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An examination of the Random Walk Model and Technical Trading Rules

of the Islamic-Compliant Stocks in Malaysia

ABSTRACT

In this paper, we use the variance ratio test to determine if the Malaysia stock market Shariah-compliant indexes follow a random walk and we use moving average trading rules to determine if excess profits can be earned trading the indexes for the period from April 17, 1999. The empirical results of the random walk hypothesis test show that the Islamic stock market in Malaysia does not follow a random walk. The overall empirical results of testing the technical trading rules show that abnormal returns for both the variable and fixed moving average rules can be earned. The moving average length of 60-days appears to be the most profitable for both the variable and the fixed moving averages and the variable moving average provides the higher profit.

INTRODUCTION

The Islamic capital market in Malaysia have been growing and offers four sharia-compliant products: sukuks, equities, indices of qualified equities, and trust funds. Malaysia was the first country to develop and introduce sharia-compliant stocks in the mid-1990s. As of May 2012, 825 companies listed on Bursa Malaysia are sharia-compliant. This includes consumer products, industrial products, mining, construction, trading and services, properties, plantation, technology, infrastructure and finance.1 There are indices that track shariah-compliant products such as the FTSE Bursa Malaysia EMAS Sharia Index and the FTSE Bursa Malaysia Hijrah Sharia Index.2

The Random Walk Hypothesis

An efficient market is defined as a market where there are large numbers of rational, profit-maximizers actively competing, with each trying to predict future market values of individual securities, and where important current information is almost freely available to all participants (Fama 1965). Testing weak form efficiency usually involves testing the profitability of technical analysis or testing the predictability of stock returns. Studies that test the predictability of stock returns focus on examining whether stock returns follow the random walk model with successive price changes that are random and serially independent (Al-Ahmad 2012).

A random walk process posits that any shock to a stock price is permanent and there is no tendency for the price level to return to a trend path over time and this suggests that future returns are unpredictable based on historical observations. On the other hand, if a stock price follows a mean reverting process, there is a tendency for the price level to return to its

1 List of Shariah-compliant Securities by the Shariah Advisory Council of the Securities Commission Malaysia,

page 18.

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This index comprises the largest 30 companies by full market capitalization of the FTSE Bursa Malaysia EMAS Index that are in compliance with Yasaar and the Securities Commission’s SAC

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trend path over time and investors may be able to forecast future returns by using information on past returns (Chaudhuri and Wu, 2003).

Research has been conducted using various techniques to test the random walk hypothesis of stock prices or indices. Among the techniques developed are the Augmented Dickey-Fuller (ADF) test (1979), the Phillips-Perron (1988) test, the Lo and MacKinlay (1988) test, the KPSS procedure developed by Kwaitkowski et al. (1992), the Chow and Denning (1993) test, and Wrights (2000) test.

Technical Trading Rules

Technical analysis applies models and trading rules based on price and volume. Technical analysis argues that market prices will reflect all relevant information, so the analysis will be based on the security's past trading pattern rather than external factors such as economic variables, fundamental variables, or news events. Technical analysis stands in contrast to the fundamental analysis approach as it analyzes price and volume data while fundamental analysis uses data from the company, market, currency or commodity.

Most large brokerage firms, trading groups, or financial institutions use both technical analysis and fundamental analysis. Technical analysis is used by traders, financial professionals, active day traders, market makers, and pit traders. Examples of technical analysis include the relative strength index, moving average analysis, regressions, inter-market and intra-inter-market price correlations, business cycles, stock inter-market cycles or recognition of chart patterns. Studies have been carried out to investigate the predictability and profitability of technical trading rule on stock market indices throughout the world as well as Asian region and Malaysia.

The Malaysian Islamic Stock Market

The Malaysian Islamic stock market is part of the Malaysian Islamic capital market. The Islamic shariah-compliant market offers shariah-compliant securities or equities for investors who are looking for shariah-compliant shares in which to invest. In an Islamic capital market (ICM) market, transactions are carried out in ways that do not conflict with the conscience of Muslims and the religion of Islam. There is an assertion of religious law so that the market is free from activities prohibited by Islam such as usury (riba), gambling (maisir) and ambiguity (gharar).3

Shariah Advisory Council (SAC) was established in May 1996 to supervise the activity of Islamic capital market and to advise the Commission on Shariah matters pertaining to the Islamic capital market. According to the Shariah Advisory Council (SAC), companies will be classified as Shariah non-compliant securities if they are involved in the following core activities:

i. Financial services based on interest (riba) ii. Gambling and gaming

iii. Manufacture or sale of non-halal products or related products iv. Conventional insurance

v. Entertainment activities that are non-permissible according to Shariah

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vi. Manufacture or sale of tobacco-based products or related products vii. Stockbroking or share trading in Shariah non-compliant securities viii. Other activities deemed non-permissible according to Shariah

In determining the tolerable level of mixed contributions from permissible and non-permissible activities towards turnover and profit before tax of a company, the SAC has established several benchmarks based on ijtihad (reasoning from the source of Shariah by qualified Shariah scholars).4 If the contributions from non-permissible activities exceed the benchmark, the securities of the company will be classified as Shariah non-compliant. The benchmarks are:

i. The five-percent benchmark is used to assess the level of mixed contributions from the activities that are clearly prohibited such as riba (interest-based companies like conventional banks), gambling, liquor and pork.

ii. The 10-percent benchmark is used to assess the level of mixed contributions from the activities that involve the element of `umum balwa which is a prohibited element affecting most people and difficult to avoid. An example of such a contribution is the interest income from fixed deposits in conventional banks. This benchmark is used for tobacco-related activities.

iii. The 20-percent benchmark is used to assess the level of contribution from mixed rental payments from Shariah non-compliant activities such as the rental payments from the premise that is involved in gambling, the sale of liquor and, et cetera.

iv. The 25-percent benchmark is used to assess the level of mixed contributions from the activities that are generally permissible according to Shariah and have an element of maslahah to the public, but there are other elements that may affect the Shariah status of these activities. Among the activities that belong to this benchmark are hotel and resort operations, share trading, stockbroking and others, as these activities may also involve other activities that are deemed non-permissible according to the Shariah.

The Islamic capital market has received large inflows of capital recently and has become an interesting alternative for investors looking for shariah-compliant investments. The market plays an important role in generating economic growth for the country and plays a complementary role to the Islamic banking system in broadening and deepening the Islamic financial markets in Malaysia.

