2013 Sixth International Conference on Business Intelligence and Financial Engineering
Evaluating the Performance of Exchange Traded Funds in the Emerging Markets LO Ka Kuen Kenneth Kin Keung Lai Kaijian He Department of Management Sciences International Business School School of Economics and Management City University of Hong Kong Shaanxi Normal University, Xi’an, China Beijing University of Chemical Technology Tat Chee Avenue, Kowloon Tong Department of Management Sciences Beijing , P.R.China Kowloon, Hong Kong City University of Hong Kong
[email protected] [email protected] Tat Chee Avenue, Kowloon Tong Kowloon, Hong Kong
[email protected]
launched the first commercially successful ETF called The SPDR fund that tracks the Standard & Poors 500 stock index [2]. The demand for ETF in Asia is expected to grow in the coming years with more innovative products and investors becoming more familiar with the features and benefits of ETFs. Also, the economic rise of Asia and notably China is likely to drive the demand for ETFs as more investors seek to diversify and expose their investment portfolios to potential areas of growth outside of their home country. Despite the good prospect, the overall size of the Asia (ex Japan) ETF industry is relatively small at US$61 billion or around 4% of global total Asset under management, compared to the amazing progress and development in the US and Europe. Based on the modest AUM in Asia (ex-Japan), ETF is still not widely utilized by investors in Asia relative to US and European investors. This paper conducts the empirical studies on the performance of different ETFs against the benchmark indexes in the emerging markets. It is found that the performance of ETFs shows time varying behaviors, in terms of return, risk and tracking error measures. More importantly the tracking error is significantly larger in more closed economy like China. This unveils the difficulty involved in achieving the balance between desired return and the risk following the underlying benchmark indexes, facing the uncertain market conditions there. To the best of knowledge, there is currently limited empirical research carried out on the performance evaluation of ETF, especially concerning the benefits of Asian investors or in particular, Hong Kong listed ETFs with emphasis on local retail investors. The remainder of the paper is organized as follows: the literature review on the performance of ETF is reviewed in section 2. Section 3 proposes the methodology. Empirical studies are conducted to evaluate the performance of both ETF and the ETF portfolio in section 4. Section 5 concludes.
Abstract—Facing the increasing level of fluctuations and risks in the financial markets, investors are increasingly shifting towards more conservative investment techniques. Exchange Traded Funds has emerged in recent years as an important alternative. This paper conducts the empirical studies on the performance of ETF across different emerging markets. We find that the performance of ETFs are inferior to the underlying indexes in general. Their performance are also sensitive to the ETF characteristics, among which geographical locations and economic development are identified as important influential factors. Keywords-Exchange Traded Funds; Sharpe Ratio; Standard Deviation; Risk Adjusted Return
I. I NTRODUCTION Since the financial crisis and the Lehman Brothers collapse in 2008, investors are increasingly looking for simplicity and transparency during the investment process, hesitating about investing into complex or difficult to understand products. Traditional mutual funds are known to be associated with high cost such as commissions, management and performance fees to compensate the Investment Manager, as well as the liquidity and price transparency issue [1]. Exchange Traded Funds (ETF) is often viewed as more cost-effective investment tools than traditional mutual funds as they have low cost and administrative expenses due to their passive nature in which investment managers simply follow the underlying tracking indices without the need to develop complicated and high-cost investing strategies [2]. ETFs are diversified and passively managed investment that aims to replicate a predefined price movement of a particular index or asset class [3]. In periods of uncertainty and volatile market environments, investors will find ETFs attractive in providing benefits of investing in a wide range of asset classes, with prior knowledge of the compositions and characteristics of the index. Given a variety of advantages, investors around the world and in particular from the United States of America and Europe have embraced the financial concept of ETFs very rapidly since 1993 when State Street Global Advisors 978-1-4799-4777-5/14 $31.00 © 2014 IEEE DOI 10.1109/BIFE.2013.75
II. L ITERATURE R EVIEW Empirical studies in the current literature show mixed evidence on the performance of ETF. 359
