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An Evaluation of Collective Investment Schemes in Ghana

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My final year thesis work.

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									               UNIVERSITY OF GHANA
                 BUSINESS SCHOOL




     AN EVALUATION OF COLLECTIVE
     INVESTMENT SCHEMES IN GHANA
               2005-2009




                        BY
               FAHIZ MOAHAMMED
                ASSUMING GEORGE
             OPPONG SAFO KANTANKA




    A DISSERTATION PRESENTED TO THE ACCOUNTING
  DEPARTMENT OF THE UNIVERSITY OF GHANA BUSINESS
SCHOOL IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR
 THE BARCHELOR OF SCEINCE DEGREE IN ADMINISTRATION
                (ACCOUNTING OPTION)

                   YEAR, MAY 2010
                                   DECLARATION

We hereby declare that except for references to other people’s materials which have been
duly acknowledged, this long essay submitted to the Academic Board of the University of
Ghana Business School (Accounting Department) is the result of our own research work and
has not been presented for any other program elsewhere.




                                                         FAHIZ MOHAMMED



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

                                                                   10227143

                                                                  (STUDENT)



DR. J.M.ONUMAH                                           ASSUMING GEORGE



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

SUPERVISOR                                                         10228417

                                                                  (STUDENT)



                                                       OPPONG SAFO KANTANKA



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

                                                                     10231715

                                                                    (STUDENT)




                                           i
                                        ABSTRACT


Mutual funds’ performance is one of the most frequently studied topics in investment areas in
the world. The reason for this popularity is the availability of data and importance of mutual
funds as vehicles for investment in the stock market for both individuals and institutions.
Since mutual funds have become popular, research has also started to include the ways of
finding the right mutual funds. Although the share price and the income from the funds may
go down as well as up, choosing the right mutual fund can have considerable effects on an
investor’s wealth. This thesis examines the past performance of mutual funds as a criterion
for investors' future choices. In particular, it examines mutual funds which invest in the
Ghanaian financial market.

In our study, the fund characteristics i.e. popularity, growth, cost and management variables
are included. These attributes were tested through correlation, simple and multiple regression
analysis. We do not find any strong evidence that the past performance is a guide to future
performance. Mostly data was collected from Databank, the Ghana Stock Exchange and
secondary data from some of the mutual funds annual reports .We analysed the data for the
last five years (2005-01-01 to 2009-12-31). Before and during our thesis, different but
relevant research papers and financial articles were studied. Our research study results shows
that the attributes which have some impact on mutual funds Returns are Risk, Fund size,
Fund age, Fund turnover and Management tenure. The results indicate that the hypothesized
relationship between mutual funds’ performance and the explanatory variables are generally
upheld.




                                              ii
                              ACKNOWLEDGEMENT
We would first like to give thanks to the almighty God express our heartfelt thanks to all the

people involved in this research thesis. Special thanks are due to our thesis supervisor, Dr.

Joseph Mensah Onumah, for his support, enthusiasm, advice and encouragement. We would

also like to thank Frederick Duvor of Databank Ghana limited, who facilitated our request

regarding Fund statistics. We would also like to thank Albert Sam of graphic communications

group limited who facilitated our access to the Ghana graphics archives.




                                              iii
                                      DEDICATION
We dedicate this thesis to our parents for their support, our lecturers for the knowledge

impacted in us and finally to the ordinary Ghanaian investor.




                                               iv
                                                    TABLE OF CONTENTS
                                                                   CHAPTER 1
INTRODUCTION
   BACKGROUND ..................................................................................................................................... 1
  RESEARCH PROBLEM………………………………………………………………………………………………………………………3
  RESEARCH OBJECTIVES……………………………………………………………………………………………………………..……3
  RESEARCH QUESTIONS……………………………………………………………………………………………………………..……3
  HYPOTHESIS………………………………………………………………………………………………………………………………..…4
  SIGNIFICANCE OF THE STUDY……………………………………………………………………………………………….………..5
  SCOPE AND LIMITATION………………………………………………………………………………………………….…….……….6
  LITERATURE REVIEW………………………………………………………………………………………………………….….……….6
  METHODOLOGY…………………………………………………………………………………………………………………..………8
  ANAYSIS AND IMPLEMENTATION OF DATA………………………………………………………………………….………..8
. THE STRUCTURE OF THE ESSAY……………………………………………………………………………………………..………9
                                                               CHAPTER 2
                                                       LITERATURE REVIEW
  SECTION A.......................................................................................................................................... 10
    FUND PERFORMANCE………………………………………………………………………………………………………………...10
    EXPENSES……………………………………………………………………………………………………………………………………12
    TURNOVER………………………………………………………………………………………………………………………………...12
    FUND AGE…………………………………………………………………………………………………………………………………..13
    FUND SIZE………………………………………………………………………………………………………………………..…………13
  SECTION B…………………………………………………………………………………………………………………………………….15
    NATURE OF COLLECTIVE INVESMENT SCHEMES IN GHANA…………………………………………………………15
                                                               CHAPTER 3
METHODOLOGY
  THE SELECTION PROCESS……………………………………………………………………………………………………………..20
  SCOPE AND LIMITATION………………………………………………………………………………………………………………20
    DATA COLLECTION………………………………………………………………………………………………………………..……20
    DATA PROCESSING……………………………………………………………………………………………………………………..21
    SELECTION OF AN APPROPRIATE BENCHMARK…………………………………………………………….….…………21
    STATISTIC METHODOLOGY………………………………………………………………………………………….……..………21
   REGRESSION ANALYSIS………………………………………………………………………………………………………………..21
   THE VALIDITY AND RELIABILITY OF THE STUDY………………………………………………………………….…………22
  DEFINITION VARIABLES…………………………………………………………………………………………………………………22
                                                               CHAPTER 4
ANALYSIS AND DISCUSSIONS
  DESCRIPTIVE STATISTICS………………………………………………………………………………………………………………27
. REGRESSION ANALYSIS…………………………………………………………………………………………………………………29
  RESULTS OF ANALYSIS…………………………………………………………………………………………………………………30
                                                               CHAPTER 5
 SUMMARY, CONCLUSION AND RECOMMENDATIONS
  SUMMARY AND CONCLUSION…………………………………………………………………………………………………...33
  RECOMMENDATIONS………………………………………………………………………………………………………………….34
REFERENCE…………………………………………………………………………………………………………………………………….35

                                                                           v
                                    LIST OF FIGURES AND TABLES

Table 4.1 Descriptive Statistics For Fund Attributes………………….……………………..27


Table 4.2 Correlation Between Variables…………………………………………….…….28


Table 4.3 Full Simple Regressions……………………….………..……………….………29

Table 4.4 Full Sample-Multiple Regressions…………….………………..……….…….…30

Appendice 1………………………………………….…………………………….....…….38

Return Vs Stardard Deviation...............................................................................................39

Return Vs Beta......................................................................................................................39

Return Vs Nav.......................................................................................................................40

Return Vs Fund Size..............................................................................................................40

Return Vs Fund Age...............................................................................................................41

Return Vs Management Tenure...............................................................................................41




                                                                   vi
                                           CHAPTER 1
                                         INTRODUCTION
BACKGROUND
Over the course of the last decade, the collective investment schemes industry has

undergone tremendous change. At the start of the millennium, it was a tiny industry

offering just one security, the Epack. Presently, there are over 10 schemes which offer a

wide array of investment policies and strategies. Investors invest in these schemes with

varying reasons and expectations. Affordability, professional management, liquidity,

denomination, flexibility, and performance monitoring, are some of the reasons people

invest in these schemes. But the overriding reason investors choose to invest in these

schemes is because of diversification.        Collective investment schemes operate on the

principle of diversification by collecting funds from a wide group of investors and investing

in a wide array of securities. The idea of diversification is an age-old concept exhibited in the

phrase “don’t put all your eggs in one basket”. It was not until 1952, however, that

Markowitz (1952) published a formal model of portfolio selection embodying diversification

principles (Bodie Investments, 2002).


