mahendra mutual fund risk return

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Mahindra & Mahindra Financial Services Limited

A summer training report on

the knowledge of Risk Tolerance that an Investor can handleto find an optimal trade-off between the risk and returns

(05 May 2008 05 Juy 2008)

Under the Guidance of

Mr. TARUN KUMAR SINGH Mr. PRASHANT DUTTA GUPTA

(INDUSTRY GUIDE) (FACULTY GUIDE)

By

MANISH PRASAD

Roll no: 27090

Batch: 2007-09

NIILM CENTRE FOR MANAGEMENT STUDIES

NEW DELHI.


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INFORMATION SHEET

1) NAME OF THE COMPANY: Mahindra & Mahindra Financial

Services Limited

2) ADDRESS OF THE COMPANY: M-8, 2nd Floor, Old DLF Colony,

Sector-14, GURGAON-121003

3) PHONE NUMBER OF THE COMPANY: 022-66526000

4) DATE OF INTERNSHIP COMMENCEMENT: 05/05/2008

5) DATE OF INTERNSHIP COMPLETETION: 50/07/2008

6) SIGATURE AND NAME OF THE INDUSTRY GUIDE: -------------------------Mr. TARUN KUMAR SINGH

7) DESIGNATION OF THE INDUSTRY GUIDE: Customer Relationship manager

8) STUDENTS NAME: Manish Prasad

9) STUDENTS ROLL NUMBER: 27090

10) STUDENTS EMAIL ID: [email protected]

11) STUDENTS MOBILE/RESIDENCE NUMBERS: 9871936904/03412240836

NIILM-CENTREFORMANAGEMENTSTUDIESB-II/66,M.C.I.E.,SherShahSuriMarg,NewDelhi-110044.Tel:(011)29894514.


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CERTIFICATE OF AUTHENTICITY

This is to certify that MR. MANISH PRASAD student of PGDBM (Full Time) 2007-2009 batch, NIILM Centre for Management Studies, NEW DELHI, has done histraining project under my supervision and guidance.

During his project he was found to be very sincere and attentive to small detailswhatsoever was told to him.

I wish him good luck and success in his future

(Manish Prasad) ( Mr. Prashant Dutta Gupta)27090 Professor



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ACKNOWLEDGEMENT

It is a pleasure to acknowledge my mentors, friends and respondents, though it is still

inadequate appreciation for their contribution.

I would not have completed this journey without the help, guidance and support

of certain people who acted as guides, friends and torchbearers along the way.

I would like to express my deepest and sincere thanks to my company guide

Mr. Tarun Kumar Singh , Customer Relationship manager, Ashutosh pankaj ofMahindra & Mahindra Financial Services Limited. and my faculty guide Mr.

Prashant Dutta Gupta for their valuable guidance and help. The project could not be

complete without their support and guidance. Thanking them is only a small gesture for

the generosity shown.

I am also thankful to all my friends, my family and all the staff members of

Mahindra & Mahindra Financial Services Limited , for cooperating with me at every

stage of the project. They acted as a continuous source of inspiration and motivated

me throughout the duration of the project helping me a lot in completing this

project.

Manish Prasad

27090Niilm-Cms



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ABSTRACT

A Mutual Fund is the most suitable investment for the common man as it offers an

opportunity to invest in a diversified, professionally managed basket of securities at a

relatively low cost.

In finance theory, investment risk is considered a precise, abstract and purely technical

statistical concept. This risk concept, however, does not reflect private investors

understanding of risk; they have a more intuitive, less quantitative, rather emotionally

driven risk perception. Empirical studies that deal with investors risk perceptions

detect four different dimensions of perceived risk:

Downside risk: the perceived risk of suffering financial losses due to negative

deviations of returns, starting from an individual reference point

Upside risk: the perceived chance of realising higher-than-average returns, starting

from an individual reference point

Volatility: the perceived fluctuations of returns over time

Ambiguity: a subjective feeling of uncertainty due to lack of information and lack of

competence.

Consumers wishing to avoid risk do not buy mutual funds, since risk is inherent in all

stock market products. Consumers may however try to minimize risks.Consumers take a big risk when they invest money in the stock market as opposed to

traditional bank deposits or bonds. Consequently, they are willing to take that risk to get

a higher return than they would get from traditional savings.

Since no prior Consumer Behaviour studies with a holistic focus on the mutual

fund market were available, all Likert-scales had to be developed for this study. Most

consumers buy mutual funds as a means to some other goal (retirement, house,

vacation, etc.). Thus, they do not consume mutual funds in the same sense that other

products and services are consumed.

CONTENTS



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Information Sheet.2

Acknowledgement . 4

Abstract..... 5

Chapter 1 Introduction 7

About Mutual Fund Industry 8

About Mahindra & Mahindra Financial Services Limited 14

Chapter 2 Review of Literature 17

Advertising in the mutual fund business 18

Risk- Return Perceptions and Advertising Content 20

Consumer Knowledge, Involvement, and Risk Willingness on Investments 24

Return and Risk on Common Stocks 33

Idiosyncratic Risk and Mutual Fund Return 36

Chapter 3 Methodology 38

BETA, Risk and Mutual Funds 46

Data: NAVs of mutual fund schemes 53

Fund analysis 59

Chapter 4 Research Analysis and Conclusion 79

Bibliography 84

References

Annexure

-Questionnaire



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CHAPTER 1

INTRODUCTION

ABOUT MUTUAL FUND INDUSTRY



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CONCEPT

A Mutual Fund is a trust that pools the savings of a number of investors who share acommon financial goal. The money thus collected is then invested in capital market

instruments such as shares, debentures and other securities. The income earned through

these investments and the capital appreciation realised are shared by its unit holders in

proportion to the number of units owned by them. Thus a Mutual Fund is the most

suitable investment for the common man as it offers an opportunity to invest in a

diversified, professionally managed basket of securities at a relatively low cost. The

flow chart below describes broadly the working of a mutual fund:

Fig. Mutual Fund Operation Flow Chart

ORGANISATION OF A MUTUAL FUND



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There are many entities involved and the diagram below illustrates the organisational

set up of a mutual fund:

Fig. Organisation of a Mutual Fund

ADVANTAGES OF MUTUAL FUNDS

The advantages of investing in a Mutual Fund are:

Professional Management

Diversification

Convenient Administration

Return Potential

Low Costs

Liquidity

Transparency

Flexibility

Choice of schemes

Tax benefits

Well regulated

TYPES OF MUTUAL FUND SCHEMES



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Wide variety of Mutual Fund Schemes exists to cater to the needs such as financial

position, risk tolerance and return expectations etc. The table below gives an overview

into the existing types of schemes in the Industry.

History of the Indian Mutual Fund Industry

The mutual fund industry in India started in 1963 with the formation of Unit Trust of

India, at the initiative of the Government of India and Reserve Bank the. The history of

mutual funds in India can be broadly divided into four distinct phases

First Phase 1964-87

Unit Trust of India (UTI) was established on 1963 by an Act of Parliament. It was set

up by the Reserve Bank of India and functioned under the Regulatory and

administrative control of the Reserve Bank of India. In 1978 UTI was de-linked from

the RBI and the Industrial Development Bank of India (IDBI) took over the regulatory

and administrative control in place of RBI. The first scheme launched by UTI was Unit

Scheme 1964. At the end of 1988 UTI had Rs.6,700 crores of assets undermanagement.



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Second Phase 1987-1993 (Entry of Public Sector Funds)

1987 marked the entry of non- UTI, public sector mutual funds set up by public sector

banks and Life Insurance Corporation of India (LIC) and General Insurance

Corporation of India (GIC). SBI Mutual Fund was the first non- UTI Mutual Fund

established in June 1987 followed by Canbank Mutual Fund (Dec 87), Punjab National

Bank Mutual Fund (Aug 89), Indian Bank Mutual Fund (Nov 89), Bank of India (Jun

90), Bank of Baroda Mutual Fund (Oct 92). LIC established its mutual fund in June

1989 while GIC had set up its mutual fund in December 1990.

At the end of 1993, the mutual fund industry had assets under management of

Rs.47,004 crores.

