the determinants of stock market development: a …

THE DETERMINANTS OF STOCK MARKET DEVELOPMENT:

A PANEL STUDY OF DEVELOPED, EMERGING AND FRONTIER

MARKETS

By

GULBAZ MAHMOOD

(REG NO 110927)

DOCTOR OF PHILOSOPHY IN MANAGEMENT SCIENCES

(FINANCE)

AIR UNIVERSITY, SCHOOL OF MANAGEMENT, ISLAMABAD

APRIL 2018

ii

THE DETERMINANTS OF STOCK MARKET DEVELOPMENT:

A PANEL STUDY OF DEVELOPED, EMERGING AND FRONTIER

MARKETS

By

Gulbaz Mahmood

(Reg No 110927)

A research thesis submitted to the Air University School of Management (AUSOM),

Islamabad in partial fulfillment of the requirement for the degree of

DOCTOR OF PHILOSOPHY IN MANAGEMENT SCIENCES

(FINANCE)

AIR UNIVERSITY, SCHOOL OF MANAGEMENT, ISLAMABAD

APRIL 2018

viii

ACKNOWLEDGEMENT

First of all, I am grateful to Almighty Allah, who bestowed me with an opportunity and

knowledge to do this project successfully. By the Grace of God, this project has been

successfully completed with the assistance of numerous wonderful people. I would like to take

this opportunity to thank all those individuals who have contributed directly or indirectly in the

successful completion of this project.

I am whole-heartedly thankful to my great supervisors, Dr. Shahnaz A Rauf and

Dr. Eatzaz Ahmed, whose valuable guidance and professional support from the initial to the

final level enabled me to develop an understanding of the subject. They have left no stone

unturned during the supervision of this project. I am also thankful to my local and foreign

examiners for their valuable comments, which made this study as a more comprehensive. The

faculty members and Dean had been very supportive in all phases of the project. Moreover,

the untiring support of our dynamic coordinator, Mr Syed Farhan Shah, had been quite

instrumental in coordinating and compiling the project activities in an amicable manner.

Ordinary words of appreciation do not cover my family’s true love and their guidance

at every corner of my life. The genuine and well-motivated support of my spouse gave me

encouragement to complete the project in addition to my present job. My mother had also been

praying all the time for the success of my project. All of my family members including my

mother, wife and kids were supported me very well in every facets. Their keen interest, prayers

and encouragement have been a very strong support for me and enabled me to finish my project

in an effective and efficient manner.

ix

TABLE OF CONTENTS

Certificate of Approval ………………………………………………………...........iii

Thesis Evaluation Report………………………………………………….…...........iv

Thesis Completion Certificate…………………………………………….…...........v

Author’s Declaration …………………………………………………………..........vi

Plagiarism Undertaking……………………………………..…………….…..........vii

Acknowledgements…………….………………………………………….….........viii

Table of Contents…………………………………………………………...………ix

List of Tables ……..……………………………………………………………….xiii

List of Figures …….……………………………………………………………….xvii

List of Abbreviations …….……………………..…………………………………xxi

Abstract…………………………………………………………………………....xxii

Chapter 1 Introduction………………………………………………………. 1

1.1. Background of the study…………………………………………………... 1

1.2. Research Gap and Motivation ……………………………………………. 4

1.3. Significance of the Study ………………………………………………… 7

1.4. Research Objectives ……………………………………………………. 8

1.5. Research Questions ……………………………………………………… 9

1.6. Contribution to Knowledge………………………………………………. 10

Chapter 2 Review of Literature …………………………………………….. 11

2.1. Introduction……………………………………………………………….. 11

2.2. Literature on Stock Market Development………………………………… 11

x

2.3. Literature on Governance Factors ………………………………………... 12

2.4. Literature on Empirical Methodology …………………………………….. 14

2.5. Stock Markets …………………………………………..………………… 17

2.6. Classification of Stock Markets ………………………………..………... 18

2.7. Concluding Remarks of Literature Review……………………………….. 18

Chapter 3 Empirical Model and Hypotheses Development………………. 20

3.1. Introduction ………………………………..…………………………….. 20

3.2. Theoretical Framework…………………………………………….…….. 20

3.2.1. Basic Caldron-Rossell Model……………………..……………………… 20

3.2.2. Augmented Caldron-Rossell Model………………………..…………...... 23

3.2.3. Diagrammatical Model of the Study……………………………………… 26

3.2.4. Empirical Models and Hypotheses Development………………………… 27

Chapter 4 Methodology……..……………………………………..………… 36

4.1. Introduction………………………………………………………...……… 36

4.2. Statistical Methodology….………………………………………….…….. 36

4.3. Econometric Methodology ……………………………………….............. 37

Chapter 5 Data and Variables………………………………………………. 40

5.1. Introduction………………………………………………………………... 40

5.2. Dependent Variable………………………………………………………... 40

5.3 Economic Factors………………………………………………………..… 41

5.4 Governance Factors……………………………………………………….. 43

5.5 Predicted Variable Signs…………………………………………………... 46

xi

5.6. Data Analysis Software…………………………………………………… 47

5.7 Data Period and Classification……………………………………………... 47

Chapter 6 Empirical Analysis..………………………………………………. 48

6.1. Introduction………………………………………………………………… 48

6.2. Statistical Analysis……………………………………................................. 48

6.2.1 Statistical Analysis of Developed Financial Markets……………………… 48

6.2.2 Statistical Analysis of Emerging Financial Markets………………………. 54

6.2.3 Statistical Analysis of Frontier Financial Markets………………………… 59

6.2.4 Statistical Analysis of World Financial Markets…………………………... 64

6.3. Econometric Analysis……………………………………………………… 70

6.3.1. Principal Component Analysis (PCA)……………………………………... 72

6.3.2. PCA of Developed Financial Markets……………………………………... 72

6.3.2 PCA of Emerging Financial Markets……………………………………… 82

6.3.3 PCA of Frontier Financial Markets………………………………………… 92

6.3.4 PCA of World Financial Markets…………………………………………...102

6.4. Panel GMM Estimation……………………………………………………..112

6.4.1. Results of Panel GMM Estimation for Model-1……………………….……114





xii

Chapter 7 Summary and Conclusions……………………..………………. 137

7.1. Combined Results…………………………………………….…………… 137

7.2. Overall Summary of the Results…………………………………………... 170

7.3 Conclusions………………………………………………………………... 173

7.4 Policy Recommendations…………………………………………………. 175

7.5 Contributions of the Study…………………………………………………. 175

7.6 Limitations of the Study…………………………………………………… 176

7.7 Future Research Avenues …………………………………………………. 177

Appendices……………………….…………………………………… 178

References…………………………………………………………….. 219

xiii

List of Tables

Table No Description of the Table Page No

Table 3.1 Country Classification of World Equity Markets by FTSE as on

30 September 2015

19

Table 5.1

Summary of the Variables along with their Predicted Signs 46

Table 6.1 Preliminary Statistics of Economic Variable of Developed Equity

Markets

52

Table 6.2 Preliminary Statistics of Governance Variables of Developed

Equity Markets

52

Table 6.3 Correlation Matrix of Economic Variables of Developed Equity

Markets

53

Table 6.4 Correlation Matrix of Governance Variables of Developed Equity

Markets

54

Table 6.5 Preliminary Statistics of Economic Variable of Emerging Equity

Markets

57

Table 6.6 Preliminary Statistics of Governance Variables of Emerging

Equity Markets

58

Table 6.7 Correlation Matrix of Economic Variables of Emerging Equity

Markets

59

Table 6.8 Correlation Matrix of Governance Variables of Emerging Equity

Markets

59

Table 6.9 Preliminary Statistics of Economic Variable of Frontier Equity

Markets

62

Table 6.10 Preliminary Statistics of Governance Variables of World Equity

Markets

63

Table 6.11 Correlation Matrix of Economic Variables of World Equity

Markets

64

Table 6.12 Correlation Matrix of Governance Variables of Frontier Equity

Markets

64

Table 6.13 Preliminary Statistics of Economic Variable of World Equity

Markets

68

Table 6.14 Preliminary Statistics of Governance Variables of World Equity

Markets

68

Table 6.15 Correlation Matrix of Economic Variables of Frontier Equity

Markets

69

Table 6.16 Correlation Matrix of Governance Variables of Frontier Equity

Markets

70

Table 6.17 List of All Variables for Statistical and Econometric Analysis 72

xiv


Table 6.18 Principal Components Analysis for Economic Variables of

Developed Stock Markets (25 Countries)

74

Table 6.19 Principal Components Analysis for Governance Variables of

Developed Stock Markets (25 Countries)

77


Emerging Stock Markets (21 Countries)

83


Emerging Stock Markets (21 Countries)

87


Frontier Stock Markets (24 Countries)

93


Frontier Stock Markets (24 Countries)

97

Table 6.24 Principal Components Analysis for Economic Variables of World

Stock Markets (70 Countries)

103


World Stock Markets (70 Countries)

106

Table 6.26 All Economic Variables of Developed Stock Markets (25

Countries) and Depended Variable : Market Capitalization as

%age of GDP

115

Table 6.27 All Economic Variables of Emerging Stock Markets (21


%age of GDP

116

Table 6.28 All Economic Variables of Frontier Stock Markets (24 Countries)

and Depended Variable : Market Capitalization as %age of GDP

116

Table 6.29 All Economic Variables of World Stock Markets (70 Countries)

and Depended Variable of Market Capitalization as %age of GDP

117

Table 6.30 All Governance Variables of Developed Stock Markets (25


%age of GDP

118

Table 6.31 All Governance Variables of Emerging Stock Markets (21


%age of GDP

118

Table 6.32 All Governance Variables of Frontier Stock Markets (24


%age of GDP

119

Table 6.33 All Governance Variables of World Stock Markets (70 Countries)

and Depended Variable of Market Capitalization as %age of GDP

119

Table 6.34 All Economic and Governance Variables of Developed Stock

Markets (25 Countries) and Depended Variable : Market

Capitalization as %age of GDP

121

xv


Table 6.35 All Economic and Governance Variables of Emerging Stock



122

Table 6.36 All Economic and Governance Variables of Frontier Stock



122

Table 6.37 All Economic and Governance Variables of World Stock Markets

(70 Countries) and Depended Variable : Market Capitalization as

%age of GDP

123

Table 6.38 All Economic Variables and Composite Index of Governance

Variables of Developed Stock Markets (25 Countries) and

Depended Variable of Market Capitalization as %age of GDP

124


Variables of Emerging Stock Markets (21Countries) and


124


Variables of Frontier Stock Markets (24 Countries) and Depended

Variable of Market Capitalization %age of GDP

125


Variables of World Stock Markets (70 Countries) and Depended

Variable of Market Capitalization as %age of GDP

125

Table 6.42 All Governance Variables and Composite Index of Economic



126


Variables of Emerging Stock Markets (21 Countries) and


127




127




128

Table 6.46 Composite Indices of Economic and Governance Variables of

Developed Stock Markets (25 Countries) and Depended Variable

of Market Capitalization as %age of GDP

129


Emerging Stock Markets (21 Countries) and Depended Variable


130


Frontier Stock Markets (24 Countries) and Depended Variable of

Market Capitalization as %age of GDP

130

xvi



World Stock Markets (70 Countries) and Depended Variable of


130

Table 6.50 Composite Indices of Economic and Governance Variables with

indirect effect of Developed Stock Markets (25 Countries) and


132


indirect effect of Emerging Stock Markets (21 Countries) and


133


indirect effect of Frontier Stock Markets (24 Countries) and


133


indirect effect of World Stock Markets (70 Countries) and


134

Table 6.54 Stock Market Development and Composite Index of Governance


Depended Variable of Composite Economic factors (Peco)

135


Variables of Emerging Stock Markets (21 Countries) and

Depended Variable of Composite Economic factors (Peco)

135

Table .6.56 Stock Market Development and Composite Index of Governance


Variable of Composite Economic factors (Peco)

136




136

Table 7.1 Determinants of Stock Market Development for Developed

Region (25 Countries) and Depended Variable : Market


138

Table 7.2 Reverse Impacts on Composite Economic Factors for Developed

Region (25 Countries) and Depended Variable : Composite

Economic Factors

141

Table 7.3 Determinants of Stock Market Development for Emerging Region


%age of GDP

146

Table 7.4 Reverse Impacts on Composite Economic Factors for Emerging


Economic Factors

149

Table 7.5 Determinants of Stock Market Development for Frontier Region


%age of GDP

154

xvii


Table 7.6 Reverse Impacts on Composite Economic Factors for Frontier


Economic Factors

157

Table 7.7 Determinants of Stock Market Development for All World Equity



162

Table 7.8 Reverse Impacts on Composite Economic Factors for the All

World Equity Markets ( 70 Countries) and Depended Variable :

Composite Economic Factors

165

Table 7.9 Overall Summary of the Empirical Results 170

xviii

List of Figures

Figure

No

Description of the Figure Page

No Figure 1.1 Diagrammatic view of relations among development of stock

market, economic and governance factors 3

Figure 1.2 Diagrammatic view of the world stock markets with all three

regions of developed, emerging and frontier markets 5

Figure 1.3 Relational Impact of governance factors on development of stock

market and economic factors 8

Figure 3.1 Diagrammatic view of the main model depicting relations among

variables of development of stock market, economic and

governance factors

26

Figure 6.1 No of Listed Companies of Developed Financial Markets

(Average No from 19996 to 2015) 49

Figure 6.2 Market Capitalization of Developed Financial Markets in USD

Billions (Average from 19996 to 2015) 50

Figure 6.3 Basic Statistics of Stock Market Development of Developed

Financial Markets 51

Figure 6.4 No of Listed Companies of Emerging Financial Markets (Average

No from 19996 to 2015) 55

Figure 6.5 Market Capitalization of Emerging Financial Markets in USD


Figure 6.6 Basic Statistics of Stock Market Development of Emerging Equity

Markets 57

Figure 6.7 No of Listed Companies of Frontier Financial Markets (Average

No from 19996 to 2015) 60

Figure 6.8 Market Capitalization of Frontier Financial Markets in USD


Figure 6.9 Basic Statistics of Stock Market Development of Frontier

Financial Markets 62

Figure 6.10 No of Listed Companies of World Financial Markets (Average No

from 19996 to 2015) 65

Figure 6.11 Market Capitalization of World Financial Markets in USD


Figure 6.12 Basic Statistics of Stock Market Development of World Financial

Markets 67

Figure 6.13 Mean of Composite of Economic Variables of Developed Stock

Markets 75

xix

Figure

No


No Figure 6.14 Standard Deviation of Composite of Economic Variables of

Developed Stock Markets 76

Figure 6.15 Mean of Composite of Governance Variables of Developed Stock

Markets 78

Figure 6.16 Standard Deviation of Composite of Governance Variables of

Developed Stock Markets 79

Figure 6.17 Mean of Cross Composite Index of Economic and Governance

Variables of Developed Stock Markets 80

Figure 6.18 Standard Deviation of Cross Composite Index of Economic and

Governance Variables of Developed Stock Markets 81

Figure 6.19 Scatter plots of Composite of Indices of Economic and

Governance Indices of Developed Stock Markets 82

Figure 6.20 Mean of Composite Index of Economic Variables of Emerging

Stock Markets 85

Figure 6.21 Standard Deviation of Composite of Economic Variables of

Emerging Stock Markets 86

Figure 6.21 Mean of Composite of Governance Variables of Emerging Stock

Markets 88


Emerging Stock Markets 89


Variables of Emerging Stock Markets 90


Governance Variables of Emerging Stock Markets 91


Governance Indices of Emerging Stock Markets 92

Figure 6.26 Mean of Composite Index of Economic Variables of Frontier

Stock Markets 95

Figure 6.27 Standard Deviation of Composite of Economic Variables of

Frontier Stock Markets 96

Figure 6.28 Mean of Composite of Governance Variables of Frontier Stock

Markets 98


Frontier Stock Markets 99


Variables of Frontier Stock Markets 100


Governance Variables of Frontier Stock Markets 101


Governance Indices of Frontier Stock Markets 102

Figure 6.33 Mean of Composite Index of Economic Variables of World Stock

Markets 105

xx

Figure

No


No Figure 6.34 Standard Deviation of Composite Economic Variables of World

Stock Markets 106

Figure 6.35 Mean of Composite Governance Variables of World Stock

Markets 108


World Stock Markets 109


Variables of World Stock Markets 110


Governance Variables of World Stock Markets 111


Governance Indices of World Stock Markets 112

Figure 7.1 Scatter Plot of Stock Market Development & Economic Variables

of Developed Markets 143

Figure 7.2 Scatter Plot of Stock Market Development & Governance

Variables of Developed Markets 144


of Emerging Markets 151


Variables of Emerging Markets 152


of Frontier Markets 159


Variables of Frontier Markets 160


of World Markets 167


Variables of World Markets 168

xxi

List of Abbreviations

CAPM Capital Asset Pricing Model

E1 GDP per capita growth (annual %)

E2 Inflation, consumer prices (annual %)

E3 Real interest rate (%)

E4 Domestic credit to private sector by banks (% of GDP)

E5 Gross domestic savings (% of GDP)

E6 Trade (% of GDP)

E7 Foreign direct investment, net inflows (% of GDP)

E8 Current account balance (% of GDP)

FTSE Financial Times Stock Exchanges

G1 Control of Corruption

G2 Government Effectiveness

G3 Political Stability and Absence of Violence/Terrorism

G4 Regulatory Quality

G5 Rule of Law

G6 Voice and Accountability

GDP Gross domestic product

G-Index Governance Index

GMM Generalized Method of Moments

IFC International Finance Corporation

IMF International Monetary Fund

ISE Islamabad Stock Exchange

ISS Institutional Shareholder Services

KSE Karachi Stock Exchange LSDV Least Squares Dummy Variable Model OECD Organization for Economic Co-operation and Development

PPP Purchasing Power Parity

RIV Residual Income Valuation Model

ROA Return on Assets

ROE Return on Equity

S3 Stocks traded, total value (% of GDP)

SBP State Bank of Pakistan

SECP Securities and Exchange Commission of Pakistan

SEO Securities and Exchange Ordinance

UAE United Arab Emirates

UK United Kingdom

UNCTAD United Nations Conference on Trade and Development

UNDP United Nations Development Program

USA United States of America

WDI World Development Indicators

WGI World Governance Indicators

Y Stock Market Development

xxii

ABSTRACT

The objective of the present study is to find the determinants of equity market

development with a panel data of Developed, Emerging and Frontier Equity Markets. The

determinants of equity market include both the quantitative and qualitative factors, whereas the

former represents the macroeconomic variables, and the later embodies the governance

variables. The study incorporates the panel data set of world stock markets of 70 countries,

which are classified by international group of Financial Times Stock Exchanges (FTSE) in

three main regions of the world as Developed (25), Emerging (21) and Frontier (24) Equity

Markets and period of the study is 20 years starting from 1996 to 2015. Given the panel nature

of the data, the econometric methodologies of dynamic Generalized Methods of Moments

(GMM) has been incorporated to find the significant relationships on subject matter Moreover,

the study has incorporated multifaceted statistical methodologies in all three regions of the

world stock markets. Despite having different dynamics and resources, there are few

similarities but there are some of the stark differences, which lead them to identify their

uniqueness. The study finds that effects of economic and governance factors on stock market

development are peculiar in nature and quite unique as per the dynamics of that particular

region. For instance, the study finds that economic and governance factors are more influential

in developed region as compared to emerging and frontier regions which is mainly due to strong

institutional quality in the developed countries. The study has formed a composite index of

Economic and Governance factors through Principal Component Analysis by using factors for

each region of the world equity markets. Afterwards, cross-index of Economic and Governance

Factors is formed for exploring the joint effects of these variables. The study reveals that there

is strong correlation in these composite indices in the developed region, where as there is no

clear pattern in the developing countries. Moreover, there is quite dispersion in the composite

xxiii

data of the developing countries. So, there is no direct correlation in the composite factors of

economic and governance in the developing countries. The studies on exploring the direct

effects are quite in abundance but the literature on indirect and cross effects of governance and

economic factors on the development of stock market are quite scarce. The present study

explores the comprehensive direct and indirect effects of governance factors as well as vice

versa effects on the development of economy and equity market in all regions of the world

equity markets. After the estimation, the study finds that effects of economic and governance

factors on stock market development are not only unidirectional, but also bidirectional as well.

Particularly, the emerging markets have dual effects on economic and governance factors. The

indirect effects of governance through economic factors are significant in developed region and

cross effects of governance and economic factors are significant in emerging markets. The

vice versa effects of stock market development and economic growth suggests that economic

growth is also affected by the development of stock market and governance factors particularly

in frontier markets. The reverse impacts of stock market development on the economic growth

are quite captivating in which development of stock market also affects the growth of economy

in all three regions of the world. In the end, the study recommends that determinants of equity

market may not solely based on economic factors rather the significance of governance factors

may be taken into account while taking the complete picture of the subject. This study will

append the knowledge of prevailing institutional works on equity markets and economy as

well. Moreover, the formation of composite indices for governance and economic factors for

all the regions of developed, emerging and frontier markets may append the existing knowledge

database of financial markets.

JEL classification: C33, C36, C38, E6, G15, P52

Keywords: Stock Market Development; Economic Factors, Governance Factors,

Developed Stock Markets, Emerging Stock Markets, Frontier Stock Markets, Composite

Index, Dynamic GMM

1

CHAPTER 1

INTRODUCTION

1.1. Background of the study

The economic performance of any country can be very well analyzed through the

development of its equity market, which plays a vital role in the economic development of any

country. For instance, Levine and Zervos (1996) stipulate that equity market development

shows an important role in foreseeing the future economic growth of particular country. During

the last few decades, the equity markets all over the world have increased extensively and this

magnitude trend of stock market development in the developing countries have been

unprecedented. The development of stock market is a complex phenomenon and its

determinants cannot be measured solely by the economic factors rather there are few other

factors that are affecting the growth of stock markets. Latest theoretical research depict that

development of stock market is affected by governance factors of a particular country apart

from its economic factors and empirical evidence provides support to this assertion. For

instance, Yartey (2008) analyzes the development of equity market by using a panel data of

forty two markets of emerging region. The result suggests that the governance factors are the

vital elements in the stock markets development in emerging markets.

According to Seeking Alpha(2008), the market size of stocks and derivatives was

estimated to US$828 trillion, which was measured as eleven times larger than the size of whole

economy of the world. Keeping in view the significance of equity markets, it is imperative to

2

understand the multifaceted dynamics of this market. From the period starting from 1984 with

ending in 1995, world stock markets had a very fast growth and equity markets in emerging

region showed a more speedy growth by taking larger chunk of development of global stock

markets. In the same context, Mohtadi & Agarwal, 2001 find that the total capitalization in the

global stock markets jumped from the amounts of US$4.7 trillion to the amounts of US$15.2

trillion and the share of emerging markets surged from 4 to 13 percent during the decade after

1985. So, the significance for the development of equity market cannot be marginalized from

any regions of world equity markets.

Critics of equity market development, however, argue that equity market is grown by

its own dynamics which is quite complex in its nature. Sometimes, it is affected by the

performance of financial condition of the country and rarely in developed and often in

underdeveloped countries, the governance factors play a major role in development of stock

market. Critics claim that liquidity of stock market may inversely affect corporate governance

because it may inspire myopia of investors. In the same context, Bhide (1993) argue that, as

investors have easy access to sell their stocks, then highly liquid stock markets may deteriorate

commitment of investors to put forth corporate control. On the other side, Acemoglu, Johnson,

and Robinson (2005) highlight that there is a dichotomy over social choices and distribution of

political power. They further explains that political institutions assign political power

according to rightful entitlement, while greater economic groups retain superior existing

political power. Despite having critics for the complexity of stock market dynamics, still it is

necessary to make use of existing framework of quantitative and qualitative factors for

analyzing these subtleties of stock market development, where quantitative represents the

economic factors and qualitative denotes the governance factors.

3

The objective of present study is to explore the determinants of equity market

development particularly joint and isolated effects by economic and governance factors. The

former factors represents the quantitative factors as macroeconomic variables, whereas the later

covers the governance factors as qualitative variables. El Wassal (2013) presents the

framework to explore the leading determinants for development of stock markets and he

presents 04 sets of factors that determine the development of stock market, that is, demand and

supply factors alongwith institutional and economic factors. The investment in equities

becomes progressively more conducive to invest as the element of governance is improved

over period of time Perotti and Van Oijen (2001). Hence, the improvement of high quality of

governance can enhance the investment size in equities, which in turn lead towards equity

market development. So, the development of stock market is mainly affected by macro

economic and governance factors as depicted below:-

Figure 1.1 Diagrammatic view of relations among development of stock market, economic

and governance factors

Macro Economic

Factors

Governance Factors

A Panel of Stock Markets Development

4

Although several empirical studies identifying the relationship between equity returns

and economic variables are concerning the developed stock markets, yet very few empirical

studies are conducted on emerging and frontier stock markets such as the work highlighted by

Sirucek (2012), Wongbangpo and Sharma (2002) and Bekaert, Harvey, and Lundblad (2001).

However, the present study analyzes the dual impact of economic and governance factors on

equity market development of all regions because it extensively believed that the improvement

of both the factors could boost the confidence in the investment of stock markets. So, the

development of good governance and improvement of economic indicators can augment the

magnetism of equity investment, which resultantly enhances the stock market development.

1.2. Research Gap and Motivation

The studies for finding the effect of economic variables on of development of equity

market are in abundance but discovering the joint impact of governance and economic factors

on equity market development are quite scarce in numbers. Particularly, studies on indirect

and cross effects of governance and economic factors are quite scarce in the existing literature.

Despite the fact that there is a dichotomy on the true determinants of equity market

development, yet this study would augment the database of factual position on qualitative as

well as quantitative factors of equity market development. In isolation, the studies are available

in the existing literature regarding impact of governance factors on the stock market

development but further research is being demanded on the comprehensive study of economic

and governance factors according different regions of the world equity markets. Moreover,

there are no ranking of countries for quantifying the effects of governance and economic factors

on the equity markets and vice versa effect of economy on the stock market which identify the

reverse effects as well. Likewise, there are total of five indices for measuring country risk, out

5

of which, four measures are from International Country Risk Guide (ICRG) and one from

Investors’ guide Erb, Harvey, and Viskanta (1996). Nevertheless, they have not covered all

political risk factors and combine effect of economic variable was ignored. Levine and Zervos

(1996) analyze the empirical evidence of equity market development and economic growth in

long-run. They find that equity market development is robustly and positively correlated with

long-term growth of economy but they have ignored the dimension of governance factors.

However, the present study encompasses wide-ranging analysis covering direct and indirect

effects including cross effects of both the economic and governance factors on the panel of

developed, emerging and frontier equity markets.

The development of equity markets is a complex phenomenon and it requires a

comprehensive and rigorous approach to explore its factors encompassing both qualitative and

quantitative nature. Yartey (2008) has conducted the study on emerging market only but the

author ignored analysis of developed and frontier markets. So there is a dire need of having a

more comprehensive study which should not only encompasses the economic and governance

factors but also covers all three regions of developed, emerging and frontier markets as per the

region classification by FTSE (accessed on 30 October, 2015) as follows .

World Stock Markets

(70)

Developed Stock

Markets (25)

Frontier Stock

Markets (24)

Emerging Stock

Markets (21)

6

Figure 1.2 Diagrammatic view of the world stock markets with all three regions of

developed, emerging and frontier markets

Emerging and Frontier economies are more prone to factors of institutional quality as

compared to developed economies. This is reflected by the weak institutions and low quality

of governance in implementation of rules and regulations in emerging and frontier markets.

The aim of the dissertation is to identify and classify the stock markets and regions, which are

more affected by the governance factors than economic factors and finding a quantitative scale

for the ranking of countries as per the effects of political and economic factors. Moreover,

efforts would be made to quantify the regional differences in affecting the economic and

governance factors on the development of equity market. On the other side, analysis would be

carried out to find vice versa effects of development of equity market on the economic and

governance factors.

This study has been conducted on three dimensions in which first dimension covers

world equity markets into three regions. The second dimension encompasses impacts of

economic factors. The third dimension comprises the impacts of governance factors on the

stock market development. Finally, the sensitivity analysis would be conducted for the direct

and indirect including cross effects of economic and governance factors on the stock market

development. The comprehensive nature of the study and latest methodologies will definitely

append the knowledge of the worthy readers. This study would fill the research gap by making

an in-depth study of developed, emerging and frontier equity markets to find the most affected

determinants of stock market development.

Although, the focus of existing studies cover the quantitative impact on the

development of stock market, yet the other dimension of the present study is on the qualitative

7

aspects. The Panel study incorporates stock markets from 70 countries, which includes 25

Developed, 21 Emerging and 24 Frontier Equity Markets and the motivation of this study is as

follows:-

(a) To analyze the impact of economic factors on the stock market development.

