Mutual Fund Risk & Return

Mahindra & Mahindra Financial Services Limited A summer training report on

“the knowledge of Risk Tolerance that an Investor can handle to find an optimal trade-off between the risk and returns” (05 May 2008 – 05 Juy 2008) Under the Guidance of Mr. TARUN KUMAR SINGH (INDUSTRY GUIDE)

Mr. PRASHANT DUTTA GUPTA (FACULTY GUIDE) By

MANISH PRASAD Roll no: 27090 Batch: 2007-09

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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) STUDENT’S NAME:

Manish Prasad

9) STUDENT’S ROLL NUMBER:

27090

10) STUDENT’S EMAIL ID:

[email protected]

11) STUDENT’S MOBILE/RESIDENCE NUMBERS: 9871936904/03412240836

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CERTIFICATE OF AUTHENTICITY This is to certify that MR. MANISH PRASAD student of PGDBM (Full Time) 20072009 batch, NIILM – Centre for Management Studies, NEW DELHI, has done his training project under my supervision and guidance. During his project he was found to be very sincere and attentive to small details whatsoever was told to him. I wish him good luck and success in his future

…………………………… (Manish Prasad) 27090

…………………………… ( Mr. Prashant Dutta Gupta) 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 of Mahindra & 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 27090 Niilm-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.

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CONTENTS Information Sheet…………………………………………………………………………….2 Acknowledgement …………………………………………………………………………. 4 Abstract…………...………………………………………………………………………….. 5 Chapter 1 Introduction

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About Mutual Fund Industry

8

About Mahindra & Mahindra Financial Services Limited

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Chapter 2 Review of Literature

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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

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ABOUT MUTUAL FUND INDUSTRY CONCEPT A Mutual Fund is a trust that pools the savings of a number of investors who share a common 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

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9 ORGANISATION OF A MUTUAL FUND 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

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11 UTI was Unit Scheme 1964. At the end of 1988 UTI had Rs.6,700 crores of assets under management. 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.

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Fourth Phase – since February 2003 In February 2003, following the repeal of the Unit Trust of India Act 1963 UTI was bifurcated into two separate entities. One is the Specified Undertaking of the Unit Trust of India with assets under management of Rs.29,835 crores as at the end of January 2003, representing broadly, the assets of US 64 scheme, assured return and certain other schemes. The Specified Undertaking of Unit Trust of India, functioning under an administrator and under the rules framed by Government of India and does not come under the purview of the Mutual Fund Regulations. The second is the UTI Mutual Fund Ltd, sponsored by SBI, PNB, BOB and LIC. It is registered with SEBI and functions under the Mutual Fund Regulations. With the bifurcation of the erstwhile UTI which had in March 2000 more than Rs.76,000 crores of assets under management and with the setting up of a UTI Mutual Fund, conforming to the SEBI Mutual Fund Regulations, and with recent mergers taking place among different private sector funds, the mutual fund industry has entered its current phase of consolidation and growth.

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13 The graph indicates the growth of assets over the years. GROWTH IN ASSETS UNDER MANAGEMENT

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 of the 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.

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ABOUT MAHINDRA & MAHINDRA FINANCIAL SERVICES LIMITED 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 India’s 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.

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15 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 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 can’t 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

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16 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 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

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18 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, investor’s 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 (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

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19 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’ riskreturn perceptions. Hypotheses are tested by means of a large-scale experimental study, and practical implications are deduced in the last section of the paper. productspecific variables of advertising effectiveness in order to understand and optimise advertising’s 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

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20 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 portfolio theory, behavioural finance deals with the descriptive analysis of actual behaviour of individuals in financial markets and analyses psychological influences on information processing and financial decision making. The typical investor is considered to be a ‘homo heuristics’ rather than a ‘homo economicus’ who makes use of judgmental heuristics in information processing and decision making instead of formal statistical analysis. Judgmental heuristics are abridged, often sub-optimal information processing strategies, so-called ‘mental shortcuts’ or ‘rules of thumb’ which are used systematically but often unconsciously to simplify decision making. Heuristics like the anchoring heuristic, the representativeness heuristic, the availability heuristic or framing lead tobiases in the perception of risks and returns. So if advertising content evokes the use of judgmental heuristics, advertising will influence investors’ risk- return perceptions by means of those heuristics. In the next sections, Explanation about two cognitive heuristics and one affect-based heuristic and deduction of the implications for advertising effects are given. The heuristics were chosen on the basis of their practical relevance in actual mutual fund advertising. Anchoring heuristic While making forecasts, predictions or probability assessments like risk-return evaluations of mutual funds, people tend to rely on a numerical anchor value which is explicitly or implicitly presented to them. Anchoring effects are not restricted to numerical values with a logical coherence to the subsequent numeric estimate. According to the so- called ‘basic anchoring effects any random and uninformative starting point might represent an initial anchor value which leads to biases in forecasts

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21 and estimates of the value of that initial starting point. Anchoring effects have been identified in many empirical studies and in various decision fields. This robust judgmental heuristic is of particular relevance in financial markets, where it applies to any financial forecast (eg stock market prices), leading to severe biases. In practice, numerical data play an important role in the informative content of mutual fund ads. Almost every print ad and many television spots highlight figures like past performance data, assets under management, distribution of dividends etc. In addition to direct anchor values (these are anchors that evoke direct associations with risks and returns, eg ‘10 per cent’), indirect anchor values with dimensions other than return or monetary 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 returnperceptions 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 baserate 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, naı¨ve 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’

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22 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

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

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23 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. Discussion Advertising in the mutual fund industry may become more effective if advertising firms are aware of and apply theoretical and empirical insights of behavioural finance theory, especially regarding investors’ systematic use of judgmental heuristics in the evaluation of risk and return. Besides more general variables of advertising effects, it is reasonable to consider private investors’ risk-return perceptions as additional, product-specific variables of advertising effects in the mutual fund industry in order to understand and optimise advertising effectiveness. Private investors make use of judgmental heuristics during the processing of advertising stimuli, regardless of their expertise in investment decisions. Uninformed investors, however, make use of heuristics to a larger extent, resulting in larger biases in the perception of risks and returns. This finding highlights the necessity of target group- or market segment-specific advertising strategies in the investment industry, as differences in knowledge and experiences lead to different risk-return perceptions. Numerical values in print ads serve as anchor values and bias expected returns, even when there is no logical connection between anchor value and return estimation. Therefore, prominent numbers and figures in mutual fund ads have to be integrated very carefully, with full awareness of their potential biasing influence.

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24 Brand awareness and brand image play a central role in the processing of ads, as they are able to distort private investors’ risk perception at the product level. Investment risk is judged lower if a highly reputable and well-known investment company offers the advertised mutual fund. As a consequence, investing in brand equity is very important. Private investors’ risk perceptions are influenced by emotional states. Emotional stimuli in the ad not only lead to a more favourable, positive affective evaluation of the advertised mutual fund, but also to a lower perception of investment risk compared to a merely informative advertising style. This finding indicates that emotional advertising is an effective tool, even in the abstract, rational, risk- returnoriented investment industry. The Impact of Consumer Knowledge, Involvement, and Risk Willingness on Return on Investments in Mutual Funds and Stocks Consumer knowledge, involvement, and risk are central concepts in consumer behav- ior research. A review of prior research shows however that there is no 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 unintentionally 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

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25 system. Sweden, as an example, has a record number of indirect investors (more than 90% of the population 18–74 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, it’s no overstatement to suggest that this movement

from

Wall

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to

Main

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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 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 naıve (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.,

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26 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) 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 hampered

behavior

and the

consequences of

behavior. This

has

the cumulation of knowledge about relationships between important

constructs in CB. Comparing and contrasting mental phenomena with actual

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27 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 situation that prevailed in service marketing regarding the role of quality, value, and satisfaction on behavioral intentions (e.g., Cronin et al., 2000). There is also a general lack of contributions from the academic world in this important domain (complex decisions or savings in MF&S) that can have a tremendous impact on consumers’ welfare (Bazerman, 2001; Lehmann, 1999; Lichtenstein et al., 1999; Mick, 2003). In the present study, therefore, we systematically examined a variety of relations between the relevant key constructs (knowledge, involvement, risk willingness, and return). Even though the literature suggests various relations between these constructs, the guidance is not

strong enough to formulate a specific model. Therefore, the modeling task

corresponds to what (MG)

situation.

