INVESTMENT PERFORMANCE OF ISLAMIC VERSUS CONVENTIONAL...

INVESTMENT PERFORMANCE OF ISLAMIC

VERSUS CONVENTIONAL MUTUAL FUNDS:

EVIDENCE FROM MALAYSIA

FADILLAH MANSOR Bachelor of Shariah (honours) (major in economics) – University of Malaya

MBA (major in finance) – University of Malaya

A thesis submitted in total fulfilment of the requirements for the degree of

Doctor of Philosophy

Department of Finance La Trobe Business School

Faculty of Business, Economics and Law La Trobe University, Bundoora

Melbourne, Victoria 3086 Australia

November 2012

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STATEMENT OF AUTHORSHIP

Except where reference is made in the text of the thesis, this thesis contains no

material published elsewhere or extracted in whole or in part from a thesis submitted

for the award of any other degree or diploma.

No other person’s work has been used without due acknowledgement in the main text

of the thesis.

All research procedures which require ethical approval reported in this thesis were

approved by the relevant Ethics Committee.

This thesis has not been submitted for the award of any degree or diploma in any

other tertiary institution.

FADILLAH MANSOR

11 NOVEMBER 2012

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DEDICATION

This thesis is dedicated to my husband, Zainal, to my mother, Rakiah, to my late

father, Mansor, and especially to my children, Farah, Yasmin, Imran, Nadia and

Fareez, in recognition of their support, sacrifice, tears and endless love throughout my

life.

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ACKNOWLEDGEMENTS

Many people have been involved in this ‘sweet and sour’ PhD journey and to them I

express my sincere appreciation. I would like to give my special gratitude to my PhD

supervisors for their kindness and friendship that I can thank you enough. I am

extremely indebted and grateful to my principal supervisor, Associate Professor Dr

Ishaq Bhatti, for his invaluable guidance, assistance, motivation, advice and

continuous support during my PhD study. I would also like to record my sincere

thanks and deep gratitude to my second supervisor, Dr Hayat Khan, for his ideas,

invaluable assistance in the computation and analysis parts using EViews and Stata,

and his cooperation and help and throughout the supervision.

I am indebted to many other individuals who provided suggestions, assistance and

friendship at different stages throughout my PhD journey. Special thanks to Dr Robin

Luo, my ex co-supervisor, and La Trobe University grant 2008 for providing me

access to the data from the Morningstar database. I really appreciate his assistance and

kindness, especially in the data collection and early computation stage during my first

year of the study. I am also indebted to Professor Mohamed Ariff for significant input,

comments and genuine interest in my work.

I would also like express my sincere appreciation to Professor Michael Skully,

Professor Munawar Iqbal, Professor Andrew Worthington and participants of the 14th

Banking and Finance Conference 2009 at the University of Melbourne for suggestions

and comments at the beginning of this study. Thanks also go to Professor Zia Haqq,

Professor Robert Clift and participants of the 13th International Business Research

Conference 2010 in Melbourne for ideas and suggestions. I also thank Professor

Leighton Vaughan Williams and participants of the 6th International Money,

Investment and Risk 2011 at Nottingham, UK, for ideas and suggestions.

Appreciation also goes to Professor Abdullah Saeed, Professor Mervyn Lewis,

Professor Shamsher Mohamad and participants in Ethics in Financial Transactions

and Society: The Way Forward 2011 at Melbourne for their interest and helpful

comments. Thanks also go to Professor Ali, Professor Loredana Ureche Rangau and

participants in the Global Finance Conference 2012 at Chicago for useful comments

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and suggestions. I would also like to express my sincere thanks to Dr Laszlo Konya

and Dr John Shahnon for consultation and assistance with EViews, Stata and other

econometrics matters.

My PhD student colleagues whom I met along this journey are worthy of special

mention. Kak Hada, Lin, Sally, Kak Sham who are always there every time I need

help, Azwan, Wahida, Faridah, Azida, Fahmida, Shima, Azni, Nani, Rami, Mas, Wan

and many more, in particular my roommate, Nga, and my ex-roomates, Sabeha, Dr

Laura, Dr Tasha, Dr Ros and Dr Angela, thanks to all of you for invaluable

friendship. My grateful thanks also go to my neighbours, Tashi and Reme, for their

helping hands to me and my family during our hard times. Sincere appreciation to my

close friends back home, Yati, Kak Na and Zan, for prayers, assistance and never-

ending motivation. My appreciation also to Head of Department and staff of the

Department of Finance (formerly the School of Economics and Finance), La Trobe

University, for providing the necessary facilities and resources needed to work on my

thesis. Thanks also to the University of Malaya and the Ministry of Higher Education

Malaysia for granting a scholarship and study leave to pursue this study. Extended

thanks also go to Phillip Thomas and Annie Ryan for editing and proofreading my

thesis according to the Australian Standards for Editing Practice (Standards D and E).

Undertaking this PhD would have been very hard without the help, motivation, love

and never-ending support from my hubby, Zainal, and my lovely kids, Farah, Yasmin,

Imran, Nadia and Fareez. I owe my life to them, and deep gratitude for cheering me

up and giving my life meaning. Deep gratitude is also extended to my mum, brothers

and sisters, my big family and in-laws for their prayers, support and encouragement,

especially during my hard times. Last but not least, I thank Allah, the Almighty, for

giving me a good health, success and strength, spiritually and physically, to complete

this tougher task.

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TABLE OF CONTENTS

........................................................................................................ PAGE STATEMENT OF AUTHORSHIP ............................................................................ ii

DEDICATION ......................................................................................................... iii

ACKNOWLEDGEMENTS ...................................................................................... iv

TABLE OF CONTENTS .......................................................................................... vi

LIST OF ABBREVIATIONS ................................................................................... ix

LIST OF TABLES .................................................................................................. xii

LIST OF FIGURES ................................................................................................ xiv

LIST OF APPENDICES ......................................................................................... xiv

LIST OF CONFERENCE PRESENTATIONS ........................................................ xv

LIST OF PUBLICATIONS ................................................................................... xvii

ABSTRACT OF THESIS ..................................................................................... xviii

CHAPTER 1- INTRODUCTION .......................................................................... 1

1.1 Introduction ................................................................................................... 1

1.2 Issues and motivation of the thesis ................................................................. 4

1.3 Objectives of the study ................................................................................... 9

1.4 Contributions of the study ............................................................................ 10

1.5 Structure of the thesis ................................................................................... 13

CHAPTER 2 - LITERATURE REVIEW AND BACKGROUND OF ISLAMIC MUTUAL FUND INDUSTRY ............................................................................. 16

2.1 Introduction ................................................................................................ 16

2.2 Background of the Islamic mutual fund industry .......................................... 19

2.3 The development of the Malaysian mutual fund industry .............................. 23

2.4 IMFs versus CMFs ....................................................................................... 32

2.4.1 Salient features of IMFs .................................................................. 34

2.5 Islamic finance and Islamic investments ....................................................... 37

2.5.1 Fundamentals of Islamic finance ..................................................... 37

2.5.2 Islamic finance and the principle of Islamic investments ................. 40

2.5.3 Islamic finance and the impact of the global financial crisis ............ 42

2.6 Theoretical framework for mutual fund performance .................................... 46

2.6.1 Performance measurement against market benchmark .................... 46

2.6.2 Market timing expertise of fund managers ...................................... 49

2.6.3 Performance persistency ................................................................. 52

2.6.4 Empirical evidence on fees and fund attributes on performance ...... 54

2.6.5 Previous studies on ethical and Islamic funds .................................. 62

2.7 Summary ..................................................................................................... 78

CHAPTER 3 - RESEARCH METHODOLOGY ................................................ 79

3.1 Introduction ................................................................................................. 79

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3.2 Variables ...................................................................................................... 79

3.2.1 Dependent variables....................................................................... 79

3.2.2 Independent variables ..................................................................... 80

3.3 Hypotheses development .............................................................................. 81

3.4 Model specifications and the methodologies................................................. 83

3.4.1 Descriptive statistics and mean pair t-test ........................................ 83

3.4.2 Standard risk adjusted performance measures ................................. 84

3.4.3 The models ..................................................................................... 88

3.4.4 Time series regression analysis ....................................................... 95

3.4.5 Panel data regression analysis ........................................................ 96

3.5 Econometric estimation issues .................................................................... 101

3.5.1 Data stationary and test of normality ............................................. 101

3.5.2 Heteroskedasticity and positive serial correlation .......................... 102

3.5.3 Multicollinearity problem ............................................................. 102

3.6 Summary ................................................................................................... 103

CHAPTER 4 - RISK AND RETURN PERFORMANCE ANALYSIS ............ 105

4.1 Introduction ............................................................................................... 105

4.2 Issues and related studies ........................................................................... 106

4.3 The data and sample selection .................................................................... 109

4.3.1 Survivorship bias .......................................................................... 111

4.4 Results and discussions .............................................................................. 112

4.4.1 Descriptive statistics of IMFs and CMFs ...................................... 112

4.4.2 Test of normality .......................................................................... 115

4.4.3 Covariance and correlation analysis .............................................. 116

4.4.4 Risk-aversion analysis ................................................................. 118

4.4.5 Trend analysis ............................................................................. 121

4.4.6 Mean differences between IMFs and CMFs .................................. 123

4.4.7 Non-risk-adjusted performance of the funds and the crises ............ 126

4.4.8 Results for risk-adjusted return performance measurements .......... 129

4.4.9 CAPM performance analysis and the crises .................................. 132

4.5 Summary ................................................................................................... 135

CHAPTER 5 - MARKET TIMING EXPERTISE AND FUND SELECTIVITY SKILL: TIME SERIES DATA ANALYSIS ...................................................... 137

5.1 Introduction ............................................................................................... 137

5.2 Issues and the significance of the chapter ................................................... 138

5.3 Data sample ............................................................................................... 142

5.4 Results for and discussions of the market benchmark ................................. 144

5.4.1 Single CAPM performance analysis.............................................. 144

5.4.2 CAPM multiple benchmarks performance analysis ....................... 147

5.5 Results for and discussion of the market timing .......................................... 150

5.5.1 Market timing expertise and fund selectivity skill ......................... 150

5.5.2 Performance analysis on market timing and asset classes .............. 155

5.5.3 Correlation between market timing and fund selectivity skill ........ 159

5.5.4 Funds diversification .................................................................... 160

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5.6 Summary ................................................................................................... 162

CHAPTER 6 - MARKET TIMING EXPERTISE AND FUND SELECTIVITY SKILL: PANEL DATA ANALYSIS .................................................................. 164

6.1 Introduction ............................................................................................... 164

6.2 Data sample ............................................................................................... 164

6.3 Results and discussions on panel data ......................................................... 165

6.3.1 Single factor CAPM performance ................................................. 165

6.3.2 Market timing performance based on TM model ........................... 170

6.3.3 Multi-factor CAPM performance .................................................. 173

6.3.4 Market timing performance based on extended TM model ............ 176

6.4 Summary ................................................................................................... 179

CHAPTER 7 – FEES IMPACT AND FUND ATTRIBUTES ON EQUITY MUTUAL FUNDS PERFORMANCE ............................................................... 181

7.1 Introduction ............................................................................................... 181

7.2 Related literatures on fees and the fund attributes ....................................... 182

7.3 Data sample ............................................................................................... 185

7.3.1 Multicollinearity ........................................................................... 189

7.4 Results and performance analysis ............................................................... 192

7.4.1 Descriptive statistics on fund samples and fund attributes ............. 192

7.4.2 Single factor OLS regression ........................................................ 198

7.4.3 Market timing expertise and fund selectivity skill ......................... 200

7.4.4 Results of panel data using FEs and REs on TM model ................. 202

7.4.5 Fees and other fund attributes on single factor regression analysis 205

7.4.6 Fees and other fund attributes on multi-factor regression .............. 210

7.5 Summary ................................................................................................... 225

CHAPTER 8 - SUMMARY AND CONCLUSION ........................................... 228

8.1 Background of the thesis ............................................................................ 228

8.2 Summary of the thesis ................................................................................ 229

8.3 Key findings .............................................................................................. 238

8.3.1 Risk and returns performance ....................................................... 238

8.3.2 Expertise in market timing and fund selectivity skill ..................... 240

8.3.3 Fees impact and fund attributes on fund performance ................... 241

8.3.4 New improved model and extended literatures .............................. 242

8.4 Implications of this study ........................................................................... 246

8.4.1 Implications for policy-makers and regulators .............................. 248

8.4.2 Implications for fund management companies and fund managers 250

8.4.3 Implications for investors ............................................................. 251

8.4.4 Implications for researchers .......................................................... 253

8.5 Limitations ................................................................................................. 253

8.6 Suggestions for future research .................................................................. 254

APPENDICES ....................................................................................................... 256

REFERENCES ...................................................................................................... 269

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LIST OF ABBREVIATIONS

ADF Augmented Dickey-Fuller

Adj R2 adjusted returns

AEFs all equity funds

AIC Akaike Information Criteria

AFC Asian financial crisis

AMFs all mutual funds

AUM asset under management

APEC Asia-Pacific Economic Cooperation

AR Appraisal ratio

ASR adjusted Sharpe ratio

BNM Bank Negara Malaysia (Central Bank of Malaysia)

BPLM Breusch and Pagan LM test

CAGR compounded annual growth rate

CAPM capital asset pricing model

CDOs collateralised debt obligations

CDS credit default swaps

CEFs conventional equity funds

CIC Capital Issues Committee

CMFs conventional mutual funds

CMP capital market plan

CRSP Center for Study of Security Prices

CV coefficient of variation

CVAR coefficient variance decomposition

DEA data envelope analysis

DJIM Dow Jones Islamic Market

DJSI Dow Jones Sustainability Index

Diff. Different

dTYPE a dummy variable, written as 1 if the Islamic fund or 0 if the

conventional fund.

DW Durbin-Watson

ETFs exchange traded funds

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FEs fixed effects

FIMM Federation of Investment Managers Malaysia (formerly known

as Federation of Malaysian Unit Trust Managers [FMUTM])

GCC Gulf Cooperation Council

GDP gross domestic products

GFC global financial crisis

ICI investment company institute

ICM Islamic capital market

IEFs Islamic equity funds

IMFs Islamic mutual funds

JA Jensen alpha

JB Jarque-Bera

KLCI Bursa Malaysia Kuala Lumpur composite index

KLIBOR Kuala Lumpur interbank rate

KLSE Kuala Lumpur Stock Exchange or Bursa Malaysia

KLSE’s MC Bursa Malaysia market Capitalization

KLSE small-cap KLSE Malaysian small-cap index

KLSI Kuala Lumpur Syariah Index

Kt kurtosis

MF mutual fund

M2 Modigliani-Modigliani measure

MSCI Morgan Stanley Capital International World Index

MYR Malaysian Ringgit

NAVs net asset values

OLS ordinary least squares

PLS profit and loss sharing

REs random effects

rf risk free rate

ROC registrar of companies

SC Securities Commission of Malaysia

SEC US Securities and Exchange Commission

SFR single factor regression

SIRCA Securities Industry Research Centre of Asia-Pacific

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Sk skewness

SR Sharpe ratio

SRI socially responsible investment

Std. Dev standard deviation

TFP total factor productivity

TI Treynor index

TM model Treynor Mazuy model

TNA total net assets

UK United Kingdom

US United States of America

VIF variance inflation factor

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LIST OF TABLES

Table 1.1: Structural framework of the thesis ........................................................... 15

Table 2.1: The main differences between IMFs and CMFs ....................................... 33

Table 2.2: Main differences between Islamic finance and conventional finance ....... 39

Table 2.3: Summary of some previous empirical evidence on fund performance ...... 68

Table 4.2: The IMFs and CMFs monthly returns performance: summary statistics . 113

Table 4.3: Summary statistics of IMFs and CMFs non-risk-adjusted returns in relation

to the AFC and the GFC ........................................................................................ 115

Table 4.4: Covariance and correlation between IMFs, CMFs and the market portfolio

.............................................................................................................................. 117

Table 4.5: Overall average returns and standard deviations for Malaysian IMFs and

CMFs, January 1990 – April 2009: Regression results ........................................... 119

Table 4.6: Results of the unit root tests .................................................................. 123

Table 4.7: Results of mean t-test assuming equal variances for IMFs and CMFs .... 125

Table 4.8: Non-risk-adjusted return performance of the mutual funds .................... 127

Table 4.9: Fund performance based on risk-adjusted return measurements ............. 130

Table 4.10: CAPM performance analysis and the crises ......................................... 132

Table 5.1: CAPM analysis of the portfolios against the conventional and Islamic

benchmarks ........................................................................................................... 145

Table 5.2: CAPM performance analysis based on multiple benchmarks ................. 149

Table 5.3: Market timing expertise of IMFs and CMFs fund managers .................. 152

Table 5.4: Comparative market timing analysis for the TM and extended TM models

.............................................................................................................................. 154

Table 5.5: Comparative market timing analysis by asset class for TM and extended

TM models ............................................................................................................ 157

Table 5.6: Correlation between fund selectivity and market timing ........................ 159

Table 5.7: Diversification level of mutual funds ..................................................... 161

Table 6. 1(a): Single CAPM analysis using panel data REs GLS regressions ......... 167

Table 6. 1(b): Single CAPM analysis using panel FEs regressions ......................... 168

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Table 6. 2(a): Market timing expertise of IMFs and CMFs fund managers using GLS

REs regression. ...................................................................................................... 171

Table 6. 2(b): Market timing expertise of IMFs and CMFs fund managers using FEs

.............................................................................................................................. 172

Table 6. 3(a) Multi-factor CAPM analysis using panel data REs GLS (within)

regression .............................................................................................................. 174

Table 6. 3(b): Multi-factor CAPM analysis using panel FEs (within) regression .... 175

Table 6.4(a) Market timing analysis using panel data REs GLS (within) regression 177

Table 6. 4(b): Market timing analysis using panel FEs (within) regression ............. 178

Table 7. 2: Description on mutual fund samples and the fund attributes ................. 194

Table 7. 3:Descriptive statistics of IEFs, CEFs and AEFs relative to market and risk

free portfolios. ....................................................................................................... 195

Table 7.4: Mean test of statistical differences between IEFs, CEFs and AEFs,

comparative to market and risk free portfolios, based on panel data ....................... 197

Table 7. 5: Results of pooled OLS using single factor regression model ................. 198

Table 7. 6: Results of market timing ability based on pooled OLS panel analysis ... 201

Table 7. 7: Panel data regression results using FEs and GLS REs .......................... 203

Table 7. 8: Cross sectional analysis of returns versus fees and other fund attributes

based on single factor panel REs regression. .......................................................... 207

Table 7.9: Fees and fund attributes on gross returns of AEFs, 1990–2009. ............. 212

Table 7. 10: Fees and fund attributes on IEFs returns performance, 1990–2009 ..... 218

Table 7. 11: Fees and fund attributes on returns of CEFs, 1990–2009. ................... 220

Table 8. 1: Summary of the results ......................................................................... 236

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LIST OF FIGURES

Figure 2.1: Islamic funds by domicile of clients, 2007 and 2011 .............................. 21

Figure2.2: Assets of Islamic funds by geographic mandate, 2007 and 2011 ............. 22

Figure 2.3: Growth of the Malaysian mutual fund industry, 1992–2012 (February) .. 24

Figure 2.4: Growth in numbers of the Malaysian mutual fund industry, 1992–2012

(February)................................................................................................................ 29

Figure 2.5: Scatter plot of the ratio percentage of the annual NAV for IMFs and CMFs

portfolios to the total NAV of the industry and to KLSE market capitalisation, from

1993 to 2012. ........................................................................................................... 31

Figure 4.1: IMFs and CMFs graphs of normality, January 1990 to April 2009 ....... 116

Figure 4.2: The Scatter Plot for the Malaysian Islamic and Conventional mean returns

versus their average standard deviation (in percentage), January 1990 to April 2009.

.............................................................................................................................. 120

Figure 4.3: The relationship between the aggregate return performance of IMFs and

CMFs relative to the market portfolio .................................................................... 121

Figure 4.4: The trend pattern for the return of the IMFs and CMFs portfolios relative

to the market portfolio, January 1990 to April 2009 ............................................... 122

LIST OF APPENDICES

Appendix A Descriptive statistics of the Islamic and Conventional mutual funds in

Malaysia, 1992 to February 2012. .......................................................................... 256

Appendix B Year on year changes in the IMFs and CMFs, 1999-2012 .................. 258

Appendix C The differences between Islamic and Conventional financial products 259

Appendix D GFC and the major events .................................................................. 266

Appendix E CAPM analysis against Islamic and conventional benchmarks ........... 267

Appendix F TM model for market timing expertise of the fund managers .............. 268

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LIST OF CONFERENCE PRESENTATIONS

1. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “The Performance of the Islamic Mutual Funds: Malaysian Example”. Paper presented at the International Symposium of Islamic Banking and Finance at the InterContinental Melbourne, The Rialto, organised by La Trobe University, National Australia Bank (NAB) and the Muslim Community Corporation of Australia (MCCA) on 6 July 2009.

2. Mansor, Fadillah (2009). “Investment in the Islamic Mutual Funds: The Malaysian Performance”. Paper presented at Internal Workshop, organised by School of Economics and Finance, La Trobe University on 27 August 2009.

3. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “The Performance of Islamic

Mutual Funds: The Malaysian Case”. Paper presented at the 14th Banking and Finance Conference, organised by Financial Services Institute of Australasia (Finsia) and Melbourne Centre for Financial Studies at University of Melbourne, on 28–29 September 2009.

4. Mansor, Fadillah and Bhatti, M. Ishaq (2009). “Islamic Unit Trusts

Development: Evidence from the Malaysian Unit Trusts Industry”. E-proceeding, paper presented at the 13th Annual Waikato Management School Student Research Conference, organised by University of Waikato at Hamilton, New Zealand, on 20 October 2009.

5. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “Developments in Islamic

Finance: Case Study on Islamic Mutual Funds”. Paper presented at the Islamic Finance Australia Conference 2010 at the Rendezvous Hotel, Melbourne, Australia on 9 June 2010.

6. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “The Performance of the Islamic

Mutual Funds in Malaysia: Risk and Return Analysis”. Paper presented at the 13th International Business Research Conference at the Novotel Hotel on Collins, Melbourne, Australia on 22–24 November 2010.

7. Mansor, Fadillah and Bhatti, M. Ishaq (2010). “Investment Performance of the

Islamic Mutual Funds: A Case Study on the Selected Fund Managers’ Companies in Malaysia – A Proposal”, Paper presented at the Malaysian Students LTU Round Table, organised by La Trobe University Postgraduate Association on 23 November 2010.

8. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Islamic Mutual Fund

Performance: New evidence on Market Timing and Stock Selectivity”. Proceeding paper presented at the 2011 International Conference on Economics and Finance Research at Singapore on 26–28 February 2011.


Performance: Evidence on Market Timing and Stock Selectivity”. Paper

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presented at the 2011 Second Foundation of Islamic Finance Conference at Kuala Lumpur, Malaysia, on 8–9 March 2011.

10. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “Islamic Mutual Fund

Performance for Emerging Market, during Bullish and Bearish: The Case of Malaysia”. Paper presented at the 2nd International Conference on Business and Economic Research at Langkawi, Malaysia, on 14–16 March 2011.

11. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “The Investment Performance of

the Islamic Mutual Funds in the Period of 1996–2009”. Paper presented at the 6th International Money, Investment and Risk at Nottingham Trent University, Nottingham, UK, on 3–5 April 2011.

12. Mansor, Fadillah, Bhatti, M. Ishaq and Abd Rahman, Nor Hadaliza (2011). “The

Performance of Islamic Mutual Funds: A Comparative Study”. Paper presented at the International Conference on Economics and Finance at Izmir, Turkey, on 15–17 April 2011.

13. Bhatti, M. Ishaq and Mansor, Fadillah (2011). “Impact of Fees on Ethics-based

and Conventional Funds: Do Ethics and Fees Make a Difference to Performance? Paper presented at Ethics in Financial Transactions & Society: The Way Forward at University of Melbourne, Australia, on 17–18 September 2011.

14. Mansor, Fadillah, Bhatti, M. Ishaq and Ariff, Mohamed (2012). “New Evidence

of the Impact of Fees on Mutual Fund Performance of Two Types of Funds”. Paper presented at the Global Finance Conference, 19th Annual Meeting, Chicago, IL, USA, on 23–25 May 2012.

15. Mansor, Fadillah and Bhatti, M. Ishaq (2012). “Islamic Mutual Funds

Performance: A Panel Analysis”. Paper presented at the 2nd Malaysian Postgraduate Conference (MCP2012), Bond University, Gold Coast, Queensland, on 7–9 July 2012.

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LIST OF PUBLICATIONS

Journals

1. Mansor, Fadillah and Bhatti, M. Ishaq (2011). “Risk and Return Analysis on

Performance of the Islamic Mutual Funds: Evidence from Malaysia”, Global

Economy and Finance Journal, Vol. 4, No. 1, pp. 19-31.

2. Mansor, F. (2010). “Developments in Islamic Banking: The Case of Pakistan” – By M. Mansoor Khan and M. Ishaq Bhatti. Asian Politics & Policy, Vol. 2, No. 2, pp. 301–303. doi: 10.1111/j.1943-0787.2010.01194.x (Book Review).

3. Mansor, Fadillah. (2009). “The Islamic Finance Position and the Global Crisis”. Dialogue Asia-Pacific, No. 21, pp. 12-14.

Conference Proceedings

1. Mansor, Fadillah and Bhatti, M. Ishaq (2012). “Islamic Mutual Funds Performance: A Panel Analysis”. Proceedings of the 2nd Malaysian Postgraduate Conference (MPC2012), Bond University, Gold Coast, Queensland, Australia, 7–9 July 2012, pp.140-154.

2. Mansor, Fadillah, Bhatti, M. Ishaq and Ariff, Mohamed (2012). “New Evidence

of the Impact of Fees on Mutual Fund Performance of Two Types of Funds”. E-Proceedings of 2012 Global Finance Conference, 19th Annual Meeting, Chicago, IL, USA, 23–25 May 2012, p. 50.

3. Fadillah Mansor and Bhatti, M. Ishaq (2011), “The Islamic Mutual Fund

Performance: New Evidence on Market Timing and Stock Selectivity”. ISI Proceedings of 2011 International Conference on Economics and Finance Research, 26–28 February 2011, pp. 487-494.


Performance: Evidence on Market Timing and Stock Selectivity”. E-Proceedings of Second Foundation of Islamic Finance Conference, Kuala Lumpur, Malaysia, 8–9 March 2011.

5. Fadillah Mansor and Bhatti, M. Ishaq (2011) “Islamic Mutual Fund Performance

for Emerging Market, during Bullish and Bearish: The case of Malaysia”. Proceedings of 2nd International Conference on Business and Economic Research, pp. 770-789.

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ABSTRACT OF THESIS

The main objective of this thesis is to investigate the performance of Islamic mutual

funds (IMFs) in comparison with the conventional mutual funds (CMFs). Risk and

return relationship is evaluated relative to market benchmark over a 20-year period

from 1990 to 2009. The data covers 479 mutual funds from the Malaysia, consisting

of 129 IMFs and 350 CMFs covering all asset classes (alternative, allocation, equity,

fixed income and money market funds). The mutual fund industry in Malaysia is

unique in the sense that IMFs account for 29 per cent of the world’s Islamic funds: the

growth worldwide in 2011 was also the highest. Several methods are employed in this

study. It starts with descriptive statistics and an analysis based on risk adjusted

performance and moves on to time series and panel data analysis. The analysis

focuses on risk and return performance, market timing and fund selectivity and the

impact of funds attributes including age and fees among other standard attributes. The

thesis uses Sharpe, Treynor and Jensen, single and multi-factor CAPM, quadratic

version of the single CAPM by Treynor and Mazuy for its analysis. The thesis finds

that the performance of IMFs is different from the CMFs peers. Unlike previous

studies, this thesis finds evidence of IMFs and CMFs outperforming market

benchmarks. In particular, results reveal that the IMFs performed better than CMFs

during the financial crisis and pre-crisis periods. In contrast, the CMFs outperformed

the IMFs during the post-crisis period. On average, IMFs are more sensitive to a

single market benchmark, while CMF performed better on multiple benchmarks.

Panel data analysis shows that IMFs managers outperformed CMFs and had superior

funds selectivity skills. There was however no evidence of market timing expertise of

the both fund managers. The impact of fees is the final evaluation. While focusing on

equity funds, the thesis finds that fee attributes had a significant impact on the

performance of equity funds. Overall, the findings reveal that fees charged have an

adverse impact on fund performance. A variety of fees were taken into account in the

analysis including management fee, expense ratio, trustee fee, and total load fee. Fees

had a hump-shaped, overall positive impact, on the performance of Islamic equity

funds (IEFs) and an inverted U-shaped, overall, negative impact on the performance

of conventional equity funds (CEFs). These factors could suggest that the fee

incentives are more important to the IEFs compared to that of CEFs, thus contributing

to the outperformance of the funds.

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CHAPTER 1- INTRODUCTION

1.1 Introduction

The rapid growth of investment in mutual funds globally and its significance to the

economic development of a country makes the study of mutual fund performance

important. In a high-tech and globally competitive financial market in the 21st century

where traders are trading funds and stocks using highly sophisticated powerful

computing aids such as iPads, tablets and/or cloud technology, the management of

portfolio funds and investment selections is both complex and versatile. Funds

managed can vary from ethics-based funds to faith-based funds with the margin

narrowing by a fraction of a cent over a million asset values in a second. The situation

makes global and domestic researchers interested in expanding their research area

from standard performance measures to more complicated measures such as

determinant factors and attributes using panel regressions. This expansion of interest

is increasing now with the introduction of various types of mutual funds, including

real estate investment trusts (REITs) and funds of funds.

The current scenario makes the study of mutual fund performance significant,

especially when it comes to new classes of asset such as Islamic mutual funds (IMFs).

Therefore, the study of the performance of IMFs compared to conventional mutual

funds in emerging markets such as Malaysia makes this thesis a timely contribution

that is of value to policy-makers and investors for understanding the benefits of

investing in this specific market.

The study of IMFs in Malaysia is significant as this is the only country that provides a

dual-system of Islamic and conventional financial markets within the same

infrastructure. Malaysia serves the Islamic mutual fund industry on a par with the

existing conventional mutual fund industry. Both Islamic and conventional funds are

in demand among investors who are looking for long-term investment with high

returns. Malaysia is also one of the countries in the emerging financial markets and a

founding member of Asia-Pacific Economic Cooperation (APEC), which was

Page | 2

established in 1989 to promote open trade and practical economic cooperation among

the Asia-Pacific economies (Worthington and Higgs 2004). Studying a country in the

emerging markets provides an opportunity to identify whether the characteristics and

behaviour of mutual funds (MFs) in less developed markets are similar to those in

developed and highly efficient markets (Bialkowski and Otten 2011).

The global demand for Islamic funds in particular was mostly driven by the steadily

increasing oil prices which peaked in 2007–2008, leaving Muslim investors in the

Gulf Cooperation Council (GCC) countries with excess liquidity to be invested. In a

growing population, the increasing numbers of Muslim clients who are looking to

invest in Islamic financial markets will increase their demand for investments in fund

portfolios. According to Pew Research Center’s Forum on Religion and Public Life,

the world’s Muslim population is expected to increase by about 35 per cent in the next

20 years, rising from 1.6 billion in 2010 to 2.2 billion by 2030. The figure will

represent 26.4 per cent of the world’s total projected population of 8.3 billion in 2030.

The Muslim population globally is forecast to grow at about twice the rate of the non-

Muslim population over the next two decades – an average annual growth rate of 1.5

per cent for Muslims compared with 0.7 per cent for non-Muslims (Pew-Research-

Center, 2011).

The issue here is the lack of research on the performance of Islamic funds. This thesis

attempts to fill this gap. The existing literature reveals that very few studies have

examined Islamic mutual funds (IMFs) or compared their performance with that of

conventional mutual funds (CMFs). The increasing demand for IMFs in particular and

the role of Islamic finance in the global market more generally are creating

opportunities for further exploration. Islamic finance is growing as a source of finance

not only for Muslim investors but also for other investors worldwide. The 2007–2008

global financial crisis (GFC) highlighted the importance of Islamic finance worldwide

and made the Islamic fund industry more popular in the international financial market.

At the same time, investment in mutual funds is becoming an increasingly arduous

task in the aftermath of the GFC. Due to the application of the Shariah principles of

Islamic finance, the Islamic fund industry is more resilient to recession. The Islamic

capital market is independent of the global financial market and therefore is insulated

Page | 3

from any external financial crisis. In addition to filling a gap in the finance literature

on Islamic funds, this study is important as it provides recent results on fund

performance, especially evidence from emerging markets.

There is also little empirical research on the relationship between fees charged by

fund managers and fund performance in an emerging market economy such as

Malaysia. This study adds new knowledge, particularly on the Islamic mutual fund

area, by investigating the relationship between fees and fund performance before and

after fees is incurred.

The investment performance of IMFs is also an interesting issue to investigate as an

alternative to CMFs and ethical mutual funds or socially responsible investment funds

(SRI). Several studies (such as Abderrezak, 2008; Abdullah et al. 2007; Elfakhani, M.

K. Hassan, and Y. Sidani, 2005; Elfakhani and Hassan 2005; Hayat and Kraeussl,

2011; Hoepner, Rammal, and Rezec, 2011; Kraeussl and Hayat, 2008) have examined

this issue. The major finding has been that IMFs perform better than CMFs only in a

bearish market. Most of these studies have employed standard performance

measurements and therefore the new research presented in this thesis enhances the

analytical methodologies to include other sophisticated econometrics and statistical

application methods.

Accordingly, this thesis builds on previous studies’ findings by examining the

performance of IMFs and CMFs. It is not limited to equity funds but includes more

diversified funds and more extensive data. This study considers a longer than usual

duration, a 20–year period from 1990 to 2009, and involves a larger number of mutual

funds in one country at one time, consisting of 535 mutual funds. Instead of using

weekly data that tend to suffer more fluctuations from market movements, the study

uses monthly and yearly returns data for analysis. The source of returns is the

Morningstar database and the returns provided are based on gross return and are net of

all expenses except the front and exit fees.

This study is conducted with the main objective of investigating investment

performance by examining mutual fund portfolios, concentrating on the returns

Page | 4

performance measurements of IMFs and CMFs. Another intention is to broaden the

evidence that the industry has improved with time, as mentioned by Elfakhani et al.

(2005), and to verify the evidence of outperformance or underperformance of the

Islamic funds and their conventional peers. This has been done by using recent data

and sophisticated statistical and econometric methods related to time series and panel

data regression analysis. The time series incorporates mean aggregate returns and the

panel data include individual fund returns. The models employed are various risk-

adjusted performance measures, standard performance based on CAPM single and

multiple benchmarks, the Treynor and Mazuy model (TM model), and also other

multiple regression models (see Section 3.4.3 for more detail).

The rest of this introductory chapter is structured as follows. Section 1.2 discusses the

related issues and motivation for conducting this study. Section 1.3 lists the objectives

of this study, and Section 1.4 explains the expected contributions of this thesis.

Finally, Section 1.5 describes the structure of the thesis.

1.2 Issues and motivation of the thesis

The main issue addressed in the study reported in this thesis is the underperformance

of mutual funds relative to the market benchmark. Therefore, the study investigated

the investment performance of mutual fund portfolios, concentrating on the

comparative performance of IMFs and CMFs. The study examined the returns

performance of IMF and CMF portfolios for a single economy – the mutual fund

domicile in Malaysia.

The major motivation for choosing a Malaysian dataset of mutual funds was to

accommodate a large sample and long period of study in which the Islamic and

conventional funds have operated within the same financial system, so that the

comparison can be rigorously analysed. Malaysia is strategically important since it

represents a single economic structure which includes Islamic and conventional funds

operating within the same financial market. A significant shortcoming of related

previous studies is their restricted data samples. In Malaysian mutual fund studies, the

largest samples have been 110 funds as examined by Taib and Isa (2007) and more

recently about 265 Islamic funds examined by Hoepner et al. (2011). This thesis

Page | 5

provides a larger sample of 479 funds consisting of 129 Islamic and 350 conventional

funds from the period of January 1990, when the Malaysian Islamic mutual fund

industry started, to April 2009.

The mutual fund industry has become increasingly popular in the last few decades

with the introduction of Islamic funds globally. This has created more awareness

among investors to include these investment funds in their portfolio selections in

order to achieve a personal preference or adequate portfolio diversification. Another

justification for choosing Malaysia is the significant growth of IMFs in that country,

which represents approximately two-thirds of the IMFs worldwide, according to the

2009 data.

Investment in mutual funds is part of an important strategy that contributes to the

development of the capital market in Malaysia. In Malaysia, Islamic fund

management activities began to grow rapidly in the 1990s. Now with more than 1000

Islamic funds worldwide, Malaysia has 167 of these funds as at May 2012. With the

establishment of the Islamic Financial Services Board centred in Malaysia in 2002

and with a significantly faster growth of the Islamic funds market in Malaysia than in

other countries, Malaysia is therefore a good case for examining the performance of

IMFs and CMFs.

This study is also motivated by the fact that Malaysia has the largest clientele of

Islamic funds in the global market and also in the emerging markets; thus, this study

is implemented in order to test whether the results are different from or similar to

those of studies which have focused on the MFs in developed markets such as the US

and the UK. About 29 per cent of Islamic funds worldwide are domiciled in Malaysia

(Eurekahedge, 2011). Most previous studies on fund performance are rooted in the US

and other developed countries, leaving the emerging markets virtually unexamined

and their huge potential1 completely unacknowledged. The gaps in the research

between these two kinds of markets are not only in the sample sizes, the database

1 The mutual fund industries in emerging markets are distinctive from those in developed markets in terms of growth, competitiveness, organisational structure and information availability (Suppa-aim, 2010).

Page | 6

providers and the models employed in the studies, but also in whether the most

important, advanced research on MFs in these developed countries could be extended

to markets in countries with emerging markets.

The studies in an emerging market such as Malaysia mainly focus on evaluation

methods and still use models such as the Sharpe, Treynor and Jensen measures and

the basic CAPM, all of which have been criticised for unsatisfactory explanations of

performance. It is still questionable whether evidence from the developed markets can

be applied to emerging markets. This study examines fund performance using recent

analysis such as panel data and sophisticated models involving various factors outside

the concept of market risk which can explain return performance and make the models

more informative and innovative. The concern is not only to evaluate performance but

also to determine whether factors such as fees and other funds’ attributes influence

fund performance.

Previous studies have agreed that there is no statistical difference in the performance

of Islamic and conventional equity funds in relation to the returns of their respective

market indices downturns (Elfakhani et al., 2005; Elfakhani and Hassan 2007). They

have concluded that the performance of funds does improve over time in that fund

managers became more expert in knowing how the market works. Elfakhani and

Hassan (2007) suggested that Islamic funds do not differ substantially from other

conventional funds, although Islamic funds do appear to perform slightly better.

Girard and Hassan (2005; 2008) also found that there is no performance difference

between Islamic and non-Islamic indices since the latter outperformed their

counterparts from 1996 to 2000 but underperformed from 2001 to 2005, and thus

similar reward, risk and diversification benefits exist for both Islamic and

conventional indices under their investigation. Abderrezak (2008) found that there is

no significant difference in performance between Islamic and ethical fund portfolios

and, in fact, both are unable to outperform the S&P 500 Index, a proxy for a

conventional portfolio.

Since Islamic and conventional funds have similar features concerning the subject of

mutual fund investments, then the results of this study are significant for identifying

Page | 7

whether they also disclose the same benefits in risk and return performance. The main

features of mutual fund investments are: (1) they provide the opportunity to individual

investors to invest in pooled stocks; (2) they provide the opportunity to individual

investors to reduce risks but invest in coverage of stocks using professional portfolio

management; and (3) they provide investors with professional investment

management and portfolio selection, thus reducing the workload of investors (Firth,

1977). Therefore, it is important to identify whether or not Islamic and conventional

funds provide similar benefits and advantages associated with their performance and

fund management and services. The conjecture of this study is that there is a

difference in the returns performance of these two funds since the Islamic funds differ

from the conventional ones in many respects, such as in the prohibition of interest and

gambling in their concept and operation. In the case of Malaysia, generally there is a

difference in terms of the growth rate of these two fund portfolios, as discussed in

Chapter 2.

In most cases, mutual funds bring diversified and professional benefits, and for these

considerations, they charge fees for expenses, namely management fees and expense

ratios, and also put in place some front-load fees and a redemption penalty. Most of

the time the fee charged is higher for investors and, in fact, it is not enough to trade

off with their returns from the investment. It is also arguable whether a higher fee is

associated with higher returns in relation to the market (see for instance, Carhart,

1997; Haslem, Baker, and Smith, 2008).

In the Malaysian market, although it possesses the largest percentage of funds, it

seems that previous studies have had mixed results. While Hayat and Kraeussl (2011)

noted that the IEFs in Malaysia underperform compared to the conventional and

Islamic benchmarks, as well as performing worse during a bearish market, Hoepner et

al. (2011) contended that the Malaysian IEFs significantly outperform the

international equity market index. However, these studies only focused on the

performance of IEFs. Previously, Abdullah et al. (2007) had contradicted these

findings by showing that CEFs perform better than IEFs during good economic times

and worse during bad economic times. Hence, these inconsistent results require

further examination.

Page | 8

The performance of IMFs and CMFs was evaluated by employing more recent and

more comprehensive data than other studies, and over a longer period of time, which

is crucial. Previous studies on Islamic funds investigated only one asset class, i.e.,

equity funds, but this thesis also provides evidence for MFs’ performance in various

asset classes within a portfolio, namely alternative, allocation, fixed income and

money market funds. The Malaysian data are appropriate due to encompassing a wide

range of data about MFs returns from various asset classes such as allocation,

alternative, fixed income and money market funds from 1990 to 2009. The data

consist of 479 fund returns over the 20-year period from January 1990 to April 2009.

The country has a long experience of managing Islamic and conventional funds side

by side in the industry. The data also represent the largest clientele of IMFs in the

global market. Furthermore, the application of data in a single economy can reduce

the bias which occurs when using cross-country data due to different national

characteristics and heterogeneity in Islamic fund performance (Hoepner et al. 2011).

Evidence from Malaysia regarding mutual fund performance is very limited, despite

the rapid growth of the industry in relation to the capital market. In comparison to

most studies on developed markets, the studies on Malaysia use only a very small

number of funds, cover a shorter period and calculate returns using hand-gathered net

asset values (NAVs) of the funds. Due to the timeframe limitations of the previous

Malaysian studies, their results could be biased. Moreover, the Islamic fund industry

only started in the 1990s and it is in the early stage of development where the industry

still lacks transparency, fund managers’ experience, limitations of product

diversification and effective fund portfolio management. With the current

development of the industry, there are external factors that can also contribute to the

final results. Thus, the findings reported in this thesis are important for providing

recent evidence on the returns performance of IMFs and CMFs, particularly following

the global financial crisis (GFC) and the liberalisation of foreign-exchange

administration rules by the Malaysian Central Bank in 2007, allowing fund managers

to invest up to 50 per cent of net asset values of their MF investments in foreign

markets. In 2008, this percentage was increasing to 100 per cent foreign ownership.

This means that Islamic fund management companies are allowed to have 100 per

Page | 9

cent foreign ownership and they are also permitted to invest 100 per cent of their

assets abroad (Securities-Commission-Malaysia, 2008).

The main and associated research questions in this thesis are as follows:

1. Do both IMFs and CMFs really underperform the market? Do these two

types of funds offer similar benefits to potential investors in risk, return

and fund diversification?

2. Is the performance of these funds sensitive to any single or multiple

benchmarks, performance measurements and any type of statistical or

econometric technique used in the analysis?

3. Do these funds really act differently in bearish and bullish markets?

4. Do Islamic and conventional funds managers offer similar advantages and

benefits to potential investors in market timing expertise and fund

selectivity skill?

5. Are there any differences with regard to the performance of Islamic funds

compared to their conventional counterparts, and what is the relevance of

certain funds attributes?

1.3 Objectives of the study

To address the various issues in the problem statements and the research questions

discussed in the previous section, the objectives of this study are as follows:

1. To examine the returns performance of IMFs and CMFs domiciled in

Malaysia. Specifically, the study analyses the returns performance of each

fund portfolio against its single market benchmark and also multiple market

benchmarks, using various standard performance measurements before and

after adjusting for risk-free rate returns.

Page | 10

It has been hypothesised that the Shariah-compliant restriction applied to IMFs

could mean worse performing fund returns (Abdullah et al. 2007; Hayat and

Kraeussl 2011). However, this study contends that the returns performance of IMFs

is better than that of CMFs because the funds have grown much better (discussed

further in Chapter 2). The study also analyses the performance of each in different

sub-periods in a complete market cycle covering bullish and bearish markets.

Chapter 4 addresses this objective.

2. To analyse whether fund managers in Islamic and conventional mutual funds

have skills in fund selectivity and ability in market timing by using time series

and panel data analysis. These issues are addressed in Chapter 5 and Chapter 6

respectively.

3. To evaluate any differences in the return performance of IEFs and CEFs. With

this in mind, this study empirically explores whether fees charged on a mutual

fund have any impact on the returns performance of the fund. The study

analyses whether the fees have an adverse or beneficial impact on returns

performance and also identifies whether the fees are associated with the skills

of good fund managers. Chapter 7 discusses the impact of fees on fund

performance and provides evidence related to this objective.

4. To empirically discover the relationship between the returns performance of a

fund portfolio and fund attributes which consist of endogenous variables such

as risk (systematic risk and residual risk), type of fees, types of fund, return

factors and also the exogenous variables such as investment place of the funds

(either local or foreign market), age and size of the funds. This is done in order

to explain the differences between the Islamic and conventional equity funds,

if any. This theme is also covered in Chapter 7.

1.4 Contributions of the study

The study adds to the finance literature, particularly that related to fund management

and the Islamic mutual fund area. Since the findings of this study could raise the

Page | 11

awareness of people who are investing in mutual funds, the development and

enhancement of the industry could attract potential investors. The thesis contributes to

the growing body of literature on IMFs globally and the mutual fund industry in

Malaysia in particular. Specifically, an empirical analysis derived from time series

and panel data regression provides some information to investors regarding the

returns performance of MFs and their prospects, especially in Malaysia and other

countries that have similar fund characteristics.

Investment in mutual funds is a considerably lower risk than some other types of

investment, such as common stocks and hedge funds. The present study indicates that

the MF industry in Malaysia generally has beta values lower than 1, suggesting that

investment funds are less volatile and low in systematic risk compared to the market

return. In Malaysia, the level of awareness among investors regarding investment in

mutual funds is lower than their awareness regarding investment in common stocks.

As at 31 December 2003, the NAV of the mutual industry represented only 10.95 per

cent of the market shares, amounting to RM70.08 billion, while by February 2012, the

NAV of the industry had increased to 19.85 per cent, representing RM267.02 billion.

Fund investment is growing and therefore attracting more investors. However, this

figure is very small compared to the amounts in developed countries, which in the UK

and the US, for example, were about $US649,010 million and $US7,567,572 million

respectively at the end of 2005 (Ramos, 2009). However, the AUM outside the US

grew from 38 per cent to 54 per cent over the 10-year period from 1997 to 2007

(Ferreira, Keswani, Miguel, and Ramos, 2011). Research in this area provides

investors and market players with sufficient information regarding the performance of

mutual funds and their prospects, particularly on risk and returns trade-offs while

investing in mutual funds compared to normal stocks, based on the Malaysian market.

The evidence in this study would help market players and regulators to maximise their

profits or to achieve their investment objectives based on different market conditions.

Generally, although the movement of mutual funds is expected to follow the market

trend (Sharpe, 1966; Treynor and Mazuy 1966), it remains arguable whether mutual

funds perform better than the market benchmark (Benos and Jochec, 2011; Blake and

Timmermann, 1998; Carhart, 1997; Elton et al. 1993; Firth, 1977; Grinblatt and

Page | 12

Titman, 1994; Jensen, 1968; Malkiel, 1995). This study investigates the returns

performance of mutual funds based on different market trends, including bearish and

bullish market periods. Since the study covers a complete market cycle, the findings

of the study give investors a good basis for understanding the market behaviour of the

mutual funds, particularly in bearish and bullish market scenarios.

Other findings can provide market players with ideas of which type of mutual fund is

likely to perform better and is more correlated to market performance. The

comparative study between Islamic and conventional funds in this research also

provides investors with an alternative to diversify their investment portfolios covering

different asset classes. This exposure will enhance the effectiveness of portfolio

selection of their asset management.

The findings reported in this thesis add to the existing literatures, as they extend

previous evidence on the returns performance of Islamic funds compared to

conventional funds based on standard performance evaluation. These findings are also

related to other areas such as multiple benchmarks based on CAPM, TM model,

extended TM model, fees and other fund attributes that also contribute to the

performance of funds. The study also enhances the financial modelling applied to

Islamic mutual funds. By using panel data regression in addition to the standard

performance measurement, this constitutes an important step for encouraging more

Islamic mutual fund research in particular, extending the analysis based on various

types of financial modelling.

The emphasis on fees in this study could provide a better understanding for regulators

and fund management companies concerning how to encourage investors to invest in

mutual funds without sacrificing their investment profit for the fees. Fees or other

charges should be associated with higher returns and better fund performance, and

should not just be treated as a trade-off for the amount of investment funds. The

findings of the study inform investors of the Islamic funds industry and the

performance of the funds. Investors can understand the Shariah-compliant matters and

methods of investing in accordance with the Shariah principles, as well as the rules

and regulations relevant to Islamic finance principles.

Page | 13

With respect to the development of the Islamic fund industry, these findings can also

provide key investment information for fund managers and regulators who are

interested in managing Islamic fund portfolios or interested in enlarging their

portfolio management to include investments which comply with Shariah principles.

The Islamic funds could also serve as an investment vehicle available to Muslim

investors and other investors such as ethical or socially responsible investors, who can

maximise their portfolio selections and minimise their risk exposure (such as from

credit risk and interest rate risk) while considering this type of investment. In fact,

Islamic investment now is not just a religious matter for Muslims who want to avoid

riba in their trade transaction, but is also considered as a subset of global traditional

finance (Amin 2009).

Finally, the findings of this study add to the growing body of literature in mutual fund

performance and could contribute ideas to governments, market players and fund

management companies in establishing and enhancing their regulations and policies

pertaining to this industry. They could also inspire regulatory bodies in other

countries, particularly the Islamic countries, to supervise, implement and monitor

Islamic mutual fund investments in a way to provide further understanding of fund

characteristics that will lead to economic growth and better establishment of the fund

industry.

1.5 Structure of the thesis

The thesis has eight chapters. Additionally, Table 1.1 presents a diagram showing the

structural framework of the thesis. Chapter 1 provides an introduction. Chapter 2

contains a review of the literature on IMFs and CMFs, and a summary description of

the empirical evidence published in previous studies. The chapter also considers

studies related to the types of performance measures of mutual funds, market

benchmarking, market timing expertise, fund selectivity skill and fees, and the impact

of fees on fund performance.

Chapter 3 presents the research methodology and the sample selection of the data

used. Chapter 4 discusses the preliminary results and evaluates performance based on

Page | 14

basic descriptive statistics and risk-adjusted performance measures. Chapter 5

analyses the results based on the single-factor CAPM model and the TM model using

time series regression analysis. Chapter 6 incorporates a similar model to that

employed in Chapter 5 and then further extends the analyses by re-examining fund

performance by using other methods such as panel data regression analysis. Chapter 7

further extends the fund performance analysis and concentrates on evaluating the

performance of IEFs and CEFs, and the impact of fees and other fund attributes on the

performance of these equity funds. Lastly, Chapter 8 provides the conclusion,

summarising the most important findings, describing the implications and limitations

of this thesis, and providing suggestions for future research.

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Table 1.1: Structural framework of the thesis

METHODS

TIME SERIES

PANEL DATA

RISK ADJUSTED PERFORMANCE MEASURES

MUTUAL FUNDS PERFORMANCE

RESEARCH QUESTIONS

OBJECTIVES FINDINGS

HYPOTHESES

SINGLE BENCHMARK

MULTIPLE BENCHMARKS

FUND ATTRIBUTES

MODEL

CAPM TM model

Multiple regressions

Multi-factor CAPM

Extended TM model

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CHAPTER 2 - LITERATURE REVIEW

AND BACKGROUND OF ISLAMIC

MUTUAL FUND INDUSTRY

2.1 Introduction

In the two decades from 1990 to 2009, the mutual fund (MF) industry played a

substantial role in the global financial market. Due to an interconnected, highly

correlated market, the performance of mutual funds has also been affected by minor

shock in small financial markets such as emerging markets and major shock in major

or developed markets. In other words, a crisis in one country has had contagious

effects in other countries, directly or indirectly. In addition, bearish and bullish

markets have also had a significant impact on the MF industry.

Hence, research in the MF industry is taking off due to its complex structure of fund

managers’ choices, trading timing selection, and closing and cloning in open-ended

funds. The closing and cloning strategy, for example, is an attractive strategy for fund

managers seeking to increase their management fees and the amount of funds they

manage (Chen et al. 2012).

This chapter discusses a number of empirical studies which have investigated the

performance of mutual funds, focusing on a variety of important issues. These issues

include measurements of performance, market efficiency with reference to the

relationship between stock markets and mutual funds, market benchmarking,

performance persistence, investment style, strategies for asset allocation, portfolio

management, market timing and fund selectivity, funds attributes and survivorship

bias (Blake and Timmermann, 1998; Brown and Goetzmann, 1995; Busse and Irvine,

2006; Carhart, 1997; Cuthbertson, Nitzsche, and O'Sullivan, 2008; Firth, 1977;

Grinblatt and Titman, 1993; Henriksson and Merton, 1981; Malkiel, 1995; Sharpe,

Page | 17

1966)2. All of these studies have focused on CMFs. Those focusing on IMFs and

other religious funds are fairly new and relatively few but growing in number (see for

example, Abderrezak, 2008; Abdullah et al. 2007; Ahmed, 2007; Elfakhani et al.,

2005; Elfakhani and Hassan 2005; Elfakhani and Hassan 2007; Hayat, 2006; Ismail

and Shakrani 2003). The scope of these IMFs studies is, however, limited as they used

standard performance evaluation based on Sharpe, Treynor and Jensen ratios and

basic CAPM in their investigation with benchmarking, persistency, and market timing

ability and fund selectivity skill.

The shortcoming of the CAPM is that it just evaluates single factor analysis based on

risk and return of a market portfolio, thus suggesting the incorporation of other

variables that also can explain the returns performance of the funds. As a result, more

models have been established in recent studies which are more efficient and more

informative in explaining fund performance relating to risk and return characteristics,

market timing expertise and fund selectivity skill, and also other aspects such as fund

style, strategy and fund attributes. The present study extends these standard

performance evaluations to include multi-factor CAPM, extended TM model and

multiple regression model based on time series and panel data analysis. This is to

ensure coverage of all the relevant issues: mutual fund performance in relation to the

market benchmark; the market timing expertise and fund selectivity skill of the fund

managers; fees and their impact on fund performance; and the relationship between

fund attributes and fund performance. The multiple regression models, for example,

enable evaluation of fees and other fund attributes and their relationship to the fund

performance. The study follows Bertin and Prather (2009) for multi-factor CAPM,

and Bello and Janjigian (1997) for the extended TM model.

There are many reasons for the importance of studying the performance of mutual

funds and why it has attracted a large number of researchers in the finance literature.

In the US, the increasing attention on mutual funds is particularly due to the

significant growth of institutional financial assets, the strict regulation of mutual funds

by the US Securities and Exchange Commission (SEC) and, most importantly, the

availability of databases and rating information from Morningstar Inc., Lipper Inc.

2 The details and significant of these previous studies are explained in Section 2.6.

Page | 18

and Wiesenberger Inc. (Gallagher, 2002). In the US, the studies on the performance of

mutual funds have grown significantly since the 1960s, particularly when Sharpe

(1964, 1965b) contributed towards an understanding of how investors could manage

risk and return in their investment portfolios using the Sharpe ratio.

With regard to Islamic funds, this subject has gathered momentum since the industry

began in the 1990s. This is due to the strong demand for Shariah-compliant products,

the continuing strength of the legal and regulatory framework of Islamic finance, the

demand from conventional investors, and the ability of the industry to develop

innovative financial instruments that meet investors’ needs (Hasan and Dridi, 2010).

The development of Islamic equity funds has also been driven by the increasing

capital value of Muslim investors to invest their funds in Shariah-compliant

investment products (Derigs and Marzban, 2009).

In this chapter, the scope of the discussion on the performance of mutual funds is

limited to the risk and return characteristics of funds, the standard measurements of

performance including the risk-adjusted return, and CAPM. Other relevant issues

focused on here include the performance of mutual funds in relation to the market

benchmark, and the market timing expertise and fund selectivity skill of fund

managers. The influence of fees and other fund attributes on fund performance is also

discussed in this literature review.

The chapter is structured as follows. Section 2.2 describes background details of the

Islamic fund industry and its development worldwide. Section 2.3 explains the

background of the mutual fund industry in Malaysia. Section 2.4 provides further

details about IMF and CMF fundamentals, particularly the salient features of IMFs.

Section 2.5 explains the fundamentals of Islamic finance and Islamic investments,

which are the basic concepts underlying the implementation of the IMFs, and relates

these to implications of the global financial crisis (GFC). The scenario of the GFC

and its implications are also explained in this section. Section 2.6 explains the

theoretical framework, issues and previous empirical studies on mutual fund

performance in the US, the UK and some countries in emerging markets, including

Malaysia. This section includes discussion on previous findings related to the

Page | 19

performance of IMFs. A summary of the empirical evidence on the performance of

mutual funds is also presented in Table 2.3. Section 2.7 concludes this chapter.

2.2 Background of the Islamic mutual fund industry

Islamic finance has grown tremendously over the decades since 1990. In the early

stage of Islamic funds in the global market, the number of the funds increased from

eight in 1991 to 95 with US$5 billion assets in 2000 (Elfakhani et al., 2005, pp. 1331-

1332), and the current global Islamic financial assets have been estimated to reach

US$750 billion and are expected to expand to US$1.6 trillion by the end of 2012.

While the Islamic financial industry is gaining momentum, the global financial

landscape on the other hand is facing a crisis of proportions in that financial assets fell

by US$16 trillion in 2008 – a significant break in the three-decade-long expansion of

global capital markets – thus presenting opportunities for a sector such as Islamic

finance, particularly when the Islamic capital market is set to play a bigger role in

driving global asset growth (Securities-Commission-Malaysia, 2009).

According to the UK Islamic Finance Secretariat (UKIFS), Islamic assets now

represents only one per cent of the global financial market. In 2011 they grew by

nearly 14 per cent to an estimated US$1,289 billion: an increase of about 150 per cent

from US$509 billion in 2006 (UK-Islamic-Finance-Secretariat, 2012). After the GFC

in 2007–2008, the Islamic fund industry remained at a plateau in 2009, with assets

under management (AUM) being worth $52 billion. The industry is fragmented, with

over 70 per cent of fund managers having AUM under US$100 million and less than

10 per cent with AUM, representing an excess of more than US$1 billion. The fees

associated with Islamic funds have also fallen. The average management fee fell to

1.15 per cent in Q1, 2010, due to an investor-driven market that forced fund managers

to reduce fees, on average, by about a quarter or 40 basis points since 2006. The

higher returns expected by investors have led to an investment pool available to the

Islamic fund managers that is estimated to be between US$360 and US$480 billion

(Ernst-&-Young (2010, pp. 6-28).

The establishment of the IMF industry globally is a recent phenomenon and is gaining

more attention as time passes. The global Islamic fund management industry

Page | 20

expanded by 7.6 per cent to US$58 billion in 2010 in AUM, which is 13 per cent

higher than in 2008. The AUM of this fund remained flat for three years (2008–2010)

after the global financial crisis (Ernst-&-Young, 2011). The growth of IMFs in

Malaysia, for instance, which functions in a similar pattern, is expected to increase

from a higher demand from local and foreign markets, as well as from increasing

levels of awareness and confidence among global investors. In Malaysia, Islamic

funds were operating in the 1970s through the introduction of Dana Amanah Bakti by

Asia Unit Trust Berhad in 1971. The growth of the industry in Malaysia has increased

significantly in relation to the global market. In February 2012, about 167 IMFs were

available in the Malaysian market, according to the SC.

The development of Islamic finance in the global market has made a substantial

contribution to the establishment of the industry. The growth rate of Islamic finance

has been maintained at 15 per cent annually (Bose and McGee, 2008). By the end of

2008, Shariah-compliant assets grew to around $US500 billion (Hassan 2008). The

trend also shows that Islamic finance is becoming an important part of the

international financial system. It is now recognised as an alternative channel of

financial intermediation not only within Islamic communities but also in Western

societies, with more conventional banking and finance systems offering Islamic

financial products.

Currently, the development of the Islamic financial system is occurring not only in the

Middle East and Southeast Asia (with Malaysia as the biggest hub), but it is also

appearing in continental Europe, the UK, the US, South Africa and other regions (see

Figure 2.1). With the higher demand for these funds in Malaysia, it is not surprising

that Malaysia now holds the largest percentage of IMF clientele in the world market.

As noted by Lewis (2009), Malaysia held about 24 per cent of funds by domicile of

clients in 2007 and the holding percentage rose to 29 per cent in 2011 (see Figure

2.1[b]). Although Malaysia is a tiny country in emerging markets, the country is

projected to become a global platform for the finance industry with its comprehensive

regulatory and governance framework, which includes the unique characteristics of

Islamic finance with stronger standards that could be seen as a global benchmark.

Page | 21

Thus the Islamic finance industry has more room to progress in practices in the

country (REDmoney, 2011).

Figure 2.1: Islamic funds by domicile of clients, 2007 and 2011 Figure 2.1(a): Islamic funds by domicile of clients in year 2007

24%

23%

8%

5%3%2%

2%

5%

18%

10%

Source: Eurekahedge Islamic Funds Database

Adapted from Lewis (2009).

Malaysia 24%

Saudi Arabia 23%

Kuwait 8%

Bahrain 5%

United States 3%

UAE 2%

Singapore 2%

Indonesia 5%

Figure 2.1(b): Islamic funds by domicile of clients in year 2011

The total AUM of IMF managers has steadily risen globally, especially the global

assets, as shown in Figure 2.2. Assets in 2007 according to geographic location were:

the Middle East and Africa 63 per cent; global market about 13 per cent; Asia-Pacific,

29%

20%11%

3%

4%

8%

4%

4%

3% 3%11%

Source: Eurekahedge (2011).

Malaysia 29%

Saudi Arabia 20%

Kuwait 11%

Bahrain 3%

United Kingdom 4%

UAE 8%

South Africa 4%

Indonesia 4%

Luxembourg 3%

Pakistan 3%

Others 11%

Page | 22

12 per cent; North America, 9 per cent; and Europe about 4 per cent, as shown in

Figure 2.2(a). By the end of July 2011, these global assets had more than doubled to

36.2 per cent and the emerging markets appeared to have 0.5 per cent of the assets

(see Figure 2.2[b]). Thus, greater internationalisation of the capital market is a critical

aspect of the strategy to strengthen Islamic finance worldwide and to position

Malaysia as a global ICM hub. The growth in Islamic financial products and

especially in the mutual fund industry will contribute to the continuing development

of the Islamic finance globally, in its diverse investment products.