Technical analysis has been widely used by investors to forecast the direction of prices through the study of historical price and volume data. There are various tools and indicators used in technical analysis that may provide supporting evidence for trading decisions. Research had been conducted to study the profitability and predictability of technical trading rules on the daily returns of the Kuala Lumpur Stock Exchange Composite Index (Annuar et al. 1991; Lai et al. 2006). Thus, technical analysis can be used to test the Islamic market. However, there is no published research studying the profitability and predictability of technical trading rules on the daily returns of Kuala Lumpur Shariah Index or currently known as FTSE Bursa Malaysia Emas Shariah Index.

4List of Shariah Compliant securities by the Shariah Advisory Council of the Securities Comission Malaysia,

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Before applying any investment strategy, it is crucial to identify whether the stock prices follow a random walk or a mean reversion process. Lo and Mackinlay (1988, 1999) report that stock prices do not completely follow the random walk and some predictable components exist in stock returns (Lo and MacKinlay 1988, 1999). Several studies have been conducted to examine the random walk hypotheses for the Kuala Lumpur Stock Exchange (Annuar et al. 1991; Barnes 1986; Kok and Lee 1994; Lai et al. 2006; Laurence 1986; Lim et al. 2003). Yet, there are still no published tests of the random walk hypotheses for the Malaysian Islamic Stock Market.

Based on the problem statement, three research questions are:

i. Does the stock index of Malaysian Islamic Stock Market follow a random walk model or a mean reversion process?

ii. Does the fixed moving average (FMA) technical trading rule predict daily returns of the FTSE Bursa Malaysia Emas Shariah Index, formerly known as the Kuala Lumpur Shariah Index (KLSI)?

iii. Does the variable moving average (VMA) technical trading rule predict daily returns of the FTSE Bursa Malaysia Emas Shariah Index, formerly known as the Kuala Lumpur Shariah Index (KLSI)?

This research is being conducted to examine the random walk model in the Malaysian Islamic Stock Market. And, the predictability of two technical trading rules inclusive of fixed moving average (FMA) and variable moving average (VMA) on daily returns of the FTSE Bursa Malaysia Emas Shariah Index or formerly known as Kuala Lumpur Shariah Index (KLSI) will be tested and compared to the alternative buy-and-hold strategy. This research aims to achieve the following objectives:

i. To determine if the FTSE Bursa Malaysia Emas Shariah Index, formerly known as Kuala Lumpur Shariah Index (KLSI), can be characterized as a random walk or a mean reversion process.

ii. To examine the predictability of the fixed moving average (FMA) technical trading rule on daily returns of the FTSE Bursa Malaysia Emas Shariah Index, formerly known as Kuala Lumpur Shariah Index (KLSI), relative to a buy-and-hold strategy.

iii. To examine the predictability of the variable moving average (VMA) technical trading rule on daily returns of the FTSE Bursa Malaysia Emas Shariah Index, formerly known as Kuala Lumpur Shariah Index (KLSI), relative to a buy-and-hold strategy.

This research will focus on the Malaysian Islamic Stock Market which is a part of Malaysian Islamic capital market. Two indices will be used as the proxy of Islamic stock market in Malaysia.

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The KLSI was launched on 17 April 1999 to meet the demands from local and foreign investors who seek to invest in securities which are consistent with Shariah principles. The KLSI acts as a benchmark for tracking the performance of Shariah-compliant securities and making informed Shariah-compliant investment decisions. KLSI was deactivated on 1 November 2007, nine months after the introduction of FTSE Bursa Malaysia Emas Shariah Index.

The FTSE Bursa Malaysia Emas Shariah Index

The FTSE Bursa Malaysia EMAS Shariah Index has been designed to provide investors with a broad benchmark for Shariah-compliant investments. This index was launched on 1 January 2007. Constituent stocks are screened according to the Malaysian Securities Commission's Shariah Advisory Council (SAC) screening methodology. It is a superior Shariah-compliant benchmark for the Malaysian market due to its investability and liquidity and its transparent ground rules.5

In spite of the growth of Islamic-based investment and the continuing strong interest in the Islamic banking and finance industry worldwide, research on the Islamic equity market is still rather limited. Moreover, most of past studies were focused on Islamic fund management and performance.

This study investigates the random walk hypothesis on Malaysian Islamic Stock Market using FTSE Bursa Malaysia Emas Shariah Index, formerly known as Kuala Lumpur Shariah Index (KLSI). Furthermore, this study seeks to study the predictability and profitability of two technical trading rules applied to the Islamic Stock Market. Findings from this study may advance our understanding and practice of technical trading rules in bull and bear markets and the profit potential of technical trading rules.

This study is organized as follows. A comprehensive analysis of past studies on the random walk hypothesis and technical trading rules in emerging markets is discussed in the literature review. The research methodology used in this study is elaborated in Section 3 which examines the general characteristics of the returns within the Islamic Shariah-compliant market. This is followed by a random walk hypothesis test and an analysis of two technical trading rules. Section 4 discussed the empirical results and Section 5 provides a discussion and conclusion of the paper.

LITERATURE REVIEW

This section will provide details and brief information on research done related to the random walk hypothesis and technical trading rules. Studies have been conducted for capital markets all over the world including Asian countries and the Malaysian stock market.

The Random Walk Hypothesis

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Lai et al. (2006) investigated the random walk hypothesis on the Malaysian stock market using daily returns of the Kuala Lumpur Stock Exchange Composite Index from January 1977 until December 1999. Research is based on the full sample period and four non-overlapping subperiods to determine the existence of any structural breaks and significant economic events. This study uses the Lo and MacKinlay variance ratio test and found non-randomness of successive price changes in the Kuala Lumpur stock market.

Chin (2008) investigated the random walk characteristics of the Malaysian stock market using the Kuala Lumpur Composite Index as the proxy for the Malaysian stock market. This study examined the full sample period and two non-overlapping sub-periods to consider the structural break in the period of 2007 until 2008 as a result of the Asian financial crisis. Similar as Lai et al. (2006), this study uses the Lo and MacKinlay variance ratio test and indicates that the Malaysian stock market can be characterized as a mean reverting process, rejecting the random walk hypothesis.

Awad and Daraghma (2009) use unit root tests using the Augmented Dickey-Fuller (ADF) test and serial correlation tests to examine the random walk theory and the efficient market hypothesis of the Palestine Security Exchange (PSE) using indices available for the PSE. This study examines the Al-Quds index which is the main index of PSE, the General Index, and sector indices which consist of five sub-indices: the Banking Sector Index, the Insurance Sector Index, the Services Sector Index, the Industry Sector Index and the Investment Sector Index. The study uses a nonparametric tests namely runs test and the Phillips-Peron (PP) test. Empirical results indicate that stock returns in the Palestine Stock Exchange are not random over the time period of the study and display predictability based on historical data.

