and residual risk capture the cross-sectional variation of mutual funds’
performance. The positive lagged returns could suggest that there is a short run
relationship between these variables and that historical performance is employed for
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measuring fund performance. The significant beta is in line with the finding of Indro,
et al (1999). The residual risk however give no impact on both funds’ performance.
The focus factor is about fees and the evidences show that the impact of fee is
different between the IEFs and CEFs. Model 2 examines the impact of all the fees in
relation to funds’ performance, but none of them are significant when there is no
control variables involved.
When control variables are included but without quadratic term of fees factors as
shown in Model 3, results reveal a significantly negative effect of management fee on
IEFs performance. On the other hand, the results indicate that the Islamic fund
performance is significantly positively correlated with expense ratio and total load
fee. The positive expense ratio on IEFs performance is in contrast to the findings of
the previous literature on the US market (see Carhart 1997; Elton et al. 1993; Golec
1996). The significant relationship between TOTLOAD and expense ratio across all
panels in Model 3 is in contrast with the findings of Berkowitz and Kotowitz (2002)
and Korkeamaki and Smythe (2004) who find no statistical relationship between the
two variables in the US and Finnish mutual funds markets respectively. In contrast,
the results show that for the CEF portfolio there is a significantly positive effect of
management fee and trustee fee but a negative impact of expense ratio on funds’
performance. The negative expense ratio on CEFs performance is in line with findings
of the previous literature on the US market (see Carhart 1997; Elton et al. 1993; Golec
1996).
On the contrary to CEFs, Model 4 shows that IEFs have positive relationship in term
of management fee, and negative sign in its square. This result indicates that an
increase in management fee could increase the effort of fund managers to get higher
fund returns. Since the relationship is non-linear, this could lead to diminishing
impact in which at certain level, the increase of this fee could reduce the fund returns.
For the IEFs portfolio, management fee gives significant positive impact and its
square term gives negative, which mean that there is a hum-shape relationship
between the variables. The high impact of management fee on the performance of
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IEFs could suggest that this is the key reason that contributes to the outperformance of
the Islamic funds. The evidence also reveals that fees factor give positive impact on
managerial incentive of the IEFs fund managers skill, thus contributing to the
evidence that the portfolios performed better than the CEFs. The positive management
fee relative to the its square indicate that the 1 dollar increase in the management fee
could stimulate the incentive for the fund managers to increase the fund returns by the
same portion. The expense ratio and trustee fee also give the same relationship like
the management fee. In contrast, the management fee gives low impact to the CEFs as
there is a significant negative impact of the variable, represents unsatisfactory returns
performance and that the investors over-compensate the fund managers for their poor
expertise. However, its square term shows the positive impact, implying that the
variables exhibit the inverted U-shape relationship, overall the negative impact on the
CEFs performance.
We get qualitatively similar results for the fees factors when we repeat the analysis for
other dependent variables, ADJUSTED, ADJUSTED LOAD and NET. All results
remain the same, except that the coefficients on management fee, expense ratio, total
load and trustee fee, reduce by 1, which is what we expect given our definitions of
these variables.
7.5 Summary
The main issue addressed in this chapter is about the relationship between mutual
funds returns performance, fees and fund attributes. The results after correcting for
heterokedasticity, based on returns performance of 106 equity funds in Malaysia,
including 53 Islamic and 53 conventional funds, indicate that there is no
outperformance of IMFs and CMFs on market timing and insignificant positive fund
selectivity of the funds in Malaysia after the imposition of fees.
The study provides evidence that the imposition of fees has a severe adverse impact
on fund returns of all fund portfolios. Once the returns to equity for investors are
recomputed with the fees, the results indicate that the equity fund performance
steadily decreases from gross returns including all fees to net returns, excluding all
fees and the impact of of fees on fund returns is more than one to one. In other words,
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all types of fees make a significant difference on the performance of all the portfolios.
Thus charging higher fees on equity mutual funds have adverse implications for the
investors in the sense that it reduces the net return of the investments. This has an
important policy implication. The reducing of fees could give positive impact to the
potential investors in long term. Fund managers have often used this argument to
support the fees charged for their professional services, this however appears to be
dubious.
The study also finds a negative relation between fees factor and the performance of
the fund portfolios. On average higher expenses lead to low returns performance of
the funds and moreover, the impact of management fee is higher on IEFs rather than
the CEFs. The expense ratio however gives higher impact on returns performance of
the IEFs but no impact on the CEFs. In particular, there is a difference in fees
attributes between Islamic and conventional equity funds. Based on the quadratic
regression results, the management fee is significantly positively correlated to the
IEFs but negatively correlated to the CEFs. In contrast, the expense ratio and trustee
fee remain positively significant impact on IEFs. The both variables are now less
important to the CEFs. The results also imply that total load is now less important to
the IEFs but not important to the CEFs.
The evidence of non-linear relationship between fees found in this study could
suggest that managerial incentives are more important to the IEFs fund managers than
the CEFs ones. These will also imply that the outperformance of Islamic fund relative
to the conventional counterparts can be explained by these fees factors. The higher
fees will stimulate the fund managers to put more effort which can lead to get higher
returns. At the same time, the increasing in fees could reduce gross returns yield by
the investors. In other words, higher fees could lead to low net returns but higher fees
would also motivate the fund managers to acquire more returns.
The findings in this chapter also suggests that the mutual fund investors should have
knowledge on fees information when making any investment decisions. For
unsophisticated investors, investing in low fees funds could probably the best choice
in order to get the reasonable returns. However, for the active investors, investing in
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higher fees could possibly give higher return. This is due to there is evidence of non-
linear relationship found in this study, which suggest that the higher fees will
stimulate the fund managers to put more effort which can lead to get higher returns.At
the same time, the increasing in fees could reduce net returns yield by the investors. In
other words, higher fees could lead to low net returns but higher fees would also
motivate the fund managers to acquire more returns from the investment funds.
Based on overall funds, the findings also indicate a negative impact of fund age and
fund size on funds’ performance. Past performance appears to be relevant and
significantly positively related to the funds’ performance. In view of the fact that the
evidence suggest a perverse market timing expertise among fund managers, active
investors should familiarise themselves with appropriate knowledge of market
conditions particularly involving significant events so that they could get a lot cheaper
once the global market starts to deteriorate. As for the long term and also for the
moderate investors, the application of dollar cost averaging could be the best choice to
ensure that they achieve their investment goals and at the same time earn their desired
expected returns from the funds’ investing.
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CHAPTER 8 - SUMMARY AND
CONCLUSION
8.1 Background of the thesis
The growth of investment in mutual funds and its significance in the development of a
country’s economy makes a study related on the performance of mutual funds important.
Tremendous growth of Islamic funds in the global market also contributed to the importance
of these investments in various types of asset classes among Muslim investors and also from
ethical investors. The investments were demanded by investors not only because they
complied with Shariah principles but they also proved to be attractive due to growth potential
and to positive expectations about performance.
This increasing demand could benefit not only market players and regulators but also
academics, scholars and practitioners involved in the area. On the market player and
regulators side, offering a variety of innovative investment products attract more investors to
participate and contribute to the development of mutual fund industry. Academics and
practitioners in this industry advocate further research on this subject because these funds
have attracted a lot of attention from around the globe (Muslim and non-Muslim investors
alike). This thesis is therefore an attempt to understand performance of mutual funds in
Malaysia as evidence from emerging markets.
The main objective of the thesis is to evaluate the returns performance of Islamic and
conventional funds compared to the market return using various statistical and econometric
methods of analysis, using a more recent, extensive and comprehensive data on returns of the
funds (comprising of 479 funds, including 129 IMFs and 350 CMFs over the period 1990–
2009). Despite of the increasing demand and tremendous growth of Islamic funds worldwide
and high expectation about its performance, the existing literatures fails to address the issue
in somewhat more detail when comparing the performance if Islamic funds with its
conventional counter parts.
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While existing comparative studies between IMFs and CMFs are still scant, the previous
studies basically report that there is seemingly no difference in returns performance of IMFs
and CMFs (Elfakhani et al., 2005; Girard and Hassan 2005; 2008; Hassan et al. 2010). Most
of the data in the studies is now out of date, and in most cases, the sample size is very small.27
The dataset used in this thesis is relatively recent and more comprehensive. This is the first
study in the Malaysian mutual fund literature to evaluate fund performance using generous
data, collected from Morningstar database which is equivalent to studies conducted in the
developed market. This gives us the liberty of using a variety of sophisticated statistical and
econometric techniques. These techniques include data analysis, time series and panel data
regressions. The performance of Islamic funds is compared with conventional counterparts
using standard CAPM and TM models with single and multiple benchmarks. This approach is
adapted to get some sense about the value added of each technique in terms of conclusions.
This thesis incorporates several empirical studies related to the performance of mutual funds
in Malaysia. The first empirical study relates to the overall performance of mutual funds in
Malaysia using the largest and longest period of the study, from the beginning of Islamic
funds industry in 1990 to current as at the end of April 2009. These mutual funds are
diversified and can be classified in five broad categories: alternative, allocation, equity, fixed
income and money market. In this evaluation, the study employed the modified models
available in the literature and extended the models to incorporate more relevant explanatory
variables.
In view of that, this chapter is structured as follows. Section 8.2 briefly summarises the
thesis, and this is followed by a discussion of the key findings achieved from empirical
analysis in this study in Section 8.3. Section 8.4 explains some implications of the thesis.
Section 8.5 acknowledges the limitations of this thesis and finally, Section 8.6 suggests
further research avenues on this topic in the future.
8.2 Summary of the thesis
This section summarises results from the empirical analysis conducted on the returns
performance of IMFs relative to the CMFs. In essence, the study covers promising insights
27 See for example, Ismail and Shakrani (2003) include only 12 funds in their sample.
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derived from the hypotheses which are parallel to the stated objectives of the study which can
be summarised into three fold. First, the study employs standard performance measures in the
existing literature to the mutual funds’ performance in Malaysia and then investigates the
performance comprehensively using time series and panel data analysis. Second, the study
examines the market timing expertise and fund selectivity skill of the IMFs and CMFs fund
managers based on TM model and extended TM model using time series and panel data
approach. Third, the study examines the factors related to mutual fund performance in terms
of fees and the funds attributes, which is one of the main concerns in determining the
differences between Islamic and conventional funds, and elaborates an extended performance
measure.