Third Phase 1993-2003 (Entry of Private Sector Funds)

With the entry of private sector funds in 1993, a new era started in the Indian mutual

fund industry, giving the Indian investors a wider choice of fund families. Also, 1993

was the year in which the first Mutual Fund Regulations came into being, under which

all mutual funds, except UTI were to be registered and governed. The erstwhile Kothari

Pioneer (now merged with Franklin Templeton) was the first private sector mutual fund

registered in July 1993.

The 1993 SEBI (Mutual Fund) Regulations were substituted by a more comprehensive

and revised Mutual Fund Regulations in 1996. The industry now functions under the

SEBI (Mutual Fund) Regulations 1996.

The number of mutual fund houses went on increasing, with many foreign mutual funds

setting up funds in India and also the industry has witnessed several mergers and

acquisitions. As at the end of January 2003, there were 33 mutual funds with total assets

of Rs. 1,21,805 crores. The Unit Trust of India with Rs.44,541 crores of assets under

management was way ahead of other mutual funds.

Fourth Phase since February 2003



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Note:

Erstwhile UTI was bifurcated into UTI Mutual Fund and the Specified Undertaking of

the Unit Trust of India effective from February 2003. The Assets under management ofthe Specified Undertaking of the Unit Trust of India has therefore been excluded from

the total assets of the industry as a whole from February 2003 onwards.

ABOUT MAHINDRA & MAHINDRA FINANCIAL SERVICES

LIMITED



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Investment Advisory Services

Company Profile

Mahindra & Mahindra Financial Services Limited, a subsidiary of Mahindra &

Mahindra Limited, was established in the year 1991 with a vision to become the

number one semi-urban and rural Finance Company. In a short span of 12 years, it has

become one of the Indias leading non-banking finance company providing finance for

acquisition of utility vehicles, tractors and cars. It has more than 350 branches covering

the entire India and services over 6,00,000 customer contracts.

It is a part of US $3 bln Mahindra Group, which is among the top 10 industrial houses

in India. Mahindra & Mahindra is the only Indian company among the top five tractor

manufacturers in the world and is the market leader in multi-utility vehicles in India.

The Group is celebrating its 60th anniversary in 2005-06. It has a leading presence in

key sectors of the Indian economy, including trade and financial services (Mahindra

Intertrade, Mahindra & Mahindra Financial Services Ltd.), automotive components,

information technology & telecom (Tech Mahindra, Bristlecone), and infrastructure

development (Mahindra GESCO, Mahindra Holidays & Resorts India Ltd., Mahindra

World City). With around 60 years of manufacturing experience, the Mahindra Group

has built a strong base in technology, engineering, marketing and distribution. The

Group employs around 30,000 people and has eight state-of-the-art manufacturing

facilities in India spread over 500,000 square meters.

Mutual Fund Distribution

Recently it has received the necessary permission from Reserve Bank of India (RBI) to

start the distribution of Mutual Fund products through its network. Hitherto the

company was only participating in the liability requirements of its customers and with

mutual fund distribution business, it can also participate in their asset allocation.

When it comes to investing everyone has unique needs based on their own objective

and risk profile. Even though many investment avenues such as fixed deposit, bonds

etc. exists, equities typically outperform these investments, over a longer period of

time. We are of the opinion that, systematic investment in equity will allow you to

create Wealth.

Hence only through proper allocation of your portfolio, you can get the maximum

return with moderate risk. Investing in equity is not as straight forward as investing in

bonds or bank deposits. It requires expertise and time. Our Investment Advisory

services will help you to invest your money in equity through different Mutual Fund



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Schemes. For instance there are some products of Mutual Fund, which allows you to

manage your cash flow by providing liquidity (liquid Funds) as well give you tax free

return.

Personalized Service

We believe in providing a personalized service enabling individual attention to achieve

your investment goal.

Professional Advice

We provide professional advice on equity and debt portfolio with an objective to

provide consistent long-term return while taking calculated market risk. Our approach

helps you to build a proper mix of portfolio, not just to promote one individual product.

Hence your long term objective are best served.

Long-term Relationship

We believe steady wealth creation requires long-term vision, it cant be achieved in a

short span of time. To achieve this one needs to take advantage of short-term market

opportunity while not loosing sight of long term objective. Hence we partner all our

clients in their objective of achieving their long-term Vision.

Access to Research Reports

Through us, you will have access to certain research work of CRISIL, so that you will

benefit from the expert knowledge of economists and analysts of one of the leading

financial research and rating company of India. This third party research gives you a

comfort of getting unbiased advice to make a proper decision for your investment.

Transparency & Confidentiality

Through email you will get a regular portfolio statement from us. You will also be

given a web access to view at your convenience the details of your investments and its

performance. Access to your portfolio is restricted to you and our monitoring system

enables us to detect any unauthorized access to your investments.

Flexibility

To facilitate smooth dealing and consistent attention, all our clients will be serviced by

their respective relationship executive. This allows us to provide tailor made advice to

achieve your investment objective.

Hassle Free Investment

Our relationship person will provide you with a customized service at your

convenience. We take care of all the administrative aspects of your investments



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including submission of application forms to fund houses along with monthly reporting

on overall state of your investments and performance of your portfolio.



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CHAPTER2

REVIEW OF LITERATURE

Advertising in the mutual fund business and the role of judgmental heuristics in

private investors evaluation of risk and return

Effective advertising strategies are of growing importance in the mutual fund industry

due to keen competition and changes in market structure. But the dominant variables in

financial decision making, investors perceived investment risk and expected return, have

not yet been analysed in an advertising context, although these product- related

evaluations can be influenced by advertising and therefore serve as additional indicators

of advertising effectiveness. In this study, I have used a large-scale experimental study



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(n=100) to detect how risk-return assessments of private investors are influenced by

specific elements of print ads. In this context, judgmental heuristics used systematically

by private investors play a crucial role.

Advertising in the Mutual Fund Industry

After 2003, the mutual fund industry was one of the fastest growing market sectors in

India. Assets held in mutual funds rose from less than Rs 2000 crores at the beginning

of the decade to Rs 87,000 crores by the end of 2003. Due to fierce competition

resulting from the internationalisation of financial markets, technological changes and

fundamental changes in private households investment behaviour, effective marketing

strategies are of great importance in the mutual fund business, and advertising has

become an important marketing instrument to attract fund sales. Accordingly,

advertising expenditures of mutual fund companies increased significantly in the last

years. In Germany, they rose to 145.61m in 2001, which is more than twice as high as

three years before (66.75m).

Similar developments can be found in other European countries and in the USA. But

what is known about the way advertising works in the mutual fund business? There is

no doubt that many theoretical and empirical findings of behavioural advertising

research apply to investment products too, for instance the attainment of brand

awareness or the creation of emotional experiences through advertising. There are,

however, special features of investment products which advertising research should

analyse explicitly. Above all, investment decisions are characterised by high exogenous

uncertainty, as future product performance must be estimated from a set of noisy and

vague variables. So investors expectations about uncertain future events play a crucial

role in investment decision making. Most importantly, purchase decisions in investment

markets follow two dominant criteria: perceived investment risk and expected return,

constructs which apply exclusively to investment products. Risk and return are crucial

variables in financial decision making, as indicated in the fundamental normative model

of investment behaviour, the mean-variance portfolio analysis. Financial services

advertising should aim to influence positively investors perceptions of these product-

specific decision criteria. This paper delivers theoretical and empirical insights into the

influence of advertising on private investors risk-return perceptions. Hypotheses are

tested by means of a large-scale experimental study, and practical implications are

deduced in the last section of the paper. product-specific variables of advertising



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effectiveness in order to understand and optimise advertisings persuasive impact in this

special business.

In finance theory, investment risk is considered a precise, abstract and purely technical

statistical concept. This risk concept, however, does not reflect private investors

understanding of risk; they have a more intuitive, less quantitative, rather emotionally

driven risk perception. Empirical studies that deal with investors risk perceptions

detect four different dimensions of perceived risk:

Downside risk: the perceived risk of suffering financial losses due to negative

deviations of returns, starting from an individual reference point

Upside risk: the perceived chance of realising higher-than-average returns, starting

from an individual reference point

Volatility: the perceived fluctuations of returns over time

Ambiguity: a subjective feeling of uncertainty due to lack of information and lack of

competence.

