Mutual funds performance, types and impacts on stock returns (financial institutions and services)

FINANCIAL INSTITUTIONS AND SERVICES MUTUAL FUNDS PERFORMANCE, TYPES AND IMPACTS ON STOCK RETURNS No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services FINANCIAL INSTITUTIONS AND SERVICES Additional books in this series can be found on Nova’s website under the Series tab Additional e-books in this series can be found on Nova’s website under the eBooks tab FINANCIAL INSTITUTIONS AND SERVICES MUTUAL FUNDS PERFORMANCE, TYPES AND IMPACTS ON STOCK RETURNS DONALD EDWARDS EDITOR New York Copyright © 2017 by Nova Science Publishers, Inc All rights reserved No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description This button is linked directly to the title’s permission page on copyright.com Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: info@copyright.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works Independent verification should be sought for any data, advice or recommendations contained in this book In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services If legal or any other expert assistance is required, the services of a competent person should be sought FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS Additional color graphics may be available in the e-book version of this book Library of Congress Cataloging-in-Publication Data ISBN: 978-1-53610-659-6 Published by Nova Science Publishers, Inc † New York CONTENTS Preface Chapter Chapter Chapter Index vii A Review of Performance, Screening and Flows in Screened Mutual Funds Ainulashikin Marzuki and Andrew C Worthington Does the Choice of Performance Measure Matter for Ranking of Mutual Funds? Amporn Soongswang and Yosawee Sanohdontree 73 Mutual Fund Prediction Models Using Artificial Neural Networks and Genetic Programming Konstantina Pendaraki, Grigorios Ν Beligiannis and Alexandra Lappa 93 129 PREFACE The authors of this book provide and discuss new research on performance measurements, types, and impacts on stock returns of mutual funds Chapter One reviews the theoretical and empirical literature relating to mutual fund performance, screening, and fund flows Chapter Two examines performance of Thai equity mutual funds over 5-year time periods of investment Chapter Three provides mutual fund prediction models using artificial neural networks and genetic programming Chapter – Islamic mutual funds (IMFs) continue to grow as an alternative investment vehicle for investors wishing to integrate Islamic values and secular financial objectives in their investments The most distinctive feature of IMFs lies in screening strategies based on the application of Shariah (Islamic law) Conventionally, this involves the application of exclusionary screening, whereby fund managers screen out companies involved in certain activities, including riba (interest), gharar (uncertainty), and maysir (gambling), and prohibited products from their portfolios as prescribed by the Quran, Sunnah and related Islamic texts The central outcome is that the managers of IMFs, unlike those of conventional mutual funds (CMFs), necessarily access only a subset of the population of investments available This has dramatic implications for many conventional dimensions of mutual fund behavior, including performance, the flow of funds, and the fund selection behavior of investors This chapter reviews the theoretical and empirical literature relating to mutual fund performance, screening, and fund flows The literature on performance starts with a discussion of the development of mutual fund performance evaluation techniques and the underlying theory This provides a general understanding of the importance of performance measurement and various ways to measure mutual fund viii Donald Edwards performance In addition, this chapter also reviews the literature on