Journal of Accounting, Management, and Economics Vol. 20, No. 1, 2018, pp. 16-36 Published by Faculty of Economics and Business, Universitas Jenderal Soedirman Published online on January 8, 2018 in http://jos.unsoed.ac.id/index.php/jame Print ISSN: 1410-9336 Online ISSN: 2620-8482  Correspondence to: 1Universitas Jenderal Soedirman, Indonesia. E-mail: ramagardika@gmail.com 2Universitas Jenderal Soedirman, Indonesia. E-mail: a.banani@yahoo.com 3Universitas Jenderal Soedirman, Indonesia. E-mail: sulistyandari.yan@gmail.com Received: December 4, 2017 Revised: December 18, 2017 Accepted: December 29, 2017 INTRODUCTION Background Investing is one of controlled strategies to gain wealth that is very effective for everyone. According to Morgan (2013), investing is the act of committing money or capital to an endeavor (a business, project, real estate, etc.) with the expectation of obtaining an additional income or profit. Investing includes the amount of time of the investors put into the study of a prospective company, especially since time is money. There are many ways to make financial investments. One of them is investing in accordance with the principles of Islamic sharia. Sharia investment avoids investors from practices forbidden by Islam. However, the transactions and facilities of sharia investment are limited right now. Investment can be done on various business activities related to the activity of producing a product or service that is not forbidden by Islam such as liquor, pork, gambling, fraud, etc. One of sharia investments is buying sharia securities including sharia stock, sharia bond, sharia mutual fund, and other securities in accordance with the principles of sharia. In the past decade, the demand for sharia- compliant financial products had increased. According to Dharani and Natarajan (2011), sharia investment had grown significantly in developed and developing countries post 1990 through the introduction of broad macroeconomic and structural reformations in the financial system, the implementation of trade liberalization policies, capital movement, privatization, and global integration of financial markets. These conditions paved the way for the Muslim community to participate in stock market operations. Sharia capital markets are relatively more resilient to crises compared to conventional capital markets. It was stated by Ahmad and Albaity (2008), as the sharia capital market has a better ability to adapt to changes from external crisis disturbances. Sharia capital markets offer a more secure investment medium for the crisis. Thus, the Indonesian capital and financial market institutions are expected to give their attention and commitment in the development of sharia capital market as an advantageous investment alternative. In the Southeast Asian region, there are two countries with the largest Muslim population. Both countries are Indonesia and Malaysia. Muslim Comparative study between the performances of two Islamic indices Study on FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) Rama Gardika1, Ade Banani2, Sulistyandari3 Faculty of Economics and Business, Universitas Jenderal Soedirman, Indonesia Abstract This study aims to analyze the differences in the performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) in Malaysia and Indonesia Sharia Stock Index (ISSI) in Indonesia by using Sharpe, Treynor, and Jensen ratio. Type of this study is quantitative research by using hypothesis testing and purposive sampling technique. Population in this study is all sharia indices listed on Indonesia Stock Exchange (IDX) and Bursa Malaysia (BM). From the sampling process obtained two indices. These indices are FTSE Bursa Malaysia EMAS Syariah (FBMS) and Indonesia Sharia Stock Index (ISSI). Objects of this study are monthly index returns during the period of July 2012 to June 2012 which amounted to 60 data. Hypothesis testing is intended to determine whether there is significant difference between the performance of FBMS and ISSI on the measurement of Sharpe, Treynor, and Jensen ratio. Test was conducted by using Two Independent Samples t-test. Results showed that there is no significant difference between the performance of FBMS and ISSI on the Sharpe, Treynor, and Jensen ratio. Implication of this study is that the results of this study can be used as a reference for companies in selecting which Islamic index prefer to issue the stock in based on the performance measured using Sharpe, Treynor, and Jensen ratio. Results of this study also can be used as a reference for investors who are interested in investing funds in sharia stocks in Indonesia and Malaysia. Keywords Index; Sharia; Sharpe ratio; Treynor ratio; Jensen ratio Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 17 population comparison between both countries can be seen in figure 1. Large number of Muslim residents in both countries led to a demand for a sharia financial system or sharia capitalization system that could keep Muslims not worried for being able to make a transaction in the economy without violating the teachings of their religion. It encourages the two countries to issue sharia-based stock index, Malaysia issued the Financial Times Stock Exchange (FTSE) Bursa Malaysia EMAS Sharia or FBMS and Indonesia issued Indonesia Sharia Stock Index (ISSI). Figure 1 Muslim population comparison between Malaysia and Indonesia (2014) Source: wikipedia.org Malaysia and Indonesia are countries with the majority of Muslim population and have launched Islamic indices. Both countries also have the same economic characteristics. Although Malaysia economic condition is not identically the same with Indonesia, but both economic conditions are almost the same and both countries are still the developing countries. Trend of sharia stock growth in Indonesia and Malaysia also showed similar characteristics. It can be seen in the growth of ISSI capitalization that grew IDR 1,200,689.06 million and FBMS capitalization that grew IDR 1,123,123.93 million from 2011 until 2016 (www.investing.com). It makes both countries leading the Muslim-majority countries in the economic sector in the Southeastern Asia. These reasons led the author to be comparing the Islamic indices of both countries in purpose to find out whether there is significant difference in performance between the indices from two different countries that have the same economic characteristics and the majority of Muslim population. Bursa Malaysia has launched three indices to track the performance of sharia-compliant securities. These indices are FTSE Bursa Malaysia EMAS Shariah Index (FBM EMAS Shariah), FTSE Bursa Malaysia Hijrah Shariah Index (FBM Hijrah Shariah), and FTSE Bursa Malaysia Small Cap Shariah (FBM Small Cap Shariah). These indices are designed for the creation of structured products, index tracking funds, and Exchange Traded Funds or as performance benchmarks. The constituents are screened and tested on their market capitalization, free-float, and liquidity. The review is conducted semi-annually in June and December. The launch of the FTSE Bursa Malaysia Hijrah Shariah Index and FTSE Bursa Malaysia GOLD Shariah Index was in response to increase interest in sharia-compliant investment. Both index were the joint initiative between FTSE, Bursa Malaysia, and the global leading sharia consultancy, Yasaar Ltd. FTSE Bursa Malaysia EMAS Sharia (FBMS) consists of sharia-compliant constituents of the FTSE Bursa Malaysia EMAS that meet the screening requirement of the Shariah Advisory Council (SAC). FTSE Bursa Malaysia Hijrah Shariah (FBMHS) index consists of 30 largest sharia-compliant companies in FBMEMAS screened by Yasaar Ltd and the Securities Commission's Shariah Advisory Council (SAC). And the FTSE Bursa Malaysia Small Cap Shariah (FBMSCAP) comprises the constituents of the FTSE Bursa Malaysia Small Cap Index that are sharia-compliant according to the SAC Screening methodology. http://www.inesting.com/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 18 Figure 2 Sharia stock constituents growth in Indonesia (2007-2016) Source: Financial Services Authority (OJK) Demand of sharia stock in Indonesia increases year to year. It is showed by the growth of sharia stock constituents in Indonesia (figure 2). It made Indonesia Stock Exchange launched Islamic indices. Indonesia has two Islamic indices. Both indices are Indonesia Sharia Stock Index (ISSI) and Jakarta Islamic Index (JII). Indonesia Sharia Stock Index (ISSI) is a stock index that reflects the total sharia stocks listed on the Indonesia Stock Exchange (IDX). Indonesia Sharia Stock Index (ISSI) constituents are all sharia stocks listed on IDX and listed in the List of Sharia Securities (DES). Growth of ISSI capitalization can be seen in figure 3. Jakarta Islamic Index (JII) is more specific than ISSI. It may be said that JII's position against ISSI is like LQ45 to IHSG. Each period, the shares that enter JII amounted to 30 (thirty) shares considered not to violate the principles of sharia. JII uses the base day of January 1, 1995 with a base value of 100. Figure 3 Indonesia Sharia Stock Index (ISSI) capitalization in billion rupiah (2011-2016) Source: Financial Services Authority (OJK) This study aims to measure the performance of two biggest Islamic indices in South Asia countries, FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) using Sharpe, Treynor, and Jensen ratio. These methods were selected instead of the other methods because Sharpe, Treynor, and Jensen ratio can be found in variety of financial and investment management literature books at both basic and advanced levels. The methods have been accepted and valid as the standard in the measurement of mutual fund performance (Rudiyanto, 2011). These methods were also consistently used by the previous research to measure the index performance. 