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Risk- Return Performance of Islamic versus Conventional Stock Market

Indices: Global Empirical Evidence

Ahmad Abu-Alkheil, Dr. Oec Assistant Professor of Finance & Accounting

German Jordanian University P.O Box 35247 Amman 11180 Jordan

Phone: +962 6 429 4444 Ext. 4626

ahmad.abualkheil@gju.edu.jo

Walayet A. Khan, PhD, Professor of Finance Research Director, Institute for Global Enterprise

University of Evansville Schroeder Family School of Business Administration

1800 Lincoln Avenue Evansville, IN 47722

Phone: +1- 812-488-2869 wk3@evansville.edu

Bhavik Parikh, PhD* Assistant Professor of Finance

Gerald Schwartz School of Business St Francis Xavier University

Antigonish, Nova Scotia – B2G W5, Canada Phone: +1-902-867-4926

bparikh@stfx.ca

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Risk- Return Performance of Islamic versus Conventional Stock Market

Indices: Global Empirical Evidence

Abstract

We use the common measures of portfolio’s risk-return tradeoff to compare the overall

monthly performances of Islamic stock indices (ISI) and conventional stock indices (CIS)

from FTSE, DJ, MSCI, S&Ps and Jakarta series covering the period from 2002 to 2014.

In terms of risk-reward trade-offs, we find a significant difference between both types of

indices. The Modigliani-Miller (MM) performance measure, the Omega ratio, Sharpe and

Treynor measures show that ISI underperform their CIS counterpart while Jensen Alpha

and Sortino ratio put ISI ahead of CIS.

As compared to the overall market returns, both indices are shown to be superior.

However, ISI exhibit lower volatility and thus offer possible long term diversification

opportunities in an investor's portfolio. Results further illustrate that CIS exhibit higher

beta which means higher potential of losses during market downturns. Consequently,

conservative investors might not prefer investing in CIS.

We also focus on the global financial crisis period to determine if the significant

differences exist among two sets of indices. Contrary to widely held view that Islamic

indexes performed better during the crisis period, we do not find any significant

differences in terms of risk adjusted returns.

Key words: Islamic stock indices, Conventional indices, CAPM, Portfolio.

JEL Classification: G11, G32, G15

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1. Introduction

Ethical investments are still niche investments (style) yet they have gained much

popularity in the international capital markets. Ethical investing is for both individual

investors and institutional investors who screen to find a certain ethically profitable

business profile. For instance, ethical indices screen companies to exclude those which

are involved in certain activities that are inconsistent with specific ethical values (such as

gambling, weapons manufacturing, etc.).

From a financial perspective, ethical investments are subset investments of all the

assets available in the market. Basso and Funari (2003) argue that they are likely to

underperform. However, many empirical studies reject this assumption, for example,

Havemann and Webster, (1999) find that social investors don't have to sacrifice

investment profits due to investing in certain ethical assets.

In this paper, we examine one key segment of ethical investments; ethical indices.

Ethical investments consist of two main parts, socially responsible investments (SRI) and

faith-based investments (FBI). Our focus is on faith based investments which attract

nearly 1.5 billion Muslims (and also increasingly many non-Muslims who are searching

for returns but in ethical investments) globally who are looking to align their investments

per the Islamic beliefs (Boassonet al. (2006), and Hassan and Mahlknecht, (2011))

Islamic investments are considerably one of the fastest growing segments in

global financial services industry, particularly due to the potential growth and

profitability. They offer investors the opportunity to identify a truly attractive investment

environment (Haroon, (1999)). Before the appearance of the Shari´ah1-compliant

benchmark stock index in the late 1990’s, it was difficult and complex process for

Muslim faith investors (and also interested non-Muslim ethical investors) to find

companies that conducted business in accordance with Islamic principles.

Islamic screens for Shari´ah-compliance exclude stocks of organizations whose

core business activities are related to conventional banking or any other interest related

activities (usury or (riba), tobacco, alcohol, gambling (maisir), life insurance, arms

manufacturing, pork production, packaging and any other processes related to pork

production, and ambiguity or speculation (gharar). Screens for Shari´ah-compliance have

also financial ratios screening where, for example, the portion of debt relative to assets

must be less than 33%, interest income must be less 5% of total revenue, and cash and

short-term investments should not exceed 50% of the total assets (Kok et al., 2009).

Historically speaking, the first Shari´ah-compliant index was established by the

Dow Jones Market in December 1995. Introduction of Islamic index enabled Islamic

index managers to invest in permissible securities and use the index as a benchmark

performance. The Dow Jones Islamic Market Index (DJIM) family includes thousands of 1 Shari´ah means the basic tenets of Islam, as conveyed by the Holy Quran and the life of the prophet Muhammad. We are using the

term in this paper referring to Islamic financial principles as established by scholars’ based on Islamic teachings.