fund attributes and their influence on mutual fund performance In the screening literature, the review includes the impact of screening and differences in screening strategies to firm and mutual fund performance Finally, the chapter reviews the literature concerning the behavior of mutual fund investors, which uses mutual fund flows as the proxy In the area of mutual fund investors, we specifically focus on how Islamic mutual funds (IMFs) investors make fund selection decision and examine if these investors are able to select funds that are able to earn positive returns in subsequent period Overall, the literature on IMFs is still scarce and lags behind compared with the literature on the conventional mutual funds (CMFs) and socially responsible investment (SRI) funds Thus, this section reviews related theoretical and empirical studies on SRI screened and unscreened funds to draw the necessary bases for the study of IMFs Chapter – This study examines performance of Thai equity mutual funds over 5-year time-periods of investment A sample of 138 funds managed by the seventeen asset management companies during the period of 2002-2007 was analyzed using both the traditional approaches: the Treynor ratio, Sharpe ratio and Jensen’s alpha and the Data Envelopment Analysis (DEA) technique The results suggest that performances evaluated using the former measures lead to more similar fund rankings compared to those applying the latter method For 3-year time-period of investment, 80% of the top ten best funds ranked based on the DEA technique are the same as those ranked using the traditional measures; however only 40% of those for 1-year and 5-year timeperiods of investment Thus, the use of diverse performance measures rather than time-periods of investment leads to different fund rankings Finally, the analyses assert that performance evaluation measure matters and choosing a measure is important for ranking of Thai equity mutual funds Chapter – In this paper, an artificial neural network (ANN) and a genetic programming (GP) approach are both applied in order to predict Greek equity mutual funds’ performance and net asset value The back propagation algorithm is used to train the weights of ANNs while jGPModeling environment is used to implement the GP approach The prediction of both the performance and net asset value of mutual funds is accomplished through historical economic information and fund-specific historical operating characteristics Our study is the first one to compare the forecasting results of the ANN approach with the results obtained through GP approach on mutual fund performance prediction The main conclusion of our work is that ANN’s results outperforms the GP’s results in the prediction of mutual funds’ net 120 K Pendaraki, G Ν Beligiannis and A Lappa matching the portfolio’s risk to that of the market Given the standard deviation of a MF’s excess return over the index  I the Modigliani measure is defined as the ratio R I  The fund with the highest Modigliani measure presents the highest return for any level of risk xv Another performance measure that is derived from comparing a fund to its benchmark is the information ratio calculated as the ratio R  R f   , where   is the standard deviation of the MF’s excess   return over the market portfolio The definition of the 17 economic variables (independent variables) is given in Table below: Table The economic variables used in the analysis Input Economic Variable Gross domestic product, constant prices Gross domestic product, current prices Gross domestic product per capita, constant prices Gross domestic product per capita, current prices Gross domestic product based on purchasing-power-parity (PPP) share of world total Inflation, average consumer prices General government balance General government structural balance Current account balance Definition-Notes [Units] Gross domestic product at constant market prices [Billions Euros] Gross domestic product at market prices (Billions Euros) See notes for: Gross domestic product, constant prices (Euros) Population (Persons) [Euros] See notes for: Gross domestic product, current prices (Euros) Population (Persons) [Euros] See notes for: Gross domestic product, current prices (Euros) [Percent] From average annual inflation, consumer prices (Index, 2000=100) [Annual percent change] Includes: Central Government, State Government, Local Government, Social Security Funds [Billions Euros] Includes: Central Government, State Government, Local Government, Social Security Funds [Billions Euros] See notes for: Gross domestic product, current prices (Euros) Current account balance (Euros) [Percent of GDP] Mutual Fund Prediction Models … Input 10 121 Economic Variable Real GDP Growth Definition-Notes [Units] GDP growth on an annual basis 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intelligence, 94, 95 artificial neural network, v, vii, viii, 93, 94, 95, 123, 124, 126, 127 Asia, 3, 27, 34, 76 assets, 2, 3, 6, 9, 13, 15, 18, 24, 43, 45, 54, 55, 69, 74, 117, 118 B bankruptcy, 25, 95 base, 15, 62, 64 bear market., benchmarking, 125 benchmarks, 6, 9, 11, 12, 13, 17, 26, 27, 29, 30, 31, 33, 34, 43, 54, 57, 61, 63, 67, 81 benefits, 5, 29, 76 bias, 9, 16, 19, 26, 27, 59, 91, 123 bond market, 66 bonds, 2, 14, 62, 79, 117 bull market, 9, 29 C Cairo, 61 candidates, 41, 52 capillary, 123, 124 capital asset pricing model (CAPM), 6, 7, 8, 11, 12, 16, 28, 29, 30, 31, 34, 118 capital gains, 118 capital markets, 3, 4, 23, 37, 47, 62 cash, 3, 10, 22, 38, 39, 49, 57, 67, 70, 82, 117 cash flow, 22, 57, 67 coefficient of variation, 33, 119 comparative analysis, 125 complexity, 94, 106, 107 compliance, 32, 36, 64 composition, 78, 126 constant prices, 98, 120 construction, 6, 97, 107, 121 Consumer Price Index, 98, 121 controversial, 77, 94 conventional mutual funds, vii, 1, 3, 27, 57, 68 corporate governance, 24, 32, 58 correlation, 24, 76, 78, 82, 83, 84, 85, 88, 115 correlation coefficient, 76, 83 cost, 11, 13, 17, 23, 24, 28, 38, 40, 41, 48, 50, 51, 58 130 Index Council of the Islamic Fiqh Academy, cross-border investment, 69 cross-validation, 107 current prices, 98, 117, 120 D DEA, viii, 73, 75, 76, 78, 79, 80, 81, 84, 85, 86, 87, 88 deficit, 121 dependent variable, 44, 54, 97, 117 depth, 25, 103, 104 derivatives, 124 developed countries, 20, 27 developing countries, deviation, 6, 7, 28, 29, 75, 79, 83, 84, 85, 98, 106, 108, 113, 118, 119, 120 dichotomy, 58 dimensionality, 112 discretization, 124 discriminant analysis, 126 disposition, 39, 49, 70 dissonance, 39, 49, 63 distribution, 18, 75, 78, 119 diversification, 5, 6, 20, 24, 28, 30, 36, 67, 76, 81, 82 E economic cycle, 24 economic development, 77 economic theory, 22 economics, 65, 88 economies of scale, 18 Efficient market hypothesis, 17 election, 8, 9, 10, 25, 28, 63, 89 electrophoresis, 123, 124 emerging markets, 20, 39, 50, 75, 76, 90, 116 empirical studies, viii, 2, 4, 36, 42, 47, 52, 94 environment, viii, 22, 93, 95, 96, 103, 104, 107, 112, 116 environmental management, 66 environmental standards, 61 environments, 116 equilibrium, 6, 23, 58, 68 equity(ies), vii, viii, 2, 3, 10, 14, 15, 17, 19, 23, 28, 31, 42, 46, 52, 56, 57, 59, 60, 61, 63, 64, 67, 68, 69, 70, 73, 74, 75, 76, 77, 78, 79, 81, 83, 84, 87, 88, 89, 90, 91, 93, 94, 96, 97, 99, 101, 104, 116, 121, 122, 123, 124, 125, 126 equity investment, 2, 68 equity market, 3, 23, 28, 31 ethical issues, 41, 51 Europe, 3, 58, 69, 125 European market, 27 evidence, 5, 10, 15, 17, 19, 20, 24, 26, 27, 32, 33, 35, 37, 40, 46, 47, 50, 56, 57, 58, 59, 62, 67, 68, 69, 70, 76, 87, 91, 94, 121, 126 exclusionary screening, vii, 1, 4, 32 experimental design, 123, 124 exposure, 6, 10, 14, 21 F factor analysis, 112 faith, 40, 51, 62, 64, 68 family