1,968,091.37 2,451,334.37 2,557,846.77 2,946,892.79 2,600,850.72 3,168,780.43 0.00 500,000.00 1,000,000.00 1,500,000.00 2,000,000.00 2,500,000.00 3,000,000.00 3,500,000.00 2011 2012 2013 2014 2015 2016 Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 19 Problem formulation Research results of Shofiyullah (2014) showed that the performances of two Islamic indices, JII and FBMHS, are not significantly different. Type of the research is hypothesis testing by using purposive sampling technique. Population in the study was all listed Islamic Indeces in Indonesia Stock Exchange and Bursa Malaysia. Two indices are obtained from the sampling process, namely Jakarta Islamic Index (JII) and the FTSE Bursa Malaysia Hijrah Shariah Index (FBMHS). Object of this study is the monthly index return for the period of January 2009 to December 2011, amounting to 36 data. The researcher used the method of Sharpe, Treynor, and Jensen index test to get the results and found out that there was no significant difference between the performance of JII and FBMHS. Author adapted the method of Sharpe, Treynor, and Jensen ratio used in Shofiyullah’s research to measure the index performance. Previous research took the sample of JII and FBMHS index that only consist of 30 sharia stock constituents with the highest liquidity. Difference between previous research and this research are that this research took the sample of ISSI and FBMS index that cover all sharia stock constituents in Malaysia and Indonesia, and this research expands the sampling period taken in the previous research from 3 years to 5 years. From the research gap above, the questions can be drawn as follow: (1) Is there difference in performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) measured by Sharpe ratio? (2) Is there difference in performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) measured by Treynor ratio? (3) Is there difference in performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) measured by Jensen ratio? LITERATURE REVIEW Sharia stock Conceptually, stocks are securities and also the proof of capital participation to the company and with that proof the shareholders are entitled to get the proceeds of the company's business (Bodie, et al., 2012). Concept of capital participation with the right to get the part of the results of the business is a concept that is not contrary to the principles of sharia. Sharia principles recognize this concept as a musyarakah or syirkah activity. Based on the analogy, then conceptually, the stock is a security that is not contrary to the principles of sharia. However, not all stocks issued by public companies may be referred to as sharia stocks. A stock may be categorized as sharia stock if the stock is issued by: (1) Issuers / public companies that clearly state in their articles of association that the business activities of the issuers / public companies are in accordance with sharia principles (sharia issuers / public companies); or (2) Issuers / public companies that do not state in their articles of association that the business activities of the issuers / public companies are not contradict with the principles of sharia in the capital market, but meet the following criteria: (a) Not conducting business as follows: gambling; trading prohibited by sharia (for example: trade not accompanied by delivery of goods / services and trade with false supply / demand); ribawi financial services (for example: interest-based banks and interest-based financing companies); buying and selling risks that contain elements of uncertainty and / or gambling (for example: conventional insurance); producing, distributing, trading and / or providing: illegitimate goods or services because of the substance; illegitimate goods or services not because of the substance; and / or goods or services that undermine morality and / or harm; and conducting transactions containing bribery elements. (b) Meet the following financial ratios: total interest-based debt compared to total assets of not more than 45%, for example Bank Muamalat Indonesia, Bank Panin Syariah, and Sofyan Hotel; or total interest income and other unlawful income compared to total business income and other income not more than 10%, for example stocks of issuers / public companies listed in the Sharia Securities List. Indonesia sharia indices Sharia capital market in Indonesia already has two stock indices which are the reference for investors in measuring the performance of their portfolio. Both indices include the Jakarta Islamic Index (JII) that has been published on July 3, 2000 and the Indonesian Sharia Stock Index (ISSI) which was launched simultaneously with the fatwa of DSN- MUI on May 12, 2011. Sharia capital market in Indonesia already has the main instrument of composite index which takes into account the performance of all sharia stocks listed on the Indonesia Stock Exchange. That index is called ISSI (Indonesia Sharia Stock Index). Indonesia Sharia Srock Index (ISSI) constituents consist of stocks listed in the Sharia Securities List as set out in the Regulation of Bapepam and LK Number II.K.1 on Criteria and Issuance of Sharia Securities List (see the explanation about sharia stocks). In addition, the sharia capital market also has a performance-based index called Jakarta Islamic Index (JII) whose constituents consist of 30 stocks that have the highest liquidity in the Indonesia Stock Exchange. To become a constituent of the Jakarta Islamic Index (JII), a stock must first be included as a stock listed in the Sharia Securities List, after that, the stocks incorporated in the ISSI constituency will be selected into 60 stocks with the highest market capitalization. Market capitalization is obtained from Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 20 the multiplication of the number of stocks outstanding multiplied by the prevailing price. Thirty of sixty stocks that have the largest transaction value will be taken. Sharia stock index plays an important role in the development of sharia capital market in Indonesia. It is not only a benchmark for investors, but also the basis for measuring the performance of an Islamic mutual fund even the foundation of an Exchange Traded Fund (ETF) product. In addition, ISSI publication ends a misunderstanding of the public regarding the number of sharia stocks, where many people think that the number is only 30 stocks. Malaysia sharia indices Bursa Malaysia as an integrated exchange offers a good breadth of quality sharia-compliant stocks. Development of the Securities Screening Methodology by the Shariah Advisory Council (SAC) of Malaysia Securities Commission (SC) determines the sharia compliance of securities listed and traded on Bursa Malaysia. The sharia compliance review is undertaken for securities of companies listed on the exchange and during pre- initial public offering companies. Each public listed company is reviewed based on the latest annual audited financial statements. The list of sharia- compliant securities is updated and published every May and November. Bursa Malaysia has launched three indices to track the performance of Shariah-compliant securities, the FTSE Bursa Malaysia EMAS Shariah Index (FBM EMAS Shariah), the FTSE Bursa Malaysia Hijrah Shariah Index (FBM Hijrah Shariah) and the FTSE Bursa Malaysia Small Cap Shariah (FBM Small Cap Shariah). These indices are designed for the creation of structured products, index tracking funds and Exchange Traded Funds or as performance benchmarks. Constituents are screened and tested on market capitalization, free- float and liquidity. The review is done semi-annually in June and December. FBMS (FTSE Bursa Malaysia EMAS Shariah) is the index that consists of sharia-compliant constituents of the FBMEMAS that meet the screening requirement of the Shariah Advisory Council (SAC). FBMHS (FTSE Bursa Malaysia Hijrah Shariah Index) consists of 30 largest sharia- compliant companies in FBMEMAS screened by Yasaar Ltd and the Securities Commission's Shariah Advisory Council (SAC). FBMSCAP (FTSE Bursa Malaysia Small Cap Shariah) comprises the constituents of the FTSE Bursa Malaysia Small Cap Index that are Shariah-compliant according to the SAC Screening methodology. FTSE Bursa Malaysia EMAS Shariah (FBMS) is a benchmark index that applies the principles set out by the SAC of the SC. These sharia-compliant companies are further filtered for FBM Hijrah Shariah which consists of the 30 largest sharia compliant companies in FBM EMAS Shariah screened by Yasaar Ltd., a leading global Shariah consultancy, to meet the requirements of international sharia investors. FTSE Bursa Malaysia EMAS Shariah (FBMS) has been designed to provide investors with a broad benchmark for sharia-compliant investment. Constituents are screened according to the Malaysian Securities Commission's Shariah Advisory Council (SAC) screening methodology. FTSE Bursa Malaysia EMAS Shariah (FBMS) applies the principles set out by the SAC in the design of this index. General criteria stipulate that sharia-compliant companies must not be involved in any of the following core activities: financial services based on riba (interest); gambling; manufacture or sale of non-halal products or related products; conventional insurance; entertainment activities that are non-permissible according to sharia; manufacture or sale of tobacco-based products or related products; stockbroking or share trading in sharia non-compliant securities; and, other activities deemed non-permissible according to sharia. FTSE Bursa Malaysia EMAS Shariah (FBMS) is chosen as the object of this study because it covers all the sharia stock constituents in Malaysia (Bursa Malaysia, 2017). Sharpe ratio Sharpe’s performance measurement method was first introduced in 1966 by William Sharpe. Sharpe's measurement is based on an excess return on risk or known as a reward-to-variability ratio. The excess return is derived from the difference between the rate of return of the investment portfolio and the risk-free investment (Indiastuti, 2008). In this study, portfolio investment is an investment in each of ISSI and FBMS issuers and riskless investment is assumed as the average interest rate of Bank Indonesia Certificates (SBI) and overnight rate of Bank Negara Malaysia. And the standard deviation portfolio used is a total risk value that combines the risk that can be diversified (unsystematic risk) and the risk that cannot be diversified (systematic risk). Treynor ratio Measurement with the Treynor method are also based on excess return (Rp-Rf), as does Sharpe. However, Treynor method uses beta (β) as denominator which is a systematic risk or also called market risk (Indiastuti, 2008). Jensen ratio Jensen's measurement method is based on Capital Asset Pricing Model (CAPM). This model states whether the investment manager can outperform the market in a predictable and statistically significant way, by indicating a positive alpha. A Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 21 good investment manager will produce significant positive alpha and vice versa. This method was developed by Michael C. Jensen in 1968. Jensen ratio measures performance by calculating the difference in returns earned from a portfolio (actual return) with expected returns based on systematic risk levels. HYPOTHESIS DEVELOPMENT AND RESEARCH MODEL Many investors mistakenly base the success of their portfolios on returns alone. Few investors consider the risk that they took to achieve those returns. Since the 1960s, investors had known how