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Chapter 3 Research Methodology

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CHAPTER 3

RESEARCH METHODOLOGY

Quantitative Techniques are the most powerful tools in the decision making

process especially concerning the business and industry. These techniques

involve the use of numbers, symbols and other mathematical expressions.

The quantitative techniques help us to supplement our judgement and

intuition.

There are various types of Statistical Analysis depending on the types of

variables to be studied. The word 'Statistics' is derived from the Latin word

'status' meaning a political state. In olden days, statistics was the scientific

method of numerical data by the state or kings. Now adays, statistics is the

scientific method of analyzing quantitative information. It includes methods of

collection, classification, description and interpretation of data. Statistics

means it refers to the data or the facts. Statistical analysis can be categorized

into two viz the Qualitative analysis and the Quantitative Analysis. The

Qualitative Analysis consists of the analysis of the categorical data. They are

the non-numeric data and cannot be measured like sex, religion, place of birth

etc. The Quantitative Analysis consists of the analysis of the numeric data

that can be measured. They are the numeric data and can be measured like

the age, members in a family, income and savings etc.

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The Research Methodology used in this research has been briefly mentioned

below before proceeding for the Quantitative Analysis.

Research Gap

As mentioned above, there have been extensive researches done on the

performance of Mutual Fund Schemes. There is lot of literature available on

the performance of the Mutual Fund Schemes. It is very significant to note

that most of the research has been done by focusing on an individual factor at

a time, whereas there are various interlinking factors affecting the

performance of the Mutual Fund schemes. Performance of the Mutual Fund

cannot be measured by isolating a single factor at a time and measuring the

performance. There is a requirement of a single performance measure by

taking all the factors at a time and giving a score by which the Fund Manager

should be able to align his scheme to increase the performance of the

scheme.

The ultimate motive of the Fund Manager is to maximize the returns of his

schemes and reduce risk and thus have a portfolio that will achieve his target.

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Related Literature Review and Earlier Study on the Topic

After an initial survey of related literature the research gap was identified. At

the initial survey, at the time of preparation of the research proposal, related

literature was collected from the public domain and published research papers

and journals. The conceptual literature on the concepts and theories on the

performance of the Mutual Fund was collected and a brief write up was

presented in the Research Proposal.

A study on a similar topic was traced on Greek Mutual Funds. This was done

by using UTADIS, by Pendaraki, Doumpos and Zopounidis. The study was

conducted only on the Greek Mutual Fund Industry and could not be applied

to the Indian Mutual Fund Industry due to various factors. The present

research is being done on the same lines on the research done on the Greek

Mutual Fund. However, there are certain other statistical tools being used in

the study. In the present research the Discriminant Analysis is used, which is

a very widely used statistical tool as compared to UTADIS used in the said

Greek Study.

After the initial literature survey and the proposal being approved an extensive

literature survey was done to trace out the related literature. The available

resources in the public domain of various published journals, research papers

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and books were explored on the research gap. After extensive exploration,

due to non-availability of the solution on the research gap, it was felt proper to

continue the research and come out with proper solution to the research gap.

The extensive literature survey has been covered in depth in the earlier

chapter 2 on related literature review.

Selecting the Present Research Problem

After the initial literature survey and the extensive literature survey, the above

topic was selected for the research. It was observed that extensive research

and emphasis was done to mark the performance of the Mutual Fund

schemes whereas limited research was done to enhance the performance of

the mutual Fund schemes. However, the Fund Managers and the Fund

houses are more inclined to enhance the performance of their Mutual Fund

schemes. By a performance measurement, the Fund Managers and the Fund

houses know at which level they stand in the race of the performance of

Mutual Fund schemes. However, it is very difficult for the Fund Managers and

the Fund houses to raise their level up the ladder and reach the prime position

or near to the prime position. Fund Managers are struggling very hard to

diversify their risk and earn more returns for their Mutual Fund schemes.

Efforts are being made to ensure that no stone is left unturned by the Fund

Managers and the Fund Houses to ensure that their schemes perform better

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than any other similar schemes. Most of the Fund Managers make the BSE

Sensex as their benchmark to mark their performance of their equity-based

schemes. Taking BSE Sensex as the benchmark, they measure the

performance of their scheme and give a comparison of how their schemes

have performed better than the market.

Taking the BSE Sensex as the benchmark and the performance of the Mutual

Fund scheme as the level indicator, the schemes were divided into two

categories viz the high performing schemes and the low performing schemes.

The high performing schemes were those which give returns higher then the

benchmark and the low performing schemes were those schemes which give

returns lower than the benchmark.

Efforts have been made by the Researcher to find out the causes for the high

performing schemes and the low performing schemes. These causes have

been identified as the factors, which are detriment for the performance of the

Mutual Fund schemes. Moving further efforts are being made on how these

low performing schemes can be moved to the high performing schemes which

is the ultimate aim of the Fund Managers and the Investors.