These different aspects have to be taken into account, as single item measures lead to an

incomplete and simplified measurement of the perceived risk construct.Expected return,

on the other hand, is a simpler, one-dimensional numerical construct, which can be

measured in absolute or relative terms.

Effects

Risk perception and return estimations are crucial constructs in the context of financial

decision making. Traditional behavioural advertising research, however, focuses on

rather general categories of advertising effects, like awareness, recall or attitude change.

Regarding investment products, private investors risk-return perceptions should be

treated as additional, in intuitively quantitative evaluations.

The Relevence of Private Investors Judgmental Heuristics for Risk- Return

Perceptions and Advertising Content

Behavioural finance, a field of research at the interface of economics, finance and

psychology, is a relatively new paradigm and was developed in the late 1980s in the

USA due to mounting empirical evidence that existing financial theories appeared to be

deficient in a real market setting. Contrary to the normative approach of classical



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units, eg 15,000 research specialists worldwide, Value Basket Fund, 1,000 dreams

come true) can also exert an influence on estimates. In accordance with the anchoring

heuristic, even those irrelevant figures will distort return- perceptions of the anchor

value when they are prominently highlighted in the ad.

H1: A low anchor value in an ad will lead to a lower return estimation, compared to a

high anchor value, even when the anchor is uninformative in nature. Representativeness

heuristic People tend to rely on stereotypes. They judge the likelihood of an event in

accordance to its fitting into a previously established schema or mental model. They

consistently judge the event that seems to be the more representative to be the more

likely, without considering the prior probability, or base-rate frequency of the

outcomes. Representativeness is a commonly used and very problematic heuristic in

financial markets, as it leads to a misinterpretation of empirical or causal coherence.

Illusory correlation, betting on trends, nave causality, misperception of randomness

and other related biases in the use of judgment criteria are typical consequences. For

instance, past performance data and trend patterns of mutual funds performance charts

are extrapolated into the future without considering the exogenous uncertainty and

randomness of financial markets. In terms of practice, mutual fund ads suggestively

promote stereotype thinking by communicating positive past performance data, fund

ratings and fund awards, and by pointing out specific brand values like trustworthiness,

competence and experience. Due to stereotypical thinking (thinking in brand

associations and brand schemata), risk-return perceptions of private investors will

heavily depend on the investment company that stands behind the investment product.

With regard to investment products, however, investors reliance on brand images or

brand stereotypes in the evaluation of risks and returns is a severe anomaly, as strong

brands cannot serve as a warranty for high returns or low risks due to the exogenous

uncertainty of financial markets.

H2: A well-known investment company with a clearly positive brand image will evoke

better risk-return perceptions at the product level compared to a relatively unknown

investment company lacking a clear and positive brand image profile, although identical

products are advertised and identical product information is provided affect heuristic



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Modern financial theory increasingly recognises the fact that financial decision making

is also determined by affective states. Negative emotions like fear, worry, anger or

shame, and positively experienced emotions like hope, greed, pleasure and joy may

influence risk-return perceptions and investment behaviour. A direct influence of

emotions on risk perception and expected returns can be deduced from the affect

heuristic which postulates that perceptions of risks and benefits of an alternative are

derived from global affective evaluations and associations. If a stimulus arouses a

positive affective impression, the decision maker will judge the risks related to this

alternative to be lower and the benefits (eg returns) to be higher, compared to neutral

emotional states. If a stimulus is associated with negative affective impressions, the

opposite effect will occur: risks are judged to be higher, the returns, on the other hand,

to be lower. In practice, mutual fund ads most often contain emotional pictures and

emotional slogans as well as product information. In terms of the affect heuristic, these

emotional elements exert a direct influence on investors risk-return perceptions if they

succeed in evoking positive affective impressions of the mutual fund.

H3: If the emotional content in the ad (pictures, slogans, tonality) succeeds in evoking

positive affective impressions of the advertised mutual fund, the investor will judge the

investment risk to be lower and the return to be higher than a purely informative ad.

The moderating impact of private investors expertise

It is important to discuss possible moderating factors in the use of heuristics.

Do only inexperienced, uninformed investors use these heuristics,

or are they also applied by novice and expert investors?

The question whether or not knowledge has an influence on heuristic information

processing has been controversially discussed. Some researchers underline the

unconsciousness and automatism of judgmental heuristics, implying that both lay and

expert investors systematically make use of them. Indeed, some empirical findings

reveal that investors expertise has no influence on the use of judgmental heuristics.

Others, however, demonstrate the moderating role of individuals knowledge, stating

that knowledgeable persons do not apply judgmental heuristics, or only to a moderate

extent.



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universally agreed understanding of how these concepts should be defined, nor on

how they are related in terms of antecedents, dimensions, and consequences. In this

study the relationship between these key concepts were explored and their impact

on consumers return on investments in mutual funds was analyzed. Theory

based alternative relationships were systematically tested in SEM analyses. The

study sheds new light on the knowledge concept by showing that the

knowledge construct should be modeled in terms of three dimensions (ability,

opportunity, and familiarity) in complex decision contexts (mutual funds and

stocks). The hypothesized importance of domain specific knowledge was

confirmed and a mediation analysis showed the relations of involvement and risk

willingness to knowledge and returns. Consumers ability and opportunity to get

access to stock market information is strongly related to their involvement, which

in turn influence both familiarity and risk willingness. Risk willingness has a

stronger effect on return than does familiarity.

In the last decade, almost all employed consumers have, intentionally or unin-

tentionally changed from being savers to being investors on the stock market.

Whereas 50% of consumers in most industrialized countries own mutual funds,

the figure can be higher for indirectly own mutual funds within a pension

system. Sweden, as an example, has a record number of indirect investors (more than

90% of the population 1874 years old). In the trade press as well as in peer reviewed

journals (e.g., Capon et al., 1996) the growth of the mutual fund industry has been

described as a revolution; In fact, its no overstatement to suggest that this

movement from Wall Street to Main Street is one of the most significant

socioeconomic trends of the past few decades (Serwer, 1999). These consumers

make risky decisions involving large amounts of money. To make wise financial

decisions, they must be able to determine how much information is needed, which

information is most useful and what sequence of information acquisition is best for

them (Jacoby et al., 2001). Their ability, motivation and opportunity to do so

influence what return they may expect on their investments. But the overwhelming

amount of technical stock market information makes it impossible for consumers to

evaluate the quality of the mutual funds on the market (e.g., Sandler, 2002; Aldridge,

1998). The situation on the stock market is, thus, typically a situation where many

consumers would use heuristics in quality assessments (Dawar and Parker, 1994)

they



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1) have a need to reduce the perceived risk of purchase;

2) they lack expertise and consequently the ability to assess quality;

3) their involvement is low (e.g., Benartzi and Thaler, 1999; Foxall and Pallister,

1998);

4) objective quality is too complex to assess or they are not in the habit of spending

time objectively assessing quality; and

5) there is a need for information.

While heuristics may serve a purpose in many other situations of less complexity,

they may be dangerous to use on the stock market. Therefore, it does not come

as a surprise that consumers who use heuristics to make complex financial decisions

are described as nave (Capon et al., 1996) and that they are regarded to be in an

unusually weak position on the financial market (e.g., Sandler, 2002). The fact that

shopping for financial instruments increasingly has become like shopping for

many other consumer items (Wilcox, 2001) and with entrepreneurs like Virgin

entering the market, consumers may not realize the risks of making bad

investment decisions. However, the long-term negative consumer welfare

implications from poor investments have been estimated to be in the hundreds of

thousands of dollars for individual consumer investors (Lichtenstein et al., 1999).

That will have a large impact on their future welfare. Extensive prior research of

behavioral data shows that there are two types of nonprofessional investors,

namely sophisticated and unsophisticated investors. Unsophisticated investors

(the majority) direct their money to funds based on advertising and advice from

brokers (Gruber, 1996), and their involvement is low (Foxall and Pallister, 1998).

The current practice in mutual fund advertising is to emphasize past performance

and advertised funds attract significantly more money than comparable funds that are

not advertised (Jain and Shuang Wu, 2000). Past performance is however not

associated with future results (ibid.), which may explain why unsophisticated investors

get lower return on investments.