characteristics, 43, 53 financial, vii, 1, 2, 3, 4, 5, 7, 21, 22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36, 37, 40, 41, 43, 46, 47, 51, 52, 54, 56, 58, 59, 63, 64, 65, 66, 67, 69, 74, 75, 77, 88, 94, 95, 103, 116, 117, 125 financial crisis, 30, 33, 34 financial development, 77 financial institutions, 2, 94 financial markets, 7, 66, 74, 116, 117 financial performance, 22, 23, 24, 35, 36, 41, 46, 51, 56, 58, 63, 64, 67, 69 firm size, 12 firm value, 23, 24, 36, 58 flow relationship, 37, 47 fluctuations, 119 forecasting, viii, 9, 64, 93, 94, 95, 96, 97, 112, 113, 115, 116, 122, 125 131 Index forecasting model, 113, 116 foreign exchange, 117 formula, 83, 118 fraud, 22 fund performance, vii, 1, 4, 5, 6, 12, 13, 14, 15, 16, 17, 18, 19, 20, 23, 26, 27, 32, 36, 37, 38, 39, 43, 44, 47, 48, 50, 53, 54, 55, 58, 59, 60, 61, 62, 63, 65, 67, 68, 69, 70, 71, 75, 76, 77, 78, 80, 88, 89, 90, 91, 94, 95, 96, 116, 117, 121, 122, 123, 124, 125, 126, 127 funds, vii, viii, 1, 2, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 97, 99, 100, 101, 103, 104, 106, 107, 108, 112, 113, 115, 116, 117, 118, 121, 123, 124, 125, 126, 127 G gambling, vii, 1, 24 GDP, 98, 120, 121 genetic programming, vii, viii, 93, 94, 122 gharar, vii, governance, 22, 24, 32, 58, 60, 63, 69 Greece, 93, 97, 116, 121, 126 grouping, 96 growth, 3, 12, 14, 15, 21, 22, 38, 43, 46, 48, 49, 54, 56, 60, 63, 70, 74, 89, 90, 121, 123 guidelines, 24, 78, 88 I idiosyncratic, 35 income, 3, 39, 49, 74, 118 independent variable, 117, 120 industry, 2, 3, 4, 16, 18, 20, 24, 58, 65, 66, 74, 77, 89, 90, 95, 116 inefficiency, 8, 16 inflation, 120, 121 ingredients, 102 institutions, 2, 77, 94 intelligence, 94, 95, 122 interest rates, 14 international investment, 64 International Monetary Fund (IMF), 4, 30, 33, 34, 36, 37, 40, 41, 47, 51, 52, 57, 97, 121 investment, vii, viii, 1, 2, 3, 4, 5, 9, 12, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 32, 35, 41, 42, 43, 51, 52, 53, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 83, 84, 87, 88, 97, 113, 115, 116, 118, 126, 127 investors, vii, 1, 4, 5, 7, 17, 19, 20, 21, 23, 24, 25, 28, 30, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 63, 64, 66, 67, 68, 71, 74, 75, 76, 77, 78, 88, 92, 94 Islamic finance, 2, 32 Islamic law, vii, 1, Islamic mutual funds, vii, 1, 2, 61, 62, 64, 68, 69 Islamic values, vii, issues, 17, 41, 51, 63, 64, 69, 83 L labor force, 121 Latin America, 90 law enforcement, 77 learning, 22, 27, 101, 102, 106, 107, 126 linear model, 38, 48, 94 liquidity, 11, 20, 60 litigation, 25 Luo, 46, 56, 67 132 Index M magazines, 20, 43, 54 magnitude, 38, 44, 48, 54, 125 Malaysia, 1, 2, 3, 4, 16, 23, 29, 33, 34, 57, 58, 68, 91 management, viii, 2, 3, 4, 5, 16, 17, 18, 25, 35, 38, 42, 43, 44, 45, 48, 52, 54, 55, 56, 60, 62, 64, 66, 69, 70, 73, 74, 78, 79, 83, 86, 89, 90, 95, 115, 127 manipulation, 80 manufacturing, 121 market capitalization, 36 market segment, 70 market share, 37, 47 market timing, 7, 9, 10, 11, 30, 31, 34, 64, 66, 67, 122, 124 marketing, 18, 37, 41, 44, 45, 47, 51, 54, 55 materials, 127 matter, 65, 88, 89, 91 maysir, vii, measurement, vii, 2, 4, 5, 6, 7, 8, 9, 14, 15, 16, 29, 38, 48, 60, 61, 63, 70, 75, 79, 80, 89, 90 media, 43, 44, 53, 54 meta-analysis, 22, 69 methodology, 96, 100, 103, 107, 116, 117, 125 Middle East, 3, 34, 61 model trees, 104 modelling, 102 models, vii, ix, 6, 7, 8, 9, 11, 13, 15, 16, 29, 30, 31, 38, 48, 80, 94, 96, 97, 99, 100, 102, 103, 104, 106, 107, 108, 111, 112, 113, 114, 115, 116, 117, 123, 125, 126 Modern Portfolio Theory, momentum, 13, 19, 20, 76, 106 multidimensional, 95 Multifactor Models, 11 mutation, 102, 103 mutual fund performance prediction, viii, 93, 96 N negative relation, 18, 19, 35, 45, 55 Netherlands, 27, 57, 64, 122 neural network, vii, viii, 93, 94, 101, 115, 116, 122, 123, 124, 125, 126, 127 neurons, 98, 99, 101, 106 O omission, operations, 2, 