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broad-market, blue-chip, fixed-income, strategy and thematic indices that have passed

rules-based screens for Shari´ah compliance (Bank Negara Malaysia, 2013). Since the

establishment of DJIM, investment in Islamic stocks has experienced remarkable growth.

Consequently, various capital markets and financial institutions around the world have

now established their own ISI. Nowadays, there are more than 250 global and local

ethical indices schemes (Ernst & Young, 2014)

Many stocks performed poorly during the Global Financial Crisis (GFC07) period

and consequently many international investors have suffered heavy losses. There is,

however, a little evidence as to how ISI performed compared to the CIS during the crisis

period. Abbes (2012) believes that the difference in performance between the ISI and CIS

should be minimal. On other hand, Al-Khazali et al., (2014) have shown that CIS

dominate (outperformed) ISI in normal economic conditions while ISI outperform CIS

during meltdown conditions. We aim to add to the emerging literature by investigating

the impact of the global financial crisis on both ISI and CIS. To do so, we investigate

whether the CIS and ISI have performed differently during the global financial crisis of

2008.

Previous studies are empirically useful but are still limited to the coverage of

countries and scope, as several Islamic indices series are not included, thus the results

offer limited scope. To make the study more meaningful and avoid the shortcomings of

previous studies, we have collated data from Thomson Reuters DataStream for the period

2002-2014 to investigate whether investors in Shari´ah products earn a higher rate of

return compared to the CIS. We used Islamic and conventional stock indices from FTSE,

DJ, MSCI, S&Ps and Jakarta series. The findings of our study will provide investors

some valuable guidelines regarding optimal investment choices between conventional

and Islamic stocks indices. At the same time, this information will benefit market

participants and potential investors who plan to diversify their portfolios in a new

emerging Islamic market.

The rest of this paper will be organized as following: Section 2 presents the

literature review. Section 3 describes the data sources and the methodology. Section 4

explains the results. And section 5 provides summary and conclusion.

2. Literature Review

Fowler and Hope (2007) argue that the performance comparison of Islamic versus non-

Islamic indices is complicated because of differences in size and industry-weighting. El

Khamlichi and Laaradh (2012) also claimed that Islamic indices have not received

significant attention by researchers due to their short history.

Screened investments have been subject of an intense discussion, as to whether

the portfolio performance is reduced when certain investments are excluded by ethical or

religious reasons. Not surprisingly, the theoretical and empirical literature has mixed

results due to different performance measures and data used by researchers. The

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persistent differences in results among these studies suggest that even these efforts may

not have successfully and accurately addressed the empirical challenges.

For example, Atta (2000) investigates the performance of Dow Jones Islamic

Index (DJIM) against both market index and risk-free rate (RF) and found that DJIM

outperformed its conventional counterparts and offered more returns than risk-free rate.

Hassan (2001) reported the same results when he analyzed the performance of six Dow

Jones Islamic indices.

Recent literature has examined the relationship between Islamic and conventional

indices regarding returns and volatility performance. For instance, Hussein (2004)

utilized the Capital Asset Pricing Model (CAPM) to estimate the risk-adjusted returns of

Financial Times Stock Exchange (FTSE) and Global Islamic Stock Index from July 1996

to August 2003. The results indicated that the application of ethical screens did not have

an adverse impact on the performance of the index. He offers evidence that the Islamic

index yields statistically significant positive returns in the bull market period, but it

underperforms the conventional peers in the bear market period.

Using daily data for the period from December 1999 to April 2002, Hakim and

Rashidian (2004) applied the co-integration and causality tests to investigate the effect of

Shari´ah restrictions on the performance of the Dow Jones Islamic Market Index

(DJIMI). They also analyzed the relationship between the DJIMI, the Wilshire5000 and

the risk-free rate which is proxied by the 3-months Treasury Bills. Authors found no

correlation between the (DJIMI) neither with the Wilshire 5000 index nor with the three-

month Treasury bills over the sample period. Estimations revealed that the DJIMI had a

special risk – return profile that was unaffected by the broad non-Shari´ah compliant

equity market.

Albaity and Ahmad (2008) provide new evidence on the risk-return performance

of the Kuala Lumpur Shari´ah Index (KLSI) and the Kuala Lumpur Composite Index

(KLCI). They find insignificant differences in performance and the movements in those

two indices which appeared to have the same behavior in both the short-run and the long-

run.

Girard and Hassan (2008) employed Sharpe Ratio, Treynor Ratio, and Jensen’s

Alpha to compare five FTSE Islamic indices and five conventional benchmarks MSCI.

They also employ Fama’s measure to examine the style and timing ability of fund

managers. Authors report insignificant performance differences between FTSE Islamic

indices and their counterparts due to style and timing ability of fund managers.