This brief review indicates that there are certain key variables that need to be

considered in an holistic study. Prior research (e.g., Alba and Hutchinson, 1987;

Lichtenstein et al., 1999; Jacoby et al., 2001) emphasizes the important role of

consumer knowledge. The effects of knowledge on consumer behavior can however not

be regarded only as main effets, but must be studied along with a wide range of

moderating variables (Alba and Hutchinson, 1987). Within consumer behavior (CB)



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involvement is assumed to influence subsequent consumer behaviors (e.g., Alba and

Hutchinson, 1987; Zaichkowsky, 1985a, 1985b, 1986, 1994; Laurent and Kapferer,

1985; Dholakia, 2001). The cornerstone principle in traditional finance is that

expected return on investments in stocks is positively related to willingness to take risks

(Shefrin, 2001), and most research on mutual funds has employed these two

explanatory variables, i.e., risk and return (Capon et al., 1996). Harry M. Markowitz,

Nobel Laureate in Economic Sciences 1990, has argued that investors can not expect a

higher return than for example the bank interest rate if they are not willing to take risks

(Bernhardson, 2004).

The aim of this study is to explore and clarify the relationships among the key

constructs and to develop a parsimonious model that captures the relative

importance of these constructs on return on investments in mutual funds and

stocks (MF&S). Earlier research on knowledge, involvement and risk has focused on

perceptual variables only, not on what matters most to consumers and firms alike;

actual behavior and the consequences of behavior. This has hampered the

cumulation of knowledge about relationships between important constructs in CB.

Comparing and contrasting mental phenomena with actual behavior has special

research benefits (Mick, 2003). Poiesz and de Bont (1995) concluded for example that

there is a lack of conceptual clarity, a seemingly uncontrolled application, an overlap

with presumed antecedents, and an unavoidable lack of consistent operationalisa-

tions of the involvement concept. Similarly, Dholakia (1997) concluded that there is

confusion in the literature whether perceived risk should be treated as an antecedent of

involvement, one of its dimensions, or as its consequence. Laurent and Kapferer (1985)

regarded perceived risk (i.e., risk avoidance and negative consequences) as an

antecedent of situational involvement, whereas Venkatraman (1989) and Dholakia

(2001) suggested that enduring involvement precedes risk. None of them discussed

situations where consumers are willing to take risks (e.g., investments in mutual

funds). Diacon and Ennew (2001) who studied risk perceptions of UK investors

included (poor) knowledge as a dimension of risk perception rather than treating

knowledge as a separate construct. Researchers who have studied consumers with

high versus low knowledge have done so with no regard to the involvement studies. No

research has simultaneously compared the relative influence of these three

important constructs on behavioral intentions or behavior, which is similar to the



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In a classic study of consumer knowledge Alba and Hutchinson (1987) made a

fundamental distinction between two major components of knowledge: expertise and

familiarity. Expertise has been defined as knowledge about a particular domain,

understanding of domain problems, and skill about solving some of these problems

(Hayes-Roth et al., 1983: 4). It is difficult to be an expert on the stock market. Earlier

research compiled from different sources (Jacoby et al., 2001) indicates that general

market and industrywide factors (e.g., deregulation of an industry) account for

perhaps 40%50% of the changes in a stocks price, approximately 300 fundamental

factors (those involving a companys financial statement) account for approximately

30%35% of the variance, and that other company-unique non-financial variables (e.g.,

changes in leadership) account for 20%25% of the variance. It is,

consequently, almost an understatement to say that financial decision-making is a

complex and multifaceted task. An American survey showed for example that 66% of

mutual fund investors could not confidently name a single company in which their

mutual funds invest (from Krumsiek, 1997). The majority, 58%, of the respondents

(employees at USC) in Benartzi and Thalers (1999) study spent an hour or less on their

retirement allocation decision, and they read only the material provided by the vendors

and consulted only family members. Nonetheless they expressed confidence that they

had made the right choice and many of them never changed their initial choice. Against

this background it may come as no surprise that large groups of consumers both

in the US and in Europe are classified as financially illiterate. That is considered a

major problem in many countries (Aldridge, 1998; Nefe, 2002; Sandler, 2002).

Earlier research has found systematic differences between better and poorer

performers (professional analysts) in regard to the type of information access (the

content of the search), the order in which different items of information are accessed

(the sequence of the search) and the amount of information accessed (the depth of the

search) (Jacoby et al., 2001). Better performers engage in significantly greater

amounts of within-factor search. They select one factor, such as earnings per share, and

check its value for all stocks of interest before moving on to the next factor.

Poorer performers tend to do more within-stock search. They select one stock and

check its value on all factors of interst. The better-performing analysts tend to access

more information overall and maintain the same relatively high level of information

search across all four periods of the task, while the poorer performers typically taper off



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This implies that high personal relevance may be associated with low

involvement, and that involvement may be considered a determinant or antecedent to

behavioral phenomena (Poiesz and de Bont, 1995). Most consumer researchers

(e.g., Laurent and Kapferer, 1985; Zaichkowsky, 1985a, 1985b, 1986, 1994) have

focused on the motivational aspects of involvement only and not on the behavioral

aspects of it. Furthermore, most consumer behaviour (CB) research on involvement

deals with familiar search products rather than with complex credence products.

That difference may explain why earlier CB research has not included ability

and opportunity when defining involvement. As noted by Poiesz and de Bont (1995:

450), to the extent that ability and opportunity conditions become more

favorable, the difference between personal relevance and involvement becomes

smaller. This study deals with a domain where the ability and opportunity

conditions are highly unfavorable.

Risk Willingness

As noted by Dholakia (2001: 1342), an important property of risk conceptualization

within consumer psychology is that risk is thought to arise only from potentially

negative outcomes, in contrast to other disciplines such as behavioral decision theory

and other areas of psychology, where both positive and negative aspects are

considered when evaluating risk. Research on risk avoidance is of limited relevance in

this study. Consumers wishing to avoid risk do not buy mutual funds, since risk is

inherent in all stock market products. Consumers may however try to minimize risks.

Venkatraman (1989) as well as Dholakia (2001) suggested that since enduring

involvement is a long-term product concern while perceived risk is limited to

the purchase situation, enduring involvement precedes risk. In this study it was assumed

that perceived risk willingness is an enduring phenomenon which lasts as long as you

own mutual funds. It is extremely hard for people to think about uncertainty,

probabability, and risk (Slovic, 1984). Repeated demonstrations have shown that

most people lack an adequate understanding of probability and risk concepts

(Shanteau, 1992). Furthermore, there is no universally agreed understanding of

how risk should be conceptualized or measured (Diacon and Ennew, 2001). But, it is

generally agreed that the stock market is driven by expectations about future

returns and by risk perceptions, where psychological risks may dominate over

simple facts. Most peoples beliefs are biased in the direction of optimism, and they



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also underestimate the likelihood of poor outcomes over which they have no control

(Kahneman and Riepe, 1998). Empirical studies have shown that consumers often

claim that they take calculated risks, but that they do not gamble (De Bondt,

1998). Many households are however underdiversified, and do not define risk at

portfolio level but rather at the level of individual assets. In these contexts, risk is seen

as controllable. Based on a review of prior studies Diacon (2004: 182) concluded

that risks are perceived as being more severe if an individual has little information or

control over what may happen. Risk taking in a bull (hausse) market may create an

illusion of control, i.e., an expectancy of a personal success probability

inappropriately higher than the objective probability would warrant (Langer, 1983:

62). This may be explained by the fact that consumers lack appropriate

reference points (Lichtenstein et al., 1999).

Financial Returns

Consumers take a big risk when they invest money in the stock market as opposed to

traditional bank deposits or bonds. Consequently, they are willing to take that risk to

get a higher return than they would get from traditional savings. There is no earlier CB

research on the return concept, but the success of advertising campaigns focusing on

past returns indicates that consumers are prone to listen to the high return

argument. Such advertising is one of the most important sources of information for

individual fund investors when making investment decisions (Capon et al., 1996;

Fondbolagens Forening, 2004). The content in fund advertisements includes

information on past returns and independent research (e.g., Morningstar), whereas

measures of costs and risks are absent (Jones and Smythe, 2003).