Collective investment schemes are pools of funds that are managed on behalf of investors

by a professional money manager. The main types of collective investment schemes in

Ghana are Unit Investment Trusts, Closed-end Investment funds and Open-end Investment

funds. The Unit Investment Trust is an unmanaged fixed-income security portfolio put

together by a sponsor and handled by an independent trustee1. It offers investors

diversification and minimum operating cost. Home Finance Company limited (HFC) for

instance operates a unit trust that deals in bonds. Closed-end Investment Funds are


1
http:// www.secghana.org/investor/csischemes.asp-(3/10/2009)

                                                  1
managed funds that sell no additional shares of its own stock after the initial public offering.

Their capitalizations are fixed unless a new public offering is made. Open-end Investment

Funds are popularly referred to as mutual funds and continue to sell shares to investors

after the initial sale of shares that starts the fund. Examples in Ghana are EPACK Investment

Fund, NTHC Horizon Fund, SAS Fortune Fund, Gold Fund, Databank M-Fund and Ecobank I-

fund. Both open-end and closed-end funds have professional fund managers who buy and

sell securities periodically in order to achieve their objectives. The major distinction

between closed-end and open-end investment companies (mutual funds) is the way in

which mutual funds raise their money or redeem shares. Mutual funds raise money

continuously without limit to the number of investors or the number of shares issued.

Through this continuous offering, the fund obtains new capital to invest. Additionally, on

each trading day, the mutual fund stands ready to buy back shares from an investor who

wants to sell. This affords it more liquidity.


This paper will concentrate on mutual funds as against all the collective investment funds.


Usually, investors do not have the necessary knowledge concerning different securities and

the investment prospects associated with these securities. There are different kinds of

stocks, bonds and other more complicated financial instruments in the Ghanaian capital and

money markets that demand specialized knowledge for an investor to be able to trade

profitably in them. Thus individual investors are not able to invest in those markets because

they do not have the appropriate information; again the transactional costs are high.

Because of this, individual investors are not able to optimally diversify their portfolio.

Mutual funds help investors overcome this obstacle by helping them contribute to a pool of

diversified funds. The Ghanaian market is home to a wide variety of investment funds, a


                                                 2
large number which offer a variety of investment orientations, risks levels and prices (fees).

Recent years have seen the establishment of a variety of mutual funds all of which are there

to help the untrained investor achieve the aim of lower risk and higher returns.



RESEARCH PROBLEM
There has been a tremendous rise in the number of mutual funds to match the seemingly

increasing demand for these securities. With such an array of funds to invest in, an investor

is faced with the dilemma of choosing from the lot, that scheme which will provide the

highest return at the lowest risk. We will be investigating in this study, whether there is a

relationship between specific fund attributes and fund performance.



RESEARCH OBJECTIVES
The objectives of the study are to;


       Determine the factors affecting the performance of mutual funds in Ghana.

       Analyze the specific causes of their upturns and downturns in the past years.


RESEREARCH QUESTIONS
The following research questions will be used as a guide


       What are the types and nature of collective investment schemes in Ghana?

       What are the attributes of mutual funds?

       Does the attributes of mutual funds affect their performance?




                                              3
HYPOTHESIS
We have hypothesized generally that the performance of collective investment schemes is

determined by attributes.


In our study the fund returns will be the dependent variable and the fund specific attributes

will be the independent variables.



HYPOTHESIS TESTING
The following are the hypotheses to be tested in the study.



H0: Risk does not influence the return.

H1: Funds with high risk generate higher returns than funds with low risk.


H0: Fund size does not influence performance of mutual funds.

H2: Big funds perform worse than small funds.


H0: Net asset value does not impact return.

H3: Net asset value does impact return.


H0: Expenses have no impact on the returns of schemes.

H4: Funds with high expenses generate higher return than low expenses.


H0: Turnover does not influence the return.

H5: Funds turnover impacts return of mutual funds.




H0: Management tenure has no impact on the return of funds.

H6: Management tenure impacts the return.


H0: Fund age does not influence the performance of mutual funds.

H7: Fund age impacts the return of mutual funds.

                                              4
We will accept the null hypothesis if fund specific attributes do not influence the

performance and alternatively reject the null hypothesis if these fund specific attributes

impact the return on the scheme.



SIGNIFICANCE OF THE STUDY
Our research will benefit the under listed groups as follows;



INVESTORS
Our contributions will help the investors make more informed decisions as to which

schemes to invest in. They will have a better idea what parameters affect the performance

of these schemes and as such make more guided investment decisions. Our study will

provide factors which investors can use as a guide for comparing one fund to another and as

such make more objective investment decisions.



ACADEMIA
This study is delving into a subject area which has not been researched into detail in Ghana.

This will therefore help open a new research area which will help identify the factors which

influence collective investment schemes. This will provide a basis for future research into

this unexplored area.



PUBLIC
This study will help make the general public aware of the factors that cause the upturns and

downturns of mutual funds.



SCOPE AND LIMITATION
We will include all existing mutual funds that were in existence as at the end of the 2004

financial year. Almost all the mutual funds have their headquarters in Accra and as such


                                              5
most of our study will be concentrated in this defined area. This is because all the data we

need for our work is available from the headquarters.


Because mutual funds are a new phenomenon in the Ghanaian market, most of the funds

are extremely young compared to their global counter-parts. This therefore limited the

amount of data we could employ in our study.


Another limitation we faced was the willingness of these fund companies to help and

corporate with us.



LITERATURE REVIEW
The importance of a literature review is to make sure that researchers do not reinvent the

wheel. This means in undertaking this study we will carefully study prior written articles,

studies and other write-ups on the topic to make sure we do not replicate an already done

study. We plan to study and review existing literature on the subject area to help us better

understand how to employ quantitative and qualitative methods to this study. We believe

this will help us understand the application of regression and correlation in finding the

relationship between a dependent variable and its independent variables. Google search

engine, Jstor online academic library, the University of Ghana Business School library, and

the Balme Library will be used in searching for most of the relevant literature. We believe

we will get access to most of the information we need from these sources.



THEORETICAL FRAMEWORK
According to Prather et al (2002), fund attributes can be divided into the following broad

categories;




                                             6
POPULARITY VARIABLES


These measure the demand for a fund or a fund category which reflects the pressure that

results from the buying and selling processes and the funds ability to adapt to that pressure.