Jo¨reskog

(1993:

295)

calls

the

Model

Generating

The alternative links between concepts in the alternative models

tested were derived from previous literature. We used a nationally representative sample of owners of MF&S. In terms of methodology we focused more on generalizability than on precision and realism. Conceptually, the focus was more on parsimony than on differentiation of

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28 detail or on the scope of the focal problem (Brinberg and McGrath, 1985: 43). These choices seemed quite natural considering the importance of mutual funds to most consumers, the lack of earlier research in the field, and the need to use a representative sample to get enough variety in the answers. To summarize, this study will clarify relations between knowledge, involvement and risk, and it will estimate their impact on consumers’ return on investment in MF&S. The report is organized in the following way. First, earlier research on the key constructs is reviewed. Then the analyzed alternative models derived from the literature are described. Third, results from the analyses of the alternative models are presented in the following sequence: the knowledge construct, alternative relations

between

relations between knowledge, MF&S

knowledge involvement,

and risk

involvement,

and

alternative

willingness and return

on

investments. Fourth, results are evaluated and interpreted and the

limitations of the study are acknowledged. Theoretical Background Consumer Knowledge 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 stock’s price,

approximately 300 fundamental factors (those involving a company’s 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

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29 Benartzi and Thaler’s (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 their search considerably after the first period. Similar results were found by Hershey and Walsh (2000–2001). They found that experts are more goal-oriented and efficient and are able to impose a meaningful structure on ill-structured tasks and

focus

their attention on

a

smaller number of more diagnostic items of information. The prior knowledge of the range of acceptable parameters for a variety of variables allows experts to form a general impression of whether an investment is indicated or not, and on that basis they are able to specify a reasonably accurate intuitively based investment amount. Novices are more likely to sample the opinions of others and to use ‘nonfunctional’ attributes such as brand name and price. In extreme cases, they may rely primarily on brand familiarity. As novices gain some familiarity with the problem domain they simplify their solutions, whereas experts continue to solve the problems at a consistent level of complexity across trials. Familiarity was defined as the number of product-related experiences that have been accumulated by the consumer (Alba and Hutchinson, 1987). Experience gives a

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30 feeling of security and consequently a higher propensity to accept risks. When decision-makers see themselves as more competent and knowledgeable, they are more likely to see chance events as controllable and believe that they have skills enough to predict events on the market (Langer, 1983). That feeling (confidence knowledge) makes them more prone to attempt to master their environment. Antonides and Van Der Saar (1990) found for example that the perceived risk of an investment is lower the more the stock price has increased recently. Involvement Consumers’ (enduring) involvement was defined as the on-going mobilization of behavioral resources for the achievement of a personally relevant goal (as opposed to Poiesz

and

de

Involvement was

Bont, seen

1995, as

the

who

discussed

consequence

of

momentary the

mobilization).

combined

subjective

assessments of motiva- tion, ability and opportunity to seek, access, interpret and evaluate task-relevant information. As noted by Petty and Cacioppo (1981: 23, emphasis added), ‘the level of involvement is not the only determinant of the route to persuasion. In addition to having the necessary motivation to think about issuerelevant argumentation, the message recipient must also have the ability to process the message if change via the central route is to occur’. Opportunity was in their definition subsumed under ability. 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.

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31

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 people’s beliefs are biased in the direction of optimism, and they 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).

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32 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 Fo¨rening, 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 with expertise and involvement who do not use their knowledge for investment purposes, i.e., thereby getting familiarity. Familiarity, in turn, may create an illusion of control and con- sequently a higher willingness to take risks. Risk willingness may therefore be seen as a

consequence

of

involvement

via

familiarity. However, there may be alternative models that would describe relations between constructs in a more accurate way. Unfortunately, as already mentioned, no previous research has simultaneously compared the relative influence of three of the major constructs in this study, namely knowledge (expertise and familiarity), involvement and risk (neither risk willingness nor risk avoidance) on behavioral consequences. Earlier studies have, as mentioned, focused on the perceptual concepts only. That explains the contradictory findings that for example Poiesz and de Bont (1995) discussed regarding antecedents, dimensions, and

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33 consequences of the involvement concept. It was therefore considered essential to test alternative models to improve our understanding of the relations between these concepts. The first alternative was to treat knowledge as a one-factor model rather than as a two factor-model (expertise and familiarity) as suggested by Alba and Hutchinson (1987).

In

a

preliminary study Zaichkowsky (1985b)

emphasized that

expertise should be separated from familiarity (product use), but she studied search products rather than complex credence products. A second alternative is that involvement precedes knowledge rather than the other way around as in the proposed model. The involvement literature on search products would favor this alternative. Zaichkowsky (1985) also found that involvement and product use (familiarity) may be related, while involvement and expertise may not necessarily be related. These two alternative models were also tested. Return on investments in MF&S

is

the dependent variable and risk and return are related. Alternative

relations for the risk willingness concept were also tested. Return and Risk on Common Stocks The capital asset pricing model (CAPM) of Sharpe (1964) and others suggests that the total risk of an asset can be dissected into a market related component or systematic risk, and a company specific component or idiosyncratic risk. Idiosyncratic risk can be diversified away by investors and is therefore not priced in an efficient capital market. Systematic risk, measured by the asset s beta, is 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.

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34 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 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 dimensionof 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?

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35 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, 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

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36 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 ratio, price-to-cash-flow(P/C)ratio, and

market

(P/E)

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 fund’s 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

accounting

(Sias,

1996;

Haugen,

2002).

Sias

observes

that,

for capitalization differences, larger betas and larger residual

variances are both associated with greater institutional holdings of stocks. These findings are supported 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 Magazine’s 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

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37 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 undiversifiedidiosyncratic 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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38

CHAPTER 3 METHODOLOGY

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39 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 taskrelevant (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 market. Risk Willingness was operationalized as willingness to take risks on the stock market (RISK), feelings of uncertainty having made decisions (CERT), how long they wait to sell a fund that decrease in value (WAIT), and what percentage of total savings that they have in MF&S (SAVE%). The more they invest in the stock market, the higher risk they take. SAVE% is also included in the Familiarity construct, since a higher share of MF&S also results in a more varied experience of MF&S. The two remaining concepts in the proposed model, enduring involvement (INVOLV), and relative success or return on investments (RETURN) were measured by

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40 single variables, which were used as manifest variables in the model. Enduring involvement i.e., owners of MF&S. Expertise must be considered and accurately measured in ways that are taskrelevant (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 market. Risk Willingness was operationalized as willingness to take risks on the stock market (RISK), feelings of uncertainty having made decisions (CERT), how long they wait to sell a fund that decrease in value (WAIT), and what percentage of total savings that they have in MF&S (SAVE%). The more they invest in the stock market, the higher risk they take. SAVE% is also included in the Familiarity construct, since a higher share of MF&S also results in a more varied experience of MF&S. The two remaining concepts in the proposed model, enduring involvement (INVOLV), and relative success or return on investments (RETURN) were measured by single variables, which were used as manifest variables in the model. Enduring involvement exists when someone shows interest in an activity or in products over a long period of time (Hoyer and MacInnis, 2001: 56). Consumers are motivated to invest in MF&S for the potential returns they may get from such investments, but the majority of them lack the ability and opportunity to select and process the information required for making informed decisions.