The same research methodologies were used by Al-Jafari (2011a) to investigate the weak form efficiency of the Kuwait Stock Exchange for the period of 17 June 2001 to 8 December 2010. Findings reveal that the Kuwait Stock Exchange does not follow random walk and is inefficient at the weak-form level.

Jafari (2011b) used similar methodologies to Awad and Daraghma (2009) and Al-Jafari (2011a) to study the weak form efficiency of the Bahrain securities market from 1 February 2003 until 30 November 2010. Similar empirical results were found showing that the Bahrain securities market does not follow a random walk and is inefficient at the weak-level.

Al-Ahmad (2012) tested the weak form efficiency of the Damascus Securities Exchange (DSE) for the period of 31 December 2009 until 30 November 2011 using an autocorrelation test, a runs test, a unit root test, a variance ratio test and a GARCH (1,1) Model. This study conducted sensitivity analysis using the truncation method to minimize the impact of large negative outliers in the data series which are associated with political instability in Syria. Test results reject the random walk model and show that the stock exchange is not efficient in the weak form.

The study by Al-Saleh and Al-Ajmi (2012) on Saudi Stock Market consisting of eight industry-based indexes and a composite index show mixed results. Serial correlation tests reject the null hypothesis of a random walk except for the insurance and the telecommunication industry indices. All results of unit root tests indicate that all indexes are weak-form inefficient, using runs tests results with the exception of the cement, insurance

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and telecommunications industry indices. Empirical results obtained by applying the Lo and MacKinlay variance ratio test cannot reject the random walk hypothesis for all indices except for the banking sector and the composite index. Using the Chen and Deo multiple variance ratio tests provide similar empirical results indicating that all the indices studied follow a random walk. In contradiction, test results from rank and sign-based test and the Chow-Denning multiple variance ratio tests reject the random walk hypothesis for most of the indices. However, the insurance, telecommunication and cement industry indices show random walk characteristics using the similar test.

Hoque et al. (2007) revisit the random walk hypothesis for eight emerging equity markets in Asia: Hong Kong, Indonesia, Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand. The study uses Wright’s rank and sign, Whang Kim subsampling, and the Lo-MacKinlay and Chow-Denning tests. Empirical results indicate that in the period of 1990 to 2004, Indonesia, Malaysia, the Philippines, Singapore and Thailand show significant predictable behavior in their weekly return series while Taiwan and Korea exhibit large unpredictable patterns in the same series.

Chaudhuri and Wu (2003) investigate the random walk characteristics and mean reversion processes of the stock price indices in seventeen emerging markets consisting of Argentina, Brazil, Chile, Colombia, Greece, India, Jordan, Korea, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Taiwan, Thailand, Venezuela and Zimbabwe. The study aims to test for mean reversion of equity prices in the presence of the structural break in the period of January 1985 until February 1997. The issue of market liberalization isexamined. Augmented Dickey and Fuller (ADF) tests and Phillips-Perron (PP) tests are used to test the hypothesis. The Zivot-Andrews (1992) test is used for testing the random walk hypothesis with structural breaks. By incorporating the structural breaks into the trend function, findings suggest that stock markets in ten countries can be characterized as mean reverting processes which do not follow a random walk. These stock markets are in Argentina, Brazil, Taiwan, Zimbabwe, Colombia, Greece, Malaysia, Philippines, India and Venezuela.

Chang and Ting (2000) examined Taiwan’s stock market for the period of 9 January 1971 to 6 January 1996 using the Lo and MacKinlay variance-ratio test. Weekly, monthly quarterly and yearly returns of the value-weighted stock price index are used for random walk testing. Findings reveal that the random walk hypothesis is rejected for the weekly value-weighted market index while the null hypothesis of randomness cannot be rejected with monthly, quarterly and yearly market indices.

Smith et al. (2002) examine African stock markets which consist of South Africa, five medium-sized markets (Egypt, Kenya, Morocco, Nigeria and Zimbabwe) and two small new markets (Botswana and Mauritius). Nine market indices reflecting the respective stock markets are examined from January 1990 to August 1998. This study used the multiple variance ratio tests of Chow and Denning to test market efficiency and random walk characteristics. Results suggest that random walk hypotheses are rejected for Botswana, Mauritius, Egypt, Kenya, Morocco, Nigeria and Zimbabwe stock market while the South African market follows a random walk.

Smith and Ryoo (2003) studied five European emerging markets: Greece, Hungary, Poland, Portugal and Turkey for the period of 3 April 1991 until the last week of August 1998. Multiple variance ratio tests from Chow and Denning (1993) are used and the empirical results show that the Istanbul stock market follows a random walk while the random walk

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hypothesis is rejected for the other four markets. Liquidity is found to be an important factor in affecting random walk characteristic of a stock market.

Gilmore and McManus (2003) examined weak-form efficiency of the equity markets in the three main Central European transition economies (the Czech Republic, Hungary and Poland) for the period of July 1995 through September 2000. This study used the Augmented Dickey Fuller (ADF) test, the Phillips-Perron (PP) test and the Lo and MacKinlay variance ratio test for univariate analysis. The Johansen procedure was used for multivariate tests and the applied model-comparison tests using ARIMA, GARCH and NAÏVE models. Evidence from univariate analysis supported weak-form efficiency in the three stock markets while multivariate tests revealed that there is no common stochastic trend shared among the three markets and returns from one market are not predictable in terms of information from another market. The model-comparison approach provided strong evidence against the random walk hypothesis.

Charles and Darné (2009) examined the random walk hypothesis for the Shanghai and Shenzhen stock markets for both A and B shares over the period of 1992 to 2007. Class A shares are denominated and traded in the local currency, Renminbi (RMB) and designed for domestic investors. Class B shares are denominated in RMB but are subscribed and traded in foreign currencies. Three methods are used in this study: Whang-Kim subsampling, Kim’s wild bootstrap tests and the conventional multiple Chow-Denning test. Results indicate that Class A shares appear more efficient than Class B shares. This may be the result of differences in liquidity, market capitalization and information asymmetry.