In evaluating the returns performance of IMFs vis-à-vis to the CMFs peers, this thesis has
mainly explained the central importance of using different performance measures in order to
identify the real performance of these fund portfolios. This is done by examining the returns
performance of 479 mutual funds in the Malaysian market, divided into two groups, the IMFs
and CMFs, covering all the fund categories, namely, alternative, allocation, equity, fixed
income and money market fund. This thesis comparatively investigates the performance of
IMFs and CMFs relative to their market return benchmarks using several risk adjusted
performance measures such as Sharpe, Treynor and Jensen measures. The method is then
extended to the CAPM single factor model and quadratic regression model based on Treynor
and Mazuy (1966). Then the study employs multiple regression analysis using both a time
series and panel data approach. The results from using these different performance measures
are explained in chronology based on the chapters in this thesis. Chapters 4, 5 and 6 explain
the results of using the data based on monthly returns, and Chapter 7 provides results based
on annual returns.
The study achieves the first objective as listed in Section 1.3, in Chapter 1, by examining the
returns performance of IMFs, CMFs and Malaysian mutual funds in general against the
market return benchmark using raw returns and risk adjusted returns as discussed in Chapter
4. This is an attempt to identify any differences in risk and returns performance of the funds
in the overarching period of the study and also during the pre-crisis, post-crisis and during
crisis periods, including the AFC and GFC. Chapter 4 employs raw returns and also risk
adjusted returns and measures the returns performance using various performance
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measurements, namely, SR, JA, TI, AR, M2 and ASR. The results reveal that there is
evidence of outperformance of the IMFs and CMFs relative to the market returns using
various types of these performance measures, with the IMFs perform better than the CMFs
counterparts. The finding is in line with the findings of Chua (1985) and in contrast to other
studies (see for example, Abdullah et al. 2007; Taib and Isa 2007) on the Malaysian market.
One of the main issues addressed in previous studies is that IMFs perform better during the
bearish market, whereas CMFs perform better during the bullish market. This thesis further
evaluates the matter and it is discussed in detail in Chapter 4. In summary, results denote that
the IMFs performed better than CMFs during the pre-crisis and also during the AFC and
GFC. In contrast, the CMFs outperformed the IMFs during the post-crisis phase.
The closest study to the present study, conducted by Abdullah et al. (2007) generally
concludes that IMFs perform better in a bearish market whereas CMFs perform better in a
bullish market. This thesis further investigates risk adjusted return performance before, after
and during the crises. Results denote that the returns performance of IMFs is relatively less
severe impact than their CMFs counterparts using various types of performance measures, as
previously mentioned. The negative returns of both fund portfolios during bearish markets
(during the AFC and GFC) suggest that the Malaysian MFs followed the market movement
and were directly impacted by these crises. Further investigation using risk adjusted returns,
based on the CAPM model, reveals that IMFs perform better than the overall sample (refer to
AMFs) and CMFs. The IMF portfolio performed better than CMFs during the pre-crisis
period and during the AFC and GFC. However, it seems that the CMFs portfolio performed
better than IMFs during the post-crisis period (see Table 4.9). The present findings reveal that
IMFs significantly outperformed the market benchmark (indicated by positive alpha) over the
period of the study. IMFs also insignificantly outperformed the market benchmark during the
AFC and GFC crises.
The findings also reveal that there is a significant difference in investment style in term of
risk and returns of these two fund portfolios (as exhibit in Table 5.1 and Table 5.3), implying
that Islamic or conventional investors consider not only risk and return factors while
concerning these funds in their portfolio selection but also other substantive factors like fund
diversification, asset classes and fee charges. They are few other factors that need to consider
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as well as discussed in Chapter 7 in this thesis. The results supports our expectation that there
is a difference in returns performance between these two funds because an Islamic investment
differs from conventional ones in many ways especially in their concepts and operation, such
as the prohibition of interest and gambling. In Malaysia’s case, there is also a difference in
terms of the growth rate of these funds as previously discussed in Chapter 2 of this thesis.
Since the previous empirical studies contradict our results that IMFs and CMFs outperform
the market benchmark, this present study highlights the issue by examining two possible
explanations, with regard to the type of empirical analysis either using time series or panel
data analysis and also with regard to the attributes or characteristics of these funds, as next
proceed in second and third approaches.
The regression methods based on single and multiple-benchmarks are employed using time
series and panel data analysis in order to examine the returns performance of IMFs and CMFs
against single and multiple market benchmarks. The analysis is discussed in Chapter 5. The
results on monthly average returns performance of both IMFs and CMFs outperform single
and multi-benchmarks. As expected, IMFs also perform slightly better than CMFs and both
of them outperform the market return when using time series analysis. This is contradictory
to the findings of Hayat and Kraeussl (2011) and Abderrezak (2008) based on Islamic funds
in the global market. The results of Chapter 5 also show that the IMFs are significantly
outperformed the single market benchmark and relatively good in respect of the single
benchmark performance, the CMFs perform better via multi-benchmarks. On average, in line
with Chapter 4, the IMFs perform better than the CMF counterparts via a single market
benchmark. On the contrary, CMFs perform better than their IMF counterparts when panel
data analysis is implemented. The findings in this thesis document that fund portfolios are
sensitive to different analyses and models and therefore, suggest that various methods of
analysis should be implemented.
Panel data analysis is then employed in Chapter 6 using similar sample data as used in
Chapter 5. Panel data is applied in order to accommodate individual returns of each of the
funds within the period of the study thus increase the number of observations in the analysis.
The results contend that there is insignificant outperformance for both IMFs and CMFs over
the single and multi-factor market benchmarks. The panel results denote significant superior
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performance for both portfolios on fund selectivity skills via the TM and extended TM
models, and also confirm those for the time series in that there is perverse or no market
timing expertise among Islamic and conventional fund managers.
Second, the objective is to examine the ability of fund managers, and it is achieved by
analysing the fund managers on fund selectivity and market timing performance using time
series analysis (Chapter 5) and panel data analysis (Chapter 6). Results show that the
Malaysian fund managers, both IMF and CMF fund managers, similarly exhibit poor market
timing. With regard to market timing expertise and fund selectivity skill, the positive alpha
indicated by the regression via time series data means that there is a superior fund selectivity
skill among the respective fund managers. However, the present study denotes neither IMF
nor CMF fund managers have market timing expertise for the period of the study chosen
from 1990 to 2009 - either with the TM model or with the extended TM model. The findings
also support the underlying theory that fund managers’ investment strategy in market timing
does not add value to investors. This is in line with Abdullah et al. (2007) who suggest that
both Islamic and conventional fund managers do not possess market timing expertise.
Previous studies documenting consistent results in line with our findings are Low (2007) and
Annuar et al. (1997).
Third, to scrutiny results obtained from Chapter 5 (based on time series regression) and from
Chapter 6 (based on panel data regression), further investigation focusing on equity funds and
fees, and other fund attributes is implemented in Chapter 7. The chapter aims to investigate
the performance of IEFs and CEFs relative to their benchmarks and their relationship with
fees and other fund attributes like age, size and investment style, and as a results, the panel
data analysis is employed using the yearly returns data of the fund portfolios. All funds from
the equity fund category were chosen, consisting of the final sample of 53 IEFs, which were
then matched to the 53 top performers of CEFs. The issues about heteroskedasticity and serial
correlation which are related to the statistical and econometric method used in evaluating the
market timing expertise and fund selectivity skill among Islamic and conventional equity
fund managers are also addressed here through panel data analysis. On overall performance,
Chapter 7 provides findings that are in line with the results in Chapters 5 and 6, in the sense
that there is no market timing expertise but superior fund selectivity skills among equity fund
managers in Malaysia during the period 1990 to 2009.
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Since many issues are still not clear particularly related to factors that contribute to the
returns performance of the funds, the empirical study using panel data analysis is conducted
in order to achieve the objective to explore any impact of fees on fund performance (Chapter
7). This model not only help to improve performance modelling but also allow for further
analysis on fund attributes using fixed effects or random effects.
The results reveal that fees substantially reduce the fund returns performance. There is a
negative relationship between fees and fund performance with the effects is less severe in
gross return before fees compared to net returns after fees. On overall, the study provides
evidence that the imposition of fees has a severe adverse impact on the performance of all
fund portfolios, with more severe impact on the IEFs rather than the CEFs peers. These
results are also of interest to potential regulators and portfolio managers to converge their
investment areas. At present, investment in emerging markets (as one of the countries is
Malaysia) is often encouraged because of relatively low correlation with the developed
markets. Therefore, these findings brought new perspective for the market players of the
similarity between developing market and developed market so that they can collaborate in
the future.
This further investigation (as in Chapter 7) indicates that the imposition of fees on returns
performance of IEFs and CEFs has an adverse impact on the real performance of the funds.
The results also reveal that although there is a strong positively significantly performance of
IEFs and CEFs over the market returns, the CEFs seem to perform better than the IEF
counterparts, even after fees. The study provides evidence that the imposition of fees has a
severe and adverse impact on the performance of all fund portfolios. All portfolio returns
decline after the various fees have been imposed. Interesting, the study provides evidence that
the returns performance after fees of all the portfolios is relatively higher than the market
returns.
The study finds there is a negative relation between fees and IEFs and CEFs returns
performance based on panel data multi-factor regression. On average, higher expenses lead to
low returns performance of the fund and the effect becomes severe in respect of the returns
performance after fees. The findings also show a negative relationship between fund age and
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fund size attributes on overall fund performance. In addition, past performance appears to be
relevant and significantly positively correlated to the overall fund performance. Result also
shows that there is a different style for fees between the IEFs and CEFs. While the expense
ratio and front fee are positively related to Islamic fund performance, they are negatively
correlated to the conventional counterparts. In contrast, the management fee is significantly
positively correlated to conventional fund performance, but vice versa to the Islamic peers.
There is also a significant relationship between fund attributes and fund performance. The
important finding emerged in this study that the results reveal the attributes that influenced
return performance of the fund are different for the Islamic and conventional equity funds.