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Research Objectives

The present study has the following research objectives: -

� To identify the core factors that affects the performance of Open

Ended Equity Schemes.

� To suggest a predictive model, which will be able to predict, which

schemes are a potential high performer or a potential low performer.

� To help the Fund Managers to come out with vital strategies to align

their schemes and make the scheme a high performer.

� To help the Investor identify the potential high performing scheme.

Research Paradigm

The enclosed diagram gives us the conceptual mapping of the factors

affecting the performance of the Open Ended Equity schemes. The variables

that have been identified have classified into two categories viz Independent

Variables and the Dependent Variables.

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Figure 4 : Conceptual Mapping of the Factors Affecting the Performance of Mutual Fund Schemes

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Independent Variables

These are the predictor variables, which have been assumed to be the factor

having functional relationship with the dependent variables (Hair, Bush and

Ortinau).

Dependent Variables

The factor performance estimate of Open Ended Equity schemes is the

dependent variable in this present research. It is the observable outcome

that is derived from the manipulation of the independent variables (Hair, Bush

and Ortinau).

Identification of the Independent and Dependent Variables

Primary Data

In the collection of the primary data by means of the questionnaire and testing

of the Hypotheses, the following 18 variables were identified.

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Independent Variable

1. Returns of 3 Year Period

2. Mean Return

3. Standard Deviation of the Returns

4. Coefficient of variation of the returns

5. NAV percentage change in the 3 Year Period

6. Geometric mean of the Excess Return over benchmark

7. Value At Risk (VaR)

8. Sharpe Index

9. Modigliani Measure

10. Information Ratio

11. Beta Coefficient β

12. Treynor Index

13. Jensen Alpha α Coefficient

14. Treynor & Mazuy's α Coefficient

15. Treynor & Mazuy's γ Coefficient

16. Henriksson & Merton's α Coefficient

17. Henriksson & Merton's γ Coefficient

18. Treynor & Black Appraisal Ratio

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Dependent Variable

Performance

Definition of Performance Level

The Performance Level has been categorized into high performer and low

performer, based on the returns over the Benchmark. For the purpose of this

research, the BSE Sensex returns over the 3 years period have been taken

as the Benchmark.

The categorization is as under: -

High Performer

The returns of the 3 years period of all those schemes, which are higher than

the returns of the 3 years period of the Benchmark, are defined as the high

performer.

Low Performer

The returns over the 3 years period of all those schemes, which are less than

the returns of the 3 years period of the Benchmark, are defined as the Low

Performer.

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Research Framework

Primary Data

Research Problem What are the Factors that affect the

performance of Mutual Fund Schemes?

Hypothesis Null Hypothesis H0 : There is no

interrelationship between variables affecting

the performance of Mutual Fund Scheme.

Alternate Hypothesis H1 : There may be a

statistical significant interrelationship

between variables affecting the performance

of Mutual Fund Scheme.

Population Sample 8 Fund Houses out of 22 Eligible Fund

Houses covering 32 Respondents (36% of

the Universe)

Tools Factor Analysis

Methodology Semi Structured Questionnaire

Sample Size : 32 Respondents

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Secondary Data

Research Problem What are the characteristics of a scheme,

which would make it a potential high

performer or a potential low performer?

Hypothesis Null Hypothesis H0 : There may be no

significant discriminating power in the

variables. (which enables classification into

high-low performer)

Alternate Hypothesis H1 : There may be a

statistical significant discriminating power in

the variables.

Population Sample Universe

Tools Discriminant Analysis

Methodology Secondary Data collected from AMFI

Sample Size : 78 Scheme with

approximately 750 NAV observation per

scheme.

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Execution of the Project

After identifying the research gap, it was first found necessary to collect the

secondary data and to identify the schemes into high performing schemes

and low performing schemes. Accordingly after collection of the secondary

data, the schemes were grouped into high performing schemes and low

performing schemes keeping the BSE Sensex as the benchmark.

The second step taken was to identify those variables, which affect the

performance of Mutual Fund schemes thus making it into high performing

schemes or a low performing schemes. Accordingly, those variables affecting

the performance of Mutual Fund schemes were collected from earlier

Research Paper available on this topic. There were 18 variables, which were

used to measure the performance of Mutual Fund schemes. These variables

were shortlisted to collect the primary data on the Indian Mutual Fund

Industry.

A semi-structured questionnaire was prepared to collect the required primary

data for the further analysis of the secondary data. The questionnaire was

framed keeping in mind the essentials of a good questionnaire. The data

was collected by one to one interviews in the questionnaire.

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As mentioned above the project was executed in a systematic manner,

however there was some delay in completion of the project due to lack of time

in collection of the secondary and the primary data.

Research Flow Chart

A research flow chart was designed to give the flow of the research done by

the Researcher. The enclosed diagram gives us the research flow chart

used in the research.

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Figure 5 : Framework of the Research Design