Guidi and Gupta (2011) examined the efficient market hypothesis (EMH) for the Association of South-East Asian Nations (ASEAN) stock markets for the period of January 2000 until April 2011. These include the stock markets of Indonesia, Malaysia, the Philippines, Vietnam, Singapore and Thailand. Methodologies used in this study include the Augmented Dickey-Fuller (ADF) test, the Kwiatkowsky, Phillips, Schmidt and Shin (KPSS) test, the variance-ratio (VR) test, the multiple variance-ratio test, the Wright test and runs test. Both ADF and KPSS tests indicate that ASEAN stock markets returns do not follow a random walk process. The Lo-Mackinlay variance ratio test, The Chow and Denning variance ratio test and the runs test provided similar results which rejects the random walk hypothesis for all stock market except the stock markets in Singapore and Thailand. Rank-based and sign-based variance ratio tests confirm that only Singapore and Thailand stock markets are weak-form efficient.

Karemera et al. used the variance-ratio test of Lo and MacKinlay (1988) and Chow and Denning (1993) to test the random walk characteristics of local currency and US dollar-based equity returns in 15 emerging markets: Argentina, Brazil, Chile, Hong Kong, Indonesia, Israel, Jordan, Korea, Malaysia, Mexico, Philippines, Singapore, Taiwan, Thailand and Turkey. The study covers the period of January 1986 and finishes in May 1997. According to the Lo and MacKinlay (1988) test, Argentina, Hong Kong, Israel, Korea, Malaysia and Singapore follow a random walk while Brazil, Chile, Jordan, Indonesia, Mexico, the Philippines, Taiwan, Thailand and Turkey do not follow a random walk. When multiple variance ratio tests are used, five of the series, Chile, Indonesia, Jordan, Mexico and Turkey, which originally were not consistent with the random walk hypothesis become consistent with it.

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Lai et al. (2006) examined the use of technical trading rules in the Malaysian stock market for the period of January 1977 until December 1999. Moving average rules which consist of fixed moving average (FMA) and variable moving average (VMA) are tested to determine if these technical rules can be sued to generate above average returns in the Malaysian stock market in four non-overlapping sub-periods. Results indicate that the 60-days variable moving average (VMA) rule was found to earn significantly higher returns compared to the buy-and-hold strategy while the same moving average length of the 60-days fixed moving average (FMA) rule was found to generate significantly higher profits as compared to the buy-and-hold strategy.

Lai and Lau (2006) studied the profitability of the application of variable and fixed moving averages as well as trading range breakout (TRB) on nine popular daily Asian market indices from the period of January 1988 until December 2003. Test results provided strong evidence for the use of the variable moving average (VMA) in particular and the fixed moving averages (FMA) in the China, Thailand, Taiwan, Malaysian, Singaporean, Hong Kong, Korean and Indonesian stock markets. The length of 20 days and 60 days appeared to be the most profitable for variable and fixed moving averages, respectively.

Parisi and Vasquez (2000) tested moving average and trading range break-out trading rules in the Chilean stock market for the period of January 1987 to September 1998. Overall results are in line with Brock et al. (1992) providing strong support for the technical analysis strategy. Buy signals are found to consistently generate higher returns as compared to sell signals.

Gunasekarage and Power (2001) analyzed the profitability of moving average rules using index data for four emerging South Asian capital markets: the Bombay Stock Exchange, the Colombo Stock Exchange, the Dhaka Stock Exchange and the Karachi Stock Exchange. The findings reveal that technical trading rules provide predictive ability in these markets thus rejecting the null hypothesis that the returns earned by applying moving averages values are equal to those generated from the buy and hold strategy. The application of this technique is shown to produce excess return to investors in South Asian markets.

Chang et al. (2004) tested the forecasting power in emerging stock markets using variable moving average (VMA) and trading range break (TRB) technical trading rules. Results reveal that only a few rules generate positive excess returns after taking into account the trading costs. This study tested different sub-samples to conduct analysis in bear and bull markets. It is shown that the variable moving average (VMA) trading rules do not seem to have predictive power for the sample used due to the widely usage of the trading rules by market participants.

Metghalchi (2007) tested two moving average technical trading rules for the Austrian stock market with samples ranging from January 1990 to May 2006. Methodologies used in this study are the standard moving average rule (SMA) and the increasing moving average rule (IMA). Test results revealed that moving average rules do indeed have forecasting power and the authors found recurring price trends for profitable trading. The empirical results of this study indicate that technical trading rules may outperform the buy-and-hold strategy.

Cheung et al. (2009) investigate the profitability of two popular technical trading rules: the simple moving average (SMA) and the trading range break (TRB) in Hong Kong

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for the period of 1972 until 2006. The study only shows that one trading rule, the (1,50) rule is able to outperform the market. With regards to trading range break (TRB) rule, the returns achieved are all small and insignificant.

Hudson et al. (1996) replicated the study by Brock et al. (1992) on the UK data ranging from July 1935 until January 1994. Methodologies used are moving average rules and trading range breakout rules. The empirical results indicated that use of technical the trading rules examined would not allow investors to make excess returns in the situation of costly trading.

DATA AND RESEARCH METHODOLOGY

The accuracy of findings of a study depends on the depth and comprehensiveness of analysis undertaken. A thoroughly structured and well executed research design would yield less dubious results which can generate reliable empirical results and conclusions. This section will provide detailed information on data and methodologies adopted in this study.

RESEARCH DATA

Analysis will be carried out using daily and weekly data of the Kuala Lumpur Shariah Index (KLSI) from 17th April 1999 to 31st October 2007. Due to the introduction of new index by FTSE to replace the existing KLSI, daily and weekly6 data of FTSE Bursa Malaysia Emas Shariah Index will be used starting 1st November 2007 until 29th February 2012.

Computation of Daily and Weekly Returns

To examine the randomness of stock returns, the daily and weekly returns are calculated as follows: 1 ln t t t P R P where t

R are the market returns at period t

t

P is the price of stock index at instant t

1

t

P is the price index at period t-1

lnis the natural logarithm

The natural logarithm is used as it is more likely to be normally distributed, Al-Ahmad (2012).

Statistical Summary of Data

Analysis will begin with the statistical analysis of the data with analysis of mean, median, maximum, minimum, standard deviation, skewness, kurtosis and Jarque-Bera. This is a

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According to Lai et al. (2006), the variance ratio test required a sample size of at least 256 observations to have reasonable power against other alternative tools, the weekly market returns are appropriate.

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preliminary analysis of the data to obtain a better understanding of the behavior of the stock index returns.