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41 Financial

returns

was operationalized

as

consumers’ evaluations

of

how

successful they have been compared with index. Consumers are familiar with

index

comparisons

from advertising, from articles in newspapers and

magazines, as well as in information from fund companies. There are several reasons why this type of measure was chosen, although a more precise and reliable measure had been highly preferable if such a measure exists. One reason is that this is the measure with which most consumers are familiar. Another reason is that an increase by 10% in a year when the index increased by 40% is a very poor result. Similarly, a decrease by 5% a year when

the

index

decreased

by

60% is a very good result. It would also be unreasonable to expect that respondents would take the time or be willing to provide detailed information about their

returns

in

a

survey.

Other

measures

would encounter a myriad of

problems such as defining whether returns had to be realized or not, how to consider tax effects, etc. The design of the study was also adapted to low involvement, inexperienced investors, due to the fact that the majority of consumers belong to this group. The aim was therefore to use as few variables as possible in the study and to focus more on the holistic approach than on the details. Frequency analyses showed that 19% of respondents had a high or very high subjective knowledge (SUBJ). A relevant question in this study is whether it is possible to have realistic expectations about the stock market if you know very little about it. Respondents were asked—in a follow-up question to the self-

Beta Definition:A quantitative measure of the volatility of a given stock, mutual fund, or portfolio, relative to the overall market, usually the S&P 500. Specifically, the performance the stock, fund or portfolio has experienced in the last 5 years as the S&P moved 1% up or down. A beta above 1 is more volatile than the overall market, while a beta below 1 is less volatile. Risk Definition: Risk is the chance that an investment’s actual return will be different than expected. This includes the possibility of losing some or all of the original investment.

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42 It is usually measured by calculating the standard deviation of the historical returns or average returns of a specific investment. A fundamental idea in finance is the relationship between risk and return. The greater the amount of risk that an investor is willing to take on, the greater the potential return. The reason for this is that investors need to be compensated for taking on additional risk. For each fund, Morningstar offers two sets of data to help investors get a sense of the risk of owning a particular fund Volatility Measurements: •

Mean

•

Standard Deviation

•

Sharpe Ratio

•

Bear Market Decile Rank

Modern Portfolio Theory Statistics: •

R-Squared

•

Beta

•

Alpha

Mean is the mathematical average of a set of data. If, for example, a stock XYZ’s annual return in the past three years are 10%, 5% and 15%, respectively, then the arithmetic mean of the stock’s return is 10%, the average 10%, 5% and 15%. Once the mean is known, we can calculate stock XYZ’s standard deviation , which measures the dispersion of the stock’s annual returns (i.e., 10%, 5% and 15%) from the mean expected return (10%). Therefore, the further away an equity’s annual return from the mean, the higher the standard deviation. In finance, standard deviation is used to gauge an equity’s 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

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43 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 equity’s movement against a benchmark. A R-squared that equals to 100 means all the equity’s movements are inline 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, let’s 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 of the two funds are shown in the following table (obtained from Morningstar.com, trailing 3-year data through February 28, 2007):

Funds 2004 2005 2006 Mean STD R-squared Beta Alpha DODGX 19.2 9.4 18.5 13.76 7.46 86 0.98 4.16 CGMFX 12.3 25.4 15.0 19.42 20.32 19 1.26 7.45 •

Mean: The mean represents the annualized average monthly return. Therefore, a higher mean suggests a higher return the fund has delivered. In this case, CGMFX has a superior average return of 19.46.

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44 •

Standard deviation (STD): Though CGMFX has a higher average return, this fund is by no means less volatile, which is indicated by its much higher STD (20.32) than DODGX’s 7.46.

•

R-squared: If we recall that R-squared measures a fund’s movement against the benchmark and a value close to 100 means the fund follows the benchmark very closely. Also, R-squared can help investor assess the usefulness of a fund’s beta or alpha statistics. A higher R-squared means the fund’s beta is more trustworthy. In this case, CGMFX’s 19 R-squared value says that only 19% of its movements can be explained by the fluctuations of S&P 500 index, an ill-fitted benchmark for CGMFX (indeed, Morningstar points that the best fit index for CGMFX is the Goldman Sachs Natural Resources index, which will give the fund a R-squared value of 80). On the other hand, DODGX’s 86 R-squared value indicates the fund is well represented by S&P 500 and its beta value can be trusted.

•

Beta: Now we know S&P 500 is not a good benchmark for CGMFX, its beta value, though higher, is not particularly helpful in assessing the fund’s risk in comparison to the benchmark. Generally, beta measures a fund’s risk associated with the market and a low beta only means that the funds marketrelated risk is low. For DODGX, a beta value of 0.98 tells us that the fund has performed 2% worse than S&P 500 index (beta equals to 1.00) in up markets and 2% better in down markets.

•

Alpha: With a R-square value that we can trust, beta can be used to predict the fund’s expected return and alpha is the yardstick for the difference between a fund’s actual return and the predication. A large, positive alpha then means a fund has performed better than what its beta would predict. For DODGX, its alpha of 4.16 means the fund has outperformed the benchmark (S&P 500 index) by 4.16% (according to Morningstar data, DODGX has indeed outperformed S&P 500 by 4.41% in the 3-year annualized total return category).

The management

objectives of

a particular mutual fund,

to a large extent,

determine the risk and return structure of the fund's portfolio. Thus, if funds are grouped by objectives such as growth or income, it is expected that risk will be

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45 homogeneous within those mutual fund groups and adjustment for risk is then redundant. The main thrust of this research is to (1) test for homogeneity of risk within and between fund groups within and between time periods and (2) attempt to develop a new composite risk-return index for use in comparing the performance of funds with different objectives. Risk homogeneity was investigated for two risk proxies: the standard deviation of return (Sharpe) and the beta value (Treynor). One time period was taken from May 2008 to June 2008.

Mutual

fund

objectives

were derived

from

Wiesenberger's publication and funds were classified into four groups: growth, growth-income, income and Gold funds. The results obtained from testing the standard

deviations

of return

are: a) the hypothesis

of

a

within-group

homogeneous standard deviation of return is rejected during the time period, b) standard deviations of return are different between subgroups; c) standard deviations within fund groups have grown more similar over time; d) significant positive rank correlation over time occurs when fund

types were mixed;

however, the within group correlation between the ranks of the individual funds is not significant over time. The following results are obtained by testing the homogeneity of beta values: a) the beta coefficients of mutual fund subgroups do not significantly differ from the average beta coefficient. Income funds during one of the two time periods investigated for this fund type provide the only definite exception to this conclusion; b) all mutual fund groups combined display only one average beta value during the time period; during the period the growth and growth-income funds do not significantly differ from their average beta value. Thus, two distinct mutual fund groups can be formed (income funds are not investigated for that particular period); during the time period four distinct fund groups evolve; c) a majority of the beta values for individual funds are stable over time, whereas stability of average beta values of fund groups over time is not found. The interpretation of the empirical tests of the risk proxies lend credence to the basic hypothesis that (1) homogeneous risk groups of funds with similar objectives do exist, and (2) it is preferable to assume that risk as measured by