to quantify and measure risk with the variability of returns, but no single measure actually looked at both risk and return together (Pareto, 2017). Today, investors have three sets of performance measurement tools for assisting with the portfolio evaluations. Sharpe, Treynor, and Jensen ratio combine risk and return performance into a single value, but each model is slightly different. In measuring the index performance, this research used three methods. These methods are Sharpe, Treynor, and Jensen ratio. These methods were selected instead of the other methods because Sharpe, Treynor, and Jensen Index can be found in variety of financial and investment management literature books at both basic and advanced levels. The methods have been accepted and valid as the standard in the measurement of mutual fund performance (Rudiyanto, 2011). Those methods are also used as a reference in assessing the performance of mutual funds and consistently used by the previous researches to measure the index performance. Hypothesis in this research can be formulated as follows: H1: There are differences in the performance of ISSI and FBMS measured by the Sharpe ratio. H2: There are differences in the performance of ISSI and FBMS measured by the Treynor ratio. H3: There are differences in the performance of ISSI and FBMS measured by the Jensen Alpha ratio. Figure 4 Analysis framework RESEARCH METHOD This research is an empirical study; the study of empirical facts based on observation of the FTSE Bursa Malaysia EMAS Shariah (FMBS) on Bursa Malaysia and Indonesian Sharia Stock Index (ISSI) on Indonesia Stock Exchange (IDX) on the period of 2012-2017. Observation periods in this study were taken during 2012 to 2017. Required data are the monthly index return for the period of July 2012 to June 2017 amounting to 60 data. Data of this study were historical data collected from Bursa Malaysia and Indonesia Stock Exchange (IDX). Both data were be obtained from the website of www.investing.com. Populations in this study are all Islamic indices in Indonesia Stock Exchange and Bursa Malaysia. Sampling was taken by using purposive sampling method. Samples were chosen based on characteristic suitability with the criteria that has been determined to get the representative sample. These criteria are: (1) Islamic indices operating on the Indonesia Stock Exchange and Bursa Malaysia in the period of 2012 to 2017. (2) Islamic indices covering all sharia stocks listed on the stock index of each country. From these criteria, samples of this study were Indonesian Sharia Stock Index (ISSI) on Indonesia Stock Exchange (IDX) and FTSE Bursa Malaysia EMAS Syariah Index (FBMS) on Bursa Malaysia. ANALYSIS TOOLS Sharpe ratio (Sp) Sharpe ratio is a method used to compare portfolio performance by using the concept of Capital Market Line (CML) or better known as Reward to Variability Ratio (RVAR). Sharpe states the portfolio performance series by calculating the net result of the portfolio with a risk-free rate per risk unit with the symbol of Sp. Sharpe's performance index is calculated by the following formula: 𝑆𝑝 = 𝑅𝑝 − 𝑅𝑓 𝜎𝑝 Where: Sp = Sharpe performance ratio Rp = portfolio return or market rate of return Rf = risk-free return or risk-free interest rate σp = standard deviation of the portfolio return p during the time of the study Treynor ratio (Tp) Treynor is the ratio used to measure portfolio performance. Treynor assumes that the highly diversified portfolio is known as Reward to Valatility Ratio (RVOR). The Treynor index, therefore, states that the portfolio performance series that is calculated as the net result of the portfolio with the risk-free interest rate per unit of market risk of the portfolio with the symbol of Tp. Treynor's t-test Mann-Whitney U test ISSI financial performance Sharpe Treynor Jensen FBMS financial performance Sharpe Treynor Jensen H Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 22 performance ratio is calculated by the following formula: 𝑇𝑝 = 𝑅𝑝 − 𝑅𝑓 𝛽𝑝 Where: Tp = Treynor performance ratio Rp = portfolio return or market rate of return Rf = risk-free return or risk-free interest rate βp = market risk of portfolio or systematic risk of portfolio (beta) Jensen ratio (ap) Jensen ratio or Jensen alpha ratio is one of measurement methods of portfolio performance. Jensen is very concerned about CAPM in measuring the performance of the portfolio which is often called Jensen alpha (differential return measure). Jensen alpha is an absolute measure that estimates the constant rate of return during the investment period in which the level of Jensen alpha returns are above (below) the buy-hold strategy with the same systematic risk. The Jensen Alpha formula as follows: 𝑎𝑝 = 𝑅𝑝 − [𝑅𝑓 + 𝛽𝑝 (𝑅𝑚 − 𝑅𝑓)] Higher the positive ap, better the portfolio performance. Jensen ratio can be calculated in another way by simplifying the above equation into the equation below: 𝑅𝑝 − 𝑅𝑓 = 𝑎𝑝 + 𝛽𝑝 (𝑅𝑚 − 𝑅𝑓) Where: Rp - Rf = excess return (the difference between portfolio return with risk free rate) Rm - Rf = market premium (the difference between market return with risk free rate) ap = Jensen ratio βp = beta coefficient from the portfolio Index return (Rp) Return is calculated by reducing the index value at the end of the unit of a certain period (t) with the end of the unit of previous period (t-1), then divided by the index value at the end of the unit of previous period (t-1). The equation form as follows: 𝑅𝑝 = 𝑃𝑡 − 𝑃𝑡−1 𝑃𝑡−1 Where: Pt = index value in period t (end of period) Pt-1 = index value in period t-1 (beginning of period) Standard deviation (σ) Standard deviation (σ) gives a description of the magnitude of the risk of fluctuations in the change in returns per unit of a sub-period into subsequent sub-period, and is referred to as total risk. The greater the σ value, the higher the risk of change in return per unit that occurs. Calculation of standard deviation in this study is conducted on the return rate of all Islamic indices and market return. In Microsoft Excel program, this calculation can be done with the formula "STDEV (...)". So can for standard deviation calculations. In the application of STDEV, the calculation of standard deviation can be done manually using the following formula: 𝜎 = √∑ (𝑥1 − �̅�) 2 𝑛 Where: x = n-th data �̅� = x mean = mean value of the sample n = amount of data Risk-free rate (Rf) Risk free asset defined as definite return, this type of asset must be a fixed-interest security that has no default possibility. Since all corporate securities are principally defaulted, risk-free assets cannot be issued by the company, but must be government- issued securities. In this study, riskless investment is assumed to be the Indonesia Government Securities (SUN) series FR0061 issued on July 3, 2012 with the maturity period of 9.8 years obtained from the website of www.bi.go.id and the coupon rate of Malaysian Government Security (MGS) series ZI120012 which was issued on July 12, 2012 with the maturity period of 5 years obtained from the website of www.bondinfo.bnm.gov.my. The SUN and MGS bonus levels are already in percentage form so that the returns can be calculated by dividing the bonus rate of SUN and MGS respectively by the number of months until maturity to reflect the rate of return on a monthly basis. Market return (Rm) Market return used in this research as a benchmark for ISSI is Jakarta Composite Index (JCI). JCI is an index that shows the movement of stock prices in general listed on the stock exchange which became a reference about the development of activities in the capital market. JCI can be used to assess the general market situation or measure whether stock prices increase or decrease. JCI also involves all stock prices listed on the exchange. In addition to inflation and deposit rates, stock returns will be influenced by market indices used as indicators for market conditions and market returns. Capital http://www.bondinfo.bnm.gov.my/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 23 market performance can be seen through the JCI. While, market return as a benchmark for FBMS is Kuala Lumpur Composite Index (KLCI). Beta (β) Beta (β) is a market risk that provides an overview of the relationship between the excess return of portfolio and the excess return from the market. Index with β = 1 has the same risk with market risk (JCI), so it is expected to get the same return with that produced by Jakarta Composite Index (JCI). The portfolio with β < 1 has a lower risk than market risk, so the potential return is generally below the return generated by the JCI. An index with β > 1 has a greater risk than market risk, so it is expected to have a return above the JCI return. Calculation of beta in this study is the beta of ISSI and FBMS, and the market beta is not calculated because the value is 1 (one). The calculation of beta in this research uses linear regression of Y = a + bX in Microsoft Excel application with the function of “SLOPE (...)” with the condition of return of ISSI and FBMS as Y axis and return market as X axis (JCI as a benchmark of Indonesian index and FBMKLCI as a benchmark of Malaysian index). In formula form, beta can be calculated using: 𝛽𝑖𝐼 = 𝜎𝑖𝐼 𝜎2𝐼 Where: βiI = beta of portfolio σiI = covariance of portfolio return of i with market portfolio σ2I = variance of the market portfolio Two independent samples t-test After conducting the normality test mentioned above, to test whether there are differences in the performance of ISSI and FBMS, conducted an independent sample t-test. Independent sample t- test is a type of statistical test that aims to compare the average of two groups that are not paired or unrelated. Unpaired can be interpreted that the study was conducted for two different sample subjects. Independent sample t-test is used to determine whether two unrelated samples have different mean values. Principle of this testing is to see the differences in the variances of the two groups of data, so before the test, must first know whether the variances are equal (equal variance) or different variances (unequal variance). The homogeneity of the variances is tested based on the formula: 𝐹 = 𝑆1 2 𝑆2 2 Where: F = value of F count S12 = biggest variance value S22 = smallest variance value Data is determined to have the same variance when F count < F table, and vice versa. Data variance is not equal (unequal variance) when F count > F table. Form of variance of both groups of data will affect the standard error value that will eventually differentiate the test formula. Testing of t-test for the same variance (equal variance) is using the formula of polled variance: 𝑡 = �̅�1 − �̅�2 √ (𝑛1 − 1)𝑆1 2 + (𝑛2 − 1)𝑆2 2 𝑛1 + 𝑛2 − 2 ( 1 𝑛1 + 1 𝑛2 ) Testing of t-test for different variance (unequal variance) is using the formula of separated variance: 𝑡 = �̅�1 − �̅�2 √ 𝑆1 2 𝑛1 + 𝑆2 2 𝑛2 Where: �̅�1 = mean of sample 1 �̅�2 = mean of sample 2 S12 = variance of sample 1 S22 = variance of sample 2 n1 = number of sample 1 n2 = number of sample 2 Purpose of independent sample t-test is to compare the average of two groups that are not related to each other. Whether the two groups have the same mean or the mean is significantly different. Mann-Whitney U test In statistics, Mann-Whitney U test, or also called the Mann-Whitney-Wilcoxon (MWW), Wilcoxon rank- sum test, or Wilcoxon-Mann-Whitney test, is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample. Unlike the t-test it does not require the assumption of normal distributions. It is nearly as efficient as the t-test on normal distributions. This test can be used to determine whether two independent samples were selected from populations having the same distribution; a similar nonparametric test used on dependent samples is the Wilcoxon signed-rank test. Test involves the calculation of a statistic, usually called U, whose distribution under the null Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 24 hypothesis is known. In the case of small samples, the distribution is tabulated, but for sample sizes above 20, approximation using the normal distribution is fairly good. Some books tabulate statistics equivalent to U, such as the sum of ranks in one of the samples, rather than U itself. Mann- Whitney U test is included in most modern statistical packages. It is also easily calculated by hand, especially for small samples. RESULT Descriptive statistics Table 1 Descriptive statistics for FBMS and ISSI monthly returns (July 2012 - June 2017) FBMS ISSI Mean 0.002882 0.00631 Standard error 0.003358 0.004484 Median 0.00715 0.01255 Mode N/A 0.0475 Standard deviation 0.02601 0.034735 Sample variance 0.000677 0.001206 Kurtosis 0.45981 0.054002 Skewness -0.46592 -0.71055 Range 0.1387 0.1476 Minimum -0.0752 -0.0789 Maximum 0.0635 0.0687 Sum 0.1729 0.3786 Count 60 60 Source: Microsoft Excel Based on table 1, resulted statistic data as follows: (1) Index returns of FBMS with 60 samples of data has minimum value of -0.0752, maximum value of 0.0635, mean / average value of 0.002882, and standard deviation of 0.02601. (2) Index returns of ISSI with 60 samples of data has minimum value of -0.0789, maximum value of 0.0687, mean / average value of 0.00631, and standard deviation of 0.034735. It means that the average return of the Indonesia Sharia Stock Index (ISSI) is higher than the FTSE Bursa Malaysia EMAS Shariah (FBMS) in the period of July 2012 - June 2017. From the range, it can be concluded that the return of ISSI (0.1476) is more fluctuated than the FBMS (0.1387). Table 2 Descriptive statistics for ISSI performance measured by Sharpe ratio (July 2012 - June 2017) Source: Microsoft Excel Based on table 2, resulted statistic data as follows: (1) Index performance of FBMS with 60 samples of data has minimum value of -3.36404, maximum value of 1.968442, mean / average value of -0.3621, and standard deviation of 1. (2) Index performance of ISSI with 60 samples of data has minimum value of -2.60739, maximum value of 1.641974, mean / average value of -0.1709, and standard deviation of 0.988161. It means that the average performance of the Indonesia Sharia Stock Index (ISSI) measured by Sharpe ratio is higher than the FTSE Bursa Malaysia EMAS Shariah (FBMS) in the period of July 2012 - June 2017. Both indices performances ratio are negative which means that the excess returns of both indices are below the expected returns. Table 3 Descriptive statistics for ISSI performance measured by Treynor ratio (July 2012 - June 2017) FBMS ISSI Mean -0.01194 -0.00552 Standard error 0.004258 0.004619 Median -0.00653 0.00091 Mode N/A 0.036911 Standard deviation 0.032981 0.03578 Sample variance 0.001088 0.00128 Kurtosis 0.45981 0.054002 Skewness -0.46592 -0.71055 Range 0.175872 0.15204 Minimum -0.11095 -0.09329 Maximum 0.064922 0.058749 Sum -0.71655 -0.33107 Count 60 60 Source: Microsoft Excel Based on table 3, resulted statistic data as follows: (1) Index performance of FBMS with 60 samples of data has minimum value of -0.11095, maximum value of 0.064922, mean / average value of -0.01194, and standard deviation of 0.032981. (2) Index performance of ISSI with 60 samples of data has minimum value of -0.09329, maximum value of 0.058749, mean / average value of - 0.00552, and standard deviation of 0.03578. It means that the average performance of the Indonesia Sharia Stock Index (ISSI) measured by Treynor ratio is higher than the FTSE Bursa Malaysia EMAS Shariah (FBMS) in the period of July 2012 - June 2017. Both indices performances ratio are negative which means that the excess returns of both indices are below the expected returns. FBMS ISSI Mean -0.3621 -0.1709 Standard error 0.129099 0.127571 Median -0.198 0.023034 Mode N/A N/A Standard deviation 1 0.988161 Sample variance 1 0.976462 Kurtosis 0.45981 0.118362 Skewness -0.46592 -0.71419 Range 5.332477 4.249365 Minimum -3.36404 -2.60739 Maximum 1.968442 1.641974 Sum -21.7259 -10.2539 Count 60 60 Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 25 Table 4 Descriptive statistics for ISSI performance measured by Jensen ratio (July 2012 - June 2017) FBMS ISSI Mean -0.0012 -0.00092 Standard error 0.001507 0.001208 Median -0.00104 3.86E-05 Mode N/A N/A Standard deviation 0.011671 0.009354 Sample variance 0.000136 8.75E-05 Kurtosis 1.716452 -0.2413 Skewness 0.730388 -0.14842 Range 0.064489 0.04045 Minimum -0.02725 -0.02002 Maximum 0.037241 0.020428 Sum -0.07196 -0.05511 Count 60 60 Source: Microsoft Excel Based on table 4, resulted statistic data as follows: (1) Index performance of FBMS with 60 samples of data has minimum value of -0.02725, maximum value of 0.037241, mean / average value of -0.0012, and standard deviation of 0.011671. (2) Index performance of ISSI with 60 samples of data has minimum value of -0.02002, maximum value of 0.020428, mean / average value of -0.00092, and standard deviation of 0.009354. It means that the average performance of the Indonesia Sharia Stock Index (ISSI) measured by Sharpe ratio is higher than the FTSE Bursa Malaysia EMAS Shariah (FBMS) in the period of July 2012 - June 2017. Both indices performances ratio are negative which means that the excess returns of both indices are below the expected returns. Normality testing Before applying the hypothesis testing, Normality testing needs to be conducted. Normality testing determines whether the data is normally distributed or not. This study used two-sample Kolmogorov- Smirnov test for the normality testing. Two sample Kolmogorov-Smirnov test was applied to identify whether the data of FBMS and ISSI are normally distributed or not. Table 5 One sample Kolmogorov-Smirnov test for FBMS and ISSI monthly returns data (July 2012 - June 2017) FBMS ISSI N 60 60 Normal parameters Mean 0.002882 0.006310 Std. deviation 0.0260104 0.0347346 Most extreme differences Absolute 0.083 0.133 Positive 0.071 0.085 Negative -0.083 -0.133 Test statistic 0.083 0.133 Asymp. Sig. (2-tailed) 0.200 0.010 Source: SPSS Based on table 5, resulted statistic data as follows: (1) Kolmogorov-Smirnov test for the data of FBMS monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) > alpha (α) or 0.200 > 0.05. It means that the data is normally distributed. (2) Kolmogorov-Smirnov test for the data of ISSI monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) < alpha (α) or 0.010 < 0.05. It means that the data is not normally distributed. Table 6 One sample Kolmogorov-Smirnov test for FBMS and ISSI performance data (July 2012 - June 2017) FBMS ISSI N 60 60 Normal parameters Mean -0.3620973 -0.1708996 Std. deviation 1.00000053 0.098816108 Most extreme differences Absolute 0.083 0.140 Positive 0.071 0.085 Negative -0.083 -0.140 Test statistic 0.083 0.140 Asymp. Sig. (2-tailed) 0.200 0.005 Source: SPSS Based on table 6, resulted statistic data as follows: (1) Kolmogorov-Smirnov test for the data of FBMS monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) > alpha (α) or 0.200 > 0.05. It means that the data is normally distributed. (2) Kolmogorov-Smirnov test for the data of ISSI monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) < alpha (α) or 0.050 < 0.05. It means that the data is not normally distributed. Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 26 Table 7 One sample Kolmogorov-Smirnov test for FBMS and ISSI performance data (July 2012 - June 2017) FBMS ISSI N 60 60 Normal parameters Mean -0.0119425 -.00055175 Std. deviation 0.03298108 0.03577907 Most extreme differences Absolute 0.083 0.133 Positive 0.071 0.085 Negative -0.083 -0.133 Test statistic 0.083 0.133 Asymp. Sig. (2-tailed) 0.200 0.010 Source: SPSS Based on table 7, resulted statistic data as follows: (1) Kolmogorov-Smirnov test for the data of FBMS monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) > alpha (α) or 0.200 > 0.05. It means that the data is normally distributed. (2) Kolmogorov-Smirnov test for the data of ISSI monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) < alpha (α) or 0.010 < 0.05. It means that the data is not normally distributed. Table 8 One sample Kolmogorov-Smirnov test for FBMS and ISSI performance data (July 2012 - June 2017) FBMS ISSI N 60 60 Normal parameters Mean -0.0011997 -0.0009188 Std. deviation 0.01167119 0.00935425 Most extreme differences Absolute 0.083 0.072 Positive 0.083 0.060 Negative -0.049 -0.072 Test statistic 0.083 0.072 Asymp. Sig. (2-tailed) 0.200 0.200 Source: SPSS Based on table 8, resulted statistic data as follows: (1) Kolmogorov-Smirnov test for the data of FBMS monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) > alpha (α) or 0.200 > 0.05. It means that the data is normally distributed. (2) Kolmogorov-Smirnov test for the data of ISSI monthly returns during July 2012 - June 2017 shows that Asymp. Sig. (2-tailed) < alpha (α) or 0.200 < 0.05. It means that the data is normally distributed. Hypothesis testing H1: There are significant differences in the performance of FBMS and ISSI measured by the Sharpe ratio. Because the data of ISSI performances measured by Sharpe ratio is not normally distributed, hypothesis testing will be conducted by using Mann-Whitney U test which is one of non- parametric methods used in statistics to measure the differences between the means of two groups of data. Mann-Whitney U test doesn’t require the data normally distributed. Table 9 Mann-Whitney U test for FBMS and ISSI performances measured by Sharpe ratio (July 2012 - June 2017) Mann-Whitney U 1566.500 Wilcoxon W 3396.500 Z -1.226 Asymp. Sig. (2-tailed) 0.220 Source: SPSS Based on table 9, showed that the value of Asymp. Sig. (2-tailed) > alpha (α) or 0.220 > 0.05. So, Ho is accepted and Ha is rejected. It means there is no significant difference in the performance of ISSI and FBMS measured by the Sharpe ratio. H2: There are significant differences in the performance of FBMS and ISSI measured by the Treynor ratio. Because the data of ISSI performances measured by Treynor ratio is not normally distributed, hypothesis testing will be conducted by using Mann-Whitney U test which is one of non- parametric methods used in statistics to measure the differences between the means of two groups of data. Mann-Whitney U test doesn’t require the data normally distributed. Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 27 Table 10 Mann-Whitney U test for FBMS and ISSI performances measured by Treynor ratio (July 2012 - June 2017) Source: SPSS Based on table 10, showed that the value of Asymp. Sig. (2-tailed) > alpha (α) or 0.214 > 0.05. So, Ho is accepted and Ha is rejected. It means there is no significant difference in the performance of ISSI and FBMS measured by the Treynor ratio. H3: There are significant differences in the performance of FBMS and ISSI measured by the Jensen ratio. Because the data of FBMS and ISSI performances measured by Jensen ratio is normally distributed, hypothesis testing will be conducted by using two independent samples t-test. Before conducting Two Independent Samples t-test, two samples F-test must be conducted first to find out whether two groups of data have the equal or unequal variances. Table 11 Two samples F-test for FBMS and ISSI performances measured by Jensen ratio (July 2012 - June 2017) FBMS ISSI Mean -0.0012 -0.00092 Variance 0.000136 0.000088 Observations 60 60 df 59 59 F 1.556802 P(F<=f) one-tail 0.0459 F critical one-tail 1.539957 Source: SPSS Based the table 11, it is showed that F statistic (1.556802) > F table (1.539957). It means that two groups of data have the unequal variances. So, kind of Two Independent Samples t-test that will be conducted is t-test for unequal variances. Table 12 Two independent samples t-test for FBMS and ISSI performances (July 2012 - June 2017) FBMS ISSI Mean -0.0012 -0.000919 Variance 0.000136 0.0000875 Observations 60 60 Hypothesized mean Difference 0 df 113 t stat -0.14545 P(T<=t) one-tail 0.442309 t critical one-tail 1.65845 P(T<=t) two-tail 0.884618 t critical two-tail 1.98118 Source: SPSS Based on table 12, it is showed that the value of t statistic (-0.14545) > t table (1.98118). So, Ho is accepted and Ha is rejected. It means there is no significant difference in the performance of ISSI and FBMS measured by the Jensen ratio. DISCUSSION Performance differences of FBMS and ISSI measured by Sharpe ratio Statistic test shows that there is no significant difference between the performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) in the period of July 2012 - June 2012 measured by Sharpe ratio, so the Ho is accepted and the Ha is rejected. It means that there is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) (α = 0.05) with the ISSI Sharpe value which outperformed the FBMS. Sharpe performance ratio measures how much excess return generated from each portfolio for each unit of total risk. Greater Sharpe performance value shows better portfolio performance. By looking at the results on negative Sharpe performance measurements on both indices, can be seen that FBMS and ISSI are not able to give positive excess return for each unit of total risk. ISSI has a greater Sharpe value when compared to FBMS, so the excess return of ISSI is greater than FBMS for each unit of total risk. It means that the benefits of investing in FBMS and ISSI stocks are lower than the benefits of investing in risk-free assets (SUN and MGS), but the investment return on FBMS is greater than ISSI profit when viewed from the total risk of each index. However, there is no significant difference in the Sharpe ratio values of both indices. Performance differences of FBMS and ISSI measured by Treynor ratio Statistic test shows that there is no significant difference between the performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) in the period of July 2012 - June 2012 measured by Treynor ratio, so the Ho is accepted and the Ha is rejected. It means that there is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) (α = 0.05) with the ISSI Treynor value that outperformed the FBMS. FBMS and ISSI have negative Treynor index. These negative values are due to FBMS and ISSI return rates which were lower than risk-free asset return (SUN and MGS). ISSI has a higher Treynor performance ratio than FBMS, it shows that the excess return generated by investment in ISSI is greater than FBMS for each unit of systematic risk (index risk that is affected by market risk) arising on each Mann-Whitney U 1563.000 Wilcoxon W 3393.000 Z -1.244 Asymp. Sig. (2-tailed) .214 Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 28 index. However, there is no significant difference in the Treynor ratio values of both indices. Performance differences of FBMS and ISSI measured by Jensen ratio Statistic test shows that there is no significant difference between the performance of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) in the period of July 2012 - June 2012 measured by Jensen ratio, so the Ho is accepted and the Ha is rejected. It means that there is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Index (ISSI) (α = 0.05) with the ISSI Jensen value that outperformed the FBMS. Jensen or Alpha ratio performance measurement is based on the Capital Asset Pricing Model (CAPM) theory, which is comparing the excess return with the required return predicted from CAPM. FBMS and ISSI have negative Jensen values. It shows that the performances of FBMS and ISSI are under the expected return. However, there is no significant difference in Jensen ratios of both indices. CONCLUSION Based on the description on data analysis and discussion, can be concluded things as follows: (1) There is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Indices (ISSI) in Sharpe ratio. (2) There is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Indices (ISSI) in Treynor ratio. (3) There is no significant difference between the performances of FTSE Bursa Malaysia EMAS Shariah (FBMS) and Indonesia Sharia Stock Indices (ISSI) in Jensen ratio. Although there is no significant difference in the performances of both indices, calculations showed that the performance of ISSI in period of July 2012 - June 2017 outperformed the FMBS in all the three measurements (Sharpe, Treynor, and Jensen ratio). It differs to the results of the research of Shofiyullah (2014) where although there was no significant difference between the performances of FBMHS and JII in the period of 2009-2011 measured by the same methods, performance of FBMHS in Malaysia outperformed the JII in Indonesia. It might be due to the increasing of ISSI constituents in the period taken in this study compared to the previous period (see figure 2). All of the three hypothesis in this research are rejected. It is the same with the research conducted by Shofiyullah (2014) where the research investigated the differences between the performances of Jakarta Islamic Index (JII) and FTSE Bursa Malaysia Hijrah Shariah (FBMHS) with the same methods with this study, that are Sharpe, Treynor, and Jensen ratio. All of the hypothesis of the Shofiyullah was rejected which means that there was no significant difference in the performances of JII and FBMHS measured by Sharpe, Treynor, and Jensen ratio. Shofiyullah argued that it due to the similarity of excess returns of both indices. JII and FBMHS. In the descriptive statistics in Table 1, it can be seen that the average return of ISSI (0.00631) is higher than FBMS (0.002882) although the return of ISSI (range = 0.1476) is more fluctuated that FBMS (range = 0.1387). These reasons might cause the ISSI outperformed the FBMS although the difference of the performances of both indices is not significant. IMPLICATION Based on the conclusions of the research results, there are some suggestions that may be useful for the investors. Results of this study can be used as a reference for investors who are interested to invest funds in sharia stocks in Indonesia and Malaysia. Although there are differences in the results of Sharpe, Treynor, and Jensen ratio calculations, where ISSI is superior to FBMS on the Sharpe ratio indicating that excess return on ISSI issuers' shares is greater than FBMS issuers for each total index risk. ISSI outperformed FBMS in Treynor and Jensen ratio. It means that for the ISSI Treynor excess return is greater than the FBMS for each unit of systematic risk. And for Jensen ratio, return of ISSI is greater than FBMS although both ratio values are under the expected return, but there is no significant difference between both indices on the three ratios. Therefore, investment decisions are still adjusting to the tendency of investment managers. Besides using the results of this study, investors should be still monitoring the performance of each stock issuer of FBMS and ISSI which will be used as an investment instrument to see the performances of these stocks individually. LIMITATION OF STUDY This study only examines the performances of sharia stock indices, so it cannot be generalized to the conventional stock indices listed on Indonesia Stock Exchange and Bursa Malaysia. Suggestions for further research as follows: (1) Due to the limitations of index data owned by the author, the author recommend that the findings from this study to be tested on the next year's index return achievement to find out whether the findings are still relevant or not. (2) Further research on the index performance will be better if it uses other ratios besides the ratios used by the author in order to increase the accuracy of the research, especially the ratios related to the portfolio assessment. Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 29 REFERENCE Albaity, M. and Ahmad, R. (2008). Performance of syariah and composite indices: evidence from Bursa Malaysia. Journal of Accounting and Finance, 4(1), 23-43. Bapepam-LK and DSN-MUI. (2010). Set of Bapepam-LK regulations about sharia capital market and fatwa of DSN-MUI related to sharia capital market. Jakarta: Bapepam-LK. Bodie, Z., Kane, A., and Marcus, A. J. (2012). Essentials of investments (9th ed.). Texas: McGraw-Hill. Bursa Malaysia. Live responsibly, invest responsibly, invest shariah. Bulletin. Bursa Malaysia. Dharani, M. and Natarajan, P. (2011). Equanimity of risk and return relationship between shariah index and general index in India. Journal of Economics and Behavioral Studies, 2(5), 213-222. Indiastuti, F. (2008). Evaluation against stock fund performance and formation of optimal portfolio on mutual fund equity securities. Thesis. Universitas Indonesia. Morgan, J. P. (2013). The art of investing. USA: Treasury Market Practices Group. Pareto, C. (2017). Measure your portfolio’s performance. http://www.investopedia.com/ articles/08/performance-measure.asp. (accessed on July 30, 2017). Rose, P. S. and Marquis, M. H. (2006). Money and capital markets: financial institutions and instruments in a global marketplace (9th ed.). New York: McGraw-Hill. Rudiyanto. (2011). Evaluating the performance of mutual funds when the market is volatile. http://rudiyanto.blog.kontan.co.id/2011/10/04. (accessed on July 11, 2017). Shofiyullah, N. F. (2014). Comparison of Jakarta Islamic Index and FTSE Bursa Malaysia Hijrah Shariah Index. Journal of Business and Management, 14(2), 19-34. Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 30 Appendix 1 FTSE Bursa Malaysia EMAS Shariah (FBMS) historical data (July 2012 - June 2017) Number of data Date Price Open High Low Return (%) 1 Jul 12 11,278.60 11,016.30 11,436.66 11,016.30 2.5 2 Aug 12 11,377.70 11,248.70 11,501.73 11,244.68 0.88 3 Sep 12 11,341.75 11,402.18 11,470.60 11,040.69 -0.32 4 Oct 12 11,488.38 11,352.54 11,575.89 11,318.06 1.29 5 Nov 12 10,957.26 11,513.81 11,526.41 10,775.67 -4.62 6 Dec 12 11,520.73 10,937.97 11,520.73 10,907.05 5.14 7 Jan 13 11,112.97 11,492.50 11,629.01 10,963.86 -3.54 8 Feb 13 11,105.74 11,147.75 11,178.13 10,838.73 -0.07 9 Mar 13 11,357.06 11,105.88 11,399.94 10,986.71 2.26 10 Apr 13 11,663.19 11,355.18 11,730.36 11,157.04 2.7 11 May 13 12,403.80 11,656.32 12,531.23 11,464.26 6.35 12 Jun 13 12,294.50 12,387.24 12,541.42 11,934.08 -0.88 13 Jul 13 12,373.68 12,290.23 12,640.60 12,255.78 0.64 14 Aug 13 12,046.89 12,415.43 12,591.30 11,485.88 -2.64 15 Sep 13 12,364.88 12,027.67 12,533.72 11,908.09 2.64 16 Oct 13 12,742.22 12,360.51 12,805.02 12,335.38 3.05 17 Nov 13 12,734.18 12,715.92 12,836.30 12,565.62 -0.06 18 Dec 13 13,051.60 12,729.33 13,124.78 12,721.88 2.49 19 Jan 14 12,604.02 13,046.11 13,048.59 12,389.31 -3.43 20 Feb 14 12,895.61 12,426.57 12,945.38 12,372.65 2.31 21 Mar 14 13,146.18 12,893.58 13,151.50 12,751.65 1.94 22 Apr 14 13,214.50 13,119.57 13,224.69 13,032.83 0.52 23 May 14 13,136.04 13,211.51 13,395.88 13,134.49 -0.59 24 Jun 14 13,387.34 13,121.39 13,418.80 13,075.53 1.91 25 Jul 14 13,383.98 13,362.31 13,536.80 13,314.81 -0.03 26 Aug 14 13,219.74 13,330.66 13,453.44 13,166.90 -1.23 27 Sep 14 13,185.85 13,220.12 13,335.38 13,086.12 -0.26 28 Oct 14 13,346.37 13,190.83 13,346.37 12,505.67 1.22 29 Nov 14 13,036.26 13,357.46 13,393.64 12,924.09 -2.32 30 Dec 14 12,507.03 13,028.55 13,028.55 11,804.03 -4.06 31 Jan 15 12,851.97 12,470.56 13,041.52 12,201.59 2.76 32 Feb 15 13,120.63 13,161.15 13,161.15 12,745.85 2.09 33 Mar 15 13,122.15 13,144.92 13,146.07 12,833.51 0.01 34 Apr 15 13,073.91 13,103.66 13,388.93 13,062.08 -0.37 35 May 15 12,576.45 13,122.38 13,133.74 12,548.20 -3.8 36 Jun 15 12,208.85 12,609.68 12,685.78 12,138.77 -2.92 37 Jul 15 12,373.72 12,270.45 12,477.22 12,033.14 1.35 38 Aug 15 11,443.34 12,323.04 12,539.35 10,705.63 -7.52 39 Sep 15 11,889.05 11,693.70 12,143.94 11,265.24 3.89 40 Oct 15 12,392.83 11,895.74 12,682.58 11,866.12 4.24 41 Nov 15 12,506.87 12,395.43 12,695.19 12,359.39 0.92 42 Dec 15 12,800.65 12,529.44 12,894.94 12,180.94 2.35 43 Jan 16 12,420.82 12,771.78 12,777.66 12,105.60 -2.97 44 Feb 16 12,249.24 12,421.29 12,575.25 12,168.12 -1.38 45 Mar 16 12,498.31 12,274.95 12,586.45 12,261.76 2.03 46 Apr 16 12,208.99 12,482.39 12,559.51 12,131.99 -2.31 47 May 16 11,922.64 12,187.04 12,210.50 11,775.69 -2.35 48 Jun 16 12,102.94 11,901.80 12,185.94 11,799.29 1.51 49 Jul 16 12,198.77 12,089.77 12,283.49 12,000.22 0.79 50 Aug 16 12,430.50 12,231.63 12,596.80 12,197.98 1.9 51 Sep 16 12,348.18 12,421.72 12,530.61 12,231.59 -0.66 52 Oct 16 12,384.01 12,398.66 12,477.95 12,368.19 0.29 53 Nov 16 11,901.19 12,384.53 12,398.68 11,845.46 -3.9 54 Dec 16 12,014.42 11,896.48 12,075.11 11,891.57 0.95 55 Jan 17 12,227.59 11,998.95 12,372.47 11,966.63 1.77 56 Feb 17 12,387.75 12,202.80 12,591.01 12,202.80 1.31 57 Mar 17 12,811.34 12,387.80 12,880.42 12,370.91 3.42 58 Apr 17 12,972.49 12,834.05 12,989.88 12,750.09 1.26 59 May 17 12,804.02 12,980.88 13,126.04 12,782.04 -1.3 60 Jun 17 12,822.15 12,795.63 13,019.98 12,740.17 0.14 Source: www.investing.com http://www.investing/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 31 Appendix 2 Indonesia Sharia Stock Index (ISSI) historical data (July 2012 - June 2017) Number of data Date Price Open High Low Return (%) 1 Jul 12 137.86 132.34 138.14 132.07 4.75 2 Aug 12 135.96 137.54 139.56 133.23 -1.38 3 Sep 12 143.96 135.54 144.46 135.34 5.88 4 Oct 12 147.78 143.47 147.94 142.00 2.65 5 Nov 12 143.89 147.04 147.69 142.76 -2.63 6 Dec 12 145.00 143.77 145.77 141.66 0.77 7 Jan 13 147.51 145.23 149.45 143.64 1.73 8 Feb 13 157.64 147.55 157.93 147.37 6.87 9 Mar 13 162.64 158.16 163.56 156.15 3.17 10 Apr 13 166.91 162.36 166.91 160.28 2.63 11 May 13 169.81 166.27 175.07 162.46 1.74 12 Jun 13 164.24 169.10 169.73 146.43 -3.28 13 Jul 13 154.20 161.15 163.56 149.03 -6.11 14 Aug 13 143.92 154.91 158.06 129.47 -6.67 15 Sep 13 145.16 143.84 159.93 136.66 0.86 16 Oct 13 151.31 145.29 156.30 145.24 4.24 17 Nov 13 143.03 150.10 151.78 141.43 -5.47 18 Dec 13 143.71 143.38 145.87 137.95 0.48 19 Jan 14 146.86 142.76 149.96 139.62 2.19 20 Feb 14 152.88 146.12 153.32 143.27 4.10 21 Mar 14 157.35 151.63 162.20 150.78 2.92 22 Apr 14 158.83 158.18 162.72 155.53 0.94 23 May 14 161.08 158.59 167.33 158.42 1.42 24 Jun 14 159.75 160.87 163.68 158.59 -0.83 25 Jul 14 167.34 159.86 169.64 159.25 4.75 26 Aug 14 168.98 166.34 171.86 166.04 0.98 27 Sep 14 166.76 169.34 172.71 165.07 -1.31 28 Oct 14 163.41 166.57 167.06 157.67 -2.01 29 Nov 14 166.10 163.80 166.40 158.82 1.65 30 Dec 14 168.64 166.02 168.64 161.26 1.53 31 Jan 15 171.50 169.06 173.52 165.27 1.70 32 Feb 15 174.32 170.97 175.41 169.30 1.64 33 Mar 15 174.10 174.46 175.79 168.43 -0.13 34 Apr 15 161.71 173.86 174.46 159.94 -7.12 35 May 15 167.07 162.22 171.92 162.05 3.31 36 Jun 15 157.92 167.21 167.56 155.49 -5.48 37 Jul 15 154.50 158.47 160.47 151.71 -2.17 38 Aug 15 142.31 153.57 154.79 129.84 -7.89 39 Sep 15 134.39 141.58 141.63 128.4 -5.57 40 Oct 15 140.96 134.54 148.07 134.27 4.89 41 Nov 15 139.80 140.65 145.73 138.53 -0.82 42 Dec 15 145.06 141.59 145.18 134.45 3.76 43 Jan 16 144.88 144.64 146.91 138.86 -0.12 44 Feb 16 151.15 144.98 151.67 142.26 4.33 45 Mar 16 155.91 151.02 158.76 150.98 3.15 46 Apr 16 157.46 156.12 162.43 154.79 0.99 47 May 16 156.35 156.78 158.34 152.61 -0.70 48 Jun 16 165.94 156.65 166.36 156.61 6.13 49 Jul 16 173.75 166.29 177.58 164.28 4.71 50 Aug 16 178.66 175.52 181.52 174.68 2.83 51 Sep 16 176.93 177.72 180.78 168.81 -0.97 52 Oct 16 179.22 178.41 181.14 176.19 1.29 53 Nov 16 170.00 179.37 181.21 166.31 -5.14 54 Dec 16 172.08 171.14 174.71 163.00 1.22 55 Jan 17 172.48 171.66 174.14 169.87 0.23 56 Feb 17 174.75 173.25 176.09 173.01 1.32 57 Mar 17 180.49 174.90 181.52 173.46 3.28 58 Apr 17 184.69 181.02 186.46 180.71 2.33 59 May 17 183.12 185.25 187.29 179.01 -0.85 60 Jun 17 185.22 183.33 185.35 180.47 1.15 Source: www.investing.com http://www.investing/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 32 Appendix 3 Kuala Lumpur Composite Index (KLCI) historical data (July 2012 - June 2017) Number of data Date Price Open High Low Return (%) 1 Jul 12 1,631.60 1,599.87 1,647.94 1,599.67 2.03 2 Aug 12 1,646.11 1,628.07 1,655.39 1,626.68 0.89 3 Sep 12 1,636.66 1,649.48 1,655.49 1,595.85 -0.57 4 Oct 12 1,673.07 1,635.38 1,679.37 1,633.39 2.22 5 Nov 12 1,610.83 1,675.85 1,678.57 1,590.67 -3.72 6 Dec 12 1,688.95 1,608.31 1,688.95 1,601.71 4.85 7 Jan 13 1,627.55 1,685.15 1,699.68 1,602.12 -3.64 8 Feb 13 1,637.63 1,634.54 1,639.58 1,597.00 0.62 9 Mar 13 1,671.63 1,636.47 1,681.03 1,613.94 2.08 10 Apr 13 1,717.65 1,671.88 1,718.44 1,632.28 2.75 11 May 13 1,769.22 1,715.01 1,826.22 1,685.28 3.00 12 Jun 13 1,773.54 1,771.55 1,792.67 1,723.74 0.24 13 Jul 13 1,772.62 1,771.98 1,811.65 1,762.87 -0.05 14 Aug 13 1,727.58 1,775.71 1,801.26 1,660.39 -2.54 15 Sep 13 1,768.62 1,723.77 1,805.15 1,702.57 2.38 16 Oct 13 1,806.85 1,766.37 1,822.17 1,759.66 2.16 17 Nov 13 1,812.72 1,803.68 1,814.73 1,780.54 0.32 18 Dec 13 1,866.96 1,812.75 1,882.20 1,811.67 2.99 19 Jan 14 1,804.03 1,865.73 1,868.29 1,777.62 -3.37 20 Feb 14 1,835.66 1,795.89 1,836.14 1,769.80 1.75 21 Mar 14 1,849.21 1,834.44 1,852.29 1,802.88 0.74 22 Apr 14 1,871.52 1,846.10 1,871.52 1,842.06 1.21 23 May 14 1,873.38 1,869.50 1,889.47 1,853.31 0.10 24 Jun 14 1,882.71 1,867.80 1,892.33 1,860.20 0.50 25 Jul 14 1,871.36 1,879.24 1,896.23 1,866.47 -0.60 26 Aug 14 1,866.11 1,866.11 1,879.62 1,837.28 -0.28 27 Sep 14 1,846.31 1,865.46 1,876.21 1,829.24 -1.06 28 Oct 14 1,855.15 1,847.00 1,855.15 1,766.22 0.48 29 Nov 14 1,820.89 1,856.82 1,858.09 1,805.35 -1.85 30 Dec 14 1,761.25 1,821.52 1,821.52 1,671.82 -3.28 31 Jan 15 1,781.26 1,757.15 1,810.21 1,706.18 1.14 32 Feb 15 1,821.21 1,814.72 1,831.41 1,780.21 2.24 33 Mar 15 1,830.78 1,822.91 1,832.85 1,774.30 0.53 34 Apr 15 1,818.27 1,831.35 1,867.53 1,818.27 -0.68 35 May 15 1,747.52 1,830.32 1,830.90 1,746.06 -3.89 36 Jun 15 1,706.64 1,751.82 1,752.08 1,688.44 -2.34 37 Jul 15 1,723.14 1,709.77 1,738.67 1,685.03 0.97 38 Aug 15 1,612.74 1,716.99 1,744.19 1,503.68 -6.41 39 Sep 15 1,621.04 1,655.47 1,691.93 1,567.91 0.51 40 Oct 15 1,665.71 1,622.61 1,727.41 1,617.42 2.76 41 Nov 15 1,672.16 1,665.30 1,696.99 1,644.29 0.39 42 Dec 15 1,692.51 1,670.29 1,706.25 1,622.84 1.22 43 Jan 16 1,667.80 1,686.82 1,687.89 1,600.92 -1.46 44 Feb 16 1,654.75 1,663.99 1,685.88 1,631.11 -0.78 45 Mar 16 1,717.58 1,658.42 1,726.55 1,655.47 3.80 46 Apr 16 1,672.72 1,715.36 1,729.13 1,660.92 -2.61 47 May 16 1,626.00 1,674.51 1,676.03 1,611.91 -2.79 48 Jun 16 1,654.08 1,623.33 1,664.04 1,611.88 1.73 49 Jul 16 1,653.26 1,653.16 1,674.58 1,640.68 -0.05 50 Aug 16 1,678.06 1,657.18 1,700.71 1,648.45 1.50 51 Sep 16 1,652.55 1,682.35 1,692.12 1,645.18 -1.52 52 Oct 16 1,672.46 1,658.36 1,679.11 1,652.63 1.20 53 Nov 16 1,619.12 1,673.56 1,673.90 1,614.11 -3.19 54 Dec 16 1,641.73 1,621.10 1,651.45 1,616.54 1.40 55 Jan 17 1,671.54 1,636.94 1,695.72 1,630.67 1.82 56 Feb 17 1,693.77 1,670.40 1,719.76 1,667.68 1.33 57 Mar 17 1,740.09 1,694.64 1,759.76 1,692.33 2.73 58 Apr 17 1,768.06 1,742.38 1,772.21 1,729.13 1.61 59 May 17 1,765.87 1,769.16 1,787.54 1,754.23 -0.12 60 Jun 17 1,763.67 1,764.57 1,796.75 1,755.65 -0.12 Source: www.investing.com http://www.investing/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 33 