THE VARIANCE RATIO TEST

The random walk model is tested using the variance ratio and the multiple variance ratio tests on the market returns. The Lo and MacKinlay variance ratio test is believed to be more powerful than the Dickey-Fuller unit root or the autocorrelation Q tests for testing predictability in stock price return series (Lo and MacKinlay, 1989). According to this test, the variance ratio statistics are based on the assumption that the variance of increments in the random walk series is linear in the sample interval. If a series follows a random walk, the variance of a qth differenced variable is q times the variance of its first differenced variable. The idea can be illustrated in the form of:

1

( t t q) ( t t )

V p p qV p p

where, q is any positive integer. The variance ratio can further written as:

1 2 2 1 ( ) ( ) ( ) ( ) (1) t t q t t V p p q VR q V p p q

The unbiased estimators 2(1) and 2( )q are denoted as:

2 1 2 1 ˆ ( ) ˆ (1) ( 1) nq k k k p p nq 2 2 ˆ ( ) ˆ ( ) nq k k q k q p p q q m where, ˆ 1 (pnq p0) nq and ( 1 )(1 ) q m q nq q nq

The null hypothesis of the test is that the variance ratio at lag q is defined as the ratio of the variance of the q-period return to the variance of the one-period return divided by q, which should be equal to one under the random walk hypothesis. If any of the estimated variance ratios differs significantly from one, then the random walk hypothesis is rejected.

Lo and MacKinlay (1988) developed two test statistics to test the null hypothesis, one is with the assumption of homoscedastic increments Z(q) and the other is with the assumption of heteroscedastic increments Z*(q). If the null hypothesis of homoscedastic increments of random walk is not rejected, the associated test statistics has an asymptotic standard normal distribution as follows :

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0 ( ) 1 ( ) ˆ ( ) VR q Z q q where, 1 2 0 2(2 1)( 1) ˆ ( ) 3 ( ) q q q q nq

On the other hand, the associate test statistic for heteroscedastic increments random walk, Z*(q) is given as follows:

* ( ) 1 * ( ) ˆ ( )e VR q Z q q where, 1 1 2 * 1 ˆ ˆ ( ) 4 1 q e k k k q q 2 2 1 1 1 2 1 1 ˆ ˆ ( ) ( ) ˆ ˆ ( ) nq j j j k j k j k k nq j j j p p p p p p

THE MOVING AVERAGE TRADING RULEs

After testing for a random walk in the Malaysian Islamic stock market, the predictability and profitability of two technical trading rules, namely, the fixed-length moving average (FMA) and variable length moving average (VMA) rule are investigated. In both trading rules, a buy signal is generated when a short-term moving average exceeds the long-term moving average and vice versa. In order to examine the profitability of these two trading rules, profit generated after applying these trading rules will be compared to the buy-and-hold strategy.

Fixed Moving Average

In the FMA rule, a buy signal will be generated on day t when the short term moving average on day t-1 exceeds the long-term moving average on day t-1. The sell signal will be generated on day t when the short term moving average on day t-1 falls below the long-term moving average on day t-1. -1 1 1 1 1 if 0 otherwise S L t s t l s l t P P I S L

where, S and L stands for short and long-term, respectively.

However, when a signal is generated, the position will be held for a certain period to estimate the holding period returns. The selection of the holding period is arbitrary and 10-days holding period will be used to reflect ten trading 10-days. Any signals generated within this period will be ignored and after the ten days, a new signal will be generated. The same

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10-days holding period rule will be followed and the cumulative returns of 10-10-days will be calculated.

Variable Moving Average

In the VMA rule, a buy or sell signal will be generated each day and the buy signal will be executed on day t when the short term moving average on day t-1 exceeds the long-term moving average on day t-1. The sell signal will be executed on day t when the short term moving average on day t-1 falls below the long-term moving average on day t-1.

However, when a one percent band is introduced, a buy signal is initiated only when a short-term moving average exceeds the long-term moving average by at least one percent. The same rule is applied for a sell signal. If the short-term moving average falls between the upper and lower band of the long-term moving average, no signal or a neutral signal will be generated.

The VMA rules states that investor should take a long position (buy) if the short-term VMA is above the long-term VMA and stay short (sell) if otherwise happens.

-1 1 1 1 1 if 0 otherwise S L t s t l s l t P P I S L

where, S and L stands for short and long-term, respectively.

The VMA rules that will be used in this study are 5_60, 5_120, and 5_180 where 5 represents the number of days in the short-term moving average and 60, 120 and 150 refer to the number of days in the long-term moving average. The same rules have been used by Lai et al. (2006) to perform an analysis to determine whether technical analysis can be used to forecast price changes in Kuala Lumpur Stock Exchange.

Calculation of Profit

In order to calculate profits generated from the application of the two technical trading rules and the buy-and-hold strategy, the “double-or-out” framework used by Brock et al. (1992) and Bessembinder and Chan (1998) is applied. This technique had been used by Lai et al. (2006) when examining predictability and profitability of variable moving average and fixed moving average trading rules in Malaysian stock market.

In this framework, an investor is assumed to borrow at the risk free interest rate and double the equity investment once a buy signal is generated. When a sell signal is generated, the investor will sell the shares and invest in the risk free interest rate. The average yield of the 3-months Malaysian Treasury bill is used as a proxy for the risk free interest rate. The borrowing and lending rates are assumed to be equivalent and the risk during the buy and sell periods are assumed to be equal.

Hence, profit earned due to buy signals would be:

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Profit generated or savings yielded for not being in the market once a sell signal is created can be computed as follows:

Profit ( ) = (risk free interest rate) (mean return trade per year)

Profits or extra returns generated by applying the technical trading rules without considering transaction costs is the sum of the profits from the buy signals and the cost savings upon the sell signals.

The profitability of the moving averages trading rules are analyzed to determine if the mean returns generated are statistically significantly different from zero. The returns derived by applying the moving average technical trading rules are then compared with mean returns earned from practicing the buy and hold strategy.

EMPIRICAL RESULTS AND DISCUSSION

This section will provide a thorough discussion and explanation on the empirical results. Basically, it starts with concise look on the statistical summary of the data used namely the returns of Kuala Lumpur Shariah Index (KLSI) and FTSE Bursa Malaysia Emas Shariah Index. Next, the discussion will focus on the variance ratio test of both Kuala Lumpur Shariah Index (KLSI) and FTSE Bursa Malaysia Emas Shariah Index thus followed by deep argument on the moving average technical trading rules which comprises of fixed moving average and variable moving average.