These results suggest that IEFs pay more attention to set of factors like expense ratio and
front fees as they are positively correlated and vice versa with the CEFs. On the contrary, the
CEFs pay more attention to management fees since the factor has significantly correlated to
fund performance but not to the IEF counterparts. Taken together, the findings in this thesis
do not support the recommendations of fund managers to increase the fees, for example,
management fees, as the strategy does not give any value added to the returns performance of
the investors. Summary of results obtained in this thesis are described in Table 8.1.
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Table 8. 1: Summary of the results
MODEL/ANALYSIS
FINDINGS
Standard risk adjusted performance
(SR, ASR, TI, JA, M2, AR) The IMFs perform better than the CMFs counterparts. CAPM due to the crisis The IMFs perform better than the CMFs during the pre-AFC, and
during the AFC and GFC. In contrast, the CMFs perform better than the IMFs during the post-AFC period.
Time series analysis Single benchmark • Both IMFs and CMFs outperformed the KLCI market
benchmark, with results indicating that the IMFs perform better than the CMFs.
• The IMFs and top performer of CMFs significantly outperformed the market benchmark, with on average the IMFs performed better than the CMFs counterparts, but performed slightly worse than the top performer of CMFs.
• All portfolios except the IMFs and top performer of CMFs are insignificantly underperformed the Islamic market benchmark.
Multi-factor benchmarks • The CMFs perform better when using the multiple benchmarks.
TM model • Both IMFs and CMFs outperform the market benchmark with the IMFs perform relatively better than the CMFs.
• The IMFs perform better than the CMFs in fund selectivity skill, however both IMFs and CMFs similarly have perverse or no market timing expertise since the coefficient estimates is insignificant.
• There is a negative correlation between fund selectivity skill and market timing ability of IMFs and CMFs fund managers.
Extended TM model • The CMFs perform better than the IMFs in term of outperformance relative to the market benchmark
• IMFs perform better than the CMFs on fund selectivity skill but similarly have inferior on market timing expertise.
Panel data analysis CAPM single factor • Both alphas of IMFs and CMFs outperformed the KLCI
market return benchmark. • The IMFs insignificantly outperform the Islamic benchmark. • The overall fund (AMFs) and CMFs significantly outperform
the Islamic benchmark using REs with time FEs, however, both of them are underperformed the benchmark using REs only.
TM model • The IMFs outperform the CMFs in relation to conventional market benchmark.
• The IMFs perform better than the CMFs in fund selectivity skill, however both IMFs and CMFs similarly have perverse or no market timing expertise since the coefficient estimates is very small and economically insignificant.
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Table 8.1 continued
Multi-factor CAPM Extended TM model
• All the portfolios underperformed the multiple benchmarks, with the IMFs perform relatively better than the CMFs peers when the REs with time FEs model is employed.
• The CMFs outperform the IMFs without time FEs model, however, the IMFs perform better with the time FEs. Both portfolios are underperformed relative to the market.
• Both IMFs and CMFs similarly have inferior market timing expertise since the value is very small and economically not significant.
Panel data analysis and fund attributes
• •
• The imposition of fees has an adverse impact on fund returns of all portfolios, in the sense that the fees reduce the expected gross returns obtained by the investors.
• The effects of fees on fund performance is more than one to one, with higher expenses lead to low returns performance of the funds.
• The impact of expense ratio is higher on returns performance after fees rather than gross returns.
• Based on the results of regression without quadratic term, management fee gives insignificant negative impact on IEFs but significantly positive impact on the CEFs. In contrast, the expense ratio and total load give highly positively significant impact to the performance of IEFs, but they give negative impact on CEFs. The trustee fee on the other hand, give positive impact to both portfolios.
• When the quadratic regression employed in this study, results are not consistent. The management fee is significantly positively correlated to the IEFs but negatively correlated to the CEFs. In contrast, the expense ratio and trustee fee remain positively significant impact on IEFs. The both variables are now less important to the CEFs. The results also imply that total load is now less important to the IEFs but not important to the CEFs.
• There is evidence of non-linear relationship found in this study, suggesting that managerial incentive is more important to the IEFs fund managers than the CEFs ones. The higher fees will stimulate the fund managers to put more effort which can lead to get higher returns. At the same time, the increasing in fees could reduce gross returns yield by the investors. In other words, higher fees could lead to low net returns but higher fees would also motivate the fund managers to acquire more returns
• The findings also indicate a negative impact of fund age and fund size on overall funds’ performance. Past performance appears to be relevant and significantly positively related to the funds’ performance.
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8.3 Key findings
Overall, our findings are comprehensive and achieve the objectives of this thesis (see
Section 1.3, in Chapter 1), and able to answer the research questions (see end of
Section 1.2, in Chapter 1) related to the hypotheses that have been developed in the
thesis. The key findings in this thesis based on our analysis can be summarised into
four subsections as the following. Table 8.2 provide a summary of these key findings.
8.3.1 Risk and returns performance
This key finding suggested that for our sample over the period of the study examined,
results of risk and returns performance in this study are mixed. Firstly, IMFs and
CMFs outperformed the market returns either using time series or panel data analysis.
Generally this finding is contrast with the findings of Taib and Isa (2007) and
Abdullah et al. (2007) in the Malaysian market but in line with Ippolito (1989) in the
US markets. The finding is also not in line with the finding of Firth (1977) and Blake
and Timmermann (1998) in the UK market.
Secondly, the finding also reveals that overall mutual fund industry in Malaysia
performs better than the market return benchmark. Unlike the previous findings of
Abdullah et al. (2007), results in this study show that the IMFs and CMFs outperform
the market returns under various risk adjusted performance measures like SR, TI and
JA, with IMFs performing better than the CMFs. For example, the mean return before
adjusted for the risk free rate indicate that IMFs return of 0.98 per cent compared to
the CMFs at 0.65 per cent and the market return at 0.24 per cent on monthly basis
(refer to Chapter 4). On overall, the Malaysian mutual fund industry significantly
outperform the market return by 6.10 per cent per annum, with the IMFs is about 8.23
per cent and the CMFs at 3.96 per cent over the period 1990–2009 (refer to Table 4.9)
Thirdly, on overall performance, the evidences show that the IMFs return is higher
than that of CMFs using time series and panel data analysis. It shows that the analysis
techniques play a role in influencing the outcome of the study. Concentrating on one
of the fund asset classes, namely the equity fund using panel data and include fees and
fund attributes as explanatory variables, as previously discussed in Chapter 7, the
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results consistently reveal that both IEFs and CEFs significantly outperform market
returns, the IEFs seem to perform better than their CEF counterparts.
The fourth is the study provides evidence which could also suggests that on average,
the returns performance of IMFs is sensitive to single market benchmark. The CMFs
on the other hand are far better with multiple market benchmarks, suggesting that
mutual fund performance measures are found to be sensitive to the model used.
The fifth is the analysis based on time series on different market conditions reveal that
the IMFs performed better than CMFs during a financial crisis and pre-crisis periods.
In contrast, the CMFs outperformed the IMFs during the post-crisis period.The AMFs
also have positive alpha during the GFC crisis indicating that the Malaysian mutual
funds in general outperform the market during the GFC crisis period. The IMFs also
insignificantly outperform the market benchmark during the AFC and GFC crises.
This result is not unexpected and in line with the findings of Abdullah et al.
(2007).The finding also shows CMFs have severely affected during the AFC and GFC
crisis.
The sixth is about the risk associated with fund return performance. The systematic
risk or beta results in most of the regressions reveal that they are lower than the
market risk as the beta is below than 1, suggesting that the risk and volatility of the
mutual funds in the sample is less risky rather than the relevant market benchmark. In
particular, the risk of IMFs slightly higher than the CMFs peers via time series (Table
5.1) and panel data (Table 6.1) analysis. Our findings are similar with Hayat and
Kraeussl (2011) with regard to betas of IMFs (significantly smaller than 1), implying
that the Islamic fund are low risk investments.
The seventh is regard to fund’s diversification. The CMFs have generally a better
degree of diversification compared to the IMFs (Table 5.7). In general, the degree of
diversification in this study are far better than the evidence of Abdullah et al. (2007)
who indicate the presence of a low diversification level among funds between 1992
and 2001. The findings are consistent with most mutual fund studies on the Malaysian
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market (Ahmed, 2007; Elfakhani et al., 2005; Hayat and Kraeussl, 2011), but not in
line with the result of Annuar et al. (1997).
Lastly, the eighth is this present study highlights evidence of fund persistency in
Malaysia over the period 1990–2009 in which this finding is not in line with Taib and
Isa (2007) who conclude that there is no performance persistence in the Malaysian
mutual funds over the period. However the positive relationship between the past
year’s and current performance support the previous study by Hendricks et al. (1993)
and Brown and Goetzmann (1995) in developed markets and the findings of Suppa-
aim (2010) in emerging markets. Therefore, it is suggested that the Malaysian fund
managers respond to the historical return performance of the funds and the movement
of the stock market while deciding on their portfolio selection.
Since the results of panel data reveal that there is no difference between investment
style of IMFs and CMFs (represented by dTYPE, as shown in Table 6), implying that
Islamic or conventional investors risk no penalty and do not need to consider any
financial penalty concerning these funds in their portfolio selection. Thus, the smart
investors could careful select the fund with low risk and more diversified but at the
same time provide reasonable profit in return. The evidence is in line with the
previous finding of Hassan et al.(2010) and Girard and Hassan (2005).
8.3.2 Expertise in market timing and fund selectivity skill
In view of the market timing expertise and fund selectivity skill among the fund
managers, this study finds evidences as the following. First, this study substantiates
the evidence of perverse or no market timing expertise among the Malaysian fund
managers using time series and panel data regression. The result further reveals that
IMFs and CMFs fund managers have similarly poor or perverse market timing over
the period of the study, either using time series or panel data analysis. The present
finding of poor market timing among the Malaysian fund managers is also in line with
Low (2007) and Abdullah et al (2007) but contradict to Hayat (2006).