Figure2.2: Assets of Islamic funds by geographic mandate, 2007 and 2011 Figure 2.2(a): Asset of Islamic funds in year 2007

62%13%

12%

9%

4%

Source: Eurekahedge Islamic Funds Database

Adapted from Lewis (2009)

Middle East/Africa 62%

Global 13%

Asia Pacific 12%

North America 9%

Europe 4%

Figure 2.2(b): Assets of Islamic funds in year 2011

Middle East/Africa

42.5%

Global 36.2%

Asia Pacific 12.6%

North America

7.9%

Europe 0.3%

Emerging

markets 0.5%

Source: Eurekahedge (2011).

Page | 23

2.3 The development of the Malaysian mutual fund industry

Compared to the mutual fund industry in the US and the UK, the Malaysian mutual

fund industry is fairly new, beginning in 1959 when the first mutual fund was

introduced by a company called Malayan Unit Trust Ltd. At that time industry

regulation involved several parties, namely, the Registrar of Companies, the Public

Trustee and Bank Negara Malaysia. During the first two decades (1959–1979),

investment in mutual funds was not popular due to the lack of public awareness.

Shortage of information about the funds and lack of marketing and advertising also

explained this situation.

When the Malaysian government intervened in the mutual fund industry in the period

1980–1990, the industry started to grow (Bala and Matthew, 2003). However, sales of

mutual funds were very limited due to the lack of public interest and lack of variety in

this new investment product. For example, from 1980 to 1990 only 18 funds were

introduced. This period also exhibited the entry of government participation in the

mutual funds industry with a committee called the Informal Committee for Unit Trust

to regulate the mutual funds industry. This committee consisted of representatives

from the Registrar of Companies (ROC), the Public Trustee of Malaysia, Bank

Negara Malaysia (BNM) and the Capital Issues Committee (CIC). The development

of the industry grew rapidly and consistently from 1990 to 1996 with the

establishment of a few new management companies and the launch of new fund

opportunities.

The introduction of guidelines on unit trusts and the enactment of the Securities

Commission Act 1993 generated greater public confidence in the mutual funds

industry and indirectly contributed towards its tremendous growth. During this period

the total NAV under management grew more than three-fold from RM15.72 billion

from the end of 1992 to RM59.96 billion by the close of 1996 (see Figure 2.3[a]).

This phase witnessed great product innovation, new investment products and

deregulation in the industry (Federation-of-Malaysian-Unit-Trust-Managers, 2004).

Page | 24

Figure 2.3: Growth of the Malaysian mutual fund industry, 1992–2012 (February)

13 1523 27 27

36 36 38 39 39 39 39 40 4034 34 37 39 36 36 38 38 39 39 39 39 40 40

0

15

30

45

60

75

90

105

120

135

150

165

180

195

210

225

240

255

270

15

.72

28

.13

35

.72

44

.13

59

.96

33

.57

38

.73

43

.26

43

.3

47

.35

53

.7

70

.08

87

.38

98

.49

12

1.7

6

16

9.4

1

13

4.4

1

19

1.7

1

22

6.8

1

24

9.4

6

26

7.0

2

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

RM

billi

on

Total NAV of the industry/Year

Figure 2.3 (a) Growth of fund managers and mutual fund industry

NAV IMFs NAV CMFs No, of Islamic Fund managers No. of Conventional Fund Managers

0

20

40

60

80

100

120

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

NA

V

Figure 2.3 (b) The ratio of the NAV for the IMFs and CMFs as part of the total NAV for the mutual fund industry

% NAV of IMFs to total

industry

% NAV of CMFs to total

industry

total industry

Page | 25

Moreover, the period of 1990–1996 witnessed expansion of the Islamic fund industry.

The availability of Shariah-compliant stocks boosted the development of this industry.

The NAV of the IMFs increased significantly from only RM 0.19 billion in 1993 to

approximately RM29.24 billion at the end of February 2012, as shown in Figure

2.3(a). The percentage of the IMF’s NAV to the total NAV of the mutual fund

industry is now about 2.17 per cent. Meanwhile, the percentage of NAV of the total

industry towards market capitalisation was about 19.85 per cent in the same period.

As shown in Figure 2.3(a), the total industry has maintained its upward trend – even

though the industry did suffer from the Asian financial crisis (AFC) during 1997–

1998 – with the net asset values to the Kuala Lumpur Stock Exchange (KLSE) market

capitalisation rising from 8.93 per cent in 1997 to 10.34 per cent in 1998 (see

Appendix A for details).

Interestingly, the main trend has been that IMFs portfolios have outdone CMFs, as

shown in Figure 2.3(b). The graph shows that the movement of IMFs is steadily

increasing and that of CMFs is decreasing. The graph also shows that there is a

positive relationship between the movement of the IMFs and the total industry

towards market movement, implying that the IMFs directly follow the market trend.

The decreasing movement of the CMFs could indicate that more investors, including

non-Muslim investors, are shifting their investments into IMFs. The results imply that

the portfolio of the IMFs is becoming increasingly important to the total industry. It

can be seen that both funds have parallel trends. The trend of IMFs mimics the market

trend, as the pattern is nearly similar to that of the total industry. The mutual fund

industry had become vital to the development of the Malaysian economy when the

government introduced the Capital Market Master Plan (CMP) in February 2001. The

CMP introduced a vision for the Malaysian capital market as one that should be

internationally competitive in all core areas necessary to support Malaysia’s basic

capital and investment needs, as well as its longer-term economic objectives. What

was wanted was a highly efficient conduit for the mobilisation and allocation of

funds, and for these to be supported by a strong and facilitative regulatory framework

that would enable the capital market to perform its functions effectively and provide a

high degree of confidence to its users (Securities-Commission-Malaysia, 2001, pp.1-

2).

Page | 26

Regarding making Malaysia an Islamic financial hub and the international Islamic

capital market centre, the CMP also identified the Islamic capital market (ICM) as a

key component in Malaysia’s capital market. This capital market currently plays an

important role in mobilising savings through the investment management industry,

and is not limited to the area of mobilising the effective Islamic funds. In fact, it

facilitates innovative products and services in the Islamic capital market and

strengthens the taxation, accounting and regulatory framework for the ICM.

The mutual fund industry in Malaysia began to gather momentum in the early 2000s,

when the industry recorded double digit growth of approximately 291 per cent within

seven years, growing in NAV from RM43.30 billion to RM169.41 billion between

2000 and 2007 (see Figure 2.3[a]). This mutual fund industry growth represented

15.32 per cent of the local market capitalisation in 2007. The NAV of the industry

was maintained at RM43.30 billion in the year 1999–2000, although in the meantime,

the KLSE market capitalisation suffered due to the crisis with the amount reducing

from RM552.69 billion in 1999 to RM444.35 billion in 2000. These figures show that

the mutual fund industry in terms of the NAV to the KLSE market capitalisation

increased from 7.83 per cent to 9.74 per cent in 2000 and grew to 15.32 per cent by

the close of 2007 (see Appendix A). This was particularly due to the government

playing an important role in the development of the mutual fund industry in Malaysia.

For example, even though there were only 31 government-linked funds in May 2001,

the NAV of the fund represented almost two-thirds of the total NAV of the fund

industry during this period (Bala and Matthew, 2003).

During the peak market cycle, it is evident that the NAV of CMFs increased from

RM112.59 billion in 2006 to RM152.55 billion in 2007. Meanwhile, the total NAV

for the Malaysian Islamic funds almost doubled in 2007, from RM9.17 billion to

RM16.86 billion (see Appendix A). This indicates a growth rate of 35.49 per cent for

CMFs, whereas the Islamic funds increased by 83.86 per cent. This figure,

furthermore, confirms this growth in terms of the NAV where the conventional fund

portfolio had a smaller growth rate compared to its Islamic counterpart, as shown in

Figure 2.3(b).

Page | 27

However, the strong growth was punctuated by the global financial crisis (GFC) in

2007–2008, which began with the infamous collapse of subprime loans in the US.

This spread to the property bubble, the global credit crunch and the banking crisis,

which greatly reduced share prices worldwide. As a result, the NAV of the Malaysian

mutual fund industry fell from RM169.41 billion in 2007 to RM134.41 billion in

2008. The KLSE market capitalisation dropped worryingly from RM1106.15 billion

to RM663.82 billion in the same period. Surprisingly, the percentage of the overall

industry NAV to market capitalisation grew to 20.25 per cent in 2008 from 15.32 per

cent in 2007 (see Appendix A). This could have been due to the new catalysts and the

removal of impediments by the SC and other regulations such as the liberalisation of

foreign-exchange administration rules by the BNM concerning mutual fund

investments in the foreign market. These allowed fund managers to invest up to 50

per cent of their net asset value in foreign currency after 2007. Furthermore, the

removal of impediments approved by the SC for overseas market investments in

March 2008 potentially boosted the growth of the global mutual fund domicile in

Malaysia. However, it declined to 19.18 per cent by December 2009 (further details in

Appendix A).

At the end of 2010, the NAV of the mutual fund industry in Malaysia rose to

RM226.8 billion from RM191.7 billion in 2009, representing a net growth of RM35.1

billion. Out of the total of 564 funds in the industry, the IMFs constituted 152 funds

(27%) while the CMFs represented 412 funds (73%). The value of the IMFs’ NAV

grew at about 22.6 per cent to RM26.6 billion (Federation-of-Investment-Managers-

Malaysia, 2010). However, the industry’s NAV in 2010 represented 17.8 per cent of

KLSE’s market capitalisation against 19.18 per cent in the previous year of.

The numbers of mutual funds in Malaysia are increasing tremendously. The number

of launched IMFs in 2007 was 128 funds compared to 95 funds in 2006. During the

same period, the number of CMFs grew to 367 funds in 2007 from 297 in 2006. The

figures have shown that in terms of growth, the IMFs have grown faster than the

conventional funds, with rates of 34.74 per cent and 23.60 per cent respectively

(Federation-of-Malaysian-Unit-Trust-Managers, 2009). Beginning with only two

funds in 1993, the net asset values (NAV) of the Islamic funds increased from

Page | 28

RM0.19 billion in 1993 to RM22.08 billion at the end of 2009. The updated figure in

Appendix A shows that the number of approved Islamic mutual funds grew from

eight funds in 1996 to 13 funds in 1999 and rose to 17 funds in 2000. By February

2012 there were 167 funds. With regard to asset values, at the end of February 2012,

the percentage of the NAV of the IMFs as part of the total industry was about 10.95

per cent (with units in circulation amounting to 61.96 billion units) with the CMFs

89.05 per cent. In relation to the KLSE market capitalisation, the IMF portfolio

contributed 2.17 per cent while the CMFs contributed 17.67 per cent (see Appendix

A).

Figure 2.4(a) shows a continuous increase in terms of the numbers of funds in both

portfolios from year to year. The figure further indicates growth in number of funds as

the IMFs reached 71 in 2004, up from five funds in 1995. By February 2012 there

were 167 funds. Although the number of funds is relatively small compared to the

CMFs, the number represents more than 20 per cent of the Islamic funds worldwide.

The growth in Islamic funds is estimated to be higher due to the establishment of new

funds from time to time. Moreover, subscriptions in new IMFs are very encouraging

(Mohd, 2007). Figure 2.4(a) also shows that there were about 150 IMFs relative to the

total of 565 funds in the Malaysian market at the end of 2009. This number rose to

605 funds in total comprising 167 IMFs and 438 CMFs by the end of February 2012.

In order to analyse the continuous growth of fund over a period of this study, Figure

2.4(b) is presented. This results when the data in Appendix A are simplified into year-

by-year changes as reported in Appendix B. Appendix B describes the comparative

ratio analysis of the IMFs and CMFs over the 13–year period from 1999 to 2012.3

Column 2 in Appendix B shows the number of fund managers managing the IMFs in

Malaysia and the percentage increase from year to year. It is evident that all fund

management companies in Malaysia have been managing IMFs in their portfolios

since 2004. Column 3 describes the funds that have been approved for the Islamic and

conventional counterparts, and notes that the percentage of the Islamic funds

approved rose from 12.15 per cent in 1999 to 26.54 per cent in 2009. The percentage 3 Although the IMF industry started in 1993, a comparative analysis cannot be done from that year due to some of the comparative data between these two portfolios not being available, as mentioned in Appendix B.

Page | 29

of the conventional funds approved declined from 87.85 per cent to 73.45 per cent in

the same period. Figure 2.4(b) displays both IMFs and CMFs funds forming an

overarching mutual funds industry. The legend total in the figure is the total number

of funds when the IMFs and CMFs are combined. The result is consistent with the

trend highlighted in Figure 2.3(b).

Figure 2.4: Growth in numbers of the Malaysian mutual fund industry, 1992–2012 (February)

5

15

25

35

45

55

65

75

85

95

92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12

Ra

tio

Figure 2.4(b)The ratio based on number of IMFs and CMFs for totalnumber of funds

IMFs

CMFs

0

100

200

300

400

500

600

700

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

Figure 2.4(a) The ratio of the numbers of the IMFs and CMFs fortotal number of mutual funds in Malaysia

IMFs

CMFs

Total Funds

Page | 30

Columns 4 and 5 of Appendix B report on the funds’ accounts and funds’ sizes in

terms of unit circulations for both portfolios. It is clearly seen from the table that the

account and the size of the IMFs kept growing from 1999 to February 2012 (from

2.33 per cent and 4.02 per cent to 12.80 per cent and 18.97 per cent respectively). In

contrast, the CMFs show a reduction in the percentages of account and size from

97.67 per cent and 95.98 per cent in 1999 to 87.14 per cent and 81.03 per cent

respectively in February 2012.

Columns 6 and 7 of Appendix B show the NAV of both portfolios as part of the total

mutual funds industry and the NAV of the IMFs, CMFs and total industry in relation

to the values of the market capitalisation (see Appendix B for details). The graph of

the NAV of the IMFs and CMFs portfolios relative to the NAV of the total industry is

previously exhibited in Figure 2.3.

Despite the encouraging growth of IMFs over a 10–year period from 0.25 per cent

(1999) to 2.21 per cent (2009), the market share of the IMFs industry remains very

small compared to the conventional mutual funds. In fact, the percentage of IMFs is

only 2.17 compared to CMFs which are about 17.67 per cent in relation to the market

share for the year 2012 (see Appendix A). Therefore, the question remains how well

Islamic funds are truly performing when compared to conventional funds. To

investigate this further by looking at the statistics, a scatter plot was developed from

the data in Appendix A. As described in Appendix A, there are two panels of data,

and the bottom line of the table (Panel A and Panel B) shows the percentage of the

NAV of IMFs and CMFs compared to firstly, the percentage of the total mutual fund

industry and secondly, the percentage of the KLSE market capitalisation. The

percentage shows an increasing value of IMFs but not CMFs. To further explain these

relationships, the study developed the trend based on scatter plot analysis for each

category, as presented in Figure 2.5.

Figure 2.5 describes the percentage ratio of the NAV for both Islamic and

conventional funds in relation to the total mutual fund industry, representing the

growth rate of each portfolio. For each point, the x-axis coordinate is the fund’s

annual NAV to total NAV and the y-axis coordinate is the fund’s NAV to KLSE

Page | 31

market capitalisation. Figure 2.5 shows a fairly strong linear relationship with a

positive slope for IMFs and a negative relationship with regard to the CMFs.

Whenever the NAV for Islamic portfolio increases, the NAV of the funds to KLSE

tends to increase. However, the conventional portfolio has a negative slope and this

demonstrates that as the NAV of the funds increases, the percentage of the funds to

KLSE decreases, regardless of the initial value of the NAV. It can be seen that the

growth rate of the IMFs is continuously increasing. However, the growth rate of the

CMFs portfolio seems to be decreasing over time.

The findings are consistent with the trend between IMFs and CMFs compared to the

total mutual funds industry as described in Figure 2.3. The results based on Figure 2.5

imply that the gaps between IMFs and CMFs are shown in Figure 2.3(b). The

negative relationship between IMFs and CMFs means that the gaps between IMFs and

CMFs were larger in 1993 but a little bit smaller in 2009. The smaller gaps mean that

IMFs and CMFs portfolios are converging as a proportion of the total mutual funds

industry.

Figure 2.5: Scatter plot of the ratio percentage of the annual NAV for IMFs and CMFs portfolios to the total NAV of the industry and to KLSE market capitalisation, from 1993 to 2012.

-0.5

0

0.5

1

1.5

2

2.5

3

0 10 20

IMF

s to

KLS

E M

C

IMFs to total industry

Figure 2.5(a) IMFs

% NAV of

IMFs to

Market

Capitalization

Linear (% NAV

of IMFs to

Market

Capitalization)

0

2

4

6

8

10

12

14

16

18

20

80 90 100

CM

Fs t

o K

LSE

MC

CMFs to total industry

Figure 2.5(b) CMFs

% NAV of

CMFs to

Market

Capitalization

Linear (% NAV

of CMFs to

Market

Capitalization)

Page | 32

2.4 IMFs versus CMFs The IMFs and CMFs operate in a parallel financial system as a whole. Unlike the

conventional financial system counterpart, the basic framework for an Islamic

financial system is a set of rules and laws, collectively referred to as Shariah

principles, governing economic, social, political and cultural aspects of Islamic

societies as way of life. This is because Shariah originated from the rules dictated by

the Quran (the Holy book of the muslims), the Sunnah (authentic traditions of the

prophet Muhammad PBUH) and Islamic jurisprudence. The basic differences between

Islamic and conventional mutual funds are illustrated in Appendix C. The detailed

differences between other Islamic and conventional investment products are also

described in the appendix.

A mutual fund is a common financial product available in financial market. There are

several types of mutual funds in the market: open-ended funds, closed-ended funds

and exchange traded funds (ETFs). Each type of fund has its own characteristics that

colour the risk and return to the portfolio investments. The IMFs and CMFs applied in

this thesis are open-ended funds. An open-ended fund is defined as a fund that is open

to public investment via the sale of shares. However, for the closed-ended mutual

fund, the investment opportunities are offered to a limited set of investors or simply

require investors to keep their shares or wait for a buyer. The funds can also be

classified into several types, basically based on the investment objective of the funds

and asset classes: equity funds; asset allocation funds; alternative funds; fixed income

or bond funds; and money market funds.

The CMFs in this study can be defined as a form of collective investment that allows

investors with similar investment objectives to pool their funds to be invested in a

portfolio of securities or other assets. The fund managers then invest the pooled funds

in the portfolio funds, which are assets classes such as cash, bonds, deposits, stocks,

commodities and others. The IMFs are operationally similar but differentiate

themselves from the CMFs in that they must conform to Shariah investment precepts.

In particular, the IMFs are managed by an investment company which initially raises

money from participants or investors to buy a diverse set of stocks, sukuks, and other

Page | 33

equity securities and to invest them in a group of assets with specific demands but

limited to Shariah-compliant products. The participants become shareholders and

receive an equity position on the underlying securities of the funds. The features of

IMFs and CMFs are compared in Table 2.1.

Table 2.1: The main differences between IMFs and CMFs Features IMFs

CMFs

The contract It is based on profit and loss sharing (PLS). Basically according to the Musharakah and Mudharabah principles. The Islamic financial system facilitates lending, borrowing and investments contracts based on risk-sharing basis or profit-loss sharing (Khan and Bhatti, 2008).

It is also a commercial-based contract, according to the lender and borrower contract. The investor is a lender who lends the money in order to get a high rate of return and the dividend.

Shariah- compliant

It is a legal requirement. The fund managers need to appoint the Shariah supervisory board.

It is not a legal requirement.

Investments Involvement is limited to certain activities which comply with Shariah principles and Islamic jurisprudence. The activities should not involve short selling and harmful investments, gambling, alcoholic beverages, non-halal products, cigarettes, prostitution, drugs, weapons and pornography. It is also not involved in interest-bearing deposits and interest-based banking and finance (Alhabshi, 1995; Bhatti 2009).

Involved in all activities, which can provide above the required rate of return.

Return Objectives

Profit maximisation is allowed but according to the Shariah principles and the Islamic jurisdiction.

Profit maximisation is always the fund’s objective without any restrictions.

Rate of Return It is based on profit rate. In Islamic equity financing, profit cannot be predetermined, but the proportion of the profit can be predetermined based on the capital ratio.

It is based on interest rate. It is predetermined and stated in the contract.

Riba element It is not allowed, according to Shariah law and a legal requirement. It is because riba rate is fixed and predetermined at the beginning of a contract.

It is accepted under the legal requirement.

Consequently, IMFs only offer investors the opportunity to invest in a diversified

portfolio of Shariah-compliant securities managed by professional fund managers

Page | 34

according to Shariah principles and guidelines. There is also a guideline for the

Islamic fund managers to appoint a Shariah advisory board or Shariah adviser for the

relevant funds. In Malaysia, this is regulated by the Guidelines of Unit Trust Funds

and supervised by the SC to ensure that their operations do comply with Shariah law.

The Shariah guidelines provide some important instructions on Islamic investing,

including asset allocation, portfolio screening, investment practices and income

distribution, and particularly on the enforcement of zakah (alms) as part of the

purification process (Girard and Hassan 2008).

More specifically, investment activities must follow certain criteria according to

Islamic law, such as the prohibition of riba (interest rate), the prohibition of maysir

(gambling), the involvement of only halal (legally permitted or permissible according

to Shariah law), and the activities and the obligation of zakah. Activities should also

not involve any short selling and harmful investments, alcoholic beverages, non-halal

products, cigarettes, prostitution, drugs, weapons and pornography. The investment

must also not involve interest-bearing deposits and interest-based banking and finance

(Alhabshi, 1995; Bhatti 2009). After fulfilling these criteria, all Islamic investment

products must be approved by the appointed Shariah supervisory board before they

are available on the market. This is to ensure that the main goal in developing Islamic

investments and their financial products is to obtain social-economic justice based on

abolishing the riba (interest rate) and other exploitative elements (Khan and Bhatti,

2008) as well as making profits.

2.4.1 Salient features of IMFs

The concept of Islamic mutual funds has its roots in the musharakah principle, an

Islamic investment vehicle wherein a syndicate of investors invests their capital in one

or more potential projects to share profits or possibly losses, also called profit and loss

sharing ventures. Common to this musharakah principle, the risks and rewards in

Islamic mutual funds are shared according to the equity participation (such as profit

and loss sharing) of each investor in the panel or the contract.

The list of Shariah funds excludes those companies whose major activities are

involved with interest-based banking and finance and conventional finance, gambling,

Page | 35

alcoholic beverages and non-halal products. According to (Ismail 1999), Islamic fund

management in Malaysia takes many forms, including in-house fund/portfolio

management, discretionary fund management by professional institutions and retail

Islamic unit trust. The Shariah guideline also ensures that those companies offering

Shariah funds undertake activities that are consistent with the mores of Islamic

investment and conform to Shariah principles.

Usually, in Islamic investments, investors have a range of choices when constructing

a financial portfolio. These include riba-free bank deposits; investments in Islamic

unit trusts and investment companies; private placements in Muslim businesses; and

investments in conventional institutions and businesses that undertake to deploy

funding from Islamic investors on a halal basis. Options regarded as haram include:

conventional bank savings and investment deposits; the purchase of interest yielding

bonds; and the acquisition of shares in companies involved in alcohol production or

distribution or in pork products. Participatory finance through musharakah as

previously discussed was one of the earliest forms of Islamic finance involving a

partnership between the provider of the capital and the user or entrepreneur (Wilson

1997, pp. 1331-1332).

According to Usmani (2007), the principles of Shariah governing IMFs or Islamic

investment funds should be subject to two basic conditions (pp. 203-204):

1. Instead of a fixed return tied up with their face value, they must carry a

pro-rata profit actually earned by the fund. Therefore, neither the

principal nor a rate of profit (tied to the principal) can be guaranteed.

The subscribers must enter into the fund with a clear understanding

that the return on their subscription is tied to the actual profit earned or

loss suffered by the fund. If the fund earns huge profits, the return on

their subscription will increase to that proportion. However, if the fund

suffers loss, they will have to share it also, unless the loss is caused by

negligence or mismanagement, in which case the management, and not

the fund, will be liable to compensate it.

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2. The amounts so pooled must be invested in a business acceptable to

Shariah principles. It means that not only the channels of investment

but also the terms agreed with the clients must conform to Islamic

principles.

The other feature is the presence of the Shariah board. Its primary mission is to ensure

that stocks selected are halal and remain so. The stocks invested in or scrutinised

must not involve companies engaged in any forbidden trade, incorporate an

unbearable amount of debt (debt-to-capital ratio > 33 %) or profit excessively from

interest income (non-operation interest income > 5 %). Considering these limits

requires constant attention and supervision of Islamic companies. Rebalancing the

portfolio of stocks is therefore done in close partnership with the fund managers.

Their role is to audit and monitor the firm, checking the company’s operations and

ensuring strict adherence to Islamic precepts (i.e., principles of Shariah and Fatwas).

They are also responsible for advising the fund managers on stock selection and for

inspecting closely the company’s stock activities (DeLorenzo, 2000).

Undoubtedly, the fund and its management also benefit as the services performed by

Shariah supervisors are directed towards the investors. Therefore, the supervisors

must take every possible step to ensure that the Islamic funds available in the market

represent halal investments for Muslim investors in particular. According to

DeLorenzo (2000), this is the vital challenge that needs to be resolved in order to

encourage Muslim people to participate in Islamic investment. This challenge is

related to the role and responsibility of the Shariah Supervisory Board to ensure that

all such income is free of impurities and completely halal according to Shariah law.

On behalf of the investors, again, it is the role of Shariah supervisors to ensure that the

purification process takes place according to Shariah principles and Islamic law. This

is unlike a Western company’s ethics, whose primary business and capital structure

are highly subjective and not easily quantified.

As a result of these efforts, the primary beneficiary is the Muslim investor who can

rest assured that his/her money is being utilised in accordance with the teachings of

Islam. In this circumstance, the responsibilities of a Shariah supervisor may be

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compared to those of an independent financial auditor (in the sense that regulatory

compliance is ensured). There is a further and far more vital aspect of the role of a

Shariah supervisor. In assuming responsibility for the Shariah compliance of a fund,

including its components and its management, the Shariah supervisor places himself

in a position to directly represent the religious interests of the investor. In discussing

the different aspects of Shariah supervision, it becomes clear that a Shariah supervisor

functions in different ways – as a consumer advocate with both religious and fiduciary

responsibilities.

Apart from Shariah supervision, the element that must be taken care of is the

purification process in the presence of zakah. In terms of the purification of the funds,

Muslims regularly purify their accounts by simply donating the interest earnings to

charity. However, the main concern here is the amounts of money earned by the

corporations in which the Islamic mutual funds have invested that are unacceptable

according to Shariah principles. These earnings must be quantified and then purified.

The sources of such earnings might include non-operating income from interest-

bearing investments or earnings from prohibited business activities that are beyond

the scope of a company's primary business. Whatever their source, the fact remains

that even Shariah-compliant equities will often yield small percentages of income that

are considered impure by Shariah standards, and which must then be purified

(DeLorenzo, 2000). IMF investment has much in common with modern forms of

investing such as ethical investment, socially responsible investment, faith-based

investment and green investment.

2.5 Islamic finance and Islamic investments

This section explains the principles of Islamic finance vis-à-vis conventional finance

and how these principles are related to Islamic investment and mutual funds. The

impact of global financial crisis in relation to Islamic finance is also discussed.

2.5.1 Fundamentals of Islamic finance

Islamic finance is derived from Islamic law (also known as Islamic jurisprudence or

Shariah law). The main sources of this law are al-Quran and al-Sunnah of the Prophet.

Hence, all activities in producing and accumulating wealth must be done according to

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this law. The concept of Islamic finance as well as Islamic investment is also derived

from similar sources. To complement their governance, the sources of law are also

derived from the opinions of Islamic scholars (ijtihad of ulama'). The objective of

Islamic finance is to combine economic growth with social justice and equity needs.

In an Islamic finance system, this law provides guidelines in the form of rules and

regulations that relate to the halal (permissible) and haram (forbidden). Therefore,

besides the written contract for business activities, the main principles governing the

philosophies behind the implementation of Islamic financial system are faith in

Tawheed (God), Ihsan (goodness) and amanah (trusteeship).

The major feature of Islamic finance is the prohibition of riba. Islamic finance also

prohibits gharar (uncertainty) and maysir (gambling) activities, and other activities

which are not allowed in Shariah law. Before Muhammad (PBUH) began to spread

the message of Islam in 610 CE, Arabia was in the midst of ayyam al-Jahilliyah, “the

Age of Ignorance”. He came to advocate not only submission to God but also ethical

responsibilities towards each other such as the prohibition of riba, which was seen as

a wicked financial entrapment of the needy. This prohibition is in the Holy Quran,

2:278, which is translated as “O ye who believe! Fear Allah, and give up what

remains of your demand for usury, if ye are indeed believers” (Bhatti 2007, p. 17). In

the early stages of Islamic history, Muslims therefore created a system that freed them

from interest, enabling them to be productive and develop their economy.

These features come from the same sources of al-Quran and al-Sunnah. They are

supported by other Shariah principles such as wealth generation, wealth distribution,

economic justice, work ethic, risk sharing, the mutual interest in contract, individuals’

and property rights and al-adl wa al-Ihsan (justice and beneficence). As a result, the

Islamic financial system is not limited to banking but also covers capital formation,

capital markets, equity markets and all other types of financial intermediation.

According to the Islamic Scholar Zarqa (1983), the Islamic financial system ensures

the optimal rate of capital formulation and its efficient utilisation leading to a

sustainable economic growth and fair opportunities for all. It is a value-based system,

which complies with Shariah principles and primarily aims to ensuring the moral and

material well-being of the individual and society. The philosophical foundation of an

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Islamic financial system goes beyond the factors of production and economic

behaviour. While the conventional financial system focuses primarily on economic

and financial transaction returns, the Islamic financial system puts its emphasis on the

ethical, moral, social and religious dimensions, to enhance equality and fairness for

the good of society as a whole (Zarqa, 1983). The main differences between Islamic

and conventional finance are summarised in Table 2.2.

Table 2.2: Main differences between Islamic finance and conventional finance Islamic Finance Conventional Finance

The contract It is a bilateral contract which is based on

certain assets (asset based). The relationship is between seller and buyer. Any trading assets not owned by the trader are considered void and banned under this contract.

It is a unilateral contract, which is based on certain debts. The relationship of the parties in the contract is between lender and borrower. The contract is considered valid as long as all the requirements of the contract are fulfilled.

Investment main objective

The main objective of the Islamic investment is to apply justice and community welfare.

Its primary objective is to maximise profit and to increase the shareholders’ wealth.

Shariah Supervisory Board

All Islamic financial products offered in the market must be approved by the Shariah Supervisory Board, to ensure that they comply with Shariah principles.

This requirement does not apply to conventional financial products.

Investment activities- Shariah-compliant products

Islamic finance limits its investments to Shariah-compliant products, and prohibits short selling and harmful investments, for example, investments involving cigarettes, prostitution, drugs and weaponry. All activities must conform to Islamic law. It consists of the following (Algaoud and Lewis, 2007, p. 38): riba is prohibited in all business transactions and investment undertaken on the basis of halal (legal, permitted) activities. Maysir (gambling) is prohibited and transactions should be free from gharar (speculation or unreasonable uncertainty). Zakah (alms) is to be paid by the bank to benefit society All activities should be in line with Islamic principles, with a special Shariah board to supervise and advise the bank on the propriety of transactions.

Conventional finance allows any investments generally without reference to any criteria or any cleansing screening process, as long as they could provide above average returns. It includes activities which involved gambling, speculation, short-selling and high risk (such as CDOs and swaps).

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Table 2.2 continued

How the system makes profit: profit rate or interest rate

In debt financing, Islamic finance uses the profit rate, which includes the profit margin in the selling price. The profit is fixed and it is determined at the beginning of the contract. The purchaser (borrower) needs to pay the whole selling price at the end of the contract time, without any extra cost. In equity financing, Islamic finance uses profit sharing or profit and loss sharing (PLS), which refers to mudharabah and musharakah respectively. Only the proportion of profit is determined during the contract, but the actual return is unknown, depending on the actual gain or loss from the project. It means that the profit is not fixed.

In debt financing, conventional finance uses the interest rate, which is excluded from the selling price. This interest rate is variable based on the current economic situation. At the end of the period, the borrower needs to pay to the lender the selling price and the nominal rate, which is the BLR and the actual rate. In equity financing, conventional finance also uses the interest rate to calculate the rate of return. The rate of return from a project is predetermined at the beginning of the contract. It is a fixed rate and not subject to the profit or loss obtained from the project.

2.5.2 Islamic finance and the principle of Islamic investments

Islamic finance is derived from the Shariah law, which can basically be divided into

two major themes: ibadah and muamalah.4 Muamalah refers to political, social and

economic activities (Ismail 1992).

Although the Islamic finance system considers all activities that are related to tasarruf

maliyyah (management of wealth) in the economy, the system is generally built on the

basis that all activities should be free from the element of riba and all investment

activities must be in line with the principles of Shariah, such as bay` (sale), bay’

bithaman ajil (deferred payment sale), ijarah (leasing), rahn (collateralised debt or

Islamic pawn broking), wakalah (agency or representative), wadiàh (safe-keeping or

safe custody), juàlah (wage, pay or reward), hiwalah (bill of exchange or

remittance), hibah (gift), al-qard hasan (benevolent loan) and the like, which have

been laid down in a specific contract or aqad. Specifically, Islamic finance should

comprise two important elements (Ab-Mumin, 1999):

4 Ibadah is concerned with the practicalities of a Muslim’s worship of Allah, whereas muamalah is concerned with human relationships.

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1. It should have the Islamic essence as a whole, which is not limited to its

labels. The application of the system should be Shariah-based and should

reflect its philosophy, values, ethics and the general objective. The main

objective of Islamic finance is to promote social justice and fairness through

economics and financial activities. Moreover, as indicated by (Lewis and

Algaoud 2001), the main objectives of the Islamic banking and finance can

be summarised as:

• The abolition of interest from all financial transactions and the reform

of all bank activities to accord with Islamic principles

• The achievement of an equitable distribution of income and wealth

• The promotion of economic development.

2. It should have the characteristics of a comprehensive and up-to date system

which is viable, competitive and comparable with the conventional financial

system.

Conclusively, a key principle of investment in Islamic finance is the prohibition of

riba. Riba means ‘interest’ in conventional economics terminology. Specifically, it

denotes the prohibition of payment and receipt of interest on deposits and loans. A

verse in al-Quran, al-Baqarah: 275, reveals: “But Allah [God] permitted trade, And

forbidden usury”. Therefore, Islamic investments limit their activities to investments

which comply with the Islamic finance principles derived from the Shariah law.

In the mid-1980s, as a result of the awareness of Muslim people to avoid usury5,

commercial activities were stimulated under the umbrella of interest-free schemes,

those without riba, and the term “Islamic financial system” became part of the market

parlance. However, describing the Islamic financial system simply as “interest-free”

does not provide a true picture of the system as a whole. The earlier reference to this

system as “Islamic banking”, which was first practised in 1963 and coincided with the

establishment of the Islamic Bank in Mit Ghamr, Egypt, is also not sufficient to

describe the system. However, the enhancements of banking activities in the Islamic

financial system have become milestones in the history of Islamic finance. The first

5 According to Shariah law, the word “interest” is the same as usury and refers to riba.

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benchmark was the establishment of the Islamic Development Bank in 1975 and the

most significant was the setting up of the Islamic Financial Services Board in 2002.

Overall, the system mainly aims to sustain economic growth in conjunction with

social justice needs to ensure that all activities conform to Islamic principles.

2.5.3 Islamic finance and the impact of the global financial crisis

Nobody denies that the current global financial crisis (GFC) requires a global

solution. It has been suggested that the most striking feature of the crisis is the

diversity of its impact. Different countries have been affected in different ways and

now face very different economic futures. The major financial shock that began the

crisis originated in the US when the sub-prime mortgage market collapsed and in turn

wrecked the derivatives and securitisation markets of commercial and investment

banks.

In the US, the shocks were caused by the virtual collapse of two keys industries that

rely heavily on finance and on confidence: housing and the automobile industry. The

final quarter of 2008 demonstrated that the motor vehicle output was 31 per cent

down when compared to the previous year, but other GDP factors were unchanged.

During the same period, housing investment declined by 19.5 per cent. The impact on

Britain was also immediate. The largest mortgage lender, Northern Rock, collapsed

and created major pressure on other British banks. The crisis spread due to the

implosion of housing bubbles and caused many countries to fall into recession or

nearly so. Too many countries had their banking and financial systems in utter

disarray; financial asset prices crashed and in some places real asset prices collapsed

as well (Highfill, 2008).

The global crisis in 2007–2008 is considered to have been the worst during the last

100 years (Ali, 2009). The International Monetary Fund forecast that the crisis would

be the first post-World War II demand-shock recession (Harding, 2009). Indeed, the

current global crisis has been compared to the Great Depression of the late 1920s.

Whether the current global slowdown can reach such proportions still remains to be

seen but, in reality, the world has seen how institutions underestimated the complexity

of financial products. They were unable to adequately measure tail risks and the pro-

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cyclical impact of fair value accounting, and failed to manage maturity mismatches,

which contributed to the financial crisis (Ghani, 2009). There are several reasons for

the impacts of the crisis on global finance, particularly on conventional banking and

finance. The reasons include the sub-prime mortgages, the incentive effects of

securitisation, a co-mingling in collateralised debt obligations (CDOs), monocline

insurers, insurers selling credit default swaps (CDS), US government sponsored

enterprises, and excessive leverage. The application of a new banking model which

involved “originate and sell” so that the banks could sell loans in the capital market

increased the risks associated with banking and finance products due to market

speculation (Amin 2009).

Ali (2009) further indicated that the root causes of the crisis included the debt culture,

moral failure and excessive speculation leading to poor or non-existent market

discipline, and inappropriate financial products. According to him, the size of the

CDS in 2007 alone was US$62.2 trillion and the size of the derivatives products more

than US$600 trillion. As a result, the conventional banks worldwide are now nursing

losses of more than US$400 billion from the credit crunch, particularly in subprime

mortgages, while the Islamic banks are virtually safe and sound. On the other hand,

the global crisis represents more than US$1trillion to the Islamic finance industry with

an opportunity to expand its demand beyond Muslim investors, particularly to ethical

and socially responsible investors. Nonetheless, Islamic finance has remained solid in

the face of the difficulties encountered by conventional finance due to the crisis.

The global crisis attracted world attention to Islamic financial products. Global

attention is changing from investment in conventional finance to investment in

Islamic financial products. This situation has arisen due to the development of a

modern finance that officially started in 1963, with Islamic financial institutions

growing much faster than conventional banks. It has witnessed growth from 176

banks with assets of US$148 billion in 1997 to 270 holding assets of around US$265

billion in 2001 (Hassan and Lewis 2007). During the crisis period, the total global

Islamic financial assets amounted to 40 per cent of the assets of the largest

conventional bank, with 300 market players globally (Zeti, 2008). In Malaysia the

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Islamic finance assets expanded by 22 per cent to RM$192 billion in 2008, and now

account for 15.0 per cent of the total assets in the industry (Ghani, 2009).

From the Islamic perspective, the root cause of the crisis was the fragility of the riba

system6. Thus the end result was the failure of a number of banks because of their

practices, generating uncertainty and a credit crunch. This led to a tightening of credit

to firms in other industries. The crisis may culminate in a severe recession leading to

the default of firms in the real estate sector as well as the financial sector (Ebrahim,

2008, pp. 111-112).

The Islamic finance system is not involved in activities that contain the element of

gharar – speculation, short-selling, and selling and buying debts. The system from the

beginning avoided excessive risk and instead promoted risk sharing in its principles

and some of its products. As a result, activities and products which involve greater

risk such as short-selling, some derivatives products and the CDOs are prohibited

according to Shariah principles. Thus, the crisis had a minimal impact on Islamic

finance because of it being so different from conventional finance. Western countries’

authorities intervened in the crisis by banning short-selling activities. The US

government temporarily banned short selling in 900 financial institutions and the UK

banned short selling in 34 financial institutions stocks (Securities-Commission-

Malaysia, 2008).

The subprime crisis in the US had a global impact on liquidity, causing many

conventional banks to tighten their lending criteria. It is suggested that syndicated

Islamic finance could become a mechanism for tapping alternative sources of liquidity

in the current credit environment. In 2007 there were approximately 28 syndicated

Islamic finance deals with a total value of US$15.2 billion. Due to the Shariah issues

associated with investing in subprime debt, many Islamic banks have stayed away

from this area and have been quite well insulated from the effects of the credit crunch.

Syndicated Islamic finance has strong growth potential now and for the foreseeable

future (Iqbal, 2007). On this theme, Iqbal (2007) emphasised two underlying forces

6 Riba system refers to the interest-based system which is practised in the conventional financial system but is banned in the Islamic financial system.

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that increase the appeal of Islamic finance. The first, paradoxically, is the fallout from

the subprime crisis, which has caused a tightening of global credit in conventional

financial markets. This has resulted in a need to tap new sources of liquidity, Islamic

finance being a prime option. The second is the growing requirement for larger and

more complex Islamic funding. For example, companies in Saudi Arabia are looking

to raise US$6.25 billion to finance a new phase of projects in the world’s top oil

exporter. Saudi Basic Industries Corp. (SABIC), Saudi Arabian Mining Co. and

Chevron Phillips Chemical Co. are all seeking cash for projects, primarily in

infrastructure. The size and similarity of these projects could make it hard for them to

compete for conventional funding, given the current market conditions. These projects

may therefore find alternative sources such as Islamic finance, which is appealing as it

is not severely affected by the credit crunch.

It is therefore crucial for Islamic finance to be recognized as a dual-system, which

works in parallel with the conventional financial system and at the same time could be

enhanced by its deeper knowledge and experience. As described by Amin (2009),

Islamic finance is a subset of conventional finance and it is compulsory for it to

complement the conventional system in order to conform with international rules and

regulations, despite its links to Shariah principles that cannot be compromised.

However, to move forward, the sustainability of Islamic finance in the world market

would require the following aspects to be executed (Akhtar 2007a):

1. Further deepening the efforts to enhance the legal and regulatory framework

of Islamic finance so that they are consistent with international practices.

2. Continued efforts to conform and align the structures and products with the

Shariah principles would help Muslims’ motivation to adopt this alternative

mechanism of financing, while attracting non-Muslims to explore the products

as well.

3. Recognising that Islamic finance has perpetuated and changed the dynamics of

cross-border private capital flows, this industry has the great potential to

augment the process of globalisation and financial integration, yet this requires

more cooperation and vigilance on the part of home and host regulators.

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4. Launching aggressive efforts to implement the evolving Islamic financial

regulatory and supervisory standards and capturing the different types of risks

associated with Islamic finance, while launching consumer protection

frameworks.

5. Promoting more financial diversification by encouraging financial innovation

and Islamic capital market development.

Hence, the key drivers for enhancing the competitiveness of Islamic finance include

the following (Akhtar 2007b):

1. Financial engineering and innovation

2. Global financial centres and their regulators’ support of the Islamic finance

industry

3. Standard governance, prudential regulation and supervisory guidance require

tweaking regulations to properly identify and assign proper weights for new

and different types of risks associated with the special and unique

characteristic of Islamic finance business.

4. Development and adoption of a simple, standard and cost-effective legal

framework for contracts associated with the new and hybrid products

5. Flexible and practical applications, enforcement of Shariah principles and

injunctions, and their acceptability by the public

2.6 Theoretical framework for mutual fund performance

This section elaborates theoretical framework used to assess the performance of

mutual funds. Important issues of focus for our analysis includes returns performance

against market benchmark, performance based on market timing and selectivity skills

and performance in relation to fees and selective fund attributes. A summary of the

empirical evidence of some previous studies on the mutual fund performance is

presented in Table 2.3 at the end of this section. Overall, the key relevant literatures

in this thesis can be explained into five subsections as the following.

2.6.1 Performance measurement against market benchmark

Many theories deal with the evaluation of mutual fund performance based on the

return performance of the funds in relation to their market benchmark at gross or after

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adjusted for the risk-free rate return. One of the popular performance evaluation

models is based on CAPM theory.7 The average outperformance or underperformance

of a fund in a portfolio or individually is determined by its high or low alpha, i.e., the

coefficient estimate obtained from the regression based on the CAPM. It is therefore

evident that potential investors will seek the higher alpha of the mutual funds since

the higher alpha means a higher return or abnormal return that they can obtain from

their investments. Bello (2005) validated the CAPM theory when he illustrated a

strong relationship between the mean return of mutual funds and risk. He also found

no significant linearity between return and volatility, measured by the standard

deviation, thus noting that the beta is absolute when measuring risk based on CAPM

theory.

Most of the evidence from the US and the UK indicates that, on average, return

performance of the mutual funds is not able to outperform the respective market

return portfolio (Benos and Jochec, 2011; Blake and Timmermann, 1998; Carhart,

1997; Elton et al. 1993; Firth, 1977; Grinblatt and Titman, 1994; Jensen, 1968;

Malkiel, 1995; Pollet and Wilson, 2008). For example, in the UK market, Firth

(1977) noted that none of the individual funds during 1965–1975 experienced

abnormal return performance, thus implying that, on average, UK fund managers are

not able to forecast the fund price so that it can perform better than a simple buy-and-

hold policy. Blake and Timmermann (1998) also noted similar evidence and it

appears that the funds underperformed approximately 1.8 per cent per annum on mean

excess return of the five-year UK government bond over the period 1972 to 1995.

Studies on mutual fund performance in Asia-Pacific countries such as Australia and

Malaysia reveal that the findings are in line with those in the US and the UK. The

study on Australian managed funds from 1983 to 1995 using conditional measures of

CAPM provided no abnormal returns (Sawicki and Ong, 2000). Previously, Robson

(1986) and Hallahan and Faff (1999) also reported inferior performance for overall

fund return against the respective market indices over the periods 1969 to 1978 and

1988 to 1997, respectively. Moreover, there is also evidence that, on average, active

7 Sharpe (1964) and Lintner (1965) are among the scholars who have led to the establishment and the

development of this CAPM model in measuring performance of mutual funds worldwide.

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Australian superannuation funds were unable to earn superior risk adjusted return in

relation to the relevant market benchmarks (Gallagher, 2002). There is also evidence

of the underperformance of Hong Kong mutual funds with reference to the market

benchmark (Abdel-Kader and Qing, 2007).

In Malaysia in particular, previous studies, except that of Chua (1985), have indicated

that, on average, the overall Malaysian mutual fund industry including IMFs performs

worse than the market return portfolio (see for example, Abdullah et al. 2007; Annuar,

Shamsher, and Hua, 1997; Aw, 1997; Low 2007; Shamsher and Annuar, 1995; Taib

and Isa, 2007). The most recent of the studies, (Low (2007), indicated that mutual

funds in Malaysia perform poorly in relation to the market benchmark, a proxy either

by the KLCI or the EMAS index. Taib and Isa (2007) further showed that, on average,

the Malaysian mutual funds industry underperforms compared to its market

benchmark and the portfolio for risk-free asset returns. Abdullah et al. (2007) also

stated that, on average, the whole Malaysian funds in their sample underperformed,

further noting that conventional funds perform better than Islamic funds during good

economic periods and worse during bad economic periods.

In the mid–1990s, Shamsher and Annuar (1995) examined the mutual funds’

performance from the 1980s to the early 1990s and found a contrasting result where

the return performance of mutual funds in Malaysia was below that of the market. Aw

(1997) also found 32 underperforming Malaysian mutual funds over the period from

1984 to 1996 in relation to the KLCI market benchmark. However, the only

outperformance evidence of Malaysian mutual funds indicated by Chua (1985) was

based on his study of the performance of 12 Malaysian funds managed by Amanah

Saham Mara Berhad and Asia Unit Trust Berhad from 1974 to 1984.

It is therefore believed that there has been a shift of fund performance over time in

Malaysia (Taib and Isa, 2007). This was confirmed in the study by Saad et al. (2010),

which found that the Islamic mutual fund companies in Malaysia are comparable to

their conventional counterparts. By using the data envelope analysis (DEA) approach,

they found that some of the Islamic mutual fund companies were above average in

terms of total factor productivity (TFP) performance. Recently, Hayat and Krauessl

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(2011) and Hoepner et al. (2011) found that most of the IEFs worldwide

underperform when set against the market benchmark. The underperformance consists

of 31 Malaysian IEFs included in this study, and such underperformance worsens

during a crisis period (Hayat and Kraeussl, 2011). In contrast, Hoepner et al. (2011)

found in their study that the Malaysian IEFs performed competitively well in relation

to the international equity market benchmarks. Their study from September 1990 to

April 2009 included data for 265 Islamic equity funds, of which 76 were from

Malaysia.

Another concern regarding the benchmark is the sensitivity of the choice of

benchmark (Grinblatt and Titman, 1994; Kothari and Warner, 2001). They

investigated performance using different benchmarks and confirmed that performance

is sensitive to the benchmark used, implying that the right choice of benchmark is

important. Kothari and Warner (2001) suggested that multi-factor models give a better

explanation of cross-section fund returns in the US. However, the evidence is contrast

to the finding of Low (2007), who indicated that using a different benchmark had no

impact on mutual fund performance in Malaysia.

2.6.2 Market timing expertise of fund managers

In the international market and the US market, a strategy of market timing ability is

becoming a common phenomenon and this concept is still relevant, with some mutual

funds providing evidence of negative or inferior market timing ability (Chang and

Lewellen, 1984; Chen et al. 1992; Henriksson, 1984), while others reveal positive or

superior market timing (see Bello and Janjigian 1997; Lee and Rahman, 1990;

Lehmann and Modest, 1987). Other important earlier studies in the US demonstrating

poor performance by MF managers are those by Chang and Lewellen (1984) and

Henriksson (1984). They provided evidence of negative market timing skills. Chang

and Lewellen (1984) applied parametric tests covering 67 MFs over the period

January 1971 to December 1979 and noted that fund managers were collectively

unable to outperform a passive investment strategy or to initiate market timing skills.

Henriksson (1984) on the other hand, applied parametric and non-parametric tests

methods to analyse the stock selectivity and the market timing of 116 MFs for the

period 1968 to 1980, and found no empirical evidence that MF managers could

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outperform the investment selection strategy or perform successfully on market

timing.

Kon (1983) and Henriksson (1984) also found a negative correlation between market

timing and stock selection skills. Kon (1983) studied 37 funds from January 1960 to

June 1976 and observed that fund managers as a group had no special information to

outperform on returns of the market portfolio. However, his study provided evidence

of significantly superior timing ability and selection skills performance at the

individual level. Kon (1983) reported that 14 out of 37 funds had overall positive

timing, yet none of them was statistically significant. Compared to the findings of

Kon (1983), Lehmann and Modest (1987) and Lee and Rahman (1990) found

evidence of positive selection abilities and superior market timing being executed by

fund managers at the individual funds level. Lehmann and Modest (1987) used the

arbitrage pricing theory model and found significant measurements of abnormal

market timing and stock selectivity performance among fund managers.

Admati et al. (1986) confirmed that the TM model developed by Treynor and Mazuy

(1966) was a valid measure of market timing ability, with Lee and Rahman (1990)

further detecting selection ability and market timing ability of a fund manager based

on monthly returns for 87 months from January 1977 to March 1984 for a sample of

93 funds. They reported that 14 funds out of 37 funds had overall positive timing, but

none of them was statistically significant. Furthermore, 10 funds had both significant

selection and timing skills, four funds had significant selection skill with no timing

skill, while five funds had significant timing skill with no selection skill. Lee and

Rahman (1990) also indicated that the test of market timing that ignores

heteroskedasticity rejects the null hypothesis of no market timing too often, when, in

fact, the null hypothesis is accepted after the heteroskedasticity correction. The

implication is that the correction decreases with the number of superior market timing

occurrences.

Ippolito (1989) studied fund performance for the period 1965 to 1984, using a sample

of 143 MFs, and found evidence of positive alpha within that period, implying that

there was a superior fund or stock selectivity performance among the fund managers.

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On the other hand, Chen et al. (1992) conducted a study on 93 MFs over a period of

87 months and indicated that a trade-off existed between market timing and security

selection ability. They provided evidence consistent with the findings of Chang and

Lewellen (1984), Henriksson (1984) and Kon (1983) that collectively fund managers

had no market timing ability for the period of January 1977 to March 1984.

Elton et al. (1993) also argued that no evidence was found that MF managers were

able to time the market successfully. They examined the overall portfolio performance

of MFs for the period 1965 to 1984 by adopting the original TM model. Their study

found that a fund manager had no selection ability and, specifically, they provided

contrasting evidence to Ippolito (1989) and concluded that fund managers

underperformed in passive portfolios, with funds consisting of higher fees and

turnover underperforming funds with lower fees and turnover in the portfolios.

Less than a decade later, Bello and Janjigian (1997) documented positive and

significant market timing abilities and security selection abilities for 633 funds from

1984 to 1994. They used the extended TM model by controlling the effects of non-

S&P 500 assets held in the fund portfolios. The evidence from the original TM model

failed to reveal a positive market timing ability. However, the extended TM model

revealed that on average there was a positive market timing ability and superior fund

selectivity skill in this period.

In the Malaysian market, the findings from most of the studies, excluding that of

Hayat (2006), have indicated a negative market timing ability of fund managers

(Abdullah et al. 2007; Ahmed, 2007; Annuar et al.1997; Elfakhani et al., 2005; Low,

2007). Annuar et al. (1997) provided evidence that, on average, mutual funds in

Malaysia had a positive selectivity performance but no market timing ability over the

period 1990–1995. Elfakhani et al. (2005) indicated similar results for the period

from January 1997 to August 2002. There was also poor selectivity performance of

the stock selection ability and market timing ability of the Islamic and conventional

fund managers in Malaysia for the period from January 1992 to December 2001

(Abdullah et al. 2007).

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Ahmed (2007) concluded that generally fund managers perform poorly in security

analysis and market timing. The study tested 60 individual funds from 1998 to 2004

and found that only two funds were superior in timing ability. Similarly, Low (2007)

indicated that fund managers had poor timing ability of over the five-year period from

1996 to 2000 regardless of the market benchmarks used. She found that, on average,

there was negative timing for the full sample of 40 funds, with some weak evidence of

positive timing at the individual level. She also revealed a significant negative

correlation between timing and fund selectivity, implying that good fund managers

with selectivity skills tend to be poor market timers. Hayat (2006), however, found a

relatively better market timing ability among IMF fund managers in Malaysia from

August 2001 to August 2006.

In other markets, Hallahan and Faff (1999) suggested that there was negative

selection performance and little evidence of market timing ability (8 out of 65 funds)

in the Australian market from 1988 to 1997 and that most of the individual funds

exhibited negative alpha. Imişiker and Özlale (2008) found weak evidence for

selection ability and some evidence for superior market timing quality in the Turkish

market, and remarked that experience had emerged as an important factor contributing

to superior market timing ability. Their study showed that out of the 49 mutual funds

in their samples, 20 and 22 funds were superior before and after correction for

heteroskedasticity. Thus, in contrast to the findings of Lee and Rahman (1990),

adjusting for heteroskedasticity increases the number of occurrences of superior

market timing ability. Abdel-Kader and Qing (2007) in their study on Hong Kong

actively managed mutual funds concluded that there was no selectivity and market

timing ability for a sample of 30 funds from August 1995 to July 2005.

2.6.3 Performance persistency

Many studies in the US have found that, on average, performance persistence exists in

mutual funds (Brown and Goetzmann, 1995; Busse and Irvine, 2006; Carhart, 1997;

Grinblatt and Titman, 1993; Henriksson and Merton, 1981; Malkiel, 1995; Sharpe,

1966). Sharpe (1966) discussed the relationship between the stock market and its

persistence in relation to the performance of mutual funds. He noted that the

Page | 53

implications of the capital market model on the performance mutual funds are

relatively straightforward and directly significant.8

Sharpe (1966) employed the average rate of return of a portfolio (��) and the actual

standard deviation of its rate of return (��) and defined performance persistence in the

scenario where all funds provide rates of return, giving��; �� values lying generally

along a straight line. In other words, performance persistence occurs where all funds

hold properly diversified portfolios and spend the appropriate amount on analysis and

administration. However, in the situation where some funds fail to diversify properly

or spend too much on research and administration, they persistently give rates of

return yielding inferior ��; �� values. Thus, performances are poorer and could be

expected to remain so (Sharpe, 1966, p. 122).

Hendricks et al. (1993) and Brown and Goetzmann (1995) provided evidence that

mutual funds in the US market perform in the short term. Carhart (1997) also

evaluated fund performance in the US and explained that performance persistence is

largely occurring in the worst performing funds, while in the emerging market

performance persistence largely occurs among the winner funds (Huij and Post,

2011). Blake and Timmermann (1998) indicated weak evidence for both top and

bottom performers in the period 1972–1995, while Cuthbertson et al. (2008) found

that the performance of past-winner funds did not exhibit persistence and past-loser

funds remained losers in the UK market over the period of study, 1975–2002,.

In the Hong Kong finance market, Abdel-Kader and Qing (2007) found that

performance persistence occurred among good and poor fund performers when a two-

year interval was used to define a short-term period. Meanwhile, Suppa-aim (2010)

found that there was a short-term persistence in mutual fund performance in Thailand

from June 2000 to August 2007. Similarly, Low and Ghazali (2007) discovered that

8 Sharpe (1966) in his study employed the ex post values of the average rate of return of a portfolio

(��) and the actual standard deviation of its rate of return (��) to predict future performance. Since the capital market model implies that the values for �� and �� for efficient portfolios should lie along a straight line, therefore, the higher values of �� are associated with higher values of�� . The model also indicates that a fund with a higher risk is expected to give a higher return. However, in this case, the values of �� and �� will not lie precisely along the straight line. For example, due to there being an element of risk in the stock market, this relationship of risk and return could still be visible and statistically significant, yet this needs further investigation.

Page | 54

there was a short-term relationship between Malaysian mutual funds and the stock

market in 1996–2000. This was because the price of mutual funds is related to the

stock market index, the KLCI, thus implying that fund managers respond to historical

performance and the movement of the stock market while determining their portfolio

selection. In contrast, Taib and Isa (2007) revealed that there was no persistence in

return performance in Malaysia over the period 1991–2001.

2.6.4 Empirical evidence on fees and fund attributes on performance

Fees are an important part of mutual funds and they are normally associated with the

returns performance of the funds. The fees are higher in normal funds than in index

funds. In other words, fees associated with active management funds are higher than

those with passive funds. From the investment point of view, the strategy of active

management funds seeks to create value and achieve alpha. As a result, fees are

higher and weighted towards better performance. On the other hand, the strategy of

passive management funds aims to track indices and to achieve beta. Therefore, the

fees of passive funds are normally lower than for the active management style and

weighted towards management (Ernst-&-Young, 2009). For fund managers, higher

fees could give more opportunities to create better returns. However, investors are

interested in funds associated with less expense since high expense funds have lower

net returns.

With reference to mutual fund investments, there are several types of fees and

expenses involved. These can be categorised into two types: load fees and operating

expense fees. The load funds normally charge both. And no-load funds charge only

expense fees without compulsory or load fees. Part of load fees is a sales load or a

sales charge, collected at the inception date of the fund being bought. Another part is

the exit fee or redemption fee, which is a deferred sales charge collected when the

mutual fund is redeemed. Some mutual funds charge only front-end fees but not back-

end or exit fees.

Operating expenses can be in the form of management fees, 12b-1 fees, trustee fees

and other fees. Management fees are investment management fees that are based on

an annual percentage of assets under management to help pay off the fund managers.

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The fees are calculated daily in the calculation of the NAV of the fund. These are paid

by all funds, load and no-load funds. There are also other fees such as 12b-1 fees,9

which are used to compensate brokers or to pay for advertising. In the Malaysian case,

the expenses fees consist of management fees, expense ratio, trustee fees and

switching fees, if any.

The findings from the conventional fund literature have indicated that fees do have an

adverse impact on investors. Most of the funds are not able to outperform their market

benchmarks; in fact, they perform even worse after deducting fee expenses from the

gross returns (see for example, Carhart, 1997; Haslem et al., 2008; Iannotta and

Navone, 2012; Malkiel, 1995).

Earlier studies highlighted the performance of mutual funds compared to the fees in

the US market. Malkiel (1995) showed that investors would have been better off

buying low-expenses index funds, since the funds did not achieve sufficient gross

returns to compensate for their management fees incurred during the 1971 to 1991

period. The funds underperformed their market benchmarks both after management

expenses and even gross of expenses excluding load fees. This is in line with the

study by Sharpe (1966), who noted that a higher Sharpe ratio (reward-to-volatility

ratio) is related to the fund performance with lower expenses.

Ippolito (1989), on the other hand, found that mutual fund returns were not related to

expense ratios and turnover during the period 1965 to 1984. He revealed that risk-

adjusted returns appeared to exhibit a negative correlation with expense ratios. He

further indicated that the risk-adjusted returns performance of the US mutual funds

(net of fees and expenses but including the load charges) are relatively comparable to

the returns performance of the index funds and adequate enough to compensate the

higher fees. He also argued that the relationship between mutual fund return and both

expense ratio and turnover in previous studies is associated with an active investment

management (Ippolito, 1993). Similarly, Elton et al. (1993) found that risk-adjusted

returns appear to exhibit a negative correlation with the expense ratios.

9 The 12b-1fees are popular in US mutual funds but currently are not imposed on Malaysian mutual funds.

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However, in contrast to Ippolito (1989), Elton et al. (1993) and Carhart (1997) argued

that high fees do not perform as well as the low fee funds, and therefore higher fees

are negatively correlated to fund performance. Elton et al. (1993) found that funds

with higher fees and turnover underperform compared to those with lower fees and

turnover. Carhart (1997) further explained that investment costs in mutual funds such

as expense ratios, transaction costs, turnover and load fees have a direct negative

impact on performance. Carhart (1997) also identified that persistence in expense

ratios is attributable to long-term persistence in mutual fund performance. He also

revealed that portfolio turnover and load fees are significantly and negatively related

to fund performance. More precisely, the expense ratios appear to reduce performance

to little more than one-for-one. Turnover reduces performance about 95 basis points

for every buy-and-sell transaction. The differences in costs per transaction account are

spread in the best and worst performing mutual funds.

Golec (1996) observed a similar relationship between fund performance and expenses

as found by Elton et al. (1993) and Carhart (1997). In fact, Golec found general

evidence that funds with low fees tend to perform better than those with high fees. He

also noted that funds with low administrative expenses perform relatively well. Older

and larger funds are associated with lower fees. Load fund is significantly negatively

correlated with management fees, implying that the fund trade-off is between lower

management fees and front fees. However, he indicated that funds with high

management fees do not necessarily imply poor performance but signal superior

investment skills leading to better performance. He therefore suggested that investors

should avoid funds with high operating expenses but not necessarily funds with high

management fees.

In the early 1990s, Chance and Ferris (1991) studied mutual fund distribution fees and

defined expense ratio as total expenses divided by total assets. They developed a

multiple regression model of the expense ratio as a function of six explanatory

variables: objective, growth and income, age, size, the existence of load charge and

the 12b-1 fees over each of the years from 1985 to 1988. The results – based on the

percentage of cross-sectional variation in expense ratios at about 42–50 per cent –

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revealed that both growth and income coefficients were negative and significant,

indicating that funds with growth or income as an objective have a lower expense

ratio than funds with the objective to maximise capital gains. Load variables were

negative and significant, except for the year 1986, implying that load funds have

lower expense ratios than no-load funds. However, the 12b-1 was positive for most of

the years and highly significant, suggesting that the 12b-1 fees increase the expense

ratio. Chordia (1996) further revealed that funds with load and redemption fees (exit

fees) hold less cash than those with no-load counterparts, therefore suggesting that

mutual funds dissuade redemptions through front and back-end load fees. On the

other hand, funds hold more cash when there is uncertainty about redemptions.