STATISTICAL SUMMARY OF DATA

Statistical analysis of the data covers the computation of mean, median, maximum, minimum, standard deviation, skewness and kurtosis. This works as a preliminary analysis of the data in order to obtain better understanding of the behavior of both stock indices.

Index Returns of Kuala Lumpur Shariah Index (KLSI)

The summary statistics of daily and weekly index returns of Kuala Lumpur Shariah index (KLSI) are shown in Table 4.1. The mean of daily returns is 0.00035 the mean of the weekly returns is 0.00174. Daily return of 0.04698 on 12th August 1999 recorded the highest return while the lowest value of -0.07089 was observed on 17th April 2000. For weekly index returns, return on 8th February 2000 was the maximum return of the series and the return on 18th September 2001 was the minimum.

The summary statistics reported in Table 4.1 indicate non-normality of returns computed on a daily and weekly basis. The values of skewness and kurtosis indicate that the distributions are not normal. Skewness characterizes the degree of asymmetry of a distribution around its mean. Both daily and weekly index returns exhibit negative skewness showing a distribution with an asymmetric tail extending toward more negative values. Kurtosis characterizes the relative peakedness or flatness of a distribution compared with the normal distribution. Both daily and weekly index returns exhibit positive kurtosis indicating a relatively peaked distribution.

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Table 4.1 Summary Statistics of the Kuala Lumpur Shariah Index (KLSI) from April 1999 to November 2007

Daily Returns Weekly Returns Number of Observations 2229 445 Mean 0.00035 0.00174 Median 0.00000 0.00155 Maximum 0.04698 0.08036 Minimum -0.07089 -0.13319 Standard Deviation 0.00913 0.02422 Skewness -0.58999 -0.62167 Kurtosis 7.42044 4.11594

Figure 4.1 and Figure 4.2 illustrates the plots of the natural logarithm of daily and weekly indices for the Kuala Lumpur Shariah Index from April 1999 to November 2007. There is an upward trend with no extreme drop throughout the period indicating that no structural break occurs from April 1999 through November 2007.

Figure 4.1 Natural Logarithm of Daily Price of Kuala Lumpur Shariah Index

4.00 4.20 4.40 4.60 4.80 5.00 5.20 5.40 5.60 N atu ral Lo g Pr ic e In d e x Date

Kuala Lumpur Shariah Index (KLSI)

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Figure 4.2 Natural Logarithm of Weekly Price of Kuala Lumpur Shariah Index 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 N atu ral Lo g Pr ic e In d e x Date

Kuala Lumpur Shariah Index (KLSI)

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Index Returns of FTSE Bursa Malaysia Emas Shariah Index

The summary statistics of daily and weekly index returns of FTSE Bursa Malaysia Emas Shariah Index is shown in Table 4.2. Mean of daily returns is 0.00036 while for weekly returns is 0.00180. For daily index returns, the return of 0.04075 on 20th February 2007 was the maximum return of the series and the return of -0.11320 on 10th March 2008 was the minimum return. The weekly return of 0.08018 on 4th November 2008 was the highest return and the lowest return of -0.11269 was observed on 22th January 2008.

The summary statistics reported in Table 4.2 indicate non-normality of returns computed on a daily and weekly basis. The values of skewness and kurtosis indicate that the distributions are not normal. Skewness characterizes the degree of asymmetry of a distribution around its mean. Both daily and weekly index returns exhibit negative skewness showing a distribution with an asymmetric tail extending toward more negative values. Kurtosis characterizes the relative peakedness or flatness of a distribution compared with the normal distribution. Both daily and weekly index returns exhibit positive kurtosis indicating a relatively peaked distribution.

Table 4.2 Summary Statistics of the FTSE Bursa Malaysia Emas Shariah Index from October 2006 to June 2012

Daily Returns Weekly Returns Number of Observations 1482 296 Mean 0.00036 0.00180 Median 0.00049 0.00350 Maximum 0.04075 0.08018 Minimum -0.11320 -0.11269 Standard Deviation 0.00933 0.02450 Skewness -1.61328 -1.17886 Kurtosis 17.67809 4.64683

Figure 4.3 and Figure 4.4 depicts the plots of natural logzrithm of daily and weekly indices for FTSE Bursa Malaysia Emas Shariah Index from October 2006 to June 2012. We can observe a structural change happened in the index series within the year of 2008 to 2009. Other than that, the movement of the index for both daily and weekly basis seems to be stable in general. Overall, we can notice a flatty upward trend with an extreme drop throughout the period indicating the existence of a structural break. The extreme drop or structural break around 2008 to 2009 was due to the Asian financial crisis (Chin 2008).

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Figure 4.3 Natural Logarithm of Daily Price of FTSE Bursa Malaysia Emas Shariah Index

Figure 4.4 Natural Logarithm of Weekly Price of FTSE Bursa Malaysia Emas Shariah Index

8.2 8.4 8.6 8.8 9 9.2 9.4 1/1/2007 1/1/2008 1/1/2009 1/1/2010 1/1/2011 1/1/2012 N atu ral Lo g Pr ic e In d e x Date

FTSE Bursa Malaysia Emas Shariah Index

FTSE Bursa Malaysia Emas Shariah Index

8.2 8.4 8.6 8.8 9 9.2 9.4 1/1/2007 1/1/2008 1/1/2009 1/1/2010 1/1/2011 1/1/2012 N atu ral Lo g Pr ic e In d e x Date

FTSE Bursa Malaysia Emas Shariah Index

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THE VARIANCE RATIO TEST

The random walk model is tested by applying the variance ratio and the multiple variance ratio tests on the market returns. Homoscedasticity, Z(q), and heteroscedasticity, Z*(q), are determined for all lags of daily and weekly returns of both Kuala Lumpur Shariah Index and FTSE Bursa Malaysia Emas Shariah Index.

Table 4.3 and Table 4.4 report the variance ratios and the Z-statistics for lags of 2 days to 24 days and 2 weeks to 52 weeks for the Kuala Lumpur Shariah Index daily and weekly returns. Daily lags consists of 2 days, 4 days, 6 days, 8 days, 10 days, 12 days, 14 days, 16 days, 18 days, 20 days, 22 days and 24 days lag and the weekly lags are for 2 weeks, 4 weeks, 8 weeks, 12 weeks, 16 weeks, 24 weeks, 32 weeks and 52 weeks. Monthly and yearly returns will not be used due to an insufficient number of data points using these two return intervals. This is consistent with Lai et al. (2006) who state that the variance ratio test requires a sample size of at least 256 observations to have reasonable power against other alternative tools. Thus, only daily and weekly market returns are appropriate in this case.

