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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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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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