Dharani and Natarajan (2011) analyzed the performance of the Islamic index and

conventional index in India. They utilized the two sample t-test to examine the difference

of the mean returns between Nifty Shari´ah index and the Nifty index. The results

showed no significant difference between average daily returns of the Nifty Shari´ah

index and the Nifty Index. They also found that Nifty Shari´ah underperformed during

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the period from January 2007 to December 2010, mainly during the crisis and the post

crisis period.

Beik and Wardhana (2011) evaluated the effect of financial crisis of 2007 on

Jakarta Islamic Index (JII). They used co-integration test to examine the long-run

relationship and the VAR model to evaluate the short-run dynamic interactions. Results

pointed out that the JII was significantly affected by the disturbance taking place in the

other markets. However, JII was found to be the least volatile and more stable index in

the short run as compared with other Islamic and conventional stock indices in Malaysia

and the USA.

Sukmana and Kholidin (2012) examined the risk performance of Islamic stock

index (Jakarta Islamic Index/JAKISL) and its conventional counterpart (Jakarta

Composite Index/JCI) in Indonesia over the period from January 2001 to December

2009. They employed the ARCH and GARCH methodologies. Their results revealed that

the volatility of JCI was greater than that of JAKISL and the Islamic index demonstrated

more resilience during the crisis period compared to conventional stock index. They

concluded that during crisis period JAKSIL was less risky than JCI.

To compare the investment performance of the Islamic portfolios and

conventional benchmark portfolios, Hassan et al., (2005) used the total return data of the

Dow Jones Islamic Market Index (DJIMI) and the Dow Jones Index-Americas for the

period from 1996 to 2003.They concluded that the returns for DJIMI were not

substantially negatively affected using Islamic "ethical" screens. Also the DJIMI

outperformed the conventional Dow Jones Index-Americas as well as the benchmark

index (i.e., CRSP (Centre for Research and Security Prices) in terms of the expected

returns.

In summary, there is no consensus in the literature regarding the directional

relationship between Islamic equity indices and the conventional equity indices. We

differentiate our study from previous studies as we use the most recent data. Also

contrary to most other studies our analysis is based solely on indices instead of

investment funds. The analysis of FTSE, DJ, MSCI, S&Ps and Jakarta series carries the

benefits of not having to consider any transaction costs or skill based management

accompanied by investment funds.

3. Data and Methodology

Thirty-two ISI and thirty-two CIS (Table 1 below) are used to examine the differences

between the performances of both indices. The monthly stock prices were collected from

Thomson Reuters DataStream for the period 2002-2014. The risk and return of both

indices are calculated using the risk adjusted measurements: The Sharpe ratio, Treynor

ratio, Jenson Alpha, Omega measure, The Modigliani and Modigliani Measure, and

Sortino Ratio. Standard deviation and beta are the proxies for the total risk and market

risk which are estimated to measure the volatility of the returns. We use independent

samples T-test and the Mann Whitney U-test to test the null hypothesis of no significant

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difference between the return performances of these two groups of indices. The data

selection takes into consideration the availability of data and its consistency within the

accessible time frame.

Table 1: List of Islamic Stock Indices (ISI) and Conventional Stock Indices (CIS) # ISI CIS 1 DJ ISLAMIC ASIA/PACIFIC - PRICE INDEX DJGL ASIA PACIFIC $ - PRICE INDEX