(b) To analyze the impact of governance factors on the stock market development.

(c) To form composite factors of economic and governance factors according to

regional markets of developed, emerging and frontier regions.

(d) To analyze the indirect and joint impacts of governance and economic factors

on the stock market development.

(e) To conduct the sensitivity analysis of quantitative and qualitative factors and

find the array of countries, where the development of stock markets are more

prone to qualitative than quantitative factors.

(f) To find vice versa effects of governance and economic factors

1.3. Significance of the Study.

This study is different from other empirical studies for couple of reasons that need to

be ponder upon. Firstly, it focuses more on the governance factors than economic factors.

Secondly, it is aimed at all regions of the world, namely, developed (25 markets), emerging

(21 markets) and frontier (24 markets) region. Thirdly, it is intended to find indirect and cross

impacts of governance factors on the equity markets as well as on economic factors. Fourthly,

identifying region, which are having reverse impact of stock market development on economic

indicators. If we focus on the reality on ground that the efficacy of economic factors is quite

fragile when the institutions controlling and affecting the economy are quite weak. So, the

8

governance factors plays pivotal role in the development of economic factors as well as equity

market development. Whereas, several studies are ignoring the pivotal factors which are

responsible for the development of stock markets as well.

Figure 1.3 Relational Impact of governance factors on development of stock market and

economic factors

However, this study has been aimed to identify the effects of governance factors on the

development of stock market as well as economic factors. In return, the effects of economic

factors on the development of stock market is also be explored in the study. Finally, a

quantified portion of economic and governance factors have been identified according to

regional standing.

1.4. Research Objectives.

Keeping in view the motivation and significance of the study, the major research

objectives are enumerated below:

(a) To develop indices for the composite factors of economic and governance

factors along with their cross indices according to regional markets of

developed, emerging and frontier regions.

(b) To analyze the impact of economic factors on the equity market development.

Governance Factors

Stock Market Development

Economic Factors

9

(c) To investigate the impact of governance factors on the stock market

development.

(d) To analyze the combined effects of governance and economic factors on the

development of equity markets.

(e) To investigate the indirect effects of governance factors through economic

factors.

(f) To examine the cross effects of governance and economic factors on the

development of stock market

(g) To explore the inter-dependence of governance and economic factors?

1.5. Research Questions.

In order to meet the study objectives, the research questions are as follows:-

(a) What are the composite factors for economic and governance variables along

with their cross composite factors according to regional markets of developed,

emerging and frontier regions?

(b) What are the impacts of Economic variables on the equity market development

of World Stock Markets?

(c) What are the impacts of Governance variables on the equity market

development of World Stock Markets?

(d) What are the combined impacts of Governance and Economic variables on the

development of stock market concerning World Equity Markets?

(e) What are the indirect effects of governance factors through economic factors on

the development of stock markets?

(f) What are the cross effects of governance and economic factors on the

development of stock market?

10

(g) How much is the inter-dependence of Governance and Economic factors with

each other?

Abovementioned research questions will be tested for all segments of world stock

markets separately, that is, all world (70), developed(25), emerging(21), and frontier(24)

markets. The estimation of the study models is based on these questions according to region

wise.

1.6. Contribution to Knowledge.

There are number of studies conducted for analyzing the impact of economic variables

on development of stock market but the literature is quite limited on the comprehensive study

of both quantitative and qualitative variables along with their indirect and cross effects

according to region wise. Moreover, this study has been planned to analyze the dual role of

governance factors on the returns of equity markets as well as economic factors. As there is a

scarcity of the literature concerning qualitative institutional variables, so this study will append

the knowledge of prevailing institutional works. Moreover, the formation composite indices

of each region may enhance the knowledge database of financial markets.

The rest of the study is presented in seven chapters. In Chapter 2, the literature review

of the study is presented and it also describes the Stock Markets as classified by Financial

Times Stock Exchange (FTSE) Group. Chapter 3 represents empirical model and hypotheses

development and Chapter 4 describes the methodology of Dynamic GMM Model. Chapter 5

encompasses the data & variables and Chapter 6 presents and discusses the empirical results of

the study. Finally, the summary and conclusions of the study are drawn in seventh Chapter.

11

CHAPTER 2

REVIEW OF LITERATURE

2.1. Introduction

The literature review of the study has been presented in three sections. Section 2.2

describes literature on stock market development. Section 2.3 covers the literature on

Governance Indicators and Section 2.4 describes empirical methodologies concerning present

study. Finally, concluding remarks of the literature review are presented in Section 2.5.

2.2. Literature on Stock Market Development

In this section, the literature review of main determinants for the development of equity

markets is presented. It is quite imperative to clarify the main terms of stock markets right at

the outset of the study. In the literature of stock markets, its nomenclature of stock market is

referred by the names of equity or financial market. However, in this study, the term of stock

market is mostly referred as equity market. Calderon-Rossell (1991) is considered as a pioneer

to present a partial equilibrium model for the growth of equity market. Uptill now, this model

symbolizes the most somber endeavor to make the foundational financial theory for the

development of equity market. In the recent times, El Wassal (2013) identifies the structure

for the major determinants for the development of equity market and he suggests four factors

as demand factors, supply factors, economic and institutional factors. On the other side, Yartey

(2008) analyzes the economic and institutional determinants of equity market development by

employing the panel dataset of forty two emerging economies. He finds that economic factors

12

are important determinants of stock market development. His results furthermore show that

governance, bureaucratic quality and law & order are vital determinants of equity market

development.

There is sufficient proof exists amid the previous couple of decades that securities exchanges

have turned out to be more corresponded with each other in regard of universal exchange and

capital streams Forbes and Chinn (2004). It shows that the cross-fringe exchange and capital

streams have improved the probability for the transmission of stuns started in a monetarily and

fiscally vital nation to the worldwide market. Bekaert, Hodrick, and Zhang (2005) inspect the

level of territorial and worldwide combination utilizing securities exchange returns in twenty

two nations amid the period beginning from January 1980 and finishing off with December

1998. They discover that the level of incorporation of stock returns in these twenty two nations

is not as awesome as was for the most part thought at the time. A compelling examination by

King and Wadhwani (1990) inspects the distinction between the relationship coefficients of the

stock exchange returns of Japan, UK, and US for the periods previously, then after the fact the

share trading system crash in 1987. The examination finds that there had been a sensational

increment in the coefficients of connections after the crash. The investigation additionally

contends that the stock returns in these business sectors fell together after the rate of securities

exchange crash in light of the fact that the private data set contains both eccentric and orderly

segments. Bayraktar (2014) studied the measurement of relative development level of stock

markets in the panel of 104 developed and developing countries. The author finds that effective

financial system is one of the main determinants of equity market development.

13

2.3. Literature on Governance Indicators

Governance factors plays an important role in the management of country’s economy

and output of the economy is depended on the quality of institutions controlling them. If the

institutions of any country are weak, then long term survival of the economy quite fragile. To

the degree that open dispositions impact an open authority's appraisal of the social stigma

joined to a corrupt demonstration, open discernments about the nature of administration may

affect the level of defilement. There are number of endeavors have been done by different

agencies to develop governance indicators and recent endeavors at the World Bank, depicted

by Kaufmann (2007), to develop an arrangement of total Worldwide Governance Indicators

(WGI), give a wellspring of high-quality information on these open observations. They have

developed governance indicators in six categories. One of the factors in WGI set is the Absence

of Violence and Political Stability, which measures "view of the probability that the

administration will be toppled by illegal or rough means, including abusive behavior at home

and psychological warfare" Kaufmann, Kraay, and Mastruzzi (2007) p.3.

Another factor from the WGI is the Voice and Accountability, which measures

discernments concerning "the degree to which a nation's natives can take an interest in choosing

their legislature, and additionally opportunity of articulation, flexibility of affiliation, and free

media" (Kaufmann et al., 2007 p. 3). If there is no accountability, then level of corruption is

increased in manifold. An open authority in a nation whose natives trust these flexibilities are

solid and all around ensured is probably going to feel that a degenerate demonstration will be

immediately found and rebuffed.

The Rule of Law factor of WGI measures open impression of "the degree to which

specialists have trust in and submit to the tenets of society, and specifically the nature of

14

agreement implementation, the courts and the police and also the probability of wrongdoing

and brutality" Kaufmann et al. (2007) p.4. This factor demonstrate the strength of law

enforcement and establishment of the strong judiciary system. Since enhanced level of peace

expands the likelihood of recognizing and rebuffing unlawful lease apportionments, a

recognition that the control of law is solid brings down the motivating forces to act deceptively.

2.4. Literature on Methodology

This study looks at the governance and economic determinants for the development

stock market in the panel data of developed, emerging and frontier markets. As we probably

aware that both governance and macroeconomic components are imperative in securities

exchange improvement. Garcia and Liu (1999) demonstrated that economic factors, like,

savings rate, stock market liquidity, real income and financial intermediary development are

the vital determinants for the development of equity market. On the other side, Pagano (1993)

demonstrates that governance components may impact the effective working of securities

exchanges. For instance, required revelation of dependable data about firms may improve

financial specialist cooperation, and controls that ingrain speculator's trust in dealers ought to

empower venture and exchanging the stock exchange. La Porta et al (1996).

Naceur, Ghazouani, and Omran (2007) examine the role of equity markets in the growth

of an economy and ponder on economic determinants that influence the development of stock

markets. They find that stock market liquidity, financial intermediary, stabilization and saving

rate variable are the vital determinants of equity market development. Beck and Levine (2004)

using a panel data set examine the effect of equity markets and banking sector on economic

growth by applying GMM techniques developed for dynamic panels. They find that banking

sector and equity markets effect positively on the growth of economy. Kanetsi (2015)

investigates the relationship between economic growth and equity market development in

15

seven countries of Sub-Saharan African region and the study finds that the stock markets are

partially and in some countries marginally responsible for output growth.

Apergis, Artikis, and Eleftheriou (2011) examine the dynamic relationship between

economic factors and excess returns for the region of emerging markets by applying the panel

GMM estimator methodology. Their findings indicate that several economic factors has a

significant part in explaining excess returns of emerging economies. However, in the case for

the implementations in statistical terms, the economic models carries few significant

disadvantages. For instance, Clare and Thomas (1994) discover that that the factor structure

changes over time and not a robust to the formation of portfolio criteria. The studies that have

applied APT model discover that the same kinds of variables used by Chen, Roll, and Ross

(1986) are more country-specific and priced as well.

Regarding, panel data estimation of financial markets, Arellano and Bond (1991)

suggest applying an estimator of dynamic panel data based on the methodology of General

Methods of Moments that optimally exploits the linear moment restrictions implied by the

growth model of dynamic panel. The dynamic estimator of GMM is an instrumental variable

that applies with current and lagged values of all strictly exogenous regressors and lagged

values of all endogenous regressors as instruments.

2.5. The Stock Markets

The stock markets are likely to boost economic growth by enhancing the domestic

savings and increasing the quantity and the quality of Investment. Stock markets are also

refereed as equity or financial markets where a common person can invest his or her earnest

money without having pressure. Moreover, it is a platform for buying or selling equity of large

companies with any magnitude of investment. But this market is driven by certain factors which

16

creates trading movements in certain direction depending upon the dynamics of that particular

country. At the national level, the factors of economy and governance issues play a vital role

in determining its trend, which may also differ from, developed to developing region. The

stock market is not developed at its own, rather the development in this sector is dependent

upon external factors. So, the phenomena of stock market development is not quite simple,

rather intricate in comprehending the true determinants of this market. At present, majority of

the researchers in this field are making predictive analysis based on the past movements and

trends in the economic conditions but governance issues are not being fully analyzed. As it is

clear that dynamics of developed region are quite dissimilar than developing region, so the

investors in that particular region are also behave differently depending upon the regional

dynamics. There may be a role for governance in elucidating the difference in magnitude, as

slight change in the policy might have significant effect. Baumol (1965) explains the economic

efficiency of assets and stock market returns. The conjecture of the author is that, if

management does not increase value of the firm value, then another economic agent may take

control and manage the firm efficiently and resultantly reap the benefits of highly efficient

firm.

2.6. Classification of Stock Markets

The stock markets play a vital role in the economics of any country which are further

classified into Developed, Emerging or Frontier Markets according to their size and operations.

Financial Times Stock Exchange (FTSE) Group annually publishes the results of country

classification by a process in which stock markets are classified as either Developed, Emerging

or Frontier.

The developed equity markets contains the largest and highly industrialized economies.

The economic systems of developed countries are well established, they are politically stable

17

and the rule of law is well deep-rooted. Developed markets are typically considered as the

safest investment terminuses, but their economic growth rates frequently follows those

countries, which are in an earlier development stage.

Emerging markets face swift development and often depict extremely high economic

growth. This high economic growth can occasionally translate into investment returns that are

higher to those available in developed markets. Nevertheless, emerging equity markets are

more riskier having political uncertainty with high fluctuations.

Frontier markets denote "the next wave" of investment terminuses. These equity

markets are usually either smaller than emerging markets, or having constraints on foreign

investment. Although frontier markets can be extremely risky and suffer from low liquidity,

yet they also offer above-average returns. Frontier equity markets are also not well correlated

with other markets.

The country classification method has been adopted for more than a decade, and with

the passage of time, it has developed into a transparent and unbiased mechanism of classifying

markets to meet the prerequisites of institutional investors. The country classification of world

equity markets duly classified by FTSE as on 30 September 2015 is shown in the table 2.1.

18

Table 2.1

Country Classification of World Equity Markets by FTSE as on 30 September 2015

2.7. Concluding remarks of Literature Review

This section is concluded on the observation that there are number of studies for

economic and governance factors in isolation but there are very few empirical studies that

conduct comprehensive analysis of both economic and governance factors according to the

combined and isolated panels of developed, emerging and frontier markets. Moreover, there

is no quantification of regional variances in affecting the economic and governance factors on

development of equity market. As there is a scarcity of the literature concerning qualitative

institutional variables, so this study will append the knowledge of qualitative works.

19

Furthermore, this study has been planned to analyze the dual role of governance factors on the

returns of equity markets as well as economic factors, which will fairly contribute the existing

literature.

20

CHAPTER 3

EMPIRICAL MODEL AND HYPOTHESES DEVELOPMENT

3.1. INTRODUCTION

This section encompasses the Empirical Model and Hypotheses Development of the

study. In the former section, the proposed model has been linked up with existing literature

and further explanation of the model and its variable is presented in diagrammatically and

equation wise as well. However, in the later part of this section, the main and subsidiary

hypotheses of the study are presented in chronological order.

3.2. Theoretical Framework

Economic theory concerning the relationship between equity market development and

economic variables is quite intricate. Since 1980s, there have been several studies to identify

factors in the arbitrage pricing theory (APT) model with economic variables affecting returns

of assets and one of leading studies is conducted by Chen et al. (1986). Later on, Calderon-

Rossell (1991) presented a basic model which is still considered as the basis to analyze

determinants of equity market development.

3.2.1. Basic Caldron-Rossell Model

Calderon-Rossell (1991) analyzed a behavioral and structural approach to equity

market development in which equity market liquidity and economic growth are considered as

the vital determinants of equity market development. It is hypothesized in the model that equity

21

market development is derived from the economic development, which is further calculated by

market liquidity and output growth. The Basic Calderon-Rossell model states that equity

market capitalization is a function of the number and value of listed companies. In this model,

liquidity of equity market and economic growth are referred as the vital determinants for the

development of equity market. Therefore, the market capitalization is shown as appended below:

Y = P*V (1)

Where:

P = Number of listed firms in the equity market;

Y = Market capitalization of stocks; and

V = Mean price of listed companies.

The model can be presented formally as follows:

Y = P*V = Y (G,T) (2)

V = V (G,P) (3)

P = P (T,V) (4)

The exogenous variable T denotes the turnover ratio and variable G denotes per capita Gross

National Product (GNP). The endogenous variables are P, V, and M. As it is evident that

Calderon-Rossell model signifies as a set of interrelated functions. So, the equation (3) and (4)

can expressed in terms of growth rates as follows:-

22

Now by combining equations (5) and (6) with equation (2), the result would be as follow:-:

After factoring, the equation (7) becomes

The model specification in Equation (8) can be articulated as the reduced form behavioral

model:

Where,

)10).......(( 111

)11).......(( 222

Equation (8) illustrates the effects of stock market liquidity (T) and economic growth

(G) on equity market development (Y).

To validate this model, Calderon-Rossell incorporated data from the annual observations of

forty-two countries from the world’s major stock markets. The analysis depicts that equity

)5...(....................21 LogTLogGLogV

)6..(....................21 LogTLogGLogP

)7.....()( 2121 LogTLogGLogTLogGPVLogLogY

)8.(..........)()( 2211 LogTLogGLogY

)9..(..............................21 LogTLogGLogY

23

economic growth and market liquidity are the vital determinants for the growth of stock

markets.

3.2.2. Augmented Caldron-Rossell Model

Calderon-Rossell (1991) developed a comprehensive partial equilibrium model which

served as a conceptual underpinning of financial theory for development of equity markets.

The study identified economic growth and stock market liquidity as the vital determinants for

the growth of equity markets. Garcia and Liu (1999) while analyzing the data of Asian and

Latin American markets have found that economic factors such as growth rate in financial

intermediary sector development, domestic investment and gross domestic product are vital

elements for the growth of equity market. Furthermore, La Porta, Lopez-de-Silanes, Shleifer,

and Vishny (1997) argued that legal origin have significant impact on the equity market

development.

In order to capture the role of qualitative variables (like governance, legal system and

accountability), an additional element of governance indicator is to be introduced into the Basic

model. Now the econometric model would be as follows:

Y = f ( Yt-1, E, G )……………………………………………………(12)

Where: Y –Equity market capitalization in %age of GDP;

Y t-1 –Lag dependent variable;

E –Vector of economic variables (inflation rate, income level and banking

sector development);

24

G – Vector of Governance variables (which include: rule of law, govt

effectiveness etc).

The econometric model as mentioned above is then transformed to logarithmic model

as we are interested in elasticity (growth rate) of equity markets as shown below:

The Augmented model of Calderon-Rossell is based on undermentioned assumptions:

(a) Generally, Investors prefer doing business in a safe economy with strong

fundamentals.

(b) Investors, in general, tends to make their investments in a country with having

better political stability.

By applying logarithms on both sides of the equation, the abovementioned model would

become as follows:

Log Yt = Log A + δ LogYt -1 + β LogEt +ϖ LogGt + µt …………………(14)

Where µ ~ NID ( 0 , σ2 )

Now by assuming natural logarithms on both sides of equation (14), we will get

)13.(..........1 tt

tttt GEAYY

25

LogY = LnY, Log A = α, LogYt−1 = Ln Yt-1, LogE = LnE, LogG = LnG

Hence, the general econometric model applied in the present study becomes as follows:

LnYt = α + δ LnYt-1 + β LnEt +ϖ LnGt + µt……………………………………(15)

The equation (15) represents an Augmented model of Calderon-Rossell, which

is the theoretical foundation of the present study. It is combination of basic determinants

of equity market development and governance factors. In particular, we will examine

the role of governance and economic factors in explaining stock market development

and further endeavors would be made to find the cross effects of governance and

economic factors. Due to the dynamic characteristics of the data, the undermentioned

regression would be estimated:-

Where, Yit is the matrix of equity market capitalization in proportion to GDP of

particular country i in year t , αi is the unobserved country wide fixed effect, and εit denotes

usual white noise. Eit is a vector of economic variables and G it is a vector of governance

factors. The study also incorporates dependent variable (Yit-1) in lag form as one of the right

hand side variables because it is believed that development of equity market is a dynamic

concept.

)16.(..........1 ititjitkitiit GEYY

26

3.2.3. Diagrammatical Model of the Study

The determinants of stock market development include both the quantitative and

qualitative variables. The quantitative variables encompass the macroeconomic factors,

whereas the qualitative variables consist of governance factors and world governance

indicators. These variables can predict the behavior of the stock market which is shown in a

diagrammatic view as below.

Figure 3.1 Diagrammatic view of the main model depicting relations among variables of

development of stock market, economic and governance factors

Stock Market

Development

-Market capitalization as %

of GDP

Economic Factors

GDP per capita growth rate

Gross Dom Savings as %of GDP

Real Interest rate

Annual Inflation Rate

FDI as %age of GDP

Broad Money as % of GDP

Trade as %age of GDP

Credit by financial sec % of GDP

World Governace Factors

Voice and Accountability

Political Stability and Absence of Violence

Government Effectiveness

Regulatory Quality

Rule of Law

Control of Corruption

27

3.2.4. Empirical Models and Hypothesis Development

Keeping in view the aforementioned theoretical and diagrammatical models, five

empirical models have been formulated that are to be tested for meeting our research objectives.

Each model is to encompass the diagrammatical model, mathematical equation and its related

Hypotheses. Then in the estimation phase, each model is to be tested for the panel data of

world and all of the three regions, developed, emerging and frontier markets. The first model

is as follows:

4.2.4.1. Empirical Model No 1: The impact of economic variables on stock market

development

The equation for testing the model-1 would be:

Economic FactorsStock Market Development

)17.(..........1 ititkitiit EYY

28

Where, Yit is the matrix of stock market capitalization relative to GDP of

particular country i in year t , αi is the unobserved country specific fixed effect,

and εit is the usual white noise. Eit is a vector of macroeconomic variables

4.2.4.2. Hypothesis Set for Model-1

The Null Hypotheses for the Model-1 with exogenous variables as 08 Economic

Variables and one composite economic variable are appended below:-

H011: GDP growth rate do not affect the development of stock markets.

H012: Annual Inflation Rate do not affect the development of stock markets.

H013: Real Interest rate do not affect the development of stock markets.

H014: Domestic credit to private sector as %age of GDP do not affect the development

of stock markets.

H015: Gross Dom Savings as % GDP do not affect the development of stock markets.

H016: Trade as %age of GDP do not affect the development of stock markets.

H017: FDI as %age of GDP do not affect the development of stock markets.

H018: Current Account Balance % of GDP do not affect the development of stock

markets.

4.2.4.3. Empirical Model No 2: The impact of governance variables on stock market

development

29


Where, Yit is the matrix of financial market capitalization relative to GDP of particular

country i in year t , αi is the unobserved country specific fixed effect, and εit is the

usual white noise. G it is a vector of governance factors.


The Null Hypotheses for the Model-2 with exogenous variables as 06

Governance Variables are appended below:-

H021: Voice and Accountability do not affect the development of stock markets.

H022: Political Stability and Absence of Violence do not affect the development of

stock markets.

H023: Government Effectiveness do not affect the development of stock markets.

H024: Regulatory Quality do not affect the development of stock markets.

H025: Rule of Law do not affect the development of stock markets.

H026: Control of Corruption do not affect the development of stock markets.

Governance FactorsStock Market Development

)18.(..........1 ititjitiit GYY

30

3.2.4.5. Empirical Model No 3: This model measures the joint impact of economic and

governance factors on the stock market development


Where, in the above equation, Yit is the matrix of stock market capitalization relative

to GDP of particular country i in year t , αi is the unobserved country specific fixed

effect, and εit is the usual white noise. E it is a vector of macroeconomic variables and

G it is a vector of governance factors.



Economic Variables and 06 Governance Variables with are appended below:-

Economic Factors

+

Governance Factor



31





of stock markets.





markets.



stock markets.





3.2.4.7. Empirical Model No 4: This model measures the channel effects of governance

factors on stock market development and further the indirect effect governance variables

through economic variables.

32

The set of equations for testing the model-4 would be:



effect, and εit is the usual white noise. Eco it is a matrix of composite macroeconomic

variables and Gov it is a matrix of composite of governance factors.

Now for estimating the direct effect of Governance(Gov) on Stock Markey Development(Y)

and Indirect effect of Governance(Gov) factors on the development of Stock Markey (Y)

through Economic Factors(Eco), the following methodology is adopted:-

Governance Factors

Economic Factors


)20...(*1 itititkitjitkitiit GovEcoGovEcoYY

)21...(ititktit GovEco

33

First of all, above mentioned equations No 20 and 21 are estimated as a System and then

estimated values are placed in the equations and following steps are adopted:-

The expression is to be tested for Wald test for its significance

Where,

= Direct effect of Governance factors (Eco) on the development of stock market (Y)

= Indirect effect of Governance factors (Gov) on the development of stock market

(Y) through Economic Factors (Eco).


To test the significance of values in model-4, a set of hypotheses is formed. Therefore,

the Null Hypotheses for the Model-4 with exogenous variables as 08 Economic Variables and

06 Governance Variables are appended below:-

H011: A composite of Economic variables do not affect the development of stock

markets.

H012: A composite of Governance variables do not affect the development of stock

markets.

H013: There is no direct effect of composite Governance variables on the development

of stock markets.

)22(...........).().(

GovY

).(

.

34

H014: There is no indirect effect of composite Governance variables through

Economic variables on the development of stock markets.

.

3.2.4.9. Empirical Model No 5: This model measures the cross effects of governance

factors on economic variables and stock market development viz-a-viz impact of economic

variables on stock market development.

The set of equation for testing the model-5 would be:



Governance Factors

Economic Factors



35

effect, and εit is the usual white noise. Eco it is a composite of macroeconomic variables

and Gov it is a composite of governance factors.

= Direct effect of Economic factors(Eco) on the development of stock market (Y)

= Direct effect of Governance factors(Gov) on the development of stock market (Y)

= Cross effects of Governance factors(Gov) and Economic Factors (Eco).on the

development of stock market (Y)



Economic Variables and 06 Governance Variables with are appended below:-


markets.


markets.

H013: A cross composite of Economic and Governance variables do not affect the

development of stock markets.

H014: A composite of Economic variables do not affect the composite of Governance

variables.

36

CHAPTER 4

METHODOLOGY

4.1. Introduction

In order to combat the challenges of panel data analysis, the study has adopted two

pronged methodology to empirically analyze the panel data. In the first part, the statistical

analysis is carried out and later part covers the econometric analysis of panel observations.

4.2. Statistical Methodology

As the scope of our study is to analyze 70 countries of world stock markets, so it is

imperative to know their trends and results of basic statistical technique. The list of statistical

technique is appended below:-

(i) Basic Statistics of Stock Market Development (Dependent Variable)

(ii) Basic Statistics of Economic Factors (Predictor Variable)

(iii) Basic Statistics of Governance Factors (Predictor Variable)

(iv) Correlation among Economic Factors (Predictor Variable)

(v) Correlation among Governance Factors (Predictor Variable)

(vi) Scatter Plots of Stock Market Development and Economic Composite Variable

with their respective Distribution and Kernel Regressions.

(vii) Scatter Plots of Stock Market Development and Governance Composite

Variable with their respective Distribution and Kernel Regressions.

(viii) Scatter Plots of Economic and Governance Composite Variables with their

respective Distribution and Kernel Regressions.

37

4.3. Econometric Methodology

The study incorporates panel dataset with time period of 20 years (t) alongwith four

country groups of 24 (developed), 21 (emerging), 24 (frontier) and 70 (world) equity markets.

Keeping in view, the complexities and comprehensive nature of data, several techniques have

been studied and appropriate econometric methodology has been adopted in this study. Given

the panel nature of our study, the estimation of proposed models in dynamic panel data can

have numerous problems due to following reasons:

(a) Inconsistent and biased estimators due to time invariant country fixed

characteristics.

(b) Heteroscedasticity and serial correlation may lead to the idiosyncratic

disturbances.

(c) Possibility of endogeneity of regressors would be there. Consequently, the

orthogonality condition between regressors and error term would not be true.

(d) Perfect instrumental variable does not exist that can satisfy the requirement of

strict exogeneity.

(e) Inclusion of lagged dependent variable as an explanatory variable for dynamic

process creates autocorrelation, which biases estimator upward by applying Ordinary

Least Squares.

To resolve abovementioned problems, there would be mainly one econometric

methodology, that is, Dynamic General Methods of Moments (GMM), and the same has been

38

incorporated in this study to find the nature of relationship among the panel data sets of Equity

Market Development, Economic and Governance Factors. The details of this econometric

methodology is enumerated in the subsequent paragraphs.

4.3.1. Dynamic Panel Generalized Methods of Moments (GMM)

As the nature of our dataset is in panel form of different countries of three regions,

which is, developed, emerging and frontier across number of years, so this study incorporates

panel data methods for the estimation of regression models. Entire relationships to be

premeditated can be described by the joint endogeneity of included variables of Economic and

Governance factors. It means that most exogenous variables are either have a two-way causal

relationship or simultaneously determined with the dependent variable. The possibilities for

the unobserved country specific effects could be present.

The econometric technique of Dynamic General Methods of Moments (GMM) has been

incorporated in estimating the regression models of the present study. For estimating dynamic

panel models, there are two methods for estimating panel models, first one is the Arellano-

Bond dynamic panel, in which fixed or individual effects are taken by differencing the data.