102 opportunities, 23, 74 optimization, 61, 101, 102, 105 optimization method, 101, 102, 105 P Pacific, 3, 27, 34, 59, 70 Pakistan, 29, 31, 33, 34, 67, 69 passive benchmark, performance appraisal, 121 performance benchmarking, 125 performance measurement, vii, 2, 4, 5, 9, 14, 15, 16, 29, 60, 61, 70, 75, 80, 90 performers, 38, 39, 49, 83, 84, 87 poor performance, 16, 37, 38, 39, 40, 41, 42, 47, 48, 49, 50, 51, 52, 103 portfolio, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 20, 21, 24, 28, 29, 32, 36, 43, 53, 57, 61, 63, 69, 74, 77, 78, 79, 80, 81, 82, 83, 85, 90, 95, 97, 119, 120, 126 positive relationship, 19, 22, 24, 37, 44, 47, 54 prediction models, vii, ix, 94, 97, 107, 112, 113, 115, 126 predictive accuracy, 125 principles, 102 private information, productive efficiency, 89 professional management, 16 profit, 22, 25, 42, 52 133 Index programming, vii, viii, 93, 94, 102, 103, 122 propagation, viii, 93, 95, 100, 101, 108, 116, 127 pruning, 104 Q Quran, vii, R rate of return, 8, 13, 82 rational expectations, 58 reality, 43, 53 recombination, 102 regression, 8, 15, 31, 36, 38, 44, 48, 54, 66, 95, 96, 116 reputation, 18, 22, 41, 43, 44, 51, 53, 54 response, 14, 39, 49, 50, 60, 89 retail, 39, 43, 49, 53, 122 revenue, 45, 55 riba, vii, risk, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 34, 35, 36, 38, 39, 40, 41, 42, 45, 48, 49, 51, 52, 55, 57, 62, 63, 65, 66, 67, 74, 75, 76, 79, 80, 81, 82, 83, 85, 89, 91, 97, 98, 108, 112, 113, 115, 116, 117, 118, 119 risk aversion, 75 risk factors, 12, 13, 14, 62 S Saudi Arabia, 4, 31, 33, 34, 59, 68 screening strategies, vii, 1, 4, 20, 21, 32, 36, 57 securities, 6, 7, 9, 11, 17, 18, 21, 24, 36, 61, 66, 70, 71, 118 security selection, selectivity, 15, 28, 34, 67, 122, 124 sensitivity, 44, 54, 65 services, 2, 3, 18, 80 shareholders, 22, 25, 41, 52, 82 Shariah, vii, 1, 2, 4, 23, 28, 29, 32, 36, 61 Sharpe ratio, viii, 7, 28, 29, 33, 34, 35, 73, 75, 76, 77, 78, 79, 80, 84, 85, 87, 92 Smart Money, 47 social norms, 65 social responsibility, 22, 58, 62 Social Security, 120 socially responsible investment, viii, 2, 3, 61, 66, 70 stakeholders, 21, 22, 24, 25 standard deviation, 6, 7, 28, 29, 75, 79, 83, 108, 113, 118, 119, 120 standard error, 100, 101 stock, vii, 6, 8, 9, 10, 11, 12, 13, 14, 24, 25, 28, 58, 59, 60, 62, 64, 65, 66, 67, 71, 74, 89, 95, 115, 117, 122, 123, 124, 127 stock markets, 74 stock picking skill, stock price, 12, 25, 95, 117, 122 strategy use, 100 structure, 44, 55, 77, 100, 101, 102, 106, 107, 117 style, 12, 15, 27, 43, 53, 59, 60, 61, 64, 70, 71, 77, 116, 125 success rate, 108, 112, 113, 115 Sunnah, vii, T techniques, vii, 2, 4, 7, 29, 63, 78, 94, 96, 102, 107, 112, 113, 115 testing, 99, 106, 107, 108, 121 Thailand, 73, 74, 75, 76, 77, 78, 79, 81, 83, 88, 91 time periods, vii, 77 time series, 103, 124, 125 training, 97, 99, 101, 106, 107, 108 Treasury, 16, 97, 117 Treynor ratio, viii, 7, 16, 28, 29, 33, 73, 75, 76, 77, 78, 79, 80, 84, 85, 87 trust fund, 58, 64, 67 134 Index turnover, 18 V U universe, 32, 36 unsystematic risk, 6, 21, 24, 35, 79 variables, 9, 12, 14, 18, 19, 24, 94, 95, 96, 97, 98, 99, 100, 101, 106, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 120 vector, 99, 100, 101, 106 volatility, 5, 9, 41, 42, 45, 51, 52, 55, 58 ... found on Nova’s website under the eBooks tab FINANCIAL INSTITUTIONS AND SERVICES MUTUAL FUNDS PERFORMANCE, TYPES AND IMPACTS ON STOCK RETURNS DONALD EDWARDS EDITOR New York Copyright © 2017 by Nova... Beligiannis and Alexandra Lappa 93 129 PREFACE The authors of this book provide and discuss new research on performance measurements, types, and impacts on stock returns of mutual funds Chapter One reviews... literature on the conventional mutual funds (CMFs) and socially responsible investment (SRI) funds Thus, this section reviews related theoretical and empirical studies on SRI screened and unscreened funds