All variance ratios are found to be exceeded one (VR(q)>1) for the various lags indicating positive correlation in the daily and weekly market returns. These results show that the index series does not follow random walk. The Kuala Lumpur Shariah Index can be categorized as a mean reverting or a trend stationary process. If the index series follows a mean reverting process, there exists a tendency for the index level to return to its trend path over time and investors may be able to forecast future returns by using information about past returns. A random walk process indicates that any shock to stock price is permanent and there is no tendency for the index level to return to a trend path over time. This characteristic suggests that future returns are unpredictable based on historical observations and the volatility of the index can grow without bound in the long run (Chauduri and Wu, 2003).

Table 4.3 Variance Ratios of the Kuala Lumpur Shariah Index (KLSI) Daily Return from April 1999 to November 2007

Lag Variance Ratio Homoscedasticity Z(q) Heteroscedasticity Z*(q) 2 1.16545 7.81114 0.07761 4 1.31248 7.88564 0.07736 6 1.38976 7.44376 0.07264 8 1.41938 6.69359 0.06599 10 1.45679 6.38736 0.06379 12 1.52022 6.55110 0.06624 14 1.55491 6.40813 0.06554 16 1.56086 6.01577 0.06219 18 1.55573 5.58906 0.05837 20 1.54292 5.15753 0.05439 22 1.53631 4.84041 0.05153 24 1.53723 4.84041 4.62865

Furthermore, it can be observed that the variance ratio appreciated with an increase in the number of lags but the homoscedasticity and heteroscedasticity Z-statistic decreased with

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increasing of number of lags. It can be concluded that the random walk hypothesis was rejected under the assumption of homoscedasticity for most of the lags of daily and weekly returns.

Table 4.4 Variance Ratios of the Kuala Lumpur Shariah Index (KLSI) Weekly Return from April 1999 to November 2007

Lag Variance Ratio Homoscedasticity Z(q) Heteroscedasticity Z*(q) 2 1.07554 1.59359 0.01830 4 1.13602 1.53372 0.01736 8 1.20332 1.44993 0.01659 12 1.25361 1.42701 0.01669 16 1.32906 1.57700 0.01877 24 1.42365 1.63088 0.02003 32 1.50876 1.68252 0.02103 52 1.57479 1.47751 0.01899

Table 4.5 and Table 4.6 summarize the variance ratios and the Z-statistics for lags of 2 days to 24 days and 2 weeks to 52 weeks on the FTSE Bursa Malaysia Emas Shariah Index daily and weekly returns. Daily lags consists of 2 days, 4 days, 6 days, 8 days, 10 days, 12 days, 14 days, 16 days, 18 days, 20 days, 22 days and 24 days lag whereas the weekly lags comprises of 2 weeks, 4 weeks, 8 weeks, 12 weeks, 16 weeks, 24 weeks, 32 weeks and 52 weeks. Similar as Kuala Lumpur Shariah Index case, monthly and yearly returns of FTSE Bursa Malaysia Emas Shariah Index will not be used due to insufficient number of data by using these two types of return basis. This is in line with Lai et al. (2006) who state that the variance ratio test required a sample size of at least 256 observations to have reasonable power against other alternative tools. Thus, only daily and weekly market returns are suitable in this case.

All variance ratios are greater than one (VR(q)>1) for the various lags showing positive correlation in the daily and weekly market returns. This leads to a conclusion that FTSE Bursa Malaysia Emas Shariah Index series does not follow a random walk. This index is characterized as a mean reverting or a trend stationary process. According to Chauduri and Wu (2003), if the index series follows a mean reverting process, then there exists a tendency for the index level to return to its trend path over time and investors may be able to forecast future returns by using information on past returns or known as historical data. On the other hand, a random walk process says that any shock to stock price is permanent and there is no tendency for the index level to return to a trend path over time. This characteristic suggests that future returns are unpredictable based on historical observations and the volatility of index can grow without bound in the long run.

Besides, it can be observed that the variance ratio increased with rise in the number of lags but the homoscedasticity and heteroscedasticity Z-statistic declined with increasing of number of lags. It can be concluded that the random walk hypothesis was rejected under the assumption of homoscedasticity for most of the lags of daily and weekly returns.

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Table 4.5 Variance Ratios of the FTSE Bursa Malaysia Emas Shariah Index Daily Return from October 2006 to June 2012

Lag Variance Ratio Homoscedasticity Z(q) Heteroscedasticity Z*(q) 2 1.11733 4.51664 0.00090 4 1.24344 5.00940 0.00098 6 1.32985 5.13658 0.00099 8 1.35693 4.64513 0.00091 10 1.37383 4.26232 0.00085 12 1.39844 4.09130 0.00083 14 1.42118 3.96596 0.00082 16 1.43891 3.83865 0.00081 18 1.44912 3.68310 0.00078 20 1.45585 3.53097 0.00076 22 1.47031 3.46115 0.00076 24 1.48962 3.43971 0.00076

Table 4.6 Variance Ratios of the FTSE Bursa Malaysia Emas Shariah Index Weekly Return from October 2006 to June 2012

Lag Variance Ratio Homoscedasticity Z(q) Heteroscedasticity Z*(q) 2 1.06298 1.08358 0.00024 4 1.08599 0.79075 0.00018 8 1.22096 1.28518 0.00030 12 1.36359 1.66854 0.00038 16 1.49533 1.93605 0.00044 24 1.76372 2.39784 0.00056 32 1.93676 2.52665 0.00060 52 2.20471 2.52564 0.00061

MOVING AVERAGE TRADING RULES

Fixed Moving Average

Table 4.7 reports the test results of the six fixed moving average rules of different lengths with a zero and a one percent band for FTSE Bursa Malaysia Emas Shariah Index from year 2006 to year 2012. Fixed moving averages tested consists of (5,60,0), (5,120,0) and (5,180,0) and also with the presence of 1% band, (5,60,0.01), (5,120,0.01) and (5,180,0.01). Figure 4.5 until figure 4.10 depicts the application of the fixed moving average rules in generating the buy and sell signals for all six variations of moving averages tested.

For all moving averages examined, the fixed moving average (FMA) rule generated more buy signals than sell signals. However, most of the daily average returns for buy signals

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are significantly negative. In the other hand, all of the sell returns are positive. This lead to a conclusion that the fixed moving average (FMA) with the 10 days holding period may not be appropriate enough to be applied in the Malaysian Islamic stock market.