The popularity of funds may be conditional relative to its past performance and whether it

meets the investment objectives outlined in its prospectus. Thus popularity variables

include total fund size, funds market capitalization and net asset value (NAV).


COST VARIABLES


Cost variables measure the expenses of the fund incurred during the normal course of

business. These measures include the expenses ratio, front end load, differed load and

assets of funds complex. Expenses ratio represents the percentage of fund assets paid as

management fee including manager’s compensation and operating expenses such as

research support, administrative fees and all other asset-based cost incurred by the fund

excluding brokerage charges. Loads are sales charges paid either at the time of initial

investment or at the point of sales. A front-end load is a commission or sales charge paid

when an investor buys shares of a mutual fund. Differed load is a commission or sales

charge paid when an investor sells his shares of a mutual fund. Assets of fund complex

measure the dollar value of the total assets of all funds within the same fund family and

represents the ability of individual funds to realize economies of scale afforded by the

complex as a whole.


MANAGERIAL VARIABLES


Managerial variables attempt to capture managerial and organizational attributes as well as

monitoring mechanisms that bind managers to stated funds investment objectives which


                                              7
ultimately affect the performance of the fund. Managerial attributes include turnover, funds

under management, management tenure, fund age, minimum initial purchase, and

management structure.


We will not be analyzing all these attributes but a selected few as highlighted by our

hypothesis.



METHODOLOGY

This study will cover collective investment schemes in Ghana. We will include in our study all

mutual funds that were in existence at the end of 2004. We will use Microsoft Excel, Simple

regression analysis and Correlation coefficients to analyze the data. Both primary and

secondary data will be used in the project. This will incorporate both qualitative and

quantitative data.



ANALYSIS AND IMPLEMENTATION OF DATA
The work shall be in three forms; the first stage will be for conceptualization, the second will

be the data collection and analyses stage and the final will involve preparing, evaluating and

presenting the findings. The final work will be presented through graphs, tables and

qualitative analysis where necessary.



DATA COLLECTION INSTRUMENTS
The main source of our data will be the annual reports of the collective investment

schemes. Where this is not available we will use formal interviews.



THE STRUCTURE OF THE ESSAY
The study is divided mainly into five chapters.




                                                  8
Chapter one will be an introductory chapter that summarize most of the study.


Chapter two covers the literature review. This chapter will comprise our findings from

previous work on the subject. We believe this will provide us with a better understanding of

the subject area and help us identify questions already answered. Since the subject area has

not been studied much in Ghana, our study will focus more on research work done globally

on the subject area. This chapter will also cover the nature of collective investment schemes

in Ghana.


Chapter three will discuss the methodology to be used. We will discuss the study area, the

population, the nature of the research and the data collection instruments to be employed.


Chapter four will look at the analysis and discussions of findings from research.


Finally we will summarize, conclude and provide our recommendations in chapter five.




                                               9
                                       CHAPTER 2
                                 LITERATURE REVIEW

SECTION A
The father of modern portfolio theory, the 1990 Nobel Prize winner H. Markowitz, is the first

person to conclude that an investor expects to be rewarded for the risk he takes, in his paper

“Portfolio Selection”, 1952. His theory assumes that everyone is a mean-variance-optimizer

that is seeking portfolios with the lowest amount of variance for a given level of return.

Hence he viewed the dispersion of returns as the appropriate risk measure. Markowitz was

also the first to develop a matrix from which he could exemplify the importance of

diversifying a portfolio to reduce risk (Biglova et al. 2004). Markowitz’s work has been vital

to portfolio managers in making portfolio asset allocation decisions, as they try to determine

how much of the portfolio that should be invested into different asset classes such as stocks,

bonds or real estate based on the risk and return trade-off. (Grinblatt & Titman, 2001)


FUND PERFORMANCE
In one of the earliest studies of mutual fund performance, Jensen (1968) derives a risk-

adjusted measure of portfolio performance (now known as "Jensen's Alpha") that estimates

how much a manager's forecasting ability contributes to the fund's returns. The measure is

based on the theory of the pricing of capital assets by Sharpe (1966), Lintner (1965a) and

Treynor (1965). Jensen applies the measure to estimate the predictive ability of 115 mutual

fund managers in the period 1945-1964, i.e. their ability to earn returns which are higher than

those that would be expected given the level of risk of each of the portfolios. The evidence on

mutual fund performance indicates not only that the 115 mutual funds were on average not

able to predict security prices well enough to outperform a buy-the-market-and-hold policy,

but also there was very little evidence that any individual fund was able to do significantly

better than that which was expected from mere random chance. Jensen’s (1968) early work

on mutual funds supported the concept of efficient markets. The efficient-market hypothesis


                                              10
asserts that financial markets are "informationally efficient", or that prices on traded assets

(e.g., stocks, bonds, or property) already reflect all known information, and instantly change

to reflect new information. Therefore, according to the theory, it is impossible to consistently

outperform the market by using any information that the market already knows, except

through luck2. The conclusions of Jensen (1968) affirmed the earlier findings of Sharpe

(1966) and Treynor (1965). This formed the basis for the general conclusion prevalent in the

early literature that, professionally managed funds do not beat a risk-adjusted index portfolio,

suggesting that managers do not appear to possess private information.


But in the early 1990s, studies in the mutual fund industry began to produce contrary results.

Ippolito's (1993) study, suggests that mutual fund returns, after expenses (but before loads),

are equivalent or superior to those available from a risk-adjusted market index, which implies

that mutual fund managers may have access to useful private information which will help

them generate excess returns sufficient to cover expenses. Grinblatt and Titman (1992)

analyze how mutual fund performance relates to past performances based on a multiple

portfolio benchmark that was formed on the basis of securities characteristics. They find

evidence that differences in performance between funds persist over time and that this

persistence is consistent with the ability of fund managers to earn abnormal returns. In recent

studies, Wermers (2000) studies a new database to perform a comprehensive analysis of the

mutual fund industry. He finds that funds hold stocks that outperform the market by 1.3

percent per year, but their net returns underperform by 1 percent. Of the 2.3 percent

difference between these results, 0.7 percent is due to the underperformance of non-stock

holdings, whereas 1.6 percent is due to expenses and transactions costs. Thus, fund managers

pick stocks well enough to cover their costs. Again, high-turnover funds beat the Vanguard

Index 500 fund on a net return basis. Their evidence supports the value of active mutual fund

2
    www.wikepida.org

                                              11
management. The conclusions drawn by these researchers have led some people to conclude

that professionally managed funds do beat a risk-adjusted index portfolio suggesting that

managers do appear to possess private information. Hendricks et al. (1993), Goetzmann and

Ibbotson (1994) and Volkman and Wohar (1995) provide further evidence to support market

inefficiency by finding repeated winners among fund managers and positive performance

persistence.