The second method is the approach of Arellano-Bovver, which allows the fixed effects through

orthogonal deviations. The relationships to be studied can be depicted by the joint endogeneity

of most involved variables. Explicitly, the majority of exogenous variables in the model are

either have a two-way causal relationship with each other or simultaneously determined with

the dependent variable.

39

Arellano and Bond (1991) suggest an estimator of dynamic panel data based on the

methodology of Generalized Method of Moments (GMM) that is implied by the dynamic

panel growth model and optimally exploits the linear moment restrictions. The dynamic

GMM estimator is an instrumental variable estimator, which uses lagged values of all

endogenous regressors with current and lagged values of all strictly exogenous regressors as

instruments. The dynamic GMM estimator is given as follows:−

YZZAXXZZAX NN

1

Where in the model

is the vector of endogenous and exogenous coefficient estimates

X and Y are the vectors of the first differences of all the exogenous variables,

Z is the vector of instruments as specified in GMM and

NA is a vector applied to weigh the instruments.

In the present study, the exogenous variables are economic and governance variables and stock

market development as dependent variable. The instruments are taken as the dynamic

regressors of GMM as presented by Arellano and Bond with lagged values of stock market

development alongwith lag values of the exogenous variables as specified in the relevant model

(Arellano and Bond (1991)) . The care has been taken for the over-identifying restrictions on

the instruments of GMM model through Sargan Test.

40

CHAPTER 5

DATA AND VARIABLES

5.1. Introduction

The determinants of equity market development constitute both the quantitative and

qualitative variables. The former encompass the macroeconomic factors, whereas the later

consist of governance factors. In the literature, there are several Macroeconomic factors that

have been taken for the of Stock Market development but in our study, we are focusing on GDP

per capita, Savings, Macroeconomic Stability, Liquidity of Stock Market, and Private Capital

Flows. On the qualitative variable sides, we have taken Governance factors comprising

Political Stability & Absence of Violence/Terrorism, Voice & Accountability, Government

Effectiveness, Regulatory Quality, Control of Corruption and Rule of Law.

5.2. Dependent Variable : Stock Market Development

The dependent variable of our study is stock market development, which is measured

by trade and market value of listed share in the equity market. In our study, stock market

development has been measured by using market capitalization as a percentage of GDP, which

equals to the market value of listed shares divided by GDP. The postulation behind this

measure is that the size of market is correlated positively with the ability to mobilize capital

and diversify risk on an economy-wide basis. So, in the subsequent analytical part, the

development of stock market would be determined by the market capitalization as percentage

of GDP of that very country.

41

5.3. The Economic Factors

Stock markets, like other financial institutions, intermediate savings to investment

projects. Usually, larger the savings leads towards higher volume of capital flows in the stock

market. Macroeconomic stability plays an important role for the development of stock markets

in all regions of the world. Thus, we expect from our study that economic factors to be

important determinants in the development of stock market. In our study, the following

components are used to produce the effects of Economic factors on stock market development:-

(a) Income Level: Income level is calculated by using the data of GDP per

capita in US dollars. As per the hypothesis of demand driven, the expansion in

economy would generate new demands for the financial activities. Resultantly,

the escalated demand will pressurize the establishment of larger and highly

sophisticated financial institutions to meet the fresh demands.

Income Level= GDP per capita growth

(b) Savings: Equity markets play an intermediary role converting savings to

investment projects. Generally, the higher savings produce the larger capital

flows through the equity market. The savings is considered as a vital

determinant of stock market development, which is determined by gross

domestic savings as %age of GDP.

Savings= Gross domestic savings as %age of GDP

42

(c) Stock Market Liquidity: The stock market liquidity is measured by using

the Stock value traded as a %age of GDP. This ratio does not measure directly

as how effortlessly investors can trade their shares, rather it measures the value

of equity transactions in comparison with economy size. Nonetheless, it

definitely measures the degree of trading in comparison with economy size. So,

it depicts the liquidity of stock market in an economy as it is highlighted by

Levine and Zervos (1998).

Stock Market Liquidity = Stock value traded as a %age of GDP

(d) Macroeconomic Stability: The higher stability in macroeconomic factors

creates higher incentive to investors and companies for participating in the

equity market. Moreover, profitability of companies becomes quite volatile by

fluctuations in exchange rate, fiscal and monetary policies. So, developed

equity markets have stability in their macroeconomic factors. In this study, we

incorporate two measures for measuring the stability in macroeconomic factors,

that is, real interest rate and inflation rate because of their significance in the

literature as used by Garcia and Liu (1999).

Macroeconomic Stability = Inflation rate and Real interest rate

(e) Capital Flows. Errunza (1983) stipulates that inflows of foreign capital on the

equity market development creates broader impacts than initial flows and higher

investor participation. He further suggests that foreign investment is correlated

with governance factors and fair practices in trading of stocks. In this study,

43

capital flows is determined by incorporating the indicator of foreign direct

investment as a %age of GDP.

Capital Flows = Foreign direct investment as a %age of GDP

(f) Trade Openness. As per the studies of (Fischer (2003)), trade openness is

referred as the monetary relationship among nations in exchange of goods and

services. In this process, the countries make certain financial transaction for

import and export of goods services to get the benefit of cross border trade. It

is determined by using the Trade in the country as a %age of GDP.

Trade Openness = Trade as a %age of GDP

Additionally, a composite variable of under studied economic factors is formed through

Principal Component Analysis (PCA) for analyzing the combined effect of economic factors.

Additionally, this variable is used to measure the cross effects and reverse impacts of stock

market development.

5.4. The Governance Factors

The current study proposes that another set of factors which may unfavorably influence

stock market development is governance risk (Diamonte, Liew, and Stevens (1996); Erb et al.

(1996); Perotti and Van Oijen (2001)). The idea of governance contains political unsteadiness,

as well as outer clash; defilement in government; military in legislative issues; lawfulness

44

custom; racial and national pressures; political psychological oppression; common war chance;

nature of organization. All the more correctly, lawful and political instability about future

mediation of political strengths in the administration of monetary action through

nationalizations and seizures of private investment, is probably going to dampen the equity

markets.

The marvel of governance factors envelops administration, legitimate framework and

responsibility. Low governance exhibits the presence of higher quality foundations. Three

methods of institutional quality are utilized as a part of the writing. The first is the nature of

administration, including, defilement, political rights, open division productivity, and

administrative weights. The second is the legitimate assurance of private property and law

authorization. The third is responsibility and the points of confinement put on the official and

political pioneers (Edison, 2003).

The Worldwide Governance Indicators (WGI) cover more than two hundred nations

and domains, measuring six measurements of administration beginning in 1996: Political

Stability and Absence of Violence, Government Effectiveness, Voice and Accountability,

Regulatory Quality, Control of Corruption and Rule of Law. The total pointers depend on a

few hundred individual hidden factors, derived from a wide assortment of existing information

sources. The information mirror the perspectives on administration of review respondents and

open, private, and NGO segment specialists around the world. The WGI construct measures

of governance corresponding to six dimensions as appended below:

(i) Political Stability and Absence of Violence

(ii) Government Effectiveness

(iii) Voice and Accountability

(iv) Regulatory Quality

45

(v) Control of Corruption

(vi) Rule of Law

Finally, a composite index of governance factors is formed through Principal

Component Analysis (PCA) for analyzing the combined effects of governance and economic

factors. Additionally, these variables are used to measure the cross effects and reverse impacts

of stock market development.

46

5.5. Predicted Variable Signs.

The expected signs of the variables under studied are depicted below:-

Table 6.1

Summary of the Variables long with their Predicted Signs

Characteristics

Variables

Expected

Signs

Stock Market

Development

- Stock Market Size Y= Market capitalization of listed dom cos (% of

GDP) -

- Stock Market Liquidity S3= Stocks traded, total value (% of GDP) -

Economic Factors

- GDP per capita E1= GDP per capita growth (annual %) +ve

- Inflation rate E2= Inflation, consumer prices (annual %) -ve

- Real interest rate E3= Real interest rate (%) -ve

- Domestic credit to private

sector

E4= Domestic credit to private sector by banks (%

of GDP)

+ve /-ve

- Gross domestic savings E5= Gross domestic savings (% of GDP) +ve /-ve

- Trade E6= Trade (% of GDP) +ve

- Foreign direct investment, E7= Foreign direct investment, net inflows (% of

GDP) +ve

- Current account balance E8= Current account balance (% of GDP) +ve

Governance Factors

- Control of Corruption G1= Control of Corruption +ve

- Government Effectiveness G2= Government Effectiveness +ve

- Political Stability and

Absence of Violence

G3= Political Stability and Absence of

Violence/Terrorism

+ve

- Regulatory Quality G4= Regulatory Quality +ve

- Rule of Law G5= Rule of Law +ve

- Voice and Accountability G6= Voice and Accountability +ve

47

5.6. Data Analysis Software.

The challenge of equating data would be managed by using database software like MS

Access and further analysis would be done by using MS Excel. The following software has

been used to analyze the data:-

(a) Econometric Views

(b) STATA

(c) SPSS

(d) MS Excel

(e) MS Access

5.7. Data Period and Classification.

The period of data under study is 20 years starting from 1996 to 2015. The study has

applied annual panel data of 70 countries as classified by Financial Times Stock Exchange

(FTSE). On an annual basis, FTSE Group publishes the results of country classification

through a refine process for classifying the world equity markets as Developed, Emerging and

Frontier Markets. So, our financial data would be grouped into three groups as developed,

emerging and frontier groups. Finally, the data of the variables have been collected from the

official sites of World Bank, IMF and FTSE group.

48

CHAPTER 6

EMPIRICAL ANALYSIS

6.1. Introduction

The study is conducted on panel data of all three stock markets of the world, that is,

developed, emerging and frontier financial markets. In the previous chapters, the empirical

model and methodology alongwith data and variable is presented. In this chapter, the presented

data is going to be tested in all possible known directions for each equity market of the world.

To further elucidate, the empirical analysis would be segmented into Statistical and

Econometric Analysis.

6.2. Statistical Analysis

In this part, the preliminary statistics and correlation matrix of all three regional

financial markets is studied. That is, the developed(25), emerging(21) and frontier(24)

financial markets are tested separately and at the end a comparative analysis would be

presented.

6.2.1. Developed Financial Market

In the developed region, there are 25 countries as classified by FTSE and these will be

analyzed for basic statistics with correlation matrix.

49

Figure 6.1

No of Listed Companies of Developed Financial Markets (Average No from 1996 to 2015)

As it is quite evident from the abovementioned graph that United States has the highest number

of listed companies followed by Canada in the category of developed financial markets.

Whereas, at the lower level, Ireland has the least number of listed companies in its group of

countries.

0 1,000 2,000 3,000 4,000 5,000 6,000

United StatesCanada

SpainJapan

United KingdomAustralia

Korea, Rep.Hong Kong SAR, China

FranceGermany

IsraelSingapore

ItalyGreece

SwedenSwitzerland

DenmarkNetherlands

BelgiumNorway

New ZealandFinlandAustria

PortugalIreland

50

Figure 6.2

Market Capitalization of Developed Financial Markets in USD Billions (Average from

1996 to 2015)

This chart shows the average market capitalization of each financial market in the

developed region that depicts the market value of stocks in each country. According to the

above depicted graph, United States of America has the highest market capitalization of USD

16,400 Billion during the last twenty years.

Basic Statistics for the Stock Market Development as Market Capitalization %age of

GDP for the panel data of 25 developed countries from 1996 to 2015 are shown in the following

Figures 6.3

0.000

4,000.000

8,000.000

12,000.000

16,000.000

20,000.000

Austr

alia

Austr

ia

Belg

ium

Canada

Denm

ark

Fin

land

Fra

nce

Germ

an

y

Gre

ece

Hong K

ong S

AR

, C

hin

a

Irela

nd

Isra

el

Italy

Japan

Kore

a,

Rep.

Neth

erl

ands

New

Zeala

nd

Norw

ay

Port

uga

l

Sin

gapo

re

Spain

Sw

eden

Sw

itzerl

and

Unit

ed K

ingdom

Unit

ed S

tate

s

51

Figure 6.3

Basic Statistics of Stock Market Development of Developed Financial Markets

0

10

20

30

40

50

60

70

80

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0

Series: LNYSample 1996 2015Observations 485

Mean 4.280667Median 4.276242Maximum 7.025276Minimum 1.584224Std. Dev. 0.760649Skewness 0.098865Kurtosis 4.022460

Jarque-Bera 21.91638Probability 0.000017

The figure shows that the preliminary statistics on the market capitalization as

percentage of GDP. The average capitalization during the whole study period comes to 4.2807

with the standard deviation of 0.76. The Jarque Bera test rejects normality of the stock market

development.

The preliminary statistics of Economic variables for the panel data of all 25 developed

countries from 1996 to 2015 is shown in the following table 6.1

Table 6.1

Preliminary Statistics of Economic Variable of Developed Equity Markets

Statistics E1 E2 E3 E4 E5 E6 E7 E8

Mean 1.633594 1.905921 4.227565 105.2107 26.37494 97.18841 5.805275 1.992301

Median 1.677344 1.921281 3.932860 101.3587 25.72429 69.83773 2.440012 1.467750

Maximum 24.66657 11.27662 13.34727 233.2110 54.28837 442.6200 87.44259 26.05861

Minimum -8.997955 -4.479938 -5.634759 29.53919 8.330869 18.34896 -5.670905 -14.47630

Std. Dev. 2.832779 1.611234 2.802013 38.33464 8.218851 83.93756 9.646420 6.533388

Skewness 0.757940 0.521129 0.334021 0.445454 0.973990 2.513723 3.552466 0.697199

Kurtosis 13.06886 6.983954 3.783508 2.856114 4.828405 8.972572 21.20060 3.991862

Jarque-Bera 2159.997 353.2957 20.09899 15.74544 148.7018 1269.726 7809.805 57.22070

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

52

The table shows the preliminary statistics of eight economic variables. The average

during the whole study period ranges from 0.10 to 0.51. The Jarque Bera test rejects normality

of the economic variables.

The preliminary statistics of governance variables are presented in the table 6.2

Table 6.2

Preliminary Statistics of Governance Variables of Developed Equity Markets

Statistics G1 G2 G3 G4 G5 G6

Mean 89.75072 90.98496 76.62031 90.14083 90.41419 86.74615

Median 93.17073 93.17073 80.84356 93.13725 93.26923 92.21296

Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000

Minimum 51.44231 61.65049 7.109005 61.76471 60.28708 35.57692

Std. Dev. 10.41610 8.014274 20.37435 8.522897 8.927048 13.83109

Skewness -1.582314 -1.401515 -1.376391 -0.937198 -1.379488 -1.443880

Kurtosis 4.932210 4.692988 4.926033 3.010753 4.335455 4.536303

Jarque-Bera 286.4232 223.3998 235.1544 73.19736 195.7372 222.9039

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

The table shows the preliminary statistics of six governance variables of developed markets.

The average during the whole study period ranges from 76.62 to 90.98. The Jarque Bera test

rejects normality of the economic variables.

The Correlation Matrix of Economic variables for the panel data of all 25 developed


53

Table 6.3

Correlation Matrix of Economic Variables of Developed Equity Markets

Correlation E1 E2 E3 E4 E5 E6 E7 E8

E1 1.000000

E2 0.010197 1.000000

E3 -0.021103 -0.029653 1.000000

E4 -0.194476 -0.122716 -0.137786 1.000000

E5 0.302341 -0.112108 -0.074511 0.123852 1.000000

E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000

E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000

E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000

The Correlation Matrix of Governance Factors for the panel data of all 25 developed


Table 6.4

Correlation Matrix of Governance Variables of Developed Equity Markets

Correlation G1 G2 G3 G4 G5 G6

G1 1.000000

G2 0.920087 1.000000

G3 0.595142 0.549431 1.000000

G4 0.847346 0.824036 0.493675 1.000000

G5 0.912385 0.900648 0.627760 0.769201 1.000000

G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000

54

6.2.2. Emerging Financial Markets

In the emerging financial markets, there are 21 countries as classified by FTSE Group

and these are to be analyzed for basic statistics with correlation matrix. First of all, the No of

Listed Companies alongwith their Market capitalization of Emerging Financial Markets is

appended below:

Figure 6.4

No of Listed Companies of Emerging Financial Markets (Average No from 1996 to 2015)

The abovementioned graph depicts that India has the highest number of listed companies

followed by China in the category of developed financial markets. Whereas, at the lower level,

Czech Republic has the least number of listed companies in its group of countries.

0 1,000 2,000 3,000 4,000 5,000 6,000

IndiaChina

MalaysiaPakistan

Egypt, Arab Rep.Thailand

South AfricaPolandBrazil

IndonesiaRussian Federation

TurkeyChile

PhilippinesPeru

MexicoColombia

United Arab EmiratesMoroccoHungary

Czech Republic

55

Figure 6.5

Market Capitalization of Emerging Financial Markets in USD Billions (Average from

19996 to 2015)

Aforementioned chart depicts the average market capitalization of each financial

market in the developed region that depicts the market value of stocks in each country.

According to the graph, China has highest market capitalization and Czech Republic has the

lowest market capitalization in its category.

Basic Statistics of Stock Market Development for the panel data of all 21 emerging

countries from 1996 to 2015 is shown in the following Figure 6.6

0.0E+00

5.0E+11

1.0E+12

1.5E+12

2.0E+12

2.5E+12

3.0E+12

3.5E+12

Brazil

Chi

le

Chi

na

Col

ombia

Cze

ch R

epub

lic

Egypt

, Ara

b Rep

.

Hun

gary

India

Indo

nesia

Malay

sia

Mex

ico

Mor

occo

Pakista

nPer

u

Philip

pine

s

Polan

d

Rus

sian

Fed

erat

ion

South

Afri

ca

Thaila

nd

Turke

y

Uni

ted

Ara

b Em

irate

s

56

Figure 6.6

Basic Statistics of Stock Market Development of Emerging Equity Markets

0

10

20

30

40

50

60

70

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Series: LYSample 1996 2015Observations 411

Mean 3.696866Median 3.638601Maximum 5.718953Minimum 1.677979Std. Dev. 0.746575Skewness 0.161084Kurtosis 2.804618





development.

The preliminary statistics of Economic variables for the panel data of all 21 emerging

countries from 1996 to 2015 is shown in the following Table 6.5

Table 6.5

Preliminary Statistics of Economic Variable of Emerging Equity Markets


Mean 1.633594 1.905921 4.227565 105.2107 26.37494 97.18841 5.805275 1.992301

Median 1.677344 1.921281 3.932860 101.3587 25.72429 69.83773 2.440012 1.467750

Maximum 24.66657 11.27662 13.34727 233.2110 54.28837 442.6200 87.44259 26.05861

Minimum -8.997955 -4.479938 -5.634759 29.53919 8.330869 18.34896 -5.670905 -14.47630

Std. Dev. 2.832779 1.611234 2.802013 38.33464 8.218851 83.93756 9.646420 6.533388

Skewness 0.757940 0.521129 0.334021 0.445454 0.973990 2.513723 3.552466 0.697199

Kurtosis 13.06886 6.983954 3.783508 2.856114 4.828405 8.972572 21.20060 3.991862

Jarque-Bera 2159.997 353.2957 20.09899 15.74544 148.7018 1269.726 7809.805 57.22070

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

57





Table 6.6

Preliminary Statistics of Governance Variables of Emerging Equity Markets


Mean 89.75072 90.98496 76.62031 90.14083 90.41419 86.74615

Median 93.17073 93.17073 80.84356 93.13725 93.26923 92.21296

Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000

Minimum 51.44231 61.65049 7.109005 61.76471 60.28708 35.57692

Std. Dev. 10.41610 8.014274 20.37435 8.522897 8.927048 13.83109

Skewness -1.582314 -1.401515 -1.376391 -0.937198 -1.379488 -1.443880

Kurtosis 4.932210 4.692988 4.926033 3.010753 4.335455 4.536303

Jarque-Bera 286.4232 223.3998 235.1544 73.19736 195.7372 222.9039

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

The table shows the preliminary statistics of six governance variables of emerging markets.



The Correlation Matrix of Economic variables for the panel data of all 21 emerging


58

Table 6.7

Correlation Matrix of Economic Variables of Emerging Equity Markets


E1 1.000000

E2 0.010197 1.000000

E3 -0.021103 -0.029653 1.000000

E4 -0.194476 -0.122716 -0.137786 1.000000

E5 0.302341 -0.112108 -0.074511 0.123852 1.000000

E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000

E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000

E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000

The Correlation Matrix of Governance Factors for the panel data of all 21 emerging


Table 6.8

Correlation Matrix of Governance Variables of Emerging Equity Markets


G1 1.000000

G2 0.920087 1.000000

G3 0.595142 0.549431 1.000000

G4 0.847346 0.824036 0.493675 1.000000

G5 0.912385 0.900648 0.627760 0.769201 1.000000

G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000

59

6.2.3. Frontier Financial Markets

In the frontier region, there are 24 countries which are classified as frontier markets by

FTSE Group and these will be analyzed for basic statistics with correlation matrix. First of all,

the number of listed companies alongwith their market capitalization is analyzed as per

following details:

Figure 6.7

No of Listed Companies of Frontier Financial Markets (Average No from 1996 to 2015)

As it is quite evident from the abovementioned graph that Serbia has the highest number

of listed companies followed by Bangladesh in the category of frontier financial markets.

Whereas, at the lower level, Botswana has the least number of listed companies in its group of

countries.

0 50 100 150 200 250 300 350 400 450 500

SerbiaBangladesh

BulgariaVietnam

Sri LankaJordanNigeriaCroatia

LithuaniaOman

ArgentinaCyprus

SloveniaRomania

Slovak RepublicKenya

TunisiaQatar

BahrainCote d'Ivoire

GhanaEstonia

MaltaBotswana

60

Figure 6.8

Market Capitalization of Frontier Financial Markets in USD Billions (Average from 1996

to 2015)


frontier region that depicts the market value of stocks in each country. According to the data,

Qatar has highest average market capitalization during the last twenty years, where Serbia had

the highest number of listed companies and now it is standing at nowhere near to the

comparison of market capitalization.

The Basic statistics for the Stock Market Development of Frontier region from 1996 to

2015 is shown in the following Figure 6.9

0

20

40

60

80

100

120

140

Arg

entina

Bahra

in

Bangla

desh

Bots

wana

Bulg

aria

Cote

d'Iv

oire

Cro

atia

Cyp

rus

Est

onia

Ghana

Jord

an

Kenya

Lithuania

Malta

Nig

eria

Om

an

Qata

r

Rom

ania

Serb

ia

Slo

vak

Republic

Slo

venia

Sri L

anka

Tunis

ia

Vie

tnam

61

Figure 6.9

Basic Statistics of Stock Market Development of Frontier Financial Markets

0

10

20

30

40

50

60

-4 -3 -2 -1 0 1 2 3 4 5


Mean 3.001197Median 3.025950Maximum 5.700869Minimum -3.917356Std. Dev. 1.106285Skewness -1.039623Kurtosis 8.515425





development.

The preliminary statistics of Economic variables for the panel data of all 24 frontier


Table 6.9

Preliminary Statistics of Economic Variable of Frontier Equity Markets


Mean 4.243440 2.752889 9.760702 5.050776 47.20415 22.42467 100.8636 -1.806020

Median 4.489896 3.024745 4.483331 4.919558 36.75874 20.61065 92.75692 -2.356626

Maximum 33.73578 30.35658 1058.374 93.93745 253.4578 75.54961 327.0551 33.18472

Minimum -14.81416 -14.55986 -4.863278 -70.43220 0.185853 -6.725383 21.12435 -25.54857

Std. Dev. 4.271855 4.079186 50.03471 10.27559 39.35828 13.99407 49.07045 7.763565

Skewness 0.359854 -0.026442 19.62829 0.521925 2.492803 1.321858 1.511951 0.942567

Kurtosis 11.44854 8.389892 409.8423 25.16564 11.57764 5.709075 7.020425 6.191409

Jarque-Bera 1422.937 575.0217 3292508. 8822.263 1960.440 286.5663 506.1559 252.4508

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

62





Table 6.10

Preliminary Statistics of Governance Variables of Frontier Equity Markets


Mean 53.78940 57.53469 49.77110 57.17460 53.15534 49.01110

Median 57.56098 61.46341 52.39027 60.29412 57.34663 53.12500

Maximum 93.17073 92.41706 99.51691 93.26923 92.82297 92.78846

Minimum 1.463415 7.317073 0.966184 8.333333 3.827751 4.807693

Std. Dev. 22.87330 20.54213 26.38134 22.28422 22.70401 24.10185

Skewness -0.582949 -0.666706 -0.169439 -0.390822 -0.459305 -0.013586

Kurtosis 2.393753 2.609144 1.962952 2.020576 2.330239 1.736069

Jarque-Bera 34.53707 38.61515 23.80613 31.40475 25.84850 31.96519

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

The table shows the preliminary statistics of six governance variables of Frontier markets.



The Correlation Matrix of Economic variables for the panel data of all 24 Frontier


63

Table 6.11

Correlation Matrix of Economic Variables of World Equity Markets


E1 1.000000

E2 0.010197 1.000000

E3 -0.021103 -0.029653 1.000000

E4 -0.194476 -0.122716 -0.137786 1.000000

E5 0.302341 -0.112108 -0.074511 0.123852 1.000000

E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000

E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000

E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000

The Correlation Matrix of Governance Factors for the panel data of frontier markets

from 1996 to 2015 is shown in the following table 6.12

Table 6.12

Correlation Matrix of Governance Variables of Frontier Equity Markets


G1 1.000000

G2 0.920975 1.000000

G3 0.791937 0.805177 1.000000

G4 0.850098 0.866145 0.713883 1.000000

G5 0.936074 0.917416 0.789577 0.890990 1.000000

G6 0.547714 0.592083 0.578830 0.660876 0.578308 1.000000

64

6.2.4. World Financial Markets

In the world equity markets, there are total of 70 countries as classified by FTSE Group

and these will be analyzed for basic statistics with correlation matrix. First of all, the number

of listed companies is to be analyzed and its average from 1996 to 2015 is appended below:

Figure 6.10

No of Listed Companies of World Financial Markets (Average No from 1996 to 2015)

As it is quite evident from the abovementioned graph that United States has the highest number

of listed companies followed by India and Canada in the category of developed financial

0 1,000 2,000 3,000 4,000 5,000 6,000

United StatesIndia

CanadaSpain

JapanUnited Kingdom

AustraliaChina

Korea, Rep.Hong Kong SAR, China

Malays iaFrance

GermanyPak is tan

Egypt, Arab Rep.Israel

ThailandSerbia

South AfricaSingapore

PolandBraz il

Indones iaRuss ian Federation

BangladeshItaly

GreeceBulgariaTurkeyVietnamSweden

Sri LankaSwitzerland

ChilePhilippines

DenmarkNetherlands

PeruJordanNigeriaBelgiumNorwayCroatia

LithuaniaMexico

OmanNew Zealand

FinlandArgentina

CyprusAustria

ColombiaSlovenia

United Arab EmiratesRomaniaPortugal

MoroccoIreland

Slovak RepublicKenya

Tunis iaHungary

QatarBahrain

Cote d'IvoireCzech Republic

GhanaEstonia

MaltaBotswana

65

markets. Whereas, at the lower level, Botswana has the least number of listed companies in its

group of countries.

Figure 6.11

Market Capitalization of World Financial Markets in USD Billions (Average from 1996 to

2015)


developed region that depicts the market value of stocks in each country. According to the

data, United States has highest average market capitalization during the last twenty years,

where India had the second highest number of listed companies and now it is standing at quite

lower standing in comparison of market capitalization.

Basic Statistics of Stock Market Development for the panel data of all 70 countries from

1996 to 2015 is shown in the following Figures 6.12.

0

4,000

8,000

12,000

16,000

20,000

Arg

en

tina

Au

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AR, C

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itze

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66

Figure 6.12

Basic Statistics of Stock Market Development of World Financial Markets

0

40

80

120

160

200

240

-3 -2 -1 0 1 2 3 4 5 6 7


Mean 3.787584Median 3.835368Maximum 7.134465Minimum -2.893285Std. Dev. 1.043240Skewness -0.596591Kurtosis 5.823311





development.