2 DJ ISLAMIC Market EUROPE - PRICE INDEX DJ EUROPE TOTAL STOCK MKT - PRICE INDEX

3 DJ ISLAMIC CANADIAN - PRICE INDEX DJGL CANADA DJTM CANADA DEAD - PRICE INDEX

4 DJ ISLAMIC JAPAN $ - PRICE INDEX DJGL JAPAN $ - PRICE INDEX

5 DJ ISLAMIC MKT CHINA HK TITANS 30 $ - PRICE INDEX DOW JONES CHINA 88 - PRICE INDEX

6 DJ ISLAMIC US - PRICE INDEX DJ US TOTAL STOCK MKT - PRICE INDEX

7 DJ ISLAMIC US LARGE CAP - PRICE INDEX DJ US LARGE CAP TOTAL STOCK MKT - PRICE INDEX

8 DJ ISLAMIC US MID CAP - PRICE INDEX DJ US MID CAP TOTAL STOCK MKT - PRICE INDEX

9 DJ ISLAMIC US SMALL CAP - PRICE INDEX DJ US SMALL CAP TOTAL STOCK MKT - PRICE INDEX

10 DJISLAMIC MARKET INDEX DJGLOBALINDEX

11 FTSE BURSA MALAYSIA HIJRAH SHARI´AH $ - PRICE INDEX FTSE BURSA MALAYSIA EMAS $ - PRICE INDEX

12 FTSE GWA P SHARI´AH DEV $ - PRICE INDEX FTSE GWA ALL-WORLD $ - PRICE INDEX

13 FTSE SHARI´AH DEV ASIA PACIFIC - PRICE INDEX FTSE DEV ASIA PAC $: A - PRICE INDEX

14 FTSE SHARI´AH DEVELOPED - PRICE INDEX FTSE GWA DEVELOPED $ - PRICE INDEX

15 FTSE SHARI´AH JAPAN 100 $ - PRICE INDEX FTSE W JAPAN $: L - PRICE INDEX

16 FTSE SHARI´AH MULT 150 - PRICE INDEX FTSE MULTINATIONALS ($) - PRICE INDEX

17 FTSE SHARI´AH USA $ - PRICE INDEX FTSE US $ :A - PRICE INDEX

18 JAKARTA SE ISLAMIC - PRICE INDEX Jakarta IDX COMPOSITE

19 MSCI AC AMERICAS IS U$ - PRICE INDEX MSCI AC AMERICAS U$ - PRICE INDEX

20 MSCI AC ASIA IS U$ - PRICE INDEX MSCI AC ASIA PACIFIC U$ - PRICE INDEX

21 MSCI AC ASIA PACIFIC IS U$ - PRICE INDEX MSCI AC ASIA U$ - PRICE INDEX

22 MSCI AC EUROPE IS U$ - PRICE INDEX MSCI AC EUROPE U$ - PRICE INDEX

23 MSCI AC FAR EAST IS U$ - PRICE INDEX MSCI AC FAR EAST U$ - PRICE INDEX

24 MSCI AC PACIFIC IS U$ - PRICE INDEX MSCI AC PACIFIC U$ - PRICE INDEX

25 MSCI AC WORLD IS U$ - PRICE INDEX MSCI AC WORLD U$ - PRICE INDEX

26 MSCI GOLDEN DRAGON IS U$ - PRICE INDEX MSCI GOLDEN DRAGON U$ - PRICE INDEX

27 MSCI INDIA IS U$ - PRICE INDEX MSCI INDIA - PRICE INDEX

28 MSCI MALAYSIA IS - PRICE INDEX MSCI MALAYSIA - PRICE INDEX

29 MSCI PAKISTAN IS U$ - PRICE INDEX MSCI PAKISTAN - PRICE INDEX

30 MSCI ZHONG HUA IS U$ - PRICE INDEX MSCI ZHONG HUA U$ - PRICE INDEX

31 S&P 500 SHARI´AH $ - PRICE INDEX S&P 500 COMPOSITE - PRICE INDEX

32 S&P JAPAN 500 SHARI´AH $ - PRICE INDEX S&P JAPAN 500 - PRICE INDEX

Following measures were used to evaluate the risk-adjusted performance of ISI and CIS:

3.1. Sharpe Ratio

The Sharpe Ratio is a measure of risk-adjusted returns. It is the average return earned

in excess of the risk-free rate per unit of volatility or total risk. Subtracting the risk-free

rate from the mean return, the performance associated with risk-taking activities can be

isolated. Since it uses standard deviation as a measure of risk, it does not assume that the

portfolio is well diversified. The higher the Sharpe Ratio, better the performance. The

Sharpe Ratio is calculated using the following model:

Sharpe ratio = (RP- Rf)/σp (1)

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where Rp is the expected portfolio return, Rf is the risk free rate, and σp is the portfolio

standard deviation.

3.2. Treynor Ratio

Treynor ratio (also known as the "reward-to-volatility ratio") attempts to measure how

well an investment has compensated its investors given its market risk. Unlike Sharpe

Ratio, Treynor Ratio utilizes "market" or "systematic risk" risk (beta) instead of total risk

(standard deviation). The premise underlying the Treynor ratio is that systematic risk, the

kind of risk that is inherent to the entire market (represented by beta), should be penalized

because it cannot be diversified away. The higher the value of Treynor ratio, better the

performance is, since this indicates greater returns per unit of market risk. If the Treynor

ratio is positive, it means that the index has profited more than expected, whereas the

opposite holds true. The Treynor ratio is calculated using the following model:

Treynor ratio= (RP- Rf)/βp, (2)

where βP is the portfolio Beta.

3.3. Jenson Alpha

The Jensen's measure is a risk-adjusted performance estimate that represents the average

return on a portfolio over and above that predicted by the (CAPM), given the

portfolio's beta and the average market return. This is the portfolio's alpha:

(3)

where Rm, is the expected market return.

3.4. MM Performance Measure

The Modigliani and Modigliani Measure (MM) is an extension of the Sharpe Ratio. It

focuses on the risk-adjusted returns as well as the total market volatility. MM is a

function of Sharpe’s measure and, thus, both yield the same results. It represents the

surplus return of a portfolio when compared to the return of the market portfolio adjusted

for market volatility. The M&M measure is mathematically expressed as:

MM = (SRp-SRm) σm (4)

where SRp: the Sharpe Ratio of the portfolio (or index). SRm: the Sharpe Ratio of the

market. σm is the standard deviation of the market portfolio returns.