However, the studies of Elton et al. (1993), Malkiel (1995) and Carhart (1997) reaffirm the

original conclusions of Jensen (1968), Sharpe (1966) and Treynor (1965). In eliminating

survivorship bias, Carhart (1997) demonstrates that those common factors driving stock

returns also explain persistence in mutual fund performance. Elton et al (1993) corrects for

benchmark error and takes issue with Ippolito's (1993) findings, while Malkiel (1995)

considers both benchmark error and survivorship bias in concluding that the results of prior

studies suggesting market inefficiency are contaminated by these factors. Although finding

some evidence of performance persistence during the 1970s, Malkiel notes that this does not

continue in the 1980s.


Other studies that address the survivorship issue include Elton et al. (1996), Grinblatt and

Titman (1994), and Brown et al (1992) with the general conclusion that the fund returns used

in other studies may be overstated thus creating only the appearance of performance

persistence. Improper benchmark specification is also cited for causing errors in fund

performance evaluation as noted in Lehman and Modest (1987), Grinblatt and Titman (1989),

Dellva et al. (2001), Malkiel (1995), Elton et al (1993) and Carhart (1997).


From the review so far, most of the mutual fund literature focuses on the controversial issue

of fund performance relative to that of the overall market, while the related issue regarding

fund-specific factors and performance has thus far not been thoroughly addressed.


                                              12
EXPENSES
Some studies that provide important insights include Sharpe (1966) who finds that funds

with lower expenses realize better performance and more recently Golec (1996) who finds

evidence that funds which keep expenses relatively low perform better. Ippolito (1989),

however, finds no significant relationship between performance, after expenses, and turnover

and investment fees. Hooks (1996) in a study over a 15 year holding period concludes that

low expense load funds do slightly outperform average expense no-load funds. In contrast,

Dellva and Olson (1998) find that funds with front-end load charges underperform no load

funds. Chen et al. (2004) finds an insignificant relation between performance and fees.

Carhart (1997) finds a negative relationship between fees with net-fee performance. The

studies of Dahlquist et al. (2000) and Otten and Bams (2002) also found a negative relation

between fees and performance.

TURNOVER
Other studies also focused on the relationship between turnover and performance. Friend et al

(1970), in the first paper to study this relationship find a slightly positive relation between

portfolio turnover and performance. Both Malkiel (1993, 1995) and Carhart (1997) report a

negative relationship between portfolio turnover on fund returns. In contrast, Wermers (2000)

and Grinblatt and Titman (1994) demonstrate a positive relationship between performance

and turnover, suggesting that those funds engaged in more active trading may be finding

underpriced securities. In reaching this conclusion, the latter study notes the standard

survivorship bias. In contrast, Sirri and Tufano (1998) examining mutual fund flows and

Chevalier and Ellison (1999) analyzing the impact of personal managerial characteristics on

fund performance have attempted to correct for survivorship bias on a sub-sample of their

data with mixed results. Other recent studies acknowledging, the survivorship issue typically

contend that the problem may not be severe when examining funds over a short period of

time (Prather, 2002).


                                              13
FUND AGE
Fund age provides a measure of a fund’s longevity and its manager’s ability. Research works

on fund age and returns have showed mixed relationships. Miguel and Antonio (2009)

studied the determinants of mutual fund performance around the world using a new data set

of 16,316 open-end actively managed domestic and international equity funds in 27 countries.

They concluded that fund age is negatively related to performance. Otten and Bams (2002)

and Cremers and Petajisto (2008) find newer funds perform better than older funds. However

Chen et al. (2004) find no relation between fund age and performance of a mutual fund.

FUND SIZE
In the area of fund size and its effect on fund performance, Chen et al. (2004) report that fund

performance worsens with fund size. The results are most pronounced among funds that

invest in small and illiquid stocks, suggesting that adverse scale effects are related to

liquidity. Following Stein (2002), they also suggest that in addition to liquidity, fund size

erodes performance because of organizational diseconomies. Indro et al (1999), Miguel and

Antonio (2009) and Pollet and Wilson (2008) also found evidence to complement the

findings of Chen at el (2004). Grinblatt and Titman (1989, 1994) found mixed evidence on

the relationship between fund returns and fund size.


Although an extensive catalogue of research material was available in our area of research,

most researchers seem to always reach contradictory conclusions even though they mostly

apply the same methodology during their studies, confirming subjectivity of even quantitative

research.


Our study will be unique in the sense that we will be studying a mutual fund industry that is

relatively young compared to the markets studied by previous researchers.




                                              14
SECTION B

NATURE OF COLLECTIVE INVESTMENT SCHEMES IN GHANA
Collective investment schemes are pools of funds that are managed on behalf of investors by

a professional money manager3. The manager uses the money to buy stocks, bonds, or other

securities according to specific investment objectives that have been established for the

scheme. In return for putting money into these funds, the investor receives shares or units that

represent his/her pro-rata share of the pool of fund assets. In return for administering the fund

and managing its investment portfolio, the fund manager charges a fee based on the value of

the fund’s assets.


Collective investment schemes in Ghana take the form of either a Mutual Fund or a Unit

Trust. The characteristics of collective investment schemes in Ghana are provided for in the

Securities Industry (Amendment) Law 2000, Act 590, and are not necessarily the same as

those of other jurisdictions.


A mutual fund is a public or external company incorporated solely to hold and manage

securities or other financial assets. The company accepts funds from investors and uses those

funds to buy a portfolio of securities and other financial assets and employs a professional

fund manager to manage the investment. The company issues shares which represent pro-rata

share of the pool of fund assets to investors. A mutual fund in Ghana may either be open-end

or closed-end. Open-end funds are funds which stand ready to repurchase their shares from

the holders in any quantity and whenever the holder should desire. In addition they sell shares

in any quantity to prospective investors at whatever time the investors determine. In other

words, open-end funds stand ready to issue new shares or redeem outstanding shares on a

continuous basis. The number of shares of the fund, therefore, fluctuates as investors

purchase or redeem shares. The price of a share in an open-end fund is determined by the net


3
    http://csighana.org/investor/csischemes.asp-(10/1/2010)

                                                      15
asset value per share of the fund, where net asset value per share refers to the total value of

the assets in the fund's portfolio, less any fund liabilities, divided by the number of shares

outstanding. Closed-end funds are funds which issue a fixed number of shares and do not

stand ready to repurchase their shares from their shareholders when they decide to sell them.

The Securities Industry (Amendment) Law requires that closed-end funds be listed on an

organized exchange in order to provide liquidity to the shareholders. These shares are traded

at prices determined by the laws of supply and demand.


The main parties involved in the organisation and operation of a mutual fund are:


       The Mutual Fund Company: The company established to operate as a mutual fund.
       The Manager: The professional fund manager appointed by the Mutual Fund
       Company to manage the fund’s investments. The manager must be a body corporate
       licensed by the Securities and Exchange Commission (SEC) as an Investment
       Advisor.
       The Custodian: A company appointed by the Mutual Fund Company to keep custody
       of all the securities owned by the fund. The custodian must either be a bank, an
       insurance company or a financial institution or a wholly owned subsidiary of any of
       them approved by the SEC.
For instance, the Epack is a mutual fund company, managed by Databank Investment

Services (the manager) and has custodians that include Barclays Bank Ghana and the

National Bank of Malawi.

The manager and the custodian must be independent of the mutual fund company.