Indro et al. (1999) stated that fund size, which is based on net assets under

management, affects mutual fund performance. This is in line with the findings of

Chance and Ferris (1991), who stated that size is negative and highly significant in

relation to fund performance, reflecting the economies of scale associated with large

mutual funds. As a result, Indro et al. (1999) suggested that mutual funds must attain a

minimum size in order to achieve sufficient returns to justify their costs of acquiring

and trading on information. They also found that trading on information contributes

positive returns only for the value and blend categories of funds and not for growth

funds’ counterparts, which means that the size of net assets is important for growth

funds rather than for value and blend categories of funds.

Chen et al. (2004) analysed size and fund performance and investigated the work of

equity mutual funds in the US from 1962 to 1999. They wanted to check if

performance depended on the fund size measured by the log of the total net assets

under management. They provided evidence that performance declines as the fund

size increases. This inverse relationship between funds’ performance and size is

related to liquidity. They suggested that size and liquidity erode performance and this

is due to organisational diseconomies linked to hierarchy costs. They found that the

fund size erodes the performance in a much more pronounced way among small cap

stocks.

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Elton et al. (2003) extended the study on the impact of incentives fees and identified

that a key factor that affects performance is the size of expense ratios. An incentive

fee is used to compensate the fund managers and defined as a reward structure that

makes management compensation a function of investment performance in relation to

a benchmark. The funds that employ incentive fees emerge as having better alphas

(positive stock selection ability) since they charge lower expenses. However, the

funds on average are low risk compared to the market risk, as the beta of these

incentive-fee funds is less than one.

Haslem et al. (2008) studied the performance of mutual funds in the US market and

indicated that, on average, superior performance occurs among large funds that have

low expense ratios, low trading activity and no or low front-end loads. However, there

is no difference in performance of funds with respect to whether they have 12b-1 fees

or not. They also found evidence consistent with other studies that, on average,

actively managed mutual funds underperform their market benchmark after deducting

expenses. These expenses consist of management fees, 12b-1 fees and other fees,

excluding sales loads and fees directly charged to shareholder accounts and security

transaction costs. These refer to brokerage fees, bid-ask spreads and market impact

costs. Consistent with this study, Gil-Bazo and Ruiz-Verdú (2008) suggested that

better-quality funds are not expected to charge higher prices. They revealed that

worse-performing funds set fees that are greater than or equal to those set by better-

performing funds. As a result, they suggested that the funds should disclose the level

of fees charged in comparison to the average or median fees of other corresponding

funds in the same category to avoid overcharging unsophisticated investors.

Pollet and Wilson (2008) also noted that higher expenses (expense ratio) and total

load (combination of front-end and back-end loads) associated with funds are

significantly negative to the returns, but higher industry concentration is positively

significant to the marginal effect of a fund’s market capitalisation style. Large and

small funds diversify their portfolios in response to growth, but the diversification is

less pronounced for the large-cap funds and family funds with a large numbers of

siblings. They also found that greater diversification of the small-cap funds is

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associated with better performance, after controlling for fund size and fund family

size from 1975 to 2000 in the US.

Giambona and Golec (2009) evaluated the performance of 3696 retail equity funds

from 1962 to 2002, and indicated that larger incentive management fees lead to less

counter-cyclical or more pro-cyclical volatility among fund managers, since the

additional risk could earn them larger returns and fees. There is a positive relationship

between fees and volatility timing as the fund managers’ market volatility timing

strategies are partly driven by their compensation incentives.

Massa and Patgiri (2009) argued that if higher incentives compensation only increases

risk-taking and reduces the probability of survival in the US mutual fund industry,

then there should be no relationship between performance and incentives after

controlling the risk. Therefore they expected that there should be a positive

relationship between performance and incentives even after controlling the risk and

survival. Their findings supported this hypothesis and revealed that higher incentives

induce managers to take more risks and reduce the probability of funds’ survival.

Funds with higher incentives also deliver higher risk-adjusted returns and evidence of

persistence in performance, even after controlling for survival. Consequently, they

provide investors with a surplus. Massa and Patgiri suggested that incentives could be

a useful tool to motivate fund managers and increase welfare.

A recent study based on the US equity mutual funds found that there is insignificantly

negative relationship between past return performance and fees, a proxy for the

expense ratio net of 12b–1 fees. On average, the older funds tend to charge higher

expense ratios and funds with a higher degrees of risk charge more fees. The fees’

dispersion decreases with the fund size and age. Furthermore, the fees’ dispersion is

lower for the funds that charge market and distribution fees, namely, the 12b-1 fees

(Iannotta and Navone, 2012).

In market other than the US, studies on fees and fund performance have been quite

limited. Dahlquist, Engstrom, and Soderlind (2000) found that performance is

negatively correlated to fees, with higher fee funds tending to underperform relative

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to lower fee funds in the Swedish market from 1993 to 1997. There are some cases

when higher fee funds perform better than low fee funds but only at the gross level,

i.e., before fees are deducted. They basically found that actively managed equity

funds perform better than passively managed funds, indicated by the alpha in a linear

regression of fund returns on several benchmark assets, allowing for time-varying

betas. They also studied the relationship between fund performance and other fund

attributes and provided evidence that good performance occurs among small equity

funds, low fee funds, funds whose trading activity is high and, in some cases, funds

with a good history of business. They employed a cross-sectional analysis, revealing

that large equity funds tend to operate more poorly than small equity funds

In a study on Finnish mutual funds from 1993 to 2000, Korkeamaki and Smythe

(2004) noted that the funds’ fees decreased over time, suggesting that the market is

increasing in competitiveness. The fees are usually higher for older funds and the

funds that are managed by banks cannot be offset by their superior returns. However,

funds from larger families and funds from institutional investors have lower fees.

International equity funds also have lower fees than their domestic counterparts.

Geranio and Zanotti (2005) investigated the determinants of mutual fund fees in Italy

over the period 1999–2002 using 1958 funds sold on the local market. In their study,

total expense ratio is the annual percentage reduction in investor returns that would

result from operating costs even if the fund’s portfolio were to be held or not traded

during that period. The operating costs are annual costs, including management fee

and administration, custody, audit, legal and distribution fees. Geranio and Zanotti

(2005) further stated that funds which are larger in size, bigger in asset management

companies and domiciled abroad charge lower fees. However, the age of a fund does

not show a significant relationship with the total expense ratio. The total expense ratio

is also lower for load funds. The study also contended that funds sold exclusively by

financial advisors charge lower fees than those sold only by banks. More specifically,

funds sold by financial advisors charge higher redemption fees than funds sold by

banks, which contradicts the findings that funds distributed by banks charge

significantly higher front fees than funds distributed by financial advisors.

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In Australia, Gallagher (2003) noted that there is evidence of significantly higher

management fees charged by managers with larger Australian equities that benchmark

allocation exposures. The performance of Australian investment managers is also

significantly negatively related to the age of the institution. He also stated that

management fees or expenses are not related to the fund asset or fund size. Huij and

Post (2011), however, found that the winner funds in emerging markets covering 22

countries including Malaysia provide returns more than sufficient to cover fee

expenses. They suggested that emerging market funds generally exhibit better

performance than US funds.

Another study incorporating fees in the performance of mutual funds was done by

Babalos et al. (2009), who evaluated the performance of all Greek domestic equity

funds over the period 2000–2006. They used total expense ratio and defined as the

ratio of a fund’s total expenses over its average net assets for each year. The expenses

include the operational costs charged by equity mutual funds of management fees,

custodian and auditors’ fees, transaction costs and other costs that are linked to

research or customer support. However, they excluded front and back-end load fees.

Their study found that funds’ return performance is negatively related to expenses,

whereas investors’ flows are not directly affected by expenses. They concluded that

charging an expense ratio of 3 per cent was relatively stable over the period of the

study, comparatively more than twice as high as the expense ratio charged by US

equity funds. Funds affiliated with the dominant banking groups deliver a higher

return performance than other funds with similar expense ratios. They also concluded

that funds’ performance is positively related to their age and negatively related to

their size (Babalos et al., 2009).

In Malaysia, the most relevant research on fees and their relationship to fund

performance was by Low (2010). Low (2008) illustrated that fund expense ratio

determines the fund returns performance. Funds with high returns volatility are

associated with a low expense ratio. However, she found no evidence that fund

objective and fund age are related to the expense ratio. In the other study, Low (2010)

evaluated fund performance in relation to fund characteristics such as fund size, age,

expense ratio, turnover, beta and fund type, and found that, on average, the risk-

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adjusted returns of 65 Malaysian mutual funds were not significantly related to age

and fund size over the period 2000–2004. She further remarked that there is a

statistically negative correlation between the growth in fund size and fund

performance. Low (2008; 2010) further revealed that fund size is negatively

correlated with listed fund characteristics, namely, expense ratio, fund age, turnover

and beta of the fund.

To the best of my knowledge, no other study has investigated the comparative return

performance between Islamic and conventional funds both before excluding fees and

after excluding fees from the fund returns. In IMF-related studies, since no analysis

evaluates funds after fees, this investigation of fund performance in relation to fees

could provide new insights and new evidence for investors and regulators on how

fees react on Islamic funds in particular, and on Malaysian mutual funds in general.

2.6.5 Previous studies on ethical and Islamic funds

There is a conception that IMFs constitute a kind of investment which is close in

character to ethical funds. Furthermore, the studies on IMFs are still few. It is

therefore timely to evaluate the performance of Islamic funds based on studies on

ethical funds. In terms of empirical findings, results from the global studies on ethical

funds or socially responsible funds have demonstrated the same tone as the findings

on Islamic funds. There is no evidence of significant differences between ethical and

conventional mutual funds (see for example, Abderrezak, 2008; Bauer et al. 2007;

Bauer et al. 2005; Goldreyer, Ahmed, and Diltz, 1999).

Mueller (1991) analysed the ethical mutual funds in US over the period 1984 to 1988

and reported that investing in ethical mutual funds produced an average annualised

return of 1 per cent less than the return that could have been obtained from

comparable funds. For these religious investors, the opportunity cost of ethical

investing, in the form of foregone returns on investments, is estimated as an implicit

biblical tithe (Mueller, 1991, p. 121). Mueller (1994) also examined the performance

of the Amana Income Fund, an Islamic equity fund, in the US during 1987 to 1992,

and concluded that, on average, the fund and its peers in the equity income fund index

are less risky that the respective market index, a proxy by Vanguard Index 500 fund

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for the market benchmark. The Islamic fund provides, on average, only 0.6 per cent

per annum above the risk-free return (government money market fund index).

Meanwhile, its peers gain on average of 3.6 per cent more than the similar risk-free

rate.

Goldreyer et al. (1999) looked at a sample of 49 socially responsible funds and 20

random samples of conventional funds provided by Lipper Analytical Services. They

found no significant performance difference between the socially responsible funds

and the conventional ones over the period January 1981 to June 1997. Their

evaluation was based on risk-adjusted performance measurements and they applied

one-year treasury security rate as a proxy for returns of the riskless asset.

Bauer et al. (2005) found insignificant differences in risk-adjusted returns between

ethical and conventional equity funds over the period 1990 to 2001, using data from

Germany, the US and the UK markets. The authors reported evidence of statistically

insignificant differences in return performance between ethical and conventional

mutual funds after controlling factors including size, book-to-market and momentum.

Ethical funds exhibit clearly different investment styles from conventional funds

because the ethical funds are typically less exposed to market return variability. They

also tend to be more growth-oriented but less value-oriented. Compared to their

conventional peers, the UK and German ethical funds are heavily exposed to small

caps, while the US ethical funds, on the other hand, invest more in large caps.

Bauer et al. (2007) also studied ethical funds in Canada and noted that there is no

significant performance difference between ethical funds and their conventional peers.

In fact, Renneboog et al. (2008) found that socially responsible funds underperform in

most European and Asian markets but no evidence of cost of diversification emerged.

Recently, Gil-Bazo et al. (2010) found that there was no significant difference in fees

between socially responsible investment (SRI) and conventional funds in the US for

the period 1997 to 2005. They also provided evidence that SRI funds do better before

and after fees than conventional funds with the same characteristics.

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Other than the ethical fund studies, published research on IMFs is growing, and

recently gathered momentum in the global market as well as in the segmented market

(see for instance, Abderrezak, 2008; Abdullah et al. 2007; Ahmed, 2007; Elfakhani et

al., 2005; Elfakhani and Hassan 2005; Elfakhani and Hassan 2007; Hayat, 2006;

Hayat and Kraeussl, 2011; Hoepner et al. 2011; Ismail and Shakrani 2003). The first

listed study evaluated both Islamic and ethical funds in relation to the conventional

benchmark, the S&P 500 index, a proxy for a conventional portfolio. Abderrezak

(2008) revealed that Islamic and ethical funds perform similarly compared to their

conventional counterparts. He also found that there is no significant difference in

performance between both fund portfolios and, in fact, both are not able to outperform

the benchmarks while using Fama’s performance measures.

The last-listed study used the weekly price data of just 12 Islamic funds against the

relevant index to study the relationship between the fund risk as measured by beta and

returns for the period from 1 May 1999 to 31 July 2001. Ismail and Shakrani (2003)

reported that the adjusted-R² (adj R²) and standard error of the conditional relationship

are higher in down-markets than in up-markets. This would mean beta could be used

as a tool to explain cross-sectional differences in Islamic fund returns and as a

measure of market risk. This is consistent with the risk-return paradigm, and is

therefore a verification that the Islamic funds are behaving as if risk is the determinant

of returns. They suggested that beta could be used as a tool to measure risk, but they

did not address whether this type of fund yields lower or higher returns.

Girard and Hassan (2005) did a comparative study of Islamic versus non-Islamic

market indices. They examined the indices’ performance from 1996 to 2005 and

remarked that there was no performance difference between Islamic and non-Islamic

indices, because although the Islamic indices outperformed from 1996 to 2000, they

underperformed from 2001 to 2005. The similar reward to risk and diversification

benefits exist for both Islamic and conventional indices.

Elfakhani et al. (2005) and Elfakhani and Hassan (2007) used 46 global IMFs and

found no statistical difference in the performance of Islamic and conventional equity

funds in relation to the returns of respective market indices over the period of January

Page | 65

1997 to August 2002. They concluded that the performance of funds does improve

over time as the fund managers gain more experience and a sense of how the market

is operating. Elfakhani and Hassan (2007) also suggested that the funds do not differ

substantially from other conventional funds, although some Islamic funds appear to

perform better than others.

Elfakhani and Hassan (2007) also indicated that the Islamic portfolio may have

generated higher returns at lower risk over the full period from January 1997 to

August 2002. In addition, the major observation of the study was the strong

performance of Islamic mutual funds compared to both Islamic and conventional

benchmarks during the recession period. In fact, they suggested that there is no

statistically significant risk-adjusted abnormal reward or penalty associated with

investing in Shariah-compliant mutual funds. They therefore concluded that

conventional investors could consider Islamic funds in their portfolio selection,

especially during slow market periods, and it is investors’ duty to investigate the

various potential types of mutual funds in the market to suit their needs. This must be

done regardless of whether a fund is a conventional one or Islamic or an ethical or

socially responsible fund.

Abdullah et al. (2007) contradicted this finding by showing that conventional funds

perform better than Islamic funds during good economic periods and worse during

bad economic periods. Hayat and Kraeussl (2011) estimated the performance of 145

Islamic equity funds worldwide over the period from 2000 to 2009. They found that,

on average, the funds significantly underperformed the respective Islamic benchmark

by 1.71 per annum and the conventional market benchmark by 0.28 per annum. Hayat

and Kraeussl (2011) further revealed that IEFs perform worse over the benchmark

during the bearish market compared to the bullish market. This result is contradictory

to the finding of Abdullah et al. (2007), who found that IEFs perform better than the

CEFs counterparts during the bearish market. However, according to Hoepner et al.

(2011), the results of Hayat and Kraeussl (2011) are debatable as they replaced a

missing NAV with the average of previous and subsequent observations. Hoepner et

al. (2011) revealed that in their study most of the global Islamic funds

underperformed the relevant market benchmark from September 1990 to April 2009.

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In contrast, the 76 Malaysian Islamic funds out of 265 funds in their sample were, on

average, competitive with the international equity market benchmarks. Hoepner et al.

(2011) incorporated the most recent data from 1990 to 2009, but limited the

evaluation of performance to Islamic equity funds. Meanwhile, Abdullah et al. (2007)

investigated Islamic equity funds in comparison to conventional funds but employed

obsolete data from 1992 to 2001.

The study by Hassan et al. (2010) generated similar evidence to that of Girard and

Hassan (2005) in that there were no performance differences between Islamic and

conventional Malaysian unit trust funds from January 1996 to November 2005. The

study incorporated a sample of 80 equity funds including 30 Islamic funds. The study

found that Islamic unit trust funds are small cap oriented while their conventional

peers are value-focused. The study also singled out a significant long-term

relationship between Islamic and non-Islamic portfolios, suggesting that investors in

the Malaysian unit trust industry are benefiting from the international diversification

of financial risks. However, this poses a challenge, as these results are contradictory

to the findings of Hoepner et al. (2011), who suggested that Islamic funds display a

tilt towards growth and small cap stocks orientation. Moreover, these studies did not

examine either market timing or the impact of fees on the fund performance.

It is therefore important to note that most studies on IMFs in the Malaysian market

have provided evidence of the funds underperforming, and the findings portray a

similar pattern according to the evidence for global IMFs (see for instance,

Abderrezak, 2008; Abdullah et al. 2007; Elfakhani et al., 2005). One possible reason

for this is that the duration of the studies is quite similar. There is a contradictory

result regarding the Islamic funds in Malaysia due to the longer period of the study.

The findings of Abdullah et al. (2007), who indicated the superior performance of

Islamic funds in Malaysia specifically during the bearish market, have quite different

conclusions to those of Hoepner et al. (2011) that Islamic funds are compatible in

relation to the international equity market.

Hoepner et al. (2011) found that most of the IEFs worldwide underperform their

market benchmark, yet they also found that in comparison the Malaysian equity funds

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perform competitively against the international equity market benchmarks. They

analysed 265 Islamic equity funds worldwide including 76 from Malaysia. In contrast,

Kraeussl and Hayat (2008) and Hayat and Kraeussl (2011) found, on average, that

IEFs from a sample of 145 funds underperformed the market benchmark, whether

Islamic or conventional, and that underperformance worsens during a crisis or bearish

market. Hoepner et al. (2011) and Kraeussl and Hayat (2008) limited their studies to

the performance of Islamic equity funds (IEFs), with Hoepner et al. (2011) employing

an equally weighted average based on simple mean returns.

In the market there are many mutual fund categories other than equity funds –

allocation funds, alternative funds, fixed income funds and money market funds – and

they remain largely unexplored. Therefore, this thesis not only evaluates the

performance of equity funds but also investigates the diversified funds, which involve

all the cited categories.

Other relevant studies on the comparative performance of Islamic and conventional

funds by Abdullah et al. (2007) and Elfakhani et al. (2005) found that, on average,

neither IMFs nor CMFs outperform the market and IMFs perform better during a

bearish market, while CMFs perform better during a bullish market. This is in contrast

to two former studies: Elfakhani et al. (2005) employed diversified funds but

consistently, and Abdullah et al. (2007) employed equity funds only. However, the

number of funds involved in all the studies is very limited and the duration is

relatively short.

IMFs were introduced in the late 1990s due to the higher demand for Shariah-

compliant products and securities in the global market. Since then, investment in

IMFs has risen in the global market due to the development of Islamic finance

worldwide, which is illustrated by it becoming an important part of the international

financial system. Continuous demand for the Islamic funds industry has made it the

fastest growth area of the Islamic financial system. This growth is crucial because it

indirectly influences the continuing improvement of the global Islamic financial

market. In Malaysia, the industry has grown tremendously since the 1990s, with an

increase from two funds in 1992 to 150 funds at the end of December 2009.

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Table 2.3: Summary of some previous empirical evidence on fund performance

Author (Year)

Country Sources of Data/ No. of funds

Period of the Study/Data range/Approach

Inputs (Attributes/ Characteristics)

Outputs (Findings)

Sharpe (1966) US Weisenberger database (34 funds)

1954–1963/ Yearly/ Time series

• Sharpe ratio • Expenses • Conventional funds

• The higher Sharpe ratio is associated to the fund with lower expenses.

• This study does not include front and exit fees in the experiments.

Treynor and Mazuy (1966)

US Weisenberger database (57 funds)


• Choice of funds/selectivity

• Market timing

• No evidence that the fund managers can outperform the market.

Merton (1981) US Simulated returns (growth of 1000) from market timing and protective strategies New York Stock Exchange ( for market return) and US T-bills (for riskless asset)

January 1927–December 1978/ Monthly/ Time series

• Modern capital market theory

• Forecasting skills • Microforecasting

(stock selectivity) • Macroforecasting

(market timing) • Equilibrium theory

• The equilibrium of price structure of management fees is no benefit in forecasting superior performance.

• The existence of different information among market participants plays an empirically insignificant role in the formation of equilibrium security prices.

Henriksson (1984)

US Standard & Poor’s stock price and

February 1968–June 1980/ Monthly/

• Parametrics and non-parametrics techniques based on multifactor

• Mutual fund managers are not able to follow an investment strategy that successfully times the market portfolio return.

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Weisenberger database (116 funds)

Time series CAPM • Market timing

Chang and Lewellen (1984)

US Center for Study of Security Prices (CRSP) (67 funds)


• Market timing skill • Security selection

ability

• Neither skillful market timing nor security selection abilities are evident in fund return performance.

• The funds collectively unable to outperform a passive investment strategy.

Admatti et al. (1986)

- - Conceptual • Timing ability • Selectivity ability

• The TM model is a valid performance measure for timing and selectivity ability.

Grinblatt and Titman (1989)

US Hypothetical data from Grinblatt (1986–87), and Grinblatt and Titman (1988) (279 funds)

1975–1984; 1986; 1987/ Yearly/ Time series

• Selectivity factor • Price equilibrium • Portfolio performance • Conventional funds

• Links between performance measures and particular equilibrium models are not necessary. Therefore, the existence of different information among investors plays an insignificant role in price equilibrium.

Ippolito (1989)

US Weisenberger database (143 funds)


• Expense ratio • Risk-adjusted returns • Conventional funds

• The returns after adjustment for expenses are comparable to the returns of index funds. The funds with higher fees are relatively well and sufficient to offset the higher charges.

• However, this study does not consider the front and exit load charges in the returns evaluation.

Lee and Rahman (1990)


January 1977–March 1984/ Monthly/ Time series

• Selection ability • Market timing ability

• There is evidence of superior selectivity and timing ability of some fund managers of individual funds.

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Chen et al. (1992)


January 1977–March 1984/ Monthly/ Time series

• Fund selectivity • Market timing • Fund objective • Expense ratio • Load fees

• The study finds selection performance for the fund sample.

• Collectively, the funds appear to possess no market timing ability over the period of the study, with the evidence suggesting that there is a trade-off between market timing and fund selection performance.

• Expense ratio is the dominant factor to explain timing ability of the mutual funds.

• Load funds on average seem to have no better ability in selecting individual securities than no-load funds.

Malkiel (1995)

US Lipper Analytic Services (239 funds)


• "Hot hand" phenomenon

• Expense ratios • Survivorship bias • Performance persistence • Conventional funds

• The strong evidence in favour of a "hot hand" phenomenon in mutual funds, which achieved above average returns, would continue to enjoy superior performance.

• The existence of expense ratios that vary over the universe of funds tends to produce some persistence in returns.

• The fund with the lowest expense ratio is likely to outperform high expense funds persistently .

Golec (1996) US Morningstar database (530 funds)


• Conventional funds • Expenses

• Funds with low fees tend to perform better than funds with high fees.

• Funds with low administrative expenses perform relatively well.

Chordia (1996)

US Investment Company Institute (397 funds)

January 1984–July 1993/ Monthly/ Time series

• Conventional funds • Load fees • Redemption fees

• Funds with load and redemption fees hold less cash than those with no-load counterparts. Therefore, mutual funds reduce redemptions through front and back-end load fees.

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Carhart (1997) US Micropal/Invest

ment Company Data, Inc. (ICDI)/ FundScope Magazine/ United Babson Reports/Wiesen-berger Investment Companies/The

Wall Street

Journal (1892 funds)


• Persistence • Fund characteristics • Diversified equity funds • Load fees

• Funds are not able to outperform their market benchmark and they perform even worse after fees are deducted from the gross returns.

• Performance persistence is largely explained by the worst performing funds.

• Fees are negatively correlated with performance of fund returns.

• Expense ratios, portfolio turnover and load fees are negatively related to performance, with load funds substantially underperforming no-load funds.

• The study suggests three important rules-of-thumb for wealth-maximising mutual fund investors: (1) avoid funds with persistently poor performance; (2) funds with high returns last year have higher-than-average expected returns next year but not in years thereafter; and (3) the investment costs of expense ratios, transaction costs and load fees all have a direct, negative impact on performance

Bello and Janjigian (1997)

US Morningstar database (633 funds)


• Market timing • Security selection • Extended TM model

• There were Positive and significant market timing abilities for all funds using the extended TM model, in which the results are sharply contrasted to the negative market timing abilities when using the original TM model.

• A significantly positively security selection skill and a negative correlation between market timing and this selectivity skill.

Annuar et al. (1997)

Malaysia

New Straits Times database

July 1990–August 1995/

• Fund performance • Selectivity skill

• These mutual funds did outperform the KLCI benchmark.

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(31 funds) Monthly/ Time series

• Market timing • TM model

• Mutual funds in Malaysia have a positive selectivity skill and a negative market timing ability.

• A positive correlation between selectivity and timing performance.

• The degree of diversification of the Malaysian mutual fund is below the market in general .

Black and Timmermann (1998)

UK Mocropal Ltd (2300 funds)

February 1972–June 1995/ Monthly/ Time series

• Persistence • Investment styles • Fund performance

• The average UK equity fund appears to underperform the market by around 1.8 per cent per annum based on a risk-adjusted basis.

• Evidence of persistence in performance among the best- and worst-performing funds in the UK market.

• The investment styles of the two groups of fund managers differ, with UK fund managers favouring asset allocation and market timing strategies, whereas their US counterparts favour quantitative (bottom up) stock selection.

Hallahan and Faff (1999)

Australia FPG research house (65 funds)

January 1988–September 1997/ Monthly/ Time series

• Market timing • Fund performance • Selectivity performance

• Australian mutual funds have a negative selection performance and little evidence of market timing ability over the study period.

Goldreyer et al. (1999)

Global Lipper Analytical Services (49 funds)

January 1981–June 1997/ Monthly/ Time series

• Socially-responsible investment funds

• Conventional funds

• The conventional funds appear to outperform SR funds in a larger number of circumstances.

Dahlquist et al. (2000)

Sweden TRUST database of

1993–1997/ Yearly/

• Past performance • Turnover

• Larger equity funds tend to perform less well than smaller equity funds with the exception that larger

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Findata Panel data regression

• Fund size • Fee structure

bond funds seem to perform better than smaller bond funds.

• Evidence suggests that actively managed equity funds perform better than more passively managed funds.

• Fund performance is negatively correlated to fees, that is, high fee funds seem not to perform as well as low fee funds before fees are deducted. Therefore, high fees may generate good performance but still not enough to cover the fees.

• A positive relation between lagged performance and current flows, and evidence of persistence in performance only for money market funds.

Ismail and Shakrani (2003)

Malaysia Malaysian Daily newspaper (12 Islamic funds)

May 1999–July 2001/ Weekly/ Time series

• Portfolio beta • Portfolio returns • Conventional funds

• The adjusted-R² and standard error of the conditional relationship is higher in down-markets than in up-markets.

• Beta has a role to play in explaining cross-sectional differences in Islamic Unit Trusts’ returns.

Bala and Matthew (2003)

Emerging market/ Malaysia

Malaysia-based (75 questionnaires)

2003/ Survey

• Emerging market • Past performance • Size of funds • Costs of transaction • Experienced fund

managers

• The three important factors which dominate the choice of mutual funds in emerging market are past performance consistency, size of funds and costs of transaction.

• The most important factor for investors is the final performance of the funds. This is followed by how the performance is achieved, either by experienced or educated fund managers.

Korkeamaki and Smythe (2004)

Finland Helsinki Exchanges Ltd. (150 funds)

1993–2000/ Yearly/ Timer series

• Conventional funds • Expense ratio

• This study on Finnish mutual funds shows that the funds’ fees decrease over time, suggesting that the market is increasingly in competitiveness.

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Girard and Hassan (2005)

Global Reuters and Datastream (DJIM and MSCI indices)

Jan 1996–Nov 2005/ Monthly/ Time series

• Dow Jones Islamic index

• Non-Islamic indices

• There is no difference between Islamic and non-Islamic indices. The Islamic indices outperform from 1996 to 2000 and underperform from 2001 to 2005 their conventional counterparts.

• Similar reward to risk and diversification benefits exist for both Islamic and conventional indices.

Taib and Isa (2007)

Malaysia The Star and The Edge Malaysia newspapers (110 funds)

January1990–December 2001/ Monthly/ Standard performance measures

• Performance measures • Persistency • Net asset values (NAV)

• No persistency in returns performance of the Malaysian mutual funds over the period of the study.

• On average, the performance of the Malaysian mutual funds falls below the market and risk free returns.

• The bond fund portfolio indicates superior performance rather than the market and equity funds.

Elfakhani et al. (2005)

Global Failaka Database (46 Islamic funds)

January 1997–August 2002/ Monthly/ Time series & pooled regression

• Islamic funds • Net asset values (NAV)

• The results of the Transformed Sharpe model showed that the performance of Islamic mutual funds compared to both benchmarks (S&P 500 Index and FTSE Islamic Indices) during the second period dominated by recession is better than that during the first (booming) sub-period, implying that the funds’ performance is improving with time.

• No statistically significant difference exists in fund performance compared to respective indices, suggesting that the behaviour of Islamic funds does not differ from that of conventional funds.

• No statistically significant risk-adjusted abnormal reward or penalty associated with investing in Islamic funds; thus conventional investors can consider Islamic mutual funds in their portfolio collection, especially during slow market conditions.

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Abdullah et al. (2007)

Malaysia Malaysian Daily Newspaper (65 funds including 14 Islamic funds)

January 1992– December 2001/ Monthly/ Time series

• Net asset values (NAV) • Islamic funds • Conventional funds • Diversification

• The Islamic funds performed better than the conventional funds during bearish economic trends.

• While, conventional funds showed better performance than Islamic funds during bullish economic conditions.

• The study implied that Islamic funds can be used as a hedging instrument during any financial meltdown or economic slowdown.

Renneboog et al. (2008)

17 countries (in Europe, North America and Asia-Pacific)

Standard & Poor’s Fund Service (Micropal); CRSP; Bloomberg and Datastream (432 SRI funds and 16,036 CEFs)

January 1991–December 2003/ Yearly/ Time series

• SRI funds • Conventional funds • Fund characteristics • Investment styles

• SRI fund has experienced an explosive growth around the world, reflecting the increasing awareness of investors to social, environmental, ethical and corporate governance issues.

• The SRI is expected to continue growth and relative importance as an asset allocation among investors.

Haslem et al. (2008)

US Morningstar database (1779 funds)

as at December 31, 2006

• Conventional funds • Expense ratio • Management fees • Load fees

• On average, superior performance occurs among large funds with low expense ratios, low trading activity and no or low front-end loads.

• The actively managed mutual fund underperforms its market benchmark after expenses.

Pollet and Wilson (2008)

US Center for Study of Security Prices (CRSP) and Thomson


• Conventional funds • Total net asset values • Expenses • Diversification

• Higher expenses (expense ratio) and total load (combination of front and back-end loads) associated with the funds are significantly negative to the returns.

• Greater diversification for the small-cap fund is

Page | 76

Financial (285 funds)

associated with better performance.

Giambona and Golec (2009)



• Compensation incentives

• Fund managers • Volatility timing

strategies • Flow timing strategies • Conventional funds

• The propensity of fund managers to time conditional market volatility is partly driven by their compensation incentives.

• Larger incentive management fees lead to less counter-cyclical or more pro-cyclical volatility timing.

• The volatility timing and flow timing are negatively related.

Babalos et al. (2009)

Greek Association of Greek Institutional Investors (75 funds)


• Conventional funds • Expense ratio • Management fees

• Evaluation of Greek equity funds performance using total expense ratio, including management fees, custodian and auditors’ fees, transaction costs and other costs that are related to research or customer support, but excluding front and back-end loads, finds that fund performance is negatively related to their expenses.

Bertin and Prather (2009)

Global Morningstar database (2541 funds, including 172 fund of funds (FOFs)


• Fund of funds • Management structure • Fund performance • Sharpe ratio

• FOFs are cost effective for diversification, and their performance and characteristics are comparable relative to traditional equity mutual funds.

• FOFs invest in-family or identified team managers leads to superior fund performance to their unidentified team-managed counterparts.

Low (2010) Malaysia Fund’s prospectus and annual reports of the fund management

January 2000– December 2004/ Monthly/ Time series

• Expense ratio • Fund age • turnover

• On average, the risk-adjusted returns of the funds are not significantly related to age and fund size.

• Fund size is negatively correlated with expense ratio, fund age, turnover and beta of the funds.

• There is no evidence that fund size is related to fund

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companies (65 funds)

returns.

Hayat and Kraeussl (2011)

Global Bloomberg (145 Islamic funds)

January 2000–February 2009/ Weekly/ Time series

• Islamic equity funds • Market timing • Risk and return

characteristics

• On average, the Islamic funds underperform the Islamic and also the conventional benchmarks.

• Islamic funds perform even worse during the bearish market.

• There was negative market timing for the IEF fund managers over the period of the study.

Hoepner et al. (2011)

Global (20 countries including Malaysia)

Eurekahedge database (265 funds)

September 1990–April 2009/ Monthly/ Time series

• Islamic funds • Carhart model • Investment style

• National characteristics explain the heterogeneity in Islamic fund performance, with the Islamic funds from the six largest Islamic financial centres in our study (the GCC countries and Malaysia) performing competitively to international equity market benchmarks, but the Islamic fund portfolios from less developed Islamic financial services significantly underperform their benchmarks.

• The Islamic funds’ investment style globally is somewhat tilted towards growth stocks, with funds from predominantly Muslim economies also displaying a clear small cap preference.

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2.7 Summary

This chapter introduces the global Islamic fund industry globally and provides details

of the Malaysian mutual fund market have been discussed. The chapter discusses the

relevant literature and evidence related to the performance of mutual funds, focusing

on issues related to performance measurements, market timing and fund selectivity

skill, and also the relationship of fees to fund performance.

Studies on the Malaysian market have noted a shortage of recent findings on fund

performance with regard to IMFs and CMFs. Moreover, there is no study that studies

the impact of fees on the performance of IMFs. The thesis is an attempt to fill up this

gap, particularly in the Malaysian context.

The next chapter describes the variables and methodologies employed in this study

and provide details of the hypotheses and models employed for testing.

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CHAPTER 3 - RESEARCH

METHODOLOGY

3.1 Introduction

This chapter explains the relevant statistical tests and econometric methods employed

to examine the performance of IMFs compared to their CMFs counterparts. These

techniques are: (1) univariate testing based on pair mean t-test, (2) risk-adjusted

performance measures to conduct a risk and return analysis, and (3) regressions

analysis based on time series and panel data. The use of the first two techniques is

reported in Chapter 4 and the use of the third technique in Chapters 5, 6 and 7.

Chapter 5 focuses on time series data, while Chapters 6 and 7 use panel data in the

analysis. The results obtained from the models are summarised in the final chapter,

Chapter 8.

This chapter is structured as follows. Section 3.2 describes the variables employed in

the study, followed by a discussion of the main hypotheses of the thesis in Section

3.3. The method and model specifications are discussed in Section 3.4. Section 3.5

addresses the relevant econometric issues and Section 3.6 summarises the main points

in this chapter.

3.2 Variables

3.2.1 Dependent variables

In this study, the dependent variables refer to the mean returns of each mutual fund

portfolios. In Chapters 4 to 6, the dependent variable is divided into three main

portfolios, representing: (1) all mutual funds (AMFs), which refers to the full sample

of funds (479 mutual funds), (2) IMFs for the 129 Islamic funds in the sample and (3)

CMFs for the 350 conventional funds in the sample. The funds comprise all fund

types: alternative funds, allocation funds, equity funds, fixed income funds and money

market funds.

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Chapter 7 narrows the focus to fees and uses alternative measures of portfolio returns

including GROSS (returns before excluding all fees), ADJUSTED (returns net of all

expenses), ADJUSTED LOAD (returns net of load fees), and NET (returns after

excluding all fees), for each of the IEFs and CEFs. More explanation about the

variables is given in Section 7.2. This chapter focuses on equity funds because load

fees and other expenses are not relevant to other types of funds. In this case, the

sample data are the mean return of all 106 equity funds (AEFs), comprising the mean

return of 53 IEFs and the mean return of 53 CEFs.

3.2.2 Independent variables

The independent variables can be divided into three main categories: single

benchmarks, multiple benchmarks and fund attributes. The single benchmarks are the

Kuala Lumpur composite index (KLCI) and the Kuala Lumpur Syariah Index (KLSI)

market return index. For the multiple benchmarks, this study extends the independent

variables in single benchmarks to include other benchmarks, specifically Morgan

Stanley Capital International World index (MSCI), Dow Jones Islamic Market index

(DJIM), Kuala Lumpur Stock Exchange Malaysian small-cap index (KLSE small-

cap), the Malaysian fixed deposit rate for bond index and Kuala Lumpur interbank

rate (KLIBOR) for the money market index. All the indices are converted into

monthly rates of return to suit the monthly return data (see Chapters 5 and 6), and are

converted into a yearly return for Chapter 7.

Other independent variables in the category of fund attributes (mainly employed in

Chapter 7) are AGE, LNSIZE, dINVEST, ��, AlPHA, BETA, RESIDRISK,

�� , MGMTFEE, EXPENSE, TOTLOAD and TRUSTEE. These independent

or explanatory variables are specifically treated to include the exogenous and

endogenous variables. The endogenous variables are AlPHA, BETA, RESIDRISK,

�� , MGMTFEE, EXPENSE, TOTLOAD and TRUSTEE. The exogenous

variables comprise fund AGE, LNSIZE and investment style of the funds, namely

dINVEST and dTYPE. The explanation of each explanatory variable is as follows.

1. AGE is defined as fund age, measured in years, from the inception date to

2009 which is the end year.

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2. LNSIZE defines a natural logarithm of the total net asset (TNA) values of the

fund during the inception date, measured in RM (million).

3. dINVEST is an investment style dummy variable representing 1 if the fund is

invested in the domestic market and zero otherwise, i.e., if the fund is mainly

invested in the foreign market.

4. ��,a dummy variable of type of funds, is equal to 1 if the fund belongs

to Islamic funds and 0 if it belongs to conventional funds.

5. AlPHA is an intercept and is calculated for each of the funds (106 funds) with

yearly market adjusted return data using the CAPM and one-month Malaysian

t-bills used as a proxy for risk-free rate return; the natural logarithm of KLCI

price index is the market return portfolio.

6. BETA is the systematic risk and is calculated for each of the funds (106 funds)

using a similar method when calculating alpha.

7. RESIDRISK refers to residual risk or residual return standard deviation for

each of the funds (106 funds) and is calculated using a similar method when

calculating alpha and beta.

8. �� is the lagged one year return based on return of a fund i, net of

all expenses in year t-1.

9. MGMTFEE is a percentage of assets paid as a management fee. It is part of all

operating expenses.

10. EXPENSE is an expense ratio, i.e., a percentage of assets’ values spent on all

operating expenses but excluding management fees, trustee fees and load fees

such as sales charge and redemption fees.

11. TOTLOAD is the total load fee and it is a combination of FRONT fee (sales

charge) and EXIT fee (also known as back fee or redemption fee).

12. TRUSTEE is a trustee fee. A trustee fee is also known as a custody fee and is

part of the operating expenses.

3.3 Hypotheses development

This section explains the hypotheses structure for this thesis. Hypothesis testing is

part of statistical inference and is generally conducted in the process of making

judgements about population based on the sampling data observed (DeFusco,

McLeavey, Pinto, and Runkle, 2007). If the null hypotheses for those alternative

Page | 82

hypotheses cannot be rejected, then the null hypotheses must be accepted. However, if

the null hypotheses can be rejected, then the alternative hypotheses will be accepted,

at one of the significance levels, based on 99 per cent, 95 per cent or 90 per cent

confidence intervals. In this case, the two-tailed hypotheses tests have been applied.

The first null hypothesis is to test the first objective of the thesis: whether there is any

difference in the return performance of IMFs and CMFs in relation to the market

benchmark (single and multiple).

Ha1: The performance of IMFs in terms of risk and return relative to market

benchmark is different from that of CMFs.

This hypothesis is presented in a series of alternative hypotheses as the following:

Ha1(i): The risk and return performance of IMFs differs from the CMFs counterparts.

Ha1(ii): The risk and return performance of IMFs differs from the market return.

Ha1(iii): The risk and return performance of CMFs differs from the market return.

The second hypothesis is to examine any differences in market timing expertise and

fund selectivity skill among IMFs and CMFs fund managers. This hypothesis refers to

the second and third objectives of this thesis.

Ha2: The performance of IMFs fund managers in relation to market timing

expertise and fund selectivity skills is different from that of CMFs fund

managers.

The third hypothesis of this study is to find any difference regarding fees and other

fund attributes on equity funds performance between the IEFs and CEFs portfolios.

Ha3: The difference in returns performance of the funds focusing on the IEFs

and CEFs can be explained by the impact of fees and other fund attributes.

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3.4 Model specifications and the methodologies

3.4.1 Descriptive statistics and mean pair t-test

The study conducts descriptive statistics on all the portfolio returns. The study also

performs a comparison test based on mean difference and median difference between

two groups of return data. The aim is to analyse the difference between returns

performance of each group. Two samples mean pair t-tests are conducted, firstly,

between Islamic and conventional portfolios and secondly, between each of the

portfolios and the market return.

This t-test assumes that the returns of the groups are independent and approximately

normally distributed. The Jacque-Bera (JB) test is conducted to test this normality. If

the JB test of each fund portfolio indicates that the test is not significant, we can then

conclude that the data in this time series analysis are normally distributed. The JB

statistic having a small value means that the actual values of skewness (Sk) and

kurtosis (Kt) must be relatively close to the values of 0 and 3, indicating that the data

have a normal distribution. Therefore this normality test would show that whether or

not both returns – Islamic and conventional portfolios – are normally distributed. The

JB test is calculated based on the formula shown below:

JB = Z�� +Z�� = �S� − 0!6/n %

�+�K� − 3

!24/n%�= *S�)�6/n +*K� − 3)�

24/n

(Eq. 3. 1)

The study also employs the non-parametric test, the Wilcox test, on sign rank test or

Mann-Whitney test to confirm the results from the t-test. This test is equivalent to the

normal t-test and is conducted where the t-test may not be reliable, and the JB shows

that the data are not normally distributed. The test statistic (�) converts the value to z-

score (+) using the formula below:

+ = � − ,*, + 1)/4!,*, + 1)*2, + 1)/24

(Eq. 3. 2)

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The trend analysis and scatter plot are also applied in order to identify the normality

of the data in the sample. When the data are normal, the linear regression model used

on the sample becomes more meaningful since the outcome of the study is not

influenced by the outliers that might exist in the raw data.

3.4.2 Standard risk adjusted performance measures

3.4.2.1 Sharpe ratio

There are two types of risks to consider in mutual fund investments. The first is the

risk that remains, which cannot be eliminated through diversification of a portfolio.

This is also called market risk because the risk is attributable to the market-wide risk.

It is measured by the beta of a fund or a portfolio that is also known as systematic

risk. The other risk is a non-systematic risk that can be eliminated through

diversification of a portfolio. Since this risk can be adjusted, it also called

diversifiable risk. It is calculated by deducting the systematic risk beta from the total

risk (standard deviation of a portfolio).

The Sharpe ratio (SR) has been used as one of the standard performance

measurements in mutual funds research to measure the risk and returns of a fund

portfolio. Many previous studies have employed this ratio to evaluate individual funds

or portfolio performance (Amin and Kat 2003; Bertin and Prather, 2009; Elfakhani et

al., 2005; Hodges, Taylor, and Yoder, 1997; Pilotte and Sterbenz, 2006; Sharpe, 1964,

1965a, 1966). As one of the risk adjusted performance measurements, SR is

considered to be a more precise return-risk measurement relative to risk-adjusted

measures on the list, due to its ability to recognise the existence of a risk-free return in

asset portfolios (Eling and Faust, 2010).

SR often refers to the return of an asset with zero risk. Zero risk implies zero standard

deviation. The investors or fund managers can choose this risk-free asset in their

portfolio as a combination in preference to a risky portfolio. Indirectly, the investors

or fund managers can also choose the level of absolute risk (as risk is measured by the

standard deviation of a risky portfolio) or expected return that they desire.

Page | 85

The main function of SR in finance is to identify the performance of a fund portfolio

based on its return and risk. Although risk can also be measured by standard deviation

and coefficient of variation (CV), these two measurements have shortcomings in that

both cannot measure the return and risk of a portfolio independently. While standard

deviation is extensively used to measure risk, the CV on the other hand is used to

indicate the proportion of the return based on each unit of the standard deviation.

Therefore, SR can provide both benefits since it can evaluate and compare the

performance of a fund portfolio based on its return and risk, determined by an

appropriate risk-free asset (DeFusco et al., 2007, p. 116).

The application of SR in measuring the performance of mutual funds is considered

successful in determining the extent to which differences in performance persist over

time. In other words, this ratio can predict the differences in funds’ performance

(Sharpe, 1966). It is therefore expected to form reliable expectations about future

performance.

This study adopts the ex post SR introduced by Sharpe (1965a, 1966) in order to

examine the risk return trade-off of a fund’s portfolio. The larger the ratio is, the

better the performance. This is because the ratio is the reward per unit of variability or

standard deviation (Sharpe 1966, p.123). The historical data reflect the actual

performance of a fund’s portfolio. The formula is referred to as ex post SR based on

the historical data. It is calculated as follows:

.� = /0 −/120

(Eq. 3. 3)

Where /0 represents the mean returns to each of a portfolio, /1 , the mean returns to a

risk-free asset, and 20 , the standard deviation of returns on the portfolio. The average

return of one-month Malaysian t-bills rate is used as a proxy for the risk-free rate

asset.

Page | 86

The term /0 −/3 in SR is called the mean excess return on a portfolio, Islamic or

conventional. In this case, it measures reward in terms of mean excess return per unit

of risk, which is measured by the standard deviation of the /0 . The ratio of excess

return to standard deviation of a portfolio is obtained from any combination of

portfolio p and the risk-free asset that lies on a line with slope equal to the quantity

divided by standard deviation of return, 20. Thus, risk-averse investors prefer

portfolios with larger Sharpe ratios to the smaller ones (DeFusco et al., 2007). The SR

may be of the ex-ante and ex post types. The ex-ante SR refers to a portfolio going

forward based on the expectations for excess mean return, the risk-free return, and the

standard deviation of return (Elton and Edwin, 2007; Hodges et al., 1997, p. 74). In an

ex post SR, the historical data performance is used to measure the risk and return.

While using ex ante, estimating the data cannot be done and probably more rigorous

assumptions must be made in evaluating the performance. The ex post SR is more

accurate because the historical data reflect the actual performance of a fund’s

portfolio and the ex post SR is expected to lead to an accurate expectation of future

performance. Hence, ex post is employed, even though the actual result may diverge

considerably from predictions, but the result is necessary and adequate enough to be

used for the empirical test (Sharpe, 1965b).

3.4.2.2 Treynor index

Since there is no assurance that past performance is the best forecast of future

performance as can be predicted through the SR measure, the SR alone is not enough

and is complemented by other measures, for example, the Treynor index (Sharpe

1966).

The Treynor index (TI), which is also known as Treynor’s measure, gives excess

return per unit of risk, based on systematic risk (the beta of a portfolio) instead of total

risk (standard deviation of a portfolio). The beta of a portfolio is the standard

deviation of the portfolio divided by the standard deviation of the returns from the

market as a whole. This portfolio beta represents the systematic risk of a portfolio

against the relevant benchmark (Wilson 2010).

Page | 87

The formula can be expressed as follows:

�4 = /0 −/150

(Eq. 3. 4)

where �4 refers to Treynor index; /0, average return on the portfolio, /1 , average

return of one-month Malaysian t-bills rate and 50 belongs to beta for the portfolio 6 .

50 is calculated as the following:

50 =20727�

(Eq. 3. 5)

3.4.2.3 Jensen alpha

The other risk-adjusted performance measure is Jensen alpha (also known as Jensen’s

measure). It measures average return on a portfolio over and above that predicted by

the CAPM, given the portfolio’s beta and the average market return (Bodie, Kane, and

Marcus, 2007). Jensen’s measure is the portfolio’s alpha value (80). The formula is

described below:

80 = /0 −9/1 +50:/7 −/1;< (Eq. 3. 6)

3.4.2.4 Appraisal ratio

The study also uses the appraisal ratio (AR), which is also referred to as the

information ratio. This AR divides the alpha of the portfolio (80) by the non-

systematic risk of the portfolio [σ:e?;]. It measures the abnormal return per unit of

risk that in principle σ:e?; could be diversified away by holding a diversified market

index portfolio (Bodie et al. 2002, p.813). Following Bodie et al. (2002), the

calculation is as follows:

�� = 802:A0;

(Eq. 3. 7)

Page | 88

3.4.2.5 Modigliani-Modigliani measure

The study then applies the Modigliani-Modigliani measure (M2) proposed by

Modigliani and Modigliani (1997) as an alternative for measuring risk-adjusted

performance. The advantage of this procedure is that it measures a fund performance

in relation to the market in percentage term. The higher the M2 associated with a fund

portfolio, the higher the return of the fund at any level of risk. The formula used is the

following:

B2 = /̅0 − /̅120 D27

(Eq. 3. 8)

where /0, average return on the portfolio, /1, average return of one-month Malaysian

t-bills rate 20 is standard deviation of returns of a fund portfolio and 27 is standard

deviation of market excess returns.

3.4.2.6 Adjusted Sharpe ratio

To make further comparison, the study also adopts Adjusted Sharpe ratio (ASR) in the

performance measurement. The ASR is able to avoid bias in estimating the standard

deviation compared to using SR (Abdullah et al. 2007). This ASR is based on the

modification of the SR by adding the number of observations (OBS) in the model

developed by Jobson and Korkie (1981). Following Abdullah et al. (2007), the

calculation is based on the following formula:

�.� = .�DEF.EF. + 0.75

(Eq. 3. 9)

3.4.3 The models

3.4.3.1 Single and multi-factor CAPM

From risk-adjusted performance measures as previously discussed in Section 3.4.2,

the study then further enhances the methods of evaluating fund performance by using

the capital asset pricing model (CAPM). Many studies have examined risk-adjusted

Page | 89

performance measures such as SR and extended the analysis to include the CAPM

(see for example, Amin and Kat 2003; Bertin and Prather, 2009; Elfakhani et al.,

2005; Elton et al. 2003; Hodges et al., 1997; Pilotte and Sterbenz, 2006; Sharpe,

1964). The single CAPM is also widely used to measure the Islamic and conventional

fund performances in relation to the market benchmark (Abdullah et al. 2007;

Elfakhani et al., 2005; Hayat and Kraeussl, 2011; Hoepner et al., 2011).

The development of the CAPM in measuring performance of mutual funds was by

Sharpe (1964), Lintner (1965), Mossin (1966) and Jensen (1964; 1968). In this

analysis, the study employs the single and multi-factor CAPM. CAPM has been

acknowledged in many published studies (Busse, 1999; Carhart, 1997; Fama, 1972;

Giambona and Golec, 2009; Henriksson and Merton, 1981; Renneboog et al., 2008)

and is still a popular model.

Jensen (1968; 1969) used single CAPM to study the performance of MFs and

introduced the Jensen measure as the intercept from a regression of the excess return

(return minus the risk-free rate) of the managed portfolio on the excess return of a

benchmark portfolio. In his study, he indicated there is no evidence that good

subsequent performance follows good past performance (Jensen, 1969). A few years

later, Black, Jensen, and Scholes (1972) conducted a test of the CAPM by introducing

multi-factor benchmarks, which include many stocks indices as independent variables.

Other studies have also used the multi-factor CAPM (Goldreyer et al., 1999; Grinblatt

and Titman, 1993; Low 2007; Renneboog et al., 2008; Taib and Isa, 2007).

The CAPM has developed and many studies have used the multi-factor CAPM in

association with conditional performance evaluation (Busse, 1999; Carhart, 1997;

Fama, 1972; Fama and French, 1993; Ferson and Schadt, 1996; Giambona and Golec,

2009; Grinblatt and Titman, 1989; Henriksson and Merton, 1981; James and

Karceski, 2006; Jegadeesh and Titman, 1993; Merton, 1981). Other researchers have

applied both the single and multi-factor CAPM to assess how well mutual funds

perform, for example, Henriksson (1984), Chang and Lewellen (1984), Ismail and

Shakrani (2003), Girard and Hassan (2005) and Bauer et al. (2007). Bauer et al.

(2007), for example, found that the same evidence – either by using the single-factor

Page | 90

model or the multi-factor model that controls for returns associated with size, book-

to-market and stock price momentum of the investments – amounted to no significant

difference in terms of performance between ethical and conventional mutual funds.

In this thesis, the single factor market index proxy generated by the Bursa Malaysia

Kuala Lumpur Composite Index (KLCI) is used to represent a market return portfolio

for the IMFs and CMFs in the CAPM analysis. The KLCI is chosen as the market

benchmark due to Malaysia currently having approximately 825 stocks trading in

Bursa Malaysia Kuala Lumpur Stock Exchange (KLSE), 89 per cent of which are

Shariah-compliant securities, according to Bursa Malaysia as at 25 May 2012. In

October 2003, the number of Shariah-compliant securities in Malaysia was 722

securities or 81 per cent of the total listed securities on the KLSE compared to 684

securities or 80 per cent of the total listed securities in 2002 (Securities-Commission-

Malaysia, 2003). The percentage increased to 88 per cent of stocks listed on the KLSE

being Shariah-compliant, representing two-thirds of Malaysia’s market capitalisation,

as at the end of December 2010 (Bursa Malaysia 2010). Therefore, the KLCI is

considered relevant as a market portfolio for both fund portfolios, and this application

means that the study period can be extended from January 1990 to April 2009.

When a different single benchmark is analysed, the KLCI is used to represent a

market return portfolio for a single conventional benchmark. The Kuala Lumpur

Syariah Index (KLSI) is then employed as a proxy for the single Islamic benchmark.

For the different analysis of a single benchmark, the period under investigation is

shorter, from July 1999 to April 2009, since the KLSI did not begin until July 1999.

For the other benchmarks used as independent variables (as mentioned in Section

3.2.2) in multiple benchmarks analysis, the period of study depends on the inception

date of the related benchmarks. The MSCI and KLIBOR are from January 1990 to

April 2009. The DJIM is from January 1996 and the KLSE small-cap is from

December 1995. The data for all indices are from either Datastream or SIRCA.

Page | 91

Generally, calculations of a market return for each of the market indices employed in

this thesis use the formula as set out below:

/7� = ln*7�/7��) ∗ 100 or equivalently,

/7� = [ln*7�) − ln*7��)] ∗ 100

(Eq. 3. 10)

where, /7� the average market returns. 7� is price of stocks index, i.e. KLCI at time

M. The log price obtained is then time with 100% to get the market return in

percentage. The reason is to accommodate the returns of funds, which are in

percentage as well.

The basic CAPM is defined as the expected return of a fund portfolio after adjusting

for the risk-free interest rate, as below:

�:/0; = /1 +[�*/7) −/1<50

(Eq. 3. 11)

Since the study employs historical data based on past returns performance, the model

in Eq. 3.11 is modified as follows:

/0� = 80� +50�*/7�) +N0̅� (Eq. 3. 12)

The following equations are based on risk-adjusted return. To identify the gross return

of a fund portfolio as reported in Chapter 7, then all the returns /0�are not adjusted

for the /1� .To employ risk-adjusted return, the model in Eq. 3.12 is then transformed

to mean excess returns and written as follows:

/0� − /1� = 80� +50�:/7� −/1�; +N0̅� (Eq. 3. 13)

Page | 92

where /0� −/1� is the mean excess return on the fund portfolio, in the situation

where the average return of a portfolio above a risk-free rate, 80� is the excess risk-

adjusted return. This is also referred to as Jensen’s alpha, where 50� is the systematic

risk of the security, /7� −/1� is the market risk premium, and 80� and 50� are

coefficient estimates denoting return performance and systematic risk, respectively.

/1� is based on one-month Malaysian t-bills used as a proxy for the risk-free rate10.

The portfolios,/0� refer to Islamic, conventional and the stated fund portfolios, and

represent a dependent variable in the regression model./7� is an average market

return portfolio calculated as shown in Eq. 3.10. Furthermore, N0� is the error term

allowing for time-varying beta across the model, with usual assumptions on N0� ~�*0, P��).

Based on a theory of market efficiency, when CAPM is correctly specified and

securities are being correctly priced, the α is zero. If a security demonstrates superior

performance, then the α should be positive and statistically significant (Prather and

Middleton 2002). Building on this theme, the study incorporates the multi-factor

version of CAPM to evaluate the performance of the mutual funds. Similar to the

studies of Elton and Edwin (2007, p.659) and Bertin and Prather (2009)11, this study

incorporates three different equity market factors and one bond market factor as the

independent variables in examining the performance of the IMFs and the CMFs

against the multiple benchmarks.

The four-factors formula comprises: the large capitalisation stock index (KLCI), the

KLSE small-cap, a foreign stock index (MSCI world), and a bond index. The choice

of various benchmarks is important so that the model can capture the impact of a

fund’s differential holdings of large-cap, small-cap, foreign and bond investments

(Bertin and Prather, 2009, p.1366). For the variable /3�, two benchmarks are used in

which 1 refers to a conventional foreign benchmark, the MSCI world index, and 2 10 The calculation of the monthly risk-free rate is as follows: Rft =(1+R)1/12 - 1. In the case of yearly risk-free rate, the following formula is used: Rft =(1+R)1 - 1=R. 11 Bertin and Prather (2009), for example, employed multi-factor CAPM to identify the average alphas and coefficient estimates for the benchmarks and the average Sharpe ratios in their comparative studies on the fund of funds and traditional equity fund. The results indicated that neither group outperforms the overall market since the alpha estimates for both samples are negative.

Page | 93

refers to the Islamic foreign benchmark, the DJIM index. The modified multi-factor

CAPM based on Eq. 3.14 taking care of four-factors is expressed as follows:

/0� − /1� = 80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503:/3� −/1�;+50S:/S� −/1�; +N0̅�

(Eq. 3. 14)

where, /0� is the average return of a fund portfolio being evaluated, /1� is the average

return a risk free asset (one-month Malaysian t-bills), 50Tis the sensitivity to

benchmark j (j= L, S, F,B) with L is a large stock index (KLCI), S is a small stock

index (KLSE small-cap), F is a foreign stock index (MSCI or DJIM), B is a bond

index (Malaysian fixed deposits). /T� is the average return on the benchmark at period

t. N0̅�is the random error term, with assumptions that it is normally distributed,

~�*0, P��).

Finally, this study extends Bertin and Prather (2009) to include one more market

benchmark, namely, the KLIBOR rate, which serves as a proxy for the money market

index (/U�),. The equation is based on five-factors CAPM, as the following:

/0� − /1� =80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503 :/3� −/1�;+50S:/S� −/1�; + 50U:/U� −/1�; +N0̅�

(Eq. 3. 15)

where, m is a money market index (KLIBOR) and the rest are as previously explained

in Eq. 3.14.

3.4.3.2 TM model and the extended TM model

In order to evaluate the Islamic and conventional fund managers’ market timing

ability and fund selectivity skills, the study adopts the TM model. This was developed

by Treynor and Mazuy (1966) and is based on exponential growth of the market

benchmark in the CAPM model using quadratic regression. The regression model is

Page | 94

also applied to the time series and panel data analysis. According to Admati et al.

(1986), the TM model provides a valid measurement of market-timing performance

ability. Positive values of α and β are indicative of security selection skill and market-

timing skill for Islamic mutual funds managers. The model equation is as follows:

/0� − /1� =80� +50�:/7� −/1�; +V0�*/̅7� − /̅1�;� + N0̅� (Eq. 3. 16)

where 80� denotes the ability of portfolio fund managers to use effective skills

regarding stock selection and V0� denotes the market timing expertise of each fund

manager. /7� is a market benchmark, */̅7�)�is the quadratic term for a market

benchmark. The other variables are defined as previously mentioned.

If the 50� for all the funds value is less than 1, this implies that the fluctuation in the

stock market does not infinitely influence any specific fund per se. In other words, the

higher the beta of a fund portfolio, the higher the volatility of a fund compared to the

market.

The following regression is conducted in order to identify any differences concerning

the timing and selectivity skill of fund managers by adding the dummy variable

�� in all funds portfolio category. �� is a dummy variable, written as 1 if it

is the Islamic fund or 0 if it is the conventional fund.

/0� − /1� = 80� + 50�:/7� −/1�; +V0�:/7� −/1�;² + �� +N0̅� (Eq. 3. 17)

The TM model is also developed into a new model called the extended TM model by

adding the multiple benchmarks to the original TM model, following Bello and

Janjigian (1997).

Page | 95

The extended TM model is written as follows:

/0� − /1� = 80� +50Q:/Q� −/1�; + 50R:/R� −/1�;+ 503 :/3� −/1�;+50S:/S� −/1�; + 50U:/U� −/1�;+V0�*/̅7� − /̅1�;� + N0̅�

(Eq. 3. 18)

3.4.3.3 The different portfolio regression analysis

The differences between the IMFs and CMFs are presented in different portfolio

(Diff.) with the aim to identify if there are any differences between the performances

of IMFs and CMFs concerning risk and return characteristics (more detail on results

from this equation is in Section 5.4.1). It is based on the following:

[:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;] = 80� + 5̅0�:/̅7� − /̅1�; +N0̅�

(Eq. 3. 19)

where I and C are the mean excess return of the IMFs and CMFs respectively. The

term [:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;]is the excess return of the IMFs minus the excess

return of the CMFs. Eq. 3.19 can also be extended to diff. portfolio to include the

market timing variable as shown below:

[:/̅X0� − /̅1�; −:/̅Y0� − /̅1�;] = 80� +5̅0�:/̅7� − /̅1�; + V̅0�:/̅7� − /̅1�;� +N0̅�

(Eq. 3. 20)

3.4.4 Time series regression analysis

The time series regression analysis is employed based on single and multi-factor

CAPM while evaluating the performance of IMFs and CMFs portfolios against the

single and multiple market benchmarks. The time series regression is also employed

using the TM model where the objective is to evaluate fund managers’ market timing

Page | 96

ability and fund selectivity skill. The results are reported in Chapter 5.The models

employed in this regression are those as previously discussed.

3.4.5 Panel data regression analysis

The study then revisits the research questions with panel data regression. Standard

panel data analyses applied are: (1) the Breusch and Pagan LM test for random

effects, which tests the appropriateness of the random effects model against OLS

pooled regression and (2) the Hausman test to compare the fixed effect model with

random effect, with (3) combinations of time-fixed effects. The significance of time-

fixed effects is also formally tested to see if time dummies are jointly significant or

not.