Second, this study reports that the extended TM model give similar result of poor
market timing as the TM model, suggesting that the implementation of market timing
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strategy in the Malaysian market does not give any benefits and value-added to
investors in their portfolio management. Unlike Bello and Janjigian (1997), the usage
of TM model and extended TM model give not much difference in market timing
results in this thesis, thus implying that the result does not sensitive to any particular
methods.
Third, with regard to fund selectivity skills, the evidences exist concerning the
superior fund selectivity where IMFs fund managers provide significantly better
selectivity skill than the CMFs using time series as well as when the panel data is
employed. Consistent with the results of time series,the panel data results show that
IMFs fund selectivity skill is significantly superior to CMFs using the TM model and
the extended TM model.
In summary, the key findings of positive selectivity skill and perverse or no market
timing expertise among the fund managers in this study supports the evidence of
Annuar et al. (1997) in the Malaysia mutual fund market from 1990–1995. Other
finding shows there is also a negative correlation between timing and selectivity
performance of the IMFs and CMFs.
8.3.3 Fees impact and fund attributes on fund performance
This thesis evaluates the determination factors that contribute to fund performance.
The results show that the differences exist between the characteristics of Islamic and
conventional funds, the IEFs and CEFs in this case. First, the mutual fund
performance is found to have a negative relationship with fees, implying the higher
the fees, the lower could be the fund return.
Second, the evidence in this study suggests that fees give an adverse impact on fund
returns performance either IEFs or CEFs, and therefore fees reduce the apparent
outperformance statistics reported in all studies. This is striking, and is a new finding
using a better matched sample as conducted in this study. All type of fees charged
have adverse impact on returns performance of AEFs, IEFs and CEFs with the returns
of these portfolios decrease when the imposition of various fees incurred, where
higher fees lead to low fund returns performance. The IEFs returns seem getting more
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severe impact compared to the CEFs and AEFs. This result may imply that fund
managers could not offer maximum profit to investors and this is partly due to the
high fees and expenses charged.
Third, results show even though the fees give a negative impact to the fund returns,
IEFs and CEFs still outperform their market benchmark before excluding all fees
based on gross and also after excluding all fees, based on net returns, suggesting that
the fund returns are seemly adequate just to compensate the fees charged. The results
imply that fees charges by fund managers are still relevant for investors with the
expectation to increase their profits from the investments but too high fees could raise
them a burden which might make them switch to other non-load funds with low fees
like ETF and bond fund.
The findings also show a negative relationship of expense ratio and load fee variables
to CEFs but a positive to the IEFs. On the contrary, the management fee has a
negative relationship to returns of IEFs but positively to the CEFs. The size has
similarly no impact on fund performance of IEFs and CEFs, whereas age gives a
negative impact on returns performance, with the larger impact on CEFs rather than
the IEFs. Similar to Low (2008; 2010), the evidences of the size and age have
explanatory power in fund performance are not statistically significant.
Finally, the evidence of non-linear relationship between fees found in this study could
suggest that managerial incentives are more important to the IEFs fund managers than
the CEFs ones, thus suggesting that the higher fees will stimulate the fund managers
to put more effort which can lead to get higher returns. This is implied that the
increasing in fees could reduce gross returns yield by the investors, but at the same
time, higher fees would also motivate the fund managers to acquire more returns.
8.3.4 New improved model and extended literatures
This key finding would enhance the model and extend literatures in the area. First, the
study helps to improve fund performance modelling and extends the standard model
which is not only limited to time series but panel data regression on evaluating the
Islamic fund performance.
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Second, this study enhances the knowledge in Islamic fund performance, which
contributes to the development of Islamic funds area parallel to their tremendous
growth in the global financial market. To the best of our knowledge, this is the first
literature on other issues related to fees and fund attributes on fund performance, with
regard to Islamic funds.
Third, this thesis extends the existing literatures and provides widely discussed the
existing literatures and empirical results from the previous findings related to the
issues of mutual fund performance. The previous studies in mutual fund area are more
concentrated on the developed market where the data is easily available and mostly
centred in the US and other developed countries like UK and Australia. Since very
few studies focusing on emerging markets, the employment of this study by using
Malaysia as a case in emerging markets is essential.
More importantly, the gap between the previous evidence from developed and
emerging markets are in many forms such as the sample size, the development of
models, econometrics and statistical methods, the sophisticated database and the
variety of performance modelling. The literature on mutual funds in emerging markets
also reveals that the main concern in this region lies in performance evaluation. It
concluded that very little has been written on other issues related to fees, fund
attributes and investment style on fund performance. Most studies on mutual funds in
emerging markets like Malaysia also employ a short sample period and mainly
concern on risk and returns performance evaluation which is based on standard
approach of performance measures limit to SR, TI and JA ratios and not many
literature in this market concentrate on other issues like modelling, persistency, fees
and other fund attributes. Therefore, this study arise several issues related to mutual
funds’ performance which have been done in the developed market but very few in
the emerging markets such as the impact of fees on fund returns and the fund
attributes who determined the returns performance of the funds.
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Table 8. 2 Summary of key findings
RESEARCH QUESTIONS
KEY FINDINGS MODEL/ ANALYSIS
IMFs CMFs
Do IMFs and CMFs outperform relative to the market?
The IMFs perform better than the CMFs counterparts.
Standard risk adjusted performance (SR, ASR, TI, JA, M2 and AR)
YES
NO
Do IMFs and CMFs outperform the market?
Both IMFs and CMFs outperformed the KLCI market benchmark, with results indicating that the IMFs perform better than the CMFs.
Time series
YES
NO
The CMFs perform better than the IMFs in term of outperformance relative to the market benchmark.
Extended TM model-time series
NO YES
The IMFs insignificantly outperform the Islamic benchmark, and the CMFs perform better than IMFs in relation to the Islamic benchmark.
Single factor CAPM- panel data
NO YES
The IMFs outperform the CMFs in relation to conventional market benchmark.
Single factor CAPM- panel data
YES NO
The IMFs outperform the CMFs in relation to fund selectivity skill among the fund managers.
TM model –panel data
YES NO
All the portfolios underperformed the multiple benchmarks with the IMFs perform relatively better than the CMFs peers when the REs with time FEs model is employed.
Multi-factor CAPM- panel data
YES NO
The CMFs outperform the IMFs without time FEs model,
Extended TM model- panel data
NO YES
However, the IMFs perform better with the time FEs. Both portfolios are underperformed relative to the market.
Extended TM model- panel data
YES NO
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Table 8.2 continued
Do these funds offer similar benefits in risk, return and fund diversification
Both funds’ risk are lower than the market risk, with the risk of IMFs slightly higher than the CMFs. The return of IMFs is higher than the CMFs
Time series Time series
YES YES
NO NO
The CMFs have better degree of diversification than the IMFs.
NO YES
Is the performance of fund sensitive to single or multi-factor benchmarks?
IMFs portfolio is more sensitive to single factor.
Single factor CAPM
YES
NO
Meanwhile CMFs more sensitive to multi-factor benchmarks The CMFs perform better when using the multiple benchmarks.
Multi-factor CAPM Multi-factor benchmarks
NO NO
YES YES
Do these funds act differently in bearish and bullish market?
The IMFs perform better than the CMFs during the pre-AFC, and during the AFC and GFC.
Single factor CAPM
YES
NO
In contrast, the CMFs perform better than the IMFs during the post-AFC period.
NO YES
Do the IMFs and CMFs offer similar results on market timing and fund selectivity skills?
There is similarly perverse or inferior market timing expertise among the IMFs and CMFs fund managers
TM model and extended TM model using time series and panel data
NO
NO
IMFs fund managers perform better than the CMFs on fund selectivity skill.
TM model and extended TM model using time series and panel data
YES NO
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Table 8.2 continued
Are there any differences in fund performance can be explained by fees and other fund attributes
The management fee gives insignificant negative impact on IEFs but significantly positive impact on the CEFs.
Panel data regression without quadratic term,
NO YES
The expense ratio and total load give highly positively significant impact to the performance of IEFs, but they give negative impact on CEFs.
Panel data regression without quadratic term,
YES NO
In contrast, the management fee is significantly positively correlated to the IEFs but negatively correlated to the CEFs.
Panel data regression with quadratic term,
YES NO
The expense ratio and trustee fee remain positively significant impact on IEFs. The both variables are now less important to the CEFs.
Panel data regression with quadratic term,
YES NO
The results also imply that total load is now less important to the IEFs and not important at all to the CEFs.
Panel data regression with quadratic term,
YES NO
The evidence non-linear relationship between fees and their square terms found in this study, suggesting that managerial incentive is more important to the IEFs fund managers than the CEFs ones.
Panel data regression with quadratic term,
YES NO
8.4 Implications of this study
The study employs a longer and more comprehensive dataset than previous studies
did in the context of Malaysia, and previously issues that had not been discussed are
covered - performance benchmarking, strategy of market timing, fees and other fund
attributes using the multi-factor regression model. In doing so, three empirical studies
were conducted. The first relates to the overall risk and return fund performance,
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representing the IMFs and CMFs portfolios based on existing models in studies such
as the Sharpe, Treynor and Jensen ratios, Modigliani measure and appraisal ratio.
The second empirical study relates to the ability of fund managers, IMFs and CMFs
fund managers in timing and selectivity performance. The existing CAPM and TM
models are employed and the study extends these models to include more benchmarks
using time series and panel data analysis. The evidence reveals that Malaysian fund
managers have perverse or no market timing ability but they do have positive fund
selectivity skill. This study confirms what Annuar et al. (1997) concluded, and
contributes additional evidence suggesting that Islamic fund managers have superior
fund selectivity skill than their conventional peers. This was found when using time
series analysis and vice versa when the panel data analysis is employed.
The third empirical section explains the fees and fund performance which has recently
emerged as a major concern in developed and emerging markets. This present study
also evaluates the fund attributes consisting of endogenous and exogenous factors that
have an impact on fund performance. Using Malaysia as a sample, this thesis
enhances the existing literature on the Islamic fund industry and finds new evidence
and for the performance of mutual funds.
Furthermore, the analysis can be replicated to other countries in emerging markets
with similar characteristics to Malaysia. On this theme the study fills the knowledge
gap between developed and emerging markets by using a larger sample size in the
data, accessing a credible database provider, Morningstar database28. The more
extensive data give the opportunity here to employ more statistical and econometric
methods in the analysis instead of solely concentrating on standard evaluation method
based on Sharpe, Treynor and Jensen ratios.