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46 the above proxies is generally found to be heterogeneous for fund types with different objectives. The generally made assumption of stability of risk over time for portfolios (average beta values) is maintained. These findings lead to a new ranking procedure computing each fund's performance by its average arithmetic return above the risk free rate divided by the weighted average beta coefficient of the fund's particular risk group. Correlation of this new ranking procedure with the performance

measures of Sharpe and Treynor is shown to be

significant; Jensen's measure of performance is not significantly correlated with this new ranking procedure. BETA, Risk and Mutual Funds Every investment involves risk, and it's important to determine how much risk is appropriate for any fund that you are considering. Risk means making less than your planned return or even losing capital Although not exactly ideal, the standard deviation (dispersion around the mean return) is generally accepted as a measure of risk. Unlike the standard deviation, Beta measures the volatility of a fund relative to a benchmark index. Funds of the same type can have significantly different levels of risks. Fund-rating services such as Morningstar and Value Line rank risk in terms of Beta, a measurement of how volatile a fund is in comparison to a benchmark market indicator, such as the Standard & Poor's 500-stock index. A fund with a Beta of higher than 1.0 (1.0 = the benchmark index) would be expected to outperform the market, while one below that figure would likely underperform. But a Beta of greater than 1.0 also means the fund is volatile. In bear markets, the value of these funds may fall much more than the major market indexes. Beta, a component of Modern Portfolio Theory statistics, is a measure of a fund's 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

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47 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 (*) 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

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48 Risk is composed of systematic (market risk) and unsystematic risk (companyspecific). 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: 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

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49 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-µx)(Y-µY)] 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: fl = 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 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 it’s 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)

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50 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: 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

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51 epsilon. Alpha represents on average, the portion of a security’s 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 - 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 T–bill, 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.

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52 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 you’ll have to phone the Company for the data. Expect some pain, as customer service people don’t 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, it’s best to phone the fund Company or use www.globefund.com or equivalent web-site. Filtering on Beta is not provided so you’ll have to do some trial and error to find the fund that fits the Beta that’s 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 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

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53 From Date To Date Date

1-Jan-08 30-Jun-08 NAV (Rs.)

2-May-08 3-May-08 4-May-08 5-May-08 6-May-08 7-May-08 8-May-08 9-May-08 13-May-08 14-May-08 15-May-08 16-May-08 20-May-08 21-May-08 22-May-08 23-May-08 23-May-08 26-May-08 27-May-08 28-May-08 29-May-08 30-May-08 2-Jun-08 3-Jun-08 4-Jun-08 5-Jun-08 6-Jun-08 9-Jun-08 10-Jun-08 11-Jun-08 12-Jun-08 13-Jun-08 16-Jun-08 17-Jun-08 18-Jun-08 19-Jun-08 20-Jun-08 24-Jun-08 25-Jun-08 26-Jun-08 27-Jun-08 30-Jun-08

13.3289 13.423 13.548 13.8429 13.8429 14.0419 14.3253 14.2723 14.2251 14.3339 14.5322 15.0879 15.4151 15.789 15.8423 15.6457 15.6457 15.3331 15.1992 15.0135 14.9386 14.8216 14.797 14.8768 14.6713 14.5863 14.791 14.7667 14.4802 14.1372 13.6954 13.7225 13.9544 14.049 14.0671 14.2497 14.1242 14.014 13.8426 14.1363 14.7264 15.014 average sdt. Dev.

Daily Return in % 0.706 0.931 2.177 0.000 1.438 2.018 -0.370 -0.331 0.765 1.383 3.824 2.169 2.426 0.338 -1.241 0.000 -1.998 -0.873 -1.222 -0.499 -0.783 -0.166 0.539 -1.381 -0.579 1.403 -0.164 -1.940 -2.369 -3.125 0.198 1.690 0.678 0.129 1.298 -0.881 -0.780 -1.223 2.122 4.174 1.953 0.303 1.601

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

54

DSP ML World Gold Fund 16.5 16 15.5

NAV

15 14.5 14 13.5 13 12.5

8

8

/2 00 6/ 29

6/ 22

/2 00

8 /2 00 6/ 15

20 08 6/ 8/

20 08 6/ 1/

8 5/ 25

/2 00

8 /2 00 5/ 18

/2 00 5/ 11

5/ 4/

20 08

8

12

NAVs from May to June 2008

NAVs Scheme Name From Date To Date Date 2-May-08 3-May-08 4-May-08 5-May-08 6-May-08 7-May-08 8-May-08 9-May-08 12-May-08 13-May-08 14-May-08 15-May-08 16-May-08 20-May-08 21-May-08 22-May-08 23-May-08 26-May-08 27-May-08 28-May-08 29-May-08 30-May-08 2-Jun-08 3-Jun-08

Top 100 Equity Fund - Reg 1-May-08 30-Jun-08 NAV (Rs.) 78.417 78.234 78.017 77.916 77.406 77.253 76.431 75.239 75.623 75.025 75.753 76.846 77.43 76.94 76.809 75.82 75.129 74.207 74.059 74.97 74.539 75.111 73.755 73.189

Daily Return in % -0.233 -0.277 -0.129 -0.655 -0.198 -1.064 -1.560 0.510 -0.791 0.970 1.443 0.760 -0.633 -0.170 -1.288 -0.911 -1.227 -0.199 1.230 -0.575 0.767 -1.805 -0.767

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

55 4-Jun-08 5-Jun-08 6-Jun-08 9-Jun-08 10-Jun-08 11-Jun-08 12-Jun-08 13-Jun-08 16-Jun-08 17-Jun-08 18-Jun-08 19-Jun-08 20-Jun-08 23-Jun-08 24-Jun-08 25-Jun-08 26-Jun-08 27-Jun-08 30-Jun-08

71.466 72.616 71.867 70.109 69.63 70.369 70.608 70.474 71.174 72.131 71.241 70.287 68.321 67.236 66.059 66.433 66.953 64.897 63.86

-2.354 1.609 -1.031 -2.446 -0.683 1.061 0.340 -0.190 0.993 1.345 -1.234 -1.339 -2.797 -1.588 -1.751 0.566 0.783 -3.071 -1.598 -0.481 1.200

average std. dev.

DSP ML Top 100 Equity - Reg 90 80 70

NAV(Rs)

60 50 40 30 20 10

6/ 27 /2 00 8

6/ 20 /2 00 8

6/ 13 /2 00 8

6/ 6/ 20 08

5/ 30 /2 00 8

5/ 23 /2 00 8

5/ 16 /2 00 8

5/ 9/ 20 08

5/ 2/ 20 08

0

Daily NAVs for May and June 08

NAVs Scheme Name From Date To Date Date

Govt Sec. Fund - Plan A 1-Jan-08 30-Jun-08 NAV (Rs.)

Daily Return in %

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

56 2-May-08 3-May-08 4-May-08 5-May-08 6-May-08 7-May-08 8-May-08 9-May-08 12-May-08 13-May-08 14-May-08 15-May-08 16-May-08 20-May-08 21-May-08 22-May-08 23-May-08 26-May-08 27-May-08 28-May-08 29-May-08 30-May-08 2-Jun-08 3-Jun-08 4-Jun-08 5-Jun-08 6-Jun-08 9-Jun-08 10-Jun-08 11-Jun-08 12-Jun-08 13-Jun-08 16-Jun-08 17-Jun-08 18-Jun-08 19-Jun-08 20-Jun-08 23-Jun-08 24-Jun-08 25-Jun-08 26-Jun-08 27-Jun-08 30-Jun-08

25.1042 25.1125 25.154 25.169 25.1383 25.0788 25.0639 25.0531 25.1763 25.1531 25.1492 25.0999 25.0372 25.032 24.9777 24.9473 24.8951 24.9211 24.8163 24.8802 24.8476 24.8522 24.8719 24.8471 24.8288 24.8225 24.8165 24.823 24.8178 24.8443 24.7969 24.7334 24.7732 24.8141 24.788 24.7054 24.5819 24.5876 24.6389 24.5647 24.5668 24.5688 24.5795 average std. dev.