The preliminary statistics of Economic variables for the panel data of all 70 countries


Table 6.13

Preliminary Statistics of Economic Variable of World Equity Markets


Mean 2.369998 6.202609 6.213175 67.82527 24.69457 91.07710 5.721348 -0.014181

Median 2.428009 3.037021 4.639557 55.66380 23.57400 75.21890 2.669790 -0.777464

Maximum 30.35658 1058.374 93.93745 253.4578 75.54961 442.6200 451.7155 33.18472

Minimum -14.78631 -4.863278 -70.43220 0.185853 -6.725383 15.63556 -43.46255 -25.54857

Std. Dev. 3.569523 30.13143 9.850004 45.61329 10.89235 63.77629 20.95003 6.686508

Skewness -0.094824 31.30399 2.399185 0.986900 0.990774 2.561513 14.94683 0.767272

Kurtosis 8.541588 1085.398 22.55318 3.708501 5.850949 11.65933 270.4033 5.536110

Jarque-Bera 1787.062 67248644 22277.44 249.5786 700.6647 5883.956 4196075. 479.6063

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

67





Table 6.14

Preliminary Statistics of Governance Variables of World Equity Markets


Mean 65.75201 70.05147 55.18526 69.41799 65.77213 61.85943

Median 67.39059 72.90066 59.71564 73.05807 67.46411 64.66038

Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000

Minimum 1.463415 7.317073 0.473934 8.333333 3.827751 4.694836

Std. Dev. 25.60633 22.07683 29.24921 22.92404 25.41489 27.40518

Skewness -0.508788 -0.610941 -0.257793 -0.591285 -0.455644 -0.306721

Kurtosis 2.310306 2.636630 1.790822 2.384133 2.176094 1.860424

Jarque-Bera 88.14989 94.79358 100.7966 103.7028 88.04055 97.70498

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

The table shows the preliminary statistics of six governance variables of world markets. The

average during the whole study period ranges from 55.18 to 70.05. The Jarque Bera test


The Correlation Matrix of Economic variables for the panel data of all 70 countries


68

Table 6.15

Correlation Matrix of Economic Variables of Frontier Equity Markets


E1 1.000000

E2 -0.022557 1.000000

E3 -0.067811 -0.126985 1.000000

E4 -0.195199 -0.132959 -0.186364 1.000000

E5 0.115561 -0.056593 -0.197085 0.237533 1.000000

E6 0.039468 -0.040500 -0.157520 0.341373 0.361747 1.000000

E7 0.030077 -0.016539 -0.047653 0.155165 0.019697 0.317627 1.000000

E8 -0.040251 -0.016718 -0.157900 0.153372 0.655817 0.313136 0.016081 1.00000

The Correlation Matrix of Governance Factors for the panel data of all 70 countries


Table 6.16

Correlation Matrix of Governance Variables of Frontier Equity Markets


G1 1.000000

G2 0.944768 1.000000

G3 0.822448 0.802101 1.000000

G4 0.915314 0.923145 0.766807 1.000000

G5 0.951607 0.944216 0.825524 0.912514 1.000000

G6 0.757272 0.756508 0.691295 0.785424 0.782673 1.000000

69

6.3. Econometric Analysis

In order to achieve the distinct results of the study, the econometric techniques have

been applied to the panel data sets of 70 countries by dividing into different groups separately

as depicted below.

Developed markets (25)

Emerging markets (21)

Frontier market (24)

World Markets (70)

The panel data of 70 countries (25,21,24,70) is used for the period starting from 1996 to

2015 (T=20 years). The Econometric analysis of the study has been further divided into two

categories as Principal Component Analysis (PCA) and Panel GMM. In PCA, the composite

Indices of Governance, Economic and Cross factors have been formed and the study has

applied Panel GMM as suggested by Arellano and Bond (1991) for estimating panel datasets

to obtain the empirical results.

Before embarking to the analysis, it is imperative to know the description of variables used

in the study. Though, these variables have been have been amply highlighted in the chapter of

Data and Variables, yet the summary of these variables will further elucidate the estimation of

results. There are number of variables that have been denoted to certain variables and factors

in the equations and estimation of the models. The list all these variables used in the study are

as follows:

70

Table 6.17

List of All Variables for Statistical and Econometric Analysis

Variable Description

Y = Market capitalization of listed domestic companies (% of GDP)

S3 = Stocks traded, total value (% of GDP)

E1 = GDP growth (annual %)

E2 = Inflation, consumer prices (annual %)

E3 = Real interest rate (%)

E4 = Domestic credit to private sector by banks (% of GDP)

E5 = Gross domestic savings (% of GDP)

E6 = Trade (% of GDP)

E7 = Foreign direct investment, net inflows (% of GDP)

E8 = Current account balance (% of GDP)

G1= Control of Corruption

G2= Government Effectiveness

G3= Political Stability and Absence of Violence/Terrorism

G4= Regulatory Quality

G5= Rule of Law

G6= Voice and Accountability

PECO= Composite Economic Factors of all Economic variables

PGOV= Composite Governance Factors of all Governance variables

PCROSS= Cross Factors of Composite Economic & Governance Factors (Eco*Gov)

Yit= The matrix of stock market capitalization relative to GDP of particular

country i in year t

Eit= Vector of Economic variables

Git= Vector of Governance variables

αi= The unobserved country specific fixed effect

εit= The usual white noise

71

6.2. Results of Principal Component Analysis (PCA)

The technique of Principal Component Analysis (PCA) has been incorporated in this

study for the formation of Composite Indices of Economic, Governance and Cross factors.

These three indices are created for all three regions of the world stock markets. First question

of the study pertains to the formation of composite index for Governance and Economic factors

along with their cross factor index of both the composite variables, which is as follows:

Q No.1. What are the composite index factors for economic and governance variables along

with their cross composite index factors according to regional markets of developed, emerging

and frontier regions?

Abovementioned question encompasses three indices on three regions, so the

estimation of results are categories in the same way. Therefore, the classification would be

made region wise in which each indices will be analyzed.

6.2.1. Developed Markets.

The index for composition of economic and governance factors of developed market is

formed by using Principal Component Analysis (PCA). These PCAs are formed for three

categories, that is, composite indices of economic, governance and cross factors.

6.2.1.1. Composite Index of Economic Factors : First all, the composite index of

economic factors is formed by using PCA technique and same is placed in the appendices as

Appendix-1A. Their Eigen Values and Correlation matrix is appended below:

72

Table 6.18

Principal Components Analysis for Economic Variables of Developed Stock Markets (25

Countries)

Sub table-1: Eigenvalues: (Sum = 8, Average = 1)

Cumulative Cumulative

Number Value Difference Proportion Value Proportion

1 2.931890 1.669409 0.3665 2.931890 0.3665

2 1.262481 0.213879 0.1578 4.194371 0.5243

3 1.048603 0.005208 0.1311 5.242974 0.6554

4 1.043394 0.261846 0.1304 6.286368 0.7858

5 0.781548 0.250330 0.0977 7.067916 0.8835

6 0.531218 0.302141 0.0664 7.599135 0.9499

7 0.229077 0.057288 0.0286 7.828211 0.9785

8 0.171789 --- 0.0215 8.000000 1.0000

Sub table-2: Eigenvectors (loadings):

Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8

E1 0.217698 0.598697 -0.181100 0.071286 -0.573299 0.446836 -0.138298 0.093967

E2 -0.128903 0.291779 0.137898 0.792956 0.437890 0.145397 0.066592 0.182020

E3 -0.049060 0.246085 0.830136 -0.380381 0.092192 0.227747 0.153212 0.139204

E4 0.157355 -0.697268 0.169655 0.252768 -0.258232 0.547230 0.080578 0.154093

E5 0.502280 0.085662 -0.133386 -0.068172 0.323232 0.276611 0.476680 -0.556526

E6 0.501374 -0.043280 0.292410 0.118898 0.123780 -0.104459 -0.744912 -0.256873

E7 0.408698 0.021674 0.264551 0.288484 -0.406964 -0.583465 0.405388 0.094311

E8 0.487985 -0.002290 -0.238736 -0.235692 0.344931 -0.003220 0.003893 0.728229

Sub table-3: Ordinary correlations:

Variable E1 E2 E3 E4 E5 E6 E7 E8

E1 1.000000

E2 0.010197 1.000000

E3 -0.021103 -0.029653 1.000000

E4 -0.194476 -0.122716 -0.137786 1.000000

E5 0.302341 -0.112108 -0.074511 0.123852 1.000000

E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000

E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000

E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000

73

Abovementioned table depicts the results of PCA for Economic variables of developed

stock markets constituting 25 countries. Sub table-3 shows the correlations among 08

economic variables and it is revealed that Gross Domestic Savings (E5) and Trade (E6) is

having the highest correlation of 0.64, whereas the lowest correlation of 0.01 is between GDP

growth (E1) and Interest Rates (E2). On the other side, there is negative correlation of 0.19

between GDP growth (E1) and private capital by banks (E4).

To analyze the trend of average values in the data, a graph has been generated as Means

of composite of economic variables, which is depicted below:

Figure 6.13

Mean of Composite of Economic Variables of Developed Stock Markets

The Figure 6.13. depicts the mean values of composite economic index of 25 developed

countries and it shows that Singapore has the highest mean value of its economic composite

index, whereas Greece has lowest mean value of its economic composite. Moreover, the

distribution along the bottom axis of the figure shows that majority of the values of economic

composite index falls between 0 and 1and most of them are lying on the left side of the figure.

-3 -2 -1 0 1 2 3 4 5

AustraliaAustria

BelgiumCanada

DenmarkFinlandFrance

GermanyGreece

Hong Kong SAR, ChinaIrelandIsrael

ItalyJapan

Korea, Rep.Netherlands

New ZealandNorway

PortugalSingapore

SpainSweden

SwitzerlandUnited Kingdom

United States

74

In order to observe the dispersion in the data, a graph has been generated as standard

deviation of composite of economic variables, which is depicted below:

Figure 6.14

Standard Deviation of Composite of Economic Variables of Developed Stock Markets

Aforementioned figure depicts that there is quite high dispersion in the economic

composite index of Ireland which is quite evident when we see their economic indicators tells

that there is quite volatility in the variables of interest rates and trade. Moreover, the distribution

along the bottom axis of the figure shows that most of the values of economic composite index

falls around 0.4 and majority of them are lying on the right side of the figure.

6.2.1.2. Composite Index of Governance Factors: Secondly, the composite index of

governance factors is formed by using PCA technique and same is placed in the appendices as

Appendix-1B. Their Eigen Values and Correlation matrix are appended below:

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

AustraliaAustria

BelgiumCanada


GermanyGreece


ItalyJapan


New ZealandNorway

PortugalSingapore

SpainSweden


United States

75

Table 6.19

Principal Components Analysis for Governance Variables of Developed Stock Markets (25

Countries)




1 4.280970 3.440560 0.7135 4.280970 0.7135

2 0.840410 0.315608 0.1401 5.121380 0.8536

3 0.524802 0.311264 0.0875 5.646182 0.9410

4 0.213538 0.141035 0.0356 5.859720 0.9766

5 0.072502 0.004724 0.0121 5.932222 0.9887

6 0.067778 --- 0.0113 6.000000 1.0000


Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6

G1 0.462370 -0.166899 -0.097386 -0.138624 -0.775541 0.358041

G2 0.450035 -0.257490 -0.109327 -0.374628 0.612673 0.451111

G3 0.346197 0.297471 0.885335 0.067423 0.038725 0.042383

G4 0.417765 -0.353979 -0.104481 0.807431 0.126753 -0.145750

G5 0.463050 0.022405 -0.121081 -0.386378 -0.008317 -0.788077

G6 0.273656 0.831596 -0.411302 0.186166 0.074381 0.155568


Variable G1 G2 G3 G4 G5 G6

G1 1.000000 G2 0.920087 1.000000 G3 0.595142 0.549431 1.000000 G4 0.847346 0.824036 0.493675 1.000000 G5 0.912385 0.900648 0.627760 0.769201 1.000000 G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000

Aforesaid table shows the results of PCA for Governance variables of developed stock markets

constituting 25 countries. Sub table-3 shows the correlations among 06 governance variables

and it is revealed that G1 and G2 are having the highest correlation of 0.92, whereas the lowest

76

correlation of 0.29 is between G4 and G6. On the other side, there is no negative correlation

among these variables.


of composite of governance variables, which is depicted below:

Figure 6.15

Mean of Composite of Governance Variables of Developed Stock Markets

The Figure 6.15 depicts the mean values of composite Index of governance variables

of 25 developed countries and it shows that Finland has the highest mean value of its economic

composite index Greece has lowest mean value of its governance composite. Moreover, the


composite index falls between 0.5 and 2.5 and most of them are lying on the right side of the

figure.



-5 -4 -3 -2 -1 0 1 2 3

AustraliaAustria

BelgiumCanada


GermanyGreece


ItalyJapan


New ZealandNorway

PortugalSingapore

SpainSweden


United States

77

Figure 6.16 Standard Deviation of Composite of Governance Variables of Developed Stock Markets

Aforementioned figure depicts that there is quite high dispersion the governance

composite index of Greece which is quite evident when we see their governance indicators.

Moreover, the distribution along the bottom axis of the figure shows that most the values of

economic composite index falls around 0.2 and majority of them are lying on the left side of

the figure.

6.2.1.3. Cross Composite Index of Economic and Governance Factors: The index

for cross composition of economic and governance factors of developed market is formed by

the interaction of these variables and same is placed in the appendices as Appendix-1C.


of composites of economic governance variables, which is depicted below:

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

AustraliaAustria

BelgiumCanada


GermanyGreece


ItalyJapan


New ZealandNorway

PortugalSingapore

SpainSweden


United States

Standard Deviation of PGOV by COUNTRY

78

Figure 6.17 Mean of Cross Composite Index of Economic and Governance Variables of Developed Stock Markets

The Figure 6.17. depicts the mean values of cross composite Index of economic and

governance index of 25 developed countries and it shows that Greece has the highest mean

value of its cross composite index and New Zealand has lowest mean value of its cross

composite index. Moreover, the distribution along the bottom axis of the figure shows that

majority of the values of economic composite index falls between -1 and 3 and most of them

are lying on the left side of the figure.



-2 -1 0 1 2 3 4 5 6 7 8 9 10 11

AustraliaAustria

BelgiumCanada


GermanyGreece


ItalyJapan


New ZealandNorway

PortugalSingapore

SpainSweden


United States

79

Figure 6.18 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Developed Stock Markets

Aforementioned figure depicts that the dispersion of governance composite index and

it shows that Greece has highest dispersion in the data which is quite evident when we see their

governance indicators tells that there is quite volatility in the variables interest rates and trade.


economic composite index falls between 0 and 2 and majority of them are lying on the left side

of the figure.

Now coming towards the analysis of cross effects of economic and governance

composite indices, the study has generated a scatter plot with their distributions and Kernel Fit

line of these two composite indices. The scatter plot of these variable is appended below:

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

AustraliaAustria

BelgiumCanada


GermanyGreece


ItalyJapan


New ZealandNorway

PortugalSingapore

SpainSweden


United States

Standard Deviation of PCROSS by COUNTRY

80

Figure 6.19 Scatter plots of Composite of Indices of Economic and Governance Indices of Developed Stock Markets

-8

-6

-4

-2

0

2

4

Com

po

site I

nde

x o

f G

overn

an

ce V

ari

ab

les (

PG

OV

)

-4 -2 0 2 4 6 8

Composite Index of Economic Variables (PECO)

Aforementioned figure of scatter plots depicts that values of the observations have their

positive relations with majority of the observations are lying at one place. According to Kernel

Fit line, it is deduced that as governance factors moves and in the same direction economic

factors also move.

81

6.2.2 Emerging Markets.

The index for composition of economic and governance factors of emerging market is

formed by using Principal Component Analysis (PCA), These PCAs are formed for three

categories, that is, composite index of economic, governance and cross factors.

6.2.2.1. Composite Index of Economic Factors : First all, the economic factors of

emerging markets are combined and formed a composite index by using Principal

Component Analysis (PCA) and same is placed in the appendices as Appendix-2A. Their

Eigen Values and Correlation matrix is appended below:

Table 6.20

Principal Components Analysis for Economic Variables of Emerging Stock Markets (21

Countries)




1 2.812725 1.546564 0.3516 2.812725 0.3516

2 1.266161 0.162971 0.1583 4.078886 0.5099

3 1.103190 0.127743 0.1379 5.182076 0.6478

4 0.975448 0.280409 0.1219 6.157524 0.7697

5 0.695038 0.208057 0.0869 6.852562 0.8566

6 0.486982 0.073323 0.0609 7.339543 0.9174

7 0.413659 0.166862 0.0517 7.753203 0.9692

8 0.246797 --- 0.0308 8.000000 1.0000



E1 0.195626 -0.147776 -0.772373 0.362086 -0.096917 -0.166650 0.304227 0.287240

E2 -0.244977 0.517768 0.139943 0.510101 -0.435455 -0.283683 -0.302250 0.175112

E3 -0.359950 0.267929 0.104908 0.416226 0.622683 0.117199 0.456797 -0.065319

E4 0.469089 0.076691 0.136359 0.078516 0.539969 -0.128272 -0.406190 0.525648

E5 0.493083 0.190033 -0.140366 0.295175 0.105927 -0.023611 -0.204996 -0.748414

E6 0.413012 -0.008615 0.468335 0.000109 -0.169150 -0.492585 0.581852 0.014352

E7 0.108502 -0.564812 0.339394 0.573640 -0.176678 0.433119 -0.044945 0.064303

E8 0.355845 0.526509 -0.005240 -0.114413 -0.225029 0.656333 0.244625 0.204430

82



E1 1.000000

E2 -0.144020 1.000000

E3 -0.189172 0.382387 1.000000

E4 0.115489 -0.285034 -0.260103 1.000000

E5 0.375543 -0.125489 -0.313327 0.649109 1.000000

E6 -0.044537 -0.170761 -0.358431 0.485944 0.439465 1.000000

E7 0.054455 -0.105178 -0.090552 0.105836 0.101140 0.213905 1.000000

E8 0.068512 -0.002082 -0.245719 0.371049 0.505460 0.333537 -0.169148 1.000000

Abovementioned table depicts the results of PCA for Economic variables of developed stock

markets constituting 25 countries. Sub table-3 shows the correlations among 08 economic

variables and it is revealed that Gross Domestic Savings (E5) and Domestic Credit (E4) is

having the highest correlation of 0.65, whereas the lowest correlation of 0.02 is between GDP

growth (E1) and Interest Rates (E2). On the other side, there is highest negative correlation of

0.36 between (E3) and (E6).



Figure 6.20 Mean of Composite Index of Economic Variables of Emerging Stock Markets

-3 -2 -1 0 1 2 3 4 5

BrazilChile

ChinaColombia

Czech RepublicEgypt, Arab Rep.

HungaryIndia

IndonesiaMalaysia

MexicoMoroccoPakistan

PeruPhilippines

PolandRussian Federation

South AfricaThailand

TurkeyUnited Arab Emirates

Mean of PECO by COUNTRY

83

The above Figure. depicts the mean values of composite economic index of 21

emerging countries and it shows that Malaysia has the highest mean value of its economic

composite index Turkey has lowest mean value of its economic composite. Moreover, the


composite index falls between -.2.5 and 1and most of them are lying on the left side of the

figure.



Figure 6.21 Standard Deviation of Composite of Economic Variables of Emerging Stock Markets

Aforementioned figure depicts that there is quite high dispersion the economic

composite index of Turkey which is quite evident when we see their economic indicators tells

that there is quite volatility their economic variables. Moreover, the distribution along the

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

BrazilChile

ChinaColombia


HungaryIndia

IndonesiaMalaysia


PeruPhilippines




Standard Deviation of PECO by COUNTRY

84

bottom axis of the figure shows that most the values of economic composite index falls

between 0.3 and 0.7 and majority of them are lying on the left side of the figure.


governance factors is formed by using PCA technique and same is placed in the appendices

and same is placed in the appendices as Appendix-2B.

Table 6.21

Principal Components Analysis for Governance of Emerging Markets (21 Countries)




1 4.694591 4.112365 0.7824 4.694591 4.694591

2 0.582227 0.300882 0.0970 5.276818 0.582227

3 0.281345 0.081186 0.0469 5.558163 0.281345

4 0.200159 0.055847 0.0334 5.758321 0.200159

5 0.144312 0.046944 0.0241 5.902633 0.144312

6 0.097367 --- 0.0162 6.000000 0.097367



G1 0.430023 -0.165428 -0.208272 -0.193172 -0.764404 0.350297

G2 0.425139 -0.248850 -0.325685 -0.173126 0.637768 0.463183

G3 0.408157 -0.223014 0.651213 0.580279 0.022346 0.149572

G4 0.423217 0.073346 -0.538409 0.475807 -0.002010 -0.547017

G5 0.425553 -0.146997 0.345434 -0.603204 0.075002 -0.555419

G6 0.327928 0.913227 0.131399 -0.075771 0.053010 0.180728



G1 1.000000 G2 0.853454 1.000000 G3 0.787502 0.775957 1.000000 G4 0.842034 0.842044 0.750062 1.000000 G5 0.849122 0.841745 0.819884 0.759013 1.000000 G6 0.569602 0.525796 0.527851 0.653774 0.589693 1.000000

85

Aforesaid table shows the results of PCA for Governance variables of emerging stock markets

constitutes 21 countries. Sub table-3 shows the correlations among 06 governance variables



in the governance varaibles.



Figure 6.21 Mean of Composite of Governance Variables of Emerging Stock Markets

The above Figure depicts the mean values of composite Index of governance variables

of 21 emerging countries and it shows that Chile has the highest mean value of its governance

composite index, while Pakistan has the lowest mean value of its governance composite.

-4 -3 -2 -1 0 1 2 3 4 5

BrazilChile

ChinaColombia


HungaryIndia

IndonesiaMalaysia


PeruPhilippines




Mean of PGOV by COUNTRY

86

Moreover, the distribution along the bottom axis of the figure shows that majority of the values

of economic composite index falls between -2 and 0 and most of them are lying on the left side

of the figure.


deviation of composite economic variables, which is depicted below:

Figure 6.22 Standard Deviation of Governance Variables of Emerging Stock Markets

Aforementioned figure depicts that there is quite dispersion in the governance

composite index and Egypt dispersion is quite high, when we see their governance indicators

tells that there is quite volatility. Moreover, the distribution along the bottom axis of the figure

shows that most the values of economic composite index falls below 0.4 and majority of them



for cross composition of economic and governance factors of emerging market is formed by


.2 .3 .4 .5 .6 .7 .8 .9

BrazilChile

ChinaColombia


HungaryIndia

IndonesiaMalaysia


PeruPhilippines




Standard Deviation of PGOV by COUNTRY

87



Figure 6.23 Mean of Cross Composite Index of Economic and Governance Variables of Emerging Stock Markets

Above Figure depicts the mean values of cross composite Index of economic and

governance index of 21 emerging countries and it shows that Pakistan has the highest mean

value of its cross composite index and China has lowest mean value of its cross composite

index. Moreover, the distribution along the bottom axis of the figure shows that majority of

the values of economic composite index falls between -2 and 2 and most of them are lying on

the right side of the figure.



-8 -6 -4 -2 0 2 4 6 8 10 12 14 16

BrazilChile

ChinaColombia


HungaryIndia

IndonesiaMalaysia


PeruPhilippines




Mean of PCROSS by COUNTRY

88

Figure 6.24 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Emerging Stock Markets


it shows that Greece has highest dispersion in the data which is quite evident when we see their



economic composite index falls between 0 and 2 and majority of them are lying on the left side

of the figure.




0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

BrazilChile

ChinaColombia


HungaryIndia

IndonesiaMalaysia


PeruPhilippines




Standard Deviation of PCROSS by COUNTRY

89

Figure 6.25 Scatter plots of Composite of Indices of Economic and Governance Indices of Emerging Stock Markets

-6

-4

-2

0

2

4

6

PG

OV

-6 -4 -2 0 2 4 6

PECO Aforementioned figure of scatter plots depicts that values of the observations have their

positive and negative relations with observations are lying at scattered places with no clear

pattern. According to Kernel Fit line, it is deduced that as governance factors do not move in

the same direction as economic factors move.

90

6.2.3. Frontier Markets.

The index for composition of economic and governance factors of frontier market is

formed by using Principal Component Analysis (PCA). These PCAs are formed for three

categories, that is, composite index of economic, governance and cross factors.

6.2.3.1. Composite Index of Economic Factors : First all, the composite index of

economic factors is formed by using PCA technique and same is placed in the appendices as

Appendix-3A. The Eigen Values and Correlation matrix of composite index of economic

factors of frontier markets are appended below:

Table 6.22

Principal Components Analysis for Economic Variables of Frontier Stock Markets




1 1.854284 0.139213 0.2318 1.854284 0.2318

2 1.715071 0.392208 0.2144 3.569355 0.4462

3 1.322863 0.295184 0.1654 4.892218 0.6115

4 1.027680 0.267811 0.1285 5.919898 0.7400

5 0.759869 0.188467 0.0950 6.679767 0.8350

6 0.571402 0.146710 0.0714 7.251169 0.9064

7 0.424692 0.100554 0.0531 7.675861 0.9595

8 0.324139 --- 0.0405 8.000000 1.0000



E1 -0.204530 0.027370 0.131593 0.889514 -0.214276 0.210393 0.242158 0.006768

E2 -0.141158 0.087436 -0.722512 -0.087506 0.075920 0.650924 0.084396 0.078486

E3 0.132969 -0.329535 0.590718 -0.165604 0.208358 0.669727 0.037581 0.063204

E4 0.551013 0.224081 0.013493 -0.168060 -0.428238 0.030327 0.640537 0.152153

E5 -0.271211 0.595563 0.243247 -0.092471 -0.026693 0.063981 -0.187144 0.681323

E6 0.467825 0.431892 0.039644 0.140286 -0.200690 0.249448 -0.574353 -0.375466

E7 0.414049 0.219347 -0.072929 0.276318 0.804551 -0.112111 0.155487 0.121379

E8 -0.391271 0.493419 0.213239 -0.202127 0.182291 0.060764 0.365316 -0.588847

91



E1 1.000000

E2 -0.073383 1.000000

E3 -0.063859 -0.369855 1.000000

E4 -0.210158 -0.095001 0.005505 1.000000

E5 0.082915 -0.030995 -0.166418 -0.035422 1.000000

E6 -0.019237 -0.057128 -0.074816 0.515323 0.181223 1.000000

E7 -0.035058 -0.017251 -0.036450 0.242858 -0.039874 0.366296 1.000000

E8 0.037805 0.022002 -0.128427 -0.159360 0.628036 -0.028473 -0.084244 1.000000

Abovementioned table depicts the results of PCA for Economic variables of frontier stock

markets constituting 24 countries. Sub table-3 shows the correlations among 08 economic

variables and it is revealed that (E5) and (E8) are having the highest correlation of 0.63,

whereas the lowest correlation of 0.006 is between (E3) and (E4). On the other side, there is

highest negative correlation of -0.36 between (E2) and (E3).



Figure 6.26 Mean of Composite Index of Economic Variables of Frontier Stock Markets

-3 -2 -1 0 1 2 3 4

ArgentinaBahrain

BangladeshBotswana

BulgariaCote d'Ivoire

CroatiaCyprusEstoniaGhanaJordanKenya

LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia

Slovak RepublicSlovenia

Sri LankaTunisia

Vietnam

92

The above Figure. depicts the mean values of composite economic index of 24 frontier

countries and it shows that Malta has the highest mean value of its economic composite index

and Qatar has lowest mean value of its economic composite. Moreover, the distribution along

the bottom axis of the figure shows that majority of the values of economic composite index

falls between -.1 and 1and most of them are lying on the central part of the figure.



Figure 6.27 Standard Deviation of Composite of Economic Variables of Frontier Stock Markets

Aforementioned figure depicts that there is quite high dispersion the economic

composite index of Malta which is quite evident when we see their economic indicators tells

that there is quite volatility their economic variables. Moreover, the distribution along the

bottom axis of the figure shows that most the values of economic composite index falls


0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

ArgentinaBahrain

BangladeshBotswana



LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia


Sri LankaTunisia

Vietnam

93


governance factors is formed by using PCA technique and same is placed in the appendices

and same is placed in the appendices as Appendix-3B.

Table 6.23

Principal Components Analysis for Governance Variables of Frontier Stock Markets (24

Countries)




1 4.852634 4.280553 0.8088 4.852634 4.852634

2 0.572081 0.264481 0.0953 5.424715 0.572081

3 0.307600 0.181244 0.0513 5.732315 0.307600

4 0.126356 0.041795 0.0211 5.858672 0.126356

5 0.084561 0.027794 0.0141 5.943233 0.084561

6 0.056767 --- 0.0095 6.000000 0.056767



G1 0.429490 -0.271398 -0.095280 0.461163 0.357205 0.626528

G2 0.433319 -0.181334 -0.063980 0.300400 -0.815472 -0.141508

G3 0.394789 -0.062057 0.864448 -0.300662 0.039300 0.032849

G4 0.421527 0.032456 -0.455053 -0.743652 -0.062974 0.239173

G5 0.434646 -0.211928 -0.180065 0.075624 0.446267 -0.727236

G6 0.324518 0.918501 -0.007847 0.218983 0.052401 -0.016840



G1 1.000000 G2 0.920975 1.000000 G3 0.791937 0.805177 1.000000 G4 0.850098 0.866145 0.713883 1.000000 G5 0.936074 0.917416 0.789577 0.890990 1.000000 G6 0.547714 0.592083 0.578830 0.660876 0.578308 1.000000

94

Aforesaid table shows the results of PCA for Governance variables of emerging stock markets

constitutes 21 countries. Sub table-3 shows the correlations among 06 governance variables



in the governance variables.