One of our main hypotheses is about the effect of fees on fund returns performance
and results in this study show that it is statistically significant. The evidence in this
study suggests that fees remove the apparent outperformance statistics reported in all
28 To the best of our knowledge this is the first study of mutual funds based on a Malaysian sample using the Morningstar database.
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studies. This is striking and is a new finding using a better matched sample as
conducted here. The results also reveal that attributes influencing return performance
of the fund are different for Islamic and conventional equity funds. These results
suggest that IEFs pay more attention to a set of factors like expense ratio and front
fees as they are positively correlated with returns performance of the IEFs and vice
versa with the CEFs. On the contrary, the CEFs pay more attention to management
since this factor has been significantly correlated to fund performance but not to the
IEF counterparts. Therefore, the current findings add to a growing body of literature
especially the Islamic fund literature relate to fees and other determinant factors in the
form of fund attributes and their impacts on fund performance.
8.4.1 Implications for policy-makers and regulators
Policy-makers and regulatory bodies should use this timely evidence to improve
existing policies and practices, and incorporate them so that investors and the mutual
fund industry benefit from this essential information. Since obtaining public
information regarding investment funds is still an issue for emerging market investors,
the regulator should impose a requirement for IMF funds to disclose their fees (while
investing on the funds) in their prospectus or annual report. This should apply to the
CMF funds as well.
In term of the expense ratio and fees for example, since the information is not publicly
known, the investors are deprived of valuable information for their investment
decisions. Therefore, the diffusion of this fact by the policy maker or the regulator
could significantly change the industry’s status quo. The argument is expected to hold
for most of the young fund industries especially in emerging markets (Babalos et al.,
2009). The other thing is the investors usually consider raw or gross returns in value
their investing. In contrast, most of advice in performance evaluation is based on the
risk adjusted return, thus the fund managers and also financial advisors in particular,
shall not deny the right of investors to get to know such a value piece of information.
In the Malaysian case, the SC and also FIMM shall play their role to ensure this fees
requirement is complied. At the same time, more information shall be provided to the
investors by the policy-maker in a form of legal guidance on Islamic funds which can
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be itemised such as required disclosures, any notification of legislative changes
concerning mutual funds, fees and charges information and the roles and
responsibilities of Shariah supervisory boards. This information is important and
necessary for the purpose of creating awareness among market players, policy makers
and regulators to enhance their access and capability particularly relate to vital
decision making. Making investors accessible to public information will make dealing
with mutual funds would enter a new era of transparency like in the developed
market, and this could give opportunity to mutual funds investment to reach parity as
investment in the ETF. Also, other strategy shall be considered by the mutual fund
regulators, for example, the proposal about day to day mutual fund pricing system. It
is totally similar to stocks market and ETF pricing system in which the system could
increase transparency in pricing mechanism.
The development of mutual fund industry is essential as one of the benchmark to
achieve the title of a developed country; such policy must be drawn to encourage
people to invest in the industry. This can be done by establishing the rule to reduce
fee on equity funds, or give a portion of tax exemption for profits that investors gain
from mutual funds or provide incentive like income tax relief to the brokers or fund
managers who provide initiative to lower low management fees. For example, in the
Malaysian case, the mutual fund industry only contributes about 20 percent of the
market, could imply more rooms shall be done to encourage people to invest in
mutual funds. As saving is the most important approach to stabilise a country
economic growth, the higher growth could reflect the higher saving rate of a country,
take China for instance, where their saving rate is very high. As reported in the
Starbiz, the Credit Suisse Group forecasts that China may become the world’s
wealthiest country by household assets after the US within five years time as the
nation middle class consumer grow richer. With total household wealth to become
US$38 trillion by 2017, China may surpass Japan’s of US35 trillion and less than half
of the US, US$89 trillion (Wan, 2012, 11 October), hence making saving and
investment play important role in value investing and wealth creating. As a result,
encourage people to invest in mutual funds is one of the strategy where people can
save their money and increase the saving rate. Towards the end the industry can act as
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a prompt to stimulate and enhance the economic growth in such way through
providing investment capital and increasing the savings rate of a country.
The most important is the development and maturity of this fund industry will help
investors to correctly positioning themselves based on different market cycle and will
enhance investors to directly participate more aggressively and effectively in the fund
market. The situation could contribute to the higher growth of mutual funds industry
in Malaysia particularly, in realisation of “2020 newly developed country”.
8.4.2 Implications for fund management companies and fund managers
The findings of this study have a number of important implications for future practice.
First, the evidence shows that fees have an adverse impact on fund performance.
Other factors like age and size also matter. Therefore, the fund managers shall
encourage a better understanding of fund attributes and factors that contribute to the
returns performance of the funds.
The second implication is the increase in demand for mutual fund investments, will
enforce fund managers have to be creative about offerings investment products
especially in term of the best attractive cost/price structures. Clients are demanding
greater choice of financial products that best meet their wealth management needs.
Additionally, they are not prepared to pay high fees associated with the investment
funds. Besides, investors have their statutory right to know the real cost as well as the
competitive cost involved while they are trading in mutual funds.
Third, in order to increase market share, fund managers need to increase the amount
access and availability of public information to investors, for example fees
information which could help investors better understand their real cost incurred when
investing in a mutual fund. Since there is evidence of a negative relationship between
fees and fund return, it is suggested that fund managers shall concern with fees’
impact and revise the fee structure to encourage investors’ participation. In spite of
some investors being willing to pay more on the assumption that they will earn more,
it is important to note that higher fee charges could force investors to switch to
another type of low investment fee like exchange trade funds.
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Finally, the fund managers should strategically manage selection in fund portfolio and
have the appropriate knowledge of market conditions in order to choose the time to
invest. This has been highlighted by Islamic fund managers operating within and
alongside conventional fund managers. On average, little evidence from this study
suggests that Malaysian fund managers are insignificant outperformance on fund
selectivity skill with Islamic fund managers perform better based on time series
analysis and the conventional fund managers perform better when panel data is
employed. Both Islamic and conventional fund managers also have perverse or no
market timing ability during the period of time chosen for analysis. Hence, these
findings argue the ability of fund managers to utilise this market timing strategy and
therefore they should think about another key strategy like cost averaging and risk
management in order to sustain higher returns performance and to survive during a
financial meltdown. The dollar cost averaging possibly the best alternative for them to
apply during the peak market.
Also, the fund managers could focus on innovative products which emphasis more on
fixed income and greater liquidity to minimise the risk. These strategies would ensure
the mutual fund industry as well as the potential investors would derive full benefits
of investment in mutual funds compare to other investment vehicles. Additionally, the
industry and market players shall cooperate and organise themselves towards globally
recognised and accepted Islamic investment products. The Islamic funds for example,
is considered as safe bets for global investors that immune and proof to economic
hardships conceivably after the GFC in 2008. Besides, might be the time comes when
fund managers need to initiate new business model that will incorporate better modus
operandi and develop a new niche market development to grab a wider opportunity
from the local and global mutual fund industry.
8.4.3 Implications for investors
The results could benefit investors to strategically manage their fund portfolio. By
educating investors with fees information and what the liberalisation of the local
market (as proposed in the Malaysian capital market liberalisation plan in 2008)
means, and coupled with the openness and reliability of the mutual fund, it is expected
that fees will come under much closer scrutiny by investors. Fees information is vital
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when making decisions and it is suggested investing in low fees funds to obtain a
higher returns performance. Moreover, there are many fund attributes associated with
the performance of mutual funds with some of them having a positive or negative
relationship. Therefore investors should be aware of these relationships before making
any investment decisions.
In view of the fact that fund managers have perverse market timing expertise,
investors should also familiarise themselves with appropriate knowledge of market
events particularly bullish and bearish conditions. Moreover, it is suggested that
investors could diversify their portfolios with respect to fund asset allocation, that is
invest in various categories of funds such as alternative, allocation, fixed income and
money market. They could do these things instead of focusing on larger returns funds
per se or concentrating on only equity funds in order to pursue higher returns from the
investments.
Fund diversification on different asset classes are also shall be pondered. The global
investors for example shall take opportunity to invest beyond the developed markets
and the local investors shall also take into consideration to invest beyond their
boundary. The liberalisations of regulation in Malaysia for instance, open the
investors’ opportunities to leverage their investment funds. Malaysia implements the
liberalisation of the foreign exchange rules in March 2005 where the fund manager
companies are permitted to invest about 30 per cent of their assets overseas. This
creates a greater opportunity to investors to diversify their investment portfolios cross
the border for the sake of pursuing higher returns performance. In 2008, this
percentage has been increasing to 100 per cent foreign ownership. At the moment,
Islamic fund management companies are allowed to have 100 per cent foreign
ownership and they are also permitted to invest 100 per cent of their assets abroad. In
addition, local and foreign-owned Islamic fund management companies to be given
income tax exemption on management fees and other fees received from managing
the funds from the year of assessment 2007 to 2016 with the circumstance that the
funds must be approved by the SC beforehand (Securities-Commission-Malaysia,
2008).
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8.4.4 Implications for researchers
The evidence revealed in this thesis is potentially useful and will benefit researchers
examining the performance of Islamic and conventional mutual funds worldwide.
Apart from concentrating on the underperformance and outperformance of fund
portfolios using standard performance measures against the various market return
benchmarks, this thesis incorporates key factors like the strategy of market timing and
fund selectivity skill. The strategy of market timing and fund selectivity skill would
promote the researchers to do further analysis in the area particularly on the right time
to exploit these strategies for the benefits of fund managers to be more efficient and
more competitive and for the usage of investors to gain more profits from their
investments. Other than that, the introduction of various and innovative mutual fund
products could be vital to boost the growth the investment industry. This is because
investors are demanding investment products, which could deliver steady income
particularly during the volatile market. This also needs further investigation among
the researchers.
The thesis also describes the impact of fees on fund performance. Instead of focusing
solely on fee factors, this thesis discusses more detail the evidences of the relationship
between fund performance and other fund attributes such as age, size, investment
style, past performance and risk factors like systematic risk and residual risk.