Independent means that the mutual fund company should not be a substantial shareholder of

the manager or the custodian. A substantial shareholder means a shareholder entitled to

exercise or control the exercise of 30% or more of the voting power at general meetings of

the company or one who is in a position to control the composition of a majority of the board

of directors of a company.



                                              16
A Unit Trust is an arrangement whereby investors' funds are pooled together and used to

invest in a portfolio of securities and other financial assets, with the beneficial interest in the

assets of the trust divided into units. The funds are managed by a professional manager.


A unit trust is constituted by a document known as the trust deed. Under the Securities

Industry (Amendment) law, Act 590, unit trusts are open-end funds and their managers stand

ready to issue new units or redeem outstanding units on a continuous basis.


The parties to a Unit Trust are:

       The Manager: the Company that establishes the unit trust. The law requires the
       company seeking to establish a unit trust to be the manager of the Trust. The manager
       must be a body corporate licensed by the SEC as an Investment Advisor prior to the
       establishment of the unit trust.
       The Trustee: A company appointed by the Manager to take into its custody or under
       its control the property of the unit trust and hold it in trust for the investors.



The trustee must either be a bank, an insurance company or a financial institution or a wholly

owned subsidiary of any of above approved by the SEC. The trust deed of the unit trust is

made under seal between the manager and the trustee.


The manager appoints the trustee but the manager and the trustee must be independent of

each other. Independent here means that the manager is not a substantial shareholder of the

trustee, and the trustee is not a substantial shareholder of the manager. A substantial

shareholder means a shareholder entitled to exercise or control the exercise of 30% or more

of the voting power at general meetings of the company or one who is in a position to control

the composition of a majority of the board of directors of a company.


Currently there are 2 unit trust schemes in the country. They are the HFC Unit Trust and the

HFC Real Estate Investment Trust (HFC REIT). These 2 schemes were established by the

                                                17
Home Finance Company Limited. The first one, HFC Unit Trust was established by

Legislative Instrument L.I. 1516 of 1991 and the second one, HFC REIT approved by the

Bank of Ghana acting for the SEC in 1995.


Mutual Funds and Unit Trusts are generally categorised according to their investment

objectives and their investment policies. Some mutual funds focus on stocks, others on bonds,

money market instruments, or other securities. On the international scene some funds invest

primarily in their countries, others invest internationally, and some specialise in specific

countries.


Under current Ghanaian tax laws, Collective Investment Schemes do not pay taxes on their

incomes. Again, investors in these schemes do not pay taxes on incomes received from these

schemes. These tax incentives have been designed to encourage the pooling of investors'

resources together for investments to develop the economy.


All securities in Ghana are subject to securities laws that are administered and enforced by

the Securities and Exchange Commission (SEC).




                                             18
                                               CHAPTER 3
                                            METHODOLOGY

In this chapter, the study area, the population, the nature of the research, the data collection

instruments, statistical methodology, validity and reliability of the study is discussed in detail.

There are two approaches to research, qualitative and quantitative. Both quantitative and

qualitative research involve careful, systematic methods to gather high-quality data. But

whiles qualitative research aims at gathering an in-depth understanding of human behaviour

and the reasons that govern such behaviour4, quantitative research aims at the systematic

empirical investigation of quantitative properties and phenomena and their relationships.5

We used both qualitative and quantitative research methods but most of our study was of a

quantitative nature; we collected huge amounts of data which we processed to hypothesize

and to find a relationship between the funds characteristics affecting mutual funds’

performance. Using quantitative methods have the advantage of efficiency because it is easier

to process a large quantity of data as compared to a large quantity of words. Different

researchers have different reasons for conducting their study but the extent of work already

done in the selected field will define whether the study will be exploratory, descriptive or

explanatory. When the area of interest is yet to be explored then an exploratory research will

be most appropriate. Where a few researchers have already explored the area of interest then

a descriptive research will be appropriate. But where extensive work has already been done in

the area and research models already established, then the research most appropriate is a

hypothesis verifying research or explanatory research. This technique concentrates on tests of

given assumptions to examine their accuracy (Lawrence, 2007).


After reviewing the extensive amount of information available worldwide for our research

area, we concluded that an explanatory research was best suited for our study. Even though

4
    http://en.wikipedia.org/wiki/Qualitative_research
5
    http://en.wikipedia.org/wiki/Quantitative_research

                                                         19
mutual funds have not been extensively studied in Ghana, models for our study already exist

worldwide and can be adapted for our study.



THE SELECTION PROCESS
Our target population consists of all mutual funds in Ghana that were in existence at the end

of 2004. Our study is a census since we included all mutual funds in existence at the end of

2004 in our study. We employ their returns, net asset value, turnover, funds size, fund age,

management tenure and total cost from January 2005 to December 2008. Most of the data is

obtained from the fund headquarters.



SCOPE AND LIMITATION
The study included all mutual funds in Ghana that were in existence at the end of 2004. The

study covers the period between 2005-2009. The data used in the analysis was the semiannual

monthly averages from 2005–2009. We included data from only mutual funds in Ghana.

Because the mutual funds industry in Ghana is young compared to its global counterparts this

limited the amount of elements we could include in our target population. This therefore

limited the amount of mutual funds we could employ in our study. Again some of the data we

needed to complete our study was unavailable on a monthly basis and therefore could not be

included. The study also disregarded all mutual funds that came into existence during the

study period.



DATA COLLECTION
Data sources can be divided into primary sources and secondary sources. Primary data

include the information collected by the investigator but secondary data includes information

collected by others for some other purpose. Advantages of using the primary sources include

its uniqueness and the fact that it has not been collected (Lawrence, 2007).

We employed both primary data and secondary data because of the nature of our study.


                                              20
DATA PROCESSING
Popularity, growth (risk), cost and management variables, from a 5-year period, were used to

measure the performance of funds. All fund attributes were collected and analysed using

regression analysis, correlation coefficients and the difference between the minimum and

maximum values.



SELECTION OF AN APPROPRIATE BENCHMARK
Mutual funds movement in relation to market is measured by beta. The market is defined by

an index. To calculate beta an appropriate index should be selected. We choose all the mutual

funds that were in existence at the end of 2004 and we choose GSE all share index as the

beta. The use of the index as a benchmark is very important for fund managers when

measuring performance.



STATISTIC METHODOLOGY
We used simple and multiple regression to examine whether performance depends on the

defined attributes. We used Microsoft Excel for the regression analysis.



REGRESSION ANALYSIS
Regression analysis is the statistical technique that identifies the relationship between two or

more quantitative variables: a dependent variable, whose value is to be predicted, and an

independent or explanatory variable (or variables), about which knowledge is available. The

technique is used to find the equation that represents the relationship between the variables.

A simple regression analysis can show that the relation between an independent variable X

and a dependent variable Y is linear, using the simple linear regression equation Y= a + bX

(where a and b are constants). Multiple regression will provide an equation that predicts one

variable from two or more independent variables, Y= a + bX + cX + dX.

Regression analysis is used to understand the statistical dependence of one variable on other

variables. The technique can show what proportion of variance between variables is due to

                                              21
the dependent variable, and what proportion is due to the independent variables.            The

relationship between the variables can be illustrated graphically or using an equation.