0.033 0.165 0.060 -0.122 -0.237 -0.059 -0.043 0.492 -0.092 -0.016 -0.196 -0.250 -0.021 -0.217 -0.122 -0.209 0.104 -0.421 0.257 -0.131 0.019 0.079 -0.100 -0.074 -0.025 -0.024 0.026 -0.021 0.107 -0.191 -0.256 0.161 0.165 -0.105 -0.333 -0.500 0.023 0.209 -0.301 0.009 0.008 0.044 -0.050 0.187

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

57

DSP ML Govt Sec. Fund - Plan A 25.3 25.2 25.1 25

NAV

24.9 24.8 24.7 24.6 24.5 24.4 24.3

6/ 27 /2 00 8

6/ 20 /2 00 8

6/ 13 /2 00 8

6/ 6/ 20 08

08 5/ 30 /2 0

5/ 23 /2 00 8

5/ 16 /2 00 8

5/ 9/ 20 08

5/ 2/ 20 08

24.2

NAVs form May to June 2008

NAVs Scheme Name From Date To Date

Date

Tax Saver Fund 1-Jan-08 30-Jun-08

NAV (Rs.) 2-May-08 5-May-08 6-May-08 7-May-08 8-May-08 9-May-08 12-May-08 13-May-08 14-May-08 15-May-08 16-May-08 20-May-08 21-May-08 22-May-08 23-May-08 26-May-08 27-May-08 28-May-08

14.06 14.109 13.95 13.987 13.891 13.577 13.56 13.508 13.637 13.889 14.059 13.988 14.106 13.947 13.738 13.454 13.369 13.49

Daily Return in % 0.349 -1.127 0.265 -0.686 -2.260 -0.125 -0.383 0.955 1.848 1.224 -0.505 0.844 -1.127 -1.499 -2.067 -0.632 0.905

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

58 29-May-08 30-May-08 2-Jun-08 3-Jun-08 4-Jun-08 5-Jun-08 6-Jun-08 9-Jun-08 10-Jun-08 11-Jun-08 12-Jun-08 13-Jun-08 16-Jun-08 17-Jun-08 18-Jun-08 19-Jun-08 20-Jun-08 23-Jun-08 24-Jun-08 25-Jun-08 26-Jun-08 27-Jun-08 30-Jun-08

13.39 13.428 13.172 13.048 12.705 12.872 12.747 12.404 12.337 12.451 12.547 12.581 12.649 12.834 12.705 12.494 12.109 11.803 11.612 11.774 11.824 11.506 11.302 average std. dev.

-0.741 0.284 -1.906 -0.941 -2.629 1.314 -0.971 -2.691 -0.540 0.924 0.771 0.271 0.540 1.463 -1.005 -1.661 -3.081 -2.527 -1.618 1.395 0.425 -2.689 -1.773 -0.535 1.364

DSP ML Tax Saver Fund 16 14 12 NAV

10 8 6 4 2

8 00 6/

27 /2

00 20 /2 6/

6/

13 /2

00

8

8

08 6/

6/ 20

8 5/

30 /2

00

8 00 23 /2 5/

5/

16 /2

00

8

08 9/ 20 5/

5/

2/ 20

08

0

NAVs from May to June 2008

DSP Merrill Lynch World Gold Fund – Growth Fund Facts Objective

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

59 The primary investment objective of the Scheme is to seek capital appreciation by investing predominantly in units of MLIIF - WGF. The Scheme may, at the discretion of the Investment Manager, also invest in the units of other similar overseas mutual fund schemes, which may constitute a significant part of its corpus. The Scheme may also invest a certain portion of its corpus in money market securities and/or units of money market/liquid schemes of DSP Merrill Lynch Mutual Fund, in order to meet liquidity requirements from time to time. However, there is no assurance that the investment objective of the Scheme will be realized. Type of Scheme

Open Ended

Nature

Fund of Funds

Option

Growth

Inception Date

Aug 23, 2007

Face

Value

(Rs/Unit) Fund Rs. Cr.

Size

in

10 1906.1 as on Jul 31, 2008

Dhawal Fund Manager

Dalal,

Aniruddha Naha .

SIP STP SWP Expense ratio(%)

0.74

Portfolio Turnover

NA

Ratio(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

60 Last

Divdend

Declared Minimum

5000

Investment (Rs) Purchase

Daily

Redemptions NAV Calculation Entry Load

NA

Daily Amount Bet. 0 to 49999999 then Entry load is 2.25%. and Amount greater than 50000000 then Entry load is 0%. If redeemed bet. 0 Months to 6 Months; Exit load is 1%. If

Exit Load

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

-20.64

-22.11

-19.81

NA

NA

NA

Mean

0.303

Treynor

NA

Standard

1.601

Sortino

NA

Correlation

NA

Fama

NA

Deviation Sharpe

NA

Beta

NA

12.92

DSP MERRILL LYNCH WORLD GOLD FUND - GROWTH

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

61 Fund Size as on

Jul 31, 2008

Fund Size ( Rs. in 1906.1 crores) Asset Allocation as Jul 31, 2008 on Equity

0%

Debt

1.42%

Others

98.58%

TOP 10 HOLDING AS ON JUN 30, 2008 DEBT Company Name

Instrument Rating Market Percentage Value (Rs.

of

Net

in Assets

crores) DSP Merril Lynch Liquid Plus MFDebt

26.79

1.3

Fund - Institutional Plan Growth OTHERS Company Name

Instrument

Market Value

%

of

(Rs. Net

in crores)

Assets

GOLD - BULLION

Gold

2,041.09

98.96

CBLO

Money Market

13.00

0.63

Net Receivables/(Payable)

Net

-18.43

-0.89

Receivables/(Payables)

DSP Merrill Lynch World Gold Fund – Growth

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

62 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.

Type of Scheme

Open Ended

Fund Manager

Nature

Equity

SIP

Option

Growth

STP

Inception Date

Feb 21, 2003

SWP

Face

Value

(Rs/Unit) Fund Rs. Cr.

Size

in

10 957.56 as on Jul 31, 2008

Expense ratio(%)

Apoorva Shah .

2.13

Portfolio Turnover

389.4

Ratio(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

63 Last

Divdend

Declared Minimum

NA 5000

Investment (Rs) Purchase

Daily

Redemptions 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 Load

If 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

Mean

1.07

Treynor

1.09

Standard

3.30

Sortino

0.47

Correlation

0.88

Fama

0.22

Deviation Sharpe

0.29

Beta

0.88

43.21

Portfolio Attribites

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

64 23.56 as on Jun -

P/E

2008 7.40 as on Jun -

P/B

2008

Dividend Yield

1.23 as on Jun 2008

Market Cap (Rs. 65,481.03 as on in crores)

Jun - 2008

Large

73.01 as on Jun 2008

Mid

NA

Small

NA

Top 5 Holding 29.10 as on Jun (%)

2008

No. of Stocks

43

Expense

Ratio 2.13

(%)

Top 10 Holding

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

65 Percentage

Percentage Stock

Sector

P/E

of

Net Qty

Value

Assets Nifty Miscellaneous NA Bharti Airtel Ltd Telecom 22.05 Engineering & Larsen & Toubro Industrial 33.12 Limited Machinery Oil & Gas, Reliance Petroleum & 21.09 Industries Ltd Refinery Hindustan Lever Diversified 27.50

of

Change

with month

11.49 5.41

NA 630,234

96.46 45.46

-21.14 61.73

4.63

178,122

38.91

-30.35

3.90

156,303

32.75

-34.93

3.66

1,484,330 30.75

16.31

340,818

29.25

16.03

25.40 3.30

140,836

27.67

-35.20

3.28

168,869

27.53

5.85

& 19.48 3.19

154,050

26.76

-39.05

Pharmaceuticals Pharmaceuticals 45.15 2.68

353,210

22.49

-3.42

Ltd Tata

Computers

Consultancy

Software

Services Ltd. Housing

Education Finance

- 19.08 3.48 &

Development Finance Corporation Ltd Nestle India Ltd Infosys Technologies Ltd

Food & Dairy Products Computers Software

31.35

-

Education

Glenmark Ltd.