Figure 6.28 Mean of Composite of Governance Variables of Frontier Stock Markets

The above Figure depicts the mean values of composite Index of governance variables

of 24 frontier countries and it shows that Malta has the highest mean value of its governance

composite index, while Nigeria has the lowest mean value of its governance composite.


of economic composite index falls between -2 and 2 and most of them are lying on the central

part of the figure.

-5 -4 -3 -2 -1 0 1 2 3 4

ArgentinaBahrain

BangladeshBotswana



LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia


Sri LankaTunisia

Vietnam

95


deviation of composite economic variables, which is depicted below:

Figure 6.29 Standard Deviation of Composite of Governance Variables of Frontier Stock Markets


composite index and Serbia dispersion is quite high, when we see their governance indicators

tells that there is quite volatility. Moreover, the distribution along the bottom axis of the figure

shows that most the values of economic composite index falls below 0.4 and majority of them





0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

ArgentinaBahrain

BangladeshBotswana



LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia


Sri LankaTunisia

Vietnam

96



Figure 6.30 Mean of Cross Composite Index of Economic and Governance Variables of Frontier Stock Markets


governance index of 21 emerging countries and it shows that Malta has the highest mean value

of its cross composite index and Qatar has lowest mean value of its cross composite index.


of economic composite index falls between -1 and 2 and most of them are lying on the right

side of the figure.



-6 -4 -2 0 2 4 6 8 10 12

ArgentinaBahrain

BangladeshBotswana



LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia


Sri LankaTunisia

Vietnam

97

Figure 6.31 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Frontier Stock Markets


it shows that Malta has highest dispersion in the data which is quite evident when we see their



governance composite index falls between 0 and 1.5 and majority of them are lying on the left

side of the figure.




0 1 2 3 4 5 6 7

ArgentinaBahrain

BangladeshBotswana



LithuaniaMalta

NigeriaOmanQatar

RomaniaSerbia


Sri LankaTunisia

Vietnam

98

Figure 6.32 Scatter plots of Composite of Indices of Economic and Governance Indices of Frontier Stock Markets

-5

-4

-3

-2

-1

0

1

2

3

4

PG

OV

-6 -4 -2 0 2 4 6 8

PECO


positive relations with observations are lying at scattered in upward trend pattern. According

to Kernel Fit line, it is deduced that as governance factors move in the same direction as

economic factors move.

99

6.2.4 World Markets.

Now coming towards the combination of all regional markets, that is, world equity

markets. The index for composition of economic and governance factors of world market is

formed by using Principal Component Analysis (PCA). These PCAs are composite indices of

economic and governance and further their cross factors are built to explore their cross effects

on the development of stock markets.

6.2.3.1. Composite Index of Economic Factors: First of all, the composite index of

economic factors is formed by using PCA technique. The Eigen Values and Correlation matrix

of composite index of economic factors of frontier markets are appended below:

Table 6.24

Principal Components Analysis for Economic Variables of World 70 Stock Markets




1 2.212070 0.964343 0.2765 2.212070 0.2765

2 1.247727 0.121100 0.1560 3.459796 0.4325

3 1.126627 0.044478 0.1408 4.586423 0.5733

4 1.082149 0.209703 0.1353 5.668572 0.7086

5 0.872446 0.244014 0.1091 6.541018 0.8176

6 0.628432 0.109065 0.0786 7.169450 0.8962

7 0.519367 0.208184 0.0649 7.688817 0.9611

8 0.311183 --- 0.0389 8.000000 1.0000

100



E1 0.011069 -0.358771 0.549087 0.567424 -0.231242 0.336840 0.195366 0.206378

E2 -0.062586 -0.246821 0.432829 -0.647864 0.437150 0.334160 0.159140 -0.002274

E3 -0.270704 0.131682 -0.364086 0.414601 0.652132 0.389980 0.165759 0.009480

E4 0.370984 0.443297 -0.191346 -0.191698 -0.336560 0.502208 0.445833 0.168061

E5 0.531497 -0.341032 -0.127036 0.117156 0.100341 -0.017653 0.253924 -0.704619

E6 0.476363 0.246875 0.194931 0.101976 0.185707 0.319359 -0.725808 -0.021467

E7 0.186221 0.538051 0.476857 0.159586 0.319700 -0.454879 0.337400 -0.000874

E8 0.490879 -0.360223 -0.245889 -0.008232 0.264795 -0.247712 0.071767 0.657358



E1 1.000000

E2 -0.022557 1.000000

E3 -0.067811 -0.126985 1.000000

E4 -0.195199 -0.132959 -0.186364 1.000000

E5 0.115561 -0.056593 -0.197085 0.237533 1.000000

E6 0.039468 -0.040500 -0.157520 0.341373 0.361747 1.000000

E7 0.030077 -0.016539 -0.047653 0.155165 0.019697 0.317627 1.000000

E8 -0.040251 -0.016718 -0.157900 0.153372 0.655817 0.313136 -0.016081 1.000000

Abovementioned table depicts the results of PCA for Economic variables of world

stock markets constituting 70 countries. Sub table-3 shows the correlations among 08 economic

variables and it is revealed that (E5) and (E8) are having the highest correlation of 0.65,

whereas the lowest correlation of 0.019 is between (E5) and (E7). On the other side, there is

highest negative correlation of -0.19 between (E1) and (E4).



101

Figure 6.33 Mean of Composite Index of Economic Variables of World Stock Markets

The above Figure. depicts the mean values of composite economic index of 70 frontier

countries and it shows that Singapore has the highest mean value of its cross composite index

and Ghana has lowest mean value of its cross composite index. Moreover, the distribution

along the bottom axis of the figure shows that majority of the values of economic composite

index falls between -1 and 1 and most of them are lying on the right side of the figure.



-3 -2 -1 0 1 2 3 4 5 6

ArgentinaAustralia

AustriaBahrain

BangladeshBelgium

BotswanaBrazil

BulgariaCanada

ChileChina

ColombiaCote d'Ivoire

CroatiaCyprus

Czech RepublicDenmark

Egypt, Arab Rep.EstoniaFinlandFrance

GermanyGhana

GreeceHong Kong SAR, China

HungaryIndia

IndonesiaIrelandIsraelItaly

JapanJordanKenya

Korea, Rep.LithuaniaMalaysia

MaltaMexico

MoroccoNetherlands

New ZealandNigeria

NorwayOman

PakistanPeru

PhilippinesPoland

PortugalQatar

RomaniaRussian Federation

SerbiaSingapore


South AfricaSpain

Sri LankaSweden

SwitzerlandThailandTunisiaTurkey

United Arab EmiratesUnited Kingdom

United StatesVietnam

102

Figure 6.34 Standard Deviation of Composite Economic Variables of World Stock Markets

Aforementioned figure depicts that Malta has highest dispersion in the data which is

quite evident when we see their governance indicators tells that there is quite volatility in the

variables interest rates and trade. Moreover, the distribution along the bottom axis of the figure

shows that most the values of governance composite index falls between 0.3 and 1.0 and

majority of them are lying on the left side of the figure.


governance factors is formed by using PCA technique and its Eigen values are appended below.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

ArgentinaAustralia

AustriaBahrain

BangladeshBelgium

BotswanaBrazil

BulgariaCanada

ChileChina


CroatiaCyprus



GermanyGhana


HungaryIndia

IndonesiaIrelandIsraelItaly

JapanJordanKenya


MaltaMexico

MoroccoNetherlands

New ZealandNigeria

NorwayOman

PakistanPeru

PhilippinesPoland

PortugalQatar


SerbiaSingapore


South AfricaSpain

Sri LankaSweden

SwitzerlandThailandTunisiaTurkey



103

Table 6.25

Principal Components Analysis for Governance Variables of World Stock Markets (70

Countries)




1 5.207129 4.877016 0.8679 5.207129 5.207129

2 0.330113 0.060145 0.0550 5.537241 0.330113

3 0.269968 0.179274 0.0450 5.807209 0.269968

4 0.090693 0.035605 0.0151 5.897903 0.090693

5 0.055088 0.008078 0.0092 5.952990 0.055088

6 0.047010 --- 0.0078 6.000000 0.047010



G1 0.424090 -0.199345 -0.186390 -0.313092 -0.452207 0.665695

G2 0.422540 -0.173127 -0.274330 -0.142948 0.828406 0.097666

G3 0.384231 -0.337787 0.840653 0.172705 0.041727 -0.000982

G4 0.416930 0.014610 -0.344400 0.818406 -0.171882 -0.089510

G5 0.425882 -0.122138 -0.145793 -0.414130 -0.276291 -0.731171

G6 0.372515 0.895018 0.208416 -0.102078 0.040373 0.068472



G1 1.000000 G2 0.944768 1.000000 G3 0.822448 0.802101 1.000000 G4 0.915314 0.923145 0.766807 1.000000 G5 0.951607 0.944216 0.825524 0.912514 1.000000 G6 0.757272 0.756508 0.691295 0.785424 0.782673 1.000000

Aforesaid table shows the results of PCA for Governance variables of emerging stock

markets constitutes 21 countries. Sub table-3 shows the correlations among 06 governance

variables and it is revealed that G1 and G5 are having the highest correlation of 0.95, whereas

104

the lowest correlation of 0.69 is between G1 and G6. On the other side, there is no negative

correlation in the governance variables.



Figure 6.35 Mean of Composite Governance Variables of World Stock Markets

-6 -5 -4 -3 -2 -1 0 1 2 3 4

Argentina

Austria

Bangladesh

Botswana

Bulgaria

Chile

Colombia

Croatia

Czech Republic

Egypt, Arab Rep.

Finland

Germany

Greece

Hungary

Indonesia

Israel

Japan

Kenya

Lithuania

Malta

Morocco

New Zealand

Norway

Pakistan

Philippines

Portugal

Romania

Serbia

Slovak Republic

South Africa

Sri Lanka

Switzerland

Tunisia

United Arab Emirates

United States

The above Figure shows the mean values of composite Index of governance variables

of 70 world countries and it shows that Malta has the highest mean value of its governance

composite index, while Nigeria has the lowest mean value of its governance composite.


of economic composite index falls between -2 and 2 and most of them are lying on the right

central part of the figure.


deviation of composite economic variables, which is depicted in the figure 6.36:

105

Figure 6.36 Standard Deviation of Composite of Governance Variables of World Stock Markets

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Argentina

Bangladesh

Bulgaria

Colombia

Czech Republic

Finland

Greece

Indonesia

Japan

Lithuania

Morocco

Norway

Philippines

Romania

Slovak Republic

Sri Lanka

Tunisia

United States


composite index and Slovak republic dispersion is quite high, when we see their governance

indicators tells that there is quite volatility. Moreover, the distribution along the bottom axis of

the figure shows that most the values of economic composite index falls below 0.4 and majority

of them are lying on the left side of the figure.



the interaction of these variables.



106

Figure 6.37 Mean of Cross Composite Index of Economic and Governance Variables of World Stock Markets


governance index of 70 world countries and it shows that Singapore has the highest mean value

of its cross composite index. Moreover, the distribution along the bottom axis of the figure

shows that majority of the values of economic composite index falls between -2 and 2 and most

of them are lying on the left side of the figure.


deviation of composite of economic variables, which is depicted in the figure 6.38

-6 -4 -2 0 2 4 6 8 10 12 14

ArgentinaAustralia

AustriaBahrain

BangladeshBelgium

BotswanaBrazil

BulgariaCanada

ChileChina


CroatiaCyprus



GermanyGhana


HungaryIndia

IndonesiaIreland

IsraelItaly

JapanJordanKenya


MaltaMexico

MoroccoNetherlands

New ZealandNigeria

NorwayOman

PakistanPeru

PhilippinesPoland

PortugalQatar


SerbiaSingapore


South AfricaSpain

Sri LankaSweden

SwitzerlandThailand

Tunis iaTurkey



107

Figure 6.38 Standard Deviation of Cross Composite Index of Economic and Governance Variables of World Stock Markets


it shows that Norway has highest dispersion in the data. Moreover, the distribution along the

bottom axis of the figure shows that most the values of governance composite index falls





0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5

Argentina

Austria

Bangladesh

Botswana

Bulgaria

Chile

Colombia

Croatia

Czech Republic

Egypt, Arab Rep.

Finland

Germany

Greece

Hungary

Indonesia

Israel

Japan

Kenya

Lithuania

Malta

Morocco

New Zealand

Norway

Pakistan

Philippines

Portugal

Romania

Serbia

Slovak Republic

South Africa

Sri Lanka

Switzerland

Tunis ia

United Arab Emirates

United States

108

Figure 6.39 Scatter plots of Composite of Indices of Economic and Governance Indices of World Stock Markets

-6

-4

-2

0

2

4

Com

po

site I

nde

x o

f G

overn

an

ce V

ari

ab

les (

PG

OV

)

-4 -2 0 2 4 6 8

Composite Index of Economic Variables (PECO)


positive relations with observations are lying at scattered in upward trend pattern. According

to Kernel Fit line, it is deduced that as governance factors move in the same direction as

economic factors move.

109

6.3. Results of Dynamic GMM for Panel data estimation

This study applies dynamic panel GMM and dynamic panel System GMM to tackle the

typical problems of Ordinary Least Squares (OLS) like endogeneity. The panel GMM has been

applied to all three regions of the world equity markets as developed, emerging and frontier

markets. Before applying panel GMM approach, the study has estimated the models for Fixed

Effects, Pooled and Random Effects. Their results suggest the suitability of GMM technique

and completed results are appended bellows:

Likelihood test for Redundant Fixed Effects Tests: The results of Likelihood test

for Redundant Fixed Effects Tests are depicted below:-

Effects Test Statistic d.f. Prob.

Cross-section F 1.358177 (13,953) 0.1735

Cross-section Chi-square 17.990384 13 0.1579

Lagrange Multiplier Tests for Pooled Tests: The results of Lagrange Multiplier

Tests for Pooled Tests are depicted below:-

Test Hypothesis

Cross-section Time Both

Breusch-Pagan 0.385031 3813.084 3813.469

(0.5349) (0.0000) (0.0000)

Honda 0.620509 61.75018 44.10273

(0.2675) (0.0000) (0.0000)

King-Wu 0.620509 61.75018 25.15605

(0.2675) (0.0000) (0.0000)

Standardized Honda 0.917347 67.50249 41.79053

(0.1795) (0.0000)

Standardized King-Wu 0.917347 67.50249 22.30559

(0.1795) (0.0000) (0.0000)

Gourierioux, et al.* -- -- 3813.469

(< 0.01)

110

Hausman Test for Random Effects: The results of Hausman Test for Random

Effects are depicted below:-

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 17.656298 13 0.1710

Above mentioned results of Hausman Test for Random Effects and Lagrange Multiplier

Tests for Pooled Tests shows that there are random effects in cross section, so the application

of Panel GMM is appropriate technique for our panel datasets.

Now, after it is decided that GMM is appropriate econometric technique for our panel

dataset, so the estimation of our study will be considered in the light of our panel dataset nature.

Question No 1 has been answered in the PCA results, now remaining questions are answered

by using GMM technique. The estimation of the GMM results is conducted as per research

questions of the study followed by its empirical model and concerned hypotheses, the details

of which are as follows:

6.2.2. Research Question No 2: This question of the study is related to the analysis of impact

of economic variables on stock market development, which is as follows:

Q No.2. What are the impacts of Economic variables on the equity market development of

World Stock Markets?

The Null Hypotheses for the Model-1 with exogenous variables as 08 Economic Variables

are appended below:-



111



of stock markets.





markets.

H019: Composite variable for Governance factors do not affect the development of

stock markets.


After estimating equation 1 in Dynamic GMM technique for the panel data set of developed

countries, the results are as follows:

Table 6.26

All Economic Variables of Developed Stock Markets (25 Countries) and Depended

Variable : Market Capitalization as %age of GDP

Variable Coefficient Std. Error t-Statistic Prob.

Y(-1) 0.597964 0.017148 34.86990 0.0000

E1 3.467382 0.291562 11.89245 0.0000

E2 -2.842335 0.596598 -4.764235 0.0000

E3 -0.872048 0.500641 -1.741865 0.0824

E4 0.045208 0.192031 0.235423 0.8140

)1.(..........1 ititkitiit EYY

112

E5 -5.479524 0.799343 -6.855038 0.0000

E6 0.756222 0.069139 10.93775 0.0000

E7 0.344247 0.191185 1.800598 0.0726

E8 1.333611 0.839905 1.587812 0.1133

Note: Complete estimated results of this table are referred at Appendix-4A

Table 6.27

All Economic Variables of Emerging Stock Markets (21 Countries) and Depended



Y(-1) 0.313267 0.131758 2.377600 0.0180

E1 1.189405 0.706579 1.683329 0.0932

E2 -0.562928 0.312187 -1.803174 0.0723

E3 0.060928 0.369616 0.164840 0.8692

E4 0.179470 0.293191 0.612128 0.5409

E5 0.594218 1.175760 0.505390 0.6136

E6 -0.257738 0.274936 -0.937448 0.3492

E7 0.361362 0.919325 0.393073 0.6945

E8 3.804343 0.862114 4.412807 0.0000

Note: Complete estimated results of this table are referred at Appendix-4B

Table 6.28

All Economic Variables of Frontier Stock Markets (24 Countries) and Depended Variable :



Y(-1) 0.538880 0.077532 6.950413 0.0000

E1 0.358943 0.154132 2.328806 0.0207

E2 -0.599268 0.511840 -1.170811 0.2428

E3 0.302298 0.301020 1.004244 0.3163

E4 -0.079352 0.222970 -0.355884 0.7222

E5 -0.281559 0.388632 -0.724487 0.4695

E6 0.518521 0.077727 6.671048 0.0000

113

E7 -0.021566 0.065399 -0.329759 0.7419

E8 1.285982 0.513060 2.506496 0.0128

Note: Complete estimated results of this table are referred at Appendix-4C

Table 6.29

All Economic Variables of World Stock Markets (70 Countries) and Depended Variable of



Y(-1) 0.346661 0.001628 212.9583 0.0000

E1 1.133814 0.038646 29.33851 0.0000

E2 -1.124654 0.056726 -19.82595 0.0000

E3 -0.878688 0.039527 -22.23030 0.0000

E4 0.165796 0.011069 14.97878 0.0000

E5 -1.825737 0.081713 -22.34320 0.0000

E6 0.755894 0.012985 58.21152 0.0000

E7 0.155027 0.009660 16.04792 0.0000

E8 0.839415 0.045524 18.43899 0.0000

Note: Complete estimated results of this table are referred at Appendix-4D

6.2.3. Research Question No 3: The third question of the study pertains to the analysis of

impact of governance variables on stock market development, which is as follows:

Q No.3. What are the impacts of Governance variables on the equity market development of

World Stock Markets?

The Null Hypotheses for the Model-2 are appended below:-



stock markets.

114






After estimating equation 2 in Dynamic GMM technique for the panel data set of

developed countries, the results are as follows:

Table 6.30

All Governance Variables of Developed Stock Markets (25 Countries) and Depended



Y(-1) 0.682435 0.025572 26.68715 0.0000

G1 -0.578566 1.102372 -0.524837 0.6000

G2 3.331904 0.750033 4.442345 0.0000

G3 -0.506273 0.134392 -3.767136 0.0002

G4 -3.274225 0.629735 -5.199368 0.0000

G5 3.235296 0.346588 9.334691 0.0000

G6 2.622147 0.264680 9.906852 0.0000


Table 6.31

All Governance Variables of Emerging Stock Markets (21 Countries) and Depended



Y(-1) 0.065809 0.008060 8.165054 0.0000

G1 -0.056858 0.183259 -0.310258 0.7565

G2 0.897967 0.190954 4.702532 0.0000

)2.(..........1 ititjitiit GYY

115

G3 0.183235 0.118612 1.544826 0.1233

G4 0.037555 0.272284 0.137927 0.8904

G5 -1.801663 0.379471 -4.747824 0.0000

G6 -0.246916 0.054973 -4.491621 0.0000


Table 6.32

All Governance Variables of Frontier Stock Markets (24 Countries) and Depended



Y(-1) 0.526775 0.012996 40.53328 0.0000

G1 -0.497369 0.111855 -4.446557 0.0000

G2 0.023674 0.120949 0.195737 0.8449

G3 0.591086 0.049267 11.99752 0.0000

G4 -0.050505 0.083193 -0.607082 0.5442

G5 -0.038931 0.145167 -0.268181 0.7887

G6 -0.007288 0.079142 -0.092084 0.9267


Table 6.33

All Governance Variables of World Stock Markets (70 Countries) and Depended Variable



Y(-1) 0.427911 0.000794 538.7451 0.0000

G1 -1.319646 0.043310 -30.46979 0.0000

G2 1.453818 0.056146 25.89362 0.0000

G3 -0.200078 0.021599 -9.263211 0.0000

G4 -0.246351 0.070530 -3.492870 0.0005

G5 2.153088 0.044617 48.25721 0.0000

G6 1.073616 0.017052 62.96247 0.0000


116

6.2.4. Research Question No 4: The fourth question of the study pertains to the analysis of

combined impacts of economic and governance variables on stock market development, which

is as follows:

Q No.4. What are the combined impacts of Governance and Economic variables on the

development of stock market concerning World Equity Markets?

This model measures the joint impact of economic variables, governance and governance

factors on the stock market development

The Null Hypotheses for this Model are appended below:-





of stock markets.







stock markets.


117







Table 6.34

All Economic and Governance Variables of Developed Stock Markets (25 Countries) and

Depended Variable : Market Capitalization as %age of GDP


Y(-1) 0.545137 0.059281 9.195741 0.0000

E1 3.104011 1.264182 2.455352 0.0146

E2 -2.613447 2.217913 -1.178336 0.2395

E3 0.145459 1.753669 0.082946 0.9339

E4 0.284822 0.401479 0.709433 0.4785

E5 -6.886241 2.074414 -3.319608 0.0010

E6 0.755269 0.306879 2.461132 0.0144

E7 0.381957 0.815504 0.468370 0.6398

E8 0.440446 1.831055 0.240542 0.8101

G1 1.502209 1.374600 1.092834 0.2752

G2 3.553311 1.757400 2.021913 0.0440

G3 -0.541886 0.760343 -0.712686 0.4765

G4 -2.890350 1.300561 -2.222387 0.0269

G5 -0.384385 1.685741 -0.228021 0.8198

G6 2.721788 0.994169 2.737752 0.0065



118

Table 6.35

All Economic and Governance Variables of Emerging Stock Markets (21 Countries) and



Y(-1) 0.284319 0.111553 2.548744 0.0113

E1 1.277739 0.579049 2.206615 0.0280

E2 -0.659128 0.263382 -2.502559 0.0128

E3 -0.078533 0.313548 -0.250466 0.8024

E4 0.302719 0.224006 1.351391 0.1775

E5 -0.166436 0.975932 -0.170541 0.8647

E6 0.177252 0.186361 0.951119 0.3422

E7 0.357971 0.233686 1.531842 0.1265

E8 3.259688 1.037422 3.142104 0.0018

G1 0.004531 0.333132 0.013600 0.9892

G2 0.335903 0.176556 1.902529 0.0580

G3 0.188202 0.173330 1.085805 0.2784

G4 0.335695 0.377145 0.890095 0.3741

G5 -0.713518 0.662969 -1.076246 0.2826

G6 -0.655727 0.441015 -1.486861 0.1380


Table 6.36

All Economic and Governance Variables of Frontier Stock Markets (24 Countries) and



Y(-1) 0.526180 0.049394 10.65260 0.0000

E1 0.337931 0.138198 2.445259 0.0152

E2 -0.091362 0.235204 -0.388438 0.6980

E3 -0.052941 0.083830 -0.631531 0.5283

E4 -0.347753 0.286500 -1.213795 0.2260

E5 -0.751510 0.183769 -4.089421 0.0001

E6 0.125278 0.151272 0.828164 0.4084

119

E7 0.138549 0.048630 2.849012 0.0048

E8 -0.051486 0.244437 -0.210633 0.8333

G1 -0.266518 0.183906 -1.449205 0.1485

G2 -0.453460 0.393824 -1.151429 0.2507

G3 0.183899 0.144857 1.269523 0.2054

G4 -0.332209 0.240763 -1.379817 0.1689

G5 0.440128 0.476963 0.922773 0.3570

G6 0.031788 0.159366 0.199464 0.8421


Table 6.37

All Economic and Governance Variables of World Stock Markets (70 Countries) and



Y(-1) 0.327335 0.002463 132.9053 0.0000

E1 1.053651 0.062257 16.92414 0.0000

E2 -0.983563 0.104093 -9.448874 0.0000

E3 -0.750464 0.073542 -10.20456 0.0000

E4 0.139105 0.023038 6.038110 0.0000

E5 -1.708201 0.150669 -11.33745 0.0000

E6 0.732865 0.022961 31.91849 0.0000

E7 0.171974 0.022617 7.603729 0.0000

E8 0.646026 0.116716 5.535043 0.0000

G1 -0.581906 0.099173 -5.867587 0.0000

G2 1.360316 0.101266 13.43313 0.0000

G3 -0.471558 0.031663 -14.89320 0.0000

G4 0.082546 0.138736 0.594987 0.5520

G5 1.851517 0.107640 17.20099 0.0000

G6 0.530616 0.069041 7.685482 0.0000


Another equation for testing the model-3 would be:

120



Table 6.38

All Economic Variables and Composite Index of Governance Variables of Developed Stock

Markets (25 Countries) and Depended Variable of Market Capitalization as %age of GDP


Y(-1) 0.550686 0.045371 12.13734 0.0000

E1 4.311606 1.026719 4.199402 0.0000

E2 -2.890759 1.137811 -2.540631 0.0115

E3 0.056528 0.892238 0.063355 0.9495

E4 0.534424 0.387282 1.379933 0.1685

E5 -7.159806 1.588324 -4.507775 0.0000

E6 0.415630 0.266821 1.557711 0.1202

E7 0.610334 0.402158 1.517647 0.1300

E8 2.490189 1.538691 1.618382 0.1065

PGOV 27.82690 10.81695 2.572528 0.0105


Table 6.39

All Economic Variables and Composite Index of Governance Variables of Emerging Stock

Markets (21Countries) and Depended Variable of Market Capitalization as %age of GDP


Y(-1) 0.337380 0.093790 3.597178 0.0004

E1 1.009993 0.413552 2.442238 0.0152

E2 -2.467357 1.030531 -2.394258 0.0172

E3 0.728884 0.235190 3.099128 0.0021

E4 0.340306 0.367234 0.926674 0.3548

E5 2.947501 2.750370 1.071674 0.2847

E6 -0.364526 0.438448 -0.831401 0.4064

)4.(..........1 ititjitkitiit GovEYY

121

E7 1.034320 0.808566 1.279203 0.2018

E8 4.001587 0.627373 6.378326 0.0000

PGOV -11.39619 9.027543 -1.262380 0.2078


Table 6.40

All Economic Variables and Composite Index of Governance Variables of Frontier Stock

Markets (24 Countries) and Depended Variable of Market Capitalization %age of GDP


Y(-1) 0.513626 0.076773 6.690172 0.0000

E1 0.332205 0.092064 3.608407 0.0004

E2 0.052924 0.131461 0.402583 0.6875

E3 0.082910 0.044068 1.881400 0.0609

E4 -0.277189 0.145858 -1.900407 0.0583

E5 -0.602946 0.091526 -6.587695 0.0000

E6 0.184761 0.033869 5.455203 0.0000

E7 0.160200 0.058052 2.759618 0.0061

E8 0.375126 0.140506 2.669823 0.0080

PGOV 3.243283 2.430260 1.334542 0.1830


Table 6.41

All Economic Variables and Composite Index of Governance Variables of World Stock



Y(-1) 0.252702 0.003437 73.52188 0.0000

E1 2.046103 0.060612 33.75750 0.0000

E2 -3.686475 0.182334 -20.21826 0.0000

E3 -1.586586 0.055425 -28.62560 0.0000

E4 0.583136 0.015244 38.25313 0.0000

E5 -4.384335 0.109060 -40.20123 0.0000

E6 0.841175 0.031276 26.89554 0.0000

122

E7 0.219161 0.031826 6.886194 0.0000

E8 1.538483 0.113689 13.53235 0.0000

PGOV 49.64352 1.424435 34.85137 0.0000


Another equation for testing the model-3 would be:



Table 6.42

All Governance Variables and Composite Index of Economic Variables of Developed Stock



Y(-1) 0.632040 0.024249 26.06447 0.0000

G1 -0.052337 0.689560 -0.075899 0.9395

G2 4.427687 0.613857 7.212893 0.0000

G3 -1.076544 0.150147 -7.169923 0.0000

G4 -3.405756 0.831739 -4.094743 0.0001

G5 1.705086 0.266668 6.394030 0.0000

G6 2.760608 0.303206 9.104742 0.0000

PECO 11.65072 2.101860 5.543055 0.0000


)5.(..........1 ititjitkitiit GEcoYY

123

Table 6.43

All Governance Variables and Composite Index of Economic Variables of Emerging Stock



Y(-1) 0.223543 0.032918 6.790925 0.0000

G1 0.401874 0.479664 0.837823 0.4027

G2 0.521704 0.719001 0.725595 0.4686

G3 0.480946 0.254370 1.890733 0.0595

G4 -0.176801 1.020584 -0.173235 0.8626

G5 -1.544353 1.064800 -1.450369 0.1479

G6 -0.474660 0.817542 -0.580594 0.5619

PECO 17.62040 2.314310 7.613676 0.0000


Table 6.44.