THE VALIDITY AND RELIABILITY OF THE STUDY
Validity and reliability are two ways of measuring whether a study is of high quality or not.

Validity can be explained as the ability to measure what you actually intended to measure.

Validity defines the quality of measurement and the proper use of procedures (Lawrence,

2007). Reliability refers to the extent to which your data collection techniques or analysis

procedures will yield consistent findings. Validity is concerned with whether or not the

findings are really about what they appear to be about. Reliability means that another

research using the same approach should be able to come up with the same results. It is the

measure of how stable, dependable, trustworthy, and consistent a test is in measuring the

same thing each time. Validity can be explained as, does the test measure what it purports to

measure? Reliability asks about how consistent and reliable a measure of a variable is

(Lawrence, 2007).

We adhered to validity and reliability by carefully collecting data from the sources and

carefully analysing them using the existent global models.




DEFINITION VARIABLES


Rate of Return
The rate of return on an investment in a mutual fund is measured as the increase and decrease

in net asset value plus income distributions such as dividends or distributions of capital gains

expressed as a fraction of net asset value at the beginning of the investment period. If we

denote the net asset value at the start and end of the period as NAV 0 and NAV 1

respectively, then


                                              22
On the other hand rate of return is also affected by the fund’s expenses. These charges are

periodically deducted from the portfolio, which reduces the net asset value. Thus the rate of

return of the fund equals the gross return on the underlying portfolio minus the total expenses

ratio. (Bodie Investments 2001)



NAV
NAV represents the fund price per share and is influenced by fund performance. NAV is

calculated on a per-share basis. Net asset values are like stock prices in that they measure the

value of one share of a fund. Also, they give investors a way to compare a fund's performance

with market or industry benchmarks (such as the Standard & Poor's 500, Ghana Stock

Exchange or an industry index). However, some analysts argue that comparing long-term

changes in a fund's NAV is not as meaningful as comparing long-term changes in its share

price because funds periodically distribute capital gains to their fund holders, thus reducing

the NAV. Investors buy shares in investment companies and ownership is proportional to the

number of shares purchased. The value of each share is called net asset value or NAV. Net

asset value equals assets minus liabilities expressed on a per-share basis. (Bodie Investments,

2001)




RISK
Risk means uncertainty about future rate of return. It is impossible to avoid the risk factor

when investing in mutual funds. Academics believe that equity investors are rewarded for

taking on risks in the long run (Peterson et al, 2001). To measure the risk of a fund, beta and

                                              23
standard deviation are commonly used. Beta measures the extent to which returns on the

stock and the market move together. It is a measure of the systematic risk of a company or a

portfolio where individual asset or portfolio is compared to the market. A higher beta than 1

implies that the individual or portfolio risk is higher than market. (Bodie Investments, 2001)




Where: COV(Ri,Rm) = the covariance between the return of asset i and the market return m.
VAR(Rm)      = the market variance.
Βi           = the estimated systematic risk of asset i



The standard deviation measures the risk of a fund fluctuation from the mean return, the

average return of a fund over a period of time which includes both systematic and non-

systemic risk. (Bodie Investments, 2001)




Where: ϭ = lower case sigma ‘standard deviation’.
∑ =capital sigma ‘the sum of’.
X = x bar ‘the mean’.

Risk can be measured either by using beta or standard deviation depending on the investors’

assumptions. If mutual fund represents the entire investment of an individual investor, the

standard deviation is a more accurate measure. If the investor has a diversified portfolio, beta

measure is preferable. If an investor invests in a mutual fund, it implies that the portfolio is

diversified and therefore not exposed to non-systematic but only systematic risk. (Bodie

Investments, 2001)

EXPENSES

Expenses of the fund are incurred during the normal course of business. These measures

include expense ratio and loads ratio. The following discussion explains these measures and

provides an economic rationale for their inclusion as costs variables. Expense ratio represents


                                                      24
the percentage of fund assets paid as management fees. These include manager’s

compensation and operating expenses such as research support, 12b-1 fees, administrative

fees and all other asset-based costs incurred by the fund excluding brokerage charges. The

expectation is that if these expenses effectively support research, marketing and managerial

expertise, then they should positively impact performance. 12b-1 fees are annual charges

deducted from a fund's assets reflecting distribution and marketing costs. We consider these

fees separately because they have been shown to significantly affect performance in previous

studies. Although these expenses are designed to support marketing efforts, their impact on

fund performance may be questionable, since the direct impact of 12b-1 fees is to generate

short-term sales. Loads are sales charges either at the time of initial investment or at the time

of redemption. A front-end load represents a one-time charge at the initial investment and

serves as a commission to brokers, while a deferred load or redemption charge is a sales fee

imposed as money is taken out of a fund. Unless loads serve to offset other expenses, the

expectation is that loads negatively impact performance. (Prather, 2002).




TURNOVER
A fund with a higher portfolio turnover rate can be particularly “tax inefficient.” Turnover is

the ratio of the trading activity of a portfolio to the assets of the portfolio. It measures the

fraction of the portfolio that is “replaced” every year. (Bodie Investments 2001)




Where: A = the amount of purchased securities during the period.
B = the amount of disposed securities during the period.
C = the average mutual fund wealth during the period.




                                                     25
FUND SIZE
Mutual Funds total assets represent the total cedi value of a single fund’s assets. A negative

relationship may indicate that the fund size may impact on its ability to implement a

particular investment style. Mutual funds having substantial fund assets under management

may have a harder time to make superior returns whiles small funds experience no economies

of scale.   The large mutual funds have several advantages over small ones because of

economies of scale and spread of fixed overhead expenses over a large assets base. Large

funds also have benefit to diversify their investment opportunities which might not be

available to small market participants (Prather, 2002).




MANAGEMENT TENURE
Management tenure affects the performance of fund because investors have to rely on

management tenure as a criterion for selection of fund. Experienced managers might be more

efficient in analysing information and it could affect management fee by allowing them to

charge lower fees (Filbeck & Tompkins, 2004). Some oppose this view that fresh managers

have more will to perform well. There are also some studies that show that managers close to

retirement on average underperform two years prior to departure and that they have higher

management fee and portfolio turnover (Peterson, 2001).



FUND AGE
Age of fund provides a measure of the fund's longevity or ability to survive in a highly

competitive environment. It is simply the number of years that a fund has been in operation.

Mutual fund age could affect the performance since younger funds may face higher cost in

their start-up period. There is also evidence showing that mutual funds returns may be

affected by an investment learning cycle (Gregory, 1997).




                                              26
                                       CHAPTER 4
                             ANALYSIS AND DISCUSSIONS

In this chapter, we will conduct single and multiple regression on the data.


DESCRIPTIVE STATISTICS

We studied two mutual funds and the factors affecting their performance. The funds were

averaged semi-annually over the study period. This therefore allowed us to compare changes

in the various variables and how it affects return over fairly short periods but with higher

number of observations. We made a total of 20 observations over the study period. These

funds and their characteristics are shown in appendix 1.