Therefore, it is worth replicating this research to other countries in emerging markets,
especially for those countries who have similar economic fundamental like Malaysia
so that they can materialise the inflow of huge funds in their mutual funds industry to
capitalise their growing economy. It is also worth for researchers to come out with
study that relate the returns performance of funds with other impact factors like
marketing and agency roles in term of their contribution for the betterment of the
industry.
8.5 Limitations
The current study has only examined Malaysia as a sample, which does not solely
represent the development of the Islamic fund industry. In fact it only represents one
country and as a result, the findings of this thesis might be applicable only to
Malaysia. Other countries have their own characteristics such as the investor
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behaviours, trend pattern, the regulation standards or infrastructure and therefore
caution must be applied when trying to transfer findings to other countries. Look at
the Islamic financial market, even though size is still small but it offers huge potential
over time.
Furthermore, the sample of the study was nationally representative of the Malaysian
mutual fund industry, thus it omits investors who were interested in participating in
the global market’s fund management system. This is due to many external factors
that are related to global issues and the regional market such as: legislation; and
shocks or events that need to be managed better if global investors want to invest in
IMFs in a specific market.
The current study has only examined the return performance of Islamic and
conventional funds using standard performance measurements that are limited to the
time series and panel data analysis approach. Since the data in this study is extensive,
many other approaches can be applied to the data analysis such as evaluating fund
performance using methods of copula, bootstrapping, Bayesian statistics, meta
analysis, data envelopment analysis and structural equation modelling. Future
research regarding IMFs should employ these kinds of advanced analysis approaches.
Other interesting issue that were not addressed here concerned whether fund flow has
any influence on returns performance on certain asset classes of Islamic funds. This is
particularly vital for potential investors to get more knowledge and information about
funds’ flow, their characteristics and behaviours, so that that they can decide whether
the present time is good time for investing in one asset class or another. We leave this
for future research to work out.
8.6 Suggestions for future research
Our main findings in this thesis highlight some avenues for further research issues in
the future. First, future research might explore and replicate the models and employ
them based on other sample of data in other countries, especially where evidence of
impressive Islamic mutual funds can be identified. Since Malaysia is one of a country
in emerging markets, the models can also be employed to other countries that have
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similar characteristics. Second, the thesis focuses on diversified mutual funds and
equity funds performance, comparing Islamic and conventional funds. It would be
interesting to assess bond fund performance since Islamic funds are creating much
interest in the current financial market. This is particularly to examine the bond fund
ability either it can provide reasonable investment returns with low risk or not. Third,
this thesis has thrown up another question such as the ability of fund managers to
achieve higher returns performance from investment funds using their market timing
expertise. Further research could also explore this important issue using different
methods of investigation.
In the Malaysian case, it also valuable to do more research concentrating on IMFs and
CMFs that strategically focuses and most contains investments in foreign securities.
Most of these funds are technically domestic funds but most contain the foreign
securities. Since few years ago few fund management companies such as Public
Mutual Berhad and CIMB Groups have launched a few products that invested in a
potential area like China, Australia and Japan, this may result in a more globally
affected on the whole Malaysian mutual fund industry. Hence, the talent of fund
managers could be a challenge here on risk management aspects and on how they
shall combine the stock selectivity skill and timing ability to achieve the best results.
This requires further research.
Finally, a new study on what is practising in other countries under the category of
emerging markets could benefit from the research methodology used in this thesis.
The implementation of the panel data approach in this thesis to evaluate the impact of
fees on fund performance is noteworthy and for the first time has been associated with
Islamic funds could be replicated in other mutual fund study. The models used in this
thesis also might be replicated to other countries, particularly those belonging to
emerging markets. This initiative could also be applied to other statistical and
econometric methods to analyse the appropriate fund data samples.
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APPENDICES
Appendix A Descriptive statistics of the Islamic and Conventional mutual funds in Malaysia, 1992 to February 2012.
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Mutual Fund
Industry
(MFI)
Units in
Circulation
(billion units)
na 17.03 25.12 31.94 38.99 45.25 46.54 52.63 63.85 71.39 84.53
Islamic na 0.29 1.07 1.27 1.81 2.19 3.44 2.12 3.13 4.26 5.76
Conventional na 16.74 24.06 30.67 37.17 43.06 43.1 50.52 60.71 67.13 78.78
No. of
accounts
(billion units)
na na na 6.85 7.96 8.26 8.59 8.91 9.58 9.99 10.18
Islamic na na na na na na na 0.21 0.24 0.27 0.3
Conventional na na na na na na na 8.7 9.35 9.72 9.87
No. of
approved
funds
na na na na na na na 107 127 164 188
Islamic 2 2 4 5 8 10 13 13 17 15 44
Conventional na na na na na na na 94 110 149 144
NAV (RM
billion)
15.72 28.13 35.72 44.13 59.96 33.57 38.73 43.26 43.3 47.35 53.7
Islamic na 0.19 0.46 0.51 0.76 1.03 1.76 1.39 1.68 2.42 3.21
Conventional na 27.94 35.26 43.62 59.2 32.54 36.97 41.87 41.62 44.93 50.49
Bursa
Malaysia
(BM)
Market Capitalization (RM billion)
246.00 619.70 508.85 565.63 806.77 375.80 374.52 552.69 444.35 465.00 481.62
Kuala Lumpur Composite Index (KLCI)
643.96 1275.32 971.21 995.17 1237.96 594.44 586.13 812.33 679.64 696.09 646.32
Panel A
% NAV to
Market
Capitalization
Total MF
industry(TI)
6.39 4.54 7.02 7.80 7.43 8.93 10.34 7.83 9.74 9.76 11.15
Islamic 0.03 0.09 0.09 0.09 0.27 0.47 0.25 0.38 0.5 0.67
Conventional 4.51 6.93 7.71 7.34 8.66 9.87 7.58 9.37 9.26 10.48
Panel B
% NAV to TI
Islamic 0.68 1.29 1.16 1.27 3.07 4.54 3.21 3.88 5.11 5.98
Conventional 99.32 98.71 98.84 98.73 96.93 95.46 96.79 96.12 94.89 94.02
Total 100 100 100 100 100 100 100 100 100 100
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Appendix A continued.
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Mutual Fund
Industry (MFI)
Units in Circulation
(billion units)
97.39 118.63 139.39 154.07 208.34 241.05 273.88 289.37 316.41 326.57
Islamic 8.59 13.16 18.62 18.55 36.35 49.93 56.85 56.21 61.21 61.96
Conventional 88.8 105.47 120.76 135.52 171.99 191.12 217.03 233.16 255.2 264.61
No. of accounts
(billion units)
10.22 10.43 10.86 11.16 12.28 13.05 14.11 14.62 15.43 15.55
Islamic 0.35 0.43 0.64 0.77 1.25 1.64 1.78 1.8 1.98 1.99
Conventional 9.88 10 10.22 10.4 11.03 11.41 12.33 12.82 13.45 13.55
No. of approved
funds
226 291 340 416 521 579 565 584 604 605
Islamic 55 71 83 100 134 149 150 155 167 167
Conventional 171 220 257 316 387 430 415 429 437 438
NAV (RM billion) 70.08 87.38 98.49 121.76 169.41 134.41 191.71 226.81 249.46 267.02
Islamic 4.75 6.77 8.49 9.17 16.86 17.19 22.08 24.04 27.86 29.24
Conventional 65.33 80.61 90 112.59 152.55 117.22 169.63 202.77 221.6 237.78
Bursa Malaysia
(BM)
Market Capitalization (RM billion)
640.28 722.04 695.27 848.70 1106.15 663.82 999.45 1275.28 1284.54 1345.30
Kuala Lumpur Composite Index (KLCI)
793.94 907.02 899.79 1096.24 1445.03 876.75 1272.78 1518.91 1530.7 1569.65
Panel A
% NAV to Market
Capitalization
Total MF
industry(TI)
10.95 12.10 11.00 14.35 15.32 20.25 19.18 17.79 19.42 19.85
Islamic 0.75 0.94 0.95 1.08 1.53 2.59 2.21 1.89 2.17 2.17
Conventional 10.20 11.16 10.05 13.27 13.79 17.66 16.97 15.90 17.25 17.67
Panel B
% NAV to TI
Islamic 6.78 7.75 8.62 7.53 9.95 12.79 11.52 10.60 11.17 10.95
Conventional 93.22 92.25 91.38 92.47 90.05 87.21 88.48 89.40 88.83 89.05
Total 100 100 100 100 100 100 100 100 100 100
Note: na refers to non-available data. Source: Adaptation from Securities Commission Malaysia and Federation of Investment Managers Malaysia (formerly known as Federation of Malaysian Unit Trust Managers- FMUTM).