Whereas the CAPM and TM model estimates are presented in Chapter 6 (as discussed

in Section 3.4.3.1 and Section 3.4.3.2) using panel data, Chapter 7 focuses on two

main perspectives based on raw or non-risk-adjusted return and fund attributes for

equity funds. The first perspective estimates the raw returns performance using the

CAPM and TM model as in Chapter 6. The second perspective examines the raw

returns performance and the relationship with fund attributes (the fund attributes are

as mentioned in Section 3.2.2). Raw returns are used because the study is interested in

evaluating real return performance when associated with fees and other fund attributes

– which is also in the interest of investors who take into account only raw returns

while fund managers prefer to judge based on risk-adjusted return, as exhibited in

CAPM.

The gross return before market adjustment is written as follows:

/0� =80� +50�*/7�) +N0̅� (Eq. 3. 21)

where /0� is the return of the portfolio at time M and /7�. is the corresponding market

return. The intercept 80� measures the difference in fund managers’ performance

(positive or negative) and 50� the slope parameter, which quantifies return

performance of a fund portfolio and systematic risk at the same time. N0̅� is an error

Page | 97

term. As indicated earlier, alternative measures of /0� used in the analysis (Chapter 7)

are GROSS, ADJUSTED, ADJUSTED LOAD and NET for each of the IEFs, CEFs

and AEFs portfolios.

In the case of the TM model before its adjustment for a risk-free rate of return, the

formula is written below:

/0� =80� +50�*/7) +V0� */7)² +N0̅� (Eq. 3. 22)

where the additional V0� indicates market timing expertise of the portfolio fund

managers, whereas other variables are as previously mentioned. 80� represents the

fund selectivity skill of fund managers.

The basic panel regression model is based on the ordinary panel least squares (OLS)

estimator and is expressed as:

Z�� = 8 +5��[�� + \�� i=1,...N; t=1, ...T

(Eq. 3. 23)

where Z�� , ]�� are N x 1, [ is N x k, and 5 is k x 1. 5^ is unknown constant coefficient

i and [�T^ is an observation of k explanatory variables for an Islamic and conventional

when j= 1, 2 respectively. \�� is \�� ~�*0, P��). In other words, the subscript i

denotes the cross-sectional dimension, whereas the subscript t denotes the time-series

dimension. Follow Baltagi (2005), the pooled OLS estimation, assuming one-way

error component model for the error term, is calculated based on:

Z�� = 8 +5�� [�� +\�� (Eq. 3. 24)

The single factor panel data regression is conducted in order to identify the impact of

each fund attribute and fund return performance,. It employs a cross-sectional single

Page | 98

panel regression, which is able to produce more reliable results and provide further

evidence on fund performance and the relationship with the fund attributes. The

equation is written as follows:

/0� = 8_ +50�:[̀0�; +N0̅� (Eq. 3. 25)

where N0̅� is a time-varying error term that can be written as N0̅� = a0 +b0��. The

a� denotes the unobservable factors that change over time or individual unobservable

effects of the individual mutual funds, and ]�� is the remainder error, which it is

assumed varies over time. /0� is the return for fund portfolio p in year t, 8 is an

intercept and [̀0� denotes a fund attribute, i.e., it refers to each of the endogenous and

exogenous variables applied in the study. The endogenous variables are alpha, beta,

residual risk, management fee and load fees, while the exogenous variables comprise

age, size and dummy of local or foreign investment (more detail is in Section 3.2.2).

The regression allows for fixed (year) effects, following Dahlquist et al. (2000) by

subtracting the mean of the return and the attribute during a year, represented by /0� and [̀0�respectively. The results from this regression are further discussed in Section

7.4.5. The equation yields:

/0� − /̅0� =50�:[0� −[̀0�; + *N0� −N�̅�) (Eq. 3. 26)

Then, averaging across all observations in Eq. 3.26 can also be written as:

/c0� = 50�[c0� + Nc0� (Eq. 3. 27)

where Nc0� is the error term that can be written as Nc0� = a� +]�� . The a� denotes the

unobservable effects of the individual mutual funds, and ]�� is the remainder

Page | 99

disturbance with the usual assumption that it is not correlated to the dependent

variable.

In the situation where unobserved effects a� are not correlated with the explanatory

variables due to the variables being truly random, the random effects are employed. In

the scenario where the unobserved effects a� are correlated with the explanatory

variables, the fixed effects panel data are used. The fixed or random effects are

determined by the Hausman test. If the test is significant, the fixed effects is chosen;

if not, then random effects is employed. The problems of serial correlation and

heteroskedasticity are corrected using the White cross-section standard error and

covariance test based on White (1980). Special cases of this equation are estimated for

AMFs, CMFs and IMFs as in Chapters 5 and 6 as the following:

/0� − /1� =80� + 50�:/7� −/1�; + V0� :/7� −/1�;� + d 50�:/�� −/1�;

�eQ,R,S,U,37Rf�,3gT�U+ 6a,AhAiiAjMk +N0̅�

(Eq. 3. 28)

where 80� denotes outperformance (positive sign) or underperformance (negative

sign) of the fund portfolio. 50� defines the superior fund selectivity skill of fund

managers if positive or otherwise if negative. This parameter indicates market risk

when V0� = 0. V0�, indicates superior market timing expertise of the fund managers if

positive or otherwise if negative. The rest of the variables are as previously defined in

Eq. 3.14. Results from this equation are explained in Chapters 5 and 6.

3.4.5.1 Fund attributes and panel data multi-factor regression

This study employs multiple regressions using panel data analysis to examine the

relationship between the return performance of funds and the fund attributes. The

analysis using this method is reported in Chapter 7. The fund return is gross, i.e., non-

adjusted for the market risk-free rate. This is because one of the aims is to examine

the real returns performance of funds. The analysis restricts the sample to funds that

have at least two years’ return data over the sample period. Each of the fund return

Page | 100

portfolios is grouped into four groups: GROSS, ADJUSTED, ADJUSTED LOAD and

NET. The definitions of these groups and further explanations concerning the return

data are presented in Section 7.3.

The fund attributes are the explanatory variables in this analysis and are classified

them into two groups, namely the endogenous variables and the exogenous variables.

Whereby the exogenous variables are fund’s age, size, investment style (investment in

local or foreign markets) and also the types of the funds, the endogenous variables are

the risk, return and fees factors include alpha, beta, residual risk, management fees,

expense ratio, total load fees, and trustee fees. The dependent variable is the GROSS

return. The specific regression utilised is as follows:

/̅0� = l_ +l�0��m� +l�0�n�.4+� +lo0��4��.� +lp0��

+lq0��nr� + ls0�� +lt0�F�� +lu0��.4v�4.w+lx0�BmB�y�� +l�_0��[��.� +l��0�d�E�nE�v

�

�e�

+ l��0�� .�� + 6a,AhAiiAjMk +N0̅� (Eq. 3. 29)

The discussions on the empirical results and findings of the regression equations are

in Section 7.4. Basically, there are three types of funds involved: all equity funds

(AEFs), Islamic equity funds (IEFs) and conventional equity funds (CEFs). The fund

attributes in this analysis are explanatory variables that involve endogenous and

exogenous variables. When the regression is conducted, these variables are controlled

while measuring the fund performance..

The quadratic regression is added to the Eq. 3.29 in order to clarify the relationship

between fees and fund returns. The equation is written as follows:

Page | 101

/̅0� = l_ +l�0��m� +l�0�n�.4+� +lo0��4��.� +lp0��+lq0��nr� + ls0�� +lt0�F�� +lu0��.4v�4.w+lx0�BmB�y�� +l�_0��[��.� +l��0�d�E�nE�v

�

�e�

+ l��0�� .�� +l�o0�BmB�y��^2 +l�p0��[��.�^2+l�q0�d�E�nE�v

�

�e�^2 +l�s0�� .��^2 + 6a,AhAiiAjMk

+N0̅� (Eq. 3. 30)

Our expectation that there might be an evidence of non-linear relationship between

fees and fund returns, therefore, the quadratic panel regression for the fees factors is

developed. Details about the results from regression are discussed in Sections 7.4.7.1

and 7.4.7.2, Chapter 7.

3.5 Econometric estimation issues

A few econometric issues have been highlighted and taken care of while analysing

and estimating the data. Firstly, the study ensures that the funds’ sample of data used

is stationary and normally distributed. This is identified through the scatter plot of

graph in each data portfolio. Meanwhile, for comparative purposes, the study also

evaluates the data after they have been trimmed. The trimmed data are the data after

eliminating the years of crises in the period of the study (see more detail in Section

7.3). The reason for this treatment of the data is to overcome the outlier issues

associated with the extreme values of market return being too high or too low. The

trimmed data are solely employed in Chapter 7.

3.5.1 Data stationary and test of normality

The JB test is employed for the descriptive data in order to identify the normality of

the variables. Scatter plot analysis is also used for this purpose. If the data variables

are not normal, the study further employs the non-parametric tests, the Wilcox test

and the Mann Whitney test, to overcome these factors. Furthermore, the econometric

method is used to test whether the data employed are stationary or not. If the data are

Page | 102

stationary, then they do not really suffer due to outliers. In other words, the linear

regression based on OLS is sufficient for the regression analysis. To test whether the

data are stationary or not, the unit root tests must be implemented. Normally, the

Augmented Dickey-Fuller (ADF) test is conducted for this reason. The test is based

on the following formulation:

∆D� = V + 5� +8D�� +∑ }��̂e� ∆D�� +b� (Eq. 3. 31)

where ∆ is the first-difference operator; D� is the time series variable tested for

stationary; � is a linear time trend; and b� is a covariance stationary random error.

The optimal choice of lag length removes autocorrelations in the error term. The

appropriate number of lagged differences ~ can be determined by Akaike Information

Criteria (AIC) based on Akaike (1970) and the critical value of the test developed by

MacKinnon (1996). The null hypothesis of unit root, |8| = 1 is tested against the

alternative of stationary, |8| < 1. In other words, failing to reject the null hypothesis

in the ADF test implies that the data are non-stationary.

3.5.2 Heteroskedasticity and positive serial correlation

The heteroskedasticity and positive serial correlation problems in the regression are

corrected using White’s (1980) test for the panel data. For the time series data, the

problems are corrected using both White’s (1980) and Newey and West’s (1987) tests.

The positive serial correlation or auto-correlation is identified when the Durbin-

Watson value in the regression is less than 2. The problem is also verified using the

Breusch-Godfrey Lagrange Multiplier (LM) test for serial correlation in the time

series data. If the heteroskedasticity and serial correlation problems exist in the data

regression, then these problems are corrected using the above mentioned tests.

3.5.3 Multicollinearity problem

In order to identify the presence of multicollinearity problems in the data, the study

detects them through: firstly, variance inflation factor (VIF) for the time series

analysis and secondly, either VIF or coefficient variance decomposition (CVAR) for

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the panel data analysis. If the VIF or CVAR values are more than five, this indicates

that there is a multicollinearity problem among the variables.

This test is important since the regression model with the multicollinearity problem

will lead to spurious regression where the outcome results are meaningless, since

there is a probability that few independent variables are highly significant to each

other. In this study, the diagnostic results indicate that multicollinearity is not a

problem in any of the regression models.

3.6 Summary

This chapter has explained the methodologies used to examine the performance of

479 Malaysian mutual funds, comprising 129 IMFs and 350 CMFs from 1990 to

2009. The dependent variable is represented by the average return of funds in each

portfolio, whereas the independent variables are segmented into single and multiple

benchmarks. These are applied as described in Chapters 4 to 6. In Chapter 7, the study

focuses on 106 equity funds, and the dependent variables are extended into four

groups including before and after fees, and the independent variables are also

extended to include multiple regression based on fund attributes.

This thesis has three overarching hypotheses. The objective related to the first

hypothesis is to identify any differences in fund return performance, i.e., IMFs and

CMFs, in relation to the market benchmark, either single or multiple benchmarks. The

objective related to the second hypothesis is to examine any differences in IMFs and

CMFs fund managers’ market timing expertise and fund selectivity skills. The third

hypothesis concerns any differences in the effects of fees on fund performance in

relation to the fund attributes of the IMFs and CMFs portfolios, focusing on equity

mutual funds.

Several methods are employed to evaluate mutual fund performance, as explained in

this chapter. Firstly, the study employs the descriptive and t-test analysis. Secondly,

the standard risk-adjusted performance measurements of SR, TI, JA, AR, M2 and

ASR are conducted. Finally, the study employs time series and panel data regression

analysis. With reference to regression analysis, fund performance is evaluated mainly

Page | 104

using the CAPM model and the TM model, following Bertin and Prather (2009),

Treynor and Mazuy (1966) and Bello and Janjigian (1997) respectively. Then the

regression analysis is extended to a multiple panel regression to include the effects of

fees and other fund attributes on fund performance. Each of the portfolios is measured

against the independent variables.

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CHAPTER 4 - RISK AND RETURN

PERFORMANCE ANALYSIS

4.1 Introduction

Global Islamic funds have grown tremendously, with total assets of US$77 billion by

the end of July 2011. The Islamic Funds Index has grown at an annual rate of 38.9

percent (Eurekahedge, 2011) since its inception in the 1990s. In a broader perspective,

the Islamic banking and investment industry is regarded as one of the fastest growth

segments in the Islamic finance industry, with an annual growth rate of 15 per cent,

making it a worldwide phenomenon.

The increasing demand on the type of investment funds or mutual funds has generated

investors’ and financial analysts’ interest in risk and return performance

measurements for the Islamic finance industry. The growth of the fund has also made

managing risk and returns in portfolio management crucial for fund managers. This

chapter therefore addresses the following two issues: (1) to evaluate the overall

performance of IMFs and CMFs in relation to their market return benchmark using

risk adjusted performance measures and (2) to compare the performance of IMFs to

their conventional peers in different market conditions.

The chapter is organised as follows. The next section provides a review of the

literature on the risk and return characteristics of the MFs. Section 4.3 discusses the

data and sample selection. Section 4.4 provides results and discussions. The analysis

provides descriptive statistics of the IMFs and CMFs, the results of non-risk-adjusted

(gross) and risk-adjusted returns, and also the performance analysis related to the AFC

and the GFC. Section 4.5 concludes the chapter with a summary of the results.

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4.2 Issues and related studies

The Islamic fund industry began in the 1990s and the number of Islamic funds

worldwide increased from eight prior to 1992 to 95 in 2000, with a value of

US$5billion in assets by 2000 (Elfakhani et al. 2005). This increase indicates a record

in the compounded annual growth rate (CAGR) of 48.44 per cent for the first eight

years. The period from December 1997 to April 2008, for instance, indicates the

growth of these funds at an annualised rate of 26 per cent. By the end of April 2008,

the number of Islamic funds stood at 504 funds worldwide with total assets AUM at

US$33.9 billion (Shanmugam and Zahari 2009). Despite the rapid growth of the fund,

the Islamic fund industry is small compared to the whole mutual fund industry

operating in the global market. The global mutual fund industry doubled its size from

US$9.6 trillion in 1998 to US$18 trillion in 2005 with a growth rate of 9 per cent

annually (Ramos, 2009). At the end of the third quarter of 2011, the mutual fund

assets worldwide were US$23.13 trillion, according to the data from the Investment

Company Institute (Investment-Company-Institute, 2012). Although the Islamic funds

constitute only a small portion of the total global mutual fund assets, it is nonetheless

true that the Islamic fund industry is attractive and is of interest to many investors.

In Malaysia, the Islamic fund industry is one of the fastest growing in the country’s

capital market (Lewis 2009; Nathie, 2008), and this is reflected in the high growth of

the funds since the industry began in the 1990s. According to the Securities

Commission of Malaysia (SC), from 2003 to 2008, the NAV of the Islamic funds in

Malaysia grew at a CAGR of 26.3 per cent while the total industry in relation to the

market share recorded a growth rate of 11.4 per cent in the same period. The number

of approved funds in Malaysia has also risen tremendously from two funds in 1993 to

141 funds by the end of April 2009; there are 167 funds as at the end of February

2012. The total NAV of IMFs is now RM$29.24 billion (1993 at RM$0.19 billion)

and the CMFs is around RM$237.78 billion (1993 at RM$27.94 billion), with the

NAV of the total mutual fund industry contributing approximately 19.85 per cent to

the stock market as at the end of February 2012 (Securities-Commission-Malaysia,

2008, 2012). This figure reveals that the CAGR of IMFs is approximately 7.10 per

cent annually over a 19–year period, from December 1993 to February 2012.

Page | 107

These IMFs, also known as Islamic unit trust funds, are steadily increasing as an

important selection asset in managed portfolios and have played a major role in the

development of the Islamic financial system in Malaysia. Due to their significance in

the current financial market, the superior performance of the IMFs could lead to the

continuing development of Islamic finance in the global market and particularly

Malaysia.12 Yet research on IMFs in Malaysia and worldwide has attracted little

attention in the finance literature. The lack of information in this area means that the

IMFs’ performance in the market in comparison to their conventional peers, the

CMFs, requires further investigation.

Studying the performance of Malaysian IMFs and CMFs is motivated by Malaysia

having 29 per cent of Islamic funds located there by the end of July 2011 (as

discussed in Section 2.2). The country is the most popular Islamic fund centre

worldwide and was acknowledged as a leading fund centre throughout the 2000s

(Eurekahedge, 2011). Malaysia successfully liberalised its Islamic financial system

through the introduction of an institution known as Pilgrimage Funds. This

environment is conducive for the growth of the Islamic mutual fund industry.

Furthermore, the motivation for this study is encouraged by the Islamic funds having

outperformed the Morgan Stanley Capital International World Index (MSCI) and

Dow Jones Sustainability Index (DJSI), and providing better downturn protection and

less volatile investment vehicles. The study by Eurekahedge noted that the MSCI and

DJSI declined by 41.12 per cent and 42.98 per cent respectively, but Islamic funds

dropped less severely by 28.53 per cent (Eurekahedge, 2011).13

The focus of this chapter on evaluating the risk and return performance of IMFs and

CMFs relative to their respective benchmarks is also inspired by the question of

whether the rapid growth of IMFs is associated with a higher return of the fund

portfolio or vice versa. One recent study demonstrated that IMFs perform worse over

the Islamic and conventional benchmark and the degree of underperformance was

even larger during the recent global financial crisis (Hayat and Kraeussl, 2011). In

12Islamic finance has continued to demonstrate its evolution and strong growth as assets have expanded by 22 per cent to RM192 billion in Malaysia for 2008 alone and now account for 15.0 per cent of the total assets in the Malaysian finance industry (Ghani, 2009). 13 See www.eurekahedge.com, September 2011, for details.

Page | 108

contrast, another study stated that IMFs can perform better than CMFs only during

bearish market conditions (Abdullah et al. 2007). The inconsistency in findings might

be due to the studies incorporating different data, and also might be explained by the

fact that IMFs are not subject to the interest rate risk that erodes the return

performance of the CMFs during the bearish market, particularly during the Asian

financial crisis (AFC) in 1997–98. In fact, the greater impact of the crises was due to

the extreme event of the AFC rather than the GFC (Bhatti and Nguyen 2012).

Therefore, in this study, evaluating the performance of IMFs and CMFs using a

longer time period and incorporating more recent data is expected to reveal more

robust results.

It has been argued that the restriction of Islamic investments to Shariah-compliant

products led to the IMFs performing poorly compared to the whole market. Islamic

investments are required to follow strict Shariah screening criteria, whereas the

conventional funds counterparts are free to invest in investment activities without any

restrictions.14 The constraint from the Shariah criteria could create poorer

performance due to limitation on diversification and lack of opportunities to pursue

investments in high return profiles. According to Abdullah et al. (2007), these

regulations are some part of the contribution to the underperformance of the IMFs, but

this can be arguable. This is because following the Shariah principles means avoiding

uncertain high return investments related to highly risky and debt-ridden (M. Amin,

2009). However, these findings also generated a question about whether there are any

significant differences in the performances of these two fund types – the IMFs and

CMFs – particularly in their risk and return characteristics when related to the normal

market cycle and the bullish and bearish market scenarios.

Hassan et al. (2010) found that there were no convincing performance differences

between Malaysian IMFs and CMFs during the period January 1996 to November

14

To enable a mutual fund to be categorised as a permissible fund depends on the status of the authorised investments. Basically they must be free from any interest rate and must conform to Shariah principles. In Malaysia, the investments of the funds are subject to the rules and regulations of the conditions laid down by the Securities Commission of Malaysia (SC). SC under its Shariah Advisory Council is the government body responsible for monitoring, evaluating and approving the Islamic funds in Malaysia (Majlis-Amanah-Rakyat, 2002 ). This requirement is not applied to the conventional funds and their management is free to choose any authorised investments (Majlis-Amanah-Rakyat, 2002).

Page | 109

2005 and they concluded that there is similar reward for risk and diversification

benefits for these two fund portfolios. They also noted that both funds are not

correlated with the market portfolio, the KLCI. However, they found that there is a

statistically significant long-term relationship between Malaysian Islamic and

conventional funds. In this chapter, it is contended that there is a difference between

the return performances of the portfolios of these two funds, IMFs and CMFs. Where

there is no difference in the performance of these funds, questions arise about the fund

characteristics and the investment style differences between them. These matters will

be investigated in the analysis section in this chapter. In the next section, the data and

sample selection method is discussed.

4.3 The data and sample selection15

The source of the return data of all funds was the Morningstar database up to the end

of April 2009. There are 535 Malaysian open-ended mutual funds are available in the

Morningstar database from January 1990 to April 2009, as presented in Table 4.1.

These monthly returns are the net amounts after deducting all operating expenses, but

excluding the front and exit fees. These data returns are divided into 143 IMFs and

392 CMFs, falling into five broad categories which are based on the types of the

funds, namely, alternative, allocation, equity, fixed income and money market.

The data on market indices were obtained from the Securities Industry Research

Centre of Asia-Pacific (SIRCA) and from Datastream. The market indices include the

Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Morgan Stanley Capital

International World Index (MSCI) and the Dow Jones Islamic Market Index (DJIM).

For the risk-free rate return, this study employs the Malaysian t-bills. For the bond

index, the Malaysian fixed deposit rate of return is employed, while for the money

market index, the Kuala Lumpur Interbank Rate (KLIBOR) is used. Additionally, this

chapter’s analysis employs KLCI as a large stock market index and a single market

return benchmark for both IMFs and CMFs portfolios. This is particularly due to

many companies being registered with the Bursa Malaysia Kuala Lumpur Stock

Exchange (KLSE) Shariah-compliant list. In October 2003, the number of Shariah-

compliant securities in Malaysia was 722 securities or 81 per cent of the total listed

15 The data and sample selection described in this section are also employed in Chapters 5 and 6.

Page | 110

securities on the KLSE, compared to 684 securities or 80 per cent of the total listed

securities in 2002 (Securities-Commission-Malaysia, 2003). Currently, about 88 per

cent of stocks listed in the KLSE are Shariah-compliant and they represent two-thirds

of the Malaysia’s market capitalisation (Bursa Malaysia 2010).

The sample of return data is then matched to a list of Malaysian mutual funds

obtained from SC. There are 554 approved mutual funds in Malaysia for the same

period, consisting of 141 IMFs and 413 CMFs. Thus, the sample in this study exceeds

the number of IMFs in the SC list with the two funds.16 These two funds originated

from the conventional funds based on information obtained from the fund prospectus,

and are converted to the Islamic fund category over the period of the study. They are

considered to be Islamic funds based on the current situation. Therefore, the total

IMFs is now 143 funds. In the conventional fund category, 413 CMFs are listed in the

SC as at the end of April 2009, and only 392 funds are available in the Morningstar

database. Therefore, 19 funds are missing from the list.

The study further restricts the final sample to those funds that have a minimum of 12–

month returns, thus limiting the total funds to 479, as reported in Panel B of Table 4.1.

The selection of a minimum of 12–month returns is adequate, following Bertin and

Prather (2009). As a result, 14 funds from the 143 IMFs are excluded from the

sample. As a result, 42 are also excluded from the list of 392 CMFs, so there are 350

CMFs in the final sample. The total number of funds excluded is 56, as shown in

Table 4.1. All the funds in the final sample are comprehensive, covering all fund type

categories. The final sample consists of 129 IMFs and 350 CMFs in Malaysia,

covering 232 monthly observations over the 20–year period from January 1990 to

April 2009.

Despite the restriction, the data in this study include the largest number of mutual

funds in Malaysia studied to date. According to Elfakhani et al. (2005), a 68–month

sampling period enables two distinctive market cycles to be covered. Even longer

historical performance data is likely to lead to more robust and conclusive results.

This study covers more than two complete market cycles and includes three

16 These two funds are Apex dana aslah (formerly known as Apex small cap) and Pacific dana dividen.

Page | 111

expansions and three troughs over the duration of the study. The analysis utilises raw

returns before they are adjusted for the risk-free rate return and also risk-adjusted

return performance (i.e., the returns after being adjusted for the Malaysian t-bills, a

proxy for the market risk-free rate of return). Further details regarding the data and

sample selection are given in Table 4.1, for ready reference. To the best of my

knowledge of the Malaysian mutual fund literature, this is the largest sample that has

been studied.

Table 4.1: Sample selection in the study The table presents the full sample of Malaysian mutual funds, consisting of 143 IMFs and 392 CMFs available in the Morningstar database as at the end of April 2009. The 143 IMFs include two funds that changed their status from CMFs to IMFs during the period, and this study considers them to be categorized in the IMFs portfolio following the prospectus description.

Morningstar Excluded fund Total Funds SC List Panel A: Numbers of mutual fund in Malaysia IMFs 143 0 143 141 CMFs 392 19* 411 413 Total 535 19* 554 554 Panel B: Final sample selection IMFs 143 14** 129 CMFs 392 19*

23** 350

Total 535 56** 479

Notes: * denotes 19 funds obtained from 554–535 funds. These 19 funds were excluded due to non-available data in the Morningstar database.

**indicates the number of funds that do not have a minimum of 12–month return data.

4.3.1 Survivorship bias

With favour to the IMFs, the data is considered free from survivorship bias as the

study incorporates all the funds listed in the market. Survivorship bias occurs when

funds that stop reporting information or cease operation are purged from the database

and regarded as of no interest to investors (Fung and Hsieh, 2002, p.66). In the case of

the conventional funds, there is a survivorship bias in the data in that out of 413 funds,

Morningstar provides data for only 392 funds after excluding the two converted

funds. However, the impact of survivorship bias in this conventional fund category is

not a problem for comparison purposes as the Islamic funds are expected to perform

Page | 112

better than the conventional funds. The disappearance of the non-surviving funds in

the conventional category will improve the overall performance of the conventional

funds compared to the Islamic funds.

4.4 Results and discussions

This section considers various econometric analyses from descriptive statistics to

single factor CAPM analysis using non-risk-adjusted returns and risk-adjusted returns.

The details are in the following subsections. The most important is the returns

performance, which is evaluated for different sub-periods: pre-crisis, during the AFC

and GFC crises and post-crisis.

4.4.1 Descriptive statistics of IMFs and CMFs

This sub-section describes the risk and return characteristics of the IMFs and the

CMFs for the period January 1990 to April 2009 using basic statistics. The IMFs

portfolio consists of 129 funds and the CMFs portfolio consists of 350 funds. The

study duration is divided into two sub-periods: sub-period 1 from January 1990 to

August 1999 and sub-period 2 from September 1999 to April 2009. The summary

statistics based on raw return performance (before being adjusted for the one-month

Malaysian t-bills) of the IMFs and CMFs for the period from January 1990 to April

2009 and between the two sub-periods are reported in Table 4.2.

Regarding overall performance for the period studied, on average IMFs earned a

higher monthly mean actual return percentage than CMFs: 0.98 per cent versus 0.63

per cent, as depicted in Table 4.2. Both IMFs and CMFs also obtained a mean return

higher than the market and the risk-free rate returns. The statistics results are

consistent in sub-period 1, whereas in sub-period 2 the CMFs portfolio appears to

have higher returns than the IMFs and the full sample consisting of all mutual funds

(AMFs). Building on this theme, in relation to the total risk which is based on the

standard deviation (std. dev.) of a portfolio, the IMFs portfolio is associated with

higher risk than the CMFs and AMFs portfolios in all periods.

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Table 4.2: The IMFs and CMFs monthly returns performance: summary statistics The table includes monthly average return performance of the full sample: 479 AMFs, 129 IMFs and 350 CMFs from the Malaysian market for the period January 1990 to April 2009. All returns are in percentages, based on gross return calculated using geometric mean return and net amounts from all expenses. The Kuala Lumpur Composite Index (KLCI) return is used as a proxy for a market return benchmark. The period is divided into two sub-periods: sub-period 1 from January 1990 to August 1999 and sub-period 2 from September 1999 to April 2009, to identify the impact of crises on fund return performance. Sub-period 1 includes the Asian financial crisis (AFC) in 1997–1998 and sub-period 2 includes the global financial crisis (GFC) in 2007–2008. The whole period consists of 232 observations, in which each of the sub-periods covers 116 observations. The mean returns of the IMFs and CMFs are higher than the market return and the risk-free rate of return, a proxy of the one-month Malaysian t-bill, implying that the return performance of the fund portfolios is relatively better than the market benchmark. Regarding overall performance, the results show that the IMFs’ return performance is higher than the CMFs. This means that the Islamic portfolio is more risky (as the standard deviation of the portfolio is higher) than the conventional portfolio. The Islamic and conventional returns are positively skewed, but the market returns demonstrate a negative skew (left tail). All portfolios show leptokurtic right tailed distribution since their kurtoses are greater than zero.

Overall period (Jan 1990 to April 2009) Sub-period 1 (Jan 1990 to Aug 1999) Sub-period 2 (Sept 1999 to April 2009)

Mean Return

Std. Dev. Skewness Kurtosis Mean Return

Std. Dev. Skewness Kurtosis Mean Return

Std. Dev. Skewness Kurtosis

AMFs 0.808 5.040 0.081 5.892 1.231 6.159 –0.057 4.864 0.385 3.568 0.044 3.969 IMFs 0.982 5.591 0.087 5.672 1.620 6.968 –0.124 4.373 0.345 3.664 0.108 4.683 CMFs 0.633 4.709 0.207 5.745 0.841 5.644 0.169 4.987 0.425 3.549 0.024 3.785 Market 0.244 8.092 –0.263 7.309 0.252 10.218 –0.244 5.589 0.237 5.207 –0.156 3.123 Risk free (rf)

0.365 0.153 0.495 1.817 0.494 0.112 –0.609 3.340 0.236 0.033 –0.194 3.019

Page | 114

Additionally, it is worth noting that the systematic risks of both IMFs and CMFs

portfolios, measured by the beta for each portfolio, are relatively lower than the

market risk, thus indicating that they are less volatile than the market (the lower the

beta, the less volatile a portfolio in relation to the market risk).

Table 4.2 shows that the IMFs are also associated with the higher std. dev. (5.59),

implying that the volatility and risk of the fund are also higher than CMFs’s total risk

at 4.71. These results suggest that the IMFs portfolio is more risky than the CMFs,

but provides substantially higher returns. The average monthly return of the KLCI, a

proxy for the market return portfolio, is lower (0.24%) but the risk is strongly higher

(8.09%) relative to both portfolios, indicating that the market fluctuation over time

and the volatility of the market return are substantially higher than the fund portfolio.

The low return is expected as the market did face at least two competing business

cycles (bullish and bearish markets) over this 20-year period.

The summary statistics in the form of sub-periods related to the crisis are also

presented in Table 4.3. The sub-periods are: pre-AFC (1990–1996), during AFC

(1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). More

explanation of the results relating to the crises is in Section 4.4.7 onwards. The results

show that the impact of the AFC has been greater than the impact of the GFC on the

mutual fund industry in Malaysia. The results support the evidence of a greater

impact of the AFC on the six stock indices in the Asia-Pacific, as recently discussed

by Bhatti and Nguyen (2012).

Page | 115

Table 4.3: Summary statistics of IMFs and CMFs non-risk-adjusted returns in relation to the AFC and the GFC

Mean Return

Std. Dev.

Skewness Kurtosis Obs. Mean Return

Market rf AMFs Overall 0.808 5.040 0.081 5.892 232 0.244 0.365 Pre-crisis 1.749 4.834 –0.111 5.487 84 0.940 0.499 During AFC –1.216 8.703 0.235 3.214 24 –3.115 0.536 Post-crisis 0.727 4.246 0.932 6.270 96 0.652 0.234 During GFC

–0.006 3.234 –0.515 3.152 28 –0.361 0.267

IMFs Overall 0.982 5.591 0.087 5.672 232 0.244 0.365 Pre-crisis 2.298 5.627 –0.093 4.469 84 0.940 0.499 During AFC –1.173 9.690 0.179 3.157 24 –3.115 0.536 Post-crisis 0.621 4.459 0.939 6.618 96 0.652 0.234 During GFC

0.121 3.071 –0.355 2.783 28 –0.361 0.267

CMFs Overall 0.633 4.709 0.207 5.745 232 0.244 0.365 Pre-crisis 1.201 4.485 0.203 5.801 84 0.940 0.499 During AFC –1.259 7.808 0.297 3.238 24 –3.115 0.536 Post-crisis 0.833 4.101 0.927 5.915 96 0.652 0.234 During GFC –0.133 3.437 –0.659 3.571 28 –0.361 0.267

4.4.2 Test of normality

The graphs based on histogram and kernel density are presented in Figure 4.1. It can be

seen that the histogram graphs for both portfolios are approximately and normally

distributed. In comparison, the CMFs portfolio is fairly distributed relative to the IMFs.

Similar results are obtained for both IMFs and CMFs when the line and kennel density

graphs clarify the normal distribution. Consistently, the results support the results for

descriptive statistics, as reported in Table 4.2, which basically state that the CMFs

portfolio is less risky, suggesting that it is in fact more diversified, due to the standard

deviation and the mean returns being lower than those of the IMFs.

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Figure 4.1: IMFs and CMFs graphs of normality, January 1990 to April 2009

4.4.3 Covariance and correlation analysis

The covariance and correlation between IMFs and CMFs portfolios is investigated

relative to their market return portfolios. The covariance test is conducted to examine

the co-movement between the portfolio, while the correlation test aims to find if the

portfolio is dependent on them and the market. The study assumes that both portfolios

0

10

20

30

40

50

60

70

80

-25 -20 -15 -10 -5 0 5 10 15 20 25

Fre

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IMFs

0

4

8

12

16

20

24

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-20 -16 -12 -8 -4 0 4 8 12 16 20

Fre

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CMFs

-30

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

IMFs

-20

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-5

0

5

10

15

20

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

CMFs

.00

.01

.02

.03

.04

.05

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are positively correlated and have dependence on their market portfolio. Table 4.4

reports the results of the analysis. They reveal that the covariance between IMFs and

CMFs for the whole period and the sub-periods are not equal to zero, using either raw

returns or risk adjusted returns. Therefore the null hypothesis can be rejected. The

results imply that these two portfolios are moving together to form an important part of

the total industry.

Table 4.4: Covariance and correlation between IMFs, CMFs and the market portfolio

Overall period Sub-period 1 Sub-period 2 IMFs CMFs IMFs CMFs IMFs CMFs Panel A: Raw Returns Covariance IMFs 31.125 48.131 13.307 CMFs 23.989 22.077 35.361 31.583 12.352 12.484 Market 37.224 33.599 57.745 50.371 16.693 16.824 Correlation IMFs 1 1 1 CMFs 0.915 1 0.907 1 0.958 1 Market 0.826 0.886 0.818 0.881 0.883 0.918

Panel B: Risk Adjusted Returns Covariance IMFs 31.073 48.297 13.333 CMFs 24.004 22.158 35.546 31.787 12.382 12.517 Market 37.288 33.729 57.969 50.467 16.730 16.863 Correlation IMFs 1 1 1 CMFs 0.915 1 0.907 1 0.958 1 Market 0.827 0.886 0.819 0.881 0.883 0.919

These results in Table 4.4 also show that both IMFs and CMFs portfolios are highly

correlated to each other. Both portfolios also have strong correlation with the KLCI (a

proxy for the market return), suggesting that these portfolios depend on the market

movement, with the conventional funds being slightly closer to reflecting the market

movement than the Islamic funds. The correlations between the IMFs and CMFs to

the market return portfolio are also relatively high in all periods.

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4.4.4 Risk-aversion analysis

In certain circumstances, the performance of the funds is based on the state of the

equilibrium of risk and return in the capital assets price. It can be predicted that

investors are risk averse and the activities of borrowing or lending funds happen at a

risk-free rate. This relationship, according to Sharpe (1965b), refers to the prediction

of expected returns from particular assets and their associated risks. Although the

actual results may diverge considerably from the predictions made by investors at the

time they purchase the assets, this forecasting is important for estimating the expected

value of distribution of the portfolios, linked to the market risk-free rate and the

standard deviation of the returns (Sharpe, 1965b). Consequently, forecasting the

future of mutual funds can be done by tracking the funds’ historical performance. In

many situations, this estimation is based on actual historical data (Sharpe, 1965b).

Finally, assumptions of the estimated values must be made, followed by doing the

empirical tests on the subject matter.

To investigate the relationship between risk and return of mutual fund performance, a

risk-aversion analysis, following Sharpe (1965b, p. 418), is conducted. The aim is to

estimate the relationship between the result obtained by regressing σ on /̅ and that

obtained by regressing /̅ on σ. The estimates for the pure rate of return and the risk

corresponding to the three lines are shown in Table 4.5. The table shows the

relationship between the mean return and the mean standard deviation of the IMFs

and CMFs portfolios based on average monthly returns from January 1990 to April

2009, the period to which the regression analysis was applied. Two types of

regressions were conducted and the results are shown in columns 4 and 5. Column 4

indicates (/̅ ) as a dependent variable and (σ) as an independent variable, and vice

versa for Column 5. Then the intermediate line is also as denoted in column 6. The α

and β for the intermediate line are obtained by adding the value of α and β from two

regression lines respectively and then dividing them into 2.

The results can be interpreted as follows. For the IMFs during this period, investors

required a monthly average rate of return of approximately 1.42 per cent on riskless

assets. Concerning the risk element, they required an additional 0.36 per cent of the

mean return per month for each 1 per cent of the standard deviation of the monthly

Page | 119

return. On the other hand, for the CMFs during the same period, investors required a

monthly average rate of return of approximately 1.35 per cent on riskless assets.

Concerning the risk, the conventional portfolio required an additional 0.18 per cent of

the mean return per month for each 1 per cent of the standard deviation of the

monthly return. Thus, the results in Table 4.5 show that the IMFs portfolio is riskier

compared to the conventional counterparts, even though the portfolio performs

slightly better. This is because the investors acquire more return (IMFs about 1.42%

compared to CMFs about 1.35%) on the risk-free assets rate.

Table 4.5: Overall average returns and standard deviations for Malaysian IMFs and CMFs, January 1990 – April 2009: Regression results Portfolio (232 Obs)

Mean Return (/̅ )

Mean Standard deviation (σ)

Regression line : (σ) to (/̅ )

Regression line : (/̅ ) to (σ)

Intermediate line

α β α β α β IMFs

0.982

3.912

–0.843

0.465

3.673

0.245

1.415

0.355

CMFs

0.633

3.027

–0.300

0.308

2.999

0.045

1.350

0.177

To illustrate the results in Table 4.5 and to identify the trend movements of both IMFs

and CMFs portfolios, a scatter plot was constructed, as shown in Figure 4.2. The

study develops the scatter plot analysis and presented the graphs that are divided into

Figure 4.2(a) and Figure 4.2(b).

Figure 4.2(a) illustrates the scatter plot for both portfolios based on their aggregate

mean returns versus their standard deviations. It can be seen that both portfolios show

similar patterns and the scatter plot shows the diversity in both. However, in

comparison, the IMFs portfolio is less scattered and this implies that the portfolio is

less diversified. Even though most of the data in both portfolios were assembled at 0

point, it seems the IMFs have more outliers. It is therefore implied that the IMFs’

movements are more volatile than the CMFs.

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Figure 4.2: The Scatter Plot for the Malaysian Islamic and Conventional mean returns versus their average standard deviation (in percentage), January 1990 to April 2009. Figure 4.2 (a)

Figure 4.2 (b)

To validate Figure 4.2(a), the data in Figure 4.2(b) were plotted. The figure presents

the individual relationship of the average monthly return of each portfolio versus its

degree of precision. This degree of precision is measured by dividing one from the

standard deviation of each of the portfolio (1/std.dev.). Results in Figure 4.2(b)

clearly show that both portfolios are approximately normally distributed. However,

the CMFs are more diversified and more dispersed in relation to their IMFs

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counterparts. The figure also suggests that in terms of degree of precision, the CMFs

score higher than the IMFs because the standard deviations of the funds are lower and

closer to zero. Conversely, the figure implies that the degree of precision of the IMFs

is lower than that of the CMFs. The results are somehow robust when one looks at the

results in Table 4.2, as the mean return and the standard deviation of the IMFs are

higher than the mean return and the standard deviation of the CMFs.

To identify the relationships between both portfolios with their market portfolio, the

graph in Figure 4.3 displays the scatter plot for both portfolios versus their market

portfolios, i.e., KLCI return. The KLCI return is shown as a horizontal line because it

is considered to be an independent variable. The results in this figure highlight a

relationship between the IMFs and the CMFs and the market portfolio. The results

indicate that the IMFs are relatively more centred and follow the market trend more

closely than the CMFs. It also can be seen that the movement of the CMFs is more

dispersed than that of the IMFs.

Figure 4.3: The relationship between the aggregate return performance of IMFs and CMFs relative to the market portfolio

4.4.5 Trend analysis

Both graphs (raw return and risk adjusted return) in Figure 4.4 indicate a similar

pattern trend in that they are moving together and following the market trend.

However, it can be seen that CMFs portfolio is more volatile than the IMFs while

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using raw return but vice versa based on risk adjusted return. On the other hand, the

IMFs portfolio is more stable in regard to raw return performance but not for risk-

adjusted return performance. This outcome confirms the scatter plot (shown in Figure

4.2), where the IMFs are more diversified than the CMFs. However, both portfolios

seem to follow the market trend movement.

Figure 4.4: The trend pattern for the return of the IMFs and CMFs portfolios relative to the market portfolio, January 1990 to April 2009 Figure 4.4(a) Raw returns of IMFs and CMFs

Figure 4.4(b) Risk adjusted returns of IMFs and CMFs

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The trend analysis depicts the trend pattern data as fluctuating over time. To see the

robustness of the data, a unit root test is done in order to identify whether the data is

stationary or otherwise. Therefore, the data are tested using the Augmented Dickey

Fuller (ADF) test. The results are presented in Table 4.6. Failing to reject the null

hypothesis in the ADF test implies that the data are non-stationary.

Table 4.6: Results of the unit root tests

This table presents the results of the unit root tests. The asterisks ***, ** , * indicate significance at the 1%, 5% and 10% levels respectively. The critical values of the ADF statistics adopted from MacKinnon (1996) are: –3.47, –2.88 and –2.58 respectively for no trend; and are –4.02, –3.44 and –3.14 respectively. Phillip and Perron unit roots tests represent the same results for the no-trend level.

Table 4.6 reports the ADF test statistics for the IMFs, CMFs, total industry and the

market. The ADF test is conducted for all the portfolios without a trend and with a

linear trend level. The null hypothesis of the unit root for all portfolios, in a level

form with or without a trend, is rejected at all levels of significance, confirming that

the trend variables constitute a stationary series. In other words, each variable

represents the ∆� as a stationary series with no pattern trend in their time series of

data, suggesting that the data fluctuate all the time.

4.4.6 Mean differences between IMFs and CMFs

This sub-section aims to identify any significant difference in the risk and return

characteristics of the IMFs and the CMFs for the period January 1990 to April 2009

and between the two sub-periods: sub-period 1 from January 1990 to August 1999

and sub-period 2 from September 1999 to April 2009. Table 4.7 reports mean

Portfolio Level No trend Trend

IMFs ADF-0 lag –10.46*** –10.42***

ADF-1 lag –7.30*** –7.27***

CMFs ADF-0 lag –9.88*** –9.85***

ADF-1 lag –6.84*** –6.81***

ALL ADF-0 lag –10.17*** –10.14***

ADF-1 lag –7.05*** –7.02***

Market ADF-0 lag –10.37*** –10.36***

ADF-1 lag –7.03*** –7.04***

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difference in the returns performance of IMFs and CMFs. The analysis of these two

sub-periods is important for the purpose of identifying the differences in the funds’

performance during the earlier 10–year period. This is associated with the AFC while

the later 10–year period is linked to the GFC. The results reveal that there is

insignificant difference in the performance of a fund in sub-period 1 and sub-period 2.

Both sub-periods reveal the positive mean score, which implies that the IMFs have a

positive return of 1.62 per cent in sub-period 1 as well as a positive return of 0.35 per

cent in sub-period 2. The higher mean return of the IMFs in sub-period 1 than in sub-

period 2 could suggest that the Islamic funds’ return performance is less severe due to

the AFC rather than the GFC.

Results in Table 4.7 also reveal that there is no significant difference in the return

performance of the CMFs in both sub-periods. Similar to IMFs, the mean score for

the CMFs is higher in sub-period 1 than for sub-period 2, implying that the funds

perform better in sub-period 1. They are 0.84 and 0.43 per cent respectively. On

average, the IMFs portfolio outperforms better than the market return and the CMFs.

Interestingly, Table 4.7 denotes that the returns for both IMFs and CMFs are higher

than the market, suggesting that both portfolios performed well during the period of

analysis with the return of IMFs being higher than CMFs. This is indicated by the

mean score for the Islamic portfolio (0.98%) being higher than the mean score for the

conventional portfolio (0.63%). Results in the table validate the evidence presented in

Tables 4.1, 4.2 and 4.3. Indirectly, the results suggest that Islamic mutual funds in

Malaysia constitute a good long-term investment compared to their conventional

counterparts because the returns will be relatively higher. The findings could also

suggest that the Islamic funds are more strongly correlated to the market movement in

the sense that a market event could have a greater and quicker impact on IMFs than

on CMFs. Yet the insignificant difference between these two fund portfolios could

indicate that investors face a similar risk and reward penalty for these two types of

investment. Therefore, the lower mean score of the CMFs compared to their Islamic

counterparts in sub-periods 1 and 2 may imply that the conventional funds are more

affected in return underperformance relative to the market than the Islamic funds, due

to the AFC and GFC.

Page | 125

Table 4.7: Results of mean t-test assuming equal variances for IMFs and CMFs The results show the performance of the IMFs and the CMFs for the whole period (January 1990 to April 2009), sub-period 1 (January 1990 to August 1999) and sub-period 2 (September 1999 to April 2009). Panels A, B and C show the results for these respective periods. The study tests the hypothesis that the return performance of each portfolio is not equal to each other either in the sub-periods or the total period. The null hypothesis is that there is no difference in the performance of the portfolios for the sub-periods using t-test, assuming equal variances. The results show t-test is an insignificant difference from zero in all the panels, thus the null hypothesis cannot be rejected. The results confirm an insignificant difference for both returns performance of IMFs and CMFs relative to the KLCI, the market return portfolio.

IMFs CMFs KLCI

Panel A: Overall period (232 observations)

Mean 0.982 0.633 0.244 Variance 31.260 22.184 65.478 Pooled Variance 48.369 43.831 Hypothesized Mean Difference 0 0 df 462 462 t Stat 1.143 0.633 P(T<=t) one-tail 0.127 0.264 t Critical one-tail 1.648 1.648 P(T<=t) two-tail 0.254 0.527 t Critical two-tail 1.965 1.965

Panel B: Sub-period 1 (116 observations)


Panel C: Sub-period 2 (116 observations)


Page | 126

4.4.7 Non-risk-adjusted performance of the funds and the crises

This sub-section reports on the scrutiny of the relative raw return performance of

IMFs and CMFs in relation to the market before and during the AFC and GFC events.

It aims to identify any impacts of these crises in particular market cycles on mutual

fund performance. Therefore, the return before market risk-adjusted performance is

used, and the study period is sub-divided into four different periods based on bullish

and bearish market cycles. These four periods are: pre-AFC (1990–1996), during

AFC (1997–1998), post-AFC (1999–2006), and during GFC (2007–2009). The

results for the return performance of fund portfolios against the KLCI, a proxy for the

market return, are illustrated in Table 4.8.

During the pre-AFC, the IMFs portfolio obtains a higher monthly average return of

1.68 per cent in relation to the CMFs (0.63%) and the market (0.94%). On average,

the AMFs are also relatively higher than the market at 1.16 per cent. These results

imply that the Malaysian mutual funds do outperform the market counterparts based

on gross or non-adjusted return basis net of all expenses.

During the AFC (using a similar period to Abdullah et al. [2007]), the results indicate

that the market was badly affected and resulted in a negative return of monthly

average at 3.12 per cent. The CMFs portfolio also experienced negative return but

less than the market at 0.04 per cent. Surprisingly, the IMFs retained a positive return

but this is smaller than that for the pre-crisis at 0.32 per cent. The positive return of

IMFs could possibly be due to the portfolio not involving highly speculative

investments that require higher risks (Amin 2009). The other reason could be due to

the IMFs having expanded rapidly and their current performance being relatively

well-managed in comparison to the infancy stage. Regarding overall performance, the

results mean that the mutual funds portfolio performed better than the market during

the AFC. However, all results are statistically insignificant.

Page | 127

Table 4.8: Non-risk-adjusted return performance of the mutual funds The table presents the non-risk-adjusted return performance of the IMFs and CMFs fund portfolios. The table includes alpha and beta estimates of a single regression for each of the four sub-periods. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006), and during GFC (2007–2009). The results reported are adjusted for heteroskedasticity and serial correlation problems using White’s (1980) and Newey and West’s (1987) correction models. The asterisks ***, **, * indicate that the coefficient estimates are different from zero at 1%, 5 % and 10% level respectively. The symbols +++, ++, + denote that the coefficient estimates are different across fund samples at 1%, 5 % and 10% level respectively.

Portfolio Pre-AFC During AFC

Post-AFC During GFC

Overall

Panel A: Return AMFs 1.156*** 0.140 0.308* 0.194 0.675*** (4.463) (0.156) (1.692) (0.747) (4.087) IMFs 1.684*** 0.319 0.194 0.310 0.843*** (4.288) (0.307) (0.898) (1.243) (4.022) CMFs 0.628*** –0.039 0.421** 0.077 0.507*** (3.126) (–0.049) (2.613) (0.267) (3.456) Market 0.940 –3.115 0.652 –0.361 0.244 RF 0.499 0.536 0.234 0.267 0.365 Pearson t-statistic

1.398 0.034 –0.342 0.292 0.728

Wilcox on/ Mann Whitney (p-value)

1.654+

(0.040) 0.093 0.378 0.156 0.779

Panel B: Systematic Risk (Beta) AMFs 0.632*** 0.435*** 0.643*** 0.552*** 0.543*** (13.447) (5.731) (14.520) (11.950) (1.835) IMFs 0.654*** 0.479*** 0.654*** 0.523*** 0.571*** (11.061) (5.708) (12.886) (12.524) (11.885) CMFs 0.610*** 0.392*** 0.631*** 0.581*** 0.515*** (14.970) (5.648) (16.120) (10.342) (11.425) Market 1.000 1.000 1.000 1.000 1.000

Panel C: F-test for equally variances (IMFs-CMFs) 1.574++ 1.540 1.182 1.252 1.410+++ (IMFs-Market) 1.443+ 3.141+++ 1.830+++ 2.968+++ 2.095+++ (CMFs-Market)

2.272+++ 4.837+++ 2.163+++ 2.371++ 2.953+++

Obs 84 24 96 28 232 With regard to the post-AFC period, results indicate a statistically significant positive

return of CMFs at 0.42 per cent on monthly average, higher than the IMFs at 0.19 per

cent. None of them is higher than the market (0.65%). Again, in contrast to the

Page | 128

findings of Abdullah et al. (2007), this result reveals that on average, the return

performance of IMFs was higher during the pre-AFC than the return recorded during

the post-AFC stage. The contradictory results could possibly be due to a different

length of period in this study for the post-AFC phase from 1999 to 2006, rather than

1999 to 2001 asused by Abdullah et al. (2007).

These results indicate that the overall fund return is relatively higher than the risk-free

rate, which seems to contradict the findings of Taib and Isa (2007). However, this

study employs the one-month Malaysian t-bills, while Taib and Isa (2007) used

KLIBOR as the risk-free rate of return in their study. The duration of this study also

includes the crisis period of January 2007 to April 2009 for the GFC.

The results reveal that both IMFs and CMFs remain positive returns on this non-risk-

adjusted basis and the IMFs insignificantly outperformed their CMFs peers. However,

unlike the study by Hayat and Kraeussl (2011), who found that the IMFs had a

negative return performance over the market benchmark during the GFC, this study’s

finding is that the IMFs return performance is still positive and statistically

significant.

Panel B in Table 4.8 indicates the results of systematic risk compared to the market,

measured by the beta of a fund portfolio. All the portfolios regardless of the sub-

periods reveal significant results of betas less than 1, suggesting that, on average, all

the fund portfolios, IMFs and CMFs, are less volatile and less risky than the market.

Moreover, the risk of IMFs in all periods is relatively higher than CMFs except

during the GFC, suggesting that potentially the IMFs could provide higher returns

due to the fund’s higher risk.

The F-test results are also reported in Table 4.8. The F-test with null hypothesis of

equally variances in all sub-groups can be rejected, thus indicating that IMFs and

CMFs exhibit different risk exposures in pre-crisis and throughout the period of

study. The F-test also reveals that IMFs and CMFs exhibit significant different risks

relative to the market for the whole period and all sub-periods.

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4.4.8 Results for risk-adjusted return performance measurements

This sub-section reports the comparative performance of IMFs and CMFs over the

20–year period. The risk-adjusted performance measures are: Sharpe Ratio (SR),

Treynor Index (TI), Jensen Alpha (JA), Modigliani-Modigliani Measure (M2),

Appraisal Ratio (AR) and Adjusted Sharpe Ratio (ASR). Table 4.9 reports the results

of the risk-adjusted performance for the overall 20–year period and the periods of the

crises. The overall period is divided into four sub-periods: pre-AFC, during AFC,

post-AFC and during GFC. The results reveal that, on average, the IMFs portfolio

outperforms the CMFs peers and the overall sample, represented by AMFs, regardless

of type of performance measurements in all periods except for the post-AFC phase.

In the pre-AFC stage, the IMFs perform relatively better that the CMFs and the

AMFs in all types of risk-adjusted return measurements. In contrast, the results show

that the CMFs perform better than the IMFs and the AMFs for the post-AFC period.

These findings are completely different from the findings of Abdullah et al. (2007),

who contended that IMFs perform better during a post-crisis phase, whereas the

CMFs perform better in a pre-crisis period.

One possible reason for the difference in findings could be the larger numbers

employed in this study (about 479 funds, of which 129 are IMFs) than the sample

used by Abdullah et al. (about 65 funds, of which 14 are IMFs). Furthermore, the

duration of their study is generally shorter for both pre- and post-crisis periods than

the periods in this study.

With regard to the AFC period, the finding in this chapter is in line with the findings

of Abdullah et al. (2007) that IMFs perform better than CMFs in all types of

performance measurements. This study employs the same period as Abdullah et al.

for the AFC period. Both portfolios experience negative returns due to the crisis, with

the IMFs performing less poorly than the CMFs and the full sample, i.e., the AMFs.

In this crisis, the systematic risk and total risk of the IMFs are relatively higher than

those of the CMFs, suggesting the ability of these funds to obtain more abnormal

returns.

Page | 130

Table 4.9: Fund performance based on risk-adjusted return measurements The table presents the results of risk-adjusted return performance using various types of measurements. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). The asterisks*** indicate that the Jarque Bera (JB) is statistically significant at 1% level, implying that the data is not normally distributed. For overall performance, these results show that the IMFs portfolio performs better than their CMFs counterparts.


Post-AFC During GFC

Overall

Panel A: AMFs SR 0.259 –0.201 0.116 –0.084 0.088 TI 1.980 –4.015 0.766 –0.493 0.814 JA 0.972 –0.159 0.224 0.075 0.508 M2 1.748 –3.453 0.699 –0.446 0.711 AR 0.232 –0.019 0.062 0.028 0.113 ASR 0.257 –0.195 0.115 –0.081 0.087 Beta 0.631 0.436 0.643 0.553 0.543 Std.Dev 4.826 8.734 4.248 3.246 5.042 JB 21.443*** 0.272 54.433*** 1.242 82.903*** Panel B: IMFs SR 0.320 –0.176 0.087 –0.047 0.110 TI 2.754 –3.562 0.590 –0.277 1.082 JA 1.511 0.043 0.113 0.184 0.686 M2 2.161 –3.026 0.523 –0.250 0.895 AR 0.304 0.005 0.030 0.072 0.137 ASR 0.317 –0.171 0.086 –0.046 0.110 Beta 0.653 0.480 0.655 0.524 0.570 Std. Dev 5.617 9.720 4.463 3.083 5.586 JB 7.252*** 0.157 63.981*** 0.629 71.271*** Panel C: CMFs SR 0.157 –0.229 0.146 –0.116 0.057 TI 1.151 –4.568 0.948 –0.688 0.519 JA 0.433 –0.360 0.335 –0.035 0.330 M2 1.057 –3.940 0.880 –0.615 0.460 AR 0.112 –0.048 0.096 –0.012 0.079 ASR 0.155 –0.222 0.145 –0.112 0.057 Beta 0.609 0.393 0.631 0.582 0.516 Std.Dev 4.479 7.841 4.102 3.449 4.717 JB 28.626*** 0.415 45.896*** 2.378 74.395*** Market-adjusted return

0.441 –3.652 0.418 –0.628 –0.121

Risk free (rf) 0.499 0.536 0.234 0.267 0.365

Table 4.9 also examines the performance of IMFs and CMFs during the GFC, when it

is shown that IMFs perform better than CMFs. The impact of the crises on all funds’

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performance is more severe during the AFC than during the GFC. During both crises,

all performance measures reveal a negative return for all the portfolios except for the

JA and AR of the IMFs and the JA of the AMFs, demonstrating a positive return in

the GFC period. In other words, all the performance measures consistently represent a

negative return of the CMFs during both financial crises. Despite all the portfolios

being badly affected by these crises, IMFs can be seen as less affected than CMFs and

AMFs. This is evident in the smaller decline in returns in both crisis periods and a

positive return for IMFs and AMFs based on their JAs during the GFC. The results

also reveal that the IMFs portfolio is less volatile than the CMFs during the GFC. The

standard deviation of the portfolios is relatively lower at 3.08 than the standard

deviation of CMFs at 3.45 during the GFC.

The systematic risk of IMFs is also lower at 0.52 relative to CMFs at 0.58. The lower

systematic risk and the residual risk of the IMFs could be explained by the risk

exposure of this investment vehicle being lower because the investment is not

involved in the stock market. The stock market is associated with usury, gambling,

alcohol and the speculative investments that do not meet the criteria of the Shariah

screening process. It is evident that both IMFs and CMFs are relatively low-risk

investments regardless of the duration and all the betas are smaller than one. The

results for low risk (beta is smaller than 1) of the IMFs during the bull and bear

markets support the findings of Hayat and Kraeussl (2011). However, unlike their

findings, this study provides evidence that the IMFs portfolio does relatively

outperform the market not only in bullish markets but in bearish ones as well.

With reference to overall performance, the SR, TI and JA confirm that the IMFs

portfolio performs better than the CMFs. The SR estimates the return to risk trade-off

by dividing the average excess return of a fund portfolio over the sample period with

the standard deviation of returns within the same period. It is therefore evident that

the higher ratio indicates that a portfolio performing better Jensen alpha is an

intercept of the single factor CAPM model representing the outperformance of a

return portfolio in relation to the market, suggesting that the higher increment

provides a better excess return. The TI measurement is similar to the SR, except that

the performance of excess return in TI is related to systematic risk, but the SR is

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related to the residual risk or total risk of the fund return portfolio. Moreover, the

performance measures based on M2, AR and ASR indicate a similar pattern. The

outperformance of IMFs using ASR and M2 measures is consistent with what

Abdullah et al. (2007) found. However, the positive return of the full sample, i.e., the

AMFs, for the whole period of this study using various risk-adjusted returns is not in

line with the finding of Taib and Isa (2007), who found evidence of Malaysian mutual

funds underperforming based on a 110-fund sample for the period January 1990 to

December 2001.

4.4.9 CAPM performance analysis and the crises

To further investigate the return performance of the IMFs and CMFs for different

market conditions, the study empirically analyses the data based on CAPM

performance analysis. The crisis in 1997 refers to the Asian financial crisis (AFC)17 in

1997–1998. The global financial crisis (GFC)18 was in 2007–2009. The major events

that led to the GFC are demonstrated in Appendix D. The CAPM regression is

conducted on risk-adjusted return (mean excess return) of a portfolio as a dependent

variable for different market conditions. This is due to the fact that the overall results

might not be robust for the bear market. The excess returns refer to returns of a

portfolio over the one-month Malaysian t-bills, a proxy for risk-free rate portfolio.

The results are illustrated in Table 4.10.

Table 4.10: CAPM performance analysis and the crises The table presents the risk-adjusted return performance of the IMFs and the CMFs fund portfolios. Returns are in percentage and net of all expenses. The table includes alpha and

17 In Malaysia, the AFC had an impact when the Ringgit Malaysia (MYR) began to experience waves of speculative pressure following the depreciation of the Thai Baht on 2 July 1997. By the end of August 1998, the Ringgit had depreciated by 40% against the US dollar in relation to its level at the end of June 1997. The Kuala Lumpur Stock Exchange Composite Index (KLCI) fell by 79.3% from a high of 1271.57 points in February 1997 to a low of 262.70 points on 1 September 1998 (Bank Negara Malaysia 1999, p.560). In 1997, real GDP was 7.5% (10% in 1996), and it declined by 7.5% in 1998, the first negative growth in 13 years (Bank Negara Malaysia 1999). 18 The GFC crisis begin in early 2007 and the effect could be seen as in October 2007 in the US, when motor vehicle output was demonstrated to be 31% down in the final quarter of 2008, s compared to a year earlier. Within the same period, housing investment fell by 19.5%. The impact on Britain was also immediate. The largest mortgage leader, Northern Rock, collapsed and created major pressure on British banks. The crisis, therefore, was started off by the implosion of housing bubbles and caused many countries to go into recession or nearly so. Many countries had banking and financial systems in utter disarray; financial asset prices crashed; and in some places real asset prices as well (Highfill, 2008).

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beta estimates of a single regression for each of the four sub-periods. The overall period is from January 1990 to April 2009. The sub-periods are: pre-AFC (1990–1996), during AFC (1997–1998), post-AFC (1999–2006) and during GFC (2007–2009). The results reported are adjusted for heteroskedasticity and serial correlation problems using White’s (1980) and Newey and West’s (1987) correction models. The one-month Malaysian t-bills and KLCI are used as proxies for risk-free rate and market return respectively. Standard errors are given in parentheses. The asterisks ***, **, * indicate that the coefficient estimates are different from zero at 1%, 5 % and 10% level respectively.