Appendix 4 Jakarta Composite Index (JCI) historical data (July 2012 - June 2017) Number of data Date Price Open High Low Return (%) 1 Jul 12 4,142.34 3,976.71 4,149.71 3,963.47 4.72 2 Aug 12 4,060.33 4,129.81 4,183.03 3,978.08 -1.98 3 Sep 12 4,262.56 4,052.89 4,272.83 4,047.28 4.98 4 Oct 12 4,350.29 4,249.69 4,366.86 4,214.52 2.06 5 Nov 12 4,276.14 4,331.75 4,381.75 4,255.27 -1.70 6 Dec 12 4,316.69 4,277.19 4,340.26 4,222.13 0.95 7 Jan 13 4,453.70 4,322.58 4,472.11 4,298.61 3.17 8 Feb 13 4,795.79 4,458.60 4,795.79 4,457.45 7.68 9 Mar 13 4,940.99 4,798.49 4,940.99 4,721.32 3.03 10 Apr 13 5,034.07 4,927.12 5,034.07 4,856.30 1.88 11 May 13 5,068.63 5,020.20 5,251.30 4,907.60 0.69 12 Jun 13 4,818.90 5,053.54 5,055.83 4,373.38 -4.93 13 Jul 13 4,610.38 4,757.18 4,815.73 4,403.80 -4.33 14 Aug 13 4,195.09 4,618.96 4,699.73 3,837.74 -9.01 15 Sep 13 4,316.18 4,196.72 4,791.77 4,012.68 2.89 16 Oct 13 4,510.63 4,314.96 4,611.26 4,314.96 4.51 17 Nov 13 4,256.44 4,473.73 4,518.65 4,202.92 -5.64 18 Dec 13 4,274.18 4,269.08 4,331.59 4,109.31 0.42 19 Jan 14 4,418.76 4,240.39 4,510.22 4,161.19 3.38 20 Feb 14 4,620.22 4,407.00 4,665.27 4,320.78 4.56 21 Mar 14 4,768.28 4,589.62 4,903.50 4,567.76 3.20 22 Apr 14 4,840.15 4,796.16 4,933.11 4,721.60 1.51 23 May 14 4,893.91 4,845.34 5,091.32 4,828.22 1.11 24 Jun 14 4,878.58 4,900.97 4,971.95 4,835.04 -0.31 25 Jul 14 5,088.80 4,877.65 5,165.42 4,862.42 4.31 26 Aug 14 5,136.86 5,076.23 5,223.98 5,043.52 0.94 27 Sep 14 5,137.58 5,159.94 5,262.57 5,082.73 0.01 28 Oct 14 5,089.55 5,148.57 5,165.39 4,900.72 -0.93 29 Nov 14 5,149.89 5,102.54 5,157.08 4,965.39 1.19 30 Dec 14 5,226.95 5,150.38 5,226.95 5,005.27 1.50 31 Jan 15 5,289.40 5,233.80 5,325.04 5,121.81 1.19 32 Feb 15 5,450.29 5,277.15 5,464.22 5,254.04 3.04 33 Mar 15 5,518.67 5,452.83 5,518.67 5,350.47 1.25 34 Apr 15 5,086.42 5,516.80 5,524.04 5,015.01 -7.83 35 May 15 5,216.38 5,093.33 5,347.13 5,089.42 2.55 36 Jun 15 4,910.66 5,212.13 5,215.55 4,826.13 -5.86 37 Jul 15 4,802.53 4,924.07 4,982.91 4,711.49 -2.20 38 Aug 15 4,509.61 4,778.04 4,868.07 4,111.11 -6.10 39 Sep 15 4,223.91 4,484.20 4,484.79 4,033.59 -6.34 40 Oct 15 4,455.18 4,231.41 4,696.16 4,207.80 5.48 41 Nov 15 4,446.46 4,442.42 4,621.26 4,395.97 -0.20 42 Dec 15 4,593.01 4,504.22 4,595.51 4,330.76 3.30 43 Jan 16 4,615.16 4,580.17 4,639.24 4,408.80 0.48 44 Feb 16 4,770.96 4,620.15 4,803.61 4,545.14 3.38 45 Mar 16 4,845.37 4,760.24 4,908.26 4,757.80 1.56 46 Apr 16 4,838.58 4,843.39 4,920.40 4,766.81 -0.14 47 May 16 4,796.87 4,828.96 4,845.12 4,690.56 -0.86 48 Jun 16 5,016.65 4,801.85 5,033.24 4,754.36 4.58 49 Jul 16 5,215.99 5,027.62 5,334.12 4,971.58 3.97 50 Aug 16 5,386.08 5,280.21 5,476.22 5,279.59 3.26 51 Sep 16 5,364.80 5,368.52 5,474.31 5,128.17 -0.40 52 Oct 16 5,422.54 5,403.86 5,482.84 5,332.08 1.08 53 Nov 16 5,148.91 5,430.75 5,491.70 5,043.35 -5.05 54 Dec 17 5,296.71 5,168.63 5,334.79 5,022.85 2.87 55 Jan 17 5,294.10 5,290.39 5,360.06 5,228.29 -0.05 56 Feb 17 5,386.69 5,319.94 5,418.38 5,317.49 1.75 57 Mar 17 5,568.11 5,389.17 5,606.02 5,350.91 3.37 58 Apr 17 5,685.30 5,583.35 5,726.53 5,577.49 2.10 59 May 17 5,738.15 5,703.87 5,874.44 5,577.52 0.93 60 Jun 17 5,829.71 5,749.42 5,831.34 5,668.72 1.60 Source: www.investing.com http://www.investing/ Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 34 Appendix 5 Sharpe ratio calculation for FBMS and ISSI monthly returns (July 2012 - June 2017) Number of data Date Index return (Rp) Risk-free rate (Rf) Rp-Rf Std. deviation (σp) Sharpe ratio (Sp) FBMS ISSI ZI120012 SPN12130704 FBMS ISSI FBMS ISSI FBMS ISSI 1 Jul 12 0.0250 0.0475 0.0123 0.01167 0.01270 0.03583 0.03473 0.02601 0.48827 0.03071 2 Aug 12 0.0088 -0.0138 0.0123 0.01167 -0.00350 -0.02547 0.03473 0.02601 -0.13456 -0.73318 3 Sep 12 -0.0032 0.0588 0.0123 0.01167 -0.01550 0.04713 0.03473 0.02601 -0.59591 1.35696 4 Oct 12 0.0129 0.0265 0.0123 0.01167 0.00060 0.01483 0.03473 0.02601 0.02307 0.42705 5 Nov 12 -0.0462 -0.0263 0.0123 0.01167 -0.05850 -0.03797 0.03473 0.02601 -2.24910 -1.09305 6 Dec 12 0.0514 0.0077 0.0123 0.01167 0.03910 -0.00397 0.03473 0.02601 1.50324 -0.11420 7 Jan 13 -0.0354 0.0173 0.0123 0.01167 -0.04770 0.00563 0.03473 0.02601 -1.83388 0.16218 8 Feb 13 -0.0007 0.0687 0.0123 0.01167 -0.01300 0.05703 0.03473 0.02601 -0.4998 1.64197 9 Mar 13 0.0226 0.0317 0.0123 0.01167 0.01030 0.02003 0.03473 0.02601 0.39600 0.57675 10 Apr 13 0.0270 0.0263 0.0123 0.01167 0.01470 0.01463 0.03473 0.02601 0.56516 0.42129 11 May 13 0.0635 0.0174 0.0123 0.01167 0.05120 0.00573 0.03473 0.02601 1.96844 0.16506 12 Jun 13 -0.0088 -0.0328 0.0123 0.01167 -0.02110 -0.04447 0.03473 0.02601 -0.81121 -1.28018 13 Jul 13 0.0064 -0.0611 0.0123 0.01167 -0.00590 -0.07277 0.03473 0.02601 -0.22683 -2.09493 14 Aug 13 -0.0264 -0.0667 0.0123 0.01167 -0.03870 -0.07837 0.03473 0.02601 -1.48786 -2.25616 15 Sep 13 0.0264 0.0086 0.0123 0.01167 0.01410 -0.00307 0.03473 0.02601 0.54209 -0.08829 16 Oct 13 0.0305 0.0424 0.0123 0.01167 0.01820 0.03073 0.03473 0.02601 0.69972 0.88480 17 Nov 13 -0.0006 -0.0547 0.0123 0.01167 -0.01290 -0.06637 0.03473 0.02601 -0.49595 -1.91068 18 Dec 13 0.0249 0.0048 0.0123 0.01167 0.01260 -0.00687 0.03473 0.02601 0.48442 -0.19769 19 Jan 14 -0.0343 0.0219 0.0123 0.01167 -0.04660 0.01023 0.03473 0.02601 -1.79159 0.29461 20 Feb 14 0.0231 0.0410 0.0123 0.01167 0.01080 0.02933 0.03473 0.02601 0.41522 0.84450 21 Mar 14 0.0194 0.0292 0.0123 0.01167 0.00710 0.01753 0.03473 0.02601 0.27297 0.50478 22 Apr 14 0.0052 0.0094 0.0123 0.01167 -0.00710 -0.00227 0.03473 0.02601 -0.27297 -0.06526 23 May 14 -0.0059 0.0142 0.0123 0.01167 -0.01820 0.00253 0.03473 0.02601 -0.69972 0.07293 24 Jun 14 0.0191 -0.0083 0.0123 0.01167 0.00680 -0.01997 0.03473 0.02601 0.26143 -0.57484 25 Jul 14 -0.0003 0.0475 0.0123 0.01167 -0.01260 0.03583 0.03473 0.02601 -0.48442 1.03163 26 Aug 14 -0.0123 0.0098 0.0123 0.01167 -0.02460 -0.00187 0.03473 0.02601 -0.94577 -0.05374 27 Sep 14 -0.0026 -0.0131 0.0123 0.01167 -0.01490 -0.02477 0.03473 0.02601 -0.57285 -0.71303 28 Oct 14 0.0122 -0.0201 0.0123 0.01167 -0.00010 -0.03177 0.03473 0.02601 -0.00384 -0.91455 29 Nov 14 -0.0232 0.0165 0.0123 0.01167 -0.03550 0.00483 0.03473 0.02601 -1.36484 0.13915 30 Dec 14 -0.0406 0.0153 0.0123 0.01167 -0.05290 0.00363 0.03473 0.02601 -2.03380 0.10460 31 Jan 15 0.0276 0.0170 0.0123 0.01167 0.01530 0.00533 0.03473 0.02601 0.58823 0.15355 32 Feb 15 0.0209 0.0164 0.0123 0.01167 0.00860 0.00473 0.03473 0.02601 0.33064 0.13627 33 Mar 15 0.0001 -0.0013 0.0123 0.01167 -0.01220 -0.01297 0.03473 0.02601 -0.46904 -0.37331 34 Apr 15 -0.0037 -0.0712 0.0123 0.01167 -0.01600 -0.08287 0.03473 0.02601 -0.61514 -2.38571 35 May 15 -0.0380 0.0331 0.0123 0.01167 -0.05030 0.02143 0.03473 0.02601 -1.93384 0.61706 36 Jun 15 -0.0292 -0.0548 0.0123 0.01167 -0.04150 -0.06647 0.03473 0.02601 -1.59551 -1.91356 37 Jul 15 0.0135 -0.0217 0.0123 0.01167 0.00120 -0.03337 0.03473 0.02601 0.04614 -0.96062 38 Aug 15 -0.0752 -0.0789 0.0123 0.01167 -0.08750 -0.09057 0.03473 0.02601 -3.36404 -2.60739 39 Sep 15 0.0389 -0.0557 0.0123 0.01167 0.02660 -0.06737 0.03473 0.02601 1.02267 -1.93947 40 Oct 15 0.0424 0.0489 0.0123 0.01167 0.03010 0.03723 0.03473 0.02601 1.15723 1.07194 41 Nov 15 0.0092 -0.0082 0.0123 0.01167 -0.00310 -0.01987 0.03473 0.02601 -0.11918 -0.57196 42 Dec 15 0.0235 0.0376 0.0123 0.01167 0.01120 0.02593 0.03473 0.02601 0.43060 0.74661 43 Jan 16 -0.0297 -0.0012 0.0123 0.01167 -0.04200 -0.01287 0.03473 0.02601 -1.61474 -0.37043 44 Feb 16 -0.0138 0.0433 0.0123 0.01167 -0.02610 0.03163 0.03473 0.02601 -1.00344 0.91072 45 Mar 16 0.0203 0.0315 0.0123 0.01167 0.00800 0.01983 0.03473 0.02601 0.30757 0.57100 46 Apr 16 -0.0231 0.0099 0.0123 0.01167 -0.03540 -0.00177 0.03473 0.02601 -1.36099 -0.05086 47 May 16 -0.0235 -0.0070 0.0123 0.01167 -0.03580 -0.01867 0.03473 0.02601 -1.37637 -0.53741 48 Jun 16 0.0151 0.0613 0.0123 0.01167 0.00280 0.04963 0.03473 0.02601 0.10765 1.42893 49 Jul 16 0.0079 0.0471 0.0123 0.01167 -0.00440 0.03543 0.03473 0.02601 -0.16916 1.02012 50 Aug 16 0.0190 0.0283 0.0123 0.01167 0.00670 0.01663 0.03473 0.02601 0.25759 0.47887 51 Sep 16 -0.0066 -0.0097 0.0123 0.01167 -0.01890 -0.02137 0.03473 0.02601 -0.72663 -0.61514 52 Oct 16 0.0029 0.0129 0.0123 0.01167 -0.00940 0.00123 0.03473 0.02601 -0.36139 0.03551 53 Nov 16 -0.0390 -0.0514 0.0123 0.01167 -0.05130 -0.06307 0.03473 0.02601 -1.97229 -1.81567 54 Dec 17 0.0095 0.0122 0.0123 0.01167 -0.00280 0.00053 0.03473 0.02601 -0.10765 0.01535 55 Jan 17 0.0177 0.0023 0.0123 0.01167 0.00540 -0.00937 0.03473 0.02601 0.20761 -0.26966 56 Feb 17 0.0131 0.0132 0.0123 0.01167 0.00080 0.00153 0.03473 0.02601 0.03076 0.04414 57 Mar 17 0.0342 0.0328 0.0123 0.01167 0.02190 0.02113 0.03473 0.02601 0.84197 0.60842 58 Apr 17 0.0126 0.0233 0.0123 0.01167 0.00030 0.01163 0.03473 0.02601 0.01153 0.33492 59 May 17 -0.0130 -0.0085 0.0123 0.01167 -0.02530 -0.02017 0.03473 0.02601 -0.97269 -0.58059 60 Jun 17 0.0014 0.0115 0.0123 0.01167 -0.01090 -0.00017 0.03473 0.02601 -0.41906 -0.00480 Mean 0.00288 0.00631 0.0123 0.01167 -0.00942 -0.00536 0.03473 0.02601 -0.36210 -0.17090 Source: Microsoft Excel Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 35 Appendix 6 Treynor ratio calculation for FBMS and ISSI monthly returns (July 2012 - June 2017) Number of data Date Index return (Rp) Risk-free rate (Rf) Rp-Rf Index beta (βp) Treynor ratio (Tp) FBMS ISSI ZI120012 SPN12130704 FBMS ISSI FBMS ISSI FBMS ISSI 1 Jul 12 0.0250 0.0475 0.0123 0.01167 0.01270 0.03583 0.78864 0.9708 0.01610 0.03691 2 Aug 12 0.0088 -0.0138 0.0123 0.01167 -0.00350 -0.02547 0.78864 0.9708 -0.00444 -0.02623 3 Sep 12 -0.0032 0.0588 0.0123 0.01167 -0.01550 0.04713 0.78864 0.9708 -0.01965 0.04855 4 Oct 12 0.0129 0.0265 0.0123 0.01167 0.00060 0.01483 0.78864 0.9708 0.00076 0.01528 5 Nov 12 -0.0462 -0.0263 0.0123 0.01167 -0.05850 -0.03797 0.78864 0.9708 -0.07418 -0.03911 6 Dec 12 0.0514 0.0077 0.0123 0.01167 0.03910 -0.00397 0.78864 0.9708 0.04958 -0.00409 7 Jan 13 -0.0354 0.0173 0.0123 0.01167 -0.04770 0.00563 0.78864 0.9708 -0.06048 0.00580 8 Feb 13 -0.0007 0.0687 0.0123 0.01167 -0.01300 0.05703 0.78864 0.9708 -0.0165 0.05875 9 Mar 13 0.0226 0.0317 0.0123 0.01167 0.01030 0.02003 0.78864 0.9708 0.01306 0.02064 10 Apr 13 0.0270 0.0263 0.0123 0.01167 0.01470 0.01463 0.78864 0.9708 0.01864 0.01507 11 May 13 0.0635 0.0174 0.0123 0.01167 0.05120 0.00573 0.78864 0.9708 0.06492 0.00591 12 Jun 13 -0.0088 -0.0328 0.0123 0.01167 -0.02110 -0.04447 0.78864 0.9708 -0.02675 -0.04580 13 Jul 13 0.0064 -0.0611 0.0123 0.01167 -0.00590 -0.07277 0.78864 0.9708 -0.00748 -0.07496 14 Aug 13 -0.0264 -0.0667 0.0123 0.01167 -0.03870 -0.07837 0.78864 0.9708 -0.04907 -0.08072 15 Sep 13 0.0264 0.0086 0.0123 0.01167 0.01410 -0.00307 0.78864 0.9708 0.01788 -0.00316 16 Oct 13 0.0305 0.0424 0.0123 0.01167 0.01820 0.03073 0.78864 