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Appendix B Year on year changes in the IMFs and CMFs, 1999-2012
1 2 3 4 5 6 7
No of Fund Managers
manage the fund (%)
Funds Approved (%)
Funds Account (%)
Funds Size (Units
circulation) (%)
NAV to total industry (%)
NAV to market
(%)
Year IMFs CMFs IMFs CMFs IMFs CMFs
IMFs CMFs IMFs CMFs IMFs CMFs Total
1999 38.24 100 12.15 87.85 2.33 97.67 4.02 95.98 3.21 96.79 0.25 7.58 7.83
2000 44.12 100 13.39 86.61 2.47 97.53 4.91 95.09 3.88 96.12 0.38 9.37 9.74 2001 62.16 100 9.15 90.85 2.67 97.33 5.97 94.03 5.11 94.89 0.50 9.26 9.76 2002 69.23 100 23.4 76.6 2.98 97.02 6.81 93.19 5.98 94.02 0.67 10.48 11.15
2003 75.00 100 24.34 75.66 3.39 96.61 8.82 91.18 6.78 93.22 0.75 10.20 10.95 2004 100 100 24.4 75.6 4.10 95.9 11.09 88.91 7.75 92.25 0.94 11.16 12.10 2005 100 100 24.41 75.59 5.89 94.11 13.36 86.64 8.62 91.38 0.95 10.05 11.00 2006 100 100 24.04 75.96 6.86 93.14 12.04 87.96 7.53 92.47 1.08 13.27 14.35
2007 100 100 25.72 74.28 10.19 89.81 17.45 82.55 9.95 90.05 1.53 13.79 15.32
2008 100 100 25.73 74.27 12.54 87.46 20.71 79.29 12.79 87.21 2.59 17.66 20.25 2009 100 100 26.54 73.45 12.62 87.38 20.76 79.24 11.52 88.48 2.21 16.97 19.18 2010 100 100 26.54 73.46 12.31 87.17 19.42 80.58 10.60 89.40 1.89 15.90 17.79
2011 100 100 27.65 72.35 12.83 87.14 19.35 80.65 11.17 88.83 2.17 17.25 19.42 2012 100 100 27.60 72.40 12.80 87.14 18.97 81.03 10.95 89.05 2.17 17.67 19.85
Avrg - - 22.50 77.50 7.43 92.53 13.12 86.88 8.27 91.73 1.29 12.90 14.19
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Appendix C The differences between Islamic and Conventional financial products
No Types of Financial Products
Conventional Islamic
Equity Investments – mainly involved equity securities like stock and shares, and also included equity-linked instruments and equity derivatives (Halim, 1999)
Stock means a share of ownership in a corporation (company). Stock typically takes the form of shares of either common stock or preferred stock. As a unit of ownership, common stock typically carries voting rights that can be exercised in corporate decisions. Preferred stock differs from common stock in that it typically does not carry voting rights but is legally entitled to receive a certain level of dividend payments before any dividends can be issued to other shareholders. Common stocks are similar to Islamic stocks if they have invest in companies which are comply to Shariah principles. In contrast, preferred stock is considered unlawful/ illegal under Islamic Laws/Shariah laws due to involvement of interest.
Islamic equity investment starts with Shariah compliant securities. Shariah-compliant securities Securities (ordinary shares / equities) of a company listed on Bursa Malaysia which is classified as Shariah permissible for investment Primary business and investment activities that generate income for the company are consistent with Shariah principles In Malaysia, the body that gives Shariah endorsement is the Shariah Advisory Council (SAC) of the Securities Commission of Malaysia (SC) Stock is based on Musharaka and Mudaraba (Trustee
Profit sharing) principles. Musharaka ( Joint-venture profit sharing) an equity participation contract under which a bank and its client contribute jointly to finance a project. It is a profit and loss sharing partnership. An ownership is distributed according to each party's share in the financing; and Mudaraba, a trustee type finance contract under which one party provides the capital for a project and the other party provides the labour. Profit sharing is agreed between the two parties to the Mudaraba contract and the losses are borne by the provider of funds except in the case of misconduct, negligence or violation of the conditions agreed upon by the bank. Equity investments are permissible under Shariah within certain parameters. Islamic scholars prescribed three minimum requirements: (i) the fund must not deal in the equities of companies whose business activities are banned by the Shariah principles, like alchoholics, casinos and
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conventional banks; (ii) interest income earned by the fund must be negligible (the current standard is less than 10 per cent) and separable so that the fund’s income can be cleansed of it; (iii) since the sale of debt is not permissible except at face value, the proportion of debts receivable in the portfolio of the company should not exceed an acceptable proportion. According to the standard that being used at present is 50 per cent.(Iqbal and Molyneux, 2005, p.106)
Debt instruments – Ismail (1999) defined as the debt-financing products which have been formally securitised for trading in the secondary markets. Debt securities may be short-term, called as trade finance is traded in the inter-bank market or private debt securities traded in the money market or public debt securities listed in the stock market(p.29).
Based on several Shariah principles like Murabaha, a purchase and resale contract in which a tangible asset is purchased by a bank at the request of its customer from a supplier, with the resale price determined based on cost plus profit markup; Salam, a purchase contract with deferred delivery of goods (opposite to Murabaha), which is mostly used in agricultural finance; Istisna, a predelivery financing and leasing instrument used to finance long term projects; Qard al-Hasan (benevolent loan), an interest-free loan contract that is usually collateralized; and Ijara, a leasing contract whereby a party leases an asset for a specified rent and term. The owner of the asset (the bank) bears all risks associated with ownership. The asset can be sold at a negotiated market price, effectively resulting in the sale of the Ijara contract. The Ijara contract can be structured as a lease-purchase contract whereby each lease payment includes a portion of the agreed asset price and can be made for a term covering the asset's expected life.
Bonds It is an certificate instrument with coupon interest that can be changed to cash during the maturity period.
Called as sukuk or Islamic bond. It is an instrument for pooled securitizations. Sukuk is secondary instrument based on a return from a real asset or its usufruct.
Mutual funds The funds are not necessarily complied with the Shariah principles. Also, the business activities of the companies involved are unlimited. The main objective
Unlike its conventional counterpart, an Islamic mutual fund must conform to Shariah investment precepts. The shari`a
encourages the use of profit sharing and partnership , and forbids riba (interest), maysir (gambling) and gharar (
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is to maximize the profits and get higher return.
uncertainty . ie. selling something that is not owned or that cannot be described in accurate detail in terms of type, size and amount). In addition , there is a desire to have investment portfolios which are morally purified. Thus investment in companies that are not in compliance with Muslims` moral orientations are not permitted and are eliminated from the portfolio. To ensure compliance with the foregoing condition, Islamic mutual are governed by shari`a advisory board whose role is mainly to give assurance that money is managed within the framework of Islamic laws. (Hassan and Lewis, 2006, 260).
Insurance It is part of risk management, to reduce risk due to some perils. It is based on bi-lateral contract, which the participants buy the insurance policy and they need to pay the premium according to the contract.
Called as takaful or Islamic insurance. It is based on uni-lateral contract.Takaful has the following features (Iqbal and Molyneux, 2005, p.57). The company is not the one who assumes risks nor the one taking any profit. Instead, it is the participants, the policy holders, who cover each other. All contributions (premiums) are accumulated into a fund. This fund is invested using Islamic modes of investment and the net profit resulting from these investment is credited back to the fund. All claims are paid from this fund. The policy holders, as groups, are the owners of any net profit that remains after paying all the claims. They are also collectively responsible if the claims exceed the balance of the fund. The company acts as a trustee on behalf of the participants to manage the operations of the takaful business. The relationship between the company and the policy holders is governed by the terms of mudarabah contract. Therefore, should there be a surplus from the operation, the company (mudarib) will share the surplus with the participants ( rab al-mal) according to the pre-agreed profit-sharing ratio.
Future markets Future contract is similar to forward contract except it is standardized in quantity, quality, delivery, location and is traded in organized markets. Price is the
Based of Bay` salam principle.
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only variable which is decided through the forces of supply and demand.
Forward markets Forward contract is an agreement in which seller agree to deliver specific commodities to buyers sometime in the future, while seller and buyer agree on the quality and the quantity of the commodity sold. Both parties will then know in advance the price and when these commodities will be delivered. Thus, they do not negotiate price at the time of delivery. Forwards are not confined to grains and foodstuffs, but prevalent in all sectors of the economy, rentals, lease and other commodities.
Based on Bay` salam principle.
Real estate Investment trusts (REITs)
Real estate is referred to real property fixed to the land, such as land and buildings. However, real estate investment trusts (REITs) is a security that sells like a stock on the major exchanges and invests in real estate directly, either through properties or mortgages. There are many kinds of REITs, like the following: Equity REITs: It is the investment in the properties and own it, thus the investors are responsible for the equity or value of their real estate assets. Their revenues come principally from their properties' rents. Mortgage REITs: it is the dealing in investment and ownership of property mortgages. These REITs loan money for mortgages to owners of real estate, or purchase existing mortgages or mortgage-backed securities. Their revenues are generated primarily by the interest that they earn on the mortgage loans. Hybrid REITs: This kind of REITs is a
An Islamic REIT is permitted to own (purchase) real estate in which its tenant(s) operates mixed activities that are permissible and non-permissible, according to the Shariah. However, the Islamic REIT fund manager must perform some additional compliance assessments before acquiring real estate that has a tenant(s) who operates mixed activities. The list of activities that are classified as non-permissible as decided by the Shariah Advisory Council are: 1. financial services based on riba (interest); 2. gambling/gaming; 3. manufacture or sale of non-halal products or related products; 4. conventional insurance; 5. entertainment activities that are non-permissible according to the Shariah; 6. manufacture or sale of tobacco-based products or related products; 7. stockbroking or share trading in Shariah non-compliant securities; and 8. hotels and resorts.
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combination of the investment strategies of equity REITs and mortgage REITs by investing in both properties and mortgages.
Wealth management Consist of wealth generation and wealth accumulation.
Besides the wealth generation and wealth accumulation, there are wealth distribution and wealth purification.
Leasing and hire-purchase
Based on loans concept. According to Islamic laws, granting loans to customers for profit is unlawful. The leasing facility is based on Ijarah concept. It is sale of usufruct of an asset. The lessor retains the ownership of the asset with all the rights and the responsibilities that go with ownership. A hire-purchase concept is based on principles of mudaraba, musharaka and murabahah, through equity participation or partnership.
Deferred instruments The contract for deferred payments in conventional side is based on loan contract. The selling price is the nominal value of a loan plus the total interest payment. The interest payment is fluctuated over time according to the based lending rate (BLR), denoted by the central bank. In other words, the interest rates vary from time to time, as dictated by market forces. Any variation in the profit portion of the selling price will cause the selling price to change as well. Interest rates on loans are adjustable to reflect changes in the cost of fund. The contract was totally different with the BBA contract. The profit rate in the BBA must stay fixed even though cost of funds has changed. Therefore, according to Khir et. al (2008), when Islamic banks see higher interest rates on loans, there is nothing they can do to upgrade the BBA profit rate, as this will alter the existing BBA price. In contrast, the conventional banks can revise interest rate upward, and customers may
Based on Bay mu`ajjal or bay bithamin ajil (BBA). It is kinds of deferred payment sales. It is a sale in which goods are delivered immediately but payment is deferred. BBA contract is a sale with deferred payment and is not a spot sale. This contract is based on buying and selling activities. The asset that a customer wants to purchase is bought by a bank and sold to the customer at an agreed price after the bank and customer determines the tenure and the installments. The price at which the bank sells the asset to the customer will include the actual cost of the asset and will also incorporate the bank’s profit margin. From Shariah point of view, the profit gained by the bank is legitimate since the transaction is based on a sale contract. The monthly installment is determined by the selling price, repayment period and the percentage of profit margin of financing. The basic feature of BBA is the selling price is fixed throughout the duration of the tenure. Normally, the bank must make sure that the selling price remains unchanged until the contract expires. Any changes in price will make the contract void and null.