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

last

66 Auto

&

Auto

ancilliaries

1.52

Banks

8.93

Cement

1.10

Computers - Software & Education

4.98

Current Assets

8.78

Diversified

2.97

Electricals

&

Electrical Equipments Electronics

4.41 2.15

Engineering

&

Industrial Machinery

2.61

Entertainment

2.16

Finance

7.00

Food

&

Dairy

Products Housing

&

Construction

2.41 2.47

Metals

0.51

Miscellaneous

16.32

Oil & Gas, Petroleum & Refinery

9.86

Paints

0.62

Pharmaceuticals

9.60

Power

Generation,

Transmission & Equip

3.67

Steel

0.11

Telecom

6.44

Textiles

1.36

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

67 DSP Lynch Equity

Merrill Top

100

Fund

Growth BSE100 BSE Sensex DSP Merrill Lynch Government Sector Fund – Growth Fund facts Objective The primary investment objective of the Scheme is to seek to generate medium to long-term capital appreciation from a diversified portfolio that is substantially constituted of equity and equity related securities of corporates, and to enable investors to avail of a deduction from total income, as permitted under the Income Tax Act, 1961 from time to time.

Type of Scheme

Open Ended

Nature

Equity

Fund Manager

Option

Growth

SIP

Inception Date

Dec 21, 2006

STP

Face

Value

(Rs/Unit) Fund Rs. Cr.

Size

in

10 467.31 as on Jul 31, 2008

Anup Maheshwari .

SWP Expense ratio(%)

2.36

Portfolio Turnover

243.78

Ratio(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

-

68 Last

Divdend

Declared Minimum Investment (Rs) Purchase Redemptions NAV Calculation Entry Load Exit Load

NA 500 Daily Daily Amount Bet. 0 to 49999999 then Entry load is 2.25%. and Amount greater than 50000000 then Entry load is 0%. Exit Load is 0%.

Risk & return SCHEME PERFORMANCE (%) AS ON AUG 8, 2008 1 Month 3 Months 6 Months 1 Year

3 Years

5 Years

Since Inception

10.43

-11.45

-17.43

-1.02

NA

NA

Mean

NA

Treynor

NA

Standard

NA

Sortino

NA

Correlation

NA

Fama

NA

Deviation Sharpe

NA

Beta

NA

14.23

Portfolio

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

69 Portfolio attributes Style Box

25.18 as on Jun -

P/E

2008 4.54 as on Jun -

P/B

2008

Dividend Yield

0.87 as on Jun 2008

Market Cap (Rs. 25,677.66 as on in crores)

Jun - 2008 43.21 as on Jun -

Large

2008 28.82 as on Jun -

Mid

2008 9.40 as on Jun -

Small

2008

Top 5 Holding 15.51 as on Jun (%)

2008

No. of Stocks

87

Expense

Ratio 2.36

(%)

DSP

Merrill

Lynch

G

Fund

Plan

Sec

Long Duration - (G)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

A

70

CNX500 BSE

Sensex

DSP Merrill Lynch Tax Saver Fund – Growth Fund facts Objectives The primary investment objective of the Scheme is to seek to generate medium to long-term capital appreciation from a diversified portfolio that is substantially constituted of equity and equity related securities of corporates, and to enable investors to avail of a deduction from total income, as permitted under the Income Tax Act, 1961 from time to time.

Type of Scheme

Open Ended

Nature

Equity

Fund Manager

Option

Growth

SIP

Inception Date

Dec 21, 2006

STP

Face

Value

(Rs/Unit) Fund Size in Rs. Cr.

10 467.31 as on Jul 31, 2008

Anup Maheshwari .

SWP Expense ratio(%)

2.36

Portfolio Turnover

243.78

Ratio(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

71 Last

Divdend

Declared Minimum

500

Investment (Rs) Purchase

Daily

Redemptions NAV Calculation Entry Load Exit Load

NA

Daily Amount Bet. 0 to 49999999 then Entry load is 2.25%. and Amount greater than 50000000 then Entry load is 0%. Exit Load is 0%.

Risk & Return

SCHEME PERFORMANCE (%) AS ON AUG 8, 2008 1 Month 3 Months 6 Months 1 Year

3 Years

5 Years

Since Inception

10.43

-11.45

-17.43

-1.02

NA

NA

Mean

NA

Treynor

NA

Standard

NA

Sortino

NA

Correlation

NA

Fama

NA

Deviation Sharpe

NA

Beta

NA

14.23

Portfolio Portfolio attributes

style box

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

72 25.18 as on Jun -

P/E

2008 4.54 as on Jun -

P/B

2008

Dividend Yield

0.87 as on Jun 2008

Market Cap (Rs. 25,677.66 as on in crores)

Jun - 2008 43.21 as on Jun -

Large

2008 28.82 as on Jun -

Mid

2008 9.40 as on Jun -

Small

2008

Top 5 Holding 15.51 as on Jun (%)

2008

No. of Stocks

87

Expense

Ratio 2.36

(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

73

Top 10 holdings Percentage

Percentage Stock

Sector

P/E

of

Net Qty

Value

Assets Dr

of

Change

with month

Reddys

Laboratories

Pharmaceuticals 17.33 4.02

262,399 17.58

-6.19

49.23 3.69

363,756 16.11

-33.28

& 29.01 2.69

144,654 11.75

-12.81

Pharmaceuticals 25.33 2.58

84,408

11.28

11.31

Finance

56,393

11.08

23.99

Ltd Reliance Communication Telecom Ventures Ltd. Allied

Digital

Services Ltd

Computers Software

-

Education

Divis Laboratories Limited Housing Development Finance

Corporation Ltd Tata Computers Consultancy

Software

Services Ltd.

Education Oil &

Oil & Natural Gas Corpn Ltd Hero

Petroleum

Refinery Honda Auto &

25.40 2.54

& 19.08 2.41

122,484 10.51

3.73

114,541 9.34

-5.75

114,317 7.91

98.83

340,635 7.57

-21.63

396,081 7.44

-32.25

Gas, & 11.77 2.14 Auto

15.29 1.81 Motors Ltd ancilliaries ING Vysya Bank Banks 14.87 1.73 Ltd Tobacco & Pan ITC Ltd 22.95 1.70 Masala

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

last

74

Sector allocation(%)