All Governance Variables and Composite Index of Economic Variables of Frontier Stock



Y(-1) 0.632040 0.024249 26.06447 0.0000

G1 -0.052337 0.689560 -0.075899 0.9395

G2 4.427687 0.613857 7.212893 0.0000

G3 -1.076544 0.150147 -7.169923 0.0000

G4 -3.405756 0.831739 -4.094743 0.0001

G5 1.705086 0.266668 6.394030 0.0000

G6 2.760608 0.303206 9.104742 0.0000

PECO 11.65072 2.101860 5.543055 0.0000


124

Table 6.45

All Governance Variables and Composite Index of Economic Variables of World Stock



Y(-1) 0.412911 0.001438 287.2115 0.0000

G1 -1.003143 0.036920 -27.17038 0.0000

G2 1.476385 0.052826 27.94794 0.0000

G3 -0.462321 0.014483 -31.92139 0.0000

G4 -0.063720 0.036346 -1.753157 0.0800

G5 2.141841 0.036974 57.92854 0.0000

G6 0.972312 0.019071 50.98503 0.0000

PECO 14.09162 0.200610 70.24373 0.0000


6.2.5. Research Question No 5: Fifth question of the study pertains to the analysis of cross

effects of economic and governance variables on stock market development, which is as

follows:

Q No.5. What are the cross effects of governance and economic factors on the development of

stock market?

This model measures the cross effects of governance factors and economic variables on stock

market development viz-a-viz impact of economic & governance variables on stock market

development.

The Null Hypotheses for the Model-4 with exogenous variables as Composite Economic and

Governance Factors are appended below:-

125


markets.


markets.




Where,

= Direct effect of Economic factors on the development of stock market (Y)

= Direct effect of Governance factors on the development of stock market (Y)

= Cross effects of Governance factors (Gov) and Economic Factors (Eco).on the

development of stock market (Y)



Table 6.46

Composite Indices of Economic and Governance Variables of Developed Stock Markets (25

Countries) and Depended Variable of Market Capitalization as %age of GDP


Y(-1) 0.662996 0.006466 102.5361 0.0000

PECO 13.43409 0.884676 15.18533 0.0000

PGOV 10.37992 1.365497 7.601572 0.0000

PECO*PGOV 2.477447 0.402805 6.150488 0.0000



126

Table 6.47

Composite Indices of Economic and Governance Variables of Emerging Stock Markets (21



Y(-1) 0.222044 0.007481 29.68213 0.0000

PECO 20.11261 0.772316 26.04195 0.0000

PGOV -4.352256 1.292038 -3.368520 0.0008

PECO*PGOV 0.959003 0.424660 2.258281 0.0246


Table 6.48

Composite Indices of Economic and Governance Variables of Frontier Stock Markets (24



Y(-1) 0.603374 0.000783 770.2145 0.0000

PECO 1.266656 0.203741 6.216999 0.0000

PGOV 0.758107 0.088048 8.610106 0.0000

PECO*PGOV -0.568153 0.083030 -6.842738 0.0000


Table 6.49

Composite Indices of Economic and Governance Variables of World Stock Markets (70



Y(-1) 0.386455 0.001405 275.0485 0.0000

PECO 18.22461 0.141548 128.7525 0.0000

PGOV 23.50915 0.582312 40.37210 0.0000

PECO*PGOV 7.614872 0.045824 166.1748 0.0000


127

6.2.6. Research Question No 6: Fifth question of the study pertains to the analysis of direct

and indirect effects governance variables on stock market development, which is as follows:

Q No.5. What are the indirect effects of governance factors through economic factors on the

development of stock markets?

This abovementioned question is to be answered by measuring the channel effects of

governance factors on stock market development and further the effect through economic

variables .

The Null Hypotheses for the Model-5 are appended below:-


markets.


markets.

H013: There is no direct effect of composite Governance variables on the development

of stock markets.

H014: There is no indirect effect of composite Governance variables through

Economic variables on the development of stock markets.

The set of equations for testing the model-5 would be:


)8...( aGovEco ititktit

128

Now for estimating the direct effect of Governance(Gov) on Stock Market

development(Y) and indirect effects of Governance(Gov) factors on the development of Stock

Market (Y) through Economic Factors(Eco), the following methodology is adopted:-

First of all, above mentioned equations are estimated as a System and then estimated

values are placed in the equations and following steps are adopted:-

The expression is to to tested for Wald test for its significance

= Direct effect of Governance factors(Gov) on the development of stock market (Y)

= Indirect effect of Governance factors(Eco) on the development of stock market

(Y) through Economic Factors (Eco).

After estimating the set of equations in System option of Dynamic GMM technique for the

panel data set of developed countries, the results are as follows:

Table 6.50

Composite Indices of Economic and Governance Variables with indirect effect of Developed

Stock Markets (25 Countries) and Depended Variable of Market Capitalization as %age of

GDP


C(11) 0.646604 0.130420 4.957863 0.0000

C(12) -0.010593 0.014358 -0.737769 0.9395

C(13) 0.015863 0.010555 1.502991 0.0000

C(14) 0.851001 0.029454 28.89264 0.0000

C(21) -0.307497 0.214673 -1.432399 0.0001

C(22) 0.184279 0.089369 2.061990 0.0000


)8(...........).().( bGovY

).(

.

129

Table 6.51

Composite Indices of Economic and Governance Variables with indirect effect of Emerging


GDP


C(11) 10.33597 3.095994 3.338499 0.0009

C(12) 0.816095 0.867415 0.940835 0.3471

C(13) 0.425459 0.504257 0.843734 0.3991

C(14) 0.793465 0.067957 11.67599 0.0000

C(21) -0.327752 0.260178 -1.259722 0.2082

C(22) 0.189012 0.094144 2.007693 0.0450


Table 6.52

Composite Indices of Economic and Governance Variables with indirect effect of Frontier


GDP


C(11) 6.056385 1.059980 5.713679 0.0000

C(12) 0.370360 0.628811 0.588984 0.5561

C(13) 0.557779 0.418102 1.334075 0.1826

C(14) 0.856991 0.032050 26.73908 0.0000

C(21) -0.009275 0.160960 -0.057625 0.9541

C(22) 0.301746 0.058461 5.161530 0.0000


130

Table 6.53

Composite Indices of Economic and Governance Variables with indirect effect of World


GDP


C(11) 4.324358 1.645948 2.627275 0.0087

C(12) 3.618091 0.911072 3.971247 0.0001

C(13) -0.002804 0.289226 -0.009695 0.9923

C(14) 0.951191 0.023176 41.04266 0.0000

C(21) -0.273180 0.141945 -1.924544 0.0544

C(22) 0.210139 0.061519 3.415829 0.0007


6.2.7. Research Question No 7: This question of the study pertains to the analysis of inter-

dependence of Governance and Economic factors with each other, which is as follows:

Q No.7. How much is the inter-dependence of Governance and Economic factors with each

other?

The Null Hypotheses for the Model-4 with exogenous variables as Composite Economic and

Governance Factors are appended below:-


markets.


markets.



131


Where,

= Direct effect of development of stock market (Y) on Composite Economic factors

(Eco)

= Direct effect of Governance factors on Composite Economic factors (Eco)

After estimating equation 9 in Dynamic GMM technique for the panel data set of

developed countries, the results are as follows:

Table 6.54

Stock Market Development and Composite Index of Governance Variables of Developed

Stock Markets (25 Countries) and Depended Variable of Composite Economic factors (Peco)


PECO(-1) 0.342913 0.009164 37.41869 0.0000

Y 0.001422 0.000354 4.021204 0.0001

PGOV -0.096181 0.013402 -7.176911 0.0000


Table 6.55

Stock Market Development and Composite Index of Governance Variables of Emerging

Stock Markets (21 Countries) and Depended Variable of Composite Economic factors (Peco)


PECO(-1) 0.484705 0.025322 19.14169 0.0000

Y 0.004823 0.000131 36.88157 0.0000

PGOV 0.140191 0.053639 2.613622 0.0094


)9...(1 ititjitkitiit GovYEcoEco

132

Table 6.56

Stock Market Development and Composite Index of Governance Variables of Frontier

Stock Markets (25 Countries) and Depended Variable of Composite Economic factors

(Peco)


PECO(-1) 0.342913 0.009164 37.41869 0.0000

Y 0.001422 0.000354 4.021204 0.0001

PGOV -0.096181 0.013402 -7.176911 0.0000


Table 6.57

Stock Market Development and Composite Index of Governance Variables of World Stock

Markets (70 Countries) and Depended Variable of Composite Economic factors (Peco)


PECO(-1) 0.057129 0.002154 26.52363 0.0000

Y 0.000865 4.95E-06 174.7373 0.0000

PGOV 0.029588 0.003113 9.504310 0.0000


133

CHAPTER 7

SUMMARY AND CONCLUSIONS

7.1. Combined Results

This study focuses on finding the Determinants of Stock Market Development in the

panel data of Developed, Emerging and Frontier Markets. The econometric technique of

Dynamic Panel GMM has been applied to the four regions of the global markets and there are

total of five models tested and each model has been tested in each region to explore the

following effects:

(i) Direct impact of Economic on the Development of Stock Markets

(ii) Direct and Indirect impact of Governance on the Development of Stock Markets

(iii) Combined Effect of Economic and Governance Factors on the Development of

Stock Market.

(iv) Cross Effects of Economic and Governance Factors on the Development of

Stock Market.

(v) Reverse impact of Development of Stock Markets on Economic Indicators

The study has summarized the results into four categories, that is, Developed,

Emerging, Frontier and World equity markets.

7.1.1. Determinants of Stock Market Development for Developed Region. First part

of the summarized result table pertains to determinants of stock market development for

134

developed region (25 countries) with depended variable of market capitalization as %age of

GDP, which is as follows:

Table 7.1

Determinants of Stock Market Development for Developed Region (25 Countries) and


Variables Model-1 Model-2 Model-3 Model-4 Model-5

Lagged Depended 0.545137*** 0.5512*** 0.6320*** 0.6629*** 0.6428***

GDP growth (annual %) 3.104011*** 4.2395***

Inflation, CPI (annual %) -2.613447 -2.7855**

Real interest rate (%) 0.145459 -0.2114

Domestic Bank credit (% of GDP) 0.284822 0.5249

Gross domestic savings (% of GDP) -6.886241*** -6.6194***

Trade (% of GDP) 0.755269*** 0.3964

FDI (% of GDP) 0.381957 0.6911*

Current A/C balance (% of GDP) 0.440446

Control of Corruption 1.502209 -0.0523

Government Effectiveness 3.553311** 4.4276***

Political Stability -0.541886 -1.0765***

Regulatory Quality -2.890350 -3.4057***

Rule of Law -0.384385 1.7050***

Voice and Accountability 2.721788** 2.7606***

Composite Economic Factors 11.650*** 13.434*** 11.135***

Composite Governance Factors 2.4413** 10.379*** 21.278***

Cross Factors of Composite

Economic & Governance Factors

(Eco*Gov)

2.477***

No. of observations 352 352 352 352 352

Instruments Rank 25 25 25 25 25

J-statistics (Sargan test for over

identifying restrictions)

15.6295 18.0643 22.0245 23.3441 23.1675

Arellano-Bond serial correlation test

AR(1)

0.0021 0.0000 0.0000 0.0000 0.0000


AR(2)

0.7325 0.8567 0.9143 0.6315 0.6471

Note: Table shows Panel GMM Estimation Results of Market Capitalization as % GDP regressed on Economic

and Governance Factors alongwith their composite factors. The annual data period is from 1996 to 2015 The

symbols of ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. The results of shown

Models have been extracted from the estimated Models in Chapter-6.

135

The estimation of the panel data of developed equity has been conducted by using of

Panel GMM regression, the estimated results are presented in table 7.1. In this regression,

Market Capitalization as %age of GDP is regressed on various explanatory variables. This table

depicts the results five Models (1, 2, 3, 4 & 5) of developed market.

In Model-1 of the Table 7.1, the results indicate that the coefficient on lag dependent

variable is positive indicating that past values of Stock Market Development (Y) positively

affects the current values of Y of developed equity markets. The values of Lag dependent

variable further reveal that the speed of adjustment is 0.54 indicating that companies in

developed markets make 54% adjustment towards their target market capitalization. Further,

it is evident from the results that GDP growth, Gross Domestic Savings and Trade have their

significant impact on the development of stock market of developed region. If GDP growth is

to grow by one unit, then Market Capitalization is affected by 3.1 times, so the economic

growth plays a vital role in the movements of stock market capitalization. In the Governance

indicators, the Government Effectiveness and Accountability have the significant impacts on

the market capitalization.

In Model-2, the market capitalization of developed equity market has been regressed

on all economic variables and one composite index of governance variables. The result of this

model depict that GDP growth, inflation and gross domestic savings are statistically significant.

GDP growth affects the market capitalization by 4.3 times and gross domestic savings is

inversely affecting the relation by 6.6 times. Additionally, Inflation is inversely affects the

market capitalization by 2.8 times. Lag dependent variable in this model shows that the speed

of adjustment is 0.55 indicating that companies in developed markets make 55% adjustment

towards their target market capitalization.

According to Model-3, the market capitalization of developed equity market has been

regressed on all governance variables and one composite index of economic variables. The

136

result of this model depict that Government Effectiveness, Political Stability, Regulatory

Quality, Rule of Law, Voice & Accountability and composite index of Economic variables are

statistically significant. Whereas, the composite index of Economic variables affects the

market capitalization by 11.6 times, which is also a quite positive significant value. Lag

dependent variable in this model shows that the speed of adjustment is 0.63 indicating that

companies in developed markets make 63% adjustment towards their target market

capitalization.

The Model-4 regresses market capitalization of developed equity market on composite

indices of governance and economic variables alongwith their cross effects. The result of this

model depict that all explanatory variables are statistically significant. The composite indices

of governance and economic variables affects the market capitalization by 13.4 and 10.4 times

respectively. Additionally, cross effect of composite indices of governance and economic

variables affects the market capitalization by 2.5 times. Lag dependent variable in this model

shows that the speed of adjustment is 0.66 indicating that companies in developed markets

make 66% adjustment towards their target market capitalization.

In Model-5, market capitalization of developed equity market regresses on composite

indices of governance and economic variables without their cross effects. The result of this


of governance and economic variables affects the market capitalization by 11.1 and 21.3 times

respectively. Lag dependent variable in this model shows that the speed of adjustment is 0.64

indicating that companies in developed markets make 64% adjustment towards their target

market capitalization.

137

7.1.2. Reverse Impacts on Composite Economic Factors for Developed Region. In

order to depict the reverse impacts on composite economic factors for developed region (25

countries), the summarized results are on the following table:

Table 7.2

Reverse Impacts on Composite Economic Factors for Developed Region (25 Countries)

and Depended Variable : Composite Economic Factors

Variables Model-1A Model-2A Model-3A

Lagged Depended 0.3429*** 0.3393*** 0.3888***

Market Capitalization as %age of GDP 0.0014*** 0.0015***

Composite Governance Factors -0.0961*** -0.024***

No. of observations 339 347 306

J-statistics (Sargan test for over identifying

restrictions)

22.0343 23.3526 23.4962

Arellano-Bond serial correlation test AR(1) 0.0000 0.0000 0.0000


Note: Table shows Panel GMM Estimation Results of Composite Economic factors regressed on Stock Market

Capitalization as % GDP and Governance Factors. The annual data period is from 1996 to 2015. The

symbols of ***, ** and * indicate the significance levels at 1%, 5% and 10% respectively. The results of

shown Models have been extracted from the estimated Models in Chapter-6.

In Table 7.2, the Composite Index of Economic Variables is regressed with explanatory

variables of Composite Index of Governance Variables and Market Capitalization as %age of

GDP. This table depicts the results of three Models (1A, 2A & 3A) of developed market.

In Model-1A, composite index of economic variables regresses on composite indices

of governance variables and market capitalization of developed equity market. The result of

this model shows that the explanatory variables are statistically significant. The composite

indices of governance variables affects the composite index of economic variables by 0.14%

and stock market development inversely affects the composite index of economic variables by

9.6%. Lag dependent variable in this model shows that the speed of adjustment is 0.34

138



Furthermore, composite index of economic variables are regressed on composite

indices of governance variables and market capitalization of developed equity market

separately. In Model-2A, composite index of economic variables are regressed on composite

index of governance variables and the result of this model shows that the explanatory variables

are statistically significant. The composite index of governance variables negatively affects

the composite index of economic variables by 2.4%. In Model-3A, composite index of

economic variables are regressed on stock market development and the result of this model

shows that the explanatory variables are statistically significant. The composite indices of

governance variables negatively affects the composite index of economic variables by 0.15%.

Lag dependent variable in this model shows that the speed of adjustment is 0.38 indicating that

companies in developed markets make 38% adjustment towards their target market

capitalization.

7.1.3. Scatter Plots of Stock Market Development for Developed Markets. To further

elucidate the diversity of relationship among stock market development, economic and

governance variables, the scatter plots along with their statistical distribution and Kernel Fit

line has been generated in this study. These are shown separately in the pairs of scatter plots

of stock market development & economic variables and scatter plots of stock market

development & governance variables. Firstly, the scatter plots of stock market development &

economic variables are shown as follows:

139

Figure 7.1

Scatter Plot of Stock Market Development & Economic Variables of Developed Markets

-4

-2

0

2

4

6

8

Com

posite I

nd

ex o

f E

co

no

mic

Facto

rs (

PE

CO

)

1 2 3 4 5 6 7 8

Stock Market Development (LY)

Figure 7.1 depicts that observed data of stock market development and economic

variables are lying across the graph and showing its trend upward. The Kernel Fit posit the

mix trend that in the very few initial values behave in opposite direction and afterwards as the

stock market development increases, so the increase in economic factors in the same direction

and vice versa if economic factors going down then it also affects development of stock

markets. On each axis, the distribution of their data is also displayed and it shows that the stock

market development data is mainly centrally located, whereas the data of economic variables

is heavily located in the first half of the distribution.

140

Secondly, the scatter plots of stock market development & governance variables in the

developed markets are shown as follows:

Figure 7.2

Scatter Plot of Stock Market Development & Governance Variables of Developed Markets

-8

-6

-4

-2

0

2

4

Com

po

site I

nde

x o

f G

overn

an

ce F

acto

rs (

PG

OV

)

1 2 3 4 5 6 7 8


Figure 7.2 illustrates that observed data of stock market development and governance

variables are lying across the graph and showing its trend upward. Most of the values lie at the

middle top of the graph. The trend line drawn by using Kernel Fit posit quite mix trend that as

the stock market development increases, so the governance factors behave in different

141

directions and vice versa if governance factors going down do not necessarily affects

development of stock markets. On each axis of the figure, the distribution of their data is also

displayed and it shows that the stock market development data is mainly centrally located,

whereas the data of governance variables is scattered across the distribution.

7.1.4. Determinants of Stock Market Development for Emerging Region. Second

part of the summarized results table pertains to determinants of stock market development for

emerging region (21 countries) with depended variable of market capitalization as %age of

GDP, which is as follows:

142

Table 7.3

Determinants of Stock Market Development for Emerging Region (21 Countries) and



Lagged Depended 0.284319*** 0.33738*** 0.222044*** 0.6629*** 0.2095***

GDP growth (annual %) 1.277739** 1.00999***

Inflation, CPI (annual %) -0.659128*** -2.46735***

Real interest rate (%) -0.078533 0.728884***

Domestic Bank credit (% of GDP) 0.302719 0.340306

Gross domestic savings (% of GDP) -0.166436 2.947501

Trade (% of GDP) 0.177252 -0.364526

FDI (% of GDP) 0.357971 1.034320

Current A/C balance (% of GDP) 3.259688*** 4.00158***

Control of Corruption 0.004531 0.401874

Government Effectiveness 0.335903** 0.521704

Political Stability 0.188202 0.480946**

Regulatory Quality 0.335695 -0.176801

Rule of Law -0.713518 -1.544353

Voice and Accountability -0.655727 -0.474660


Composite Governance Factors -11.39619 -4.3522*** -4.8667***



(Eco*Gov)

0.95900**





318.06 8.0538 14.2326 23.3441 23.1675


AR(1)

0.0021 0.0000 0.0000 0.0032 0.0021


AR(2)

0.5050 0.9929 0.8511 0.52960 0.6061





143

The estimation of the panel data of emerging equity has been done by incorporating the

econometric method of Panel GMM, the estimated results are presented in table 7.3. As per

this method, Market Capitalization as a %age of GDP is regressed on various explanatory

variables of economic and governance factors. This table depicts the results in five Models (1,

2, 3, 4 & 5) of emerging market.


variable is positive indicating that past values positively affects current values of emerging

equity markets. Lag dependent variable further reveal that the speed of adjustment is 0.28


market capitalization. Furthermore, it is evident from the results that GDP growth, Inflation

and Current Account balance have their significant impact on the development of stock market

of emerging region. If GDP growth is to grow by one unit, then Market Capitalization is

affected by 1.27 times, so the economic growth plays a vital role in the movements of stock

market capitalization. In the Governance indicators, the Government Effectiveness has the

significant impacts on the market capitalization.

In Model-2 of the table 7.2 of the emerging equity markets, the market capitalization of

emerging equity market has been regressed on all economic variables and one composite index

of governance variables. The result of this model depict that GDP growth, inflation, interest

rates and current account balance are statistically significant. GDP growth affects the market

capitalization by 1.01 times and. Inflation is inversely affecting the relation by 2.4 times.

Additionally, Interest rate and current account balance affects the market capitalization by 0.7

and 4.0 times respectively. Lag dependent variable in this model shows that the speed of

adjustment is 0.34 indicating that companies in developed markets make 34% adjustment

towards their target market capitalization.

144

According to Model-3, the market capitalization of emerging equity market has been


result of this model depict that Political Stability and composite index of Economic variables

are statistically significant. Whereas, the composite index of Economic variables affects the

market capitalization by 17.6 times, which is also a quite positive significant value. Lag


companies in emerging markets make 22% adjustment towards their target market

capitalization.

The Model-4 regresses market capitalization of emerging equity market on composite

indices of governance and economic variables along with their cross effects. The result of this


of governance and economic variables affects the market capitalization by 20.1 and -4.3 times

respectively. Additionally, cross effects of composite indices of governance and economic


shows that the speed of adjustment is 0.66 indicating that companies in emerging markets make

66% adjustment towards their target market capitalization.

In Model-5, market capitalization of emerging equity market regresses on composite



of governance and economic variables affects the market capitalization by 21.3 and -4.8 times


indicating that companies in emerging markets make 21% adjustment towards their target


145

7.1.5. Reverse Impacts on Composite Economic Factors for Emerging Region. In

order to depict the reverse impacts on composite economic factors for emerging region (21

countries), the summarized results are on the following table:

Table 7.4

Reverse Impacts on Composite Economic Factors for Emerging Region (21 Countries) and

Depended Variable : Composite Economic Factors


Lagged Depended 0.484705*** 0.478638***

0.423222***

Market Capitalization as %age of GDP 0.004823***

0.00494***

Composite Governance Factors 0.140191*** 0.085739***



restrictions)

17.8444 19.7793 13.654








variables of Market Capitalization as %age of GDP and Composite Index of Governance

Variables. This table depicts the results in three Models (1A, 2A & 3A) of emerging markets.

In Model-1, composite index of economic variables regresses on composite indices of

governance variables and market capitalization of emerging equity market. The result of this

model shows that the explanatory variables are statistically significant. The composite indices

of governance variables affects the composite index of economic variables by 0.4% and stock

market development affects the composite index of economic variables by 14% in positive

direction. Lag dependent variable in this model shows that the speed of adjustment is 0.48

146

indicating that companies in emerging markets make 48% adjustment towards their target



indices of governance variables and market capitalization of emerging equity market

separately. In Model-2, composite index of economic variables are regressed on composite

indices of governance variables and the result of this model shows that the explanatory

variables are statistically significant. The composite indices of governance variables affects

the composite index of economic variables by 8.5% in positive direction. In Model-3,

composite index of economic variables are regressed on stock market development and the

result of this model shows that the explanatory variables are statistically significant. The

composite indices of governance variables negatively affects the composite index of economic

variables by 0.5%. Lag dependent variable in this model shows that the speed of adjustment is

0.42 indicating that companies in emerging markets make 42% adjustment towards their target


7.1.6. Scatter Plots of Stock Market Development of Emerging Markets. To further


governance variables, scatter plots along with their distribution and Kernel Fit line has been

generated in this study. These are shown separately in the pairs of scatter plots of stock market

development & economic variables and scatter plots of stock market development &

governance variables. Firstly, the scatter plots of stock market development & economic

variables are shown as follows:

147

Figure 7.3

Scatter Plot of Stock Market Development & Economic Variables of Emerging Markets

-6

-4

-2

0

2

4

6

Com

posite I

nd

ex o

f E

con

om

ic v

ara

ible

s (

PE

CO

)

1 2 3 4 5 6



variables of emerging markets that are lying across the graph and showing its trend upward.

The Kernel Fit line posits the positive trend that as the stock market development increases, so

the economic factors and vice versa if economic factors going down then it also affects

development of stock markets. On each axis, the distribution of their data is also displayed and

it shows that the stock market development data is mainly centrally located, whereas the data

of economic variables is heavily located in the first half of the distribution.

148


emerging markets are shown as follows:

Figure 7.4

Scatter Plot of Stock Market Development & Governance Variables of Emerging Markets

-6

-4

-2

0

2

4

6

Com

po

site I

nde

x o

f G

ove

rna

nce

va

raib

les (

PG

OV

)

1 2 3 4 5 6



variables are lying across the graph and not showing any linear trend. The Kernel Fit line posits

quite mix trend that as the stock market development increases, so the governance factors

149

behave in different directions and vice versa if economic factors going down do not necessarily

affects development of stock markets. On each axis of the figure, the distribution of their data

is also displayed and it shows that the stock market development data in emerging markets is

mainly centrally located, whereas the data of governance variables is scattered across the

distribution.

7.1.7. Determinants of Stock Market Development for Frontier Region. Third part

of the summarized results table pertains to determinants of stock market development for

frontier region (24 countries) with depended variable of market capitalization as %age of GDP,

which is as follows:

150

Table 7.5

Determinants of Stock Market Development for Frontier Region (24 Countries) and



Lagged Depended 0.526180*** 0.513626*** 0.61592*** 0.60337*** 0.6033***

GDP growth (annual %) 0.337931** 0.332205***

Inflation, CPI (annual %) -0.091362 0.052924

Real interest rate (%) -0.052941 0.082910**

Domestic Bank credit (% of GDP) -0.347753 -0.277189**


Trade (% of GDP) 0.125278 0.184761***

FDI (% of GDP) 0.138549*** 0.160200***

Current A/C balance (% of GDP) -0.051486 0.375126***

Control of Corruption -0.266518 0.45851***

Government Effectiveness -0.453460 -0.24620

Political Stability 0.183899 0.43215***

Regulatory Quality -0.332209 -0.196686

Rule of Law 0.440128 0.242199

Voice and Accountability 0.031788 -0.265916

Composite Economic Factors 0.214651 1.266656*** 0.010182

Composite Governance Factors 3.243283 0.758107*** -0.11489**



(Eco*Gov)

-0.56815***





12.8384 14.7391 12.6583 21.5611 23.1675


AR(1)

0.0252 0.0000 0.0000 0.0000 0.0204


AR(2)

0.8089 0.9994 0.9934 0.3182 0.7877





151

In this section of, the estimation of the panel data of frontier region has been done by

incorporating the econometric method of Panel GMM regression, the estimated results are

presented in table 7.5. As per this regression, Market Capitalization as %age of GDP is

regressed on various explanatory variables economic and governance factors. This table depicts

the results in five Models (1, 2, 3, 4 & 5) of frontier equity markets.


variable is positive indicating that past values positively affects current values of frontier equity

markets. The values of Lag dependent variable reveal that the speed of adjustment is 0.52

indicating that companies in frontier region make 52% adjustment towards their target market

capitalization. Furthermore, it is evident from the results that GDP growth, Gross Domestic

Savings and FDI have their significant impact on the development of stock market of frontier

region. If GDP growth is to grow by one unit, then Market Capitalization is affected by 0.34

times, so the economic growth plays a vital role in the movements of stock market

capitalization.