Table 4.1 Descriptive Statistics For Fund Attributes

                       N    Mean            Median          Min. Value     Max Value
Fund Return            20 1.090             1.083           -2.026         3.451
STDEV                  20 1.243             0.778           0.056          4.385
Beta                   20 0.108             -0.003          -0.440         1.474
Fund Size              20 34650168.637      26357451.261    3734220.127    101243752.142
Management Tenure      20 6.580             6.475           0.400          13.250
Fund Age               20 6.625             6.625           1.000          12.250
NAV                    20 0.415             0.332           0.115          0.912

Source: Research study-(01-01-2005 to 31-12-2009)


Table 4.1 shows that we calculated mean, median, minimum and maximum values for six of

the variables we set out to measure. We studied 2 mutual funds semi-annually over a five

year period. The mean and median values for fund return were very close. However since the

inception of both funds, the same individuals have managed the Epack, with a change in the

management of the Mfund half way through the study period, this accounted for the closeness

of the summary statistics for Fund age and management tenure.




                                              27
Table 4.2 Correlation Between Variables
                                                     FUND                FUND      MANAGEMENT
                  RETURN     STDEV      NAV          SIZE    BETA        AGE       TENURE

RETURN            1.000

STDEV             -0.493     1.000

NAV               -0.155     0.701      1.000

FUND SIZE         -0.163     0.631      0.948        1.000

BETA              -0.433     0.593      0.434        0.446   1.000

FUND AGE          -0.151     0.694      0.951        0.838   0.443       1.000
MANAGEMENT
TENURE            -0.221     0.721      0.934        0.797   0.434       0.947     1.000
Source: Research study-(01-01-2005 to 31-12-2009)


We calculated correlations because there are more than two variables for each N subject. The

correlation is a measure of the extent to which two measurement variables “vary together”.

The value of correlation is between -1 and +1 inclusive. Table 4.2 shows that some fund

attributes might be correlated with each other. Return and standard deviation have a high

negative correlation which shows a strong inverse relationship between standard deviation

and return. Again we observed a very strong inverse relationship between beta and return.

This leads us to the conclusion that there exists a strong inverse relationship between risk and

return of mutual funds.

Because the correlation between fund age, size and net asset value is high as predicted, large

mutual funds tend to be oldest. The high positive relationship observed between management

tenure, fund age, fund size and net asset value was due to the fact that the fund management

had remained relatively the same since inception, and funds size increasing with time.

The high positive relationship between standard deviation and the fund size means that the

higher the asset base of the fund the higher the fund specific risk. Again the high positive

correlation between fund age and management tenure means that the older a fund is and the

longer a manager stays in office respectively, the higher the fund specific risk becomes and



                                                28
the lower the returns. The negative and significant coefficient estimate of return to fund age

does not support the winners repeat hypothesis, and in fact our results suggest that mutual

fund performance is more likely to exhibit a reversal pattern.

Appendix II shows the regression analysis between return and the other variables.



REGRESSION ANALYSIS
Regression analysis is performed to examine how the attributes influence the return

individually. As seen in the table 4.3 below, in the last column standard deviation explains

24.27 percent of mutual fund return in this regression, and Beta is the variable which has the

highest coefficient. Beta again is at an explanatory level of 18.73 percent.           The other

attributes as expected have very low explanatory levels.

Table 4.3 Full simple regressions

                      Coefficients     t Stat              P-Value             R-Square (%)

Standard Deviation    -0.5014          -2.4019             0.0273              24.27

BETA                  -1.5287          -2.0368             0.0567              18.73

Fund Size             -0.0000          -0.6994             0.4933              2.65

NAV                   -0.7499          -0.6654             0.5142              2.40

Fund Age              -0.0499          -0.6474             0.5255              2.28

Management Tenure     -0.0499          -0.6474             0.5376              2.15

Source: Research study-(01-01-2005 to 31-12-2009)                    Confidence level 95 percent


We performed multiple regression to determine the percentage of the changes in fund return

that the fund characteristics could explain together. Here we performed multiple regression

with the risk, popularity, growth and management variables. The last column shows that

standard deviation and beta together explain 27.32 percent of the changes in return and the

popularity variables i.e. fund size and NAV together also explain 2.65 percent of mutual fund

return in multiple regression. Management variables accounted for 8.24 percent.


                                                 29
Table 4.4 Full sample-multiple regressions

                           Coefficient   t Stat             P-value              R-Square

Standard Deviation         -0.3704       -1.4178            0.1743
                                                                                 0.2732
BETA                       -0.7664       -0.8451            0.4098

Fund Size                  -0.0000       -0.2070            0.8385
                                                                                 0.0265
NAV                        -0.0323       -0.0088            0.9930

Management Tenure          -0.2261       -1.0513            0.3078
                                                                                 0.0824
Fund Age                   0.1892        0.7880             0.4415

Source: Research study-(01-01-2005 to 31-12-2009)                    Confidence level 95


The low level of the explanatory power of the attributes is due to the fact that some variables

were not included in the study. Mainly the risk variable (Alpha), popularity variable (market

capitalization), management variable (turnover) and all the cost variables were not included

due to the unavailability of data. The coeffecients of regression cannot be used in the analysis

of the data because both the “p” and “t” stat of the single and multiple regressions were

statistically insignificant.



Results of Analysis
A relationship between return and the predictor variable can be sought in the scatter plot

(Appendix II). All scatter plots indicate some kind of linear relationship. Our analysis of

descriptive statistics, correlation between variables and regression analysis gives results that

will help us either accept the null hypothesis if managerial attributes does not influence the

performance and alternatively reject the null hypothesis if these managerial attributes impact

the return of mutual funds.




                                                  30
Results of first Hypothesis
H0: Risk does not influence the return.
H1: Funds with high risk generate higher returns than funds with low risk.

In the simple regression the coefficient level for standard deviation and beta is highest with

highest explanatory level. Again in the correlation table, Beta and standard deviation

recorded the highest correlation values. Chang’s (2004) study shows that low risk provide

investors with higher return. We will reject the null hypothesis because risk influences the

return of mutual funds.


Results of second and third Hypothesis
H0: Fund size does not influence performance of mutual funds.
H2: Big funds perform worse than small funds.

H0: Net asset value does not impact return.
H3: Net asset value does impact return.


Among the popularity variables we tested NAV and Fund size. The results show a positive

correlation between fund size and standard deviation. Since standard deviation varied

inversely with returns, fund size and NAV also move in the same direction. Thus the fund

size influences the fund performance because larger funds have more diversification but have

a more difficult time finding high yielding investments for their excess funds. A negative

relationship indicates that the fund size and NAV impact on a funds ability to implement a

particular investment style, as the total assets of a fund increases, its ability to continue a

successful strategy focusing on small cap investments may be impeded. We therefore reject

the null hypothesis.


Results of fourth Hypothesis
H0: Expenses have no impact on the returns of schemes.
H4: Funds with high expenses generate higher return than low expenses.




                                              31
Due to the unavailability of data on expenses incurred by the funds on a monthly basis, we

were not able to analyse the impact of expenses on returns.


Results of fifth Hypothesis
H0: Turnover does not influence the return.
H5: Funds turnover impacts return of mutual funds.