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

75 Auto

&

Auto

ancilliaries Banks

2.88 6.12

Breweries

&

Distilleries

0.19

Cement

1.79

Chemicals

0.79

Computers

-

Software

& 7.85

Education Consumer Durables

3.15

Current Assets

6.22

Diversified

1.87

Electricals

&

Electrical

1.64

Equipments Engineering

&

Industrial Machinery Entertainment

4.56

Fertilizers, Pesticides & Agrochemicals Finance Food

2.70

1.62 7.31

&

Dairy

Products Hotels & Resorts

2.32 0.30

Housing

&

Construction

4.92

Metals

2.54

Mining & Minerals

0.38

Miscellaneous

0.09

Oil

&

Petroleum Refinery

Gas, & 6.74

NIILM- CENTRE FOR MANAGEMENT STUDIES

B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

76

Asset allocaton

Equity Debt 93.78

0.00

Cash

&

Equivalent 6.22

DSP

Merrill

Lynch

Tax

Saver Fund Growth CNX500 BSE Sensex

NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

77

CHAPTER 4 RESEARCH ANALYSIS AND CONCLUSION NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

78

DSPML World Gold Fund invests in stocks of companies engaged in gold mining & production. The fund's assets have more than doubled in a span of 4 months, all thanks to the returns it earns... DSPML Gold Fund's returns have given investors reasons to cheer. The previous year gave investors of the DSPML World Gold fund many reasons to smile. The fund, which listed in September last year, has delivered 42 per cent returns since its launch. This year (till February 1, 2008), the fund, which is part of the Equity Specialty category has delivered around 8 per cent returns compared to the category's 11 per cent loss during the same period. The Sensex and Nifty were down 10 per cent and 13.4 per cent respectively during this period. In the December 2007 quarter, the fund's returns at 16 per cent were much ahead of the benchmark FTSE loss of 0.15 per cent. However this is less than the Sensex's gain of 17 per cent in that quarter. The DSP World Gold fund does not buy gold directly but invests in stocks of companies engaged in gold mining and production world over. It does so by buying units of Merrill Lynch International Investment Funds-World Gold Fund (MLIIFWGF). In fact MLIIF -WGF forms over 97 per cent of the fund's portfolio. The fund's good returns can be attributed more to the fact that the gold prices have peaked to a 30-year high, resulting in a bonanza for the companies in this field. A weakening US dollar and an unprecedented rise in oil prices have also made gold an attractive

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79 investment avenue. However, investors looking to invest in gold must not confuse this fund with gold exchange traded funds (ETFs), which invest directly in gold. Another difference between DSPML Gold Fund and other ETFs is that the former is managed actively. According to the DSPML website, DSPML World Gold Fund has invested over 80 per cent in gold followed by platinum (9 per cent) and silver (5.10 per cent). As per the December 2007 portfolio, Australia based Newcrest Mining is the top holding of the fund accounting for 8.4 per cent of the fund's assets, followed by Barrick Gold (7.50 per cent), Kinross Gold (5.50 per cent) and Lihir Gold (5.20 per cent). So far, many investors have flocked to this fund. The fund's assets under management, which stood at Rs 692 crore in September 2007, have more than doubled to over Rs 1487 crore in December 2007.

This fund has done well in its short life, but will DSPML Taxsaver suffer from an over-diversified portfolio? Despite the fund manager's explanation, we believe that this fund is unreasonably spread out. No doubt, DSP Merrill Lynch is known for its extremely diversified portfolios, but this one is very bloated with 86 stocks (it was 97 in March). Being a small fund, one should not be surprised that 51 of the stocks have an allocation of less than 1 per cent and an additional 26 stocks, less than 2 per cent. The top 10 holdings corner a modest 30.1 per cent of the portfolio. While this may be interpreted as a lack of conviction which could hinder performance, one can't really argue with the numbers. While it outperformed the category average in the first two quarters of its existence, its performance in the December quarter (2007) was impressive. The fund's success stemmed from its exposure to surging mid- and small-caps. The size of the fund favoured a mid- and small-cap tilt and the fund manager capitalised on that wave. The BSE Mid Cap delivered almost 32 per cent that quarter and BSE Small Cap 46.69 per cent, against 17.33 per cent of the Sensex. But it was this very exposure that proved to be the chink in the fund's armour. In the very next quarter, mid- and small-caps were massacred. Naturally, the fund followed suit. It delivered 47.23 per cent in its best quarter (October 8, 2007 - January 7, 2008) and delivered its worst immediately after with -37.14 per cent (January 4, 2008 - April

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80 4, 2008). Subsequently, the fund's large cap exposure rose from 23.27 per cent in December to 44 per cent by April. What the fund manager seeks for this fund is simple: to go about delivering returns in a consistent manner with no market-cap or sector bias. In some cases, he holds on to his stocks. In others, he exits the moment he has achieved his price objective and will probably re-enter at a later date. So stocks like Yes Bank, Development Credit Bank and Welspun-Gujarat Stahl Rohren Ltd that made money for him were in his portfolio for 12 months or less. And while he probably did make money in Educomp Solution and BF Utilities, he would have made more money had he stayed on for a few more months. But then you can't really hold it against him since fund management is all about taking a view. Though the fund was off to a good start with notable quarterly numbers, it is still very young. However, going by the pedigree of the fund house and the manager at the helm, this one could turn out to be a strong contender in the tax-saving category. The data given below consists of daily NAVs of benchmark index S&P CNX 500 for two months. Opening value. closing, high and low values during the day is given. The daily returns in percentage form is calculated and tabulated. Also the calculated daily returns of two DSP ML schemes, who follow this benchmark index is also tabulated. The analysis is explained below the table:

Date

Open

2-May-08 5-May-08 6-May-08 7-May-08 8-May-08 9-May-08 12-May08 13-May08 14-May08 15-May08 16-May08 20-May-

High

Low

Close

daily returns in % S & P CNX500 govt.sec. A tax saver

4290.95 4298.3 4260 4217.9 4151.9 4131.2

4291.2 4315.95 4271.55 4227.3 4164.55 4161.45

4258.6 4260.1 4189.9 4176.4 4137.15 4052.65

4280.4 4267.85 4217.5 4198.6 4152.3 4064.1

-0.29 -1.18 -0.45 -1.10 -2.12

0.17 0.06 -0.12 -0.24 -0.06

0.35 -1.13 0.27 -0.69 -2.26

4038.95

4074.4

3992.9

4066.3

0.05

-0.04

-0.13

4101.4

4126.4

4032.25

4041.5

-0.61

0.49

-0.38

4022.1

4087.5

4022.1

4081.3

0.98

-0.09

0.95

4103.85

4161.15

4103.85

4159.3

1.91

-0.02

1.85

4190.9 4170.5

4204.9 4189.2

4159.65 4133

4198.1 4158.9

0.93 -0.93

-0.2 -0.25

1.22 -0.51

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81 08 21-May08 22-May08 23-May08 26-May08 27-May08 28-May08 29-May08 30-May08 2-Jun-08 3-Jun-08 4-Jun-08 5-Jun-08 6-Jun-08 9-Jun-08 10-Jun08 11-Jun-08 12-Jun08 13-Jun08 16-Jun08 17-Jun08 18-Jun08 19-Jun08 20-Jun08 23-Jun08 24-Jun08 25-Jun08 26-Jun08 27-Jun08 30-Jun08