3.5. Sortino Ratio

Sortino’s ratio (Sortino & Price, 1994) measures the risk-adjusted return of an investment

asset or portfolio. Sortino ratio simply replaces total risk in the Sharpe ratio by downside

risk; portfolio managers will not be punished for upside variability but will be penalized

RREβ R Rα fMpfpp

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for variability below the minimum target return while the Sharpe ratio penalizes both

upside and downside volatility equally.

It represents the differential return of a portfolio by unit of downside risk (returns over

required return for 1 unit of downside risk), as shown in equation 5:

SoR = (Rt - Rrf)/tdR (5)

where SoR is the portfolio’s Sortino’s measure; Rt is the average period return for a

portfolio. Rrf is the minimum expected target or required rate of return defined by the

investor for the investment strategy under consideration.tdR is the target downside risk or

deviation of portfolio.

Investors would hope to see substantial excess return above the RF, accompanied by little

downside deviation. Therefore, like most of other ratios, the higher the Sortino ratio the

better it is.

3.6. Omega Measure

This measure is a function of the portfolio returns. It is calculated by dividing the

probability of obtaining a return superior to a minimum expected return by the

probability of obtaining a return inferior to it, as shown in equation 6:

(6)

where Ωi (Rmin) is the portfolio’s Omega measure as a function of Rmin and F(x) is the

cumulative distribution function of the portfolio returns defined by the interval [a, b].

The higher the ratio the better it is.

3.7. The Risk Adjusted CAPM using OLS

We use the Capital Asset Pricing Model (CAPM) of Sharpe (1964) to provide

additional insights into the performance of Islamic indices as compared to conventional

indices. The empirical representation of the CAPM is as follows:

Rit−Rf= α+ βRMER + ɛi (7)

where, Rit is the return on Islamic index i, RMER is the return on the conventional market

index more above the risk-free rate. CAPM beta (β) measures the sensitivity of Islamic

index to the market movements. An index with a β greater than one is more sensitive to

movements in the market and therefore, riskier than an index with a beta lower than

one. The CAPM alpha (α) is the risk adjusted return of Islamic index as compared to

the conventional index. ɛ i is the error term (Abbes, 2012).

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4. Empirical Results

4. 1 Descriptive Statistics

The descriptive statistics for the returns of the 32 indices for the entire sample period are reported

in Table (2). The means and standard deviations vary widely across the 32 indices. Results show

that ISI mean return (0.003) is lower than CIS mean return (0.004). The lower average

returns earned by ISI from DJ, FTSE, and S&Ps series are supported by lower standard

deviation of (0.0475), a measurement of total risk, and the lower average βp = 0.713 (the

measure of systematic risk). The results for beta suggest that ISI are less sensitive to the

market compared to the CIS. The lower portfolio beta of the ISI is a logical result of

Shari´ah screening process. As compared to the market, results show that both indices

however, are riskier than the market (σm =0.0040). Therefore, ISI and CIS record higher

returns than the market (0.003 and 0.004 ˃0.0018).

Table 2. Descriptive Statistics of Monthly Returns for ISI and CIS (2002-2014) Index No.* Min. CSI Max. CSI. Mean CSI. σ CSI**. Min. ISI Max. ISI Mean ISI σ ISI

1 -0.21 0.11 0.00295 0.040847 -0.19 0.11 -0.0021 0.03638

2 -0.21 0.12 0.0017 0.045838 -0.21 0.12 -0.002 0.04194

3 -0.23 0.12 0.00199 0.048258 -0.23 0.12 -0.0004 0.04434

4 -0.25 0.13 0.00099 0.052799 -0.2 0.12 -0.0007 0.04677

5 -0.2 0.12 0.00173 0.04476 -0.2 0.12 -0.0001 0.0409

6 -0.22 0.12 0.00208 0.046895 -0.22 0.12 0.000 0.04373

7 -0.24 0.15 0.00285 0.058065 -0.22 0.14 0.0007 0.05236

8 -0.25 0.16 0.00599 0.065804 -0.24 0.16 0.0053 0.06516

9 -0.2 0.11 0.00054 0.029202 -0.2 0.11 0.0006 0.03958

10 -0.36 0.29 0.00439 0.060344 -0.36 0.29 0.009 0.08437

11 -0.24 0.16 0.00426 0.038547 -0.24 0.16 0.008 0.05215

12 -0.69 0.24 0.00538 0.07147 -0.69 0.24 0.0081 0.09844

13 -0.22 0.13 0.00404 0.049314 -0.22 0.13 0.0036 0.05063

14 -0.19 0.1 0.00362 0.042122 -0.16 0.1 0.0034 0.04103

15 -0.26 0.15 0.00712 0.052322 -0.26 0.13 0.0075 0.05392

16 -0.24 0.18 0.00696 0.067044 -0.23 0.16 0.0067 0.05773

17 -0.21 0.13 0.00308 0.044369 -0.21 0.12 0.0024 0.04919

18 -0.37 0.22 0.00426 0.058936 -0.37 0.22 0.0044 0.07356

19 -0.22 0.12 -0.0001 0.042777 -0.015 0.96 0.0012 0.0324

20 -0.15 0.14 0.00285 0.044634 -0.15 0.1 0.0022 0.04292

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21 -0.19 0.11 0.00433 0.043262 -0.17 0.11 0.0045 0.04266