Analyzed Models

It is well known in the trade that the majority of consumers are reluctant to

buy complex financial products, and that they, in many cases, must be sold to buy the

product. It is therefore reasonable to assume that consumers must have a minimum

amount of motivation, ability and opportunity to get access to and

process information about the stock market. Without motivation, one does not

acquire expertise in such a complex domain. By adopting the definition of

involvement in the stock market used by Petty and Cacioppo (1981) as well as by

Poiesz and de Bont (1995) it follows that involvement is a consequence of expertise.

Furthermore, in this particular domain, it would be unlikely to find consumers



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therefore the only relevant measure of risk in an informationally efficient

market. Accordingly, in an efficient market, the CAPM predicts a linear

relation between security returns and beta.

As predicted by the CAPM, several studies using sample periods prior

to 1969 find significant linearity between beta and stock returns (Miller and

Scholes, 1972; Black, Jensen, and Scholes, 1972; Fama and MacBeth,

1973).Miller and Scholes (1972) find a linear association between average returns

and beta, as well as a positive association between average returns and idiosyncratic

risk, using a 1954 to 1963 sample period. In line with other previous studies, which

they report, they find that the relation between idiosyncratic risk and average

returns is even stronger than between beta and average returns. They also find a linear

relation between beta and idiosyncratic risk. Black, Jensen, and Scholes (1972) report

a positive linear relation between average returns and beta and demonstrate that

the relation between average returns and beta for 17 non-overlapping two year

periods, from 1932 to 1965, is unstable and negative for at least 7 of the 17 periods.

Finally, Fama and MacBeth (1973) find a linear relation between average returns and

beta from January 1926 to June 1968, and find that no measure of risk besides beta

systematically affects expected returns.

Recent studies are not supportive of linearity between beta and security

returns. Fama and French (1992) find that, controlling for firm size, stock beta is not

linearly related to average returns from 1963 to 1990.1 Their results are supported

by Malkiel and Xu (1997), who suggest that firm size is a better proxy of risk than

stock beta. Furthermore, Malkiel and Xu (2002) find that beta estimated using the

market model is important in explaining cross-sectional return differences from

1935 to 1968, but that beta s role weakened considerably during the more

recent 1963 to 2000 period.2 Idiosyncratic risk, on the other hand, is important in

both periods whether it is measured using the market model or the Fama and

French (1992) three-factor model.

The relation between average returns and such firm characteristics as size,

price-to- earnings (P/E) ratio and price-to-book (P/B) ratio are well documented.

For example, Banz (1981) finds that firm size varies negatively with average

returns.3 Basu (1983), on the other hand, demonstrates that P/E ratio varies

negatively with average returns even after controlling for the effect of firm size.

Furthermore, Rosenberg, Reid and Lanstein (1985) find that P/B ratio varies negatively



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with average returns, and Fama and French (1992) find a strong univariate

relation between average returns and both firm size and P/B ratio. Using a bivariate

regression, Fama and French (1992) show that firm size and P/B ratio together absorb

the role of P/E ratio in stock returns. They argue that stock risks are

multidimensional, one dimension of risk proxied by firm size and another proxied by

P/B ratio. Moreover, Malkiel and Xu (1997) report that both firm size and P/B ratio

appear to be good proxies of risk over the 1963 to 1994 sample period.

Is Idiosyncratic Risk Relevant?

Studies that find significant association between idiosyncratic volatility and

stock returns include Miller and Scholes (1972), Friend, Westerfield, and

Granito (1978), Levy (1978), Amihud and Mendelson (1989) and Lehman

(1990). In line with Miller and Scholes (1972), Malkiel and Xu (1997) find a

significant linear relation between idiosyncratic risk and average returns. Their results

indicate that the relation between idiosyncratic risk and average returns is even

stronger than between firm size and returns. Malkiel and Xu also find a negative

relation between idiosyncratic risk and firm size and suggest that idiosyncratic risk is

a proxy for firm size and is perhaps a proxy for a wide range of systematic factors.

They argue that idiosyncratic risk may serve as a useful risk proxy since portfolio

managers are often called upon to explain why they invest in a stock that declined

considerably during a reporting period. Accordingly, such portfolio managers may

demand a risk premium on individual stocks with high perceived idiosyncratic risk.

Noting that a significant proportion of investors are either not able or not willing to

hold the market portfolio, Malkiel and Xu (2002) contend that idiosyncratic risk could

be priced to compensate investors who are not fully diversified. Malkiel and Xu

(2002) show that idiosyncratic volatility is more powerful than either beta or

firm size in explaining the cross- sectional differences in stock returns. They

show also that the explanatory power of idiosyncratic volatility is not subsumed

by either firm size or P/B ratio. Furthermore, Goyal and Santa-Clara (2003) show that

lagged average stock variance, which they find to be mostly driven by idiosyncratic

volatility, is positively related to returns on the market. They find this relation to be

stronger for smaller firms after controlling for the effect of P/B ratio.Campbell, Lettau,



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Malkiel and Xu (2001) find that idiosyncratic volatility is the largest component

of the total volatility of an average firm from1962 to 1997. They also find a

significant positive trend in idiosyncratic volatility and find no significant trend

in market volatility during that period. They demonstrate that the increase in

idiosyncratic volatility from1962 to 1997 has increased the number of randomly

selected stocks needed to achieve a relatively complete diversification. For example,

20 stocks reduced annual excess standard deviation to 5% from 1963 to 1985,

whereas 50 stocks were required to achieve the same level of diversification from 1986

to 1997.

Idiosyncratic Risk and Mutual Fund Return

The purpose of the present study is to find out if previous evidence regarding

the relation between common stock return and idiosyncratic risk can be generalized to

mutual fund prices. A secondary objective is to investigate the relation between mutual

fund return and price-to-book (P/B) ratio, price-to-earnings (P/E) ratio,

price-to-cash-flow(P/C)ratio, and market capitalization of the companies

held by mutual funds. According to the CAPM, there should be no significant linear

relation between return and idiosyncratic volatility. There should also be no linear

relation between return and such firm characteristics as P/B ratio, P/E ratio, P/C ratio

and market capitalization unless such characteristics are proxies of systematic risk.

However, since previous studies of common stock return find positive relation between

idiosyncratic volatility and return, as discussed above, I predict a positive relation

between mutual fund return and undiversified-idiosyncratic volatility. I also

predict a negative relation between mutual fund return and P/B ratio, P/C ratio, P/E

ratio, and the capitalization of companies held by mutual funds. Moreover, I predict

positive relation between return and funds net assets, since mutual fund costs are

known to vary inversely with fund size.

The increase in idiosyncratic risk for individual stocks over time, the

decline in the explanatory power of the market model, and the increase in the

number of randomly selected stocks needed to achieve diversification, as

demonstrated by Campbell, Lettau, Malkiel and Xu (2001), have special significance

to institutional investors who are known to be attracted to the more volatile stocks

(Sias, 1996; Haugen, 2002). Sias observes that, accounting for

capitalization differences, larger betas and larger residual variances are both

associated with greater institutional holdings of stocks. These findings are supported



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by Falkenstein (1996) who finds that mutual funds generally prefer the larger stocks

with high visibility and are averse to stocks with low idiosyncratic risk.

Falkenstein argues that mutual funds are not driven by conventional proxies for

risk and that idiosyncratic risk, rather than beta, is a significant factor in explaining

stock holdings of mutual funds. Moreover, Lakonishok, Shleifer, and Vishny (1994)

find that individuals and institutional investors prefer stocks of glamorous firms with

high P/B ratios. Furthermore, based on Fortune Magazines annual survey

of company reputation, Shefrin and Statman (1995) find that financial analysts,

senior corporate executives and outside directors rank companies as if they believe

that good companies are companies with high P/B ratios, and that good stocks are

stocks of well run, highly visible companies. They also rank stocks as if they

are indifferent to beta.