In Model-2 of the table 7.5 of the frontier equity markets, the market capitalization of

frontier equity markets has been regressed on all economic variables and one composite index

of governance variables. The result of this model depict that almost all of the economic

variables are statistically significant except annual inflation rate of frontier equity markets.

GDP growth affects the market capitalization by 0.33 times and. FDI and current account

balance affects the market capitalization by 0.16 and 0.37 times respectively. Lag dependent

variable in this model shows that the speed of adjustment is 0.51 indicating that companies in

frontier markets make 51% adjustment towards their target market capitalization.

According to Model-3, the market capitalization of frontier equity market has been


152

result of this model depict that Political Stability and Control on corruption are statistically

significant. Lag dependent variable in this model shows that the speed of adjustment is 0.61

indicating that companies in frontier markets make 61% adjustment towards their target market

capitalization.

The Model-4 regresses market capitalization of frontier equity market on composite



of economic and governance variables affects the market capitalization by 1.26 and 0.75 times


variables affects the market capitalization by -0.56 times. Lag dependent variable in this model

shows that the speed of adjustment is 0.60 indicating that companies in frontier markets make


In Model-5, market capitalization of frontier equity market regresses on composite


model depict that all explanatory variables are not statistically significant, rather composite

index of governance variables have inverse relation with the development of stock market of

frontier region. The composite index of governance variables affects the market capitalization

4.8 times. Lag dependent variable in this model shows that the speed of adjustment is 0.61



7.1.8. Reverse Impacts on Composite Economic Factors for Frontier Region. In

order to depict the reverse impacts on composite economic factors for frontier region (24

countries), the summarized results are produced on the following table:

153

Table 7.6

Reverse Impacts on Composite Economic Factors for Frontier Region (24 Countries) and

Depended Variable : Composite Economic Factors


Lagged Depended 0.3715*** 0.4026*** 0.3743***





restrictions)

21.3671 22.2252 21.1069









Variables of frontier markets. This table depicts the results in three Models (1A, 2A & 3A) of

frontier markets.

In Model-1A, composite index of economic variables regresses on composite indices

of governance variables and market capitalization of frontier equity market. The result of this

model shows that all the explanatory variables are statistically significant. The composite

indices of governance variables affects the composite index of economic variables by 0.2% and

stock market development affects the composite index of economic variables by 34% in

positive direction. Lag dependent variable in this model shows that the speed of adjustment is

0.37 indicating that companies in frontier markets make 37% adjustment towards their target


154


indices of governance variables and market capitalization of frontier equity market separately.

In Model-2, composite index of economic variables are regressed on composite indices of

governance variables and the result of this model shows that the explanatory variables are

statistically significant. The composite indices of governance variables affects the composite

index of economic variables by 8.5% in positive direction. In Model-3, composite index of


shows that the explanatory variables are statistically significant. The composite index of

governance variables positively affects the composite index of economic variables by 27%.

Lag dependent variable in this model shows that the speed of adjustment is 0.40 indicating that

companies in frontier markets make 40% adjustment towards their target market capitalization.

7.1.9. Scatter Plot of Stock Market Development of Frontier Markets. To further



generated in this study. These are shown separately in the pairs of scatter plots of stock market

development & economic variables and scatter plots of stock market development &

governance variables. Firstly, the scatter plots of stock market development & economic

variables are shown as follows:

155

Figure 7.5

Scatter Plot of Stock Market Development & Economic Variables of Frontier Markets

-6

-4

-2

0

2

4

6

8

Cio

mposit

e I

nd

ex o

f E

co

no

mic

Va

ria

ble

s (

PE

CO

)

-4 -2 0 2 4 6

Stock Market Devlopment (LY)


variables of frontier markets that are mainly lying at the right upper side of the graph except

few isolated observation lying in the left side of the graph and rest of the graph is showing its

upward trend. The Kernel Fit posits mainly the positive trend that as the stock market

development increases, so the economic factors and vice versa if economic factors going down

then it also affects development of stock markets. On each axis, the distribution of their data

is also displayed and it shows that the stock market development data is mainly rightly located,

whereas the data of economic variables is mainly located in the central part of the distribution.

156


frontier markets are shown as follows:

Figure 7.6

Scatter Plot of Stock Market Development & Governance Variables of Frontier Markets

-5

-4

-3

-2

-1

0

1

2

3

4

Com

po

site I

nde

x o

f G

overn

an

ce V

ari

ab

les (

PG

OV

)

-4 -2 0 2 4 6




quite mix trend that as the stock market development increases, so the governance factors

157

behave in different directions and vice versa if economic factors going down do not necessarily

affects development of stock markets. On each axis of the figure, the distribution of their data

is also displayed and it shows that the stock market development data in frontier markets is

located on the right side of the distribution, whereas the data of governance variables is

scattered across the distribution similar to the characteristics of other regions.

7.1.10. Determinants of Stock Market Development for World Markets. Third part

of the summarized results table pertains to determinants of stock market development for all

World Markets (70 countries) with depended variable of market capitalization as %age of GDP,

which is as follows:

158

Table 7.7

Determinants of Stock Market Development for All World Equity Markets (70 Countries)

and Depended Variable : Market Capitalization as %age of GDP


Lagged Depended 0.327335*** 0.25270*** 0.4129*** 0.3864*** 0.4309***

GDP growth (annual %) 1.053651*** 2.04610***

Inflation, CPI (annual %) -0.98356*** -3.6864***

Real interest rate (%) -0.75046*** -1.5865***

Domestic Bank credit (% of GDP) 0.139105*** 0.58313***


Trade (% of GDP) 0.73286*** 0.84117***

FDI (% of GDP) 0.17197*** 0.21916***

Current A/C balance (% of GDP) 0.64602*** 1.53848***

Control of Corruption -0.58190*** -1.0031***

Government Effectiveness 1.36031*** 1.47638***

Political Stability -0.47155 -0.4623***

Regulatory Quality 0.08254*** -0.06372*

Rule of Law 1.851517*** 2.1418***

Voice and Accountability 0.530616*** 0.9723***


Composite Governance Factors 49.643*** 23.509*** 18.296***



(Eco*Gov)

7.6148***





58.5999 54.2453 61.5439 61.9803 65.3912


AR(1)

0.0000 0.0000 0.0000 0.0000 0.0000


AR(2)

0.9999 0.9985 0.9990 0.9910 0.9568





159

This is final section of estimation, in which, estimation of the panel data of whole world

equity markets has been done by incorporating the econometric method of Panel GMM

regression, the estimated results are presented in table 7.7. According to this regression, Market

Capitalization as %age of GDP is regressed on various explanatory variables of economic and

governance factors. This table portrays the results in five Models (1, 2, 3, 4 & 5) of world

equity markets.

In Model-1 of the Table 7.7, the results depict that the coefficient on lag dependent

variable is positive indicating that past values positively affects current values of world equity

markets. The values of Lag dependent variable reveal that the speed of adjustment is 0.32

indicating that companies in frontier region make 32% adjustment towards their target market

capitalization. Furthermore, it is evident from the results that all economic variables have their

significant impact on the development of stock market of world region. However, Inflation,

Real interest rate and Gross Domestic Savings have negative impact on the development of

stock markets. Furthermore, the results indicate that if GDP growth is to grow by one unit, then

Market Capitalization is affected by 1.05 times, so the economic growth plays a vital role in

the movements of stock market capitalization. Regarding, the results of governance variables,

it is revealed that all variables are statistically significant except political stability. These

results are quite in consonance with previous studies as conducted by Yartey (2008), Cherif

and Gazdar (2010) and Bayraktar (2014).

In Model-2 of the table 7.7 of the world equity markets, the market capitalization of

world equity markets has been regressed on all economic variables and one composite index

of governance variables. The result of this model depict that almost all of the economic

variables are statistically significant annual inflation rate of world equity markets. GDP growth

affects the market capitalization by 2.04 times and. FDI and current account balance affects

160

the market capitalization by 0.21 and 1.53 times respectively. Lag dependent variable in this

model shows that the speed of adjustment is 0.25 indicating that companies in world markets

make 25% adjustment towards their target market capitalization.

According to Model-3, the market capitalization of world equity market has been


result of this model depict that all governance variables are statistically significant. Lag


companies in world markets make 41% adjustment towards their target market capitalization.

The Model-4 regresses market capitalization of world equity market on composite



of economic and governance variables affects the market capitalization by 18.2 and 23.5 times



shows that the speed of adjustment is 0.38 indicating that companies in world markets make


In Model-5, market capitalization of world equity market regresses on composite


model depict that all explanatory variables are statistically significant. The composite index of

economic and governance variables affects the market capitalization 16.28 and 18.29 times


indicating that companies in world markets make 43% adjustment towards their target market

capitalization.

161

7.1.11. Reverse Impacts on Composite Economic Factors for all World Equity Markets.

In order to depict the reverse impacts on composite economic factors for all world

equity markets (70 countries), the summarized results are produced on the following table:

Table 7.8

Reverse Impacts on Composite Economic Factors for the All World Equity Markets

( 70 Countries) and Depended Variable : Composite Economic Factors


Lagged Depended 0.05712*** 0.14356*** 0.05838***





restrictions)

65.7542 68.8099 64.9826


Arellano-Bond serial correlation test AR(2) 0.4657 0..4913 .4728







Variables of world markets. This table depicts the results in three Models (1A, 2A & 3A) of

world markets.

In sub Model-1, composite index of economic variables regresses on composite indices

of governance variables and market capitalization of world equity market. The result of this

model shows that all the explanatory variables are statistically significant. The composite

indices of governance variables affects the composite index of economic variables by 2.9% and

162

stock market development affects the composite index of economic variables by 0.1% in

positive direction. Lag dependent variable in this model shows that the speed of adjustment is

0.05 indicating that companies in world markets make 5% adjustment towards their target



indices of governance variables and market capitalization of world equity market separately.

In Model-2, composite index of economic variables are regressed on composite indices of

governance variables and the result of this model shows that the explanatory variables are

statistically significant. The composite indices of governance variables affects the composite

index of economic variables by 9.9% in positive direction. In Model-3, composite index of


shows that the explanatory variables are statistically significant. The stock market

development positively affects the composite index of economic variables by 0.1%. Lag


companies in world markets make 14% adjustment towards their target market capitalization.

7.1.12. Scatter Plots of Stock Market Development of World Equity Markets. To

further elucidate the diversity of relationship among stock market development, economic and


generated for all world equity markets in this study. These are shown separately in the pairs of

scatter plots of stock market development & economic variables and scatter plots of stock

market development & governance variables. Firstly, the scatter plots of stock market

development & economic variables are shown as follows:

163

Figure 7.7

Scatter Plot of Stock Market Development & Economic Variables of World Markets

-4

-2

0

2

4

6

8

Com

posite I

nd

ex o

f E

co

no

mic

Vari

able

s (

PE

CO

)

-4 -2 0 2 4 6 8



variables of emerging markets that are mainly lying at the lower right side of the graph except

few isolated observation lying in the left side of the graph and rest of the graph is showing its

upward trend. The Kernel Fit line of world equity markets posits mainly the positive trend that

as the stock market development increases, so the economic factors and vice versa if economic

factors going down then it also affects development of stock markets. On each axis, the

164

distribution of their data is also displayed and it shows that the stock market development data

is mainly centrally located at the right side of the graph, whereas the data of economic variables

is mainly located in the lower central part of the distribution.


world markets are shown as follows:

Figure 7.8

Scatter Plot of Stock Market Development & Governance Variables of World Markets

-6

-4

-2

0

2

4

Com

po

site I

nde

x o

f G

ove

rna

nce

va

riab

les (

PG

OV

)

-4 -2 0 2 4 6 8


165

Figure 7.8 portrays that observed data of stock market development and governance


quite mix trend at beginning and further upward trend in the majority values of the distribution.

Therefore trend shows that as the stock market development increases, so the governance

factors behave in different directions in few values and majority of the observations follow

upward trend. On each axis of the figure, the distribution of their data is also displayed and it

shows that the stock market development data in world markets is almost normally distributed

on the right side of the distribution, whereas the data of governance variables is scattered across

the distribution.

166

7.2. Overall Summary of the Empirical Results

The results of determinants of equity market development have been analyzed by

using statistical and econometric techniques on the panel data sets of world equity markets.

After thorough analysis, the overall summary of the results is appended below:

Table 7.9

Overall Summary of the Empirical Results

Models/Effects

Developed

Markets

(25)

Emerging

Markets

(21)

Frontier

Markets

(24)

World

Markets

(70)

Effects of Economic variables on the

equity market development

Positively

Significant

Positively

Significant

Positively

Significant

Positively

Significant

Effects of Governance variables on

the equity market development

Positively

Significant

Marginally

Significant

Marginally

Significant

Positively

Significant

Indirect Effects of Governance

variables through Economic

Variables on the equity market

development

Marginally

Significant

Not

Significant

Not

Significant

Positively

Significant

Combined Effects of Economic and

Governance variables on the equity

market development

Positively

Significant

Partially

Significant

Partially

Significant

Positively

Significant

Cross effects of Economic and

Governance variables on the equity

market development

Positively

Significant

Positively

Significant

Negatively

Significant

Positively

Significant

Reverse impacts of Stock market

development on Economic variables

Positively

Significant

Positively

Significant

Positively

Significant

Positively

Significant

Impacts of Governance variables on

Economic variables

Negatively

Significant

Positively

Significant

Positively

Significant

Positively

Significant

Table 7.9 depicts the model/effect wise summary of the results in four categories, that

is, Developed, Emerging, Frontier and World Equity Markets. The brief summaries of the

results according to different regions of the equity markets are as follows:

167

7.2.1. Short Summary of the Results for Developed Equity Markets: In the developed

region, there are 25 stock markets and it is found that Economic and Governance variables

have their significant effects on the development of stock market. In Economic variables, the

GDP growth, Gross Domestic savings and Trade are the significant variables. In Governance

variables, Government Effectiveness and Accountability are the significant variables which are

in conformity with the results of Pagano (1993), Perotti and Van Oijen (2001) and El Wassal

(2013). While, the analysis of composite variables depict that both the composite variables of

Economic and Governance factors are having significant impact on the development of stock

markets. There is also significant indirect effect of Governance through Economic variables on

the development of equity markets. Moreover, the cross effects of Governance and Economic

factors are significant on the development of stock market. Both the indirect and cross effects

are the contributions of the study as well. On the other hand, the reverse analysis depicts that

development of stock market is also contributing the growth of economy which is in line with

study of Levine and Zervos (1996).

7.2.2. Short Summary of the Results for Emerging Equity Markets: In the

emerging region of the equity markets, there are 21 stock markets and it is found that mostly

Economic variables have their significant effects on the development of stock market except

few Governance variables have their significant effects. In Economic variables, the GDP

growth, Gross Domestic savings and Current Account Balance are the significant variables.

These results are in conformity with the studies as conducted by Yartey (2008) and Bayraktar

(2014). In Governance factors, only Government Effectiveness has significant impacts. The

analysis of composite variables depict that both the composite variables of Economic and

Governance factors are having significant impact on the development of stock markets but

Governance variables have their negative impacts. The cross effects of Governance and

Economic factors are significant on the development of stock market. On the other hand, the

168

analysis for knowing contribution of stock markets in an economy of emerging markets, the

reverse analysis depicts that development of stock market is also contributing the growth of

economy that is in conformity with the results of Levine and Zervos (1996).

7.2.3. Short Summary of the Results for Frontier Equity Markets: In the frontier

region, there are 24 stock markets and it is found that Mainly Economic variables have their

significant effects on the development of stock market. In Economic variables, the GDP

growth, Gross Domestic savings and FDI are the significant variables. In Governance factors,

there are marginal significant impacts. The analysis of composite variables depict that both the

composite variables of Economic and Governance factors are having significant impact on the

development of stock markets but Governance variables have their negative impacts.

Moreover, the cross effects of Governance and Economic factors are significant on the

development of stock market. On the other hand, the analysis for knowing contribution of

stock markets in an economy of frontier markets, the reverse analysis depicts that development

of stock market is also contributing the growth of economy as per the study of Levine and

Zervos (1996).

7.2.4. Short Summary of the Results for World Equity Markets: As a whole of

world equity markets, there are 70 stock markets that have been identified by FTSE and it is

found that Economic and Governance variables have their significant effects on the

development of stock market. In Economic variables, mainly all are the significant variables

which are conformity with the results of Apergis et al. (2011). In Governance variables, all are

the significant variables except political stability and these results are in consonance with the

studies of El Wassal (2013) and Perotti and Van Oijen (2001). While, the analysis of

composite variables depict that both the composite variables of Economic and Governance

factors are having significant impact on the development of stock markets. There is also

positive significant effect of Governance through Economic variables on the development of

169

equity markets. There is also significant indirect effect of Governance through Economic

variables on the development of world equity markets. In the same way, the cross effects of

Governance and Economic factors are significant on the development of world stock market.

Both the indirect and cross effects are the contributions of the study as well. On the other

hand, the reverse analysis depicts that development of world stock market is also contributing

the growth of their economy and these results are consonance with the studies of Levine and

Zervos (1996) and Kanetsi (2015).

170

7.3. Conclusions

The study has explored multifaceted analysis of equity markets in all three regions of

the world stock markets. Despite having different dynamics and resources, there are few

similarities but there are some of the stark differences, which lead them to identify their

uniqueness. To narrow down the final words on this study, the conclusion is divided into two

segments, that is, micro and macro views.

First in the micro view, the study finds that effects of economic and governance factors

on stock market development of developed, emerging and frontier markets are peculiar in

nature and unique as per the dynamics of that particular region. For instance, this study finds

that economic and governance factors are more influential in developed region as compared to

their impacts in emerging and frontier region. In economic factors, it is found that GDP growth,

FDI, Trade and Stock market liquidity are found to be leading determinants of stock market

development. On the governance factors, it is revealed that Governance effectiveness and

Accountability are the main determinants in the development of stock markets.

Another dimension of micro view is conducted on the formation of composite indices

of Economic and Governance factors through Principal Component Analysis by using factors

for each region of the world equity markets. Afterwards, cross-index of Economic and

Governance Factors is formed for exploring the joint effects of these variables. The study

reveals that there is strong correlation in these composite variable in the developed region,

where is there is no clear pattern in the developing countries. The postulation behind this trend

is that institutions are strong in the developed countries and if there is any negative thing

171

happens in the governance issues, the economy respond quickly. Whereas, in the developing

countries, the institutions are not fully strong and there are frequent governance issues as well.

Moreover, there is quite dispersion in the composite data of the developing countries. So, there

is no direct correlation in the composite factors of economic and governance in the developing

countries. The plausible postulation behind this phenomena could be the weak institutional

quality and frequent lower governance level in the developing countries.

Now coming towards macro view, the study finds that effects of economic and

governance factors on stock market development are not only unidirectional, but also

bidirectional as well. Particularly, the developed markets have dual effects on economic and

governance factors. The indirect effects of governance through economic variables revealed

the significant effects in developed markets and no indirect effects in emerging and frontier

markets. While analyzing the cross effects, it is revealed that it is quite significant in developed

markets and found quite trivial in emerging stock markets. The plausible reason for this

significance in developing market is due to the fact that developing countries, due to their

strong institutional quality, are more reactive to both the governance and economic issues. The

reverse impact of stock market development on the economic growth is also quite captivating

in which development of stock market also effect the growth of economy in all three regions

of the world.

At the outset of this study, it was postulated that governance factors might play a

significant role in emerging than developed markets, but the results show that markets in

emerging and frontier regions are effecting negatively by governance factors. There is no vivid

picture to identify its pattern except the weak institutional issues in developing countries.

Regarding the negative behavior of governance factors in emerging and frontier markets, the

plausible cause could be the governance issues in these countries. When the governance factors

are in positive side, the investor may not be necessarily investing in stock markets, rather they

172

may invest in other sectors like banking, real estate or industry which are more safer and less

volatile than equity markets. On the other side, when the governance factors are not good, then

investor invests their money in stock market to get higher gain on their investment. The same

is validated by the negative significant values of gross domestic investment in emerging and

frontier markets. So, the inverse trend of investment augment the assertion of negative trend

in composite governance factors in developing countries.

While investigating the reverse impact of stock market development on the economic

factors, it is found that economic factors in developed and emerging markets are also prone to

the effects of stock market development. The postulation behind this phenomena is that stock

market is depicting the market performance of companies which are directly and indirectly

contributing to the growth of economy and in return the growth of economy is also effected by

the development of stock markets.

7.4. Policy Recommendations

Keeping in view the findings of the study on the economic and governance factors of

equity markets, the following policy recommendations are suggested:

1. The study recommends that determinants of equity market may not solely based

on economic factors rather the significance of governance factors may be taken into

account while taking the complete picture of the subject.

2. The spectrum for the application of governance factors may be enhanced

particularly in emerging and frontier regions of the world equity markets.

3. The linkage of economic and governance factors may be thoroughly researched

and common factors may be established for effective determinants of financial markets.

7.5. Contributions of the Study

The aim of the study is to find the important determinants of stock market development

by using the panel data of developed, emerging and frontier regions. During the empirical

173

testing, it is found that economic factors and stock markets have their dual effects. The

significant contributions of the study are as follows:

1. First, the study has analyzed the isolated and combined effects of economic and

governance factors on the development of stock markets on all three stock markets of

the world. Moreover, the correlation effects of economic and governance factors for

each region are also analyzed to know the dynamics of factors in isolation and collective

as well.

2. Secondly, the cross effects of governance and economic variable on the

development of stock markets on all three equity markets of the world are explored to

know the joint impacts of these variables on the development of stock market.

3. Thirdly, the indirect effects of governance factors through economic variable

on the development of stock markets for all regional markets by estimating their

systems of equations.

4. Last but not the least, the formation of index for the isolated and cross composite

variable of economic and governance factors with classification of developed, emerging

and frontier regions.

7.6. Limitations of the Study

Apart from the limitations in this part of the world, the main limitations of the study are

appended below:

1. The scope of the study was limited to the stock market development, however,

after analyzing the data of economic and governance factors of all regions of the world,

it is found that there is lot more to do in the fields of economic and governance factors.

174

The reverse impacts of governance and stock market development on the economy

poses multiple questions to investigate other side of the study as well.

2. Stock market data is quite in abundance on daily, monthly and yearly basis, but

there are number of challenges for economic and governance factors data. The data on

factors of economic and governance is compiled after end of the year, which may not

reflect the real-time picture of the economic condition and its governance at certain

time for a particular year.

3. There was quite dearth of qualitative data and availability of that data in this

part of the world, like Pakistan, is a big challenge to the researcher. The high quality

data is not available without huge funding which is far from the reach University

resources for one researcher. Moreover, there were number challenges in the existing

data of World Development Indicators (WDI), like, at number of places, either the data

was missing or wrongly placed in another year. Therefore, an interim solution was

evolved by extracting individual countries data from the respective state bank web sites

and at some places, the method of extrapolation was also incorporated to make the

meaningful data.

7.7. Future Research Avenues

The section comprises the areas to be focused for future research. This study has been

focused on the determinants of stock market development on the panel study of all regions of

the world. During the research, it was revealed that there are number of other directions which

require deliberations and further research. The salient areas for future research are as follows:-

1. The reverse impacts of governance factors and stock market development on

economic factors may be investigated. By which, we will be having more vivid picture

175

of the fact that governance factors play vital role in its growth apart from traditional

factors particularly in developing countries.

2. Existing research has focused its attention in the direction of external factors

rather if the internal plus governance factors are resolved, then market is automatically

going to be improved as the investor may find safe investment inland than going abroad.

So, due focused may be placed in the future studies for tackling the issues of internal

and governance factors.

3. Keeping in view the dynamics of different regions of the world, economic

indicators may be revised according to the nature and realities of that particular region.

The production and types of goods and services produced in developed country is quite

different than developing country, then comparison of both the developed and

underdeveloped country at the same scale is not at a parity. This disparity may be

resolved by creating the economic indicators according to governance dynamics of each

country.

176

APPENDICES

Appendix-1A

Composite Index of Economic Variables of Developing Stock Markets (25 Countries)

COUNTRY Mean Std. Dev. Skew. Kurt.