22 -0.3 0.28 0.00301 0.065818 -0.14 0.1 0.001 0.02867

23 -0.21 0.16 0.00404 0.043249 -0.19 0.14 0.0038 0.04284

24 -0.22 0.11 0.00231 0.052666 -0.18 0.1 0.0006 0.03754

25 -0.23 0.16 0.00103 0.040665 -0.16 0.11 0.0003 0.03469

26 -0.16 0.12 0.00125 0.042472 -0.16 0.1 0.0006 0.03632

27 -0.2 0.11 0.00304 0.040702 -0.17 0.1 0.0022 0.03377

28 -0.04 0.05 0.00061 0.007689 -0.04 0.05 0.0012 0.01086

29 -0.19 0.11 -0.00045 0.052798 -0.19 0.11 -0.0009 0.04005

30 -0.2 0.12 0.00304 0.036898 -0.1 0.08 0.0028 0.02644

31 -0.38 0.18 0.00968 0.038281 -0.39 0.18 0.0156 0.07136

32 -0.37 0.16 0.0159 0.0677 -0.38 0.18 0.0163 0.06665

Average -0.245 0.15 0.004 0.048017 -0.221 0.159 0.003 0.0475

*Please refer to table 1 for the names of the indices. ** σ (sigma) refers to the standard deviation. Volatility (average βp) = 0.713 and

0.737 for ISI and CIS. Average Return of the Market (ARm)= 0.0018. Risk of the Market (σm) = 0.0040.

Figure 1 illustrates the volatility trends under each indices series over the sample period

from 2002 to 2014. We use variance of returns as a measure of volatility. There appears

to be no apparent differences in returns. However, this trend of returns is only an

arbitrary deduction and requires further detailed analysis for verification.

Figure 1: The monthly returns for the thirty-two stock indices illustrated in Table 1 (2002–2014)

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4.2. Mean Differences

We further proceed to test whether there is a significant difference between the means of

Islamic and conventional indices. We first employed the non-parametric Wilcoxon–

Mann–Whitney two-sample rank-sum test. This test does not require a particular normal

distribution. Table 3 reports the actual significance values of the mean average returns,

and mean standard deviation for a portfolio of Islamic indices and conventional indices.

Specifically, the test statistics table provides the test statistic, U statistic, as well as the

asymptotic significance (2-tailed) p-value.

Table 3 shows that the null hypothesis can be rejected and thus there is a significant

difference in the mean of both indices. This result is in line with results of Girard and

Hassan (2008) and Rana and Akhter (2015) who found that returns of Islamic indexes are

significantly different from their conventional counterparts.

Table 3: Wilcoxon Scores (Rank Sums) for Average Returns (ARp), and Standard

Deviation (σp): Comparison of ISI and CIS*

We also use the parametric t-test. In fact, the Central Limit Theorem illustrates that the t-

test (the "robust" test) is valid for non-normal data sets if the datasets are sufficiently

"large". Our dataset is large enough, so it is valid to use the parametric Independent

Samples t-test to check the robustness of our previous results obtained from MWW test.

Table 4 shows the results of the t-test of differences in mean returns, standard deviations,

and beta between the ISI and CIS. These findings again offer an evidence of a significant

difference between two sets of indexes (T-values are large i.e. close or above 2.00)

Index Mann

Whitney

U Test

Mean Score Index Mann

Whitney U

Test

Mean Score

CIS

CIS

σp 3917.6865 ISI

ISI

σp 3701.3135

ARp 3802.2571 ARp 3818.7429

*Two-Sided test [Pr > |Z|], where p <.0001at α=0.05. The Z scores are as follow; for σp= -4.2935, Z

for ARp= -0.3271. The U-statistics are as follow: σp= 14922468 and U-statistics ARp=14486599.5.

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Table 4: Comparison of ARp, σp and βp for ISI versus CIS: Value-Ind. sample t-test.

Index Mean of ARp, βp, andσp*

ISI

(ARp) 0.00450

CIS 0.00426

Difference in Means 0.00024

T- Value for Difference in Means Test 1.97

(βp)

ISI 0.07135

CIS 0.07374

Difference in Means -0.0239

T- Value for Difference in Means Test -3.39

(σp)

ISI 0.00495

CIS 0.00514

Difference in Means Test -0.00019

T Value for Difference in Means Test -2.66

*The results are significant at the α =5% level.