Consistent with the CAPM and inconsistent with several studies of stock returns, I find

no significant linear relation between mutual fund returns and undiversified-

idiosyncratic risk, even though idiosyncratic variance is approximately 45% of the

average fund s variability of returns from 1992 to 2001. Instead, the study finds

a significant nonlinear relation between returns and idiosyncratic risk. Suggestive

of economies of scale, my results show a positive linear relation between returns

and fund size after controlling for the effects of portfolio beta. Furthermore, the study

finds a negative linear relation between returns and P/B ratio after controlling for

the effects of beta, and it finds no significant linear relation between returns and either

the P/E ratio or market capitalization of companies held by mutual funds.



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Methodology

Since no prior Consumer Behaviour studies with a holistic focus on the mutual

fund market were available, all Likert-scales had to be developed for this study. Most

consumers buy mutual funds as a means to some other goal (retirement, house,

vacation, etc.). Thus, they do not consume mutual funds in the same sense that other

products and services are consumed.

Expertise must be considered and accurately measured in ways that are task-

relevant (Alba and Hutchinson, 1987). In this study expertise was measured by five

variables: perception of own knowledge (subjective knowledge; SUBJ), frequency of

information search, i.e., how often the stock market was monitored (FREQ), access to

information and stock market analyses in six leading business magazines (INFO),

perceived ability to make own analyses of the stock market (EVAL), and perceived

ability to interpret annual reports (ANREP). Familiarity was operationalized as

respondents experience with the MF&S market in terms of own investments and

how long they had been investors. People who have invested in MF&S for

many years and who have a larger share of their savings in MF&S would by this

definition be likely to have more familiarity with the stock market. It was for example

assumed that the longer consumers have invested in the stock market, the more

tolerance they will have for the volatility in the market. Familiarity was measured

by three variables: percentage of total savings in MF&S (SAVE%), MF&S as a

percentage of annual income (INC%), and how many years the respondent had

owned MF&S (YEARS). Consumers who invest in MF&S have decided to risk

their money by investing in products that by nature are risky. Thus, they do not avoid

risk as such, although they may be more or less willing to take high risks on the stock



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the mean is known, we can calculate stock XYZs standard deviation , which

measures the dispersion of the stocks annual returns (i.e., 10%, 5% and 15%) from

the mean expected return (10%). Therefore, the further away an equitys annual

return from the mean, the higher the standard deviation. In finance, standard

deviation is used to gauge an equitys volatility, whether the equity is a stock or a

mutual fund.

During the recent market sell-off, the majority of stocks followed the movement of the

general market and turned lower, the only difference among stocks is the extent of the

downturn as compared to the benchmark. The risk that a stock tends to go along with

the general market is captured by beta, also known as systematic risk (or market risk),

which measure how an individual stock or fund reacts to the general market

fluctuations. By definition, a benchmark (or index) has a beta of 1.00 and the beta of an

equity is relative to this value. If the movement of a stock or fund can be completely

explained by the movements of the general market, then this stock or fund will have a

R-squared of 100. According to Morningstar, R-squared, represented by a percentage

number ranging from 0 to 100, characterizes an equitys movement against a

benchmark. A R-squared that equals to 100 means all the equitys movements are in-

line with the benchmark.

With the Greek letter beta, investors can have an sense of how sensitive an equity is in

relation to the broad market. If investors decide to take on a higher risk by investing in

a volatile equity that carries a larger beta, then in theory, they should be rewarded with

a higher than average return. The difference between the realized return and the average

expected return is measured by another Greek letter alpha. A positive alpha indicates

that the equity exceeds its expectations against the respective benchmark.

How they work

Now we know what the risk measurements are, lets see how we can use them to assess

the risk/reward of an investment.

To illustrate, I use two funds, Dodge & Cox Stock Fund (DODGX) and CGM Focus

Fund (CGMFX), that I own to show how they are measured up against each other in

each category. Using S&P 500 index as the benchmark, the performance and risk data

http://quicktake.morningstar.com/FundNet/Snapshot.aspx?Country=USA&Symbol=DODGX&fdtab=snapshothttp://quicktake.morningstar.com/FundNet/Snapshot.aspx?Country=USA&Symbol=CGMFX&fdtab=snapshothttp://quicktake.morningstar.com/FundNet/Snapshot.aspx?Country=USA&Symbol=DODGX&fdtab=snapshothttp://quicktake.morningstar.com/FundNet/Snapshot.aspx?Country=USA&Symbol=CGMFX&fdtab=snapshot


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sensitivity to market movements. It measures the relationship between a fund's excess

return over T-bills and the excess return of the benchmark index.

By definition, the Beta of the market benchmark (in this case, an index) is 1.00.

Accordingly, a fund with a 1.10 Beta has performed 10% better than its benchmark

index--after deducting the T-bill rate--than the index in up markets and 10% worse in

down markets, assuming all other factors remain constant. Conversely, a Beta of 0.85

indicates that the fund has performed 15% worse than the index in up markets and 15%

better in down markets. The Beta calculation involves a bit of math, but the resulting

number is very easy to understand.

Beta is only indicative for funds with a relatively high correlation with the index.

In other words, the higher R-Squared is, the more relevant the fund's Beta.

The Beta Calculation Process

Here is an example showing the inner details of the Beta calculation process:

Suppose we collected end-of-the-month prices and any dividends for a stock and the

S&P/TSX index for 36 months (0..36). We need n + 1 price observations to calculate n

holding period returns, so since we would like to index the returns as 1...36, the prices

are indexed 0.... 36. Also, professional Beta services use monthly data over a 36-month

period.

Now, calculate monthly holding period returns using the prices and dividends. For

example, the return for month 2 will be calculated as: r_2 = ( p_2 - p_1 + d_2 ) / p_1

Here r denotes return, p denotes price, and d denotes dividend. The following table of

monthly data may help in visualizing the process.

(Monthly data is preferred in the profession because investors' horizons are said to be

monthly.)

Nr. Date Price Div. (*) Return

0 12/31/86 45.20 0.00 --

1 01/31/87 47.00 0.00 0.0398

2 02/28/87 46.75 0.30 0.0011

. ... ... ... ...

35 11/30/91 46.75 0.30 0.0011

36 12/31/91 48.00 0.00 0.0267



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(*) Dividend refers to the dividend paid during the period. They are assumed to be paid

on the date. For example, the dividend of 0.30 could have been paid between 02/01/87

and 02/28/87, but is assumed to be paid on 02/28/87. So now, we'll have a series of 36

returns on the security and the index (1.36). Plot the returns on a graph and fit the

best-fit line (using the least squares regression curve fitting process):

Modern Portfolio Theory-the underpinning

Risk is composed of systematic (market risk) and unsystematic risk (company-

specific). Systematic risk includes currency risks, inflation risks, foreign investment

risk, political and regulatory risks, interest rate risk, economic risks, and lately terrorist

risk. Even bad weather risk can affect certain market sectors such as retailers,

agriculture, forest products, insurance, airlines and tourism. Systematic risk cannot be

eliminated by diversification within a given market. Systematic risk captures the

reaction of individual stocks or portfolios to general market swings. Some stocks and

portfolios tend to be very sensitive to market movements. Others are more stable. This

relative volatility or sensitivity to market moves can be estimated on the basis of the

past record, and is popularly denoted by Beta. Beta is the numerical description of

systematic risk. Despite the mathematical manipulations involved, the basic idea

behind the Beta measurement is one of putting some precise numbers on the subjective

feelings money managers have had for years. Beta is essentially a comparison between

the movements of individual stocks (or portfolios) and the movements of the market as

a whole. Professionals often call high- Beta stocks aggressive investments and label

low- Beta stocks as defensive investments. The Beta of a portfolio is the weighted

average of the Betas of individual securities making up the portfolio.

Modern Portfolio Theory says that the total risk of each individual security is irrelevant.

It is only the systematic component that counts as far as extra rewards go. Because

stocks (30 or more at least) can be combined in portfolios to eliminate or reduce

specific (unsystematic) risk, only the undiversifiable or systematic risks will command

a risk premium. Investors will not get rewarded for bearing risks that can be diversified

away. This is the basic logic behind the Capital Asset Pricing Model (CAPM), which

itself is a very simplified model.