Due to the unavailability of data on turnover values for the funds, we were not able to analyse

the impact of turnover on returns.


Results of sixth and seventh Hypothesis
H0: Management tenure has no impact on the return of funds.
H6: Management tenure impacts the return.


H0: Fund age does not influence the performance of mutual funds.
H7: Fund age impacts the return of mutual funds.


Management tenure has a positive relationship with fund performance according to the earlier

studies by Golec (1996). Our study contrasts this showing a negative correlation. This

therefore leads us to conclude that younger managers do present new ideas which yield

higher returns. An earlier study by Gregory et al (1997) show that mature funds perform

better than younger funds whereas Otten and Bams (2002) finds opposite. Our study shows a

negative coefficient for fund age to return, which again contrasts Golec (1996) but is in

agreement with the findings of Otten and Bams (2002). This result suggests that an older fund

may have achieved past success but past does not necessarily secure future performance.

The negative coefficient in the correlation table between the management variables and

return, indicates that focus is important, and the effectiveness of management expertise

declines as the manager attempts to cover more funds. This reduction in effectiveness

associated with spreading oneself too thin outweighs any benefits (i.e. economies of scale,

etc.) arising from managing multiple funds.



                                              32
                                      CHAPTER 5
              SUMMARY, CONCLUSION AND RECOMMENDATIONS



SUMMARY AND CONCLUSION
The study provides a first step of a comprehensive and integrated examination of the nature

of collective investment schemes in Ghana and how the attributes of mutual funds affect their

performance. We included different types of categories of popularity, growth, cost and

managerial variables which influence the performance of mutual funds. These included detail

discussion of the relationship between mutual fund performance and fund specific factors

with a recent data set of mutual funds and fund characteristics. Our study builds upon earlier

research and provides investors a framework of which factors they should consider while

investing in mutual funds. We included all the mutual funds that existed at the beginning of

our study period. The study shows the relationship between return and how it is affected by

the fund size, net asset value, risk, fund age and management tenure. The study reveals that

the investor is not rewarded for choosing older funds and funds with managers with high

periods in office. Secondly, beta and standard deviation show an inverse relationship with

fund return; high beta funds perform worse off than low beta funds and funds with low

standard deviation perform better than those with high standard deviation. Relationship

between fund size, age and management tenure reveals that investor should choose the

younger fund with a manager in charge for a shorter time period.


The study provides a significant update to the previous literature by examining a mutual

funds industry at a relatively young age. Unlike most prior research on the determinants of

fund performance, the data used in this study is free of survivorship bias. The relationship

between mutual fund performance and fund specific factors shows the results are generally

consistent with the studies conducted by previous researchers.




                                             33
RECOMMENDATIONS
We studied different research papers and thesis related to mutual fund performance which

has given us an idea for future studies. We include a small population size and only a few

variables influencing the performance of mutual funds. The future study can be done

including more variables such as diversification level, number of holdings, funds under

management, and education level of management. Again the population size can be increased

by choosing a shorter period.


A study into how the various categorizations of mutual funds are performing in Ghana can be

carried out; mainly how money market funds, equity funds, and fixed income funds are

performing against each other in the Ghanaian economy.


A study of how Unit Trusts are performing as against mutual funds can also be carried out.


It will also be interesting to study the effect the global economic crisis on the Ghanaian

mutual funds industry in the 2008-2010 period.




                                             34
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                                              37
APPENDIX I
                      6
    Fund       Year        Return   STDEV    BETA    NAV     FUND SIZE    MANAGEMENT   FUND
    Name                                                                    TENURE      AGE
    Epack     2005-       1.1101    1.3986   0.39   0.4361   30243338.4      8.75      8.75
                1
              2005-       0.4182    2.8077   0.38   0.4272 25618241.99       9.25      9.25
                2
              2006-       1.0860    2.2386     -    0.4687 27096660.54       9.75      9.75
                1                            0.05
              2006-       2.5132    3.0578   0.51   0.5478   34067872.7      10.25     10.25
                2
              2007-       3.2732    1.1068   0.46   0.6394 48207398.58       10.75     10.75
                1
              2007-       3.4511    0.9868     -    0.7876 72891557.48       11.25     11.25
                2                            0.28
              2008-       1.7706    2.2101     -    0.9119 101243752.1       11.75     11.75
                1                            0.08
              2008-       2.0259    4.3851   1.47   0.9064 99710903.36       12.25     12.25
                2
              2009-       1.5243    3.7560     -    0.7648 62091678.93       12.75     12.75
                1                            0.02
              2009-       0.6984    0.5684   0.03   0.7769 55553525.82       13.25     13.25
                2

    Mfund     2005-       0.8268    1.3969     -    0.1151 3734220.127        1
                1                            0.44                                       1.2
              2005-       1.3235    0.1360   0.00   0.1250 6216965.725        1.5
                2                                                                       1.7
              2006-       1.0798    0.0772     -    0.1343 8660914.172        2
                1                            0.03                                       2.2
              2006-       1.1745    0.1295     -    0.1435 10397176.77        2.5
                2                            0.12                                       2.7
              2007-       0.9686    0.0671   0.01   0.1529 12137759.59        3
                1                                                                       3.2
              2007-       0.9255    0.0680     -    0.1618 13803080.28        3.5
                2                            0.01                                       3.7
              2008-       1.0496    0.0721   0.00   0.1716 15956113.05        4
                1                                                                       4.2
              2008-       1.7302    0.2056     -    0.1869 17508719.58        4.5
                2                            0.06                                       4.7
              2009-       2.0433    0.1397     -    0.2096 20411075.87        5         5.2
                1                            0.01
              2009-       2.1283    0.0559   0.00   0.2376 27452417.66        5.5       5.7
                2




6
    Semi-annual averages

                                                    38
APPENDIX II
Relationship between return and the predictor variables showing in the scatter plots

                                   Return VS Standard Deviation

            4


            3


            2


            1
  RETURN




            0


           -1


           -2


           -3
                 0.00   0.75         1.50           2.25           3.00           3.75   4.50
                                             STANDARD DEVIATION



                                            Return VS BETA

           4



           3



           2



           1
  Return




           0



           -1



           -2



           -3
                -0.8    -0.4          0.0              0.4          0.8            1.2    1.6
                                                    BETA




                                                  39
                                           Return VS NAV

         4



         3



         2



         1
Return




         0



         -1



         -2



         -3
              0.0         0.2              0.4               0.6           0.8             1.0
                                                     NAV


                                      Return VS FUND SIZE

         4



         3



         2



         1
Return




         0



         -1



         -2



         -3
              0     20000000    40000000         60000000     80000000   100000000   120000000
                                                 FUND SIZE




                                                  40
                               RETURN VS FUND AGE

         4



         3



         2



         1
Return




         0



         -1



         -2



         -3
              0.0   2.5      5.0             7.5        10.0   12.5   15.0
                                        FUND AGE




                          RETURN VS MANAGEMENT TENURE

         4



         3



         2



         1
Return




         0



         -1



         -2



         -3
              0.0   2.5      5.0             7.5        10.0   12.5   15.0
                                    Management tenure




                                        41

								
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