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have to pay more monthly. This is not possible in Islamic banking because by taking a similar move, it will increase the contractual selling price, thus violating the BBA contract as the principle of aqad (contract) requires only one price in one sale.
Deposits It is also called as savings account. This account caters to those who wish to save money and at the same time earn income in the form of interest.
Savings account in Islamic term function as same as conventional, unlike they use different principles, such as wadiah, mudaraba and qard al-hassan.
Fixed deposit Fixed deposit is for those who keep money from investment purposes, to get better returns on their funds. The fixed returns is in the form of interest, either quarterly, semi-annually or yearly paid to the clients.
In Islamic finance, this facility is familiar with a term called investment deposit.This deposit is governed by the principle of mudaraba. Islamic banks act as agent-manager or mudarib and the depositors act as investors or rabb al-mal. The bank would provide no guarantee or fixed return on the amount deposited. Customers who hold their funds in this investment deposit will be treated as if they were shareholders of the bank and are entitled to a share of profits or losses made by the bank. The agreement on how the percentage of the profit or loss will be distributed between the bank and the depositor is made at the beginning of the deposit period and cannot be amended during the tenure of the deposits, except by the consent of both parties. The distribution of profit to the depositors may be on a monthly, quarterly, half-yearly, or yearly basis and advance notice is required for those who wish to withdraw the funds before the maturity date.
Money market Based on currency exchanges, which refer to the current interest rate movements.
Based on sarf (Islamic rules governing currency exchange)
Capital market The capital market from conventional perspective basically based on the loan contract, which provide returns to the investors in the form of interest rate, dividend and also capital gains. The main
Shariah compliance is the fundamental thrust of ICM, include (i) Shariah Advisory Council for regulator, and (ii) Shariah committees or advisers for the industry. The Islamic contracts are the underlying principles for all ICM products & services, consist of the following:
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objective of its capital market is to maximize profit for the benefits of the investors (lender) as well as the entrepreneurs (borrower). The range of products offered in traditional capital market are quite similar to the Islamic capital market products except they are not complied with the Shariah principles.
• Sale/Purchase principles– BBA, murabahah, istisna` and bay` Salam
• Rental/Hire principles– ijarah, ijarah thumma bay`, ijarah wa iktina`
• Profit/Loss Sharing principles– mudarabah and musharakah
• Loan principle– Qard hasan
The ICM products in various markets, include; • Islamic equity market • Shariah-compliant stocks; Islamic mutual funds;
Islamic REITs; Islamic indices; and Islamic ETFs. • Sukuk (Islamic debt) market • Islamic asset-based financing; Islamic equity based
financing; and Islamic asset-backed securities • Islamic stockbroking • Shariah-compliant trading; and Shariah-compliant
margin financing • Islamic structured products • Dual-Currency Product; Commodity-Linked
Product; and • Capital Protected Product • Islamic derivatives • Crude Palm Oil futures; Crude Palm Kernel
Futures; and • Single Stock Futures
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Appendix D GFC and the major events
.
Source: Ariff and Farrar (2011)
0407
0707
0807
0907
0108
0308
0708
1
0
0
7
1207
0508
0408
Century Financial
Bears Stearn I
BNP Paribas
Northern Rock
BCRS Citi Merryll
$700 bill auction
Stock market 1st Drop 3.6 %
Chase grabs Bears Stearn II taken
UK loans to Bank rescue
UBS; Barclay lose capital; raise cap
Fannie & Freddie Mae Rescue
0908
15 Financial firms fail worldwide and Banks not lending: No US$ Worldwide on 11-15 Oct: 2008
2nd Stock market Down 8% (NY) AIG Rescued HBOS takeover Fortis Bradford-Bigley Gitnir, Iceland
$700 bil rescue US$50 bil Germany $88 bil UK rescue Wachovia fails, etc
$16.4 bil IMF $586 bil China More rescues UK VAT 2.5%
1108
1208
CAR sales SME loans Irish bank Lowest g 0.5
Global financial crisis
1008
1008
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Appendix E CAPM analysis against Islamic and conventional benchmarks The table presents results on returns performance based on different market benchmark before correction for heterocedasticity. Panel A employs mean aggregate returns whereas Panel B indicates the mean equally weighted returns. The mean excess returns are reported in percentage net from all expenses.The Malaysian t-bills is employed as a proxy for the risk-free rate of return. The period of study for conventional benchmark is from January 1990 to April 2009, whereas the period for Islamic benchmark is from July 1999 to April 2009. Standard errors based on the cross-section of the estimated coefficients are given in parentheses. The asterisks ***,**,* indicate significant level at 1%, 5% and 10%, respectively. N represent number of funds, whilst n is the number of observations. Conventional benchmark Islamic benchmark α β Adj R2
α β Adj R2 Without correction for heteroskedasticity
Panel A: Mean equally weighted return All funds (479)
–0.2631*** (0.0241)
0.0377*** (0.0030)
0.4082 –0.2343*** (0.0078)
0.0142*** (0.0016)
0.4121
IMFs (129)
–0.1883*** (0.0415)
0.0587*** (0.0051)
0.3598 –0.2348*** (0.0124)
0.0215*** (0.0025)
0.3859
CMFs (350)
–0.3379*** (0.0120)
0.0167*** (0.0015)
0.3544 –0.2337*** (0.0039)
0.0068*** (0.0008)
0.3960
Top performer (129)
–0.3238*** (0.0132)
0.0212*** (0.0016)
0.4211 –0.2304*** (0.0042)
0.0100*** (0.0008)
0.5411
Middle performer (129)
–0.3000*** (0.0393)
0.0556*** (0.0049)
0.3596 –0.2312*** (0.0084)
0.0163*** (0.0017)
0.4416
Bottom performer (129)
–0.3361* (0.1717)
0.2316*** (0.0206)
0.4236 –0.2596*** (0.0868)
0.1601*** (0.0174)
0.4172
Difference –0.2155*** (0.0360)
0.0435*** (0.0045)
0.2896 –0.2376*** (0.0103)
0.0156*** (0.0021)
0.3256
Panel B: Mean aggregate return All funds (479)
0.5081*** (0.1618)
0.5432*** (0.0200)
0.7610 0.0220 (0.1589)
0.6193*** (0.0318)
0.7636
IMFs (129)
0.6861*** (0.2065)
0.5704*** (0.0255)
0.6831 –0.0158 (0.1805)
0.6130*** (0.0361)
0.7101
CMFs (350)
0.3301** (0.1438)
0.5159*** (0.0178)
0.7844 0.0598 (0.1501)
0.6256*** (0.0301)
0.7870
Top performer (129)
0.5116*** (0.1501)
0.4989*** (0.0186)
0.7575 0.2594* (0.1517)
0.6751*** (0.0304)
0.8081
Middle performer (129)
0.0950 (0.1449)
0.5338*** (0.0179)
0.7932 –0.0050 (0.1517)
0.5168*** (0.0304)
0.7114
Bottom performer (129)
–0.2622 (0.3366)
0.6906*** (0.0404)
0.6273 –0.4350* (0.2331)
0.7372*** (0.0467)
0.6799
Difference
0.3556** (0.1480)
0.0544*** (0.0183)
0.0328 –0.0762 (0.0966)
–0.0130 (0.0257)
–0.0047
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Appendix F TM model for market timing expertise of the fund managers The table presents results of market timing expertise and stock selectivity skill of IMFs and CMFs Fund Managers using TM model. The KLCI is used a proxy for the market return. The returns are based on mean aggregate and mean equally weighted and reported in percentage. The returns are net from all expenses and adjusted for the risk-free rate using Malaysian t-bills as a proxy. The period of study is from January 1990 to April 2009. Standard errors based are given in parentheses. The asterisks ***, **, and * indicate significant level at 1%, 5% and 10%, respectively. N represents the total numbers of funds in each portfolio and Obs is the numbers of observations.
Portfolio α β θ Adj R2 Obs Obs Without correction for heteroskedasticity
Panel A: Mean equally weighted return
AMFs (N=479)
–0.2584*** (0.0260)
0.0375*** (0.0030)
–0.0001 (0.0001)
0.4063 232 232
IMFs (N=129)
–0.1843*** (0.0447)
0.0585*** (0.0052)
–0.0001 (0.0003)
0.3572 232 232
CMFs (N=350)
–0.3325*** (0.0129)
0.0165*** (0.0015)
–0.0001 (0.0001)
0.3552 232 232
Top performer (N=129)
–0.3197*** (0.0142)
0.0210*** (0.0016)
0.0001 (0.0001)
0.4201 232 232
Middle performer (N=129)
–0.3069*** (0.0424)
0.0559*** (0.0049)
0.0001 (0.0002)
0.3573 232 232
Bottom performer (N=129)
–0.4715** (0.1817)
0.2380*** (0.0206)
0.0020 (0.0009)
0.4350 172 172
Panel B : Mean aggregate return AMFs (N=479)
0.3303* (0.1717)
0.5502*** (0.0199)
0.0027*** (0.0010)
0.7679
232 232
IMFs (N=129)
0.5339** (0.2211)
0.5764*** (0.0256)
0.0023* (0.0013)
0.6864 232 232
CMFs (N=350)
0.1267 (0.1508)
0.5240*** (0.0175)
0.0031*** (0.0009)
0.7954
232 232
Top performer (N=129)
0.3378** (0.1589)
0.5058*** (0.0184)
0.0027*** (0.0009)
0.7654 232 232
Middle performer (N=129)
–0.1352 (0.1509)
0.5429*** (0.0175)
0.0035*** (0.0009)
0.8067 232 232
Bottom performer (N=129)
–0.7123** (0.3476)
0.7119*** (0.0395)
0.0066*** (0.0018)
0.6523 174 174
Page | 269
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