The logic behind it is as follows:



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If investors did get an extra return / risk premium for bearing unsystematic risk it would

turn out that the diversified portfolios made up of stocks with large amounts of

unsystematic risk would give larger returns than equally risky portfolios of stocks with

less unsystematic risk. Investors would jump at the chance to have these higher returns,

bidding up the prices of stocks with large unsystematic risk and selling stocks with

equivalent Betas but lower unsystematic risk. This process would continue until the

prospective returns of stocks with the same Betas were equalized and no risk premium

could be obtained for bearing unsystematic risk. Any other results would be

inconsistent with the existence of an efficient market.

Mathematically Beta is defined as: Beta=COV (RF, RM) / VAR (RM)

where COV is the covariance between RF and RM

RF =the return of the mutual fund

RM=the return of the index

VAR (RM)=the variance of the index

VAR-the variance is the square of the standard deviation usually denoted by the Greek

letter Sigma

Covariance is defined as COV (X, Y)=E [(X-x)(Y-Y)] and measures the direction

and strength of the relationship between random variables X and Y where E is the

expected value and =the population mean. If X and Y are statistically independent

(no relationship) than E (X*Y)=E (X)*E (Y). Beta is a dimensionless number. Dividing

the covariance by the benchmark variance merely normalizes the measure of Beta.

Another equivalent, but perhaps more intuitive definition of Beta is:

= Correlation (Fund, Market) x Std Dev (F) / Std Dev (M)

Beta values can be roughly characterized as follows:

* Beta less than 0

Negative Beta is possible but not likely. People thought gold

stocks should have negative Betas but that hasn't been true.

* Beta equal to 0

Cash under your mattress, assuming no inflation

* Beta between 0 and 1

Low-volatility investments (e.g., utility stocks)

* Beta equal to 1



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Matching the index (e.g., for the S&P 500, a U.S. index fund, in Canada an Index ETF

like i60; TSX: XIU. XIU which mirrors the S&P/TSX 60, has a turnover of about 13 %

to remain congruent with its index changes

* Beta greater than 1

Anything more volatile than the index (e.g., small cap. funds)

* Beta much greater than 1 (tending toward infinity)

Impossible, because the stock would be expected to go to zero on any market decline.

It has been shown that Betas are approximately normally distributed with a standard

deviation of around 0.3. Hence, about 95 percent of shares have Betas which lie

between 0.4 and 1.6.

High Beta funds are expected to do better than the market. During declines they are

expected to do worse than the market average. Betas are not stable from period to

period), and they are very sensitive to the particular market proxy/ benchmark against

which they are measured (the S&P 500 itself has a annual turnover of about 8 % due to

changes and mergers/divestitures). The choice of index is huge for obvious reasons.

There is only a handful of Canadian equity funds that truly deserve to be benchmarked

to a 100% TSX Composite Index. Most have at least 10% foreign content, with many at

20%+. Also, some U.S. equity funds (i.e. Janus American Equity, Spectrum American

Growth, and Templeton Mutual Beacon to name a few) have a mandate to hold a

certain

amount in overseas stocks. Benchmarking a fund seems a difficult task since few funds

offer pure exposure to a single market/ asset class.

Meaning of Beta

A lot of disservice has been done to Beta in the popular press because of trying to

oversimplify the concept. A Beta of 1.5 does not mean that if the market goes up by 10

points, the stock (or fund) will go up by 15 points. It also doesn't mean that if the

market has a return (over some period, say a month) of 2%, the stock will have a return

of 3%. To understand Beta, look at the equation of the line representing the best fit

using the least squares linear regression technique:



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stock return = alpha + Beta * index return+ epsilon where epsilon is a random error

term

Beta indicates the average sensitivity of an individual security to the market return, and

is a measure of the market or systematic risk of a security (or portfolio). As the

coordinates do not fall exactly on the line of best fit, an error term, epsilon, is

introduced to represent the unexplained security return. The specific returns arise

because of events affecting the economy, and are represented by alpha as well as

epsilon. Alpha represents on average, the portion of a securitys return that is not

associated with general movements in the economy. Alpha therefore represents the

average return of an individual security when the return of the market index is zero. It is

taken to be equal to the risk-free rate i.e. T-bill rate.

One shot at interpreting Beta is the following. On a day the (S&P-type) market index

goes up by 1%, a stock with a Beta of 1.5 will go up by 1.5% + epsilon (can be positive

or negative). Thus it won't go up by exactly 1.5%, but by something different.

The good thing is that the epsilon values for different stocks are guaranteed to be

uncorrelated with each other. Hence in a diversified portfolio , you can expect all the

epsilons (of different stocks) to cancel out. Thus if you hold a diversified portfolio, the

Beta of a stock characterizes that stock's response to fluctuations in the market index.

So in a diversified portfolio like a mutual fund, the Beta of a fund is a not an

unreasonable summary of its risk properties with respect to the "systematic risk", which

is fluctuations in the market index. A fund or stock with high Beta responds strongly to

variations in the market, and a fund or stock with low Beta is relatively insensitive to

variations in the market.

The main practical problem in applying the Markowitz approach to portfolio

management is the large amounts of data which is required. The calculation of Beta

makes it necessary to estimate how returns of every individual security would move or

covary with those of every other individual security.

With a view to simplifying the computations and reducing the quantity of data required

for the Markowitz approach, Dr. William Sharpe and others side-stepped the difficult

task of estimating covariances between all securities. This was achieved by including

risk-free securities in the analysis, identifying the market portfolio on the Markowitz

efficient frontier and generating a market sensitivity measure (Beta) for each security.

Without going into all the details, this results in the equation E (RF)= alpha + E (RM



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- alpha)] which from our Grade 11 math is a straight line with slope Beta and Y

intercept alpha. In plain English this means that the expected Return of the fund is =to

the risk-free rate, say a GIC or Tbill, plus Beta times the expected return of the market

index less the risk-free return. So, Beta can be a useful tool in assessing the risk/reward

appropriateness of a fund.

So, if the market return is 2% above the risk-free rate , the stock return would on

average be 3% above the risk-free rate, if the stock Beta is 1.5.

Using Beta

Current Government regulations do not require Fund Companies to publish the value of

Beta in the Prospectus. They only publish return data, portfolio turnover % and the

MER so youll have to phone the Company for the data. Expect some pain, as customer

service people dont get this type of question every day.

In general, Beta values are a useful way of determining how a mutual fund has done,

and how well it may do from a risk perspective in the future. Beta values for many U.S.

mutual funds can be found in financial magazines or special investing periodicals such

as Investor's Business Daily. In Canada, its best to phone the fund Company or use

www.globefund.com or equivalent web-site. Filtering on Beta is not provided so youll

have to do some trial and error to find the fund that fits the Beta thats right for you.

A conservative investor whose main concern is preservation of capital should focus on

funds with low Betas, whereas one willing to take high risks in an effort to earn high

rewards should look for high-Beta funds. Some funds go better together than others.

You do not diversify if you buy two funds that have a history of moving up and down at

the same time. Also,never forget your personal financial goals and risk tolerance.

If you had a portfolio of Beta 1.2, and decided to add a fund or stock with Beta 1.5, then

you know that you are slightly increasing the riskiness (and potential average return) of

your portfolio. This conclusion is reached by merely comparing two numbers (1.2 and

1.5). That parsimony of computation is the major contribution of the notion of "Beta".

Conversely if you got cold feet about the variability of your Beta = 1.2 portfolio, you

could augment it with a few companies with Beta less than 1.The Beta of a portfolio is



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the dollar -weighted average of the securities held in the portfolio (i.e. mutual fund)

relative to a given market.