Post-AFC During GFC

Overall

Panel A: All mutual funds (AMFs) in the sample α 0.972*** –0.159 0.224 0.075 0.508*** (0.253) (0.897) (0.180) (0.253) (0.161) β 0.631*** 0.436*** 0.643*** 0.553*** 0.543*** (0.047) (0.076) (0.044) (0.046) (0.046) Adj R² 0.776 0.728 0.832 0.810 0.761 Residual risk 4.826 8.734 4.248 3.246 5.042 Observations 84 24 96 28 232

Panel B: IMFs α 1.511*** 0.043 0.113 0.184 0.686*** (0.386) (1.040) (0.215) (0.246) (0.206) β 0.653*** 0.450*** 0.655*** 0.524*** 0.570*** (0.059) (0.084) (0.051) (0.041) (0.048) Adj R² 0.611 0.709 0.781 0.807 0.683 Residual risk 5.617 9.720 4.463 3.083 5.586 Observations 84 24 96 28 232

Panel C: CMFs α 0.433** –0.360 0.335** –0.035 0.330** (0.197) (0.794) (0.159) (0.280) (0.143) β 0.609*** 0.393*** 0.631*** 0.582*** 0.516*** (0.041) (0.069) (0.039) (0.056) (0.045) Adj R² 0.841 0.732 0.859 0.793 0.784 Residual risk 4.479 7.841 4.102 3.449 4.717 Observations 84 24 96 28 232 Results in Table 4.10 shows that beta values in Panel A, B, C, which represent the

systematic risk of AMFs, IMFs and CMFs, are relatively stable in different market

conditions. The overall beta of IMFs and CMFs is significantly smaller than 1,

implying that they are low-risk investments. The results support the findings of

Abdullah et al. (2007) and Hayat and Kraeussl (2011). The findings of this study

show that the overall beta for IMFs is 0.57 and for CMFs is 0.52, in which the value

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is greater than that found by Abdullah et al. (2007) at 0.25 and 0.38 for Islamic and

conventional funds respectively.

Unlike the findings of Hayat and Kraeussl (2011), the present findings reveal that

IMFs significantly outperform the market benchmark (indicated by positive alpha)

over the period of the study. IMFs also insignificantly outperform the market

benchmark during the AFC and GFC crises. The contradictory results here might be

due to Hayat and Kraeussl limiting their study to Islamic equity funds, whereas this

study includes diversified mutual funds consisting of alternative, allocation, equity,

fixed income and money market funds. The results reveal that the CMFs and the

AMFs are seemingly affected by the AFC crisis. However, the AMFs have positive

alpha during the GFC crisis, indicating that the Malaysian mutual funds in general

outperformed the market during the GFC period.

In the pre AFC period, all the portfolios have significantly positive alphas, suggesting

that they perform better than the market return benchmark. The IMFs achieve the

highest level of mean excess returns at 1.51 per cent compared to CMFs at 0.43 per

cent and AMFs at 0.97 per cent. Meanwhile, in the post-AFC period, results indicate

that all portfolios outperform the market benchmark; however, they are statistically

insignificant except for the CMFs. The results show that CMFs outperform the

market benchmark better than IMFs. The results also show that the returns

performance of all fund portfolios is higher in the pre-crisis period than in the post-

crisis period. Additionally, the IMFs perform better during the pre-crisis whereas

CMFs perform better during the post-crisis. This evidence is in contrast to the

findings of Abdullah et al. (2007) who found that both the Islamic and conventional

funds underperformed the market and that the average returns of these funds in post-

crisis were better than those documented during the pre-crisis period.

The results show that during the AFC, IMFs perform better than the overall fund and

the CMFs. The study provides evidence that IMFs outperform the market; however,

CMFs and AMFs underperform the market (indicated by negative alphas) during the

AFC. The CMFs also underperform during the GFC crisis; however, with less severe

impact than during the AFC. On the other hand, the IMFs perform better during the

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GFC than during the AFC period. The AMFs have positive alphas during the GFC

but underperform the market (with negative alpha) during the AFC, suggesting that

the Malaysian mutual fund performed better during the GFC than the AFC.

4.5 Summary

This chapter has investigated the performance of Islamic funds compared to

conventional ones. It has also evaluated the performance of both funds using monthly

average returns before and after adjusting for the market risk-free rate over the period

January 1990 to April 2009. It can be stated here that the performance measures

indicate that the IMFs portfolio performs relatively well compared to its CMFs

counterpart. The major outcome here is that both IMFs and CMFs perform better than

the market portfolio, which is the proxy used by the KLCI index. In particular, the

returns performance of the Islamic funds is slightly better than the returns

performance of the conventional funds, but there is no evidence of significant

difference between these two portfolios over the overall period, or between the two

sub-periods from January 1990 to August 1999 and from September 1999 to April

2009.

In view of the risk-adjusted returns performance, Islamic funds also perform better

than CMFs using various types of performance measures, namely Sharpe ratio (SR),

Adjusted Sharpe ratio (ASR),Treynor index (TI), Jensen alpha (JA), M2 measure and

Appraisal ratio (AR). Further investigation using these performance measures related

to before, after and during the crises also indicate that the returns performance of

IMFs is relatively less severe than that of CMFs, as shown in Table 4.9. On average,

the negative returns of both funds during the bearish markets suggest that both IMFs

and CMFs follow the market movement and were directly impacted on by these

crises.

The evidence here argues for the previous findings in the literature that Islamic funds

perform better during the bearish market and worse during the bullish market.

Overall, the IMFs perform better than the overall sample (the AMFs) and the CMFs

(see details in Table 4.10). The IMFs perform better than the CMFs during the pre-

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AFC period and during the AFC and GFC. In contrast, the CMFs portfolio performs

better than the IMFs during the post-AFC period.

Since the results reported in this chapter are contrary to the findings of previous

studies, detailed investigation is therefore required to explain the differences between

risk and return performance of these two types of funds. Moreover, the

outperformance and underperformance of both funds in different sub-periods could

mean that normal risk-adjusted performance measurements are not really meaningful,

thus requiring further investigation. The extended analysis using more reliable

methods such as the CAPM with different market benchmarks and the TM model

developed by Treynor and Mazuy (1966) could provide more rigorous findings on

these matters. These will be discussed in the next chapter.

The next chapter also examines fund performance using risk-adjusted return

performance based on the CAPM model with single and multiple benchmarks. The

performances of both IMFs and CMFs portfolios based on the ability of fund

managers in their timing expertise and fund selectivity skills are also assessed using

the TM model and the extended TM model, following Bello and Janjigian (1997), to

evaluate these abilities among the fund managers.

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CHAPTER 5 - MARKET TIMING

EXPERTISE AND FUND SELECTIVITY

SKILL: TIME SERIES DATA ANALYSIS

5.1 Introduction

The preliminary results reported in Chapter 4 were based on descriptive statistics and

risk-adjusted performance measures such as Sharpe ratio, Treynor index, Jensen alpha

and others, as introduced in Sub-section 3.4.2. As noted in Sub-section 3.4.2.3, Jensen

alpha is an intercept term which is derived from a single factor CAPM. This chapter

uses alternative benchmarks (Islamic and the conventional benchmarks) in single

factor CAPM performance analysis. The single factor CAPM is then expanded to

multi-factor CAPM by incorporating the multiple regression models adapted from

Bertin and Prather (2009). The CAPM regression is also broadened to a quadratic

regression, namely TM model, following Treynor and Mazuy (1966), and extended

TM model, following Bello and Janjigian (1997).

This chapter deals with the first and second hypotheses as previously stated in Section

3.3, and focuses on several specific objectives: (1) to identify whether these two

portfolios, the IMFs and the CMFs, differ significantly from each other in regard to

market benchmark using time series analysis, (2) to compare the returns performance

of the funds between IMFs and CMFs using a different Islamic and conventional

single market benchmark, (3) to specifically observe whether the IMFs are

significantly sensitive to either the Islamic benchmark or the conventional benchmark,

and (4) to evaluate fund managers’ performance on market timing expertise and fund

selectivity skill among the Malaysian fund managers in general, and more specifically

among IMFs and CMFs fund managers.

The structure of this chapter is as follows. Section 5.2 discusses the main issues and

the significance of the chapter. Section 5.3 further elaborates the data sample used

here (as described in Section 4.3). Section 5.4 provides results and discussions on

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firstly, the performance of the funds against the market benchmark. Secondly, results

based on an analysis of market timing and fund selectivity performance are described

in Section 5.5. Finally, Section 5.6 concludes the chapter.

5.2 Issues and the significance of the chapter

In view of the fact that this study compares the performance between two substantial

portfolios of mutual funds, the IMFs and the CMFs, in which the fundamentals of

these funds’ investment rationales differ totally, the outperformance of these two

portfolios is expected to be different when using single or multiple benchmarks. It is

also assumed that these two portfolios act differently in relation to different market

benchmarks since their fundamental concept and investment styles are different.

Previous studies have indicated that there is no difference in the performance between

Islamic and conventional benchmarks (Albaity and Ahmad, 2008; Elfakhani et al.,

2005; Girard and Hassan 2005; 2008; Hakim and Rashidian, 2004; Hassan et al.

2010). The available literature for Malaysia indicates that the KLCI and KLSI indices

act no differently in providing abnormal returns to the stock market (Albaity and

Ahmad, 2008). Therefore, it seems that using Islamic or conventional benchmarks

makes no difference when evaluating the performance of an Islamic fund. This

chapter re-investigates the issue.

It is evident that the return performance of mutual funds is directly responsive to stock

market performance (Low and Ghazali 2007). Thus, the analysis of the relationship

between fund and stock markets is of fundamental importance; in fact, the rise (fall)

of stock return could indirectly increase (decrease) the return performance of a mutual

fund. Moreover, currently about 20 per cent of the stock market shares in Malaysia

belong to the mutual fund industry. Therefore, knowledge of market efficiency and,

more specifically, market trends could reveal further insights when investigating

mutual funds’ performance, particularly with reference to their risk and return

characteristics. There is a lack of such evidence concerning the IMFs’ performance.

Since the right choice of benchmark is important (Grinblatt and Titman, 1994), it is

incorporated to conduct a multiple benchmark analysis based on the CAPM. Thus, the

standard CAPM is extended, following Bertin and Prather (2009), and relevant market

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benchmarks are included, ranging from large stock and small cap stock to foreign and

bond stock. The reason for following the Bertin and Prather model is to include

various benchmarks covering the asset class of the mutual funds. Next, the study

further extends this model to include the money market benchmark. This is because

the mutual funds in this study consist of diversified mutual funds including

alternative, allocation, equity, fixed income and money market. The purpose of

incorporating these market benchmarks is to examine whether the various mutual fund

categories are sensitive to any one or various benchmarks. Explanations about the

construction of the models were documented in Chapter 3.

The preliminary results reported in Chapter 4 also highlight the presence of a

statistical difference in fund risk and return characteristics for IMFs and CMFs. In this

chapter, not only a difference in the returns performance of the funds in relation to the

market benchmark is expected to be observed, but also differences in the expertise of

IMF and CMF fund managers on market timing and fund selectivity skill. A rationale

of this hypothesis was that Shariah screening criteria are foreseen as the restrictions

that may make it difficult the Islamic fund managers to avail timing opportunities but

at the same time induce the fund managers to carefully select. In other words, the

restrictions make the fund managers put more attention to selectivity outperformance

to offset the adverse impact of poorer timing ability, if there is a case.

Fund managers generally forecast on two distinct aspects of mutual funds

management: firstly, forecasts on the price movements of selected individual stocks

and secondly, forecasts on the price movement of the general stock market as a whole

(Merton, 1981). The former is usually associated with the analysis of the securities.

They are either undervalued or overvalued compared to their market benchmarks. The

fund managers endeavor to identify the fund or securities whose expected returns lie

significantly above the security market line in order to ensure above average returns.

In other words, the first forecasting relates to the fund managers’ stock selectivity

skill. In the second type of forecasting, the fund managers attempt to identify whether

the securities are undervalued or overvalued relative to the fixed-income securities or

bonds. This is because fund managers strategically change their investment portfolios

and asset classes to suit the bear and bull markets. For example, fund managers will

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switch to options such as fixed income securities and dividend funds during the bear

market and will make a staggered swap to equity funds when the market is in the

cycle of bottoming out or signals a bull market. The right forecast with regard to

timing when the equity fund will outperform or underperform bonds is a tough

challenge for the fund managers.

Hence, this chapter concentrates on measuring fund managers’ market timing

expertise and fund selectivity skills, and employs the TM model for measuring market

timing ability (Admati et al. 1986). Bello and Janjigian (1997) are also followed to

provide more benchmark indices in the TM model representing various types of

mutual funds in our sample. The model named as the extended TM model in Section

5.4 is used to analyse this further, using time series regression analysis.19

The existing literature has shown mixed results on fund managers’ market timing

expertise. In the US market, this ability is accepted as a common phenomenon with

some mutual funds providing evidence of negative market timing ability (for example,

Chang and Lewellen, 1984; Chen et al. 1992; Henriksson, 1984) and others revealing

positive market timing (see Bello and Janjigian 1997; Lee and Rahman, 1990;

Lehmann and Modest, 1987). In the Malaysian market, the findings from most

studies, with the exception of Hayat (2006), have illustrated a negative or inferior

market timing ability among fund managers (for example, Abdullah et al. 2007;

Ahmed, 2007; Annuar et al., 1997; Elfakhani et al., 2005; Hayat and Kraeussl, 2011).

Hayat (2006) indicated that about 29 per cent of Malaysian Islamic funds in the

sample had a positive market timing ability on an individual basis. In contrast, Hayat

and Kraeussl (2011) in their recent study found evidence of negative market timing

expertise among global Islamic equity fund managers. Their results, based on the TM

model, further indicated that Malaysian Islamic fund managers display inferior or no

market timing. As the findings are not consistent, further analysis in this area is

required.

The investigation in this study will provide a useful aid for market players, fund

managers and investors in particular, deepening their understanding of the efficiency

19 The issue based on panel data regression analysis l is discussedin Chapter 6.

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of asset allocations, portfolio diversification and portfolio selections in their

investment funds management. Furthermore, a reflection on the linear relationship

between superior forecasting skills in managing funds and the stock market’s

efficiency can offer useful insights to investors.20 In fact, the superiority (inferiority)

of fund managers’ strategies on market timing could reflect the inefficiency

(efficiency) of the market equilibrium. Superior market timing would directly

undermine the theory of the market being efficient (Henriksson and Merton, 1981).

Market efficiency means that the market players have access to complete information

reflecting the price value of the stock market. This implies that the fund values could

be equal to the values of the stock market. As a result, investment strategies such as

market timing and fund selectivity skills provide no benefits but are instead costly to

investors. However, in reality, the assumption of market efficiency based on perfect

information is not fulfilled; in most cases, some markets are efficient and others are

not. This situation explains a negative correlation between the expertise of fund

managers in timing the market and the theory of market efficiency, in that the more

efficient the particular market, the less the benefits of market timing. Hence, with the

assumption that the market is not fully efficient, an evaluation of fund managers’

market timing expertise requires further investigation.

If it is true that fund managers are not able to anticipate a rise or fall in the common

stocks market and adjust their portfolios based on market movements, it is necessary

to revise the accountabilities of investment management across the board. Indeed, this

suggests that fund managers do not actually have market timing expertise (Treynor

and Mazuy 1966). Therefore, the issue of market timing expertise – whether the fund

managers can really guess the market – remains important. It is the role of fund

managers to ensure that they can time the market correctly and thus provide higher

abnormal returns to their clientele.

20 The theory of CAPM proposes that there is a positive linear relationship between stock market return and systematic risk (measured by the beta). Bello (2005), for instance, proved this theory when he found a strong relationship between mean return of mutual funds and risk. He also found no significant linearity between return and volatility measured by the standard deviation, thus convincing his readers that the beta is absolute when measuring risk based on this CAPM.

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The analyses in this chapter are important since investors would gain an advantage in

knowing whether investments in Islamic funds could incur more risk or give more

returns than conventional funds. This is due not only to the fact that investment

activities in Islamic funds are governed by Shariah principles (Usmani, 2007), but

also to the fact that they are associated with other special risks such as insufficient

track record and low working capital (Hayat and Kraeussl 2011), which are not

present in their conventional counterparts. Since an investment in Islamic funds needs

to follow a Shariah screening process to ascertain that the investment is permissible, it

is arguable that such investment could provide more returns than the normal or

conventional fund. Therefore, the results of underperformance (outperformance) of

the IMFs investigated in this chapter indirectly suggest that the imposition of Shariah

screening of Islamic investments actually adds risk and extra costs to the potential

investors, or conversely.

5.3 Data sample

The analysis employs time series return data based on a sample of 479 mutual funds

domiciled in Malaysia between January 1990 and April 2009. These consist of 129

IMFs and 350 CMFs from various fund categories. Each of the fund portfolios is

divided into five categories based on their asset classes: allocation, alternative, equity,

fixed income and money market. January 1990 is chosen as the start of the period as it

was when the Islamic fund industry started to develop. April 2009 is the end of the

period since this is the most recent date for fund return data available in the

Morningstar database. Compared to previous studies, this study explores a longer

period and uses more extensive data concerning the Malaysian mutual fund industry

in relation to IMFs and CMFs.21

The monthly return of the data is calculated in percentages based on geometric mean

and net of all expenses but excluding front and exit fees. The returns in this analysis

are then adjusted for market risk-free return. The mean aggregate return is calculated

21 See, for example, Abdullah et al. (2007) and Taib and Isa (2007), who evaluated 65 and 110 mutual funds using Malaysian data from January 1992 to December 2001and from January 1991 to December 2001, respectively. A recent study by Hayat and Kraeussl (2011) evaluated the performance of 145 funds for 2000 to 2009. Another recent study by Hoepner et al. (2011) investigated 265 funds for September 1990 to April 2009. Unfortunately both studies limited their analysis to Islamic equity funds.

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based on simple return and also for the portfolio return. When necessary, the equally

weighted return is constructed for each of the stated categories and also for each asset

class category. The mean aggregate for IMFs is calculated based on the monthly

average of 129 funds while the mean aggregate for CMFs is based on the monthly

average of 350 funds. The other categories are top performer, middle performer and

bottom performer of the CMFs. These are generated based on 129 funds at the top,

middle and bottom of the mean return of all 350 CMFs. For these categories, only 129

funds were chosen to provide relatively matched pairs with the IMFs portfolio.

Other categories which are involved in the asset classes of the fund are also

constructed. For each of the asset classes (see Section 5.4.2 ), the mean return of each

is based on the number of funds included in each fund category. For the allocated

category, there are about 109 funds, 32 IMFs and 77 CMFs. For the alternative

category, there are about 31 funds, consisting of 8 IMFs and 23 CMFs. The equity

category consists of approximately 235 funds, of which 58 are IMFs and 177 are

CMFs. For the fixed income category, there are 64 funds consisting of 17 IMFs and

47 CMFs; and for the money market category, about 40 funds, of which 14 are IMFs

and 26 are CMFs.

In most cases, the KLCI is used as a market return benchmark for the IMFs and the

CMFs fund portfolios.22 However, for the benchmark analysis based on the single

CAPM model, the study employs the Islamic market index, known as the Kuala

Lumpur Syariah Index (KLSI), in order to examine the performance of the funds

against this particular benchmark. The funds’ performance is examined against the

Kuala Lumpur Composite Index (KLCI) market return as a proxy for the conventional

benchmark and then uses KLSI, a proxy for the Islamic benchmark, for the period

January 1990 to April 2009 (232 observations). The KLSI was launched in July 1999

and therefore investigating the performance of IMFs and CMFs using a different

benchmark is available for the period July 1999 to April 2009 (118 observations).

22 See Section 4.3.

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5.4 Results for and discussions of the market benchmark

5.4.1 Single CAPM performance analysis

This section examines the performance of all mutual funds in the sample (AMFs), the

IMFs and the CMFs in the Malaysian mutual funds, using the standard CAPM

regression model of the monthly excess return for January 1990 to April 2009. The

estimated alpha and beta coefficients are presented after correction for

heteroskedasticity and standard errors covariance, following White (1980). A non-

heteroskedasticity-adjusted regression analysis is also conducted (see results in

Appendix F) and since the results are similar, only the findings after the correction are

discussed. The outperformance (underperformance) alpha and high (low) beta are

obtained using two different market benchmarks, namely KLCI and KLSI, as proxies

for the conventional and Islamic market benchmarks correspondingly. The results are

presented in Table 5.1.

Table 5.1 presents the CAPM regression results for the time series of mean excess

return performance concerning the AMFs, IMFs, CMFs and other fund categories,

i.e., top performer, middle performer and bottom performer of the CMFs, against their

Islamic and conventional benchmarks. The corrections for heteroskedasticity and

serial correlation problems are managed using White’s (1980) and Newey and West’s

(1987) tests. However, the standard errors reported in parentheses are based on White

in order to verify the consistency of the report with the rest of the regression reports in

this thesis.

The results in Table 5.1 vividly illustrate that, on average, all the portfolios except the

middle and bottom performers perform strongly, and significantly outperform the

conventional benchmark. However, only IMFs underperform the market return when

the Islamic benchmark is applied. The adj. R2 obtained from the regressions is

relatively higher and it explains more than 60 per cent of the model’s variability.

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Table 5.1: CAPM analysis of the portfolios against the conventional and Islamic benchmarks The table presents results on mean excess returns performance based on different market benchmarks after correction for heteroskedasticity. All returns are reported in percentages and net of all expenses. The overall sample period is based on monthly data from January 1990 to April 2009 and the full sample consists of 479 funds, made up of 129 IMFs and 350 CMFs. However, the period varies depending on the availability of the funds in a category. For example, the sample is from November 1994 to April 2009 for the bottom performer. For the Islamic benchmark, the period is from July 1999 to April 2009, due to the Islamic benchmark being launched in July 1999. The difference portfolio (Diff.) is constructed by subtracting the returns of CMFs from the returns of IMFs. Standard errors obtained from the cross-section of the estimated coefficients are reported in parentheses. The asterisks ***, **, * indicate significant levels at 1%, 5% and 10%, respectively. Obs is the number of observations.

According to the traditional interpretation of alpha as portfolio performance

measurement, a positive alpha higher than zero denotes outperformance of a fund

against the market return benchmark. The significant outperformance of AMFs as

shown in Table 5.1 suggests that, on average, Malaysian mutual funds outperformed

the market returns by 6.10 per cent per annum over the 20–year period of 1990–2009.

The IMFs and CMFs also performed better than the market as they gained abnormal

annual returns of 8.23 per cent and 3.96 per cent respectively within the same period.

These results clearly indicate that the Islamic funds perform better than their

conventional peers.

Conventional market benchmark Islamic market benchmark

α β Adj R2

Obs α β Adj R2

Obs

AMFs (N=479)

0.508*** (0.161)

0.543*** (0.046)

0.76 232 0.022 (0.159)

0.619*** (0.037)

0.76

118

IMFs (N=129)

0.686*** (0.206)

0.570*** (0.048)

0.68 232 –0.016 (0.181)

0.613*** (0.043)

0.71 118

CMFs (N=350)

0.330** (0.143)

0.516*** (0.045)

0.78 232 0.060 (0.150)

0.626 (0.034)

0.77 118

Top performer (129)

0.512*** (0.149)

0.499*** (0.046)

0.76 232 0.259* (0.152)

0.675*** (0.032)

0.81 118

Middle performer (129)

0.095 (0.144)

0.534*** (0.043)

0.79 232 –0.005 (0.151)

0.517*** (0.038)

0.71 118

Bottom performer (129)

–0.262 (0.336)

0.691*** (0.081)

0.63 232 –0.440* (0.234)

0.737*** (0.055)

0.68 118

Diff. 0.356**

(0.148) 0.054*** (0.016)

0.03 232 –0.076 (0.097)

–0.013 (0.026)

–0.00 118

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Table 5.1 reveals the results for conventional fund performance in the categories of

top performer, middle performer and bottom performer. The outcome shows that fund

portfolios, namely top and middle performers, obtain abnormal returns relative to the

market benchmark; however, the latter is insignificant. For the bottom performer, as

expected, the returns performance drops to 0.26 per cent below the market returns;

however, the result is not significant. The outperformance of the top performer is

significantly higher than the overall performance of CMFs, but surprisingly its return

is slightly lower than the IMFs, with a decline of 2.09 per cent annually. Surprisingly,

using the Islamic benchmark, the IMFs portfolio experiences in its return performance

a drop of about 0.19 per cent per annum from 1999 to 2009. The short duration of the

Islamic benchmark could probably influence this result.

The difference portfolio (Diff.) is also constructed by subtracting the returns for

CMFs from the returns for IMFs, as shown in Table 5.1. The equation is explained in

Section 3.4.3.3. The aim is to identify if there is any difference between the

performance of IMFs and CMFs concerning risk and return characteristics. There is a

strong statistical significance regarding Diff. portfolio for alpha and beta, indicating

that there is a difference in the investment return style and systematic risk of the IMFs

and CMFs (see Table 5.1). These results imply importantly that there is a strong

statistically significant difference between systematic risk (beta) and return

characteristics (alpha) of IMFs and CMFs when the KLCI (a proxy for conventional

market benchmark) is employed. However, this significance disappears when the

explanatory variable, the Islamic benchmark, is employed.

Unlike previous studies which had indicated that there is no difference in IMFs and

CMFs risk and return characteristics in relation to the market benchmark (see for

example, Elfakhani et al.2005; Elfakhani and Hassan 2005; Hassan et al. 2010), the

findings of this study reveal a big difference. Other studies have claimed that there is

no difference between Islamic and conventional market indices (Albaity and Ahmad,

2008; Girard and Hassan 2005; 2008; Hakim and Rashidian, 2004). Therefore, the

results for the strong performance of alpha and beta in Table 5.1 would imply that it is

continually open to further investigation. In order to identify the robustness, further

Page | 147

investigation is undertaken to verify the finding using different approaches via panel

data analysis. This is explored in more detail in Chapter 6.

5.4.2 CAPM multiple benchmarks performance analysis

This section extends the single regression analysis to multi-regression CAPM to

evaluate fund performance after controlling factors related to the asset classes of the

funds. This means estimating alpha using the multi-factor benchmarks of the CAPM

model. Table 5.2 presents the results of the regression which include four different

models in each portfolio. The alpha estimate is an intercept term of the regression,

representing a measure of outperformance concerning a fund portfolio if it is a

positive value, and a measure of underperformance when the value is negative.

In this table, the regression analyses are conducted for each model for all portfolios.

Models 1 and 2 follow Bertin and Prather’s (2009) four-factor benchmarks to include

large, small, foreign and bond market benchmarks. Model 1 is modified to include the

conventional foreign benchmark and Model 2 incorporates the Islamic foreign

benchmark. As in the studies by Abdullah and Abdullah (2009), Bertin and Prather

(2009) and Cumby and Glen (1990), this study employs the MSCI World index to

represent the conventional foreign index benchmark. For the Islamic foreign index,

the analysis employs the DJIM Index. The latter began in February 1999, despite been

operational since 31 December 1995. The DJIM is one of the world pioneer indices

representing the Islamic benchmark.

The KLCI is used to represent a large stock index benchmark and the KLSE small-cap

index is used to represent a small stock index benchmark. Similar large and small

stock indices are used for the Islamic and conventional funds and take into account

the uniqueness of the Malaysian stock market in which more than 80 per cent of listed

stocks comply with Shariah law. Therefore, it is expected that using similar indices

for large and small stock indices for the IMFs and the CMFs portfolios will not matter

much. However, the overall sample period is reduced to December 1995 to April

2009, due to the fact that the KLSE small-cap stock benchmark has operated since

1995. For Models 2 and 4, the duration is shorter – from January 1996 to April 2009 –

due to the DJIM operating from 1996. For the bond index, this study uses the

Page | 148

Malaysian fixed deposits as a bond benchmark. Models 3 and 4 are extensions of

Models 1 and 2, and include the money market benchmark in the models. The

KLIBOR is used as a proxy for the money market benchmark.

Looking at the overall performance (results are reported in Table 5.2), it can be seen

that all the portfolios have positive alpha estimates, implying that the excess return

performance of the IMFs, CMFs and AMFs are comparable to the overall market

benchmarks. The alphas of the CMFs seem to be higher than the Islamic counterparts

in all models, mirroring the outperformance of that portfolio in relation to the others.

However, none of the alphas is statistically significant. This finding seems not to be in

line with the evidence of single factor CAPM (as explained in Table 5.1), which

indicates that the magnitude of IMFs’ positive alpha in a single benchmark is

significantly higher than the CMFs counterparts.

The coefficient estimates for the betas of the multi-benchmark models in Table 5.2

show similar results to the single benchmark, stating that the large stock index is

significantly different from zero. This suggests that the benchmark exerts a strong

influence on the funds’ performance. Surprisingly, the coefficient estimates of the

conventional and Islamic foreign benchmarks are significantly different from zero for

the CMFs portfolio. This indicates that the returns’ performance is sensitive to these

benchmarks, as shown in Models 1 and 2 in columns 9 and 10. This finding is quite

similar to that of Ahmed (2007), who stated that the international market index adds a

premium to the overall performance of alphas in the Malaysian mutual funds. The

IMFs returns’ performance does not seem to be sensitive to any benchmark except for

the KLCI, a proxy for the large stock index benchmark. Hence, the multi-benchmark

model appears to be a better market benchmark for the CMFs. Nevertheless, the single

benchmark performs relatively well when evaluating the portfolio for the IMFs.

Page | 149

Table 5.2: CAPM performance analysis based on multiple benchmarks This table presents coefficient estimates using AMFs, IMFs and CMFs monthly average return as the dependent variable. The sample period is from 1995M12 to 2009M04 for Models 1 and 3, and from 1996M01 to 2009M04 for Models 2 and 4. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected by using White’s (1980) and Newey-West’s (1987) correction tests. Standard errors based on White (1980) are given in parentheses. Variance inflation factor (VIF) to detect multicollinearity problems for each variable is also presented. The asterisks ***, **, * denote the significant level of the coefficient estimates that are different from zero at 1%, 5% and 10% levels respectively.

Variable AMFs IMFs CMFs Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 α 0.289

(0.518) 0.327

(0.538) 0.325

(0.506) 0.368

(0.526) 0.089

(0.584) 0.162

(0.606) 0.117

(0.572) 0.196

(0.594) 0.488

(0.487) 0.492

(0.508) 0.532

(0.472) 0.544

(0.493) VIF 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 βpL 0.492***

(0.056) 0.495*** (0.057)

0.501*** (0.051)

0.503*** (0.052)

0.527*** (0.059)

0.525*** (0.061)

0.533*** (0.056)

0.531*** (0.057)

0.457*** (0.053)

0.465*** (0.054)

0.468*** (0.048)

0.475*** (0.048)

VIF 1.464 1.503 1.455 1.488 1.440 1.493 1.466 1.497 1.457 1.467 1.415 1.430

βpS 0.012 (0.036)

0.009 (0.037)

0.014 (0.036)

0.012 (0.037)

0.004 (0.039)

0.001 (0.040)

0.006 (0.039)

0.003 (0.040)

0.019 (0.035)

0.018 (0.035)

0.022 (0.035)

0.021 (0.035)

VIF 2.223 1.955 2.000 1.821 2.067 1.875 1.957 1.803 2.307 1.969 2.036 1.831

BpFMSCI 0.074 (0.053)

-

0.063 (0.057)

-

0.050 (0.055)

-

0.042 (0.061)

-

0.097* (0.054)

-

0.084 (0.056)

-

VIF 1.831 - 2.539 - 1.747 - 2.499 - 1.785 - 2.315 -

BpFDJIM -

0.066 (0.040)

-

0.059 (0.042)

-

0.061 (0.045)

-

0.056 (0.048)

-

0.070* (0.038)

-

0.062 (0.039)

VIF - 1.666 - 1.932 - 1.631 - 1.899 - 1.571 - 1.783

BpB –0.006 (0.178)

–0.017 (0.185)

–0.092 (0.155)

–0.107 (0.163)

0.047 (0.200)

0.025 (0.207)

–0.019 (0.174)

–0.041 (0.181)

–0.058 (0.167)

–0.060 (0.174)

–0.164 (0.149)

–0.172 (0.158)

VIF 1.342 1.461 1.154 1.299 1.223 1.335 1.088 1.198 1.400 1.475 1.210 1.358

BpM - -

6.663 (6.345)

6.835 (6.097)

- -

5.153 (6.983)

5.099 (6.772)

- - 8.173 (5.861)

8.571 (5.588)

VIF - - 1.959 1.554 - - 1.786 1.418 - - 2.097 1.681

Adj R2 0.77 0.77 0.77 0.77 0.73 0.73 0.73 0.73 0.77 0.77 0.78 0.78 Obs 161 160 161 160 161 160 161 160 161 160 161 160

Page | 150

5.5 Results for and discussion of the market timing

5.5.1 Market timing expertise and fund selectivity skill

This section examines the performance on return (alpha) and risk (beta) as previously

discussed, allowing for the time varying systematic risk. This is done by using the

quadratic regression model known as the TM model, developed by Treynor and

Mazuy (1966). The regression procedure is similar to the one applied in the single

CAPM model with the addition of a squared-market return variable (details explained

in Chapter 3). The heteroskedasticity and serial correlation problems in the time series

regression (when existing) are corrected using White’s (1980) and Newey and West’s

(1987) procedures.

A comparison is made by using KLCI for the period 1990 to 2009 and KLSI for the

shorter period 1999 to 2009. For this reason, the study employs KLCI as a proxy for

the market benchmark in all portfolios. This is due to the fact that the Islamic

benchmark, the KLSI, is too recent (launched in July 1999). Evidence indicates that

about 88 per cent of the stocks in Malaysia are Shariah-compliant, representing two-

thirds of Malaysia’s market capitalisation (Bursa Malaysia 2010). A previous study

also showed that there is no difference in how KLCI and the KLSI perform (Albaity

and Ahmad, 2008). Therefore, we can consider the KLCI as a representative proxy for

the market benchmark for the Islamic fund as well.

Table 5.3 reports the results of the regression on the average alpha and the coefficient

estimates of the TM model for all the fund portfolios. The table indicates a positive

alpha estimate with all the other portfolios, with the exception of the middle and

bottom performers, implying that the fund managers of these portfolios have actively

managed the funds and done well in selecting funds in relation to the market

benchmark. Most of the alphas are statistically significant except for the CMFs and

the middle performer, indicating that both IMFs and CMFs fund managers in

Malaysia had superior fund selectivity skills over the period 1990–2009.

Page | 151

The finding of positive selectivity skill is in contrast to most findings in the Malaysian

market, such as the results elicited by Ahmed (2007), Abdullah et al. (2007),

Elfakhani et al. (2005) and Hayat and Kraeussl (2011), except Annuar et al. (1997).

Such contradictory findings are possibly related to the crisis period which caused

outliers in the data which were not treated in the sample. This may be due to the

shorter duration of the study or to the limited sample of Islamic mutual funds.23

Table 5.3 also shows that the coefficient estimates of theta θ for all the portfolios are

positive but not significantly different, with the exception of the bottom performer,

implying that the fund managers have tried to time the market but their activities end

up with perverse or no market timing. The higher Adj. R2 in this return performance

could suggest that the model used is appropriate. The evidence of no market timing is

consistent with the previous findings of Annuar et al. (1997) and Ahmed (2007), who

revealed that the Malaysian mutual funds performed relatively poorly in terms of

market timing during 1998–2004 and 1990–1995.

On the other hand, our finding is in contrast to the finding of Hayat (2006), who

contended that the Malaysian Islamic fund does have a positive market timing on an

individual basis. The results of no market timing here also contradict those of

Lehmann and Modest (1987), Bello and Janjigian (1997), Ippolito (1989) and Lee and

Rahman (1990), but are consistent with those of Chen et al. (1992), Kon (1983),

Henriksson (1984), Chang and Lewellen (1984) and Elton et al. (1993) in the

developed market. At the same time, our finding is also consistent with Abdel-Kader

and Qing (2007) and Suppa-aim (2010), but is a little different from that of Imisiker

and Ozlale (2008) for the emerging market.

23

Elfakhani et al. (2005) also reported that Malaysian Islamic funds had negative market timing and fund selectivity skills over the period between 1997 and 2002, and Hayat and Kraeussl (2011) confirmed the same results for the period 2000–2009. The evidence is consistent with the findings of Lehmann and Modest (1987), Bello and Janjigian (1997), Ippolito (1989) and Lee and Rahman (1990), but it contrasts with those of Chen et al. (1992), Kon (1983), Henriksson (1984), Chang and Lewellen (1984) and Elton et al. (1993) in the international market.

Page | 152

Table 5.3: Market timing expertise of IMFs and CMFs fund managers The table presents the results of market timing expertise and stock selectivity skill of IMFs and CMFs Fund Managers using the TM model. The KLCI is used as a proxy for the market return. The returns are based on mean aggregate and mean equally weighted, and reported in percentages. The returns are net from all expenses and adjusted for the risk-free rate using Malaysian t-bills as a proxy. The period of study is from January 1990 to April 2009. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors based on White (1980) are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. N represents the total number of funds in each portfolio and Obs is the number of observations.

Table 5.3 also compares the IMFs portfolio to the overall performance of the 129 top

performers, 129 middle performers and 129 bottom performers of the CMFs.

Unexpectedly, the study posits that the performance of the IMFs is relatively better

than the top performer of the CMFs. This is probably due to the fact that the CMFs’

top performer was more strongly affected during the crisis relative to the IMFs’

counterparts.

The Diff. portfolio is also examined in Table 5.3 by subtracting the CMFs return from

the IMFs return. The portfolio is added to the analysis in Table 5.3 in order to identify

the style differential between these two portfolios in relation to their market timing

ability and fund selectivity skill. The coefficient estimate of the Diff. portfolio denotes

that there is a statistically significant difference in the mean excess return

performance of the IMFs and CMFs, implying that fund managers of both are

Portfolio α β θ Adj R2 Obs

AMFs (N=479)

0.330** (0.186)

0.550 *** (0.042)

0.003 (0.003)

0.77 232

IMFs (N=129)

0.534** (0.231)

0.576*** (0.044)

0.002 (0.003)

0.69 232

CMFs (N=350)

0.127 (0.169)

0.524*** (0.042)

0.003 (0.003)

0.80 232

Top performer (N=129)

0.338* (0.181)

0.506*** (0.045)

0.003 (0.003)

0.77 232

Middle performer (N=129)

–0.135 (0.159)

0.543*** (0.037)

0.004 (0.003)

0.81 232

Bottom performer (N=129)

–0.712** (0.326)

0.712*** (0.066)

0.007* (0.003)

0.65 174

Diff.

0.407* (0.224)

0.064*** (0.016)

–0.001 (0.001)

0.13 231

Page | 153

different in their fund selectivity skill. However, they are no different in their market

timing expertise. The reason might be that Islamic funds focus on selecting portfolios

consisting of Shariah-compliant investments. Therefore, there is expected to be a

difference in the fund selectivity skill.

The beta coefficient estimates of both IMFs and CMFs in Table 5.3 are also positive

but lower than 1, implying that both funds are less risky than the market portfolio.

This strongly significant finding with regard to the β values of both portfolios

indicates that none has a volatility level greater than the market, thus implying that

mutual funds in Malaysia are relatively more stable and more diversified. In fact, such

funds are not greatly influenced by market conditions. The fact that both portfolios are

less volatile suggests that this is a good indicator to potential investors when choosing

mutual funds, as this portfolio investment could provide diversification and stable

returns. On the other hand, less volatility also implies that the fund regulatory bodies

are concerned about introducing new funds in a riskier category in order to cater for

some demands from aggressive or risk-seeking investors.

To deepen the investigation, the study also compares the market timing expertise of

fund managers, using both the TM and extended TM models. The results are reported

in Table 5.4. Results in the table illustrate that IMFs and CMFs have no market timing

expertise but the results are insignificant. However, both portfolios have positively

insignificant fund selectivity skills. More specifically, the coefficient estimate of

alpha is strongly significant for the IMFs using the TM model and for the CMFs using

the extended TM model. The results provide consistency with the findings previously

discussed in Section 5.4, i.e., that single and multi-benchmarks are better for IMFs

and CMFs respectively.

Page | 154

Table 5.4: Comparative market timing analysis for the TM and extended TM models The dependent variable in each regression is the fund’s mean excess monthly return. The overall sample period is from January 1990 to April 2009. However, the extended model sample period is reduced to January 1996 to April 2009 due to the fact that the KLSE small-cap benchmark was only launched in 1995. The foreign benchmark is MSCI for the all funds and CMFs portfolios, whereas for the IMFs portfolio, the DJIM is employed. All the returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected by using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors are given in parentheses below the coefficient estimates. VIF is also presented in italic form. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively.

Variable TM Model Extended TM Model

AMFs IMFs CMFs AMFs IMFs CMFs Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF Coefficient VIF α 0.330*

(0.186) 0.000 0.534**

(0.231) 0.000 0.127

(0.169) 0.000 0.619

(0.460) 0.000 0.411

(0.542) 0.000 0.829**

(0.416) 0.000

β 0.550*** (0.042)

1.012 0.576*** (0.046)

1.000 0.524*** (0.042)

1.061 0.499*** (0.053)

1.760 0.532*** (0.057)

1.737 0.466*** (0.050)

1.719

θ 0.003 (0.003)

1.012 0.002 (0.003)

1.000 0.003 (0.003)

1.061 0.003 (0.003)

1.427 0.003 (0.003)

1.259 0.003 (0.003)

1.677

BPS - - - - - - –0.002 (0.026)

1.316 –0.006 (0.029)

1.368 0.010 (0.024)

1.296

BPF - - - - - - 0.089 (0.046)

1.997 0.068 (0.053)

2.090 0.110*** (0.044)

1.809

BPB - - - - - - –0.205 (0.151)

1.211 –0.132 (0.176)

1.179 –0.277** (0.140)

1.254

BPm - - - - - - 3.161 (5.413)

1.775 1.671 (5.992)

1.580 4.651 (5.066)

2.023

Mean var.

0.442 0.617 0.268 0.103 0.0085 0.122

Residual 5.042 5.586 4.717 4.963 5.313 4.704 �� 0.77 0.69 0.80 0.78 0.74 0.79 N (Obs) 479 (232) 129 (232) 350 (232) 479 (161) 129 (161 350 (161)

Page | 155

5.5.2 Performance analysis on market timing and asset classes

Table 5.5 shows the performance analysis based on various asset classes of the fund

portfolios using both the TM and extended TM models. The asset classes are divided

into five categories: allocation, alternative, equity, fixed income (fix_income) and

money market (m_market). Panel A reports the results of the full sample for all

mutual funds (AMFs), whereas Panel B and Panel C show the results of IMFs and

CMFs respectively. It can be seen that the equity category has a positive and

significant alpha in the extended TM model, which would suggest that the multi-

benchmark model is the best choice for evaluating the Malaysian equity fund

performance.

In Panel B of Table 5.5, the results indicate that the allocation fund has negative

selectivity and slightly positive timing. The results are consistent with the previous

studies reported by Kon (1983), Henriksson (1984) and Chen et al. (1992), showing

the existence of a trade-off between stock selection and market timing ability for the

allocation of funds involved in both activities. The alternative has a statistically

insignificant negative selectivity and timing ability. The equity fund type has a

positive selectivity and significant positive timing, whereas the money market has

significant positive selectivity but insignificant positive timing ability. Furthermore,

the fixed income category has statistically significant positive selectivity and

insignificant negative market timing ability.

Panel C in Table 5.5 shows the asset classes for the CMFs portfolio. The allocation

and equity funds have insignificant positive fund selectivity skill and market timing

expertise. The alternative fund has insignificant positive selectivity skill but

statistically significant negative market timing. The fixed-income fund has

insignificantly no market timing expertise and positively fund selectivity skill. In

contrast, the money market fund has significantly positively fund selectivity skill but

no market timing. Moreover, the alphas of the money market category for both IMFs

and CMFs portfolios seem to be strongly significant and different from zero,

suggesting that this category outperforms the market, using the standard TM model.

Page | 156

However, the alphas become negative when applied to the extended TM model;

specifically, the alpha of the CMFs money market has a strong statistical significance.

To sum up, the results indicate that some categories in IMFs and CMFs outperform

and underperform the market benchmarks in the models. This finding supports

strongly the theory of the right choice of benchmark – that different categories of

funds require different benchmarks. In this case for example, only the equity,

alternative and allocation categories of the IMFs obtained higher alphas using the

extended TM model, whereas it is more suitable to use this model for all CMFs

categories. Another important contribution of the analysis is that the outperformance

of all the portfolios could be considered more reliable when the extended TM model

is employed rather than the normal TM model.

Page | 157

Table 5.5: Comparative market timing analysis by asset class for TM and extended TM models The table presents the results of investment type category based on TM and extended TM models. The dependent variable in each regression is the fund’s mean excess monthly return in each of the investment type categories. The overall sample period is from January 1990 to April 2009. For the extended TM model, the duration is varied depending on the establishment of the fund category and the market benchmark. For allocation, it is from 1995M12 to 2009M04. For the alternative, the sample is from 2004M09 to 2009M04. For equity and money market, the period is from 1996M01 to 2009M04. Furthermore, for fixed income it is from 1995M12 to 2009M04. Whenever necessary, the heteroskedasticity and serial correlation problems are corrected using White’s correction test (1980) and Newey-West’s correction test (1987). Standard errors are given in parentheses. VIF is presented in italic form. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively.

Asset Class

TM Model Extended TM Model Allocation Alternative Equity Fix_income M_market Allocation Alternative Equity Fix_income M_market

Panel A: AMFs α 0.220

(0.199) 0.053 (0.072)

0.148 (0.202)

0.140 (0.140)

0.945*** (0.302)

0.473 (0.451)

0.721 (0.801)

1.126** (0.566)

0.134 (0.213)

–1.009 (1.020)

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 β 0.509***

(0.053) 0.081*** (0.017)

0.618*** (0.046)

0.151*** (0.038)

0.244*** (0.035)

0.398*** (0.048)

0.053* (0.029)

0.559*** (0.062)

0.032** (0.012)

0.233*** (0.047)

1.166 1.169 1.011 1.302 1.009 1.770 3.108 1.744 3.157 2.060 θ 0.003

(0.004) –0.004 (0.002)

0.004 (0.003)

0.001 (0.002)

0.001 (0.002)

0.002 (0.003)

–0.003 (0.003)

0.005 (0.003)

–0.001 (0.002)

0.001 (0.002)

1.166 1.169 1.011 1.302 1.009 1.734 1.631 1.797 3.972 1.607 Mean var. 0.323 –0.012 0.307 0.175 0.985 0.208 –0.012 0.071 0.170 0.506 Residual 4.856 0.616 5.596 2.627 4.640 4.238 0.616 5.825 0.800 3.864 �� 0.71 0.35 0.79 0.21 0.17 0.72 0.33 0.79 0.11 0.23 N (Obs) 109 (232) 31 (56) 235

(232) 64 (232) 40 (232) 109 (161) 31 (56) 235 (160) 64 (161) 40 (160)

Page | 158

Table 5.5 continued

Asset Class

TM Model Extended TM Model Allocation Alternative Equity Fix_income M_market Allocation Alternative Equity Fix_income M_market

Panel B : IMFs α –0.049

(0.193) –0.035 (0.061)

0.361 (0.242)

0.159* (0.090)

1.605*** (0.616)

0.375 (0.547)

0.574 (0.622)

0.799 (0.656)

0.014 (1.068)

–1.490 (2.159)

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 β 0.542***

(0.039) 0.023 (0.018)

0.608*** (0.046)

–0.002 (0.012)

0.511*** (0.072)

0.530*** (0.041)

0.025 (0.021)

0.561*** (0.061)

–0.007 (0.014)

0.466*** (0.093)

1.157 1.011 1.00 1.067 1.007 1.408 1.694 1.521 1.489 1.703 θ 0.002

(0.002) –0.002 (0.002)

0.003 (0.003)

–0.001 (0.002)

0.002 (0.004)

0.002 (0.003)

–0.001 (0.002)

0.004 (0.003)

–0.001 (0.002)

0.002 (0.005)

1.157 1.011 1.00 1.023 1.007 1.600 1.303 1.315 1.549 1.531 Mean var. –0.103 –0.064 0.458 0.129 1.677 –0.103 –0.066 0.019 0.129 0.812 Residual 5.201 0.458 5.847 0.550 9.479 5.201 0.4622 5.770 0.550 8.032 �� 0.79 0.03 0.70 0.19 0.19 0.79 0.10 0.74 0.20 0.26 N (Obs) 32 (155) 8 (56) 58 (232) 17 (103) 14 (231) 32 (155) 8 (55) 58 (159) 17 (102) 14 (231) Panel C : CMFs α 0.246

(0.205) 0.067 (0.097)

0.086 (0.203)

0.142 (0.140)

0.222*** (0.037)

0.558 (0.465)

1.002 (0.906)

1.120** (0.519)

0.141 (0.213)

–0.219*** (0.045)

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

β 0.501*** (0.055)

0.103*** (0.020)

0.623*** (0.047)

0.153*** (0.038)

–0.001 (0.003)

0.385*** (0.049)

0.062* (0.034)

0.568*** (0.060)

0.032** (0.012)

0.001 (0.003)

1.151 1.199 1.055 1.298 1.038 1.772 3.295 1.685 3.154 2.274

θ 0.002 (0.004)

–0.005** (0.002)

0.004 (0.003)

0.001 (0.002)

0.000*** (0.000)

0.001 (0.003)

–0.004 (0.003)

0.005 (0.004)

–0.000 (0.001)

0.000 (0.000)

1.151 1.199 1.055 1.298 1.038 1.665 1.667 1.626 3.997 2.106

Mean var. 0.347 –0.028 0.264 0.177 0.251 0.242 –0.028 0.104 0.172 0.168 Residual 4.837 0.783 5.636 2.629 0.638 4.208 0.783 5.889 0.811 0.262 �� 0.69 0.35 0.79 0.21 0.04 0.70 0.35 0.79 0.13 0.57 N (Obs) 77 (232) 23 (53) 177 (232) 47 (232) 26 (231) 109 (161) 31 (53) 177(161) 47(161) 40 (161)

Page | 159

5.5.3 Correlation between market timing and fund selectivity skill

This section reports on the correlation test conducted for each fund type and for the

overall performance of both portfolios, i.e. the Islamic and conventional mutual funds.

The aim is to identify the existence (or otherwise) of a correlation between selectivity

and market timing ability performance based on the TM model. Results show

evidence of a substantial negative mild correlation between timing and selectivity of

the Islamic and conventional mutual funds for January 1990 to April 2009. The details

of the results are presented in Table 5.6 below.

Table 5.6: Correlation between fund selectivity and market timing The table presents results for the correlation coefficient among IMFs, CMFs and AMFs fund managers between market timing and fund selectivity skill. The asterisk *** denotes that the coefficient estimates are significant at 1% level.

Asset Class Correlation Coefficient

AMFs IMFs CMFs

Allocation

–0.126***

–0.154***

–0.126***

Alternative –0.103 –0.103 –0.110 Equity –0.126*** –0.126*** –0.126*** Fixed–income –0.126*** –0.091 –0.126*** Money market –0.126*** –0.126*** –0.126*** Overall –0.126*** –0.126*** –0.126***

Table 5.6 indicates the presence of a negative correlation between selectivity and

market timing performance among Islamic and conventional fund managers in

Malaysia over the period of the study from January 1990 to April 2009. Overall, there

is a negative mild correlation between selection and timing performance of all types

of portfolios: AMFs, IMFs and CMFs. The evidence for a negative correlation

regarding the IMFs is consistent with previous findings by Kon (1983) and

Henriksson (1984), but different from those of Lee and Rahman (1990) and Annuar et

al. (1997), both of which found that all Malaysian mutual funds between 1990 and

1995 appeared to show a positive correlation between selectivity and timing

performance at 0.53.

Page | 160

5.5.4 Funds diversification

This section further examines the degree of diversification of the mutual funds when

IMFs and CMFs are compared. Diversification is measured by estimating the R2 of all

the portfolios. Most previous studies measured the degree of diversification using the

coefficient of determination, R2, which is calculated on the basis of the degree of

diversification of a fund in relation to the market portfolio’s diversification. It

normally ranges between 0 and 1. Since the TM model is based on a multivariate

analysis in this study, the usage of Adj. R2 is preferred to the R2. 24 Table 5.7 presents

the summary of the Adj. R2 while using the TM model and extended TM model.

The results in Table 5.7 illustrate that the CMFs generally have a better degree of

diversification than the IMFs. This evidence is consistent with the results of Abdullah

et al. (2007), particularly in relation to the fixed income asset classes of funds. The

level of diversification of IMFs is smaller when compared to the full sample (AMFs)

using both models.

In terms of details, Table 5.7 reports that the diversification level of funds is relatively

higher with more than 50 per cent, suggesting that the larger excess return of the

mutual funds in all portfolios is explained by the models. By extending the TM model

to the extended TM model, the explanatory power of the model for the AMFs and

IMFs is slightly improved. Nevertheless it does not have much effect on the CMFs.

In contrast to Abdullah et al. (2007), who indicated the presence of a low

diversification level among funds between 1992 and 2001, the results of this study

show that Islamic and conventional mutual funds in Malaysia have a reasonably high

level of diversification. This means that the ability of managers of both funds to

diversify their investment in funds is relatively higher. Our results are also contrary to

those of Annuar et al. (1997). They evaluated fund performance for the period 1990–

1995 and noted that the degree of diversification was relatively poor and below their

expectations. They explained this as being due to the regulatory constraints imposed

by the SC, the lack of advertising and lack of fund managers’ expertise. 24

This is because the econometric procedure for computing R2 never decreases when extra variables are added to the regression. It could, however, increase, even though the variables do not add explanatory power to the model. Therefore to mitigate this caveat, the Adj. R2 is applied.

Page | 161

Table 5.7: Diversification level of mutual funds The table presents results of the correlation coefficient for IMFs, CMFs, and AMFs fund managers between market timing and fund selectivity skill.

Asset Class Adj R2

AMFs IMFs CMFs Panel A: TM Model Allocation

0.71

0.79

0.69

Alternative 0.35 0.03 0.35 Equity 0.79 0.70 0.79 Fixed-income 0.21 0.19 0.21 Money market Overall

0.17 0.77

0.19 0.69

0.04 0.80

Panel A: Extended TM Model Allocation

0.72

0.79

0.70

Alternative 0.33 0.10 0.35 Equity 0.79 0.74 0.79 Fixed-income 0.11 0.20 0.13 Money market Overall

0.23 0.78

0.26 0.74

0.57 0.79

On the other hand, the findings are consistent with most mutual fund studies on the

Malaysian market (Ahmed, 2007; Elfakhani et al., 2005; Hayat and Kraeussl, 2011).

Hayat and Krauessl (2011) indicated that Islamic funds are well-diversified as the

percentage of R2 of the portfolio is relatively high, about 75 per cent over the period

2000–2009. Elfakhani et al. (2005) also found that, on average, the Islamic mutual

fund portfolio was diversified by about 68 per cent over the period from January 1997

to August 2002. Ahmed (2007) indicated further that the percentage range of

diversification of the Malaysian mutual funds rose from 8 per cent to 95 per cent over

the period 1998–2004.

On an asset class basis, the results suggest that allocation and equity funds are well

diversified, as confirmed by an R2 with values between 69 to 79 per cent. On the other

hand, fixed income and money market funds are relatively less diversified using both

of the models. The results also suggest that the IMFs portfolio is better diversified in

allocation funds. In the meantime, the CMFs portfolio performs better in equity funds.

Page | 162

5.6 Summary

This chapter has addressed the question of whether IMFs and CMFs outperform the

market benchmark or not when using the single factor CAPM model, when compared

to the Islamic and conventional benchmark. This chapter also investigated whether

IMFs and CMFs perform better in relation to multiple benchmarks. Both IMFs and

CMFs portfolios significantly outperform the conventional market benchmark, with

results indicating that the IMFs portfolio performs better than the CMFs. Furthermore,

results for multi-benchmarks indicate that the CMFs perform better than the IMFs,

although none of the alphas is statistically significantly different from zero. The

inconsistency in these results needs further examination of fund performance.

Therefore, the next chapter employs panel data regression analysis to explore fund

performance with the aim of producing more robust results.

The most important evidence arising from this chapter is the impact of different single

and multi-benchmarks and how they affect the performance of the alphas for the fund

portfolios. In other words, what exactly is the right choice of market benchmark for

the IMFs and CMFs? The findings provide evidence that IMFs perform relatively

better when using a single benchmark, and CMFs perform better when using multi-

factor benchmarks analysis. The implication is that fund managers can apply relevant

benchmarks to particular mutual funds. At the same time, the choice of benchmark is

a focal factor in determining fund performance.

Further investigation was done in order to identify whether IMFs and CMFs do have

market timing expertise and fund selectivity skill using the standard TM and extended

TM models. As expected, while using the TM version the results show that both IMFs

and CMFs outperform the market return benchmark. Consistently, the IMFs portfolio

performs better than the CMFs on fund selectivity skill using mean aggregate return.

However, in terms of market timing expertise, both portfolios have perverse or no

market timing since the estimated coefficient of each portfolio is relatively small

despite being positive. These results remain after the extended TM model has been

used. The result showing inferior market timing expertise suggests that fund

managers’ talents in timing the market have no impact on fund returns or adversely

Page | 163

affect the returns performance of the mutual funds. The extended TM model also

reveals that the outperformance of IMFs and CMFs portfolios in terms of their asset

classes are improving compared to the standard TM model. A more detailed

exploration of these issues is addressed in Chapter 6.

On average, IMFs and CMFs in Malaysia are considered well-diversified portfolios,

hence individual investors could benefit from including a mutual fund in their

investment vehicles. The time series analysis also indicates that, on average, there is a

negative correlation between fund selectivity skill and market timing expertise in the

Malaysian market. Therefore, investors could consider this trade-off while making a

decision whether to invest in any type of mutual funds. Finally, the significant

contribution reported in this chapter is that the study extends the multi-factor CAPM

and extended TM models to include a money market component. The proposed model

is more reliable due to the diversity of the asset classes or categories of mutual funds

employed in this study.

Page | 164

CHAPTER 6 - MARKET TIMING

EXPERTISE AND FUND SELECTIVITY

SKILL: PANEL DATA ANALYSIS

6.1 Introduction

Following the time series analysis reported in Chapter 5, this chapter reports on the

performance of funds using a relatively sophisticated and recent econometric method

of panel data analysis instead of time series. This is done in order to compare the

results reported in this chapter with those in Chapter 5 by replicating models similar

to those applied in Chapter 5 using panel data regressions.

One of the benefits of using panel data regression is that it reduces survivorship bias

and is able to accommodate funds with different inception dates. In the context of this

thesis, this technique has the advantage of controlling for fund-specific and time

related variations in a variety of ways. The findings of such analysis are expected to

be relatively more rigorous as the technique works with actual returns without

resorting to calculating the simple mean of the portfolio returns. This increases the

sample size, which comes at an efficiency gain.

The rest of the chapter is structured as follows. Section 6.2 briefly explains the data

sample and is followed by the presentation of results and discussion in Section 6.3.

Section 6.4 provides a summary of the chapter.

6.2 Data sample

The analysis in this chapter uses the same dataset as that reported in Chapters 4 and 5:

129 IMFs and 350 CMFs, giving a total of 479 AMFs from various categories, these

being allocation, alternative, equity, fixed income and money market asset classes,

over the same period from January 1990 to April 2009. These funds have different

inceptions dates. The panel structure in this case is therefore unbalanced with a

maximum of 31,614 observations in total.

Page | 165

6.3 Results and discussions on panel data

Table 6.1 to Table 6.4 present the results based on special cases of the following

regression equation, refer to Eq. 3.28 (see Chapter 3, p. 99). Each table contains two

sub-tables: (a) and (b). Tables suffixed with (a) report REs GLS panel regressions and

those suffixed with (b) report FEs panel regressions: (1) the Hausman test to compare

the fixed effects model with random effects and (2) combinations of time-fixed effects

where appropriate. The significance of time-fixed effects is formally tested to see

whether time dummies are jointly significant or not. Problems related to serial

correlation and cross-sectional heteroskedasticity are also accounted for. Regressions

without time-fixed effects are labelled Model (1) and those with time FEs are labelled

Model (2). While comparing these results across IMFs and CMFs, the alphas in FE

regressions cannot be directly compared because every cross-section has its own alpha

(α) which is not reported. This comparison is, however, possible in REs estimation.

6.3.1 Single factor CAPM performance

The results of the single CAPM are reported in Table 6.1(a) using random effects

(REs) and generalised least squares (GLS), and in Table 6.1(b) using fixed effects

(FEs) panel data regression. These results show the performance for each of the

portfolios (i.e., IMFs, CMFs and AMFs) against Islamic and conventional

benchmarks.

Table 6.1 depicts the results of the single factor CAPM using REs (as shown in Table

6.1[a]) and using FEs (as shown in Table 6.1[b]) without and with time-fixed effects.

The results in both tables show the total variability of adj. R2 ranging from 42 to 59

per cent of the returns, implying that the amount of percentage is explained by the

models. The Hausman tests in Panel B and in Model (2) of Panel A denote that the

tests are not significant, implying that the REs model is appropriated as opposed to

the FEs model. The Hausman tests also indicate that the AMFs and CMFs are

appropriated with the FEs as shown in Model (1) before the time FEs are added.

Table 6.1(a) shows that all the alphas are positively significant using the REs model

with time FEs (as shown in Model 2 in Panel A), suggesting that all the portfolios

Page | 166

outperform the KLCI, a proxy for the conventional market benchmark, with the IMFs

performing better relative to the CMFs and the overall funds, AMFs. Model 2,

however, shows that the CMFs outperform the IMFs counterparts in relation to the

Islamic benchmark.

On average, the IMFs portfolio significantly outperforms the conventional market

benchmark by 17.51 per cent per annum over the period 1990–2009. The evidence of

the outperformance of IMFs compared to the market benchmark is generally

consistent with the findings of Hayat and Kraeussl (2011). They found that, on

average, 51 Islamic equity funds in Malaysia outperformed the KLSI by 0.73 per cent

per annum over the period 2000–2009. Surprisingly, the results also show that the

IMFs insignificantly outperform the market (when using the Islamic benchmark) by

1.09 per cent per annum from 1999 to 2009. The KLSI serving as a proxy for the

Islamic benchmark in Malaysia was launched in August 1999.

Page | 167

Table 6. 1(a): Single CAPM analysis using panel data REs GLS regressions The table presents results of returns performance of the portfolios compared to the conventional and Islamic market benchmarks. All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills, a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total number of funds (cross-section) is 479 funds (129 IMFs and 350 CMFs). However, for the Islamic benchmark the period is from August 1999 to April 2009 because it began only in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is an initial for the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively. Regression model (1): REs only, and model (2) REs with time FEs.

Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) Diff. (N=31,614) α 0.100***

(0.025) 1.117* (0.573)

0.209*** (0.080)

1.459*** (0.045)

0.052* (0.030)

0.935 (0.638)

0.077*** (0.028)

1.090* (0.571)

β 0.532*** (0.017)

0.458*** (0.059)

0.540*** (0.024)

0.368*** (0.044)

0.529*** (0.022)

0.497*** (0.067)

0.532*** (0.017)

0.458*** (0.059)

dTYPE - - - - - - 0.090 (0.061)

0.121*** (0.055)

Rho 0.009 0.022 0.000 0.000 0.016 0.031 0.009 0.021 Adj. R2 0.456 0.572 0.417 0.545 0.471 0.593 0.456 0.572 Hausman test 0.001 1.000 0.656 1.000 0.001 1.000 0.001 1.000 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000 Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) Diff. (N=31,614) α –0.045*

(0.023) 0.399*** (0.148)

0.011 (0.034)

0.091 (0.241)

–0.077** (0.031)

0.512*** (0.182)

–0.070** (0.030)

0.370** (0.148)

β 0.557*** (0.017)

0.481*** (0.025)

0.523*** (0.030)

0.451*** (0.046)

0.569*** (0.021)

0.492*** (0.029)

0.557*** (0.017)

0.481*** (0.025)

dTYPE -

- - - - - 0.095** (0.045)

0.107** (0.047)

Rho 0.020 0.031 0.000 0.003 0.027 0.036 0.019 0.030 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 0.443 0.550 Hausman test 0.328 0.680 0.242 0.834 0.110 0.261 0.329 0.639 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000

Page | 168

Table 6. 1(b): Single CAPM analysis using panel FEs regressions The table presents results of returns performance of the portfolios compared to the conventional and Islamic market benchmarks. All returns are net of all expenses and reported in percentage. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills, a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total number of funds is 479 funds (129 IMFs and 350 CMFs). However, for the Islamic benchmark the period is from August 1999 to April 2009 because it began only in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is an initial for the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively. Regression model (1): FEs only, and model (2) FEs with time FEs.

Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) Diff. (N=31,614) α 0.158***

(0.003) 0.803 (0.579)

0.209*** (0.080)

1.602*** (0.135)

0.139*** (0.005)

1.241** (0.574)

0.158*** (0.003)

0.803 (0.579)

β 0.531*** (0.017)

0.784*** (0.083)

0.539*** (0.024)

0.791*** (0.078)

0.528*** (0.022)

0.209*** (0.020)

0.531*** (0.017)

0.784*** (0.083)

dTYPE - - - - - - (omitted) (omitted) Rho 0.036 0.044 0.018 0.022 0.044 0.054 0.036 0.044 Adj. R2 0.456 0.572 0.417 0.545 0.471 0.593 0.456 0.572 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000 Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) Diff. (N=31,614) α –0.010***

(0.001) 0.197 (0.228)

0.011*** (0.002)

0.656 (0.441)

–0.017** (0.001)

0.194 (0.328)

–0.010*** (0.001)

0.197 (0.228)

θ 0.557*** (0.017)

0.829*** (0.041)

0.524*** (0.030)

0.952*** (0.052)

0.568*** (0.021)

0.818*** (0.043)

0.557*** (0.017)

0.829*** (0.041)

dTYPE - - - - - - (omitted) (omitted) Rho 0.046 0.056 0.023 0.028 0.052 0.065 0.046 0.056 Adj. R2 0.443 0.550 0.416 0.527 0.453 0.564 0.443 0.550 Test for time FEs - 0.000 - 0.000 - 0.000 - 0.000

Page | 169

Systematic risk, which is estimated by the beta in the regression when V0� = 0,

generally shows that the IMFs portfolio has the highest beta compared to the CMFs

and AMFs. However, both IMFs and CMFs indicate betas less than 1, suggesting that

both funds are less volatile and less risky than the market portfolio. The average beta

of IMFs between 0.36 and 0.54 in Table 6.1(a) is smaller in this study than the beta

estimate in the study by Hayat and Kraeussl (2011), which was 0.75 in relation to the

Malaysian Islamic equity funds.

The systematic risk of the CMFs is greater than that of the IMFs, but more so in the

case of the conventional benchmark as opposed to the Islamic benchmark. The

findings, in contrast to previous studies, indicate that there is no difference in the risk

and return characteristics of IMFs and CMFs (for example, Elfakhani et al. 2005;

Hassan et al. 2010). Traditionally, it has been argued that IMFs are more risky

because Islamic funds are associated with some specific risks that are not present in

the conventional counterparts. Examples of these risks are inconsistency with Shariah

scholars’ judgements, the lack of a track record, and high exposure to companies with

poorly leveraged and low amount of working capital (Hayat and Kraeussl, 2011). The

evidence here, however, suggests the opposite, that IMFs are less risky. This can be

explained with the help of a counter argument, which is that these restrictions induce

managers to resort to more careful decision making which leads to reduction in risk-

taking.

In this regression, the difference portfolio (Diff.) is also constructed by adding

dummy variable dTYPE to the model of AMFs in order to identify the significant

difference between the IMFs and CMFs portfolios. If the fund belongs to the IMFs,

the value is 1 and if it belongs to the CMFs, the value is 0. The positive sign shows

that the IMFs perform better than the CMFs and vice versa for the negative sign.

Consistent with the results for the time series (as previously discussed in Section

5.3.1), the results of the panel data show that there is a significant difference that is

consistent with our conclusions from the results of CMFs and IMFs shown in Table

6.1(a), as discussed above.

Page | 170

The analysis of single CAPM is extended to each of the asset classes and the results

show consistency with the previous results when using time series analysis. The

results are not reported here but are available upon request.

6.3.2 Market timing performance based on TM model

This section examines the performance of the market timing and fund selectivity skill

of IMFs and CMFs fund managers over the 20-year period from 1990 to 2009 relative

to the conventional and Islamic market benchmarks. Table 6.2(a) reports results based

on REs and Table 6.2(b) reports results based on FEs regressions.

The TM model is used, which contends that any significant positive beta provides

evidence that the fund managers of the fund portfolios demonstrate superior fund

selectivity skill and any positive theta exhibits superior market timing expertise. The

results show strongly significant outperformance of IMFs managers’ fund selectivity

skill using either REs or FEs regressions, with the exception of RE estimates with

Islamic foreign benchmark, where the opposite is true. The results are consistent with

the results for the time series data shown in Table 5.3, which shows the significant

superior fund selectivity skill of the fund managers.

Surprisingly, the alpha of IMFs now shows negatively significant in relation to

conventional market benchmark when applied to Model (2) using REs regression.

However, the alpha is positively significant when the FEs regression is applied, which

is irrelevant. Table 6.2(a) indicates that all alphas are negative, with the exception of

IMFs in Model (2), in relation to the Islamic market benchmark.

Moreover, the results show that all fund managers demonstrate inferior market timing

expertise as it is economically insignificant because the values of theta are very small,

with the higher percentage at 0.003 per cent. The result of perverse or no market

timing in all the portfolios is also reliable with regard to the previous finding using

time series data. Interestingly, IMFs managers demonstrate inferior market timing

with regard to the conventional benchmark and CMFs managers demonstrate inferior

market timing with regard to the Islamic foreign benchmark.

Page | 171

Table 6. 2(a): Market timing expertise of IMFs and CMFs fund managers using GLS REs regression. The table presents the market timing expertise and stock selectivity skill of Islamic and conventional fund managers based on the Treynor Mazuy model. The table presents results of mean excess returns performance of the portfolios using panel data pooled regression after correction for heteroskedasticity standard errors and consistent covariance estimator, using the method of White (1980). All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills as a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total numbers of funds (N) is 479 (AMFs), including 129 IMFs and 350 CMFs. However, the period varies depending on the availability of the data in a category. For the Islamic benchmark, the period is from August 1999 to April 2009 due to this benchmark being launched in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.

Model(1) Model(2) Model(1) Model(2) Model(1) Model (2) Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) α 0.003

(0.025) 1.170** (0.559)

0.099 (0.076)

–1.859** (0.734)

–0.041 (0.029)

0.999 (0.638)

β 0.538*** (0.018)

0.401*** (0.043)

0.546*** (0.024)

3.960*** (0.795)

0.536*** (0.022)

0.427*** (0.049)

θ 0.003*** (0.0002)

0.005*** (0.001)

0.003*** (0.0003)

-0.294*** (0.065)

0.003*** (0.0003)

0.006*** (0.002)

Rho 0.009 0.022 0.000 0.000 0.016 0.031 Adj. R2 0.460 0.572 0.421 0.545 0.476 0.593 Hausman test

0.0003 1.000 0.656 –14.98# 0.0003 1.000

Test for time FEs

- 0.000 - 0.000 - 0.000

Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) α –0.012

(0.021) –0.157** (0.068)

–0.003 (0.041)

0.227** (0.090)

–0.027 (0.026)

–0.297*** (0.182)

β 0.556*** (0.017)

0.886*** (0.095)

0.524*** (0.030)

0.351*** (0.116)

0.567*** (0.021)

1.082*** (0.121)

θ –0.001* (0.0007)

-0.030*** (0.008)

0.0005 (0.001)

0.007 (0.010)

–0.002** (0.0008)

–0.043*** (0.009)


0.044 0.023 0.433 1.000 0.028 1.000

Test for time FEs

- 0.000 - 0.000 - 0.000

Page | 172

Table 6. 2(b): Market timing expertise of IMFs and CMFs fund managers using FEs The table presents the market timing expertise and stock selectivity skill of Islamic and conventional fund managers based on the Treynor Mazuy model. The table presents results of mean excess returns performance of the portfolios using panel data pooled regression after correction for heteroskedasticity standard errors and consistent covariance estimator using the method of White (1980). All returns are net of all expenses and reported in percentages. The returns are adjusted for the market risk-free rate using one-month Malaysian t-bills as a proxy for risk-free rate of return. The overall sample period is based on monthly data from January 1990 to April 2009 and the total numbers of funds (N) is 479 (AMFs), including 129 IMFs and 350 CMFs. However, for the Islamic benchmark, the period is from August 1999 to April 2009 due to this benchmark being launched in July 1999. Standard errors based on the cross-section of the estimated coefficients are reported in parentheses. N is the number of observations. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.

Model (1) Model (2) Model (1)

Model (2)

Model (1)

Model (2)

Panel A: Conventional benchmark AMFs(N=31,614) IMFs(N=8,403) CMFs(N=23,211) α 0.047***

(0.007) 1.344** (0.533)

0.101*** (0.013)

2.098*** (0.191)

0.027*** (0.009)

1.221** (0.593)

β 0.537*** (0.018)

0.246*** (0.023)

0.545*** (0.024)

0.298*** (0.045)

0.535*** (0.022)

0.228*** (0.026)

θ 0.003*** (0.0002)

0.0006 (0.0005)

0.003*** (0.0004)

0.0006 (0.0005)

0.003*** (0.0002)

0.0005*** (0.0006)

Rho 0.036 0.044 0.018 0.022 0.043 0.054 Adj. R2 0.443 0.550 0.421 0.545 0.476 0.593 Test for time FEs

- 0.000 - 0.000 - 0.000

Panel B: Islamic benchmark AMFs (N=27,463) IMFs (N=7,437) CMFs(N=20,026) α 0.017

(0.016) 0.324 (0.337)

–0.001 (0.029)

1.241* (0.647)

–0.024 (0.020)

0.339*** (0.328)

β 0.556*** (0.017)

0.705*** (0.032)

0.525*** (0.030)

0.721*** (0.049)

0.567*** (0.021)

0.676*** (0.039)

θ –0.001 (0.0006)

–0.015*** (0.004)

0.0005 (0.0012)

0.005 (0.006)

–0.002** (0.0008)

–0.013*** (0.004)


- 0.000 - 0.000 - 0.000

Page | 173

6.3.3 Multi-factor CAPM performance

Similar to the previous chapter, this chapter extends the CAPM analysis on multiple

benchmarks. Tables 6.3(a) and (b) present the results of the panel regressions.

With reference to overall performance, the results in Table 6.3(a) as shown in Model

(2) highlight the underperformance of all the portfolios with conventional as well as

Islamic foreign benchmarks, Since the time FEs tests are significant, the results in

Model (2) are reliable, thus imply that all portfolios relatively underperform the multi-

factor market benchmarks, with the IMFs relatively better than the CMFs

counterparts. This finding is in contrast with the previous results when the time series

analysis was applied.

The coefficient estimate results for the betas of the multi-benchmark models show no

significant differences between IMFs and CMFs betas in most cases. CMFs, however,

seem to be less risky when using a fixed-effect model with a conventional foreign

benchmark. This conclusion is different from the single factor model but consistent

with the results of Hayat and Kraeussl (2011), as discussed above.

Page | 174

Table 6. 3(a) Multi-factor CAPM analysis using panel data REs GLS (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the full sample (AMFs), IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: without Islamic foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.116***

(0.022) -3.323*** (0.310)

0.101*** (0.039)

-2.726*** (0.584)

0.115*** (0.027)

-3.538*** (0.367)

BpL 0.484*** (0.020)

0.320*** (0.020)

0.518*** (0.029)

0.322*** (0.038)

0.473*** (0.024)

0.320*** (0.024)

BpS 0.001 (0.003)

0.117*** (0.016)

-0.007 (0.005)

0.098*** (0.029)

0.004 (0.003)

0.124*** (0.019)

BpFmsci 0.094*** (0.011)

0.638*** (0.032)

0.034** (0.017)

0.540*** (0.054)

0.115*** (0.014)

0.674*** (0.040)

BPB 1.811** (0.714)

-350.311*** (19.243)

-2.506 (1.575)

-322.278*** (35.835)

3.192*** (0.776)

-360.312*** (22.902)

BPm 2.189** (0.857)

107.548*** (12.380)

7.792*** (2.520)

77.762*** (17.099)

0.485 (0.736)

118.401*** (15.728)


0.001 –146.80# 0.757 1.000 0.017 –119.18#

Test for time FEs

- 0.000 - 0.000 - 0.000

Panel B: with Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.132***

(0.022) –2.487*** (0.261)

0.110*** (0.039)

–2.055*** (0.392)

0.135*** (0.027)

–2.651*** (0.327)

BpL 0.481*** (0.020)

0.284*** (0.017)

0.515*** (0.029)

0.292*** (0.027)

0.470*** (0.024)

0.281*** (0.021)

BpS 0.003 (0.003)

0.112*** (0.016)

–0.006 (0.005)

0.094*** (0.027)

0.006** (0.003)

0.119*** (0.019)

BpFmsci 0.142*** (0.017)

0.543*** (0.030)

0.081** (0.032)

0.463*** (0.065)

0.191*** (0.020)

0.572*** (0.033)

BPFdjim –0.070*** (0.009)

–0.073*** (0.025)

–0.049** (0.022)

–0.058 (0.051)

–0.078*** (0.009)

–0.077*** (0.029)

BPB 1.353* (0.724)

–242.744*** (26.464)

–2.863* (1.615)

–236.032*** (56.265)

2.695*** (0.785)

–246.065*** (29.847)

BPm 2.830*** (0.869)

110.189*** (12.808)

8.247*** (2.602)

79.879*** (18.361)

1.193 (0.740)

121.206*** (16.174)

Rho 0.008 0.022 0.000 0.000 0.013 0.029

Adj. R2 0.470 0.579 0.442 0.566 0.482 0.591

Hausman test

0.011 –43.69# 0.855 1.000 0.043 –37.88#

Test for time FEs

- 0.000 - 0.000 - 0.000

Note: Regression model (1): REs only, model (2) REs with time FEs, and model (2) simple pooled OLS with time FEs. # results report chi2<0, model fitted on these data fails to meet the assumptions of the Hausman test. The problem is fixed by putting the additional command ‘hausman fixed random, sigmamore or sigmaless’. The asterisks ***, **, * indicate significant level at 1%, 5% and 10%, respectively.

Page | 175

Table 6. 3(b): Multi-factor CAPM analysis using panel FEs (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the AMFs, IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All portfolio returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses.

Model (1)

Model (2) Model (1) Model (2)

Model (1)

Model (2)

Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.138***

(0.014) –2.051*** (0.358)

0.104*** (0.032)

–2.813*** (0.949)

0.147*** (0.015)

–1.899*** (0.375)

BpL 0.483*** (0.020)

0.328*** (0.025)

0.518*** (0.029)

0.433*** (0.056)

0.472*** (0.024)

0.297*** (0.027)

BpS 0.002 (0.003)

0.158*** (0.015)

–0.006 (0.005)

0.163*** (0.023)

0.004 (0.003)

0.157*** (0.018)

BpFmsci 0.094*** (0.011)

–0.198*** (0.055)

0.034** (0.017)

–0.430** (0.166)

0.115*** (0.014)

–0.138*** (0.051)

BPFdjim - - - - - - BPB 0.823

(0.793) (omitted) –3.148**

(1.573) (omitted) 2.204**

(0.873) (omitted)

BPm 2.803*** (0.883)

(omitted) 8.261*** (2.625)

(omitted) 1.077 (0.755)

(omitted)


- 0.000 - 0.000 - 0.000

Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.152***

(0.014) 0.506** (0.228)

0.112*** (0.032)

0.993* (0.508)

0.164*** (0.015)

–0.614*** (0.180)

BpL 0.480*** (0.020)

0.230*** (0.024)

0.515*** (0.029)

0.287*** (0.053)

0.469*** (0.024)

0.249*** (0.031)

BpS 0.003 (0.002)

0.167*** (0.014)

–0.006 (0.005)

0.182*** (0.022)

0.006** (0.003)

0.139*** (0.014)

BpFmsci 0.161*** (0.017)

0.466*** (0.050)

0.082** (0.032)

0.437*** (0.133)

0.190*** (0.020)

0.564*** (0.060)

BPFdjim –0.069*** (0.009)

–0.317*** (0.040)

–0.049** (0.022)

–0.278*** (0.090)

–0.077*** (0.009)

–0.750*** (0.096)

BPB 0.427 (0.803)

(omitted) –3.452** (1.628)

(omitted) 1.776** (0.881)

(omitted)

BPm 3.399*** (0.897)

(omitted) 8.685*** (2.714)

(omitted) 1.737** (0.763)

(omitted)


- 0.000 - 0.000 - 0.000

Note: Regression model (1): FEs only, model (2) FEs with time FEs, and model (2) simple pooled OLS with time FEs. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. The omitted variables are due to multicollinearity problems between the variables and time (period) FEs.

Page | 176

6.3.4 Market timing performance based on extended TM model

Tables 6.4(a) and (b) report the comparative results of the TM model and extended TM

model regarding the IMFs and CMFs fund managers’ fund selectivity skill and market timing

expertise relative to the conventional and Islamic foreign benchmarks.

Comparing the results in Table 6.4(a) across IMFs and CMFs, CMFs outperform in terms of

α, without time fixed effects. However, when we add time FEs to the model, IMFs

outperform CMFS. Both α’s are, however, negative which implies underperformance relative

to the market. Since time FEs are significant, we conclude that IMFs outperform CMFs in

terms of α but both underperform the market benchmark when we include conventional

foreign benchmark (MSCI) in the regressors. Both outperform the market when we replace

MSCI with an Islamic foreign benchmark (DJIM), and CMFs now do better than IMFs. The

two seemingly conflicting results are, however, not directly comparable with each other as

they are two different models in the sense that one includes MSCI and the other includes

DJIM. The reason both are not in one regression at the same time is to compare the results

with the single factor TM model.

With regard to market timing, no economically significant (although statistically significant)

evidence is found in favour of superior market timing among fund managers when IMFs are

compared with CMFs. Both, however, exhibit inferior market timing abilities when we

replace MSCI with DJIM. Islamic managers are found to have better fund selectivity skills

when we use DJIM instead of MSCI as the foreign benchmark and there is no other

difference relative to each other.

Consistent with the results of the time series (see Table 5.4), the panel data results show that

IMFs and CMFs fund managers are positively superior in fund selectivity skill in relation to

the large stock market index, in this case, the KLCI index.

Page | 177

Table 6.4(a) Market timing analysis using panel data REs GLS (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each of the portfolios. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively. Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.055**

(0.022) –3.086*** (0.267)

0.041 (0.040)

–2.536*** (0.467)

0.054** (0.027)

–3.287*** (0.323)

θ 0.003*** (0.000)

0.002*** (0.001)

0.003*** (0.000)

0.002 (0.002)

0.003*** (0.000)

0.003*** (0.001)

BpL 0.486*** (0.020)

0.303*** (0.018)

0.520*** (0.029)

0.308*** (0.030)

0.476*** (0.025)

0.302*** (0.022)

BpS –0.007** (0.003)

0.112*** (0.016)

–0.015*** (0.004)

0.094*** (0.027)

–0.004 (0.003)

0.119*** (0.019)

BpFmsci 0.120*** (0.011)

0.587*** (0.026)

0.051*** (0.017)

0.499*** (0.050)

0.130*** (0.014)

0.620*** (0.031)

BPFdjim - - - - - - BPB –0.764

(0.741) –320.647*** (13.679)

–5.131*** (1.617)

–298.494*** (25.716)

0.621 (0.814)

–328.806*** (16.208)

BPm 1.209 (0.853)

101.452*** (11.607)

6.582** (2.569)

72.874*** (14.676)

–0.405 (0.714)

111.926*** (14.928)

Rho 0.008 0.022 0.000 0.000 0.013 0.029

Adj. R2 0.472 0.579 0.449 0.577 0.482 0.591

Hausman test 0.001 1.000# 0.799 1.000# 0.005 1.000# Test for time FEs

- 0.000 - 0.000 - 0.000

Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α –0.002

(0.022) 4.821*** (0.433)

0.011 (0.037)

4.180*** (0.850)

–0.015 (0.027)

5.057*** (0.505)

θ 0.003*** (0.000)

–0.030*** (0.002)

0.003*** (0.000)

–0.026*** (0.004)

0.002*** (0.000)

–0.032*** (0.002)

BpL 0.511*** (0.019)

0.044** (0.018)

0.534*** (0.028)

0.088*** (0.031)

0.504*** (0.024)

0.028 (0.021)

BpS 0.000 (0.003)

0.114*** (0.016)

–0.011** (0.005)

0.096*** (0.030)

0.004 (0.003)

0.121*** (0.020)

BpFmsci - - - - - - BPFdjim 0.055***

(0.007) –0.957*** (0.043)

0.020* (0.011)

–0.813*** (0.081)

0.068*** (0.008)

–1.020*** (0.050)

BPB 1.707** (0.823)

707.365*** (36.039)

–3.812** (1.723)

574.701*** (68.760)

3.470*** (0.911)

755.989*** (42.247)

BPm 0.598 (0.877)

216.747*** (12.532)

6.299** (2.582)

170.806*** (16.473)

–1.115 (0.757)

233.590*** (15.990)

Rho 0.008 0.022 0.000 0.000 0.013 0.029

Adj. R2 0.467 0.581 0.448 0.577 0.476 0.593

Hausman test 0.000 1.000# 0.787 1.000# 0.000 1.000# Test for time FEs

- 0.000 - 0.000 - 0.000

Note: Regression model (1): REs only and model (2) REs with time FEs. Number of results report chi2<0. The model fitted on these data fails to meet the assumptions of the Hausman test. The problem is fixed by putting the additional command ‘hausman fixed random, sigmamore or sigmaless’.

Page | 178

Table 6. 4(b): Market timing analysis using panel FEs (within) regression The dependent variable in each regression is the fund’s mean excess monthly return of each portfolio, i.e., the AMFs, IMFs and CMFs. This extended model sample period is from December 1995 to April 2009 when using MSCI for the conventional foreign benchmark, and from January 1996 to April 2009 when the Islamic foreign benchmark, the DJIM, is employed. All portfolio returns (in percentages) are net of all expenses and adjusted for the risk-free rate of return using Malaysian one month t-bills. Standard errors are given in parentheses. The asterisks ***, **, * indicate significant level at 1%, 5% and 10% respectively Model (1) Model (2) Model (1) Model (2) Model (1) Model (2) Panel A: Conventional foreign benchmark AMFs(N=29,998) IMFs(N=8,024) CMFs(N=21,974) α 0.074***

(0.013) –5.314*** (0.475)

0.040 (0.028)

–5.455*** (0.755)

0.084*** (0.028)

–0.215*** (0.048)

θ 0.003*** (0.000)

0.003*** (0.000)

0.003*** (0.000)

0.002*** (0.002)

0.003*** (0.000)

0.003*** (0.001)

BpL 0.485*** (0.020)

0.346*** (0.026)

0.519*** (0.029)

0.448*** (0.054)

0.475*** (0.025)

0.316*** (0.029)

BpS –0.007** (0.003)

0.095*** (0.010)

–0.016*** (0.004)

0.111*** (0.018)

–0.004 (0.003)

0.001*** (0.012)

BpFmsci 0.120*** (0.011)

–0.272*** (0.051)

0.051*** (0.017)

–0.490*** (0.148)

0.130*** (0.014)

–0.215*** (0.048)

BPFdjim - - - - - - BPB –1.787**

(0.793) (omitted) –5.548***

(1.513) (omitted) –0.446

(0.888) (omitted)

BPm 1.840** (0.877)

(omitted) 6.975** (2.687)

(omitted) 0.217 (0.731)

(omitted)

Rho 0.037 0.046 0.019 0.024 0.043 0.054

Adj. R2 0.472 0.577 0.447 0.566 0.483 0.589

Test for time FEs - 0.000 - 0.000 - 0.000 Panel B: Islamic foreign benchmark AMFs (N=29,954) IMFs (N=8,013) (N=21,941) α 0.017

(0.011) –1.171*** (0.348)

0.010 (0.028)

–0.195 (1.248)

0.016 (0.012)

–1.469*** (0.253)

θ 0.003*** (0.000)

0.003*** (0.000)

0.003*** (0.000)

0.004*** (0.001)

0.003*** (0.000)

0.003*** (0.000)

BpL 0.510*** (0.019)

0.246*** (0.024)

0.533*** (0.028)

0.274*** (0.060)

0.503*** (0.024)

0.235*** (0.025)

BpS –0.000 (0.003)

–0.031** (0.013)

–0.012** (0.005)

–0.073 (0.005)

0.004 (0.003)

–0.019** (0.009)

BpFmsci - - - - - - BPFdjim 0.056***

(0.007) 0.049 (0.033)

0.020* (0.011)

0.167 (0.138)

0.068*** (0.008)

0.015 (0.013)

BPB 0.617 (0.839)

(omitted) –4.202*** (1.557)

(omitted) 2.307** (0.943)

(omitted)

BPm 1.244 (0.895)

(omitted) 6.667** (2.697)

(omitted) –0.464 (0.765)

(omitted)

Rho 0.037 0.047 0.019 0.024 0.042 0.055

Adj. R2 0.467 0.579 0.445 0.566 0.477 0.591

Test for time FEs - 0.000 - 0.000 - 0.000 Note: Regression model (1): FEs only, model (2) FEs with time FEs, and model (2) simple pooled OLS with time FEs.. The omitted variables are due to multicollinearity problems between the variables and time (period) FEs.

Page | 179

It is not possible to conclude from the analysis whether or not FEs are more

appropriate than REs, as the Hausman test is inconclusive for our preferred model,

which is the model with time FEs. Most of the Hausman test decides in favour of FEs.

Table 6.4(b) reports results from a fixed-effect model, which shows that both CMFs

and IMFs do not have economically superior or inferior market timing among fund

managers. IMFs fund managers, however, show some evidence of better fund

selectivity skill. This is not a surprising result as Islamic investment is mostly about

sharing risk, which induces the fund managers to take their project selection more

seriously. This seriousness results in better fund selectivity. This result is consistent

with the result of the time series analysis reported in Chapter 5.

6.4 Summary

This chapter has mainly aimed to evaluate and compare the risk-adjusted return

performance of IMFs and CMFs relative to CAPM single and multi-factor

benchmarks using panel data analysis. This chapter has also aimed to compare the

results with the findings based on time series analysis as previously discussed in

Chapter 5. In summary, the results show that panel data analysis provides more

rigorous results but they are still in line with the time series.

The results based on single and multi-factor benchmarks show consistency with the

time series analysis in that, on average, alphas of both IMFs and CMFs outperform

the KLCI market return benchmark. The results demonstrate that all the portfolios are

more sensitive to the KLCI, a proxy for the large stock index, as there is a strong

coefficient estimation of this index in all models.

The findings in this chapter have revealed a significant superior fund selectivity skill

among the IMFs and CMFs fund managers using TM and extended TM models. The

findings also report that the IMFs and CMFs fund managers exhibit similarly inferior

market timing expertise using TM model and the extended TM model.

In conclusion, the empirical results from panel data FEs or REs show consistency

with the previous empirical analysis based on time series, in that: (1) both IMFs and

CMFs outperform the single conventional market benchmark, and (2) the IMFs and

Page | 180

CMFs fund managers are superior in fund selectivity skill but inferior in market

timing ability, with the IMFs fund managers slightly better than the CMFs. In

contrast, the results of IMFs and CMFs in term of negative and positive alphas are

mixed when applied to the Islamic single benchmark.

The limitation of the results reported in this chapter is that the performance

investigation is limited to identifying whether the funds are outperformed or

underperformed. Further issues arise on what determinant factors impact on fund

performance and what the reason is for the differences between the returns

performance of IMFs and CMFs, particularly when different approaches such as time

series and panel data are implemented. To examine this, Chapter 7 addresses the

determinant factors in terms of fund attributes and fees that contribute to the

performance of the fund portfolios and their relationship to mutual fund performance,

specifically focusing on equity mutual funds.

Next, Chapter 7 further investigates the impact of fees on fund performance by

evaluating one of the asset classes in mutual funds as previously mentioned, namely

equity funds. The equity fund category is also associated with load fee, with higher

fees charges on the investment funds due to the expectation of having higher payback

in return. The fees are expected to have an adverse impact on fund returns, which

could imply that higher fees reduce fund returns, thus making a significant difference

between the returns performance of funds before and after excluding fees. Ironically,

investors assume that higher fees give better returns performance and they are willing

to pay more fees for the fund managers who can provide higher investment returns.

Hence, this evaluation could be useful for actively managed investors and market

players. These issues are analysed in detail in Chapter 7.

Page | 181

CHAPTER 7 – FEES IMPACT AND

FUND ATTRIBUTES ON EQUITY

MUTUAL FUNDS PERFORMANCE

7.1 Introduction

In the previous chapters, we found some evidence indicating that IMFs managers

outperformed CMFs and demonstrated relatively superior funds selectivity skills, with

no significant difference in market timing. This is more or less true in the case of

equity fund as well. This chapter narrows down the focus to equity funds and

investigate whether or not the differences in performance could be explained by fees

and other fund attributes. These attributes include age, size, investment styles, alpha,

beta, residual risk, expense ratio, management fee and load fee as defined in Section

3.2.2 of Chapter 3.

The chapter uses yearly return rather than monthly return (as examined in Chapters 4

to 6) as fees are usually charged on yearly basis. The chapter uses an unbalanced

panel with a maximum of 106 equity funds (cross-sections) with time series

observation ranging from a minimum of 2 to a maximum of 20. Consistent with our

analysis in the previous chapters, this chapter aims to (1) investigate the performance

of IEFs and CEFs in relation to their market benchmarks, (2) examine performance of

IEFs and CEFs fund managers in terms of market timing expertise and fund

selectivity skill, and (3) examine the relationship between fund’s return, fees and the

fund’s attributes.

The rest of the chapter is organised as follows. Section 7.2 discusses the relevant

literatures on fees and other fund attributes. Section 7.3 explains the sample of the

data. Section 7.4 provides results and discussion of the findings. This section

describes the statistical descriptions of the fund samples, discusses results of fund

performance based on single factor regressions and also the performance on market

timing expertise and fund selectivity skill. The main part examined in this section is

Page | 182

the impact of fees and other fund attributes on the equity fund performance. Finally,

section 7.5 presents a summary of the chapter.

7.2 Related literatures on fees and the fund attributes

In view of the extensive discussions in existing finance literature regarding the

performance of equity mutual funds, the findings reported in this chapter are relatively

important. Generally speaking, the literature discusses relationship between the

performance of fund portfolios, the relevant market benchmarks and the different

characteristics of these funds and reports mixed results. For instance, most studies in

the US reveal that equity mutual funds are not able to outperform the corresponding

market benchmarks (Benos and Jochec, 2011; Carhart, 1997; Elton et al. 1993;

Grinblatt and Titman, 1994; Jensen, 1968; Malkiel, 1995). Similarly, in the UK, Firth

(1977), and Blake and Timmermann (1998) find that equity funds underperformed the

market return over a period of 1965–1975 and 1972–1995 respectively. Blake and

Timmermann (1998) also report that on average, UK equity fund risk adjusted return

underperformed by approximately 1.8 per cent per annum. An exception is the study

by Ippolito (1989), which finds that excess returns performance net of all expenses for

equity funds exceeds that of index funds in the market. He further reports that the US

mutual fund return performance is relatively adequate to compensate for the higher

fees associated with the returns over the period 1965–1984.

Similarly, previous studies on mutual fund performance in Asia-Pacific countries

including Australia and Malaysia, mostly find that mutual funds have generally

underperformed the market benchmark. A study on Australian managed funds

between 1983 and 1995 using conditional measures of CAPM provides no abnormal

returns (Sawicki and Ong, 2000). Previously, Robson (1986), and Hallahan and Faff

(1999) also report inferior performance on the overall Australian fund returns against

respective market indices over a period of 1969–1978 and 1988–1997 respectively.

There is also evidence that on average active Australian superannuation funds are

unable to earn superior risk adjusted returns in relation to the relevant market

benchmarks (Gallagher, 2002).

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Similarly, most studies on Malaysian equity mutual funds, with the exception of

Chua (1985), note that the returns are not able to outperform the Kuala Lumpur

Composite Index (KLCI) market returns (see for example, Abdullah et al. 2007;

Annuar et al., 1997; Aw, 1997; Shamsher and Annuar, 1995; Taib and Isa, 2007). In

fact, these returns are lower than the risk free rate of return (Taib and Isa, 2007).

With respect to Islamic and conventional funds, evidence indicates that the IMFs

significantly underperform the market benchmark, as well as their conventional

counterparts (Abderrezak, 2008; Abdullah et al. 2007; Elfakhani and Hassan 2005;

Kraeussl and Hayat, 2008). Abdullah et al. (2007) for instance finds that both Islamic

and conventional funds in Malaysia underperform the market benchmark and that

Islamic funds perform better during bearish market. Conventional funds on the other

hand perform better during bullish market. Hoepner et al. (2011) similarly finds that

most of the IMFs worldwide underperform market benchmark, however, at the same

time Malaysian equity funds perform at par with the international equity market

benchmarks (Their study includes data on 265 IEFs, 76 of which are from Malaysia,

from September 1990 to April 2009). As for the religious mutual funds, findings

indicate that Australian ethical funds on average underperform the market by

approximately 1.5 per cent per annum (Tippet, 2001). The religious funds in the US

also underperform the market and conventional mutual funds over a period of

January 1994-September 2010 (Ferruz, Muñoz, and Vargas, 2012).

The underperformance of these mutual funds gives the investors an impression that

investing in these funds is associated with losses and financial penalty. In this regard,

Grinblatt and Titman (1994) suggest that the right choice of benchmark is particularly

important for performance evaluations. Therefore, in this chapter, the study analyses

the performance of fund portfolios allowing for different benchmarks to identify

whether the IEFs and CEFs are sensitive to any particular benchmarks.

Moreover, the performance of the mutual funds is not only related to market

benchmarking but also to other fund characteristics, including the imposition of fees.

Malkiel (1995) for example, indicates that in terms of aggregate returns, funds are not

able to outperform their market benchmarks, not only after management fees, but also

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in gross, before the expenses. The discussion about fees and mutual fund performance

is quite encouraging especially in the area of fees impact and how the different kinds

of fees could influence fund performance. Most studies based on the US equity

mutual funds report that there is an insignificant negative relationship between fund

return performance and fees (Carhart, 1997; Elton et al. 1993; Gil-Bazo and Ruiz-

Verdú, 2008; Haslem et al., 2008; Iannotta and Navone, 2012; Pollet and Wilson,

2008). The superior performance of mutual funds occurs mostly among large funds

with low expenses, low trading activity and no or low front-exit fees (Haslem et al.,

2008).

However, to this point there is no existing study that discusses fees and their relative

impact on Islamic and conventional mutual funds’ performance. In Malaysia for

instance, there are no previous published findings about the relationship between fees

and Islamic fund performance. The most relevant references are the ethical fund

studies. Gil-Bazo et al. (2010) investigate fund performance in the US over a period

of 1997–2005, between the ethical or socially responsible investment (SRI) funds and

the conventional funds and find that returns performance of US SRI funds before and

after fees is better than that of conventional funds with similar characteristics. They

also find no significant differences in fees between SRI and conventional funds apart

from the finding that the SRI funds from the same fund management companies are

cheaper than their conventional counterparts.

Fees are considered important for investors, as well as, for the fund management

companies. Although higher fees could decrease the returns on investment but at the

same time can also increase the fund managers’ revenue. Therefore, for investors, fees

incurred in the mutual fund investments are the price paid for the investment services

with the expectation that the expected return from the investment will be higher and

more than enough to offset the fees, while for the management companies, they

actually generate income (Khorana, Servaes, and Tufano, 2009). Nevertheless, the

previous findings indicate that the fees have an adverse impact on the investors since

most of the funds are not able to outperform their market benchmarks, not only on an

after fees expenses basis but also in gross (see for example, Carhart, 1997; Haslem et

al., 2008; Iannotta and Navone, 2012; Malkiel, 1995).

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Therefore, the study on comparative performance on Islamic and conventional equity

funds is largely to be explored but it gains little attention in the literature. The

popularity of these funds is gaining momentum due to the high growth of these funds

and the Islamic financial markets in the global financial industry (Hasan and Dridi,

2010). Hence, it is reasonable to further investigate the performance of Islamic funds

in order to identify whether or not these funds offer similar advantages as

conventional funds, and provide significant benefits to investors especially with

respect to fees and fund managers’ expertise, and risk and return trade-off

comparative to their conventional peers. In that case, this chapter aims to investigate

any difference between returns performance of the Malaysian equity funds, across

Islamic and conventional funds and their relationship with the market benchmarks.

The findings from this chapter are also important as they attempt to clarify several

issues such as why investors are interested in mutual funds if they predominantly

acknowledge that the funds give no abnormal returns or give lesser returns as

compared to the market. In particular, Benos and Jochec (2011) conclude that small

investors in the domestic US equity funds cannot make profits from the funds and that

large investors can only earn positive abnormal returns by rebalancing their fund

portfolios annually.

7.3 Data sample

The study examines the performance of 106 equity funds in Malaysia, including 53

IEFs and 53 CEFs over a period of 1990 to 2009 using single factor CAPM and

extends to the Treynor and Mazuy (1966) model to evaluate the selectivity skill and

market timing expertise among the Islamic and conventional fund managers. Most

previous studies compare portfolio returns based on gross returns or net returns after

excluding all expenses fees. Little attention is given to returns performance after

excluding load fees or returns after excluding all fees (the load fees plus all expenses

fees). Therefore, this study aims to extend the former two categories to include the

latter two categories.

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Finally, the analysis on the fund’s performance in this chapter is based on returns

before excluding all fees (GROSS returns) and returns after excluding all fees (NET

returns). The study also employs return net of all expenses (ADJUSTED returns) and

return net of load fees (ADJUSTED LOAD returns). The returns obtained from the

Morningstar database is net of all expenses (except front and exit fees) corresponding

to the ADJUSTED return category in this chapter. These returns are calculated based

on multiplicative methods using geometric mean and all expenses are deducted except

the front and exit fees such as sales charge and redemption fees. Since Morningstar

does not provide information about fees; the data about fees for all equity funds in the

sample are gathered directly from the related fund management companies and

prospectus of the funds.

For Malaysia, previous studies use gross returns and also returns adjusted for market

risk (market risk adjusted return). Moreover, they also use hand-gathered data,

collected from newspapers and the related fund management companies. The gross

returns here mean the raw returns before deducting any fees, and are calculated from

the NAV of a fund after adjusting for dividend payments. This complex process may

encounter problems if it is not properly managed.

In this study, the analysis is done using returns which are not adjusted for the risk free

rate of return. Since the study concerned a fund manager’s performance, the

application of gross return by adding back all the fees to net returns is appropriate

(Shukla and Van Inwegen, 1995). This is also to ensure that investors get information

on real value of their investment. Therefore, besides the GROSS category, this chapter

also incorporates other groups of returns, namely ADJUSTED, ADJUSTED LOAD

and NET.

Since there is no comparable study in Islamic mutual fund literature that emphasizes

on the relationship between fees and the returns performance of the funds, this chapter

therefore attempts to fill the gaps, employing four categories of returns, namely

GROSS, ADJUSTED, ADJUSTED LOAD and NET returns as dependent variables.

The data is more comprehensive and the outcomes of this study are important in order

to provide the investors estimates of real value of expected returns on investments.

Page | 187

The process of selecting the equity mutual funds is based on the list of IEFs available

in Malaysia as of April 2009. Our sample is drawn from the 535 Malaysian mutual

funds included in the Morningstar database. We identify 143 Islamic mutual funds

falling into one of the five broad categories basically based on the fund types:

alternative, allocation, equity, fixed income and money market.

The data is matched with the list obtained from the SC. There are 554 approved

mutual funds in Malaysia as of April 2009, consisting of 141 IMFs and 413 CMFs.

The study considers all the IMFs in the list. Thus the IMFs list is free from

survivorship bias. The study also finds that there are two funds from the CMFs list

that are converting to Islamic fund operations within the period of this study based on

information from their prospectus. Therefore the study includes both in the IMFs

category ending up with 143 funds.

The study however, restricts the final sample to include only the IMFs that have a

minimum of 12 months returns. Based on this criterion 14 funds are excluded from

the sample of 143 funds, reducing the number to 129 funds from five broad categories

as previously mentioned. Out of the 129 IMFs, 58 are included in the IEFs category.

The study further restricts the number of IEFs to 53 since 5 of them are excluded as

they are state-funds and do not provide fees information.

For the conventional funds category, on the other hand, there are 413 CMFs in

Malaysia as of April 2009. We exclude 2 funds that are included in Islamic mutual

fund category, as mentioned above, and 19 funds due to unavailable data in the

Morningstar database. Again, we only include 349 CMFs having a minimum of 12

months returns out of the 392 CMFs covering all categories over a similar period.

There are 176 CEFs out of final 349 CMFs and we choose the top 53 from them based

on the highest average returns over the period of the study. As a result, the final

selected sample consists of 106 equity mutual funds, including 53 IEFs and 53 CEFs.

The full sample of 106 funds is categorized as all equity mutual funds (AEFs). Since

this chapter focuses on annual returns, we obtain annual data from the Morningstar

database for these selected funds. The yearly return from 1990 to 2008 is available for

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each of the funds. In order to have the annual returns for 2009, the monthly returns

data for each of the selected equity funds in the sample is used (similar to the monthly

data return as previously employed in Chapters 4 and 5 of this thesis). We convert the

monthly data on returns using the geometric means to obtain the annual returns.

The resulting dataset includes annual returns data for 106 equity mutual funds in

Malaysia from 1990 to 2009. The returns were net of all expenses except front and

exit fees. From the data, other portfolio returns are calculated, namely GROSS,

ADJUSTED, ADJUSTED LOAD, and NET returns. In order to develop these

portfolio returns, other information about all expenses and compulsory fees, including

front fees or sale charges fees and exit fees or redemption fees, were obtained from

fund management companies’ website and from the prospectus of these funds. (The

details about the fees structure of the equity mutual funds involved in this sample are

given in Table 7.2 of this chapter). It can be seen that on average, the management

fees of Islamic equity mutual funds in Malaysia are higher than the global market, and

also slightly higher than the conventional counterparts (about 1.57 per cent25 as

compared to 1.53 per cent).

The GROSS portfolio returns mean returns including all fees, generated by adding

back all the fund expenses (all expenses and load fees) to net returns. The

ADJUSTED portfolio returns are the returns net from all expenses fees (This is

exactly the data obtained from the Morningstar database). The ADJUSTED LOAD

portfolio returns are the returns net from compulsory fees or load fees, normally

consisting of front fee or sales charge and also exit fee or redemption fee. Meanwhile,

the NET portfolio returns are the returns after excluding all fees. This dataset is

hereafter called original data.

Other dataset employed in this study, namely trimmed data is obtained after

controlling for the outliers. This process involves excluding periods of crisis in the

mutual fund returns. The crisis periods that have been excluded are 1997, 1998 and

2008. These years correspond to the Asian financial crisis (AFC) and global financial

25 Ernst and Young (2009) reported that on average, the management fee for the active Islamic equity fund is about 1.4 per cent.

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crisis (GFC), respectively. These crises had a significant impact on the KLCI, a proxy

for the Malaysian market returns. Since mutual funds’ performance is sensitive to

market movements, the true performance of the mutual fund cannot be examined if

the data is not trimmed.

In this chapter, we use gross or raw returns before adjusting for the risk free rate of

return. This is to give equity mutual fund investors insights about the real expected

returns performance of the mutual fund investments. Moreover, raw returns are more

informative for interpreting the important events in the economic cycles as compared

to the risk adjusted or market adjusted returns (Dann, 1981). The study hypothesises

that there is a difference between returns performance of Islamic and conventional

equity funds. Furthermore, the imposition of fees on equity mutual funds could have

adverse impacts on the equity fund performance.

7.3.1 Multicollinearity

Since the regressions in this analysis involve numerous independent variables, the

correlation matrix is reported for the explanatory variables in order to identify

potential high correlations or multicollinearity issues among the explanatory

variables. Table 7.1 presents this information.

From the table, it can be seen that multicollinearity does not appear to be a severe

issue as most correlation coefficients are below 0.50. To mitigate this issue, for the

variables that have high correlations, a separate regression analysis is conducted to

exclude the variables from the relevant model. Furthermore, for front fee and exit fee

variables, we combine them to form a new variable called TOTLOAD. Where

necessary, we alternate between TOTLOAD or front and exit fees while ensuring that

the high correlation variables are dropped while running the relevant regression

models.

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Table 7. 1: Correlation matrix of the explanatory variables Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 Panel A: AEFs Exogenous 1-AGE 1 –0.372 0.248 –0.031 0.344 –0.063 0.087 0.112 –0.046 –0.016 0.348 0.659 2-LNSIZE 1 0.028 –0.135 –0.281 0.014 –0.132 0.019 0.131 –0.205 –0.181 –0.353 3-dINVEST 1 0.036 0.238 –0.070 0.106 0.053 –0.111 –0.021 –0.011 0.103 4- dTYPE 1 –0.256 0.029 –0.190 –0.039 0.184 0.017 0.055 0.124 Endogenous Return 5- ALPHA 1 –0.074 0.108 0.544 –0.097 0.006 0.222 0.102 6-RETURNt-1 1 –0.014 –0.018 0.009 –0.010 –0.008 –0.020 Risk 7- BETA 1 0.131 –0.046 –0.075 –0.131 –0.155 8- RESIDRISK 1 0.103 –0.061 0.223 –0.075 Fees 9-MGMTFEE 1 0.220 0.130 –0.021 10-EXPENSE 1 0.014 0.166 11-TOTLOAD 1 0.074 12-TRUSTEE 1 Panel B: IEFs Exogenous 1-AGE 1 –0.479 0.329 0.392 –0.096 –0.096 0.068 –0.025 0.045 0.356 0.360 –0.061 0.687 2-LNSIZE 1 –0.115 –0.554 0.047 –0.151 –0.302 0.197 –0.126 –0.228 –0.225 –0.025 –0.399 3-dINVEST 1 0.273 –0.137 0.149 –0.005 –0.231 –0.188 0.060 0.073 –0.143 0.175 Endogenous Return 4- ALPHA 1 –0.106 0.517 0.603 –0.079 0.159 0.286 0.292 –0.078 0.254 5-RETURNt-1 1 –0.078 –0.007 –0.009 0.007 0.006 0.001 0.061 –0.037 Risk 6- BETA 1 0.840 0.168 0.105 0.116 0.124 –0.087 0.017 7- RESIDRISK 1 0.274 0.193 0.213 0.214 –0.020 0.028 Fees 8-MGMTFEE 1 0.347 0.180 0.171 0.095 –0.088 9-EXPENSE 1 0.146 0.155 0.098 0.044 10-TOTLOAD 1 0.996 0.007 0.053 11-FRONT 1 –0.087 0.053 12-EXIT 1 –0.004 13-TRUSTEE 1

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Table 7.1 continued Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 Panel C: CEFs Exogenous 1-AGE 1 –0.288 0.181 0.341 –0.037 –0.034 0.157 –0.053 –0.056 0.346 0.342 0.083 –0.653 2-LNSIZE 1 0.144 0.084 –0.006 0.382 0.374 0.129 –0.266 –0.130 –0.101 –0.106 –0.290 3-dINVEST 1 0.253 –0.021 0.197 0.108 –0.012 0.081 –0.063 –0.024 –0.179 0.056 Endogenous Return 4- ALPHA 1 –0.030 0.383 0.540 –0.050 –0.259 0.309 0.326 –0.179 –0.069 5-RETURNt-1 1 –0.025 –0.026 0.013 –0.021 –0.021 –0.035 0.074 –0.014 Risk 6- BETA 1 0.753 0.020 –0.024 0.075 0.137 –0.335 –0.109 7- RESIDRISK 1 –0.045 –0.264 0.241 0.278 –0.260 –0.115 Fees 8-MGMTFEE 1 0.146 0.077 0.004 0.348 –0.011 9-EXPENSE 1 –0.071 –0.002 –0.329 0.300 10-TOTLOAD 1 0.981 –0.208 0.080 11-FRONT 1 –0.392 0.072 12-EXIT 1 0.012 13-TRUSTEE

1

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7.4 Results and performance analysis

7.4.1 Descriptive statistics on fund samples and fund attributes

Table 7.2 illustrates the summary of statistics for the total net assets (TNA), returns,

risk and the fees structure. All the variables reported in the table are calculated as 20–

year annualised averages, based on panel data. For example, the average TNA for the

total sample is RM$600.53 million. On average, IEFs are the smallest, with net assets

of RM$589.00 million as compared to CEFs that are the largest with RM$697.00

million.

Overall, all equity mutual funds (AEFs) achieved 15.77 per cent average annual

returns before fees over the 20–year period as shown in Table 7.2, approximately 510

basis points (bps) higher than the market return of the KLCI at 10.67 per cent.26

However, on average, annual returns after fees indicate that mutual funds

underperformed the market return at 6.54 per cent, approximately 413 bps lower than

the KLCI. The descriptive statistics mainly denote that CEFs’ portfolio was the best

performer over the period. However, the higher returns of the CEFs are accompanied

with a higher systematic risk (beta) and a higher unsystematic risk (residual risk).

With respect to fees structure of the portfolios, as shown by average expense ratios,

IEFs invested relatively more in research relative to CEFs. The average mutual funds

expense ratio of 1.73 per cent is higher than that in the US, where it is about 1.31 per

cent (see Indro et al., 1999, p.77). The low expense ratio of the US mutual funds

might suggest the presence of economies of scale in that industry.

In addition, higher operating expenses could lead to low performance of a fund.

Although the percentage is very small, they have the adverse impact of reducing fund

returns. The reason is that investors must allocate some of their initial investment or

profits from the investment to pay for the fees. These expenses could be in the form of

management fees, expenses ratio or trustee fee. In contrast to that, investors pay one-

26 The average KLCI market return based on trimmed data is approximately 10.67 per cent per annum over the 1990-2009 as shown next in Panel B of Table 7.3. The percentage is higher as compared to the findings of Taib and Isa (2007) who report that on average market return of the Malaysian stock market is about -0.96 per annum over the period of 1990 to 2001.

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off front and exit fees, operating expenses need to be paid overtime that most

unsophisticated investors are not aware about the impact of fees.

Furthermore, investors’ insensitivity to management fee may reflect the lack of salient

information about the fee. Since the fee is paid over time, and is calculated as a

fraction of the value of the investment, it is rarely translated into dollar terms.

Furthermore, investors seem to be more sensitive to load fees as compared to

operating expenses (Barber, Odean, and Lu, 2005; Gil-Bazo and Ruiz-Verdú, 2008).

Consequently, most of the unsophisticated investors do not realise how much

expenses were deducted from their investments in terms of fees.

Further description on the data is conducted. Table 7.3 presents the descriptive

statistics from the panel data, for AEFs, IEFs, and CEFs relative to the market and

risk free portfolios. Table 7.3 provides more rigorous results but apparently

contradicts with the findings of the time series analysis as mentioned in Chapter 5 of

the thesis. The mean returns performance of IEFs is relatively lower than the CEFs.

This suggests that CEFs perform better than IEFs while using panel data regression

analysis.

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Table 7. 2: Description on mutual fund samples and the fund attributes

Fund size: TNA (RM millions)

Age

20–year average return (%)

KLCI as a proxy for market

Fees structure

Gross Adjusted Adjusted Load

Net alpha beta Residual risk (%)

Mgmt. Fee

Expense ratio

Total load

Trustee fee

AEFs: N=843 Mean 600.53 14.730 15.766 12.423 9.887 6.544 5.424 1.081 26.675 1.537 1.728 5.878 0.079 Std. Dev 500.01 11.944 28.847 28.847 28.837 28.839 5.308 1.252 6.518 0.157 0.345 0.600 0.014 Median 500.00 10.042 16.644 13.304 10.953 7.782 5.414 0.848 27.215 1.500 1.650 6.000 0.070 IEFs: N=342 Mean 589.00 13.983 14.613 11.227 8.688 5.302 3.770 0.792 26.320 1.568 1.738 5.925 0.080 Std. Dev 456.00 12.852 29.697 29.684 29.655 29.643 7.244 0.196 7.678 0.143 0.316 0.624 0.015 Median 450.00 8.017 17.387 14.298 11.472 8.042 3.758 0.787 24.201 1.500 1.670 6.000 0.080 CEFs: N=501 Mean 697.00 15.239 16.552 13.239 10.706 7.392 6.728 0.840 26.917 1.515 1.721 5.847 0.077 Std. Dev 525.00 11.267 28.255 28.263 28.266 28.275 2.508 0.148 5.585 0.163 0.364 0.581 0.013 Median 500.00 11.028 16.515 13.275 10.644 6.895 6.426 0.848 28.320 1.500 1.640 5.500 0.070

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Table 7. 3:Descriptive statistics of IEFs, CEFs and AEFs relative to market and risk free portfolios. Fund portfolio N Mean Max Min Std. Dev JB

Panel A: Original data IEFs GROSS 342 14.613 154.111 –58.794 29.697 8.859** ADJUSTED 342 11.227 150.631 –62.064 29.684 8.608** ADJUSTED

LOAD 342 8.688 147.611 –63.794 29.655 8.307**

NET 342 5.302 144.131 –67.064 29.643 8.059** Market 1060 2.832 69.387 –73.360 29.889 92.394*** rf 1060 4.345 7.700 1.920 1.882 111.581*** CEFs GROSS 501 16.552 109.432 –54.382 28.255 0.705 ADJUSTED 501 13.239 106.272 –57.932 28.263 0.747 ADJUSTED

LOAD 501 10.706 103.932 –60.632 28.266 0.695

NET 501 7.392 100.772 –64.182 28.275 0.738 Market 1060 2.832 69.387 –73.360 29.889 92.394*** rf 1060 4.345 7.700 1.920 1.882 111.581*** AEFs GROSS 843 15.766 154.111 –58.794 28.847 7.089** ADJUSTED 843 12.423 150.631 –62.064 28.847 6.972** ADJUSTED

LOAD 843 9.887 147.611 –63.794 28.837 6.688**

NET 843 6.544 144.131 –67.064 28.839 6.578** Market 2120 2.832 69.387 –73.360 29.881 184.789*** rf 2120 4.345 7.700 1.920 1.881 223.161*** Panel B: Trimmed data IEFs GROSS 276 24.037 154.111 –25.996 23.266 131.926*** ADJUSTED 276 20.655 150.631 –29.886 23.245 130.754*** ADJUSTED

LOAD 276 18.107 147.611 –32.496 23.223 128.720***

NET 276 14.725 144.131 –36.386 23.204 127.464*** Market 901 10.669 69.387 –28.296 21.967 105.033*** rf 901 4.220 7.700 1.920 1.908 103.280*** CEFs GROSS 419 24.071 109.432 –21.871 22.950 37.030*** ADJUSTED 419 20.746 106.272 –24.781 22.980 37.133*** ADJUSTED

LOAD 419 18.219 103.932 –28.371 22.973 36.420***

NET 419 14.895 100.772 –31.281 23.003 36.565*** Market 901 10.669 69.387 –28.296 21.967 105.033*** rf 901 4.220 7.700 1.920 1.908 103.280*** AEFs GROSS 695 24.057 154.111 –25.996 23.059 132.991*** ADJUSTED 695 20.710 150.631 –29.886 23.069 131.608*** ADJUSTED

LOAD 695 18.175 147.611 –32.496 23.056 129.479***

NET 695 14.827 144.131 –36.386 23.066 128.155*** Market 1802 10.669 69.387 –28.296 21.962 210.065*** rf 1802 4.220 7.700 1.920 1.907 206.560***

However, standard deviation of the IEFs portfolio is relatively larger than the CEFs

portfolio (see Panel A, Table 7.3). This evidence validates findings in time series data

as previously discussed in Chapter 5 (that Islamic funds are more risky than the

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conventional funds). The finding is also consistent with Mansor and Bhatti (2011)

who find that Islamic funds have relatively higher risk using F-test and Bartlett test

for variances. They also find that Islamic funds are more volatile thus providing

higher expected risk in relation to their conventional peers. Therefore, the findings

here directly signify that conventional funds perform better than Islamic funds using

panel data analysis, with higher returns and lower risk.

The JB tests for each of the portfolios also indicate that the data is not normally

distributed (see Panel B, Table 7.3). In such situations normal linear regression is not

enough to provide comprehensive results, and panel data regression analysis is robust

to overcome the weaknesses. Panel data regressions are adjusted for

heteroskedasticity and cross-sectional standard errors, and covariance, and for serial

correlation when the Durbin-Watson (DW) is less than 2.

Additionally, Table 7.4 presents t-tests for statistical differences using equal variance

independent samples t-test. The results of the table compute mean differences t-test

and indicate that there is no significant difference between Islamic and conventional

funds. These results show a highly significant difference at one per cent level between

mean returns of all the portfolios and the market returns. In contrast to time series

data, panel data analysis indicates a significant difference between net returns of all

portfolios with the market. There is also a significant difference between all equity

funds portfolio and the risk free returns using trimmed data (Panel B). Since this

study covers a duration of 20–year, trimmed data is employed for controlling outliers

arising due to the AFC in 1997/1998 and the GFC in 2007/2008, to provide more

rigorous results.

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Table 7.4: Mean test of statistical differences between IEFs, CEFs and AEFs, comparative to market and risk free portfolios, based on panel data Table presents t-tests using equal variance to evaluate whether Islamic and conventional equity fund portfolios perform significantly different from each other. These t-tests are also conducted to compare the IEFs, CEFs portfolios, market portfolio and risk free (rf) portfolio. The results are based on average annualised returns over the period 1990 to 2009. The asterisks ***, **, * indicate the statistical significance based on a two-tail test at 1%, 5% and 10% respectively. Portfolio IEFs-

CEFs IEFs- market

CEFs-market

AEFs-market

AEFs- rf

Panel A: Original data GROSS Mean

difference 1.939 11.781 13.720 12.933 11.421

t-statistic –0.958 6.348*** 8.615*** 10.734*** 18.137*** ADJUSTED Mean

difference 2.013 8.394 10.407 9.590 8.078

t-statistic –0.995 4.524*** 6.534*** 7.960*** 12.828*** ADJUSTED LOAD

Mean difference

2.017 5.856 7.873 7.055 5.542

t-statistic –0.997 3.157*** 4.943*** 5.856*** 8.804*** NET Mean

difference 2.090 2.470 4.560 3.712 2.199

t-statistic –1.033 1.331 2.863*** 3.081*** 3.494*** Panel B: Trimmed data GROSS Mean

difference 0.034 13.367 13.402 13.388 19.837

t-statistic –0.019 8.722*** 10.171*** 13.462*** 36.211*** ADJUSTED Mean

difference 0.092 9.985 10.077 10.040 16.490

t-statistic –0.051 6.516*** 7.644*** 10.095*** 30.088*** ADJUSTED LOAD

Mean difference

0.112 7.438 7.550 7.505 13.955

t-statistic –0.063 4.855*** 5.728*** 7.547*** 25.476*** NET Mean

difference 0.170 4.056 4.225 4.158 10.607

t-statistic –0.045 2.648*** 3.204*** 4.181*** 19.356***

To further examine the above mentioned results, the study proceeds with the single

factor panel data regression and TM model panel data regression analysis in the

following sections (the results of single factor regression are reported in Section 7.4.2,

while Section 7.4.3 discusses the findings on the market timing expertise of fund

managers and their selectivity skill).

Page | 198

7.4.2 Single factor OLS regression

Table 7.5 presents findings of the single factor regression (SFR) model by using panel

ordinary least squares (OLS) regression using similar models that have been discussed

in Chapter 3 of this thesis. Results show that all the portfolios outperform the market

benchmark. These results also provide evidence that IEFs seem to underperform the

CEFs.

Table 7. 5: Results of pooled OLS using single factor regression model Table reports the results (in percentage) of regressions based on single factor regression, with 53 cross-sections included for IEFs and CEFs, and 106 cross-sections for AEFs portfolio, from 1990 to 2009.The results are after adjusted for heteroskedasticity and serial correlation problems. N is the number of observations. The asterisks ***, **, * indicate the statistical significance based on two-tail test at 1%, 5% and 10% respectively. Standard errors are given in parentheses. Fund portfolio Original data Trimmed data

α β ��gT� α β ��gT�

IEFs GROSS 13.468***

(3.257) 0.875*** (0.052)

0.690 10.585** (5.086)

1.047*** (0.147)

0.452

Original data (N=342)

ADJUSTED 10.082*** (3.256)

0.875*** (0.052)

0.691 7.208 (5.086)

1.047*** (0.147)

0.452

Trimmed data (N=276)

ADJUSTED LOAD

7.544** (3.262)

0.875*** (0.052)

0.691 4.663 (5.097)

1.047*** (0.147)

0.453

NET 4.157 (3.262)

0.875*** (0.052)

0.692 1.286 (5.097)

1.046*** (0.147)

0.453

CEFs GROSS 14.529*** (2.692)

0.888*** (0.055)

0.755 11.925*** (3.526)

1.052*** (0.088)

0.608


ADJUSTED 11.217*** (2.698)

0.888*** (0.055)

0.754 8.591** (3.533)

1.053*** (0.088)

0.607


ADJUSTED LOAD

8.683*** (2.699)

0.888*** (0.055)

0.754 6.073* (3.532)

1.052*** (0.087)

0.606

NET 5.371** (2.706)

0.883*** (0.052)

0.752 2.739 (3.539)

1.053*** (0.087)

0.606

AEFs GROSS 14.101*** (2.880)

0.883*** (0.052)

0.727 11.408*** (4.037)

1.049*** (0.103)

0.545


ADJUSTED 10.759*** (2.883)

0.883*** (0.052)

0.727 8.056** (4.040)

1.049*** (0.103)

0.544


ADJUSTED LOAD

8.224*** (2.885)

0.883*** (0.052)

0.727 5.528 (4.042)

1.048*** (0.102)

0.545

NET 4.881* (2.888)

0.883*** (0.052)

0.727 2.177 (4.046)

1.048*** (0.103)

0.544

The returns performance difference however varies across funds categories. It can

also be seen that the impact of fees on mean returns performance of AEFs, IEFs and

CEFs becomes more severe in NET as expected. Therefore, it seems that the

imposition of fees, either load fees or all expenses fees, would have a direct impact in

Page | 199

reducing the fund returns performance as is also supported by the various previous

studies (Babalos et al., 2009; Carhart, 1997; Haslem et al., 2008; Iannotta and

Navone, 2012; Indro et al., 1999; Malkiel, 1995; Pollet and Wilson, 2008).The main

findings in the above mentioned studies also indicate that the returns performance of

funds after fees is relatively lower than the market return and is not even able to offset

the fees. However, the present study shows that even though the imposition of fees

reduces returns performance of all portfolios, they are still superior to the market

returns performance before and after deducting fees. This is consistent with the report

of Ippolito (1989).

The results exhibit in Table 7.5 are after correcting for heteroskedasticity and cross-

sectional standard errors and they show that the returns for the gross category of the

IEFs are moderately good, at 10.59 per cent per annum. However, the returns

performance diminishes for the other categories after deducting the fees. The returns

performance of the ADJUSTED category (net of all expenses) decreases to 7.21 per

cent per annum albeit being insignificant. Similarly, the performance of ADJUSTED

LOAD category (after deducting the load fees) is insignificant but lower than the

ADJUSTED category at 4.66 per cent per annum.

Returns with fees (GROSS) performs better than returns after fees (NET) suggesting

that the fees may generate better fund performance, good enough to cover the cost of

fees. However, after deducting all fees (NET), equity investors are only able to get

1.29 per cent profit per annum for the Islamic funds and 2.74 per cent per annum for

the conventional funds.