By combining both buy and sell transactions, the fixed moving average (FMA) rule with a shorter length appeared to be more profitable for the FTSE Bursa Malaysia Emas Shariah Index. This may be associated with higher sensitivity of the index movement compared to the longer length moving average. The variable moving average (FMA) rules for the 60-days and 120-days were found to earn significantly higher profit than the buy-and-hold strategy. The shorter length of fixed moving average (FMA) rule indicated better predictive ability than the longer length moving average.

The fixed moving average (FMA) rule showed more predictive ability for the shorter length of fixed moving average (FMA) rule. This result implies that future returns can be predicted from the historical returns. Technical trading rules are found to demonstrate the predictability and economic value in the Islamic stock market in Malaysia.

Variable Moving Average

Table 4.8 shows the test results of the six variable moving average rules of different lengths with a zero and a one percent band for FTSE Bursa Malaysia Emas Shariah Index from year 2006 to year 2012. Variable moving averages tested consists of (5,60,0), (5,120,0) and (5,180,0) and with the presence of 1% band, (5,60,0.01), (5,120,0.01) and (5,180,0.01). Figure 4.11 until figure 4.16 depicts the application of the variable moving average rules in generating the buy and sell signals for all six variations of moving averages tested.

For all moving averages tested, the variable moving average (VMA) rule produced more buy signals than sell signals. Daily average returns for buy signals are significantly positive and provide evidence to reject the hypothesis that the mean return generated by technical trading rules is zero. In the other hand, all of the sell returns are negative.

The variable moving average (VMA) rule with shorter length appeared to be more profitable for the FTSE Bursa Malaysia Emas Shariah Index. This may be associated with higher sensitivity of index movement compared to longer length moving average. The variable moving average (VMA) rules particularly the 60-days and 120-days rule were found to earn significantly higher returns compared to the buy-and-hold strategy. The shorter length of the variable moving average (VMA) rule indicated greater predictive ability compared to the longer ones (Lai et al. 2006).

The variable moving average (VMA) rule with the 1% band outperformed the similar rule without the 1% band in terms of total return. The 1% band works as a filter to eliminate ‘whiplash’ signals as highlighted by Brock et al. (1992), especially when the short and long term moving averages are very close to each other. Ignoring those ‘whiplash’ signals is show to produce higher returns.

The variable moving average (VMA) rules more forecast ability in particular for shorter length of variable moving average (VMA) rule. This result implies that future returns can be predicted from historical returns.

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Table 4.7 Test Results of Fixed Moving Averages (FMA) Rules

Test N(Buy) N(Sell) Buy Sell Buy and Hold

Buy>0 Sell>0 Buy-Sell Profit (Buy) Profit (Sell) Profit 5,60,0 81 62 -0.00088 0.00179 0.00036 0.48025 0.58987 -0.00267 -0.01424 0.02223 0.00799 5,60,0.01 44 36 -0.00162 0.00217 0.00036 0.46591 0.60556 -0.00379 -0.01426 0.01562 0.00136 5,120,0 76 60 -0.00076 0.00157 0.00036 0.48677 0.58667 -0.00234 -0.01161 0.01887 0.00726 5,120,0.01 53 37 0.00641 -0.00413 0.00036 0.47909 0.59459 0.01054 0.06792 -0.03057 0.03735 5,180,0 73 58 -0.00050 0.00102 0.00036 0.49378 0.56552 -0.00152 0.00148 -0.00729 -0.00581 5,180,0.01 58 46 -0.00073 0.00117 0.00036 0.48168 0.17647 -0.00189 -0.00842 0.01074 0.00232

Table 4.8 Test Results of Variable Moving Averages (VMA) Rules

Test N(Buy) N(Sell) Buy Sell Buy and Hold

Buy>0 Sell>0 Buy-Sell Profit (Buy) Profit (Sell) Profit 5,60,0 44 45 0.00066 -0.00019 0.00036 0.54663 0.50462 0.00085 0.00580 -0.00171 0.00409 5,60,0.01 36 32 0.00084 -0.00033 0.00036 0.55140 0.50700 0.00117 0.00603 -0.00212 0.00391 5,120,0 28 27 0.00051 -0.00003 0.00036 0.53581 0.52413 0.00054 0.00283 -0.00016 0.00267 5120,0.01 27 23 0.00057 -0.00025 0.00036 0.53945 0.51238 0.00081 0.00305 -0.00114 0.00191 5,180,0 16 16 0.00024 0.00010 0.00036 0.52394 0.52782 0.00014 0.00076 0.00030 0.00107 5,180,0.01 22 17 0.00035 -0.00004 0.00036 0.52531 0.51867 0.00040 0.00154 -0.00015 0.00139

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SUMMARY AND CONCLUSIONS

The empirical results of the random walk hypothesis tests show that the Islamic stock market in Malaysia does not follow a random walk. This implies the potential for the application of technical trading rules to generate above average returns as investors may be able to forecast future returns by using information on past returns or known as historical data.

The overall empirical results of testing the technical trading rules show that abnormal returns for both the variable and fixed moving average rules are economically significant. The length of 60-days appears to be the most profitable moving average for both the variable and the fixed moving averages. The empirical results show that the variable moving average outperforms the fixed moving averages in terms of profit produced by applying any particular trading rule.

LIMITATIONS OF THIS STUDY

The findings derived based on Shariah-compliant index may be constrained by the limited data available for these instruments. For instance, the FBM Emas Shariah Index used to represent the Islamic stock market in Malaysia in this study was launched in 2007. Hence, the findings were solely based on 5 years period whilst analysis based on the sub-periods for the purpose of considering any structural break cannot be made.

Secondly, analysis of the variance ratio test and technical trading rules do not take into consideration possible structural breaks especially during the Asian financial crisis in the period of 2007 to 2008. This is due to limited number of data available if the full sample data is divided into several sub-periods.

Last but not least, in calculating the profit of the moving averages trading rule analysis, transaction costs such as agent commission and brokerage fees are ignored. Hence, profit generated in this study is without transaction costs.

SUGGESTIONS FOR FUTURE RESEARCH

Future research on this area especially analyzing the random walk hypothesis and technical trading rules in the Islamic stock market in Malaysia can be expanded to cover a longer period of study. Besides, the research can examine any structural breaks in the data series to the Asian financial crisis in the period of 2007 to 2008. This may ensure more reliable results based on the full sample of data and also based on several non-overlapping sub-periods.

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