Results from tables 3 and 4 show that CIS are riskier than ISI but surprisingly and

exceptionally the later generate higher average returns despite the intuitive appeal of a

positive risk-return relationship. This indicates that ISI offer a combination of low-risk

but high-return opportunity- that is unique. Low risk and high returns stocks are rare. But

there are indeed examples of high performing indices that have low risk and high return

profiles. Baker et al, (2013) show that, from 1968 through 2012 in the U.S. equity market

portfolios of low risk stocks deliver on the promise of lower risk as planned, but with

surprisingly higher average returns. This so-called “low risk anomaly” suggests a very

basic form of market inefficiency. The presence of such anomaly in ISI indexes may be

because the ISI managers use a proprietary stock selection models and /or portfolio

construction algorithm aimed at achieving a lower absolute risk than the market index but

with similar or higher returns. ISI have a higher return with lower risk, therefore it seems

eventually these indexes will have an appeal for investors.

4.3. Risk-adjusted Performance for ISI and CIS

Risk-adjusted returns are used to provide a comprehensive analysis of the risk and returns

for both, ISI and CIS for the period 2002 to 2014. Such analysis further examines the

robustness of aforementioned results. Table 5 reports Sharpe Ratio, Treynor Ratio, and

Jensen’s Alpha for Islamic and conventional stock portfolios.

14 | P a g e

Table 5. Risk-Return trade-off Performance for ISI and CIS

No. Return versus Risk Estimates %

1 Sharpe Ratio CIS 18.508

Sharpe Ratio ISI 12.881

2 Treynor Ratio CIS 0.222

Treynor Ratio ISI -0.081

3 Jensen alpha CIS 0.057

Jensen alpha ISI 0.121

4 Sortino CIS 0.833

Sortino ISI 0.936

5 MM CIS -134.41

MM ISI -175.29

6 Omega CIS 1.133

Omega ISI 1.099

The first performance measure is Sharpe ratio that shows that ISI yields lower risk

adjusted returns (12.881%) than CIS (18.508%). The next performance measure is

Treynor ratio, which confirms the lower risk adjusted returns earned by ISI. The Treynor

ratio takes into consideration only systematic risk (beta), whereas Sharpe ratio

incorporates both systematic and unsystematic risks. In both performance measures ISI

are ranked behind CIS indicating that ISI offer lower excess return per unit of risk. These

results are consistent with those of Ahmad and Ibrahim (2002).

The MM performance measure and the Omega ratio further confirm the results found by

Sharpe and Treynor measures. MM shows that Islamic indices earns lower returns

(-175.29%) than conventional indices (-134.41%) while the Omega ratio records a lower

return of 1.099 for Islamic indices as compared to conventional indices.

Jensen Alpha is designed to help investors determine the risk-reward profile of an

investment portfolio. Surprisingly, Jensen's alpha ratios put the ISI ahead in terms of

indices returns performance. The table reports that Jensen's alpha is positive for both

types of indices. The positive Jensen’s Alpha indicates a superior (abnormal returns)

performance of the index. However, ISI portfolio has higher Jensen's alpha as compared

to their conventional peers (CIS Jensens α=0.057 %< ISI Jensens α 0.121%)

which means better performance of ISI by providing additional return over the

benchmark.

Islamic indices have current Sortino Ratio of 0.936 (conventional indices =0.833). This

further confirms the results found by Jensen Alpha measure. The higher Sortino ratio of

ISI indicates that ISI performs better compared to CIS relative to the risk taken. Also ISI

exhibit a lower risk of large losses. Thus, a rational investor would prefer the ISI over

CSI because it means that the investment is earning more return per unit of bad risk that it

takes on.

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Findings from Jensen Alpha and Sortino ratio contradict the view based on optimal

portfolio theory in that the Islamic screening process implies inferior risk-adjusted returns

because the restricted indices may miss out on certain profitable investments. However,

our results are in line with Stone et al. (2001) who shows no significant loss in attainable

diversification possibilities by (religious) ethical screening.

4.4. Time Regression Models

Table 6 presents the results of CAPM for the entire period. The beta of ISI and CIS is on

average less than one. However, the beta of ISI is still lower than the beta of CSI

indicating they are less risky and less sensitive to the movement of market.

The CAPM alphas for both indexes are similar. They are negative and close to zero. This

suggests that Islamic indices do not exhibit a significantly different risk-adjusted

performance compared to the conventional indices. Alpha tells us how well an index

performs compared to the stated benchmark it’s trying to beat. A negative alpha indicates

the index is underperforming and thus fails to generate returns at the same rate as the

broader sector. In other words, negative alpha means that the return for both indices are

below the equilibrium predicted by the CAPM, therefore indices prices will fall until

yields rise to the equilibrium. The act of selling will drive down the price. In another

word, both indices are overpriced. Both indices have beta bellow one. However,

conventional indices show higher beta than Islamic indices.