The results in the table also indicate that the performance of Islamic and conventional

funds is better than the market although the returns are considerably low for long term

investments over a 20–year period. The NET returns performance (after deducting all

fees) diminishes for both Islamic and conventional equity fund portfolios, suggesting

that the profits that investors gain from mutual fund investments are largely used to

offset the fees.

Page | 200

7.4.3 Market timing expertise and fund selectivity skill

The findings from OLS regressions for market timing expertise and fund selectivity

among fund managers are reported in Table 7.6. Fund selectivity skills of the fund

managers (measured by the alpha (α)) have a significantly positive effect based on

both original and trimmed data before correction for heteroskedasticity (the results are

available upon request). The results are less clear cut after the correction as shown in

Table 7.6.

These results are more or less in line with results discussed in Chapters 5 and 6. The

results reveal that there is no significant impact of market timing expertise on fund

performance of IEFs, CEFs (measured by the theta θ) when using original data, after

correction for heteroskedasticity, implying perverse or inferior market timing

expertise among Islamic and conventional fund managers. Moreover, after correction

for heteroskedasticity is made, all results of thetas show insignificantly perverse or no

market timing expertise at 0.00 per cent. The findings are consistent with Low (2007)

who reports that Malaysian fund managers are poor in their timing ability over the

period 1996 to 2000, which contributes to the negative overall fund performance, thus

leading to no economic benefits accruing to fund managers involved in market timing

activities and that the fund managers should consider reducing the imposition of

relevant fees to offset the low fund performance.

In summary, the results indicate that all the portfolios outperform the market

benchmark. There is also evidence that IEFs underperform the CEFs. This finding is

in line with many previous studies on the topic. The results however are in contrast

with the findings of Gil-Bazo et al. (2010) who state that SRI perform better than the

conventional funds.

Page | 201

Table 7. 6: Results of market timing ability based on pooled OLS panel analysis This table reports the results of panel data regressions based on the TM model over a sample period from 1990 to 2009 for annual returns performance of 106 AEFs (53 IEFs and 53 CEFs). The results are based on 53 cross-sections for IEFs and CEFs and 106 cross-sections for AEFs portfolio, over the same period. The difference of alpha (α) and (θ) from zero indicates that there is superior in fund selectivity skill and market timing expertise among the fund managers. The asterisks ***,**,* denote statistical significance at 1%, 5% and 10% respectively. Standard errors are given in parentheses. The standard errors allow corrections for heteroskedasticity and serial correlation follow White (1980).

Fund portfolio Original data Trimmed data α β θ ��gT� α β θ ��gT� IEFs GROSS 10.526**

(4.831) 0.958*** (0.050)

0.004* (0.002)

0.701 10.409** (5.165)

0.916*** (0.166)

0.005 (0.004)

0.459

Original data (n=342)

ADJUSTED 7.153 (4.830)

0.958*** (0.050)

0.004* (0.002)

0.702 7.034 (5.164)

0.917*** (0.166)

0.005 (0.147)

0.459

Trimmed data (n=276)

ADJUSTED LOAD

4.618 (4.842)

0.957*** (0.049)

0.004* (0.002)

0.703 4.490 (5.177)

0.918*** (0.165)

0.005 (0.004)

0.460

NET 1.245 (4.841)

0.957*** (0.049)

0.004* (0.002)

0.703 1.115 (5.176)

0.918*** (0.166)

0.005 (0.004)

0.460

CEFs GROSS 11.861*** (3.517)

0.954*** (0.036)

0.003*** (0.001)

0.770 11.614*** (3.706)

0.971*** (0.136)

0.003 (0.003)

0.612


ADJUSTED 8.530** (3.524)

0.954*** (0.036)

0.003** (0.001)

0.769 8.277** (3.713)

0.971*** (0.136)

0.003 (0.003)

0.611

Trimmed data( n=419)

ADJUSTED LOAD

6.027* (3.528)

0.953*** (0.036)

0.003** (0.001)

0.769 5.769 (3.715)

0.973*** (0.136)

0.003 (0.003)

0.610

NET 2.696 (3.534)

0.953*** (0.036)

0.003** (0.001)

0.768 2.432 (3.722)

0.973*** (0.136)

0.003 (0.003)

0.610

AEFs GROSS 11.337*** (3.957)

0.955*** (0.035)

0.003** (0.001)

0.741 11.123*** (4.184)

0.950*** (0.139)

0.004 (0.003)

0.551


ADJUSTED 7.987** (3.960)

0.955*** (0.035)

0.003** (0.001)

0.741 7.771* (4.187)

0.950*** (0.139)

0.004 (0.003)

0.551

Trimmed data (n=695)

ADJUSTED LOAD

5.473 (3.966)

0.954*** (0.035)

0.003** (0.001)

0.741 5.249 (4.192)

0.951*** (0.139)

0.004 (0.003)

0.550

NET 2.123 (3.969)

0.955*** (0.035)

0.003** (0.001)

0.741 1.897 (4.195)

0.951*** (0.139)

0.004 (0.003)

0.550

Page | 202

7.4.4 Results of panel data using FEs and REs on TM model

The study also extends the analysis to include panel least squares regressions,

allowing for cross-sectional fixed effects (FEs) and also using panel generalised least

squares (GLS) using random effects (REs). These regressions are carried out to

examine the robustness of the results on panel regressions concerning market timing

expertise among fund managers, as previously described in Table 7.6.

Table 7.7 reports the results of these FEs and REs panel regressions. All the

regressions are also corrected for heteroskedasticity and serial correlation using

method of White (1980).The findings of the GLS using trimmed data are consistent

with the previous results as shown in Table 7.6 that there is perverse or no market

timing expertise among fund managers for both Islamic and conventional ones. These

also suggest that the previous regressions based on pooled regression are already

adequate to deal with the questions on hand.

The Hausman test (r�) is also presented in Panel B, Table 7.7. Results of Hausman

test (r�) suggests that the tests are not significant for the IEFs portfolio, implying that

FE model is not preferred over the REs. Results in Table 7.8 also show inconsistent

findings that there is a statistically significant difference on market timing expertise

between Islamic and all equity fund portfolios using panel regression with fixed

effects. These findings indicate that the CEFs and AEFs portfolios are not sensitive to

random effects while allowing for the cross-sectional fixed effects, but the same is not

true for the IMFs portfolio when the trimmed data is used.

Page | 203

Table 7. 7: Panel data regression results using FEs and GLS REs Fund portfolio Original data Trimmed data α β θ �� α β θ ��

Panel A: OLS fixed effects with correction for heteroskedasticity IEFs GROSS 10.778**

(4.697) 0.971*** (0.056)

0.003 (0.002)

0.726 10.692** (5.191)

0.852*** (0.179)

0.006* (0.003)

0.536

ADJUSTED 7.391 (4.697)

0.971*** (0.056)

0.003 (0.002)

0.726 7.310 (5.191)

0.852*** (0.179)

0.006* (0.003)

0.535

ADJUSTED LOAD

4.853 (4.697)

0.971*** (0.056)

0.003 (0.002)

0.725 4.763 (5.191)

0.852*** (0.179)

0.006* (0.003)

0.534

NET 1.466 (4.697)

0.971*** (0.056)

0.003 (0.002)

0.725 1.381 (5.191)

0.852*** (0.179)

0.006* (0.003)

0.534

CEFs GROSS 11.779*** (3.416)

0.964*** (0.040)

0.003*** (0.001)

0.769 11.656*** (3.696)

0.908*** (0.140)

0.005 (0.003)

0.618

ADJUSTED 8.466** (3.416)

0.964*** (0.040)

0.003*** (0.001)

0.769 8.332** (3.696)

0.908*** (0.140)

0.005 (0.003)

0.619

ADJUSTED LOAD

5.932* (3.416)

0.964*** (0.040)

0.003*** (0.001)

0.769 5.805 (3.696)

0.908*** (0.140)

0.005 (0.003)

0.619

NET 2.619 (3.416)

0.964*** (0.040)

0.003*** (0.001)

0.769 2.481 (3.696)

0.908*** (0.140)

0.005 (0.003)

0.620

AEFs GROSS 11.367*** (3.855)

0.967*** (0.041)

0.003** (0.001)

0.752 11.244*** (4.169)

0.891*** (0.143)

0.005* (0.003)

0.587

ADJUSTED 8.024** (3.855)

0.967*** (0.041)

0.003** (0.001)

0.752 7.897* (4.169)

0.891*** (0.143)

0.005* (0.003)

0.588

ADJUSTED LOAD

5.488 (3.855)

0.967*** (0.041)

0.003** (0.001)

0.752 5.362 (4.169)

0.891*** (0.143)

0.005* (0.003)

0.587

NET 2.145 (3.855)

0.967*** (0.041)

0.003** (0.001)

0.752 2.014 (4.169)

0.891*** (0.143)

0.005* (0.003)

0.588

Page | 204

Table 7.7 continued

Fund portfolio Original data Trimmed data α β θ �� r� α β θ �� r� Panel B: GLS random effects with correction for heteroskedasticity IEFs GROSS 10.993*

(5.790) 0.957*** (0.050)

0.003* (0.002)

0.719 0.011 8.955

11.695 (7.378)

0.889*** (0.167)

0.005 (0.003)

0.490 0.157 3.700

ADJUSTED 7.602 (5.759)

0.957*** (0.050)

0.003* (0.002)

0.720 0.013 8.754

8.302 (7.350)

0.889*** (0.167)

0.005 (0.003)

0.490 0.154 3.730

ADJUSTED LOAD

5.035 (5.711)

0.957*** (0.050)

0.003* (0.002)

0.719 0.010 9.152

5.736 (7.333)

0.890*** (0.167)

0.005 (0.003)

0.490 0.148 3.823

NET 1.644 (5.683)

0.957*** (0.050)

0.003* (0.002)

0.719 0.011 8.941

2.344 (7.308)

0.890*** (0.167)

0.005 (0.003)

0.490 0.146 3.855

CEFs GROSS 11.861*** (3.517)

0.954*** (0.036)

0.003*** (0.001)

0.770 0.000 15.693

11.614*** (3.706)

0.971*** (0.136)

0.003 (0.003)

0.612 0.000 17.371

ADJUSTED 8.530** (3.524)

0.954*** (0.036)

0.003*** (0.001)

0.769 0.000 16.424

8.277** (3.713)

0.971*** (0.136)

0.003 (0.003)

0.611 0.000 17.192

ADJUSTED LOAD

6.027* (3.528)

0.953*** (0.036)

0.003** (0.001)

0.769 0.000 17.678

5.769 (3.715)

0.973*** (0.136)

0.003 (0.003)

0.610 0.000 18.571

NET 2.696 (3.534)

0.953*** (0.036)

0.003** (0.001)

0.768 0.000 18.393

2.432 (3.722)

0.973*** (0.136)

0.003 (0.003)

0.610 0.000 18.348

AEFs GROSS 11.547*** (4.389)

0.956*** (0.035)

0.003** (0.001)

0.747 0.000 17.183

11.723** (5.276)

0.933*** (0.137)

0.004 (0.003)

0.564 0.001 13.536

ADJUSTED 8.199* (4.399)

0.956*** (0.035)

0.003** (0.001)

0.747 0.000 17.513

8.378 (5.296)

0.933*** (0.137)

0.004 (0.003)

0.564 0.001 13.512

ADJUSTED LOAD

5.678 (4.381)

0.955*** (0.035)

0.003** (0.001)

0.746 0.000 18.154

5.852 (5.277)

0.934*** (0.136)

0.004 (0.003)

0.563 0.001 14.167

NET 2.332 (4.395)

0.955*** (0.036)

0.003** (0.001)

0.746 0.000 18.446

2.508 (5.299)

0.934*** (0.137)

0.004 (0.003)

0.563 0.001 14.115

Note: Standard errors are given in parentheses. Chi.sq. stats are in italic. The asterisks ***, **, * denote statistical significance at the 1%, 5% and 10% levels respectively.

Page | 205

7.4.5 Fees and other fund attributes on single factor regression analysis

This section extends the analysis to fund attributes and fees variables and reports the

cross-sectional analysis using single panel REs regression with time period fixed

period based on 20–year data to include size, age, a dummy variable for local or

foreign fund, management fee, expense ratio, and load fee. This section conjectures

that these explanatory variables have some impact on the performance of the fund

portfolios.

The regression analysis are conducted based on each types of returns categories, using

both original and trimmed data, with N represents the number of observations in each

of the regressions. The single panel regressions estimate the coefficients where the

standard errors allow for heteroskedasticity and serial correlation based on the work

of White (1980). These regressions also allow for the time fixed effects (year), as

employed by Dahlquist et al. (2000). Table 7.8 presents results of the regressions. The

similar results obtained in IEFs, CEFs and AEFs either using original data or trimmed

data (as shown in Panel A and Panel B of Table 7.8 respectively).

The results in the table indicate that there is no relationship between returns

performance and the size of the fund, where the fund size refers to the total net asset

values (TNA) of the funds (in logs) at the inception date. For both original and

trimmed data, the results reveal that there is no statistically significant difference

between the size and fund performance of IEFs, CEFs and AEFs over the period of

1990–2009. This contradicts with the findings of Indro et al. (1999) who argue that

size has an impact on the mutual fund performance.

Results in Table 7.8 also indicate that age has negative but insignificant effect on IEFs

and AEFs. There is a negative relationship between performance and the size of the

fund. Further, size has a larger impact on the performance of CEFs, particularly in the

categories of net (after deducting all fees) and adjusted load (after deducting front and

exit fees) returns.

Page | 206

The regression coefficient estimates, where returns are regressed on a constant and

age, approximately equal –0.12 for adjusted load and net returns. Even though these

are not significant, these slope coefficients for CEFs are quite large as compared to

the IEFs, implying that the older the fund, the less it performs relative to the market

because the fund is already achieved the economics of skill.

The management fees have a negative impact on the Islamic equity funds (IEFs)

performance both with original and trimmed data. The results support the findings of

Dahlquist et al. (2000) that management fee has a strong negative relation to the

performance of the funds. The coefficient estimate for the IEFs is less than minus five

(–5.06) based on original data, and even more severe for the trimmed data (–7.70).

The higher negative impact of management fee to IEFs fund performance could imply

that IEF fund managers charge investors higher management fees than their

conventional counterparts.

For the AEFs, the regression coefficient is –2.153 (see Panel B, Table 7.8) suggesting

that the fund managers offset more than double the direct effect of the fees to the fund

performance. This evidence implies that the effect of management fee on gross mutual

fund performance in Malaysia is about two to one on a yearly basis. Although the

adverse impact is larger in magnitude, this finding is also consistent with (Dahlquist et

al., 2000) who provide evidence that management fee weakens the US fund

performance one for one per annum.

However, the results also indicate that the management fee has a positive impact on

the conventional equity funds (CEFs) performance and this is highly significant while

using original data. For example, the regression coefficient estimate is about 5.08 and

4.82 for the gross and adjusted load returns after deducting load fee, (the front and

exit fee but not the expense fees) respectively. However, the results are insignificant

when applying with the trimmed data.

Page | 207

Table 7. 8: Cross sectional analysis of returns versus fees and other fund attributes based on single factor panel REs regression. This table reports the regression results based on original data (Panel A) and trimmed data (Panel B), allowing for fixed period (year) effects, and are adjusted for heteroskedasticity and serial correlations as in White’s (1980) test. Standard errors are given in parentheses.

Attributes Coefficient estimates N SIZE AGE dINVEST MGMT FEE EXPENSE TOTLOAD

Panel A: Original data IEFs GROSS 342 0.000 –0.028 1.930*** –5.062 5.739*** 3.148*** (0.000) (0.082) (0.706) (4.533) (1.864) (1.152) ADJUSTED 342 0.000 –0.029 2.205*** –6.792 4.591** 3.035*** (0.000) (0.082) (0.710) (4.541) (1.868) (1.151) ADJUSTED 342 0.000 –0.048 1.795** –5.794 5.461*** 2.148* LOAD (0.000) (0.080) (0.714) (4.539) (1.850) (1.152) NET 342 0.000 –0.049 2.070*** –7.523 4.313** 2.035* (0.000) (0.080) (0.720) (4.548) (1.854) (1.151) CEFs GROSS 501 0.000 –0.098 –0.718 5.076*** –2.525** –0.768 (0.000) (0.074) (1.721) (1.682) (1.100) (1.120) ADJUSTED 501 0.000 –0.098 –0.845 3.934** –3.565*** –0.781 (0.000) (0.074) (1.760) (1.760) (1.079) (1.140) ADJUSTED 501 0.000 –0.116 –0.647 4.824*** –2.504** –1.768 LOAD (0.000) (0.000) (1.794) (1.712) (1.161) (1.120) NET 501 0.000 –0.117 –0.774 3.682* –3.544*** –1.781 (0.000) (0.074) (1.833) (1.801) (1.139) (1.140) AEFs GROSS 843 0.000 –0.052 0.840 1.121 0.438 0.798 (0.000) (0.071) (0.744) (2.489) (0.961) (0.964) ADJUSTED 843 0.000 –0.053 0.895 –0.234 –0.643 0.735 (0.000) (0.071) (0.751) (2.594) (0.945) (0.979) ADJUSTED 843 0.000 –0.071 0.837 0.667 0.345 –0.202 LOAD (0.000) (0.070) (0.799) (2.556) (1.007) (0.964) NET 843 0.000 –0.072 0.892 –0.687 –0.736 –0.265 (0.000) (0.070) (0.806) (2.667) (0.990) (0.979)

Page | 208

Table 7.8 continued

Attributes Coefficient estimates N SIZE AGE dINVEST MGMT FEE EXPENSE TOTLOAD

Panel B: Trimmed data IEFs GROSS 276 0.000 –0.058 2.394*** –7.703 5.604** 2.678** (0.000) (0.089) (0.785) (5.589) (2.368) (1.189) ADJUSTED 276 0.000 –0.058 2.680*** –9.424* 4.452* 2.572** (0.000) (0.089) (0.788) (5.601) (2.376) (1.188) ADJUSTED 276 0.000 –0.077 2.276*** –8.458 5.342** 1.678 LOAD (0.000) (0.087) (0.788) (5.593) (2.351) (1.189) NET 276 0.000 –0.079 2.563*** –10.179* 4.191* 1.572 (0.000) (0.087) (0.793) (5.606) (2.359) (1.188) CEFs GROSS 419 0.000 –0.087 –0.096 2.841 –2.002 –0.339 (0.000) (0.087) (2.068) (1.804) (1.347) (1.369) ADJUSTED 419 0.000 –0.086 –0.177 1.467 –3.070** –0.318 (0.000) (0.087) (2.110) (1.989) (1.326) (1.391) ADJUSTED 419 0.000 –0.105 0.021 2.513 –1.894 –1.339 LOAD (0.000) (0.087) (2.144) (1.858) (1.409) (1.369) NET 419 0.000 –0.104 –0.060 1.139 –2.963** –1.318 (0.000) (0.087) (2.185) (2.072) (1.386) (1.391) AEFs GROSS 695 0.000 –0.058 1.384 –2.153 0.692 0.825 (0.000) (0.084) (0.866) (3.038) (1.181) (1.144) ADJUSTED 695 0.000 –0.058 1.457* –3.671 –0.407 0.784 (0.000) (0.084) (0.874) (3.191) (1.163) (1.161) ADJUSTED 695 0.000 –0.077 1.415 –2.706 0.664 –0.175 LOAD (0.000) (0.083) (0.926) (3.126) (1.234) (1.144) NET 695 0.000 –0.077 1.488 –4.225 –0.435 –0.216 (0.000) (0.082) (0.935) (3.290) (1.215) (1.161)

Page | 209

In line with Ippolito (1989), for the IEFs portfolio in Table 7.8 there is a strong

positive relationship between the expense ratio and the fund performance. On the

other hand, for all CEFs expense ratio has a significantly negative effect on returns

performance. The same negative impact is also observed for adjusted returns and net

returns for the AEFs portfolio, but the results are insignificant.

The negative correlation between fund returns and expense ratio is in line with the

findings of Elton et al. (1993) and Indro et al. (1999) but does not support the

findings of Ippolito (1989). The significant negative coefficients for CEFs adjusted

returns (net after all expenses fee but not the load fee) suggest that the conventional

equity mutual fund, on average, overinvest in information (Indro et al., 1999).

Similar findings are reported for the impact of load fee. There is a positive but

insignificant relationship between load fee and the IEFs performance. However, the

relationship is negative for the CEFs.

Lastly, the results are mixed for the relationship between fund performance and

expense ratio and load fee with respect to the AEFs portfolio. Based on adjusted

returns, AEFs performance is negatively related to expense ratio but positively related

to load fees. Although the results are not significant, the adverse impact is larger for

the expense ratio as compared to load fees. The net returns performance of AEFs (see

Panel A, Table 7.8) indicates that expense ratio has a larger impact on the returns

performance (–0.74) relative to the load fee that has an impact of about –0.27.

However, the impact is less severe when using trimmed data (Panel B, Table 7.8). In

conclusion, the higher expense ratio and load fees of the IEFs could probably be

associated with low returns performance of the fund portfolio (Pollet and Wilson,

2008).

Page | 210

7.4.6 Fees and other fund attributes on multi-factor regression

The main objective of this section is to examine the relationship of fund attributes on

fund performance by employing the multi-factor panel REs with time fixed effects.

The specific regression utilised is as in the Eq. 3.30 (see p. 101). The study then

extends the analysis in order to identify the linear relationship between fund

performance and the fund attributes. Our expectation that there is a non-linear

relationship exist between the fees factors. These fund attributes are the explanatory

variables in this analysis and are classified them into two groups, namely the

endogenous variables and the exogenous variables. The endogenous variables include

risk and fees factor namely alpha, beta, residual risk, management fees, expense ratio,

total load fees, and trustee fees. Whereas the exogenous variables are fund’s age, size,

investment style (investment in local or foreign markets) and also the types of the

funds (see details in Section 3.4.5, Chapter 3).

The main issue addressed in this section is not only to investigate the fund

performance and the impact of fees on fund manager’s performance but also to

identify whether the linear relationship holds between fees and fund returns, and

between fund performance and other fund attributes. Since EViews that have been

employed in the previous analysis in this chapter has a limitation to do cross-sectional

FEs and period FEs at the same time due to multicollinearity problems exist (in which

singular matrix appear when the regression is conducted using EViews), therefore this

section employs Stata 11. This section compared on panel analysis based on FEs and

REs with GROSS as dependent variable and the independent variables are set of

attributes and fees that have been previously defined in Chapter 3 of this thesis.

The regressions of fund performance on fund attributes in this section are conducted

by using panel REs (reg. 1) and REs with for fixed effects (reg. 2). The results are

robust for cross-sectional standard errors and heteroskedasticity. The BPLM test for

random effects is also conducted, whereby if the test is not significant, then the pooled

OLS is conducted with time FEs as exhibit in reg. (3). Details about the results from

regression are discussed in the following sub-sections. Section 7.4.7.1 provides results

and discussion on performance of AEFs, meanwhile, the discussion on the findings on

comparative performance between IEFs and CEFs are presented in Section 7.4.7.2.

Page | 211

7.4.7.1 Performance of AEFs

The results of AEFs performance are exhibited in Table 7.9. The regressions of fund

performance on fund attributes is conducted using panel REs only (Reg. 1) and REs

with for fixed effects (Reg. 2).The BPLM test for random effects is conducted and if

the test is significant, it shows that REs panel is an appropriate model as opposed to

the pooled OLS estimation. When the BPLM is not significant, then the pooled OLS

with time dummies (Reg. 3) is employed. These dummies are however suppressed

and not reported. The regression based on FE cannot be done due to multicollinearity

problems as previously mentioned.

All of the exogenous variables are explained in Model 1, which contains of control

variables, return and risk factors. Model 2 contain the fees factors, which is the focus

factor in this analysis. Model 3 include all the variables but exclude the squared terms

of the fees factor as in Eq. 3.29 in Chapter 3. And, lastly Model 4 explains all the

variables including the square terms of the fee factors as mentioned in Eq. 3.30. The

dependent variable is the gross return of the portfolio.

Page | 212

Table 7.9: Fees and fund attributes on gross returns of AEFs, 1990–2009. Model 1 (N=843) Model 2 (N=843)

Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2)

Intercept 17.985** (7.692)

18.281** (7.073)

24.291** (8.389)

20.445 (46.652)

25.472 (46.154)

AGE –0.178*** (0.018)

–0.173*** (0.039)

–0.173*** (0.029)

- -

LNSIZE –0.398 (0.399)

–0.756** (0.357)

–0.756* (0.394)

- -

dINVEST 0.097 (0.881)

–0.450 (0.942)

–0.450 (0.800)

- -

dTYPE 0.590 (0.497)

0.396 (0.469)

0.396 (0.741)

- -

ALPHA 1.008*** (0.102)

0.887*** (0.087)

0.887*** (0.129)

- -

RETURNt-1 0.515*** (0.005)

0.537*** (0.015)

0.537*** (0.025)

- -

BETA –0.210 (0.311)

–0.267*** (0.183)

–0.267 (0.183)

- -

RESIDRISK –0.024 (0.069)

–0.013 (0.068)

–0.013 (0.068)

- -

MGMTFEE - - - –59.678*** (15.664)

–66.254*** (16.065)

MGMTFEE^2 - - - 15.533*** (3.662)

17.596*** (3.815)

EXPENSE - - - 17.166*** (6.487)

16.398** (7.724)

EXPENSE^2 - - - –3.723** (1.523)

–3.429* (1.843)

TOTLOAD - - - 12.222 (14.150)

5.856 (15.369)

TOTLOAD^2 - - - –0.907 (1.172)

–0.389 (1.294)

TRUSTEE - - - –19.188 (65.747)

12.313 (53.832)

TRUSTEE^2 - - - 106.303 (407.016)

–104.898 (331.590)

Adj. R2 0.710 0.937 0.710 0.095 0.855

Rho 0.000 0.000 - 0.000 0.048 BPLM 0.000 0.774 - 0.000 0.000 Test for time FEs

- 0.000 - - 0.000

Page | 213

Table 7.9 continued

Model 3 (N=737#) Model 4 (N=737#)

Reg.(1) Reg.(2) Reg.(3) Reg.(1) Reg.(2) Reg.(3)

Intercept –1.449 (9.901)

3.376 (9.762)

9.691 (11.581)

11.362 (24.782)

26.771 (24.155)

33.350 (26.554)

AGE –0.159*** (0.037)

–0.248*** (0.057)

–0.248*** (0.039)

–0.163*** (0.038)

–0.253*** (0.057)

–0.253*** (0.040)

LNSIZE –0.131 (0.419)

–0.675* (0.385)

–0.675 (0.438)

0.191 (0.352)

–0.319 (0.309)

–0.319 (0.387)

dINVEST 0.323 (0.919)

–0.165 (1.020)

–0.165 (0.808)

0.397 (0.917)

–0.117 (0.978)

–0.117 (0.795)

dTYPE 0.398 (0.505)

0.081 (0.490)

0.081 (0.766)

0.774 (0.471)

0.482 (0.442)

0.482 (0.738)

ALPHA 1.025*** (0.093)

0.907*** (0.083)

0.907*** (0.130)

0.991*** (0.086)

0.876*** (0.078)

0.876*** (0.129)

RETURNt-1 0.515*** (0.005)

0.539*** (0.014)

0.539*** (0.025)

0.514*** (0.005)

0.542*** (0.014)

0.542*** (0.024)

BETA 0.082 (0.334)

–0.058 (0.211)

–0.058 (0.195)

0.194 (0.302)

0.070 (0.181)

0.070 (0.192)

RESIDRISK –0.038 (0.072)

–0.029 (0.067)

–0.029 (0.068)

–0.002 (0.065)

0.009 (0.062)

0.009 (0.066)

MGMTFEE 0.405 (4.233)

2.760 (4.232)

2.760 (4.048)

–45.232*** (8.025)

–40.603*** (8.603)

–40.603*** (8.747)

MGMTFEE^2 - - - 12.033*** (1.728)

11.404*** (1.936)

11.404*** (1.954)

EXPENSE 0.416 (1.329)

–0.475 (1.389)

–0.475 (1.180)

8.326*** (2.917)

5.878* (3.422)

5.878* (3.187)

EXPENSE^2 - - - –1.568** (0.762)

–1.149 (0.843)

–1.149 (0.829)

TOTLOAD 1.280** (0.613)

0.839 (0.547)

0.840 (0.586)

5.105 (8.593)

0.962 (8.403)

0.962 (9.779)

TOTLOAD^2 - - - –0.281 (0.721)

0.040 (0.705)

0.040 (0.837)

TRUSTEE 7.999** (3.143)

7.337** (3.145)

7.337*** (2.436)

6.766 (32.157)

1.575 (28.816)

1.575 (26.580)

TRUSTEE^2 - - - 10.421 (200.192)

37.315 (183.484)

37.315 (164.687)

Adj. R2 0.734 0.943 0.937 0.710 0.710 0.937

Rho 0.00 0.00 - 0.000 0.000 - BPLM 0.000 0.626 - 0.000 0.544 - Test for time FEs

- 0.000 - - 0.000 -

Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.

Page | 214

The analysis aims to explain the relationship in term of the impact of fees and fund

attributes on fund return performance. Model 1 regresses the fund return on control

variables consist of exogenous variable fund age (AGE), fund size (LNSIZE) and

investment style (dINVEST), return factors, namely ALPHA and lagged return

(RETURNt-1 ) and risk factors, such as BETA and RESIDRISK, and also includes a

dummy variable for the type of fund (dTYPE). The results indicate that dTYPE has no

significant impact on fund performance, suggesting that there is no significant

difference between these two types of funds, the IEFs and CEFs.

Results show that there is a negatively significant relationship between age and fund

returns. There is also positive and significant relationship between ALPHA and lagged

return (RETURNt-1) on fund returns performance suggesting that the alpha and past

performance of the funds could contribute to higher fund returns performance. In the

model, risk is reflected by both, fund’s systematic risk (beta) and also the

unsystematic risk (residual risk), however there is a significantly negative relationship

only between systematic risk (BETA) and fund performance

The results yield that fund size (LNSIZE) now has a negative significant relationship

with fund performance based on regression in Model 3 using REs with time FEs. the

negative relationship between fund size and fund performance that we find here could

imply that the Malaysian mutual funds are small-cap rather than large-cap oriented.

The evidence is consistent with the findings on Swedish markets where there is a

negative impact on fund performance due to fund size and fee (Dahlquist et al., 2000)

and Grinblatt and Titman (1994), thus supporting the idea that a larger fund size is

associated with cost disadvantages that could lead to reduced returns performance

(Indro et al., 1999). Most importantly, this finding is consistent with Low (2010) who

reports a negative relationship between size and fund returns for the Malaysian mutual

funds suggesting that as the funds grow in size, they become less efficient in their

operations management that leads to less returns performance. However, it is not in

line with the findings of Chen et al. (1992) and Otten and Bams (2002) who find that

size is positively related to fund performance, suggesting that the larger funds perform

better. But the BPLM is insignificant, thus results could be dubious, but OLS

estimation with time FEs also provides similar results.

Page | 215

Model 2 regresses all the fees variables, Results indicate that that there is a significant

relationship between management fees and expense ratio on fund performance,

indicate that there is no significant relationship between the other fees, namely total

load and trustee fee and the fund performance.

Model 3 also reports results from the regression which include all the variables but

excluding the quadratic term of fees factors. Results show consistency with the

previous model (Models 1 and 2) with exception that now the total load and trustee

fee are positively significant on fund performance. Model 4 then includes all the

variables and the quadratic term for the fees factors. The results obtained are in line

with the results in Models 1 and 2 from the table.

In Model 4, result reveals the expense ratio is positively significant to fund returns.

The positive expense ratio could suggest that the fund managers’ earnings could

sufficiently support the cost of investment and, this is in line with the finding of Chen

et al. (1992) who study the US market over the period 1977 to 1984. This finding in

line with the finding of Ippolito (1989) and Droms and Walker (1996), but contradicts

with the finding of Elton et al. (1993), Golec (1996), and Prather et al. (2004).

Our expectation that there is a non-linear relationship between fees factors and the

fund returns. Therefore the quadratic term of fee factors are included in the model.

We expect there is a negative relationship between the fees factors and their square.

Results in Table 7.9 show that an increase in expense ratio will stimulate the efforts of

fund managers to increase more profit on fund returns. The marginal impact of each

percentage paid to fund managers in the form of expense ratio is however could lead

to diminishing impact resulting a hum-shape relationship which is captured by a

positive sign on coefficient estimates on the expense and a negative sign on its square,

which implied a positive impact on the fund performance. Based on results in Table

7.9, this situation did not hold for the management fees.

The results in Model 4 also indicate that there is a strongly negative relationship

between age and fund returns, but the alpha and lagged returns show strongly positive

relationship with the fund returns. Therefore, results suggest that longer age of the

Page | 216

fund could possibly reduce the performance of the fund returns, the table also reports

that alphas and RETURNt-1are statistically positively significant across all the

regressions. In line with our expectation, there is evidence that return factors and fees

are related to each other.

The evidence of negative relationship between fund age on the fund performance

across the model could also suggest that young funds may have potentially performed

better than older funds. this finding is in line with the finding of Otten and Bams

(2002) in the European markets, and Blake and Timmerman (1998) in the UK market.

However, this finding is not consistent with the previous findings of Ferreira et al.

(2011), Chen et al.(2004) and Prather et al.(2004) in the US market who indicate that

there is no relationship between age and fund performance.

In addition, the findings indicate that there is a strong negative effect of fund age and

management fee as shown in Model 4. However, the expense ratio is positively

significant on fund performance. The finding is in contrast with the findings of

Geranio and Zanotti (2005) who report no significant effect of age and expense ratio

in Italian funds industry. However, it is in line with the evidence of Malhotra and

McLeod (1997) and Tufano and Sevick (1997) from the US market and Korkeamaki

and Smythe (2004) based on Finnish markets.

The variables ALPHA and the lagged returns (RETURNt-1) show a positive and

significant relationship could explain the outperformance of the funds that can be

explained by the positive alphas. The evidence that fund performance is statistically

significantly correlated with RETURNt-1; returns performance of the past year

suggests that there is a short term persistency in mutual fund performance. This result

is inconsistent with the finding of Taib and Isa (2007) who find that there is no

persistency in Malaysian mutual funds over the period of 1991-2001. However, this

result is in line with the findings of Low and Ghazali (2007) who found that there is a

short run relationship between Malaysian mutual funds and the stock market in 1996–

2000. The result is also consistent with the evidences on developed market provided

by Hendricks, Patel, and Zeckhauser (1993) and Brown and Goetzmann (1995), and

Page | 217

also that from emerging markets (see for example, Suppa-aim, 2010), all of which

indicates a short-term persistency in mutual fund performance.

Results also indicate a positive relationship between lagged return (RETURNt-1) and

the expense ratio (EXPENSE), suggesting that the higher returns is compensate with

higher expenses. This evidence supports the finding of Korkeamaki and Smythe

(2004) who hypothesise that returns influence the expenses, and as a result the high

return funds charge higher expenses. However, this finding is contradict with the

finding of Gil-Bazo and Ruiz-Verdú (2008) who claim that worse-performing funds

set fees that are greater or equal to those set by better performing funds, and the

finding of Bechmann and Rangvid (2007) that the funds with higher expenses tend to

be low on returns performance.

7.4.7.2 Comparative performance between IEFs and CEFs

The results for the IEFs performance are presented in Table 7.10, and the CEFs

performance in Table 7.11 with a similar panel regression technique as previously

employed in Table 7.9.

Page | 218

Table 7. 10: Fees and fund attributes on IEFs returns performance, 1990–2009 Model 1 (N=342) Model 2 (N=342)


Intercept 7.841 (9.522)

12.340 (12.095)

20.779* (12.396)

146.688 (179.002)

146.688 (406.799)

–44.774 (143.102)

AGE –0.047 (0.033)

–0.200** (0.094)

–0.200*** (0.052)

- - -

LNSIZE 0.033 (0.448)

–0.336 (0.577)

–0.336 (0.555)

- - -

dINVEST –0.781 (1.132)

–1.627 (1.162)

–1.627 (1.269)

- - -

ALPHA 1.106*** (0.086)

0.972*** (0.103)

0.972*** (0.145)

- - -

RETURNt-1 0.519*** (0.008)

0.526*** (0.024)

0.526*** (0.044)

- - -

BETA 7.669 (4.901)

0.469 (6.383)

0.469 (6.469)

- - -

RESIDRISK –0.193* (0.111)

–0.038 (0.139)

–0.038 (0.128)

- - -

MGMTFEE - - - –243.285 (197.149)

–243.285 (408.220)

–16.983 (156.479)

MGMTFEE^2 - - - 68.269 (56.012)

68.269 (119.257)

1.712 (44.419)

EXPENSE - - - 11.447 (10.079)

11.447 (20.039)

17.895 (11.138)

EXPENSE^2 - - - –1.763 (3.143)

–1.763 (5.838)

–3.484 (3.278)

TOTLOAD - - - 23.356 (39.545)

23.356 (71.731)

4.777 (34.589)

TOTLOAD^2 - - - –1.682 (3.272)

–1.682 (6.111)

–0.180 (2.881)

TRUSTEE - - - –36.410 (117.974)

–36.410 (171.895)

91.877 (82.680)

TRUSTEE^2 - - - 245.032 (756.047)

245.032 (1048.332)

–578.311 (513.634)

Adj. R2 0.711 0.917 0.711 0.094 0.013 0.836

Rho 0.000 0.000 - 0.000 - 0.019 BPLM 0.000 0.083 - 0.214 - 0.000 Test for time FEs

- 0.000 - - - 0.000

Page | 219

Table 7.10 continued

Model 3 (N=289#) Model 4 (N=289#)

Reg.(1) Reg.(2) Reg.(1) Reg.(2)

Intercept –4.366 (9.344)

–7.426 (12.010)

–137.928** (60.704)

–237.621*** (86.640)

AGE –0.091 (0.040)

–0.284*** (0.104)

–0.127 (0.058)

–0.303** (0.117)

LNSIZE 0.290 (0.336)

–0.067 (0.466)

0.215 (0.416)

–0.311 (0.654)

dINVEST –0.242 (0.949)

–1.151 (1.337)

0.653 (1.297)

–0.418 (1.277)

ALPHA 1.036*** (0.058)

0.896*** (0.102)

1.055*** (0.056)

0.904*** (0.109)

RETURNt-1 0.517*** (0.008)

0.528*** (0.022)

0.516*** (0.008)

0.525*** (0.021)

BETA 7.242* (4.245)

–0.499 (5.925)

6.859* (4.139)

–0.664 (5.705)

RESIDRISK –0.163* (0.093)

0.016 (0.108)

–0.169* (0.091)

0.020 (0.100)

MGMTFEE –5.837** (2.887)

–6.053 (4.460)

153.221*** (54.310)

245.340*** (81.556)

MGMTFEE^2 - - –45.812*** (15.553)

–72.885*** (23.830)

EXPENSE 3.000*** (0.833)

2.727*** (0.847)

10.262*** (3.948)

11.738*** (9.104)

EXPENSE^2 - - –2.008* (1.164)

–2.580** (1.256)

TOTLOAD 2.464*** (0.598)

2.467*** (0.815)

–1.355 (13.797)

0.315 (17.369)

TOTLOAD^2 - - 0.293 (1.186)

0.128 (1.461)

TRUSTEE 1.837 (2.624)

5.441* (2.915)

1.024 (37.863)

60.610* (31.964)

TRUSTEE^2 - - 13.334 (241.291)

–338.491* (202.369)

Adj. R2 0.735 0.929 0.711 0.917

Rho 0.00 0.00 0.000 0.006 BPLM 0.000 0.029 0.000 0.000 Test for time FEs - 0.000 - 0.000 Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.

Page | 220

Table 7. 11: Fees and fund attributes on returns of CEFs, 1990–2009. Model 1 (N=501) Model 2 (N=501)


Intercept 12.075 (11.477)

13.140 (8.751)

19.234* (9.911)

48.169 (34.242)

53.118 (39.425)

53.118 (42.087)

AGE –0.039 (0.036)

–0.136*** (0.027)

–0.136*** (0.042)

- - -

LNSIZE –0.208 (0.633)

–0.624 (0.480)

–0.624 (0.513)

- - -

dINVEST –1.003 (0.869)

–0.399 (1.103)

–0.399 (1.025)

- - -

ALPHA 0.932*** (0.154)

0.876*** (0.134)

0.876*** (0.177)

- - -

RETURNt-1 0.515*** (0.006)

0.550*** (0.021)

0.550*** (0.031)

- - -

BETA 11.743*** (4.469)

4.985 (3.982)

4.985 (4.179)

- - -

RESIDRISK –0.292 (0.119)

–0.152 (0.092)

–0.152 (0.125)

- - -

MGMTFEE - - - –18.878 (15.382)

–26.084 (18.070)

–26.084 (19.976)

MGMTFEE^2 - - - 5.895 (3.706)

7.860* (4.398)

7.860* (4.979)

EXPENSE - - - 8.618 (6.298)

2.760 (8.223)

2.760 (8.142)

EXPENSE^2 - - - –2.222 (1.355)

–0.718 (1.752)

–0.718 (1.770)

TOTLOAD –2.788 (11.736)

0.138 (13.370)

0.138 (15.746)

TOTLOAD^2 0.208 (0.990)

–0.033 (1.128)

–0.033 (1.354)

TRUSTEE –36.182 (47.933)

–67.268 (45.908)

–67.268 (53.965)

TRUSTEE^2 202.423 (290.573)

380.161 (281.885)

380.161 (328.042)

Adj. R2 0.708 0.950 0.869 0.003 0.885 0.885

Rho 0.000 0.000 0.000 0.000 BPLM 0.000 0.801 0.000 0.537 Test for time FEs

- 0.000 - 0.000

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Table 7.11 continued

Model 3 (N=448#) Model 4 (N=448#)

Reg.(1) Reg.(2) Reg.(1) Reg.(2) Reg.(3)

Intercept 7.284 (14.971)

11.709 (14.081)

7.176 (35.067)

43.390 (29.938)

51.114 (36.115)

AGE –0.169*** (0.054)

–0.254*** (0.051)

–0.152** (0.060)

–0.250*** (0.053)

–0.250*** (0.059)

LNSIZE –0.564 (0.666)

–1.193** (0.552)

–0.176 (0.706)

–1.005* (0.542)

–1.005* (0.542)

dINVEST –0.446 (0.904)

0.219 (1.009)

–0.434 (0.868)

0.239 (0.840)

0.239 (1.043)

ALPHA 1.078*** (0.154)

0.981*** (0.138)

1.054*** (0.147)

0.948*** (0.142)

0.948*** (0.197)

RETURNt-1 0.514*** (0.006)

0.559*** (0.020)

0.514*** (0.006)

0.562*** (0.021)

0.562*** (0.030)

BETA 10.680*** (4.111)

4.516 (3.407)

12.765*** (4.329)

7.108** (3.281)

7.108* (4.208)

RESIDRISK –0.238 (0.145)

–0.099 (0.103)

–0.229 (0.140)

–0.092 (0.102)

–0.092 (0.139)

MGMTFEE 4.101 (2.871)

6.459** (3.093)

–34.698** (13.598)

–26.577** (10.527)

–26.577* (15.236)

MGMTFEE^2 - - 9.540*** (3.054)

8.008*** (2.454)

8.008** (3.561)

EXPENSE –2.366** (1.035)

–3.078** (1.200)

1.262 (4.559)

–4.429 (4.362)

–4.429 (5.740)

EXPENSE^2 - - –0.285 (1.044)

0.867 (0.978)

0.867 (1.298)

TOTLOAD –0.187 (0.731)

–0.449 (0.555)

9.978 (10.491)

5.561 (7.922)

5.561 (12.348)

TOTLOAD^2 - - –0.806 (0.882)

–0.457 (0.665)

–0.457 (1.045)

TRUSTEE 14.061*** (3.916)

12.189*** (3.340)

–8.754 (42.681)

–51.990 (32.830)

–51.990 (39.148)

TRUSTEE^2 - - 123.187 (267.316)

376.971* (208.092)

376.971* (243.109)

Adj. R2 0.734 0.956 0.734 0.956 0.954

Rho 0.000 0.000 0.000 0.000 - BPLM 0.000 0.000 0.000 0.175 - Test for time FEs

- 0.000 - 0.000 -

Note: # Variable RETURNt-1 denotes the lagged return, reduce the N observations. The asterisks ***, **, * denote that it is significant at 1%, 5% and 10% levels respectively. Standard errors are given in parentheses. Reg. (1): REs only, reg. (2): REs with time FEs, and reg (3):pooled OLS with time FEs.

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Model 1 reveals no significant relationship between the exogenous variables, namely

fund size and dINVEST on IEFs returns performance. Model 1 indicates that fund age

has a significant negative relationship with the IEFs returns as in reg. (2) in the model.

The results analysis show insignificantly negative effect of the fund size relative to the

IEFs fund returns using REs with time FEs across the models, which is in line with

the findings in the US market, which provides no evidence of a significant

relationship between fund performance and fund size, see for example, (Droms and

Walker, 1996; Grinblatt and Titman, 1994). In contrast, the results are not consistent

with the findings of (Dahlquist et al., 2000) on Swedish mutual funds. Results for the

CEFs performance also reveal that there is no significant relationship between fund

size and dINVEST on returns performance of the CEFs.

Results further indicate that age has significantly negative impact on both IEFs and

CEFs but more on the IEFs than the CEFs counterparts, suggesting that young funds

may have potentially performed better than older funds. This is in contrast with

previous findings that indicate that there is no relationship between age and fund

performance mostly in the US market (Chen et al. 2004; Ferreira et al., 2011; Prather

et al. 2004). However, this finding is in line with the finding of Otten and Bams

(2002) who reveal a negative relationship between fund performance and age and

expense ratio in the European markets, and a weak evidence in the UK market that

young funds (during their first year of existence) have performed better (Blake and

Timmermann, 1998).

Size has causing negative significant impact on CEFs, but it does not matter on IEFs

performance. The significant negative effect of fund size on the CEFs fund

performance supports the findings of Grinblatt and Titman (1994) and Low (2010).

The evidence is consistent with the findings on Swedish markets where there is a

negative impact on fund performance due to fund size and fee (Dahlquist et al., 2000).

The finding is also similar to Ferreira et al. (2011) who argue that smaller funds in the

US market perform better than the larger funds thus supporting the idea that a larger

fund size is associated with cost disadvantages that could lead to reduced returns

performance (Indro et al., 1999). Model 3 in regression with time FEs also reveals

that fund size and age have a significantly negative relationship with fund

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performance And lastly, the dINVEST factor also does not have any impact on both

portfolios. The evidence of dINVEST is in line with Abdullah and Abdullah (2009)

who find that there is no significant difference between internationally invested funds

and domestically diversified funds in Malaysia over a period of 2004 to 2008.

The risk factors include beta and residual risk. The betas for both portfolio indicate

risk is rewarding or it means risk –taking contributed to the CEFs returns but risk-

taking did not head on returns of IEFs, which is the finding is consistent with the

previous results that the IEFs is appear to have higher risk, but the beta gives no

impact to the returns. Residual risk however gives no impact on the both portfolios.

With regards to endogenous return factors, alpha and lagged return (RETURNt-1 ),

both variables give more a less similar impact on the both IEFs and CEFs The

findings show that alpha give a positive impact on the returns performance of IEFs

and CEFs across the models, suggesting that there is a short run relationship between

these variables and that historical performance is important for measuring fund

performance. There is also no significant difference between these portfolios on the

lagged return (RETURNt-1 ). A positive significant impact of variables ALPHA and the

lagged returns (RETURNt-1) could explain the outperformance of the funds that can be

explained by the positive alphas

Results also reveal that returns performance of CEFs have a statistically significant

relationship with beta and one year lagged returns, implying that the systematic risk

and past returns performance play important roles to explain the variation in the fund

returns. On the other hand, results based on REs in Models 3 and 4 also indicate a

significant relationship between systematic risk (BETA) and IEFs fund performance.

The models also show a significant positive effect of lagged return and beta on fund

performance, implying that the systematic risk and past returns performance play

important roles to explain the variation in the IEFs fund returns, but slightly lower the

CEFs.. The significant beta is in line with Indro, et al (1999) who find that systematic

risk and residual risk capture the cross-sectional variation of mutual funds’

performance. The positive lagged returns could suggest that there is a short run

relationship between these variables and that historical performance is employed for

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measuring fund performance. The significant beta is in line with the finding of Indro,

et al (1999). The residual risk however give no impact on both funds’ performance.

The focus factor is about fees and the evidences show that the impact of fee is

different between the IEFs and CEFs. Model 2 examines the impact of all the fees in

relation to funds’ performance, but none of them are significant when there is no

control variables involved.

When control variables are included but without quadratic term of fees factors as

shown in Model 3, results reveal a significantly negative effect of management fee on

IEFs performance. On the other hand, the results indicate that the Islamic fund

performance is significantly positively correlated with expense ratio and total load

fee. The positive expense ratio on IEFs performance is in contrast to the findings of

the previous literature on the US market (see Carhart 1997; Elton et al. 1993; Golec

1996). The significant relationship between TOTLOAD and expense ratio across all

panels in Model 3 is in contrast with the findings of Berkowitz and Kotowitz (2002)

and Korkeamaki and Smythe (2004) who find no statistical relationship between the

two variables in the US and Finnish mutual funds markets respectively. In contrast,

the results show that for the CEF portfolio there is a significantly positive effect of

management fee and trustee fee but a negative impact of expense ratio on funds’

performance. The negative expense ratio on CEFs performance is in line with findings

of the previous literature on the US market (see Carhart 1997; Elton et al. 1993; Golec

1996).

On the contrary to CEFs, Model 4 shows that IEFs have positive relationship in term

of management fee, and negative sign in its square. This result indicates that an

increase in management fee could increase the effort of fund managers to get higher

fund returns. Since the relationship is non-linear, this could lead to diminishing

impact in which at certain level, the increase of this fee could reduce the fund returns.

For the IEFs portfolio, management fee gives significant positive impact and its

square term gives negative, which mean that there is a hum-shape relationship

between the variables. The high impact of management fee on the performance of

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IEFs could suggest that this is the key reason that contributes to the outperformance of

the Islamic funds. The evidence also reveals that fees factor give positive impact on

managerial incentive of the IEFs fund managers skill, thus contributing to the

evidence that the portfolios performed better than the CEFs. The positive management

fee relative to the its square indicate that the 1 dollar increase in the management fee

could stimulate the incentive for the fund managers to increase the fund returns by the

same portion. The expense ratio and trustee fee also give the same relationship like

the management fee. In contrast, the management fee gives low impact to the CEFs as

there is a significant negative impact of the variable, represents unsatisfactory returns

performance and that the investors over-compensate the fund managers for their poor

expertise. However, its square term shows the positive impact, implying that the

variables exhibit the inverted U-shape relationship, overall the negative impact on the

CEFs performance.

We get qualitatively similar results for the fees factors when we repeat the analysis for

other dependent variables, ADJUSTED, ADJUSTED LOAD and NET. All results

remain the same, except that the coefficients on management fee, expense ratio, total

load and trustee fee, reduce by 1, which is what we expect given our definitions of

these variables.

7.5 Summary

The main issue addressed in this chapter is about the relationship between mutual

funds returns performance, fees and fund attributes. The results after correcting for

heterokedasticity, based on returns performance of 106 equity funds in Malaysia,

including 53 Islamic and 53 conventional funds, indicate that there is no

outperformance of IMFs and CMFs on market timing and insignificant positive fund

selectivity of the funds in Malaysia after the imposition of fees.

The study provides evidence that the imposition of fees has a severe adverse impact

on fund returns of all fund portfolios. Once the returns to equity for investors are

recomputed with the fees, the results indicate that the equity fund performance

steadily decreases from gross returns including all fees to net returns, excluding all

fees and the impact of of fees on fund returns is more than one to one. In other words,

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all types of fees make a significant difference on the performance of all the portfolios.

Thus charging higher fees on equity mutual funds have adverse implications for the

investors in the sense that it reduces the net return of the investments. This has an

important policy implication. The reducing of fees could give positive impact to the

potential investors in long term. Fund managers have often used this argument to

support the fees charged for their professional services, this however appears to be

dubious.

The study also finds a negative relation between fees factor and the performance of

the fund portfolios. On average higher expenses lead to low returns performance of

the funds and moreover, the impact of management fee is higher on IEFs rather than

the CEFs. The expense ratio however gives higher impact on returns performance of

the IEFs but no impact on the CEFs. In particular, there is a difference in fees

attributes between Islamic and conventional equity funds. Based on the quadratic

regression results, the management fee is significantly positively correlated to the

IEFs but negatively correlated to the CEFs. In contrast, the expense ratio and trustee

fee remain positively significant impact on IEFs. The both variables are now less

important to the CEFs. The results also imply that total load is now less important to

the IEFs but not important to the CEFs.

The evidence of non-linear relationship between fees found in this study could

suggest that managerial incentives are more important to the IEFs fund managers than

the CEFs ones. These will also imply that the outperformance of Islamic fund relative

to the conventional counterparts can be explained by these fees factors. The higher

fees will stimulate the fund managers to put more effort which can lead to get higher

returns. At the same time, the increasing in fees could reduce gross returns yield by

the investors. In other words, higher fees could lead to low net returns but higher fees

would also motivate the fund managers to acquire more returns.

The findings in this chapter also suggests that the mutual fund investors should have

knowledge on fees information when making any investment decisions. For

unsophisticated investors, investing in low fees funds could probably the best choice

in order to get the reasonable returns. However, for the active investors, investing in

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higher fees could possibly give higher return. This is due to there is evidence of non-

linear relationship found in this study, which suggest that the higher fees will

stimulate the fund managers to put more effort which can lead to get higher returns.At

the same time, the increasing in fees could reduce net returns yield by the investors. In

other words, higher fees could lead to low net returns but higher fees would also

motivate the fund managers to acquire more returns from the investment funds.

Based on overall funds, the findings also indicate a negative impact of fund age and

fund size on funds’ performance. Past performance appears to be relevant and

significantly positively related to the funds’ performance. In view of the fact that the

evidence suggest a perverse market timing expertise among fund managers, active

investors should familiarise themselves with appropriate knowledge of market

conditions particularly involving significant events so that they could get a lot cheaper

once the global market starts to deteriorate. As for the long term and also for the

moderate investors, the application of dollar cost averaging could be the best choice to

ensure that they achieve their investment goals and at the same time earn their desired

expected returns from the funds’ investing.

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CHAPTER 8 - SUMMARY AND

CONCLUSION

8.1 Background of the thesis

The growth of investment in mutual funds and its significance in the development of a

country’s economy makes a study related on the performance of mutual funds important.

Tremendous growth of Islamic funds in the global market also contributed to the importance

of these investments in various types of asset classes among Muslim investors and also from

ethical investors. The investments were demanded by investors not only because they

complied with Shariah principles but they also proved to be attractive due to growth potential

and to positive expectations about performance.

This increasing demand could benefit not only market players and regulators but also

academics, scholars and practitioners involved in the area. On the market player and

regulators side, offering a variety of innovative investment products attract more investors to

participate and contribute to the development of mutual fund industry. Academics and

practitioners in this industry advocate further research on this subject because these funds

have attracted a lot of attention from around the globe (Muslim and non-Muslim investors

alike). This thesis is therefore an attempt to understand performance of mutual funds in

Malaysia as evidence from emerging markets.

The main objective of the thesis is to evaluate the returns performance of Islamic and

conventional funds compared to the market return using various statistical and econometric

methods of analysis, using a more recent, extensive and comprehensive data on returns of the

funds (comprising of 479 funds, including 129 IMFs and 350 CMFs over the period 1990–

2009). Despite of the increasing demand and tremendous growth of Islamic funds worldwide

and high expectation about its performance, the existing literatures fails to address the issue

in somewhat more detail when comparing the performance if Islamic funds with its

conventional counter parts.

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While existing comparative studies between IMFs and CMFs are still scant, the previous

studies basically report that there is seemingly no difference in returns performance of IMFs

and CMFs (Elfakhani et al., 2005; Girard and Hassan 2005; 2008; Hassan et al. 2010). Most

of the data in the studies is now out of date, and in most cases, the sample size is very small.27

The dataset used in this thesis is relatively recent and more comprehensive. This is the first

study in the Malaysian mutual fund literature to evaluate fund performance using generous

data, collected from Morningstar database which is equivalent to studies conducted in the

developed market. This gives us the liberty of using a variety of sophisticated statistical and

econometric techniques. These techniques include data analysis, time series and panel data

regressions. The performance of Islamic funds is compared with conventional counterparts

using standard CAPM and TM models with single and multiple benchmarks. This approach is

adapted to get some sense about the value added of each technique in terms of conclusions.

This thesis incorporates several empirical studies related to the performance of mutual funds

in Malaysia. The first empirical study relates to the overall performance of mutual funds in

Malaysia using the largest and longest period of the study, from the beginning of Islamic

funds industry in 1990 to current as at the end of April 2009. These mutual funds are

diversified and can be classified in five broad categories: alternative, allocation, equity, fixed

income and money market. In this evaluation, the study employed the modified models

available in the literature and extended the models to incorporate more relevant explanatory

variables.

In view of that, this chapter is structured as follows. Section 8.2 briefly summarises the

thesis, and this is followed by a discussion of the key findings achieved from empirical

analysis in this study in Section 8.3. Section 8.4 explains some implications of the thesis.

Section 8.5 acknowledges the limitations of this thesis and finally, Section 8.6 suggests

further research avenues on this topic in the future.

8.2 Summary of the thesis

This section summarises results from the empirical analysis conducted on the returns

performance of IMFs relative to the CMFs. In essence, the study covers promising insights

27 See for example, Ismail and Shakrani (2003) include only 12 funds in their sample.

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derived from the hypotheses which are parallel to the stated objectives of the study which can

be summarised into three fold. First, the study employs standard performance measures in the

existing literature to the mutual funds’ performance in Malaysia and then investigates the

performance comprehensively using time series and panel data analysis. Second, the study

examines the market timing expertise and fund selectivity skill of the IMFs and CMFs fund

managers based on TM model and extended TM model using time series and panel data

approach. Third, the study examines the factors related to mutual fund performance in terms

of fees and the funds attributes, which is one of the main concerns in determining the

differences between Islamic and conventional funds, and elaborates an extended performance

measure.

In evaluating the returns performance of IMFs vis-à-vis to the CMFs peers, this thesis has

mainly explained the central importance of using different performance measures in order to

identify the real performance of these fund portfolios. This is done by examining the returns

performance of 479 mutual funds in the Malaysian market, divided into two groups, the IMFs

and CMFs, covering all the fund categories, namely, alternative, allocation, equity, fixed

income and money market fund. This thesis comparatively investigates the performance of

IMFs and CMFs relative to their market return benchmarks using several risk adjusted

performance measures such as Sharpe, Treynor and Jensen measures. The method is then

extended to the CAPM single factor model and quadratic regression model based on Treynor

and Mazuy (1966). Then the study employs multiple regression analysis using both a time

series and panel data approach. The results from using these different performance measures

are explained in chronology based on the chapters in this thesis. Chapters 4, 5 and 6 explain

the results of using the data based on monthly returns, and Chapter 7 provides results based

on annual returns.

The study achieves the first objective as listed in Section 1.3, in Chapter 1, by examining the

returns performance of IMFs, CMFs and Malaysian mutual funds in general against the

market return benchmark using raw returns and risk adjusted returns as discussed in Chapter

4. This is an attempt to identify any differences in risk and returns performance of the funds

in the overarching period of the study and also during the pre-crisis, post-crisis and during

crisis periods, including the AFC and GFC. Chapter 4 employs raw returns and also risk

adjusted returns and measures the returns performance using various performance

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measurements, namely, SR, JA, TI, AR, M2 and ASR. The results reveal that there is

evidence of outperformance of the IMFs and CMFs relative to the market returns using

various types of these performance measures, with the IMFs perform better than the CMFs

counterparts. The finding is in line with the findings of Chua (1985) and in contrast to other

studies (see for example, Abdullah et al. 2007; Taib and Isa 2007) on the Malaysian market.

One of the main issues addressed in previous studies is that IMFs perform better during the

bearish market, whereas CMFs perform better during the bullish market. This thesis further

evaluates the matter and it is discussed in detail in Chapter 4. In summary, results denote that

the IMFs performed better than CMFs during the pre-crisis and also during the AFC and

GFC. In contrast, the CMFs outperformed the IMFs during the post-crisis phase.

The closest study to the present study, conducted by Abdullah et al. (2007) generally

concludes that IMFs perform better in a bearish market whereas CMFs perform better in a

bullish market. This thesis further investigates risk adjusted return performance before, after

and during the crises. Results denote that the returns performance of IMFs is relatively less

severe impact than their CMFs counterparts using various types of performance measures, as

previously mentioned. The negative returns of both fund portfolios during bearish markets

(during the AFC and GFC) suggest that the Malaysian MFs followed the market movement

and were directly impacted by these crises. Further investigation using risk adjusted returns,

based on the CAPM model, reveals that IMFs perform better than the overall sample (refer to

AMFs) and CMFs. The IMF portfolio performed better than CMFs during the pre-crisis

period and during the AFC and GFC. However, it seems that the CMFs portfolio performed

better than IMFs during the post-crisis period (see Table 4.9). The present findings reveal that

IMFs significantly outperformed the market benchmark (indicated by positive alpha) over the

period of the study. IMFs also insignificantly outperformed the market benchmark during the

AFC and GFC crises.

The findings also reveal that there is a significant difference in investment style in term of

risk and returns of these two fund portfolios (as exhibit in Table 5.1 and Table 5.3), implying

that Islamic or conventional investors consider not only risk and return factors while

concerning these funds in their portfolio selection but also other substantive factors like fund

diversification, asset classes and fee charges. They are few other factors that need to consider

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as well as discussed in Chapter 7 in this thesis. The results supports our expectation that there

is a difference in returns performance between these two funds because an Islamic investment

differs from conventional ones in many ways especially in their concepts and operation, such

as the prohibition of interest and gambling. In Malaysia’s case, there is also a difference in

terms of the growth rate of these funds as previously discussed in Chapter 2 of this thesis.

Since the previous empirical studies contradict our results that IMFs and CMFs outperform

the market benchmark, this present study highlights the issue by examining two possible

explanations, with regard to the type of empirical analysis either using time series or panel

data analysis and also with regard to the attributes or characteristics of these funds, as next

proceed in second and third approaches.

The regression methods based on single and multiple-benchmarks are employed using time

series and panel data analysis in order to examine the returns performance of IMFs and CMFs

against single and multiple market benchmarks. The analysis is discussed in Chapter 5. The

results on monthly average returns performance of both IMFs and CMFs outperform single

and multi-benchmarks. As expected, IMFs also perform slightly better than CMFs and both

of them outperform the market return when using time series analysis. This is contradictory

to the findings of Hayat and Kraeussl (2011) and Abderrezak (2008) based on Islamic funds

in the global market. The results of Chapter 5 also show that the IMFs are significantly

outperformed the single market benchmark and relatively good in respect of the single

benchmark performance, the CMFs perform better via multi-benchmarks. On average, in line

with Chapter 4, the IMFs perform better than the CMF counterparts via a single market

benchmark. On the contrary, CMFs perform better than their IMF counterparts when panel

data analysis is implemented. The findings in this thesis document that fund portfolios are

sensitive to different analyses and models and therefore, suggest that various methods of

analysis should be implemented.

Panel data analysis is then employed in Chapter 6 using similar sample data as used in

Chapter 5. Panel data is applied in order to accommodate individual returns of each of the

funds within the period of the study thus increase the number of observations in the analysis.