NAVs

Scheme Name

World Gold

Fund

From Date 1-Jan-08

To Date 30-Jun-08

Date NAV (Rs.) Daily Return in

%

2-May-08 13.3289

3-May-08 13.423 0.706

4-May-08 13.548 0.931

5-May-08 13.8429 2.177

6-May-08 13.8429 0.000

7-May-08 14.0419 1.438

8-May-08 14.3253 2.018

9-May-08 14.2723 -0.370

13-May-08 14.2251 -0.331

14-May-08 14.3339 0.765

15-May-08 14.5322 1.383

16-May-08 15.0879 3.824

20-May-08 15.4151 2.169

21-May-08 15.789 2.426

22-May-08 15.8423 0.338

23-May-08 15.6457 -1.241

23-May-08 15.6457 0.000

26-May-08 15.3331 -1.99827-May-08 15.1992 -0.873

28-May-08 15.0135 -1.222

29-May-08 14.9386 -0.499

30-May-08 14.8216 -0.783

2-Jun-08 14.797 -0.166

3-Jun-08 14.8768 0.539

4-Jun-08 14.6713 -1.381

5-Jun-08 14.5863 -0.579

6-Jun-08 14.791 1.403

9-Jun-08 14.7667 -0.164

10-Jun-08 14.4802 -1.940

11-Jun-08 14.1372 -2.36912-Jun-08 13.6954 -3.125

13-Jun-08 13.7225 0.198

16-Jun-08 13.9544 1.690

17-Jun-08 14.049 0.678

18-Jun-08 14.0671 0.129

19-Jun-08 14.2497 1.298

20-Jun-08 14.1242 -0.881

24-Jun-08 14.014 -0.780

25-Jun-08 13.8426 -1.223

26-Jun-08 14.1363 2.122

27-Jun-08 14.72644.17430-Jun-08 15.014 1.953

average 0.303



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53

sdt. Dev. 1.601

DSP ML World Gold Fund

12

12.5

13

13.5

14

14.5

15

15.5

16

16.5

5/4/20

08

5/11/200

8

5/18/200

8

5/25

/200

8

6/1/20

08

6/8/20

08

6/15/200

8

6/22

/200

8

6/29

/200

8

NAVs from May to June 2008

NAV

NAVsScheme Name Top 100 Equity Fund - Reg

From Date 1-May-08

To Date 30-Jun-08

Date NAV (Rs.) Daily Return in

%

2-May-08 78.417

3-May-08 78.234 -0.233

4-May-08 78.017 -0.277

5-May-08 77.916 -0.129

6-May-08 77.406 -0.655

7-May-08 77.253 -0.198

8-May-08 76.431 -1.064

9-May-08 75.239 -1.560

12-May-08 75.623 0.510

13-May-08 75.025 -0.791

14-May-08 75.753 0.970

15-May-08 76.846 1.443

16-May-08 77.43 0.760

20-May-08 76.94 -0.633

21-May-08 76.809 -0.170

22-May-08 75.82 -1.288

23-May-08 75.129 -0.911

26-May-08 74.207 -1.22727-May-08 74.059 -0.199



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56

Scheme Name Govt Sec. Fund - Plan A

From Date 1-Jan-08

To Date 30-Jun-08

Date NAV (Rs.) Daily Return in %

2-May-08 25.1042

3-May-08 25.1125 0.0334-May-08 25.154 0.165

5-May-08 25.169 0.060

6-May-08 25.1383 -0.122

7-May-08 25.0788 -0.237

8-May-08 25.0639 -0.059

9-May-08 25.0531 -0.043

12-May-08 25.1763 0.492

13-May-08 25.1531 -0.092

14-May-08 25.1492 -0.016

15-May-08 25.0999 -0.196

16-May-08 25.0372 -0.250

20-May-08 25.032 -0.02121-May-08 24.9777 -0.217

22-May-08 24.9473 -0.122

23-May-08 24.8951 -0.209

26-May-08 24.9211 0.104

27-May-08 24.8163 -0.421

28-May-08 24.8802 0.257

29-May-08 24.8476 -0.131

30-May-08 24.8522 0.019

2-Jun-08 24.8719 0.079

3-Jun-08 24.8471 -0.100

4-Jun-08 24.8288 -0.074

5-Jun-08 24.8225 -0.025

6-Jun-08 24.8165 -0.024

9-Jun-08 24.823 0.026

10-Jun-08 24.8178 -0.021

11-Jun-08 24.8443 0.107

12-Jun-08 24.7969 -0.191

13-Jun-08 24.7334 -0.256

16-Jun-08 24.7732 0.161

17-Jun-08 24.8141 0.165

18-Jun-08 24.788 -0.105

19-Jun-08 24.7054 -0.333

20-Jun-08 24.5819 -0.50023-Jun-08 24.5876 0.023

24-Jun-08 24.6389 0.209

25-Jun-08 24.5647 -0.301

26-Jun-08 24.5668 0.009

27-Jun-08 24.5688 0.008

30-Jun-08 24.5795 0.044

average -0.050

std. dev. 0.187



57/86


58/86

58

28-May-08 13.49 0.905

29-May-08 13.39 -0.741

30-May-08 13.428 0.284

2-Jun-08 13.172 -1.906

3-Jun-08 13.048 -0.941

4-Jun-08 12.705 -2.629

5-Jun-08 12.872 1.314

6-Jun-08 12.747 -0.971

9-Jun-08 12.404 -2.691

10-Jun-08 12.337 -0.540

11-Jun-08 12.451 0.924

12-Jun-08 12.547 0.771

13-Jun-08 12.581 0.271

16-Jun-08 12.649 0.540

17-Jun-08 12.834 1.463

18-Jun-08 12.705 -1.005

19-Jun-08 12.494 -1.661

20-Jun-08 12.109 -3.08123-Jun-08 11.803 -2.527

24-Jun-08 11.612 -1.618

25-Jun-08 11.774 1.395

26-Jun-08 11.824 0.425

27-Jun-08 11.506 -2.689

30-Jun-08 11.302 -1.773

average -0.535

std. dev. 1.364

DSP ML Tax Saver Fund

0

2

4

6

8

10

12

14

16

5/2/20

08

5/9/20

08

5/16/200

8

5/23

/200

8

5/30

/200

8

6/6/20

08

6/13/200

8

6/20

/200

8

6/27

/200

8

NAVs from May to June 2008

NAV

DSP Merrill Lynch World Gold Fund Growth Fund Facts

Objective



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61

Growth

OTHERS

Company Name Instrument Market

Value (Rs.

in crores)

% of

Net

Assets

GOLD - BULLION Gold 2,041.09 98.96

CBLO Money Market 13.00 0.63

Net Receivables/(Payable) Net Receivables/

(Payables)

-18.43 -0.89

DSP Merrill Lynch

World Gold Fund

Growth

BSE

Sensex

BSE

METAL

DSP Merrill Lynch Top 100 Equity Fund Growth

Fund Facts

objective

The Fund is seeking to generate capital appreciation, from a portfolio that is

substantially constituted of equity and equity related securities of the 100 largest

corporates, by market capitalisation, listed in India.



62/86

62

Last Divdend

Declared NA

Minimum

Investment (Rs)5000

Purchase

RedemptionsDaily

NAV Calculation Daily

Entry Load

Amount Bet. 0 to 49999999 then Entry load is 2.25%. and

Amount greater than 50000000 then Entry load is 0%.

Exit LoadIf redeemed bet. 0 Months to 6 Months; Exit load is 1%. If

redeemed bet. 6 Months to 12 Months; Exit load is 0.5%.

SCHEME PERFORMANCE (%) AS ON AUG 8, 2008

1 Month 3 Months 6 Months 1 Year 3 Years 5 Years Since

Inception

10.95 -8.40 -8.30 4.50 31.35 38.25 43.21


Type of Scheme Open Ended

Nature Equity

Option Growth

Inception Date Feb 21, 2003Face Value

(Rs/Unit)10

Fund Size in Rs.

Cr.

957.56 as on Jul

31, 2008

Fund Manager Apoorva Shah .

SIP

STP

SWPExpense ratio(%) 2.13

Portfolio

Turnover

Ratio(%)

389.4


63/86

63

Portfolio Attribites

Top 10 Holding


Mean 1.07

Standard

Deviation

3.30

Sharpe 0.29

Beta 0.88

Treynor 1.09

Sortino 0.47

Correlation 0.88

Fama 0.22

P/E23.56 as on Jun -

2008

P/B7.40 as on Jun -

2008

Dividend Yield1.23 as on Jun -

2008

Market Cap (Rs.

in crores)

65,481.03 as on

Jun - 2008

Large

73.01 as on Jun -

2008

Mid NA

Small NA

Top 5 Holding

mahendra mutual fund risk return

Documents