Table 6: CAPM Estimation for Islamic and conventional Indices: the Entire

Period Factor ISI CSI

Α -0.001 -0.001

Β 0.712 0.789

We examine the impact of the global financial crisis (GFC) of 2007 on the

performance of Islamic versus conventional indices. Consequently, we employ the

(CAPM) model to estimate alpha (α) and the beta coefficient (β) for both markets

(portfolios) during (GFC) of 2007 (table 7).

Table 7: CAPM Estimation for Islamic and conventional Indices: Crisis Period Time Period Variable Mean

2007-2010 α CSI

-0.00223

α ISI

-0.00178

β CSI

0.81531

β ISI

0.75307

Results show that ISI have lower beta coefficient, while surprisingly possessing a higher

(lower negative) alpha than CSI. Investors would thus most likely prefer ISI indices with

16 | P a g e

high alpha and low beta. But some other investors might like CSI with higher beta to cash

in on the indices volatility during upswing market.

4.5. The Effect of GFC of 2007 on the Risk-Reward Performance of ISI and CSI

For further analysis, we investigate the effects of the global crisis on the

performance of the indices in terms of the risk-reward trade-off. Table 8 reports the

results. ISI exhibit added value in relation to risk. During this period, Islamic portfolios

have a higher Sharpe ratio but a lower Treynor ratio than conventional portfolio.

Table 8. Risk-Adjusted Returns of ISI and CIS– Global Crisis of 2007 Financial Crisis Period [2007-2010]

Performance Measure Mean Score Sig.2 Tailed. P-value*

CSI- Sharpe

ISI - Sharpe

CSI- Treynor

ISI - Treynor

CSI –Sortino

ISI -Sortino

CSI –MM

ISI -MM

CSI – Omega

ISI –Omega

-3.551073

-2.978552

0.090957

0.063516

-1.35334

-0.60224

-19.6212

65.72186

1.207405 1.16361

0.4335

0.8301

0.3405

0.3098

0.4369

* This column is the two-tailed p-value computed using the [t] distribution, the "Pooled" method.

In terms of the other three ratios; Sortino, Omega, and MM, results show that Sortino

and MM are higher (lower negative values) for CIS than ISI despite the adverse effects of

the global crisis.

However, interpretations above are still arbitrary. To determine if differences between

the two types of indices are significant, we employ inter-temporal analysis using

difference-of-means tests (t-test). As shown in the table, we fail to reject the null

hypothesis2. Our results are consistent with the results of Abbes, (2012).

2 Formally, our conclusion is only that we "cannot reject" the null that the two types of indices are equal.

We cannot conclude that the null hypothesis is true either. The two indices might still be different and more

data maybe show that.

17 | P a g e

5. Summary and Conclusion

We examine the overall monthly performances of 32 Islamic and 32 conventional stock

indices from FTSE, DJ, MSCI, S&Ps and Jakarta Series from 2002 to 2014. We use the

common ratios that measures portfolio’s risk-return tradeoff (i.e., the Sharpe ratio,

Treynor ratio, Jenson Alpha, Omega measure, The Modigliani and Modigliani Measure,

and Sortino Ratio).

Results obtained from the parametric t-test and the Mann Whitney U-test show a

significant difference between both indices in terms of the return performance. ISI are

found to be better than CIS in some cases and worse than CIS in some other cases in

generating returns given their specific level of risk. As compared to the market, both ISI

and CIS are shown to be riskier and thus earn a higher risk premium. In terms of the risk-

return trade off, results report a significant difference between both Islamic and

conventional indices. Specifically, the MM performance measure, the Omega ratio,

Sharpe and Treynor measures show that ISI underperform their CIS counterpart while

Jensen Alpha and Sortino ratio put ISI ahead of CIS.

Our results regarding the effects of the crisis of 2007 on the risk adjusted returns profile

indicate that the ISI are relatively more stable and less risky but surprisingly tend to have

simultaneously higher average returns. Yet, the observed differences between ISI and

CIS during the crisis are not statistically significant.

The implications of our findings for both individual and institutional investors are

obvious. A conservative investor might not be attracted to invest in the risky CIS as they

have a higher systematic risk (beta). With high beta, there are potential higher losses

during market downturns. While a value investor who strives to gain the highest possible

returns for the lowest possible risk will more likely choose ISI.

The results in this paper left some room for further investigation. We found that Islamic

indices performed differently from conventional indices over the sample period. This

could be partially due to the special characteristics and the weights of each component of

18 | P a g e

the index. Therefore, extending this paper by taking a further step of analyzing the

components of these indexes can be used to draw new conclusions from existing data.

Furthermore, in this paper we compiled composite results without making any distinction

between developing and developed markets. We may get more insights when we

segregate the data based on developed versus developing markets due to potential

institutional, regulatory, cultural, and other differences. Comparing indices from different

economic categories would be a new research inquiry.

19 | P a g e

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