0.9708 0.02308 0.03166 17 Nov 13 -0.0006 -0.0547 0.0123 0.01167 -0.01290 -0.06637 0.78864 0.9708 -0.01636 -0.06836 18 Dec 13 0.0249 0.0048 0.0123 0.01167 0.01260 -0.00687 0.78864 0.9708 0.01598 -0.00707 19 Jan 14 -0.0343 0.0219 0.0123 0.01167 -0.04660 0.01023 0.78864 0.9708 -0.05909 0.01054 20 Feb 14 0.0231 0.0410 0.0123 0.01167 0.01080 0.02933 0.78864 0.9708 0.01369 0.03022 21 Mar 14 0.0194 0.0292 0.0123 0.01167 0.00710 0.01753 0.78864 0.9708 0.00900 0.01806 22 Apr 14 0.0052 0.0094 0.0123 0.01167 -0.00710 -0.00227 0.78864 0.9708 -0.00900 -0.00233 23 May 14 -0.0059 0.0142 0.0123 0.01167 -0.01820 0.00253 0.78864 0.9708 -0.02308 0.00261 24 Jun 14 0.0191 -0.0083 0.0123 0.01167 0.00680 -0.01997 0.78864 0.9708 0.00862 -0.02057 25 Jul 14 -0.0003 0.0475 0.0123 0.01167 -0.01260 0.03583 0.78864 0.9708 -0.01598 0.03691 26 Aug 14 -0.0123 0.0098 0.0123 0.01167 -0.02460 -0.00187 0.78864 0.9708 -0.03119 -0.00192 27 Sep 14 -0.0026 -0.0131 0.0123 0.01167 -0.01490 -0.02477 0.78864 0.9708 -0.01889 -0.02551 28 Oct 14 0.0122 -0.0201 0.0123 0.01167 -0.00010 -0.03177 0.78864 0.9708 -0.00013 -0.03272 29 Nov 14 -0.0232 0.0165 0.0123 0.01167 -0.03550 0.00483 0.78864 0.9708 -0.04501 0.00498 30 Dec 14 -0.0406 0.0153 0.0123 0.01167 -0.05290 0.00363 0.78864 0.9708 -0.06708 0.00374 31 Jan 15 0.0276 0.0170 0.0123 0.01167 0.01530 0.00533 0.78864 0.9708 0.01940 0.00549 32 Feb 15 0.0209 0.0164 0.0123 0.01167 0.00860 0.00473 0.78864 0.9708 0.01090 0.00488 33 Mar 15 0.0001 -0.0013 0.0123 0.01167 -0.01220 -0.01297 0.78864 0.9708 -0.01547 -0.01336 34 Apr 15 -0.0037 -0.0712 0.0123 0.01167 -0.01600 -0.08287 0.78864 0.9708 -0.02029 -0.08536 35 May 15 -0.0380 0.0331 0.0123 0.01167 -0.05030 0.02143 0.78864 0.9708 -0.06378 0.02208 36 Jun 15 -0.0292 -0.0548 0.0123 0.01167 -0.04150 -0.06647 0.78864 0.9708 -0.05262 -0.06847 37 Jul 15 0.0135 -0.0217 0.0123 0.01167 0.00120 -0.03337 0.78864 0.9708 0.00152 -0.03437 38 Aug 15 -0.0752 -0.0789 0.0123 0.01167 -0.08750 -0.09057 0.78864 0.9708 -0.11095 -0.09329 39 Sep 15 0.0389 -0.0557 0.0123 0.01167 0.02660 -0.06737 0.78864 0.9708 0.03373 -0.06939 40 Oct 15 0.0424 0.0489 0.0123 0.01167 0.03010 0.03723 0.78864 0.9708 0.03817 0.03835 41 Nov 15 0.0092 -0.0082 0.0123 0.01167 -0.00310 -0.01987 0.78864 0.9708 -0.00393 -0.02046 42 Dec 15 0.0235 0.0376 0.0123 0.01167 0.01120 0.02593 0.78864 0.9708 0.01420 0.02671 43 Jan 16 -0.0297 -0.0012 0.0123 0.01167 -0.04200 -0.01287 0.78864 0.9708 -0.05326 -0.01325 44 Feb 16 -0.0138 0.0433 0.0123 0.01167 -0.02610 0.03163 0.78864 0.9708 -0.03309 0.03258 45 Mar 16 0.0203 0.0315 0.0123 0.01167 0.00800 0.01983 0.78864 0.9708 0.01014 0.02043 46 Apr 16 -0.0231 0.0099 0.0123 0.01167 -0.03540 -0.00177 0.78864 0.9708 -0.04489 -0.00182 47 May 16 -0.0235 -0.0070 0.0123 0.01167 -0.03580 -0.01867 0.78864 0.9708 -0.04539 -0.01923 48 Jun 16 0.0151 0.0613 0.0123 0.01167 0.00280 0.04963 0.78864 0.9708 0.00355 0.05113 49 Jul 16 0.0079 0.0471 0.0123 0.01167 -0.00440 0.03543 0.78864 0.9708 -0.00558 0.03650 50 Aug 16 0.0190 0.0283 0.0123 0.01167 0.00670 0.01663 0.78864 0.9708 0.00850 0.01713 51 Sep 16 -0.0066 -0.0097 0.0123 0.01167 -0.01890 -0.02137 0.78864 0.9708 -0.02397 -0.02201 52 Oct 16 0.0029 0.0129 0.0123 0.01167 -0.00940 0.00123 0.78864 0.9708 -0.01192 0.00127 53 Nov 16 -0.0390 -0.0514 0.0123 0.01167 -0.05130 -0.06307 0.78864 0.9708 -0.06505 -0.06496 54 Dec 17 0.0095 0.0122 0.0123 0.01167 -0.00280 0.00053 0.78864 0.9708 -0.00355 0.00055 55 Jan 17 0.0177 0.0023 0.0123 0.01167 0.00540 -0.00937 0.78864 0.9708 0.00685 -0.00965 56 Feb 17 0.0131 0.0132 0.0123 0.01167 0.00080 0.00153 0.78864 0.9708 0.00101 0.00158 57 Mar 17 0.0342 0.0328 0.0123 0.01167 0.02190 0.02113 0.78864 0.9708 0.02777 0.02177 58 Apr 17 0.0126 0.0233 0.0123 0.01167 0.00030 0.01163 0.78864 0.9708 0.00038 0.01198 59 May 17 -0.0130 -0.0085 0.0123 0.01167 -0.02530 -0.02017 0.78864 0.9708 -0.03208 -0.02077 60 Jun 17 0.0014 0.0115 0.0123 0.01167 -0.01090 -0.00017 0.78864 0.9708 -0.01382 -0.00017 Mean 0.00288 0.00631 0.0123 0.01167 -0.00942 -0.00536 0.78864 0.9708 -0.01194 -0.00552 Source: Microsoft Excel Journal of Accounting, Management, and Economics, Vol. 20, No. 1, 2018, pp. 16-36 36 Appendix 7 Jensen ratio calculation for FBMS and ISSI monthly returns (July 2012 - June 2017) Number of data Date Index return (Rp) Market return (Rm) Risk-free rate (Rf) Rm-Rf Index beta (β) Jensen ratio (ap) FBMS ISSI KLCI JCI ZI120012 SPN12130704 FBMS ISSI FBMS ISSI FBMS ISSI 1 Jul 12 0.0250 0.0475 0.0203 0.0472 0.0123 0.01167 0.00800 0.03553 0.78864 0.9708 0.00639 0.00134 2 Aug 12 0.0088 -0.0138 0.0089 -0.0198 0.0123 0.01167 -0.00340 -0.03147 0.78864 0.9708 -0.00082 0.00508 3 Sep 12 -0.0032 0.0588 -0.0057 0.0498 0.0123 0.01167 -0.01800 0.03813 0.78864 0.9708 -0.00130 0.01011 4 Oct 12 0.0129 0.0265 0.0222 0.0206 0.0123 0.01167 0.00990 0.00893 0.78864 0.9708 -0.00721 0.00616 5 Nov 12 -0.0462 -0.0263 -0.0372 -0.0170 0.0123 0.01167 -0.04950 -0.02867 0.78864 0.9708 -0.01946 -0.01014 6 Dec 12 0.0514 0.0077 0.0485 0.0095 0.0123 0.01167 0.03620 -0.00217 0.78864 0.9708 0.01055 -0.00186 7 Jan 13 -0.0354 0.0173 -0.0364 0.0317 0.0123 0.01167 -0.04870 0.02003 0.78864 0.9708 -0.00929 -0.01381 8 Feb 13 -0.0007 0.0687 0.0062 0.0768 0.0123 0.01167 -0.00610 0.06513 0.78864 0.9708 -0.0082 -0.00620 9 Mar 13 0.0226 0.0317 0.0208 0.0303 0.0123 0.01167 0.00850 0.01863 0.78864 0.9708 0.00360 0.00194 10 Apr 13 0.0270 0.0263 0.0275 0.0188 0.0123 0.01167 0.01520 0.00713 0.78864 0.9708 0.00271 0.00771 11 May 13 0.0635 0.0174 0.0300 0.0069 0.0123 0.01167 0.01770 -0.00477 0.78864 0.9708 0.03724 0.01036 12 Jun 13 -0.0088 -0.0328 0.0024 -0.0493 0.0123 0.01167 -0.00990 -0.06097 0.78864 0.9708 -0.01329 0.01472 13 Jul 13 0.0064 -0.0611 -0.0005 -0.0433 0.0123 0.01167 -0.01280 -0.05497 0.78864 0.9708 0.00419 -0.01941 14 Aug 13 -0.0264 -0.0667 -0.0254 -0.0901 0.0123 0.01167 -0.03770 -0.10177 0.78864 0.9708 -0.00897 0.02043 15 Sep 13 0.0264 0.0086 0.0238 0.0289 0.0123 0.01167 0.01150 0.01723 0.78864 0.9708 0.00503 -0.01980 16 Oct 13 0.0305 0.0424 0.0216 0.0451 0.0123 0.01167 0.00930 0.03343 0.78864 0.9708 0.01087 -0.00172 17 Nov 13 -0.0006 -0.0547 0.0032 -0.0564 0.0123 0.01167 -0.00910 -0.06807 0.78864 0.9708 -0.00572 -0.00029 18 Dec 13 0.0249 0.0048 0.0299 0.0042 0.0123 0.01167 0.01760 -0.00747 0.78864 0.9708 -0.00128 0.00038 19 Jan 14 -0.0343 0.0219 -0.0337 0.0338 0.0123 0.01167 -0.04600 0.02213 0.78864 0.9708 -0.01032 -0.01125 20 Feb 14 0.0231 0.0410 0.0175 0.0456 0.0123 0.01167 0.00520 0.03393 0.78864 0.9708 0.00670 -0.00361 21 Mar 14 0.0194 0.0292 0.0074 0.0320 0.0123 0.01167 -0.00490 0.02033 0.78864 0.9708 0.01096 -0.00221 22 Apr 14 0.0052 0.0094 0.0121 0.0151 0.0123 0.01167 -0.00020 0.00343 0.78864 0.9708 -0.00694 -0.00560 23 May 14 -0.0059 0.0142 0.0010 0.0111 0.0123 0.01167 -0.01130 -0.00057 0.78864 0.9708 -0.00929 0.00308 24 Jun 14 0.0191 -0.0083 0.0050 -0.0031 0.0123 0.01167 -0.00730 -0.01477 0.78864 0.9708 0.01256 -0.00563 25 Jul 14 -0.0003 0.0475 -0.0060 0.0431 0.0123 0.01167 -0.01830 0.03143 0.78864 0.9708 0.00183 0.00532 26 Aug 14 -0.0123 0.0098 -0.0028 0.0094 0.0123 0.01167 -0.01510 -0.00227 0.78864 0.9708 -0.01269 0.00033 27 Sep 14 -0.0026 -0.0131 -0.0106 0.0001 0.0123 0.01167 -0.02290 -0.01157 0.78864 0.9708 0.00316 -0.01354 28 Oct 14 0.0122 -0.0201 0.0048 -0.0093 0.0123 0.01167 -0.00750 -0.02097 0.78864 0.9708 0.00581 -0.01141 29 Nov 14 -0.0232 0.0165 -0.0185 0.0119 0.0123 0.01167 -0.03080 0.00023 0.78864 0.9708 -0.01121 0.00461 30 Dec 14 -0.0406 0.0153 -0.0328 0.0150 0.0123 0.01167 -0.04510 0.00333 0.78864 0.9708 -0.01733 0.00040 31 Jan 15 0.0276 0.0170 0.0114 0.0119 0.0123 0.01167 -0.00090 0.00023 0.78864 0.9708 0.01601 0.00511 32 Feb 15 0.0209 0.0164 0.0224 0.0304 0.0123 0.01167 0.01010 0.01873 0.78864 0.9708 0.00063 -0.01345 33 Mar 15 0.0001 -0.0013 0.0053 0.0125 0.0123 0.01167 -0.00700 0.00083 0.78864 0.9708 -0.00668 -0.01378 34 Apr 15 -0.0037 -0.0712 -0.0068 -0.0783 0.0123 0.01167 -0.01910 -0.08997 0.78864 0.9708 -0.00094 0.00447 35 May 15 -0.0380 0.0331 -0.0389 0.0255 0.0123 0.01167 -0.05120 0.01383 0.78864 0.9708 -0.00992 0.00800 36 Jun 15 -0.0292 -0.0548 -0.0234 -0.0586 0.0123 0.01167 -0.03570 -0.07027 0.78864 0.9708 -0.01335 0.00175 37 Jul 15 0.0135 -0.0217 0.0097 -0.0220 0.0123 0.01167 -0.00260 -0.03367 0.78864 0.9708 0.00325 -0.00068 38 Aug 15 -0.0752 -0.0789 -0.0641 -0.0610 0.0123 0.01167 -0.07640 -0.07267 0.78864 0.9708 -0.02725 -0.02002 39 Sep 15 0.0389 -0.0557 0.0051 -0.0634 0.0123 0.01167 -0.00720 -0.07507 0.78864 0.9708 0.03228 0.00551 40 Oct 15 0.0424 0.0489 0.0276 0.0548 0.0123 0.01167 0.01530 0.04313 0.78864 0.9708 0.01803 -0.00464 41 Nov 15 0.0092 -0.0082 0.0039 -0.0020 0.0123 0.01167 -0.00840 -0.01367 0.78864 0.9708 0.00352 -0.00660 42 Dec 15 0.0235 0.0376 0.0122 0.0330 0.0123 0.01167 -0.00010 0.02133 0.78864 0.9708 0.01128 0.00522 43 Jan 16 -0.0297 -0.0012 -0.0146 0.0048 0.0123 0.01167 -0.02690 -0.00687 0.78864 0.9708 -0.02079 -0.00620 44 Feb 16 -0.0138 0.0433 -0.0078 0.0338 0.0123 0.01167 -0.02010 0.02213 0.78864 0.9708 -0.01025 0.01015 45 Mar 16 0.0203 0.0315 0.0380 0.0156 0.0123 0.01167 0.02570 0.00393 0.78864 0.9708 -0.01227 0.01601 46 Apr 16 -0.0231 0.0099 -0.0261 -0.0014 0.0123 0.01167 -0.03840 -0.01307 0.78864 0.9708 -0.00512 0.01092 47 May 16 -0.0235 -0.0070 -0.0279 -0.0086 0.0123 0.01167 -0.04020 -0.02027 0.78864 0.9708 -0.00410 0.00101 48 Jun 16 0.0151 0.0613 0.0173 0.0458 0.0123 0.01167 0.00500 0.03413 0.78864 0.9708 -0.00114 0.01650 49 Jul 16 0.0079 0.0471 -0.0005 0.0397 0.0123 0.01167 -0.01280 0.02803 0.78864 0.9708 0.00569 0.00822 50 Aug 16 0.0190 0.0283 0.0150 0.0326 0.0123 0.01167 0.00270 0.02093 0.78864 0.9708 0.00457 -0.00369 51 Sep 16 -0.0066 -0.0097 -0.0152 -0.0040 0.0123 0.01167 -0.02750 -0.01567 0.78864 0.9708 0.00279 -0.00616 52 Oct 16 0.0029 0.0129 0.0120 0.0108 0.0123 0.01167 -0.00030 -0.00087 0.78864 0.9708 -0.00916 0.00207 53 Nov 16 -0.0390 -0.0514 -0.0319 -0.0505 0.0123 0.01167 -0.04420 -0.06217 0.78864 0.9708 -0.01644 -0.00272 54 Dec 17 0.0095 0.0122 0.0140 0.0287 0.0123 0.01167 0.00170 0.01703 0.78864 0.9708 -0.00414 -0.01600 55 Jan 17 0.0177 0.0023 0.0182 -0.0005 0.0123 0.01167 0.00590 -0.01217 0.78864 0.9708 0.00075 0.00244 56 Feb 17 0.0131 0.0132 0.0133 0.0175 0.0123 0.01167 0.00100 0.00583 0.78864 0.9708 0.00001 -0.00413 57 Mar 17 0.0342 0.0328 0.0273 0.0337 0.0123 0.01167 0.01500 0.02203 0.78864 0.9708 0.01007 -0.00026 58 Apr 17 0.0126 0.0233 0.0161 0.0210 0.0123 0.01167 0.00380 0.00933 0.78864 0.9708 -0.00270 0.00257 59 May 17 -0.0130 -0.0085 -0.0012 0.0093 0.0123 0.01167 -0.01350 -0.00237 0.78864 0.9708 -0.01465 -0.01787 60 Jun 17 0.0014 0.0115 -0.0012 0.0160 0.0123 0.01167 -0.01350 0.00433 0.78864 0.9708 -0.00025 -0.00437 Mean 0.00288 0.00631 0.00188 0.00710 0.0123 0.01167 -0.01042 -0.00457 0.78864 0.9708 -0.00120 -0.00092 Source: Microsoft Excel