4119.6

4186

4119.6

4174.6

0.38

-0.02

0.84

4166.55

4166.55

4090.85

4099.85

-1.79

-0.22

-1.13

4122.9

4134.25

4034.2

4037.35

-1.52

-0.12

-1.50

3987.45

3998.25

3953.2

3962.5

-1.85

-0.21

-2.07

4004.55

4007.75

3935.65

3944.2

-0.46

0.1

-0.63

3959.2

3996.95

3926.5

3991.5

1.20

-0.42

0.91

4021

4021.5

3917.25

3939.4

-1.31

0.26

-0.74

3973.25 3987.85 3810.4 3837.5 3724.75 3816.75 3661.7

3992.5 3987.85 3837.8 3849.8 3782.15 3816.75 3661.7

3927.75 3833.8 3761.65 3709.1 3669.2 3721.8 3542.65

3959.65 3853.55 3831.65 3719.4 3774.6 3730.5 3611.8

0.51 -2.68 -0.57 -2.93 1.48 -1.17 -3.18

-0.13 0.02 0.08 -0.1 -0.07 -0.03 -0.02

0.28 -1.91 -0.94 -2.63 1.31 -0.97 -2.69

3588.6 3587.65

3622.3 3643.45

3516.85 3587.65

3570.6 3632.35

-1.14 1.73

0.03 -0.02

-0.54 0.92

3533.3

3651.15

3532.1

3645.6

0.36

0.11

0.77

3659.3

3659.3

3616

3634.8

-0.30

-0.19

0.27

3677.8

3716.15

3669.5

3679.75

1.24

-0.26

0.54

3680.85

3761.25

3671.95

3753.55

2.01

0.16

1.46

3770.5

3781.15

3692.55

3701.2

-1.39

0.17

-1.01

3648.45

3668.6

3625.8

3633.45

-1.83

-0.11

-1.66

3644.25

3652.45

3498.75

3510.95

-3.37

-0.33

-3.08

3471.8

3486.45

3393.7

3416.6

-2.69

-0.5

-2.53

3423.15

3446

3337.8

3359.05

-1.68

0.02

-1.62

3306

3406.55

3292.65

3401.05

1.25

0.21

1.40

3429.95

3441.55

3390.4

3431

0.88

-0.3

0.42

3374.2

3374.2

3284.2

3293.65

-4.00

0.01

-2.69

3296.85

3310.5

3191.45

3203.35

-2.74

0.01

-1.77

0.024

2.046

0.191

1.364

co-variance std.dev

1.595

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82 variance correlation

2.543

0.036 0.079

beta

0.009

0.825

The beta value of government sector fund is almost zero. This means the fund performs as the benchmark, whereas the second fund of taxplan, the beta value is 0.825, which means the fund perform better than the benchmark index. Results from the modeling are presented in a step-wise manner, starting with the knowledge construct and ending with the dependent variable (RETURN). The central construct in this study is knowledge, and the questionnaire included several different indicators of self-assessed knowledge. The appropriate method to analyze such data is to regard the multiple measures as fallible indicators of a theoretical construct, and to test whether the implications of such a model are empirically justifiable. The presentation of alternative models for relations among the constructs is done in the following sequence: 1. Alternative relations between knowledge and involvement 2.

Alternative relations between knowledge, involvement, risk willingness and

return. When evaluating an investment (mutual fund in particular), there are many obvious factors we should consider: returns, risks, expenses, and turn-over ratio, etc. Among them, the risk factor, when used properly, can help us gauge what we can expect from the investment, though past performance does not necessarily indicate future results.

There are important systematic relationships between stock returns and economic variables that may not be captured adequately by a simple Beta measure of risk. Beta changes over time and its estimation can be “noisy” as a result. Beta is therefore not a perfect measure of market risk but its relative ease of calculation, availability of information and link to a theory make it one of the important tools in our tool bag. It has been soundly critiqued and it’s death announced, perhaps prematurely. Undoubtedly there will yet be many improvements in the techniques of risk analysis,

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1.860 0.965

83 and quantitative analysis of risk measurement. Future risk measures will likely be more sophisticated and complex. The key message for investors is to factor Beta into your investment considerations and pay as little as possible for the desired Beta (i.e. ETF’s or index funds). Pay higher MER’s only if you think you have found a manager(s) that can provide future persistent positive Jenson Alpha -a measure of risk- adjusted return. Jensen’s Alpha represents the ability of the fund manager to achieve a result that is above what could be expected given the risk in the fund. If the realized return is larger than that predicted by the overall portfolio Beta, the manager has added value. Jensen Alpha=the return of the fund above the risk-free rate less the return of the fund above the risk free rate that the Beta risk factor predicts=RF-T-bill rate-fl (RF-T-bill rate). The bigger the Beta the better. If markets were completely efficient, the Alpha value would always be zero since all available information and analysis would already be priced into the shares. It would only be possible to achieve a return according to the risk taken. However, most mutual fund managers claim that this is not the case and that selecting special under-priced shares can achieve returns in their funds above what is expected from the risk taken. When looking at Alpha values, it is important to use a long time frame. To get a realistic view, a minimum of one year is needed, while three years is preferable. There probably will never be a ultimate risk measure. In the meantime, Beta is at the least a useful tool, combined with others, in assessing an investment in a mutual fund for your portfolio.

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84

BIBLIOGRAPHY

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85

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NIILM- CENTRE FOR MANAGEMENT STUDIES B-II/66, M.C.I.E., Sher Shah Suri Marg, New Delhi- 110044. Tel: (011) 29894514.

86 9. Basu, S. (1983). The relation between earnings yield, market value and return for NYSE common stocks: Further evidence, Journal of Financial Economics 12, 129-156. 10. Bello, Z.Y., and Janjigian, V. (1997). A reexamination of the market timing and security selection performance of mutual funds, Financial Analysts Journal 53, 24-30. 11. THE JOURNAL OF FINANCE

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VOL. XXX, NO. 4

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SEPTEMBER

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SUBGROUPS* ELMAR

BERNHARD

KOCH Winthrop

College 12. Int. Rev. of Retail, Distribution and Consumer Research Vol. 15, No. 4, 449 – 469, October 2005 Success in Complex Decision Contexts: The Impact of Consumer Knowledge, Involvement, and Risk Willingness on Return on Investments in Mutual Funds and Stocks RITA MARTENSON Marketing Department, University of Goteborg, Sweden 13. Aktiefra¨mjandet (2003) Aktiea¨gandet i Sverige. Stockholm: Aktiefra¨mjandet, January, p. 4. 14. Alba, J. W. (2000) Presidential address: ‘Dimensions of consumer expertise . . . or lackthereof ’, Advances in Consumer Research, 27, pp. 1–2. 15. Alba, J. W. and Hutchinson, J. W. (1987) Dimensions of consumer expertise, Journal of Consumer Research, 13(4), pp. 411–454. 16.Aldridge, A. (1998) Habitus and cultural capital in the field of personal finance, The Sociological Review, 46(1), pp. 1–23. 17. Anderson, J. and Gerbing, D. (1988) Structural equation modeling in practice: a review and recommended two step approach, Psychological Bulletin, 103, May, pp. 411–423. 18. Antonides, G. and Van Der Saar, N. L. (1990) Individual expectations, risk perception and preferences in relation to investment decision making, Journal of Economic Psychology, 11, pp. 227–245. Arbuckle, J. L. and Wothke, W. (1999) AMOS 4.0 User’s Guide (Chicago, IL: Smallwaters). 19. Bazerman, M. H. (2001) Consumer research for consumers, Journal of Consumer Research, 27, March, pp. 499–504

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87 20. Journal of Accounting – Business & Management 15 (2008) 90-108 Short-Term Persistence in Mutual Funds Performance: Evidence from India Sanjay Sehgal and Manoj Jhanwar 21. Berk, J., and R. Green, 2004, "Mutual Fund Flows and Performance in Rational Markets", Forthcoming, Journal of Political Economy. 22. Bollen , N. and J. Busse, 2005, "Short-Term Persistence in Mutual Fund Performance", Working Paper. 23. Brown, S., and W. Goetzmann, 1995, "Performance Persistence", Journal of Finance, 50, 679-698. [ISI] 24. Carhart, M., 1997, "On Persistence in Mutual Fund Performance", Journal of Finance, 52, 57-82. [ISI]

Websites •

www.google.com

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www.mahindrafinance.com

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www.bseindia.com

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www.nseindia.com

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www.amfiindia.com

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www.mutualfundsindia.com

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www.valueresearchonline.com

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search.ebscohost.com

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88

ANNEXURE 1. Questionnaire

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