The results contend that there is insignificant outperformance for both IMFs and CMFs over

the single and multi-factor market benchmarks. The panel results denote significant superior

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performance for both portfolios on fund selectivity skills via the TM and extended TM

models, and also confirm those for the time series in that there is perverse or no market

timing expertise among Islamic and conventional fund managers.

Second, the objective is to examine the ability of fund managers, and it is achieved by

analysing the fund managers on fund selectivity and market timing performance using time

series analysis (Chapter 5) and panel data analysis (Chapter 6). Results show that the

Malaysian fund managers, both IMF and CMF fund managers, similarly exhibit poor market

timing. With regard to market timing expertise and fund selectivity skill, the positive alpha

indicated by the regression via time series data means that there is a superior fund selectivity

skill among the respective fund managers. However, the present study denotes neither IMF

nor CMF fund managers have market timing expertise for the period of the study chosen

from 1990 to 2009 - either with the TM model or with the extended TM model. The findings

also support the underlying theory that fund managers’ investment strategy in market timing

does not add value to investors. This is in line with Abdullah et al. (2007) who suggest that

both Islamic and conventional fund managers do not possess market timing expertise.

Previous studies documenting consistent results in line with our findings are Low (2007) and

Annuar et al. (1997).

Third, to scrutiny results obtained from Chapter 5 (based on time series regression) and from

Chapter 6 (based on panel data regression), further investigation focusing on equity funds and

fees, and other fund attributes is implemented in Chapter 7. The chapter aims to investigate

the performance of IEFs and CEFs relative to their benchmarks and their relationship with

fees and other fund attributes like age, size and investment style, and as a results, the panel

data analysis is employed using the yearly returns data of the fund portfolios. All funds from

the equity fund category were chosen, consisting of the final sample of 53 IEFs, which were

then matched to the 53 top performers of CEFs. The issues about heteroskedasticity and serial

correlation which are related to the statistical and econometric method used in evaluating the

market timing expertise and fund selectivity skill among Islamic and conventional equity

fund managers are also addressed here through panel data analysis. On overall performance,

Chapter 7 provides findings that are in line with the results in Chapters 5 and 6, in the sense

that there is no market timing expertise but superior fund selectivity skills among equity fund

managers in Malaysia during the period 1990 to 2009.

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Since many issues are still not clear particularly related to factors that contribute to the

returns performance of the funds, the empirical study using panel data analysis is conducted

in order to achieve the objective to explore any impact of fees on fund performance (Chapter

7). This model not only help to improve performance modelling but also allow for further

analysis on fund attributes using fixed effects or random effects.

The results reveal that fees substantially reduce the fund returns performance. There is a

negative relationship between fees and fund performance with the effects is less severe in

gross return before fees compared to net returns after fees. On overall, the study provides

evidence that the imposition of fees has a severe adverse impact on the performance of all

fund portfolios, with more severe impact on the IEFs rather than the CEFs peers. These

results are also of interest to potential regulators and portfolio managers to converge their

investment areas. At present, investment in emerging markets (as one of the countries is

Malaysia) is often encouraged because of relatively low correlation with the developed

markets. Therefore, these findings brought new perspective for the market players of the

similarity between developing market and developed market so that they can collaborate in

the future.

This further investigation (as in Chapter 7) indicates that the imposition of fees on returns

performance of IEFs and CEFs has an adverse impact on the real performance of the funds.

The results also reveal that although there is a strong positively significantly performance of

IEFs and CEFs over the market returns, the CEFs seem to perform better than the IEF

counterparts, even after fees. The study provides evidence that the imposition of fees has a

severe and adverse impact on the performance of all fund portfolios. All portfolio returns

decline after the various fees have been imposed. Interesting, the study provides evidence that

the returns performance after fees of all the portfolios is relatively higher than the market

returns.

The study finds there is a negative relation between fees and IEFs and CEFs returns

performance based on panel data multi-factor regression. On average, higher expenses lead to

low returns performance of the fund and the effect becomes severe in respect of the returns

performance after fees. The findings also show a negative relationship between fund age and

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fund size attributes on overall fund performance. In addition, past performance appears to be

relevant and significantly positively correlated to the overall fund performance. Result also

shows that there is a different style for fees between the IEFs and CEFs. While the expense

ratio and front fee are positively related to Islamic fund performance, they are negatively

correlated to the conventional counterparts. In contrast, the management fee is significantly

positively correlated to conventional fund performance, but vice versa to the Islamic peers.

There is also a significant relationship between fund attributes and fund performance. The

important finding emerged in this study that the results reveal the attributes that influenced

return performance of the fund are different for the Islamic and conventional equity funds.

These results suggest that IEFs pay more attention to set of factors like expense ratio and

front fees as they are positively correlated and vice versa with the CEFs. On the contrary, the

CEFs pay more attention to management fees since the factor has significantly correlated to

fund performance but not to the IEF counterparts. Taken together, the findings in this thesis

do not support the recommendations of fund managers to increase the fees, for example,

management fees, as the strategy does not give any value added to the returns performance of

the investors. Summary of results obtained in this thesis are described in Table 8.1.

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Table 8. 1: Summary of the results

MODEL/ANALYSIS

FINDINGS

Standard risk adjusted performance

(SR, ASR, TI, JA, M2, AR) The IMFs perform better than the CMFs counterparts. CAPM due to the crisis The IMFs perform better than the CMFs during the pre-AFC, and

during the AFC and GFC. In contrast, the CMFs perform better than the IMFs during the post-AFC period.

Time series analysis Single benchmark • Both IMFs and CMFs outperformed the KLCI market

benchmark, with results indicating that the IMFs perform better than the CMFs.

• The IMFs and top performer of CMFs significantly outperformed the market benchmark, with on average the IMFs performed better than the CMFs counterparts, but performed slightly worse than the top performer of CMFs.

• All portfolios except the IMFs and top performer of CMFs are insignificantly underperformed the Islamic market benchmark.

Multi-factor benchmarks • The CMFs perform better when using the multiple benchmarks.

TM model • Both IMFs and CMFs outperform the market benchmark with the IMFs perform relatively better than the CMFs.

• The IMFs perform better than the CMFs in fund selectivity skill, however both IMFs and CMFs similarly have perverse or no market timing expertise since the coefficient estimates is insignificant.

• There is a negative correlation between fund selectivity skill and market timing ability of IMFs and CMFs fund managers.

Extended TM model • The CMFs perform better than the IMFs in term of outperformance relative to the market benchmark

• IMFs perform better than the CMFs on fund selectivity skill but similarly have inferior on market timing expertise.

Panel data analysis CAPM single factor • Both alphas of IMFs and CMFs outperformed the KLCI

market return benchmark. • The IMFs insignificantly outperform the Islamic benchmark. • The overall fund (AMFs) and CMFs significantly outperform

the Islamic benchmark using REs with time FEs, however, both of them are underperformed the benchmark using REs only.

TM model • The IMFs outperform the CMFs in relation to conventional market benchmark.

• The IMFs perform better than the CMFs in fund selectivity skill, however both IMFs and CMFs similarly have perverse or no market timing expertise since the coefficient estimates is very small and economically insignificant.

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Table 8.1 continued

Multi-factor CAPM Extended TM model

• All the portfolios underperformed the multiple benchmarks, with the IMFs perform relatively better than the CMFs peers when the REs with time FEs model is employed.

• The CMFs outperform the IMFs without time FEs model, however, the IMFs perform better with the time FEs. Both portfolios are underperformed relative to the market.

• Both IMFs and CMFs similarly have inferior market timing expertise since the value is very small and economically not significant.

Panel data analysis and fund attributes

• •

• The imposition of fees has an adverse impact on fund returns of all portfolios, in the sense that the fees reduce the expected gross returns obtained by the investors.

• The effects of fees on fund performance is more than one to one, with higher expenses lead to low returns performance of the funds.

• The impact of expense ratio is higher on returns performance after fees rather than gross returns.

• Based on the results of regression without quadratic term, management fee gives insignificant negative impact on IEFs but significantly positive impact on the CEFs. In contrast, the expense ratio and total load give highly positively significant impact to the performance of IEFs, but they give negative impact on CEFs. The trustee fee on the other hand, give positive impact to both portfolios.

• When the quadratic regression employed in this study, results are not consistent. The management fee is significantly positively correlated to the IEFs but negatively correlated to the CEFs. In contrast, the expense ratio and trustee fee remain positively significant impact on IEFs. The both variables are now less important to the CEFs. The results also imply that total load is now less important to the IEFs but not important to the CEFs.

• There is evidence of non-linear relationship found in this study, suggesting that managerial incentive is more important to the IEFs fund managers than the CEFs ones. The higher fees will stimulate the fund managers to put more effort which can lead to get higher returns. At the same time, the increasing in fees could reduce gross returns yield by the investors. In other words, higher fees could lead to low net returns but higher fees would also motivate the fund managers to acquire more returns

• The findings also indicate a negative impact of fund age and fund size on overall funds’ performance. Past performance appears to be relevant and significantly positively related to the funds’ performance.

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8.3 Key findings

Overall, our findings are comprehensive and achieve the objectives of this thesis (see

Section 1.3, in Chapter 1), and able to answer the research questions (see end of

Section 1.2, in Chapter 1) related to the hypotheses that have been developed in the

thesis. The key findings in this thesis based on our analysis can be summarised into

four subsections as the following. Table 8.2 provide a summary of these key findings.

8.3.1 Risk and returns performance

This key finding suggested that for our sample over the period of the study examined,

results of risk and returns performance in this study are mixed. Firstly, IMFs and

CMFs outperformed the market returns either using time series or panel data analysis.

Generally this finding is contrast with the findings of Taib and Isa (2007) and

Abdullah et al. (2007) in the Malaysian market but in line with Ippolito (1989) in the

US markets. The finding is also not in line with the finding of Firth (1977) and Blake

and Timmermann (1998) in the UK market.

Secondly, the finding also reveals that overall mutual fund industry in Malaysia

performs better than the market return benchmark. Unlike the previous findings of

Abdullah et al. (2007), results in this study show that the IMFs and CMFs outperform

the market returns under various risk adjusted performance measures like SR, TI and

JA, with IMFs performing better than the CMFs. For example, the mean return before

adjusted for the risk free rate indicate that IMFs return of 0.98 per cent compared to

the CMFs at 0.65 per cent and the market return at 0.24 per cent on monthly basis

(refer to Chapter 4). On overall, the Malaysian mutual fund industry significantly

outperform the market return by 6.10 per cent per annum, with the IMFs is about 8.23

per cent and the CMFs at 3.96 per cent over the period 1990–2009 (refer to Table 4.9)

Thirdly, on overall performance, the evidences show that the IMFs return is higher

than that of CMFs using time series and panel data analysis. It shows that the analysis

techniques play a role in influencing the outcome of the study. Concentrating on one

of the fund asset classes, namely the equity fund using panel data and include fees and

fund attributes as explanatory variables, as previously discussed in Chapter 7, the

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results consistently reveal that both IEFs and CEFs significantly outperform market

returns, the IEFs seem to perform better than their CEF counterparts.

The fourth is the study provides evidence which could also suggests that on average,

the returns performance of IMFs is sensitive to single market benchmark. The CMFs

on the other hand are far better with multiple market benchmarks, suggesting that

mutual fund performance measures are found to be sensitive to the model used.

The fifth is the analysis based on time series on different market conditions reveal that

the IMFs performed better than CMFs during a financial crisis and pre-crisis periods.

In contrast, the CMFs outperformed the IMFs during the post-crisis period.The AMFs

also have positive alpha during the GFC crisis indicating that the Malaysian mutual

funds in general outperform the market during the GFC crisis period. The IMFs also

insignificantly outperform the market benchmark during the AFC and GFC crises.

This result is not unexpected and in line with the findings of Abdullah et al.

(2007).The finding also shows CMFs have severely affected during the AFC and GFC

crisis.

The sixth is about the risk associated with fund return performance. The systematic

risk or beta results in most of the regressions reveal that they are lower than the

market risk as the beta is below than 1, suggesting that the risk and volatility of the

mutual funds in the sample is less risky rather than the relevant market benchmark. In

particular, the risk of IMFs slightly higher than the CMFs peers via time series (Table

5.1) and panel data (Table 6.1) analysis. Our findings are similar with Hayat and

Kraeussl (2011) with regard to betas of IMFs (significantly smaller than 1), implying

that the Islamic fund are low risk investments.

The seventh is regard to fund’s diversification. The CMFs have generally a better

degree of diversification compared to the IMFs (Table 5.7). In general, the degree of

diversification in this study are far better than the evidence of Abdullah et al. (2007)

who indicate the presence of a low diversification level among funds between 1992

and 2001. The findings are consistent with most mutual fund studies on the Malaysian

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market (Ahmed, 2007; Elfakhani et al., 2005; Hayat and Kraeussl, 2011), but not in

line with the result of Annuar et al. (1997).

Lastly, the eighth is this present study highlights evidence of fund persistency in

Malaysia over the period 1990–2009 in which this finding is not in line with Taib and

Isa (2007) who conclude that there is no performance persistence in the Malaysian

mutual funds over the period. However the positive relationship between the past

year’s and current performance support the previous study by Hendricks et al. (1993)

and Brown and Goetzmann (1995) in developed markets and the findings of Suppa-

aim (2010) in emerging markets. Therefore, it is suggested that the Malaysian fund

managers respond to the historical return performance of the funds and the movement

of the stock market while deciding on their portfolio selection.

Since the results of panel data reveal that there is no difference between investment

style of IMFs and CMFs (represented by dTYPE, as shown in Table 6), implying that

Islamic or conventional investors risk no penalty and do not need to consider any

financial penalty concerning these funds in their portfolio selection. Thus, the smart

investors could careful select the fund with low risk and more diversified but at the

same time provide reasonable profit in return. The evidence is in line with the

previous finding of Hassan et al.(2010) and Girard and Hassan (2005).

8.3.2 Expertise in market timing and fund selectivity skill

In view of the market timing expertise and fund selectivity skill among the fund

managers, this study finds evidences as the following. First, this study substantiates

the evidence of perverse or no market timing expertise among the Malaysian fund

managers using time series and panel data regression. The result further reveals that

IMFs and CMFs fund managers have similarly poor or perverse market timing over

the period of the study, either using time series or panel data analysis. The present

finding of poor market timing among the Malaysian fund managers is also in line with

Low (2007) and Abdullah et al (2007) but contradict to Hayat (2006).

Second, this study reports that the extended TM model give similar result of poor

market timing as the TM model, suggesting that the implementation of market timing

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strategy in the Malaysian market does not give any benefits and value-added to

investors in their portfolio management. Unlike Bello and Janjigian (1997), the usage

of TM model and extended TM model give not much difference in market timing

results in this thesis, thus implying that the result does not sensitive to any particular

methods.

Third, with regard to fund selectivity skills, the evidences exist concerning the

superior fund selectivity where IMFs fund managers provide significantly better

selectivity skill than the CMFs using time series as well as when the panel data is

employed. Consistent with the results of time series,the panel data results show that

IMFs fund selectivity skill is significantly superior to CMFs using the TM model and

the extended TM model.

In summary, the key findings of positive selectivity skill and perverse or no market

timing expertise among the fund managers in this study supports the evidence of

Annuar et al. (1997) in the Malaysia mutual fund market from 1990–1995. Other

finding shows there is also a negative correlation between timing and selectivity

performance of the IMFs and CMFs.

8.3.3 Fees impact and fund attributes on fund performance

This thesis evaluates the determination factors that contribute to fund performance.

The results show that the differences exist between the characteristics of Islamic and

conventional funds, the IEFs and CEFs in this case. First, the mutual fund

performance is found to have a negative relationship with fees, implying the higher

the fees, the lower could be the fund return.

Second, the evidence in this study suggests that fees give an adverse impact on fund

returns performance either IEFs or CEFs, and therefore fees reduce the apparent

outperformance statistics reported in all studies. This is striking, and is a new finding

using a better matched sample as conducted in this study. All type of fees charged

have adverse impact on returns performance of AEFs, IEFs and CEFs with the returns

of these portfolios decrease when the imposition of various fees incurred, where

higher fees lead to low fund returns performance. The IEFs returns seem getting more

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severe impact compared to the CEFs and AEFs. This result may imply that fund

managers could not offer maximum profit to investors and this is partly due to the

high fees and expenses charged.

Third, results show even though the fees give a negative impact to the fund returns,

IEFs and CEFs still outperform their market benchmark before excluding all fees

based on gross and also after excluding all fees, based on net returns, suggesting that

the fund returns are seemly adequate just to compensate the fees charged. The results

imply that fees charges by fund managers are still relevant for investors with the

expectation to increase their profits from the investments but too high fees could raise

them a burden which might make them switch to other non-load funds with low fees

like ETF and bond fund.

The findings also show a negative relationship of expense ratio and load fee variables

to CEFs but a positive to the IEFs. On the contrary, the management fee has a

negative relationship to returns of IEFs but positively to the CEFs. The size has

similarly no impact on fund performance of IEFs and CEFs, whereas age gives a

negative impact on returns performance, with the larger impact on CEFs rather than

the IEFs. Similar to Low (2008; 2010), the evidences of the size and age have

explanatory power in fund performance are not statistically significant.

Finally, the evidence of non-linear relationship between fees found in this study could

suggest that managerial incentives are more important to the IEFs fund managers than

the CEFs ones, thus suggesting that the higher fees will stimulate the fund managers

to put more effort which can lead to get higher returns. This is implied that the

increasing in fees could reduce gross returns yield by the investors, but at the same

time, higher fees would also motivate the fund managers to acquire more returns.

8.3.4 New improved model and extended literatures

This key finding would enhance the model and extend literatures in the area. First, the

study helps to improve fund performance modelling and extends the standard model

which is not only limited to time series but panel data regression on evaluating the

Islamic fund performance.

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Second, this study enhances the knowledge in Islamic fund performance, which

contributes to the development of Islamic funds area parallel to their tremendous

growth in the global financial market. To the best of our knowledge, this is the first

literature on other issues related to fees and fund attributes on fund performance, with

regard to Islamic funds.

Third, this thesis extends the existing literatures and provides widely discussed the

existing literatures and empirical results from the previous findings related to the

issues of mutual fund performance. The previous studies in mutual fund area are more

concentrated on the developed market where the data is easily available and mostly

centred in the US and other developed countries like UK and Australia. Since very

few studies focusing on emerging markets, the employment of this study by using

Malaysia as a case in emerging markets is essential.

More importantly, the gap between the previous evidence from developed and

emerging markets are in many forms such as the sample size, the development of

models, econometrics and statistical methods, the sophisticated database and the

variety of performance modelling. The literature on mutual funds in emerging markets

also reveals that the main concern in this region lies in performance evaluation. It

concluded that very little has been written on other issues related to fees, fund

attributes and investment style on fund performance. Most studies on mutual funds in

emerging markets like Malaysia also employ a short sample period and mainly

concern on risk and returns performance evaluation which is based on standard

approach of performance measures limit to SR, TI and JA ratios and not many

literature in this market concentrate on other issues like modelling, persistency, fees

and other fund attributes. Therefore, this study arise several issues related to mutual

funds’ performance which have been done in the developed market but very few in

the emerging markets such as the impact of fees on fund returns and the fund

attributes who determined the returns performance of the funds.

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Table 8. 2 Summary of key findings

RESEARCH QUESTIONS

KEY FINDINGS MODEL/ ANALYSIS

IMFs CMFs

Do IMFs and CMFs outperform relative to the market?

The IMFs perform better than the CMFs counterparts.

Standard risk adjusted performance (SR, ASR, TI, JA, M2 and AR)

YES

NO

Do IMFs and CMFs outperform the market?

Both IMFs and CMFs outperformed the KLCI market benchmark, with results indicating that the IMFs perform better than the CMFs.

Time series

YES

NO

The CMFs perform better than the IMFs in term of outperformance relative to the market benchmark.

Extended TM model-time series

NO YES

The IMFs insignificantly outperform the Islamic benchmark, and the CMFs perform better than IMFs in relation to the Islamic benchmark.

Single factor CAPM- panel data

NO YES

The IMFs outperform the CMFs in relation to conventional market benchmark.

Single factor CAPM- panel data

YES NO

The IMFs outperform the CMFs in relation to fund selectivity skill among the fund managers.

TM model –panel data

YES NO

All the portfolios underperformed the multiple benchmarks with the IMFs perform relatively better than the CMFs peers when the REs with time FEs model is employed.

Multi-factor CAPM- panel data

YES NO

The CMFs outperform the IMFs without time FEs model,

Extended TM model- panel data

NO YES

However, the IMFs perform better with the time FEs. Both portfolios are underperformed relative to the market.

Extended TM model- panel data

YES NO

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Table 8.2 continued

Do these funds offer similar benefits in risk, return and fund diversification

Both funds’ risk are lower than the market risk, with the risk of IMFs slightly higher than the CMFs. The return of IMFs is higher than the CMFs

Time series Time series

YES YES

NO NO

The CMFs have better degree of diversification than the IMFs.

NO YES

Is the performance of fund sensitive to single or multi-factor benchmarks?

IMFs portfolio is more sensitive to single factor.

Single factor CAPM

YES

NO

Meanwhile CMFs more sensitive to multi-factor benchmarks The CMFs perform better when using the multiple benchmarks.

Multi-factor CAPM Multi-factor benchmarks

NO NO

YES YES

Do these funds act differently in bearish and bullish market?

The IMFs perform better than the CMFs during the pre-AFC, and during the AFC and GFC.

Single factor CAPM

YES

NO

In contrast, the CMFs perform better than the IMFs during the post-AFC period.

NO YES

Do the IMFs and CMFs offer similar results on market timing and fund selectivity skills?

There is similarly perverse or inferior market timing expertise among the IMFs and CMFs fund managers

TM model and extended TM model using time series and panel data

NO

NO

IMFs fund managers perform better than the CMFs on fund selectivity skill.

TM model and extended TM model using time series and panel data

YES NO

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Table 8.2 continued

Are there any differences in fund performance can be explained by fees and other fund attributes

The management fee gives insignificant negative impact on IEFs but significantly positive impact on the CEFs.

Panel data regression without quadratic term,

NO YES

The expense ratio and total load give highly positively significant impact to the performance of IEFs, but they give negative impact on CEFs.

Panel data regression without quadratic term,

YES NO

In contrast, the management fee is significantly positively correlated to the IEFs but negatively correlated to the CEFs.

Panel data regression with quadratic term,

YES NO

The expense ratio and trustee fee remain positively significant impact on IEFs. The both variables are now less important to the CEFs.


YES NO

The results also imply that total load is now less important to the IEFs and not important at all to the CEFs.


YES NO

The evidence non-linear relationship between fees and their square terms found in this study, suggesting that managerial incentive is more important to the IEFs fund managers than the CEFs ones.


YES NO

8.4 Implications of this study

The study employs a longer and more comprehensive dataset than previous studies

did in the context of Malaysia, and previously issues that had not been discussed are

covered - performance benchmarking, strategy of market timing, fees and other fund

attributes using the multi-factor regression model. In doing so, three empirical studies

were conducted. The first relates to the overall risk and return fund performance,

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representing the IMFs and CMFs portfolios based on existing models in studies such

as the Sharpe, Treynor and Jensen ratios, Modigliani measure and appraisal ratio.

The second empirical study relates to the ability of fund managers, IMFs and CMFs

fund managers in timing and selectivity performance. The existing CAPM and TM

models are employed and the study extends these models to include more benchmarks

using time series and panel data analysis. The evidence reveals that Malaysian fund

managers have perverse or no market timing ability but they do have positive fund

selectivity skill. This study confirms what Annuar et al. (1997) concluded, and

contributes additional evidence suggesting that Islamic fund managers have superior

fund selectivity skill than their conventional peers. This was found when using time

series analysis and vice versa when the panel data analysis is employed.

The third empirical section explains the fees and fund performance which has recently

emerged as a major concern in developed and emerging markets. This present study

also evaluates the fund attributes consisting of endogenous and exogenous factors that

have an impact on fund performance. Using Malaysia as a sample, this thesis

enhances the existing literature on the Islamic fund industry and finds new evidence

and for the performance of mutual funds.

Furthermore, the analysis can be replicated to other countries in emerging markets

with similar characteristics to Malaysia. On this theme the study fills the knowledge

gap between developed and emerging markets by using a larger sample size in the

data, accessing a credible database provider, Morningstar database28. The more

extensive data give the opportunity here to employ more statistical and econometric

methods in the analysis instead of solely concentrating on standard evaluation method

based on Sharpe, Treynor and Jensen ratios.

One of our main hypotheses is about the effect of fees on fund returns performance

and results in this study show that it is statistically significant. The evidence in this

study suggests that fees remove the apparent outperformance statistics reported in all

28 To the best of our knowledge this is the first study of mutual funds based on a Malaysian sample using the Morningstar database.

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studies. This is striking and is a new finding using a better matched sample as

conducted here. The results also reveal that attributes influencing return performance

of the fund are different for Islamic and conventional equity funds. These results

suggest that IEFs pay more attention to a set of factors like expense ratio and front

fees as they are positively correlated with returns performance of the IEFs and vice

versa with the CEFs. On the contrary, the CEFs pay more attention to management

since this factor has been significantly correlated to fund performance but not to the

IEF counterparts. Therefore, the current findings add to a growing body of literature

especially the Islamic fund literature relate to fees and other determinant factors in the

form of fund attributes and their impacts on fund performance.

8.4.1 Implications for policy-makers and regulators

Policy-makers and regulatory bodies should use this timely evidence to improve

existing policies and practices, and incorporate them so that investors and the mutual

fund industry benefit from this essential information. Since obtaining public

information regarding investment funds is still an issue for emerging market investors,

the regulator should impose a requirement for IMF funds to disclose their fees (while

investing on the funds) in their prospectus or annual report. This should apply to the

CMF funds as well.

In term of the expense ratio and fees for example, since the information is not publicly

known, the investors are deprived of valuable information for their investment

decisions. Therefore, the diffusion of this fact by the policy maker or the regulator

could significantly change the industry’s status quo. The argument is expected to hold

for most of the young fund industries especially in emerging markets (Babalos et al.,

2009). The other thing is the investors usually consider raw or gross returns in value

their investing. In contrast, most of advice in performance evaluation is based on the

risk adjusted return, thus the fund managers and also financial advisors in particular,

shall not deny the right of investors to get to know such a value piece of information.

In the Malaysian case, the SC and also FIMM shall play their role to ensure this fees

requirement is complied. At the same time, more information shall be provided to the

investors by the policy-maker in a form of legal guidance on Islamic funds which can

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be itemised such as required disclosures, any notification of legislative changes

concerning mutual funds, fees and charges information and the roles and

responsibilities of Shariah supervisory boards. This information is important and

necessary for the purpose of creating awareness among market players, policy makers

and regulators to enhance their access and capability particularly relate to vital

decision making. Making investors accessible to public information will make dealing

with mutual funds would enter a new era of transparency like in the developed

market, and this could give opportunity to mutual funds investment to reach parity as

investment in the ETF. Also, other strategy shall be considered by the mutual fund

regulators, for example, the proposal about day to day mutual fund pricing system. It

is totally similar to stocks market and ETF pricing system in which the system could

increase transparency in pricing mechanism.

The development of mutual fund industry is essential as one of the benchmark to

achieve the title of a developed country; such policy must be drawn to encourage

people to invest in the industry. This can be done by establishing the rule to reduce

fee on equity funds, or give a portion of tax exemption for profits that investors gain

from mutual funds or provide incentive like income tax relief to the brokers or fund

managers who provide initiative to lower low management fees. For example, in the

Malaysian case, the mutual fund industry only contributes about 20 percent of the

market, could imply more rooms shall be done to encourage people to invest in

mutual funds. As saving is the most important approach to stabilise a country

economic growth, the higher growth could reflect the higher saving rate of a country,

take China for instance, where their saving rate is very high. As reported in the

Starbiz, the Credit Suisse Group forecasts that China may become the world’s

wealthiest country by household assets after the US within five years time as the

nation middle class consumer grow richer. With total household wealth to become

US$38 trillion by 2017, China may surpass Japan’s of US35 trillion and less than half

of the US, US$89 trillion (Wan, 2012, 11 October), hence making saving and

investment play important role in value investing and wealth creating. As a result,

encourage people to invest in mutual funds is one of the strategy where people can

save their money and increase the saving rate. Towards the end the industry can act as

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a prompt to stimulate and enhance the economic growth in such way through

providing investment capital and increasing the savings rate of a country.

The most important is the development and maturity of this fund industry will help

investors to correctly positioning themselves based on different market cycle and will

enhance investors to directly participate more aggressively and effectively in the fund

market. The situation could contribute to the higher growth of mutual funds industry

in Malaysia particularly, in realisation of “2020 newly developed country”.

8.4.2 Implications for fund management companies and fund managers

The findings of this study have a number of important implications for future practice.

First, the evidence shows that fees have an adverse impact on fund performance.

Other factors like age and size also matter. Therefore, the fund managers shall

encourage a better understanding of fund attributes and factors that contribute to the

returns performance of the funds.

The second implication is the increase in demand for mutual fund investments, will

enforce fund managers have to be creative about offerings investment products

especially in term of the best attractive cost/price structures. Clients are demanding

greater choice of financial products that best meet their wealth management needs.

Additionally, they are not prepared to pay high fees associated with the investment

funds. Besides, investors have their statutory right to know the real cost as well as the

competitive cost involved while they are trading in mutual funds.

Third, in order to increase market share, fund managers need to increase the amount

access and availability of public information to investors, for example fees

information which could help investors better understand their real cost incurred when

investing in a mutual fund. Since there is evidence of a negative relationship between

fees and fund return, it is suggested that fund managers shall concern with fees’

impact and revise the fee structure to encourage investors’ participation. In spite of

some investors being willing to pay more on the assumption that they will earn more,

it is important to note that higher fee charges could force investors to switch to

another type of low investment fee like exchange trade funds.

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Finally, the fund managers should strategically manage selection in fund portfolio and

have the appropriate knowledge of market conditions in order to choose the time to

invest. This has been highlighted by Islamic fund managers operating within and

alongside conventional fund managers. On average, little evidence from this study

suggests that Malaysian fund managers are insignificant outperformance on fund

selectivity skill with Islamic fund managers perform better based on time series

analysis and the conventional fund managers perform better when panel data is

employed. Both Islamic and conventional fund managers also have perverse or no

market timing ability during the period of time chosen for analysis. Hence, these

findings argue the ability of fund managers to utilise this market timing strategy and

therefore they should think about another key strategy like cost averaging and risk

management in order to sustain higher returns performance and to survive during a

financial meltdown. The dollar cost averaging possibly the best alternative for them to

apply during the peak market.

Also, the fund managers could focus on innovative products which emphasis more on

fixed income and greater liquidity to minimise the risk. These strategies would ensure

the mutual fund industry as well as the potential investors would derive full benefits

of investment in mutual funds compare to other investment vehicles. Additionally, the

industry and market players shall cooperate and organise themselves towards globally

recognised and accepted Islamic investment products. The Islamic funds for example,

is considered as safe bets for global investors that immune and proof to economic

hardships conceivably after the GFC in 2008. Besides, might be the time comes when

fund managers need to initiate new business model that will incorporate better modus

operandi and develop a new niche market development to grab a wider opportunity

from the local and global mutual fund industry.

8.4.3 Implications for investors

The results could benefit investors to strategically manage their fund portfolio. By

educating investors with fees information and what the liberalisation of the local

market (as proposed in the Malaysian capital market liberalisation plan in 2008)

means, and coupled with the openness and reliability of the mutual fund, it is expected

that fees will come under much closer scrutiny by investors. Fees information is vital

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when making decisions and it is suggested investing in low fees funds to obtain a

higher returns performance. Moreover, there are many fund attributes associated with

the performance of mutual funds with some of them having a positive or negative

relationship. Therefore investors should be aware of these relationships before making

any investment decisions.

In view of the fact that fund managers have perverse market timing expertise,

investors should also familiarise themselves with appropriate knowledge of market

events particularly bullish and bearish conditions. Moreover, it is suggested that

investors could diversify their portfolios with respect to fund asset allocation, that is

invest in various categories of funds such as alternative, allocation, fixed income and

money market. They could do these things instead of focusing on larger returns funds

per se or concentrating on only equity funds in order to pursue higher returns from the

investments.

Fund diversification on different asset classes are also shall be pondered. The global

investors for example shall take opportunity to invest beyond the developed markets

and the local investors shall also take into consideration to invest beyond their

boundary. The liberalisations of regulation in Malaysia for instance, open the

investors’ opportunities to leverage their investment funds. Malaysia implements the

liberalisation of the foreign exchange rules in March 2005 where the fund manager

companies are permitted to invest about 30 per cent of their assets overseas. This

creates a greater opportunity to investors to diversify their investment portfolios cross

the border for the sake of pursuing higher returns performance. In 2008, this

percentage has been increasing to 100 per cent foreign ownership. At the moment,

Islamic fund management companies are allowed to have 100 per cent foreign

ownership and they are also permitted to invest 100 per cent of their assets abroad. In

addition, local and foreign-owned Islamic fund management companies to be given

income tax exemption on management fees and other fees received from managing

the funds from the year of assessment 2007 to 2016 with the circumstance that the

funds must be approved by the SC beforehand (Securities-Commission-Malaysia,

2008).

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8.4.4 Implications for researchers

The evidence revealed in this thesis is potentially useful and will benefit researchers

examining the performance of Islamic and conventional mutual funds worldwide.

Apart from concentrating on the underperformance and outperformance of fund

portfolios using standard performance measures against the various market return

benchmarks, this thesis incorporates key factors like the strategy of market timing and

fund selectivity skill. The strategy of market timing and fund selectivity skill would

promote the researchers to do further analysis in the area particularly on the right time

to exploit these strategies for the benefits of fund managers to be more efficient and

more competitive and for the usage of investors to gain more profits from their

investments. Other than that, the introduction of various and innovative mutual fund

products could be vital to boost the growth the investment industry. This is because

investors are demanding investment products, which could deliver steady income

particularly during the volatile market. This also needs further investigation among

the researchers.

The thesis also describes the impact of fees on fund performance. Instead of focusing

solely on fee factors, this thesis discusses more detail the evidences of the relationship

between fund performance and other fund attributes such as age, size, investment

style, past performance and risk factors like systematic risk and residual risk.

Therefore, it is worth replicating this research to other countries in emerging markets,

especially for those countries who have similar economic fundamental like Malaysia

so that they can materialise the inflow of huge funds in their mutual funds industry to

capitalise their growing economy. It is also worth for researchers to come out with

study that relate the returns performance of funds with other impact factors like

marketing and agency roles in term of their contribution for the betterment of the

industry.

8.5 Limitations

The current study has only examined Malaysia as a sample, which does not solely

represent the development of the Islamic fund industry. In fact it only represents one

country and as a result, the findings of this thesis might be applicable only to

Malaysia. Other countries have their own characteristics such as the investor

Page | 254

behaviours, trend pattern, the regulation standards or infrastructure and therefore

caution must be applied when trying to transfer findings to other countries. Look at

the Islamic financial market, even though size is still small but it offers huge potential

over time.

Furthermore, the sample of the study was nationally representative of the Malaysian

mutual fund industry, thus it omits investors who were interested in participating in

the global market’s fund management system. This is due to many external factors

that are related to global issues and the regional market such as: legislation; and

shocks or events that need to be managed better if global investors want to invest in

IMFs in a specific market.

The current study has only examined the return performance of Islamic and

conventional funds using standard performance measurements that are limited to the

time series and panel data analysis approach. Since the data in this study is extensive,

many other approaches can be applied to the data analysis such as evaluating fund

performance using methods of copula, bootstrapping, Bayesian statistics, meta

analysis, data envelopment analysis and structural equation modelling. Future

research regarding IMFs should employ these kinds of advanced analysis approaches.

Other interesting issue that were not addressed here concerned whether fund flow has

any influence on returns performance on certain asset classes of Islamic funds. This is

particularly vital for potential investors to get more knowledge and information about

funds’ flow, their characteristics and behaviours, so that that they can decide whether

the present time is good time for investing in one asset class or another. We leave this

for future research to work out.

8.6 Suggestions for future research

Our main findings in this thesis highlight some avenues for further research issues in

the future. First, future research might explore and replicate the models and employ

them based on other sample of data in other countries, especially where evidence of

impressive Islamic mutual funds can be identified. Since Malaysia is one of a country

in emerging markets, the models can also be employed to other countries that have

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similar characteristics. Second, the thesis focuses on diversified mutual funds and

equity funds performance, comparing Islamic and conventional funds. It would be

interesting to assess bond fund performance since Islamic funds are creating much

interest in the current financial market. This is particularly to examine the bond fund

ability either it can provide reasonable investment returns with low risk or not. Third,

this thesis has thrown up another question such as the ability of fund managers to

achieve higher returns performance from investment funds using their market timing

expertise. Further research could also explore this important issue using different

methods of investigation.

In the Malaysian case, it also valuable to do more research concentrating on IMFs and

CMFs that strategically focuses and most contains investments in foreign securities.

Most of these funds are technically domestic funds but most contain the foreign

securities. Since few years ago few fund management companies such as Public

Mutual Berhad and CIMB Groups have launched a few products that invested in a

potential area like China, Australia and Japan, this may result in a more globally

affected on the whole Malaysian mutual fund industry. Hence, the talent of fund

managers could be a challenge here on risk management aspects and on how they

shall combine the stock selectivity skill and timing ability to achieve the best results.

This requires further research.

Finally, a new study on what is practising in other countries under the category of

emerging markets could benefit from the research methodology used in this thesis.

The implementation of the panel data approach in this thesis to evaluate the impact of

fees on fund performance is noteworthy and for the first time has been associated with

Islamic funds could be replicated in other mutual fund study. The models used in this

thesis also might be replicated to other countries, particularly those belonging to

emerging markets. This initiative could also be applied to other statistical and

econometric methods to analyse the appropriate fund data samples.

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APPENDICES

Appendix A Descriptive statistics of the Islamic and Conventional mutual funds in Malaysia, 1992 to February 2012.

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Mutual Fund

Industry

(MFI)

Units in

Circulation

(billion units)

na 17.03 25.12 31.94 38.99 45.25 46.54 52.63 63.85 71.39 84.53

Islamic na 0.29 1.07 1.27 1.81 2.19 3.44 2.12 3.13 4.26 5.76

Conventional na 16.74 24.06 30.67 37.17 43.06 43.1 50.52 60.71 67.13 78.78

No. of

accounts

(billion units)

na na na 6.85 7.96 8.26 8.59 8.91 9.58 9.99 10.18

Islamic na na na na na na na 0.21 0.24 0.27 0.3

Conventional na na na na na na na 8.7 9.35 9.72 9.87

No. of

approved

funds

na na na na na na na 107 127 164 188

Islamic 2 2 4 5 8 10 13 13 17 15 44

Conventional na na na na na na na 94 110 149 144

NAV (RM

billion)

15.72 28.13 35.72 44.13 59.96 33.57 38.73 43.26 43.3 47.35 53.7

Islamic na 0.19 0.46 0.51 0.76 1.03 1.76 1.39 1.68 2.42 3.21

Conventional na 27.94 35.26 43.62 59.2 32.54 36.97 41.87 41.62 44.93 50.49

Bursa

Malaysia

(BM)

Market Capitalization (RM billion)

246.00 619.70 508.85 565.63 806.77 375.80 374.52 552.69 444.35 465.00 481.62

Kuala Lumpur Composite Index (KLCI)

643.96 1275.32 971.21 995.17 1237.96 594.44 586.13 812.33 679.64 696.09 646.32

Panel A

% NAV to

Market

Capitalization

Total MF

industry(TI)

6.39 4.54 7.02 7.80 7.43 8.93 10.34 7.83 9.74 9.76 11.15

Islamic 0.03 0.09 0.09 0.09 0.27 0.47 0.25 0.38 0.5 0.67

Conventional 4.51 6.93 7.71 7.34 8.66 9.87 7.58 9.37 9.26 10.48

Panel B

% NAV to TI

Islamic 0.68 1.29 1.16 1.27 3.07 4.54 3.21 3.88 5.11 5.98

Conventional 99.32 98.71 98.84 98.73 96.93 95.46 96.79 96.12 94.89 94.02

Total 100 100 100 100 100 100 100 100 100 100

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Appendix A continued.

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Mutual Fund

Industry (MFI)

Units in Circulation

(billion units)

97.39 118.63 139.39 154.07 208.34 241.05 273.88 289.37 316.41 326.57

Islamic 8.59 13.16 18.62 18.55 36.35 49.93 56.85 56.21 61.21 61.96

Conventional 88.8 105.47 120.76 135.52 171.99 191.12 217.03 233.16 255.2 264.61

No. of accounts

(billion units)

10.22 10.43 10.86 11.16 12.28 13.05 14.11 14.62 15.43 15.55

Islamic 0.35 0.43 0.64 0.77 1.25 1.64 1.78 1.8 1.98 1.99

Conventional 9.88 10 10.22 10.4 11.03 11.41 12.33 12.82 13.45 13.55

No. of approved

funds

226 291 340 416 521 579 565 584 604 605

Islamic 55 71 83 100 134 149 150 155 167 167

Conventional 171 220 257 316 387 430 415 429 437 438

NAV (RM billion) 70.08 87.38 98.49 121.76 169.41 134.41 191.71 226.81 249.46 267.02

Islamic 4.75 6.77 8.49 9.17 16.86 17.19 22.08 24.04 27.86 29.24

Conventional 65.33 80.61 90 112.59 152.55 117.22 169.63 202.77 221.6 237.78

Bursa Malaysia

(BM)

Market Capitalization (RM billion)

640.28 722.04 695.27 848.70 1106.15 663.82 999.45 1275.28 1284.54 1345.30

Kuala Lumpur Composite Index (KLCI)

793.94 907.02 899.79 1096.24 1445.03 876.75 1272.78 1518.91 1530.7 1569.65

Panel A

% NAV to Market

Capitalization

Total MF

industry(TI)

10.95 12.10 11.00 14.35 15.32 20.25 19.18 17.79 19.42 19.85

Islamic 0.75 0.94 0.95 1.08 1.53 2.59 2.21 1.89 2.17 2.17

Conventional 10.20 11.16 10.05 13.27 13.79 17.66 16.97 15.90 17.25 17.67

Panel B

% NAV to TI

Islamic 6.78 7.75 8.62 7.53 9.95 12.79 11.52 10.60 11.17 10.95

Conventional 93.22 92.25 91.38 92.47 90.05 87.21 88.48 89.40 88.83 89.05

Total 100 100 100 100 100 100 100 100 100 100

Note: na refers to non-available data. Source: Adaptation from Securities Commission Malaysia and Federation of Investment Managers Malaysia (formerly known as Federation of Malaysian Unit Trust Managers- FMUTM).

Page | 258

Appendix B Year on year changes in the IMFs and CMFs, 1999-2012

1 2 3 4 5 6 7

No of Fund Managers

manage the fund (%)

Funds Approved (%)

Funds Account (%)

Funds Size (Units

circulation) (%)

NAV to total industry (%)

NAV to market

(%)

Year IMFs CMFs IMFs CMFs IMFs CMFs

IMFs CMFs IMFs CMFs IMFs CMFs Total

1999 38.24 100 12.15 87.85 2.33 97.67 4.02 95.98 3.21 96.79 0.25 7.58 7.83

2000 44.12 100 13.39 86.61 2.47 97.53 4.91 95.09 3.88 96.12 0.38 9.37 9.74 2001 62.16 100 9.15 90.85 2.67 97.33 5.97 94.03 5.11 94.89 0.50 9.26 9.76 2002 69.23 100 23.4 76.6 2.98 97.02 6.81 93.19 5.98 94.02 0.67 10.48 11.15

2003 75.00 100 24.34 75.66 3.39 96.61 8.82 91.18 6.78 93.22 0.75 10.20 10.95 2004 100 100 24.4 75.6 4.10 95.9 11.09 88.91 7.75 92.25 0.94 11.16 12.10 2005 100 100 24.41 75.59 5.89 94.11 13.36 86.64 8.62 91.38 0.95 10.05 11.00 2006 100 100 24.04 75.96 6.86 93.14 12.04 87.96 7.53 92.47 1.08 13.27 14.35

2007 100 100 25.72 74.28 10.19 89.81 17.45 82.55 9.95 90.05 1.53 13.79 15.32

2008 100 100 25.73 74.27 12.54 87.46 20.71 79.29 12.79 87.21 2.59 17.66 20.25 2009 100 100 26.54 73.45 12.62 87.38 20.76 79.24 11.52 88.48 2.21 16.97 19.18 2010 100 100 26.54 73.46 12.31 87.17 19.42 80.58 10.60 89.40 1.89 15.90 17.79

2011 100 100 27.65 72.35 12.83 87.14 19.35 80.65 11.17 88.83 2.17 17.25 19.42 2012 100 100 27.60 72.40 12.80 87.14 18.97 81.03 10.95 89.05 2.17 17.67 19.85

Avrg - - 22.50 77.50 7.43 92.53 13.12 86.88 8.27 91.73 1.29 12.90 14.19

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Appendix C The differences between Islamic and Conventional financial products

No Types of Financial Products

Conventional Islamic

Equity Investments – mainly involved equity securities like stock and shares, and also included equity-linked instruments and equity derivatives (Halim, 1999)

Stock means a share of ownership in a corporation (company). Stock typically takes the form of shares of either common stock or preferred stock. As a unit of ownership, common stock typically carries voting rights that can be exercised in corporate decisions. Preferred stock differs from common stock in that it typically does not carry voting rights but is legally entitled to receive a certain level of dividend payments before any dividends can be issued to other shareholders. Common stocks are similar to Islamic stocks if they have invest in companies which are comply to Shariah principles. In contrast, preferred stock is considered unlawful/ illegal under Islamic Laws/Shariah laws due to involvement of interest.

Islamic equity investment starts with Shariah compliant securities. Shariah-compliant securities Securities (ordinary shares / equities) of a company listed on Bursa Malaysia which is classified as Shariah permissible for investment Primary business and investment activities that generate income for the company are consistent with Shariah principles In Malaysia, the body that gives Shariah endorsement is the Shariah Advisory Council (SAC) of the Securities Commission of Malaysia (SC) Stock is based on Musharaka and Mudaraba (Trustee

Profit sharing) principles. Musharaka ( Joint-venture profit sharing) an equity participation contract under which a bank and its client contribute jointly to finance a project. It is a profit and loss sharing partnership. An ownership is distributed according to each party's share in the financing; and Mudaraba, a trustee type finance contract under which one party provides the capital for a project and the other party provides the labour. Profit sharing is agreed between the two parties to the Mudaraba contract and the losses are borne by the provider of funds except in the case of misconduct, negligence or violation of the conditions agreed upon by the bank. Equity investments are permissible under Shariah within certain parameters. Islamic scholars prescribed three minimum requirements: (i) the fund must not deal in the equities of companies whose business activities are banned by the Shariah principles, like alchoholics, casinos and

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conventional banks; (ii) interest income earned by the fund must be negligible (the current standard is less than 10 per cent) and separable so that the fund’s income can be cleansed of it; (iii) since the sale of debt is not permissible except at face value, the proportion of debts receivable in the portfolio of the company should not exceed an acceptable proportion. According to the standard that being used at present is 50 per cent.(Iqbal and Molyneux, 2005, p.106)

Debt instruments – Ismail (1999) defined as the debt-financing products which have been formally securitised for trading in the secondary markets. Debt securities may be short-term, called as trade finance is traded in the inter-bank market or private debt securities traded in the money market or public debt securities listed in the stock market(p.29).

Based on several Shariah principles like Murabaha, a purchase and resale contract in which a tangible asset is purchased by a bank at the request of its customer from a supplier, with the resale price determined based on cost plus profit markup; Salam, a purchase contract with deferred delivery of goods (opposite to Murabaha), which is mostly used in agricultural finance; Istisna, a predelivery financing and leasing instrument used to finance long term projects; Qard al-Hasan (benevolent loan), an interest-free loan contract that is usually collateralized; and Ijara, a leasing contract whereby a party leases an asset for a specified rent and term. The owner of the asset (the bank) bears all risks associated with ownership. The asset can be sold at a negotiated market price, effectively resulting in the sale of the Ijara contract. The Ijara contract can be structured as a lease-purchase contract whereby each lease payment includes a portion of the agreed asset price and can be made for a term covering the asset's expected life.

Bonds It is an certificate instrument with coupon interest that can be changed to cash during the maturity period.

Called as sukuk or Islamic bond. It is an instrument for pooled securitizations. Sukuk is secondary instrument based on a return from a real asset or its usufruct.

Mutual funds The funds are not necessarily complied with the Shariah principles. Also, the business activities of the companies involved are unlimited. The main objective

Unlike its conventional counterpart, an Islamic mutual fund must conform to Shariah investment precepts. The sharià

encourages the use of profit sharing and partnership , and forbids riba (interest), maysir (gambling) and gharar (

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is to maximize the profits and get higher return.

uncertainty . ie. selling something that is not owned or that cannot be described in accurate detail in terms of type, size and amount). In addition , there is a desire to have investment portfolios which are morally purified. Thus investment in companies that are not in compliance with Muslims` moral orientations are not permitted and are eliminated from the portfolio. To ensure compliance with the foregoing condition, Islamic mutual are governed by sharià advisory board whose role is mainly to give assurance that money is managed within the framework of Islamic laws. (Hassan and Lewis, 2006, 260).

Insurance It is part of risk management, to reduce risk due to some perils. It is based on bi-lateral contract, which the participants buy the insurance policy and they need to pay the premium according to the contract.

Called as takaful or Islamic insurance. It is based on uni-lateral contract.Takaful has the following features (Iqbal and Molyneux, 2005, p.57). The company is not the one who assumes risks nor the one taking any profit. Instead, it is the participants, the policy holders, who cover each other. All contributions (premiums) are accumulated into a fund. This fund is invested using Islamic modes of investment and the net profit resulting from these investment is credited back to the fund. All claims are paid from this fund. The policy holders, as groups, are the owners of any net profit that remains after paying all the claims. They are also collectively responsible if the claims exceed the balance of the fund. The company acts as a trustee on behalf of the participants to manage the operations of the takaful business. The relationship between the company and the policy holders is governed by the terms of mudarabah contract. Therefore, should there be a surplus from the operation, the company (mudarib) will share the surplus with the participants ( rab al-mal) according to the pre-agreed profit-sharing ratio.

Future markets Future contract is similar to forward contract except it is standardized in quantity, quality, delivery, location and is traded in organized markets. Price is the

Based of Bay` salam principle.

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only variable which is decided through the forces of supply and demand.

Forward markets Forward contract is an agreement in which seller agree to deliver specific commodities to buyers sometime in the future, while seller and buyer agree on the quality and the quantity of the commodity sold. Both parties will then know in advance the price and when these commodities will be delivered. Thus, they do not negotiate price at the time of delivery. Forwards are not confined to grains and foodstuffs, but prevalent in all sectors of the economy, rentals, lease and other commodities.

Based on Bay` salam principle.

Real estate Investment trusts (REITs)

Real estate is referred to real property fixed to the land, such as land and buildings. However, real estate investment trusts (REITs) is a security that sells like a stock on the major exchanges and invests in real estate directly, either through properties or mortgages. There are many kinds of REITs, like the following: Equity REITs: It is the investment in the properties and own it, thus the investors are responsible for the equity or value of their real estate assets. Their revenues come principally from their properties' rents. Mortgage REITs: it is the dealing in investment and ownership of property mortgages. These REITs loan money for mortgages to owners of real estate, or purchase existing mortgages or mortgage-backed securities. Their revenues are generated primarily by the interest that they earn on the mortgage loans. Hybrid REITs: This kind of REITs is a

An Islamic REIT is permitted to own (purchase) real estate in which its tenant(s) operates mixed activities that are permissible and non-permissible, according to the Shariah. However, the Islamic REIT fund manager must perform some additional compliance assessments before acquiring real estate that has a tenant(s) who operates mixed activities. The list of activities that are classified as non-permissible as decided by the Shariah Advisory Council are: 1. financial services based on riba (interest); 2. gambling/gaming; 3. manufacture or sale of non-halal products or related products; 4. conventional insurance; 5. entertainment activities that are non-permissible according to the Shariah; 6. manufacture or sale of tobacco-based products or related products; 7. stockbroking or share trading in Shariah non-compliant securities; and 8. hotels and resorts.

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combination of the investment strategies of equity REITs and mortgage REITs by investing in both properties and mortgages.

Wealth management Consist of wealth generation and wealth accumulation.

Besides the wealth generation and wealth accumulation, there are wealth distribution and wealth purification.

Leasing and hire-purchase

Based on loans concept. According to Islamic laws, granting loans to customers for profit is unlawful. The leasing facility is based on Ijarah concept. It is sale of usufruct of an asset. The lessor retains the ownership of the asset with all the rights and the responsibilities that go with ownership. A hire-purchase concept is based on principles of mudaraba, musharaka and murabahah, through equity participation or partnership.

Deferred instruments The contract for deferred payments in conventional side is based on loan contract. The selling price is the nominal value of a loan plus the total interest payment. The interest payment is fluctuated over time according to the based lending rate (BLR), denoted by the central bank. In other words, the interest rates vary from time to time, as dictated by market forces. Any variation in the profit portion of the selling price will cause the selling price to change as well. Interest rates on loans are adjustable to reflect changes in the cost of fund. The contract was totally different with the BBA contract. The profit rate in the BBA must stay fixed even though cost of funds has changed. Therefore, according to Khir et. al (2008), when Islamic banks see higher interest rates on loans, there is nothing they can do to upgrade the BBA profit rate, as this will alter the existing BBA price. In contrast, the conventional banks can revise interest rate upward, and customers may

Based on Bay muàjjal or bay bithamin ajil (BBA). It is kinds of deferred payment sales. It is a sale in which goods are delivered immediately but payment is deferred. BBA contract is a sale with deferred payment and is not a spot sale. This contract is based on buying and selling activities. The asset that a customer wants to purchase is bought by a bank and sold to the customer at an agreed price after the bank and customer determines the tenure and the installments. The price at which the bank sells the asset to the customer will include the actual cost of the asset and will also incorporate the bank’s profit margin. From Shariah point of view, the profit gained by the bank is legitimate since the transaction is based on a sale contract. The monthly installment is determined by the selling price, repayment period and the percentage of profit margin of financing. The basic feature of BBA is the selling price is fixed throughout the duration of the tenure. Normally, the bank must make sure that the selling price remains unchanged until the contract expires. Any changes in price will make the contract void and null.

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have to pay more monthly. This is not possible in Islamic banking because by taking a similar move, it will increase the contractual selling price, thus violating the BBA contract as the principle of aqad (contract) requires only one price in one sale.

Deposits It is also called as savings account. This account caters to those who wish to save money and at the same time earn income in the form of interest.

Savings account in Islamic term function as same as conventional, unlike they use different principles, such as wadiah, mudaraba and qard al-hassan.

Fixed deposit Fixed deposit is for those who keep money from investment purposes, to get better returns on their funds. The fixed returns is in the form of interest, either quarterly, semi-annually or yearly paid to the clients.

In Islamic finance, this facility is familiar with a term called investment deposit.This deposit is governed by the principle of mudaraba. Islamic banks act as agent-manager or mudarib and the depositors act as investors or rabb al-mal. The bank would provide no guarantee or fixed return on the amount deposited. Customers who hold their funds in this investment deposit will be treated as if they were shareholders of the bank and are entitled to a share of profits or losses made by the bank. The agreement on how the percentage of the profit or loss will be distributed between the bank and the depositor is made at the beginning of the deposit period and cannot be amended during the tenure of the deposits, except by the consent of both parties. The distribution of profit to the depositors may be on a monthly, quarterly, half-yearly, or yearly basis and advance notice is required for those who wish to withdraw the funds before the maturity date.

Money market Based on currency exchanges, which refer to the current interest rate movements.

Based on sarf (Islamic rules governing currency exchange)

Capital market The capital market from conventional perspective basically based on the loan contract, which provide returns to the investors in the form of interest rate, dividend and also capital gains. The main

Shariah compliance is the fundamental thrust of ICM, include (i) Shariah Advisory Council for regulator, and (ii) Shariah committees or advisers for the industry. The Islamic contracts are the underlying principles for all ICM products & services, consist of the following:

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objective of its capital market is to maximize profit for the benefits of the investors (lender) as well as the entrepreneurs (borrower). The range of products offered in traditional capital market are quite similar to the Islamic capital market products except they are not complied with the Shariah principles.

• Sale/Purchase principles– BBA, murabahah, istisna` and bay` Salam

• Rental/Hire principles– ijarah, ijarah thumma bay`, ijarah wa iktina`

• Profit/Loss Sharing principles– mudarabah and musharakah

• Loan principle– Qard hasan

The ICM products in various markets, include; • Islamic equity market • Shariah-compliant stocks; Islamic mutual funds;

Islamic REITs; Islamic indices; and Islamic ETFs. • Sukuk (Islamic debt) market • Islamic asset-based financing; Islamic equity based

financing; and Islamic asset-backed securities • Islamic stockbroking • Shariah-compliant trading; and Shariah-compliant

margin financing • Islamic structured products • Dual-Currency Product; Commodity-Linked

Product; and • Capital Protected Product • Islamic derivatives • Crude Palm Oil futures; Crude Palm Kernel

Futures; and • Single Stock Futures

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Appendix D GFC and the major events

.

Source: Ariff and Farrar (2011)

0407

0707

0807

0907

0108

0308

0708

1

0

0

7

1207

0508

0408

Century Financial

Bears Stearn I

BNP Paribas

Northern Rock

BCRS Citi Merryll

$700 bill auction

Stock market 1st Drop 3.6 %

Chase grabs Bears Stearn II taken

UK loans to Bank rescue

UBS; Barclay lose capital; raise cap

Fannie & Freddie Mae Rescue

0908

15 Financial firms fail worldwide and Banks not lending: No US$ Worldwide on 11-15 Oct: 2008

2nd Stock market Down 8% (NY) AIG Rescued HBOS takeover Fortis Bradford-Bigley Gitnir, Iceland

$700 bil rescue US$50 bil Germany $88 bil UK rescue Wachovia fails, etc

$16.4 bil IMF $586 bil China More rescues UK VAT 2.5%

1108

1208

CAR sales SME loans Irish bank Lowest g 0.5

Global financial crisis

1008

1008

Page | 267

Appendix E CAPM analysis against Islamic and conventional benchmarks The table presents results on returns performance based on different market benchmark before correction for heterocedasticity. Panel A employs mean aggregate returns whereas Panel B indicates the mean equally weighted returns. The mean excess returns are reported in percentage net from all expenses.The Malaysian t-bills is employed as a proxy for the risk-free rate of return. The period of study for conventional benchmark is from January 1990 to April 2009, whereas the period for Islamic benchmark is from July 1999 to April 2009. Standard errors based on the cross-section of the estimated coefficients are given in parentheses. The asterisks ***,**,* indicate significant level at 1%, 5% and 10%, respectively. N represent number of funds, whilst n is the number of observations. Conventional benchmark Islamic benchmark α β Adj R2

α β Adj R2 Without correction for heteroskedasticity

Panel A: Mean equally weighted return All funds (479)

–0.2631*** (0.0241)

0.0377*** (0.0030)

0.4082 –0.2343*** (0.0078)

0.0142*** (0.0016)

0.4121

IMFs (129)

–0.1883*** (0.0415)

0.0587*** (0.0051)

0.3598 –0.2348*** (0.0124)

0.0215*** (0.0025)

0.3859

CMFs (350)

–0.3379*** (0.0120)

0.0167*** (0.0015)

0.3544 –0.2337*** (0.0039)

0.0068*** (0.0008)

0.3960

Top performer (129)

–0.3238*** (0.0132)

0.0212*** (0.0016)

0.4211 –0.2304*** (0.0042)

0.0100*** (0.0008)

0.5411


–0.3000*** (0.0393)

0.0556*** (0.0049)

0.3596 –0.2312*** (0.0084)

0.0163*** (0.0017)

0.4416


–0.3361* (0.1717)

0.2316*** (0.0206)

0.4236 –0.2596*** (0.0868)

0.1601*** (0.0174)

0.4172

Difference –0.2155*** (0.0360)

0.0435*** (0.0045)

0.2896 –0.2376*** (0.0103)

0.0156*** (0.0021)

0.3256

Panel B: Mean aggregate return All funds (479)

0.5081*** (0.1618)

0.5432*** (0.0200)

0.7610 0.0220 (0.1589)

0.6193*** (0.0318)

0.7636

IMFs (129)

0.6861*** (0.2065)

0.5704*** (0.0255)

0.6831 –0.0158 (0.1805)

0.6130*** (0.0361)

0.7101

CMFs (350)

0.3301** (0.1438)

0.5159*** (0.0178)

0.7844 0.0598 (0.1501)

0.6256*** (0.0301)

0.7870

Top performer (129)

0.5116*** (0.1501)

0.4989*** (0.0186)

0.7575 0.2594* (0.1517)

0.6751*** (0.0304)

0.8081


0.0950 (0.1449)

0.5338*** (0.0179)

0.7932 –0.0050 (0.1517)

0.5168*** (0.0304)

0.7114


–0.2622 (0.3366)

0.6906*** (0.0404)

0.6273 –0.4350* (0.2331)

0.7372*** (0.0467)

0.6799

Difference

0.3556** (0.1480)

0.0544*** (0.0183)

0.0328 –0.0762 (0.0966)

–0.0130 (0.0257)

–0.0047

Page | 268

Appendix F TM model for market timing expertise of the fund managers The table presents results of market timing expertise and stock selectivity skill of IMFs and CMFs Fund Managers using TM model. The KLCI is used a proxy for the market return. The returns are based on mean aggregate and mean equally weighted and reported in percentage. The returns are net from all expenses and adjusted for the risk-free rate using Malaysian t-bills as a proxy. The period of study is from January 1990 to April 2009. Standard errors based are given in parentheses. The asterisks ***, **, and * indicate significant level at 1%, 5% and 10%, respectively. N represents the total numbers of funds in each portfolio and Obs is the numbers of observations.

Portfolio α β θ Adj R2 Obs Obs Without correction for heteroskedasticity

Panel A: Mean equally weighted return

AMFs (N=479)

–0.2584*** (0.0260)

0.0375*** (0.0030)

–0.0001 (0.0001)

0.4063 232 232

IMFs (N=129)

–0.1843*** (0.0447)

0.0585*** (0.0052)

–0.0001 (0.0003)

0.3572 232 232

CMFs (N=350)

–0.3325*** (0.0129)

0.0165*** (0.0015)

–0.0001 (0.0001)

0.3552 232 232


–0.3197*** (0.0142)

0.0210*** (0.0016)

0.0001 (0.0001)

0.4201 232 232


–0.3069*** (0.0424)

0.0559*** (0.0049)

0.0001 (0.0002)

0.3573 232 232


–0.4715** (0.1817)

0.2380*** (0.0206)

0.0020 (0.0009)

0.4350 172 172

Panel B : Mean aggregate return AMFs (N=479)

0.3303* (0.1717)

0.5502*** (0.0199)

0.0027*** (0.0010)

0.7679

232 232

IMFs (N=129)

0.5339** (0.2211)

0.5764*** (0.0256)

0.0023* (0.0013)

0.6864 232 232

CMFs (N=350)

0.1267 (0.1508)

0.5240*** (0.0175)

0.0031*** (0.0009)

0.7954

232 232


0.3378** (0.1589)

0.5058*** (0.0184)

0.0027*** (0.0009)

0.7654 232 232


–0.1352 (0.1509)

0.5429*** (0.0175)

0.0035*** (0.0009)

0.8067 232 232


–0.7123** (0.3476)

0.7119*** (0.0395)

0.0066*** (0.0018)

0.6523 174 174

Page | 269

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