Australia -0.965754 0.206988 -0.235267 2.416603

Austria -0.020635 0.453560 1.086004 2.796837

Belgium 0.148951 0.550040 -0.392688 1.812621

Canada -0.329221 0.262239 -0.147106 2.274181

Denmark -0.219381 0.590056 0.755236 2.478274

Finland -0.213434 0.648560 -0.453611 1.816350

France -0.930771 0.188327 -0.506843 2.948668

Germany -0.260907 0.421799 -0.160253 1.666128

Greece -2.238874 0.537864 -0.593165 2.437103

Hong Kong 3.109351 0.955035 -1.240720 4.180725

Ireland 1.956902 1.921279 1.946727 6.309741

Israel -0.881353 0.610179 -0.672300 2.373559

Italy -1.175533 0.253756 -0.642280 2.613781

Japan -0.423302 0.349008 -0.106963 2.174554

Korea, Rep. 0.350447 0.413523 -0.137820 2.122725

Netherlands 1.377850 1.194165 1.071487 3.510925

New Zealand -0.962546 0.298254 -0.334904 2.824914

Norway 0.936153 0.662562 -0.440915 2.246504

Portugal -1.466352 0.432565 0.771124 2.381135

Singapore 4.723261 0.867499 0.170697 1.979008

Spain -0.895517 0.282663 0.858171 3.266056

Sweden 0.247483 0.396757 -0.577912 2.762711

Switzerland 1.256337 0.483430 0.111595 2.219408

United Kingdom -1.038344 0.231107 -0.725392 3.907891

United States -1.694127 0.232620 0.106421 2.044053

All -1.05E-16 1.714394 1.505926 5.431351

177

Appendix-1B

Composite Index of Governance Variables of Developing Stock Markets (25 Countries)


Australia 1.089340 0.200782 -0.353185 1.716721

Austria 1.217786 0.272650 0.457234 2.977997

Belgium -0.036496 0.246959 0.214047 2.565366

Canada 1.297703 0.150367 -0.798390 4.663154

Denmark 2.087905 0.188092 -1.367705 4.187274

Finland 2.278833 0.206717 -0.812653 3.062307

France -0.614126 0.268572 0.110590 1.988135

Germany 0.686426 0.221552 -0.397381 1.894194

Greece -4.470563 1.312934 -0.596092 1.742103

Hong Kong -0.052191 0.947192 -0.668476 1.735310

Ireland 0.862161 0.226033 -0.507899 2.222526

Israel -3.039911 0.418212 -0.337373 2.559646

Italy -4.264235 1.035477 0.237419 1.319231

Japan -0.917697 0.656976 -0.345975 2.361420

Korea, Rep. -3.795460 0.914866 -0.602105 2.129513

Netherlands 1.726172 0.290358 0.260651 1.803974

New Zealand 1.887547 0.286199 0.147645 1.801809

Norway 1.662479 0.293047 -0.092778 1.885976

Portugal -1.289040 0.835635 0.067164 1.518722

Singapore 0.744807 0.238273 0.794563 2.613796

Spain -1.539454 1.015319 -0.086846 1.448040

Sweden 1.837521 0.229779 0.299867 2.163824

Switzerland 1.853912 0.166713 0.185566 2.330356

United Kingdom 0.728759 0.348729 0.080685 1.970068

United States 0.057821 0.446286 0.359859 1.772998

All 5.51E-16 2.071123 -1.139304 3.478826

178

Appendix-1C

Composite Index of Cross Variables of Developing Stock Markets (25 Countries)


Australia -1.035869 0.233349 -0.396988 2.609203

Austria 0.061303 0.651933 1.412186 3.838495

Belgium -0.003365 0.114603 -0.471906 3.138398

Canada -0.420915 0.357186 -0.285545 2.192777

Denmark -0.481914 1.218928 0.695997 2.375537

Finland -0.472077 1.495062 -0.489665 1.870070

France 0.552501 0.267453 -0.068158 1.865060

Germany -0.189689 0.339007 -0.760367 2.452431

Greece 10.10881 4.494001 0.935223 2.577739

Hong Kong 0.897412 2.336990 -1.089663 3.368527

Ireland 1.478845 1.506424 1.859503 5.982037

Israel 2.564234 1.566611 0.254741 1.815734

Italy 5.267852 1.920199 0.471055 2.025572

Japan 0.281626 0.356990 0.921568 3.381340

Korea, Rep. -1.111330 1.635903 0.091179 2.898054

Netherlands 2.121435 1.647763 0.748343 2.650031

New Zealand -1.671158 0.563261 -0.006523 2.327884

Norway 1.378623 1.009041 -0.477658 2.694128

Portugal 2.048443 1.367827 0.017120 1.671340

Singapore 3.549761 1.367043 0.442224 1.802650

Spain 1.447292 0.938253 0.214146 1.639614

Sweden 0.422898 0.666499 -0.678151 2.794817

Switzerland 2.327054 0.918478 0.218764 2.266642

United Kingdom -0.697196 0.296238 -0.137171 2.389872

United States -0.062646 0.696179 -0.280529 1.727448

All 1.257152 2.886701 2.445255 12.97456

179

Appendix-2A

Composite Index of Economic Variables of Emerging Stock Markets (21 Countries)


Brazil -1.942774 0.752322 -0.723954 2.425411

Chile 0.564227 0.639131 0.253769 2.344943

China 2.799715 0.669681 -0.276066 1.787414

Colombia -1.193455 0.536418 -0.760103 2.479304

Czech Republic 0.729795 0.506211 0.279079 2.422474

Egypt, Arab Rep. -0.913957 0.605611 0.001079 2.172584

Hungary 0.587482 0.839548 0.193664 1.679691

India 0.007839 0.550841 -0.389739 1.740199

Indonesia 0.028988 0.375987 -1.449514 5.872522

Malaysia 3.999529 0.809248 -0.104962 1.917183

Mexico -0.845606 0.317537 -0.289528 1.654079

Morocco -0.198296 0.387816 -0.215857 1.693290

Pakistan -1.938645 0.452450 0.065541 3.719674

Peru -1.046059 0.698570 -0.101534 1.834719

Philippines -0.273177 0.253658 0.038469 2.038193

Poland -0.603993 0.545307 0.385652 2.387534

Russian Federation 0.309999 1.311317 -1.316722 4.448749

South Africa -0.340938 0.224320 0.034326 2.356726

Thailand 2.141891 0.362671 0.634694 2.639749

Turkey -2.068850 1.395165 -0.357613 1.628124

All 5.48E-17 1.679278 0.662668 3.741399

180

Appendix-2B

Composite Index of Governance Variables of Emerging Stock Markets (21 Countries)


Brazil 0.080476 0.345247 -0.370429 2.295428

Chile 4.147182 0.259894 -0.830821 3.442916

China -1.603256 0.208888 0.625533 4.272645

Colombia -1.364435 0.563606 -0.065333 1.476513

Czech Republic 3.144388 0.344200 -1.655000 5.171553

Egypt, Arab Rep. -1.959756 0.863976 -0.720025 2.373738

Hungary 3.147643 0.562921 -0.549158 1.820401

India -0.895317 0.248011 -0.413207 2.466593

Indonesia -2.443294 0.828108 0.027400 1.895751

Malaysia 1.608865 0.311800 -0.277997 2.953435

Mexico -0.361182 0.391752 0.150782 2.070351

Morocco -0.604099 0.528012 0.778163 2.544536

Pakistan -3.729705 0.289851 0.062054 2.249251

Peru -1.029763 0.308005 -0.187629 2.177875

Philippines -1.081058 0.649361 0.616961 2.473367

Poland 2.587971 0.460122 -0.536775 2.100241 Russian

Federation -3.049469 0.391748 -0.746559 3.169700

South Africa 1.327535 0.239012 -0.084030 1.915885

Thailand 0.143625 0.811489 0.230379 1.286876

Turkey -0.224816 0.521788 -0.726807 2.177821 United Arab

Emirates 2.158466 0.350571 0.689920 2.599022

All -6.60E-16 2.169285 0.222681 2.212145

181

Appendix-2C

Composite Index of Cross Variables of Emerging Stock Markets (21 Countries)


Brazil -0.221073 0.699871 0.161105 2.561821

Chile 2.443935 2.710681 0.366230 2.222646

China -4.454534 1.109893 -0.027676 1.821227

Colombia 1.855126 1.439524 0.740998 2.047867

Czech Republic 2.344404 1.750691 0.452967 2.488879

Egypt, 2.038805 2.236131 1.230110 3.093461

Hungary 1.542016 2.355155 0.132725 1.917083

India 0.003645 0.464606 0.473914 1.805221

Indonesia 0.024910 1.150538 1.761255 6.430198

Malaysia 6.397100 1.642142 -0.057766 2.261986

Mexico 0.283860 0.383374 0.633501 3.719693

Morocco -0.082289 0.107279 0.102920 1.964007

Pakistan 7.258902 1.887531 0.032557 3.147607

Peru 1.011339 0.661966 0.034272 2.119076

Philippines 0.240050 0.332930 0.570219 2.530857

Poland -1.512753 1.485325 0.416524 2.541127

Russia -0.840029 4.271261 1.077596 4.196973

South Africa -0.444715 0.305019 -0.188755 2.668042

Thailand 0.353811 1.835152 0.417180 1.620842

Turkey 1.079118 1.825674 1.161474 3.138860

All 0.996084 3.035410 0.678259 4.005079

182

Appendix-3A

Composite Index of Economic Variables of Frontier Stock Markets (24 Countries)


Argentina -1.220608 0.534112 0.245688 1.865731

Bahrain -0.337962 0.619410 0.022351 2.254788

Bangladesh -1.072808 0.104767 0.590534 2.139264

Botswana -1.295906 0.649235 0.697073 3.301188

Bulgaria 0.103046 1.302010 -2.345895 9.439417

Cote d'Ivoire -0.803793 0.165826 0.087099 1.792122

Croatia 0.090416 0.268334 0.357275 2.951517

Cyprus 2.334506 1.141779 1.231037 4.062338

Estonia 0.522680 0.459411 0.741343 3.413566

Ghana -0.104609 0.231534 -0.793476 2.365102

Jordan 1.121692 0.539673 0.479710 2.720841

Kenya -0.262951 0.391249 0.516459 2.651393

Lithuania 0.054069 0.420363 0.812936 2.672201

Malta 3.115624 1.698996 1.140209 3.290800

Nigeria -1.496189 0.882823 -0.153147 3.184306

Oman -1.225697 0.728499 0.989966 4.219315

Qatar -2.634517 1.144860 0.901449 2.536023

Romania -0.385378 0.304740 0.188634 2.528231

Serbia 0.742568 0.181413 0.204842 2.166360

Slovak Republic 0.349525 0.276375 -0.204150 3.356366

Slovenia -0.001228 0.504557 0.374271 1.844806

Sri Lanka -0.659922 0.276782 -0.920464 4.258084

Tunisia 0.483210 0.420043 -0.179652 1.349893

Vietnam 0.188577 0.661479 0.127017 1.542964

All 1.30E-16 1.363422 1.174093 7.545286

183

Appendix-3B

Composite Index of Governance Variables of Frontier Stock Markets (24 Countries)


Argentina -1.113651 0.826943 0.767677 2.205729

Bahrain 0.401422 0.361682 0.682720 2.485337

Bangladesh -3.381544 0.425396 0.398719 3.105378

Botswana 1.965427 0.156058 1.580156 5.658519

Bulgaria 0.208842 0.510899 -1.831850 5.417083

Cote d'Ivoire -3.617081 1.126899 0.776771 2.357674

Croatia 0.568730 0.868217 -1.324704 3.235838

Cyprus 2.858672 0.169407 0.791708 3.419025

Estonia 2.706934 0.413493 -0.835690 2.747196

Ghana -0.344637 0.486763 -0.580833 2.002510

Jordan -0.052168 0.221243 -0.397478 2.202829

Kenya -2.788794 0.280456 0.000155 2.298052

Lithuania 1.833325 0.404564 0.141107 2.357675

Malta 3.360873 0.186914 -0.089567 1.992881

Nigeria -4.206050 0.215372 -0.791068 2.766568

Oman 0.856380 0.310863 -0.052554 2.071833

Qatar 1.412253 0.613843 -0.369490 2.878535

Romania -0.145912 0.518493 -0.331148 1.995397

Serbia -1.997467 1.605809 -0.341552 1.624412

Slovak Republic 1.894445 0.312563 -0.544374 1.680247

Slovenia 2.815675 0.285798 0.416034 2.327837

Sri Lanka -0.986006 0.261286 0.762919 2.806351

Tunisia -0.302383 0.358153 -0.761795 2.274586

Vietnam -1.947284 0.195525 0.626789 3.775588

All 3.40E-16 2.205168 -0.388274 2.258845

184

Appendix-3C

Composite Index of Cross Variables of Frontier Stock Markets (24 Countries)


Argentina 1.422364 1.175117 -0.385277 1.753637

Bahrain -0.210974 0.300304 -0.460115 1.815708

Bangladesh 3.632448 0.638240 0.647455 2.243514

Botswana -2.549830 1.266309 0.670046 3.139963

Bulgaria 0.285888 0.959387 2.279196 11.15372

Cote d'Ivoire 3.450465 0.516051 -0.148253 2.951905

Croatia 0.165580 0.246594 0.767251 3.171441

Cyprus 6.734739 3.459554 1.252704 4.225037

Estonia 1.504852 1.354307 0.862683 3.421386

Ghana -0.028050 0.040179 -1.526562 4.372070

Jordan -0.071973 0.239277 -0.507492 2.778704

Kenya 0.682430 1.113058 -0.784243 2.838036

Lithuania 0.139802 0.759261 0.873835 2.886447

Malta 10.59778 6.107309 1.206577 3.545088

Nigeria 6.286088 3.799575 0.373531 3.743734

Oman -1.034918 0.694563 -0.008863 3.388001

Qatar -5.298165 2.375804 0.991799 2.398005

Romania 0.123137 0.289584 0.411769 2.565340

Serbia -0.491378 0.340537 -0.083587 2.167255

Slovak Republic 0.693511 0.586164 -0.040657 2.861024

Slovenia -0.069010 1.456953 0.284652 1.872703

Sri Lanka 0.662511 0.422823 1.316496 4.604055

Tunisia -0.384915 0.367760 -0.070954 1.178538

Vietnam -0.308949 1.272961 -0.108585 1.637664

All 1.416327 3.669467 2.527687 13.29536

185

Appendix-4A

Model-1 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and



Y(-1) 0.597964 0.017148 34.86990 0.0000

E1 3.467382 0.291562 11.89245 0.0000

E2 -2.842335 0.596598 -4.764235 0.0000

E3 -0.872048 0.500641 -1.741865 0.0824

E4 0.045208 0.192031 0.235423 0.8140

E5 -5.479524 0.799343 -6.855038 0.0000

E6 0.756222 0.069139 10.93775 0.0000

E7 0.344247 0.191185 1.800598 0.0726

E8 1.333611 0.839905 1.587812 0.1133

Effects Specification

Cross-section fixed (first differences)

Mean dependent var 2.586788 S.D. dependent var 50.50454

S.E. of regression 59.62322 Sum squared resid 1219340.

J-statistic 20.23588 Instrument rank 25

Prob(J-statistic) 0.209784

186

Appendix-4B

Model-1 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and



Y(-1) 0.313267 0.131758 2.377600 0.0180

E0G 1.189405 0.706579 1.683329 0.0932

E2 -0.562928 0.312187 -1.803174 0.0723

E3 0.060928 0.369616 0.164840 0.8692

E4 0.179470 0.293191 0.612128 0.5409

E5 0.594218 1.175760 0.505390 0.6136

E6 -0.257738 0.274936 -0.937448 0.3492

E7 0.361362 0.919325 0.393073 0.6945

E8 3.804343 0.862114 4.412807 0.0000




S.E. of regression 29.29072 Sum squared resid 287412.1



187

Appendix-4C

Model-1 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and



Y(-1) 0.538880 0.077532 6.950413 0.0000

E0G 0.358943 0.154132 2.328806 0.0207

E2 -0.599268 0.511840 -1.170811 0.2428

E3 0.302298 0.301020 1.004244 0.3163

E4 -0.079352 0.222970 -0.355884 0.7222

E5 -0.281559 0.388632 -0.724487 0.4695

E6 0.518521 0.077727 6.671048 0.0000

E7 -0.021566 0.065399 -0.329759 0.7419

E8 1.285982 0.513060 2.506496 0.0128







188

Appendix-4D

Model-1 Panel GMM Estimation Results for All Economic Variables of World Stock



Y(-1) 0.346661 0.001628 212.9583 0.0000

E1 1.133814 0.038646 29.33851 0.0000

E2 -1.124654 0.056726 -19.82595 0.0000

E3 -0.878688 0.039527 -22.23030 0.0000

E4 0.165796 0.011069 14.97878 0.0000

E5 -1.825737 0.081713 -22.34320 0.0000

E6 0.755894 0.012985 58.21152 0.0000

E7 0.155027 0.009660 16.04792 0.0000

E8 0.839415 0.045524 18.43899 0.0000







189

Appendix-5A




Y(-1) 0.682435 0.025572 26.68715 0.0000

G1 -0.578566 1.102372 -0.524837 0.6000

G2 3.331904 0.750033 4.442345 0.0000

G3 -0.506273 0.134392 -3.767136 0.0002

G4 -3.274225 0.629735 -5.199368 0.0000

G5 3.235296 0.346588 9.334691 0.0000

G6 2.622147 0.264680 9.906852 0.0000







190

Appendix-5B




Y(-1) 0.065809 0.008060 8.165054 0.0000

G1 -0.056858 0.183259 -0.310258 0.7565

G2 0.897967 0.190954 4.702532 0.0000

G3 0.183235 0.118612 1.544826 0.1233

G4 0.037555 0.272284 0.137927 0.8904

G5 -1.801663 0.379471 -4.747824 0.0000

G6 -0.246916 0.054973 -4.491621 0.0000







191

Appendix-5C




Y(-1) 0.526775 0.012996 40.53328 0.0000

G1 -0.497369 0.111855 -4.446557 0.0000

G2 0.023674 0.120949 0.195737 0.8449

G3 0.591086 0.049267 11.99752 0.0000

G4 -0.050505 0.083193 -0.607082 0.5442

G5 -0.038931 0.145167 -0.268181 0.7887

G6 -0.007288 0.079142 -0.092084 0.9267







192

Appendix-5D

Model-2 Panel GMM Estimation Results for All Governance Variables of World Stock



Y(-1) 0.427911 0.000794 538.7451 0.0000

G1 -1.319646 0.043310 -30.46979 0.0000

G2 1.453818 0.056146 25.89362 0.0000

G3 -0.200078 0.021599 -9.263211 0.0000

G4 -0.246351 0.070530 -3.492870 0.0005

G5 2.153088 0.044617 48.25721 0.0000

G6 1.073616 0.017052 62.96247 0.0000







193

Appendix-6A




Y(-1) 0.545137 0.059281 9.195741 0.0000

E1 3.104011 1.264182 2.455352 0.0146

E2 -2.613447 2.217913 -1.178336 0.2395

E3 0.145459 1.753669 0.082946 0.9339

E4 0.284822 0.401479 0.709433 0.4785

E5 -6.886241 2.074414 -3.319608 0.0010

E6 0.755269 0.306879 2.461132 0.0144

E7 0.381957 0.815504 0.468370 0.6398

E8 0.440446 1.831055 0.240542 0.8101

G1 1.502209 1.374600 1.092834 0.2752

G2 3.553311 1.757400 2.021913 0.0440

G3 -0.541886 0.760343 -0.712686 0.4765

G4 -2.890350 1.300561 -2.222387 0.0269

G5 -0.384385 1.685741 -0.228021 0.8198

G6 2.721788 0.994169 2.737752 0.0065







194

Appendix-6B




Y(-1) 0.284319 0.111553 2.548744 0.0113

E1 1.277739 0.579049 2.206615 0.0280

E2 -0.659128 0.263382 -2.502559 0.0128

E3 -0.078533 0.313548 -0.250466 0.8024

E4 0.302719 0.224006 1.351391 0.1775

E5 -0.166436 0.975932 -0.170541 0.8647

E6 0.177252 0.186361 0.951119 0.3422

E7 0.357971 0.233686 1.531842 0.1265

E8 3.259688 1.037422 3.142104 0.0018

G1 0.004531 0.333132 0.013600 0.9892

G2 0.335903 0.176556 1.902529 0.0580

G3 0.188202 0.173330 1.085805 0.2784

G4 0.335695 0.377145 0.890095 0.3741

G5 -0.713518 0.662969 -1.076246 0.2826

G6 -0.655727 0.441015 -1.486861 0.1380 Effects Specification






195

Appendix-6C




Y(-1) 0.526180 0.049394 10.65260 0.0000

E0G 0.337931 0.138198 2.445259 0.0152

E2 -0.091362 0.235204 -0.388438 0.6980

E3 -0.052941 0.083830 -0.631531 0.5283

E4 -0.347753 0.286500 -1.213795 0.2260

E5 -0.751510 0.183769 -4.089421 0.0001

E6 0.125278 0.151272 0.828164 0.4084

E7 0.138549 0.048630 2.849012 0.0048

E8 -0.051486 0.244437 -0.210633 0.8333

G1 -0.266518 0.183906 -1.449205 0.1485

G2 -0.453460 0.393824 -1.151429 0.2507

G3 0.183899 0.144857 1.269523 0.2054

G4 -0.332209 0.240763 -1.379817 0.1689

G5 0.440128 0.476963 0.922773 0.3570

G6 0.031788 0.159366 0.199464 0.8421 Effects Specification






196

Appendix-6D

Model-3 Panel GMM Estimation Results for All Economic and Governance Variables of

World Stock Markets (70 Countries) and Depended Variable of Market Capitalization as

%age of GDP


Y(-1) 0.327335 0.002463 132.9053 0.0000

E1 1.053651 0.062257 16.92414 0.0000

E2 -0.983563 0.104093 -9.448874 0.0000

E3 -0.750464 0.073542 -10.20456 0.0000

E4 0.139105 0.023038 6.038110 0.0000

E5 -1.708201 0.150669 -11.33745 0.0000

E6 0.732865 0.022961 31.91849 0.0000

E7 0.171974 0.022617 7.603729 0.0000

E8 0.646026 0.116716 5.535043 0.0000

G1 -0.581906 0.099173 -5.867587 0.0000

G2 1.360316 0.101266 13.43313 0.0000

G3 -0.471558 0.031663 -14.89320 0.0000

G4 0.082546 0.138736 0.594987 0.5520

G5 1.851517 0.107640 17.20099 0.0000

G6 0.530616 0.069041 7.685482 0.0000 Effects Specification






197

Appendix-7A




Y(-1) 0.550686 0.045371 12.13734 0.0000

E0G 4.311606 1.026719 4.199402 0.0000

E2 -2.890759 1.137811 -2.540631 0.0115

E3 0.056528 0.892238 0.063355 0.9495

E4 0.534424 0.387282 1.379933 0.1685

E5 -7.159806 1.588324 -4.507775 0.0000

E6 0.415630 0.266821 1.557711 0.1202

E7 0.610334 0.402158 1.517647 0.1300

E8 2.490189 1.538691 1.618382 0.1065

PGOV 27.82690 10.81695 2.572528 0.0105







198

Appendix-7B




Y(-1) 0.337380 0.093790 3.597178 0.0004

E0G 1.009993 0.413552 2.442238 0.0152

E2 -2.467357 1.030531 -2.394258 0.0172

E3 0.728884 0.235190 3.099128 0.0021

E4 0.340306 0.367234 0.926674 0.3548

E5 2.947501 2.750370 1.071674 0.2847

E6 -0.364526 0.438448 -0.831401 0.4064

E7 1.034320 0.808566 1.279203 0.2018

E8 4.001587 0.627373 6.378326 0.0000

PGOV -11.39619 9.027543 -1.262380 0.2078







199

Appendix-7C




Y(-1) 0.513626 0.076773 6.690172 0.0000

E0G 0.332205 0.092064 3.608407 0.0004

E2 0.052924 0.131461 0.402583 0.6875

E3 0.082910 0.044068 1.881400 0.0609

E4 -0.277189 0.145858 -1.900407 0.0583

E5 -0.602946 0.091526 -6.587695 0.0000

E6 0.184761 0.033869 5.455203 0.0000

E7 0.160200 0.058052 2.759618 0.0061

E8 0.375126 0.140506 2.669823 0.0080

PGOV 3.243283 2.430260 1.334542 0.1830







200

Appendix-7D

Model-4 Panel GMM Estimation Results for All Economic Variables and Composite Index

of Governance Variables of World Stock Markets (70 Countries) and Depended Variable of



Y(-1) 0.252702 0.003437 73.52188 0.0000

E1 2.046103 0.060612 33.75750 0.0000

E2 -3.686475 0.182334 -20.21826 0.0000

E3 -1.586586 0.055425 -28.62560 0.0000

E4 0.583136 0.015244 38.25313 0.0000

E5 -4.384335 0.109060 -40.20123 0.0000

E6 0.841175 0.031276 26.89554 0.0000

E7 0.219161 0.031826 6.886194 0.0000

E8 1.538483 0.113689 13.53235 0.0000

PGOV 49.64352 1.424435 34.85137 0.0000







201

Appendix-8A




Y(-1) 0.632040 0.024249 26.06447 0.0000

G1 -0.052337 0.689560 -0.075899 0.9395

G2 4.427687 0.613857 7.212893 0.0000

G3 -1.076544 0.150147 -7.169923 0.0000

G4 -3.405756 0.831739 -4.094743 0.0001

G5 1.705086 0.266668 6.394030 0.0000

G6 2.760608 0.303206 9.104742 0.0000

PECO 11.65072 2.101860 5.543055 0.0000 Effects Specification






202

Appendix-8B




Y(-1) 0.223543 0.032918 6.790925 0.0000

G1 0.401874 0.479664 0.837823 0.4027

G2 0.521704 0.719001 0.725595 0.4686

G3 0.480946 0.254370 1.890733 0.0595

G4 -0.176801 1.020584 -0.173235 0.8626

G5 -1.544353 1.064800 -1.450369 0.1479

G6 -0.474660 0.817542 -0.580594 0.5619

PECO 17.62040 2.314310 7.613676 0.0000







203

Appendix-8C




Y(-1) 0.615926 0.017504 35.18684 0.0000

G1 0.458516 0.090900 5.044160 0.0000

G2 -0.246200 0.437428 -0.562835 0.5741

G3 0.432157 0.171076 2.526111 0.0122

G4 -0.196686 0.389381 -0.505125 0.6140

G5 0.242199 0.807948 0.299771 0.7646

G6 -0.265916 0.285089 -0.932746 0.3520

ECO -0.214651 0.168485 -1.274011 0.2040







204

Appendix-8D

Model-2 Panel GMM Estimation Results for All Governance Variables and Composite

Index of Economic Variables of World Stock Markets (70 Countries) and Depended



Y(-1) 0.412911 0.001438 287.2115 0.0000

G1 -1.003143 0.036920 -27.17038 0.0000

G2 1.476385 0.052826 27.94794 0.0000

G3 -0.462321 0.014483 -31.92139 0.0000

G4 -0.063720 0.036346 -1.753157 0.0800

G5 2.141841 0.036974 57.92854 0.0000

G6 0.972312 0.019071 50.98503 0.0000

PECO 14.09162 0.200610 70.24373 0.0000







205

Appendix-9A




Y(-1) 0.662996 0.006466 102.5361 0.0000

PECO 13.43409 0.884676 15.18533 0.0000

PGOV 10.37992 1.365497 7.601572 0.0000

PECO*PGOV 2.477447 0.402805 6.150488 0.0000







206

Appendix-9B




Y(-1) 0.222044 0.007481 29.68213 0.0000

PECO 20.11261 0.772316 26.04195 0.0000

PGOV -4.352256 1.292038 -3.368520 0.0008

PCROSS 0.959003 0.424660 2.258281 0.0246







207

Appendix-9C




Y(-1) 0.603374 0.000783 770.2145 0.0000

PECO 1.266656 0.203741 6.216999 0.0000

PGOV 0.758107 0.088048 8.610106 0.0000

PCROSS -0.568153 0.083030 -6.842738 0.0000







208

Appendix-9D

Model-6 Panel GMM Estimation Results for Composite Indices of Economic and

Governance Variables of World Stock Markets (70 Countries) and Depended Variable of



Y(-1) 0.386455 0.001405 275.0485 0.0000

PECO 18.22461 0.141548 128.7525 0.0000

PGOV 23.50915 0.582312 40.37210 0.0000

PCROSS 7.614872 0.045824 166.1748 0.0000







209

Appendix-10A



Coefficient Std. Error t-Statistic Prob. C(11) 0.646604 0.130420 4.957863 0.0000

C(12) -0.010593 0.014358 -0.737769 0.4609

C(13) 0.015863 0.010555 1.502991 0.1333

C(14) 0.851001 0.029454 28.89264 0.0000

C(21) -0.307497 0.214673 -1.432399 0.1525

C(22) 0.184279 0.089369 2.061990 0.0396 Determinant residual covariance 0.445211

J-statistic 0.015135

Equation: LY = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*LY(-1)

Instruments: LY(-1) PECO(-1) PGOV(-1) C

Observations: 364

R-squared 0.729094 Mean dependent var 4.348144

Adjusted R-squared 0.726837 S.D. dependent var 0.768536


Durbin-Watson stat 1.951553

Equation: PECO= C(21) + C(22)*PGOV

Instruments: LY(-1) PECO(-1) PGOV(-1) C

Observations: 368





Wald Test:

System: sys_peco_pgov Test Statistic Value df Probability Chi-square 0.165066 1 0.6845

Null Hypothesis: C(13)+C(12)*C(22)=0

Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(13) + C(12)*C(22) 0.246344 0.606338

Delta method computed using analytic derivatives.

210

Appendix-10B




C(12) 0.816095 0.867415 0.940835 0.3471

C(13) 0.425459 0.504257 0.843734 0.3991

C(14) 0.793465 0.067957 11.67599 0.0000

C(21) -0.327752 0.260178 -1.259722 0.2082



Equation: Y = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*Y(-1)

Instruments: C Y(-1) PECO(-1) PGOV(-1)

Observations: 363






Instruments: C Y(-1) PECO(-1) PGOV(-1)

Observations: 365





Wald Test:





211

Appendix-10C




C(12) 0.370360 0.628811 0.588984 0.5561

C(13) 0.557779 0.418102 1.334075 0.1826

C(14) 0.856991 0.032050 26.73908 0.0000

C(21) -0.009275 0.160960 -0.057625 0.9541




Instruments: C Y(-1) PECO(-1) PGOV(-1) PCROSS(-1)

Observations: 333 c(12)+c(13)*c(22)=0

c(12)+c(13)*c(22)=0

c(12)+c(13)*c(22)=0 c(12)+c(13)*c(22)=0






Instruments: C Y(-1) PECO(-1) PGOV(-1) PCROSS(-1)


c(12)+c(13)*c(22)=0

c(12)+c(13)*c(22)=0 c(12)+c(13)*c(22)=0





Wald Test:


Null Hypothesis: c(12)+c(13)*c(22)=0



212

Appendix-10D

Model-6 Panel GMM Estimation Results for Composite Indices of Economic and

Governance Variables with indirect effect of World Stock Markets (70 Countries) and



C(12) 3.618091 0.911072 3.971247 0.0001

C(13) -0.002804 0.289226 -0.009695 0.9923

C(14) 0.951191 0.023176 41.04266 0.0000

C(21) -0.273180 0.141945 -1.924544 0.0544




Instruments: Y(-1) PECO(-1) PGOV(-1) C


c(13)+c(12)*c(22)=0

c(13)+c(12)*c(22)=0 c(13)+c(12)*c(22)=0






Instruments: Y(-1) PECO(-1) PGOV(-1) C


c(13)+c(12)*c(22)=0

c(13)+c(12)*c(22)=0 c(13)+c(12)*c(22)=0





Wald Test:

System: %system Test Statistic Value df Probability Chi-square 4.367647 1 0.0366




213

Appendix-11A




PECO(-1) 0.342913 0.009164 37.41869 0.0000

Y 0.001422 0.000354 4.021204 0.0001

PGOV -0.096181 0.013402 -7.176911 0.0000







214

Appendix-11B




PECO(-1) 0.484705 0.025322 19.14169 0.0000

Y 0.004823 0.000131 36.88157 0.0000

PGOV 0.140191 0.053639 2.613622 0.0094







215

Appendix-11C




PECO(-1) 0.345052 0.001972 175.0095 0.0000

Y 0.001642 0.000380 4.317262 0.0000

PGOV 0.642321 0.007697 83.45177 0.0000







216

Appendix-11D

Model-6 Panel GMM Estimation Results for Stock Market Development and Composite

Index of Governance Variables of World Stock Markets (70 Countries) and Depended



PECO(-1) 0.057129 0.002154 26.52363 0.0000

Y 0.000865 4.95E-06 174.7373 0.0000

PGOV 0.029588 0.003113 9.504310 0.0000







217

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