On the positive side, ETFs show positive performances of high degree of accuracy. Many researchers have made comparisons between ETFs and other investment tools. For example, [4] makes a comparison between Open-Ended Funds and ETFs, concluding that ETFs overcome the constraints of Open-Ended Funds (OEFs), while maintaining the benefits of low cost, diversification and tax efficiency [4]. [5] shows that the ETF is a more suitable investment vehicle when investors have more correlated liquidity shocks or when the underlying indexes are narrower or less liquid [5]. [6] compare the risk and return performance of ETFs with Closed-End Funds (CEFs) for 14 countries. Results show that ETFs exhibit higher mean returns and higher Sharpe ratios than CEFs. This indicates that a passive investment strategy utilizing ETFs may be superior to an active investment strategy using CEFs. ETFs may be able to offer higher risk-adjusted returns as part of an internationally diversified portfolio [6]. [7] find that ETFs offer taxable investors a method of holding broad baskets of stocks that deliver returns comparable to those of low-cost index funds [7]. [8] investigate the performance and persistence of 20 iShares MSCI ETFs in comparison with S&P 500 index. The research indicates that ETFs can beat the U.S. market index based on risk-adjusted performance measures and there is evidence of ETFs performance persistence based on annual return [8]. [9] apply the spread decomposition models of Glosten et al. to a sample of ETFs and their control securities. They find that ETFs offer lower levels of adverse selection than investments in individual securities [9]. On the negative side, there are some researches reporting negative ETF performance during the comparative studies. [5] and [10] compare the efficiency of ETFs and OEFs. The studies show that conventional funds and ETFs are substitutes [5], [10]. While ETFs are no more efficient than OEFs. [11] and [12] examines the performance of ETFs relative to their respective benchmarks and conventional index funds. Study shows that ETFs have generally underperformed competitive conventional mutual funds and their benchmark indices. [13] estimate tracking errors from 26 ETFs utilizing three different methods and test their relative performance using Jensen’s model [13]. The finding of negative Jensens alphas implies that investing in ETFs does not provide a significant benefit compared to their benchmark returns. [5] compare ETFs and target market index portfolios and conclude that while ETFs may offer more diversified benefits than target market, there are no significant performance differences between indirect invest method (ETFs) and direct one [5]. Research by Blitz et al. (2012) shows that European index funds and exchangetraded funds underperform their benchmarks by 50 to 150 basis points per annum. [14] examined the performance of sector ETFs in relation to their S&P industry sectors and prospectus benchmark indexes. They applied regression
analysis to analyze diversification and employed Sharpe’s Single Index Market Model and the Sharpe ratio to analyze performance. They found that ETFs do not provide an investor with a level of sector risk exposure equal to that of the S&P sector. ETFs are not perfect substitutes for their S&P sectors, but may provide the investor with excess risk adjusted returns when compared to the S&P sector. Meanwhile, another focus of ETF related research in the current literature is on the investigation of seasonality and persistence of ETFs performance and volatility. For example, [15] investigates the seasonal characteristics of ETFs return, risk, and tracking error. He reveals the existence of a strong November effect in performance. The study shows that the return and tracking error of ETFs are conversely related in November. On one hand, the average return in November is steadily positive for all the years, while it is the highest average return for the whole period. On the other hand, the average November tracking error is the lowest for the whole period, the existence of a significant negative November effect in ETFs tracking error. Moreover, he finds that the average risk in November is the second lowest risk among all the calendar months. Since November offers the highest average return we conclude that investors could have the possibility to gain respectably positive and low risky returns during November. Then [16] reveals the existence of a strong reverse December effect along with a modest reverse effect that affect the ETFs volatility in his another paper. Moreover, Return and volatility are also another aspect where active research is engaged in. [17] analyze return and volatility of Asian iShares traded in the U.S. By taking advantage of the trading schedule difference between the U.S. and Asian markets, they find that Asian ETF returns are explained by both U.S. returns and local Asian market returns [17]. The location of trade and investor sentiment effects are further supported by the high return correlation between Asian and U.S. ETFs. III. M ETHODOLOGY Data consist of the historical daily Net Asset Values (NAVs) of 33 ETFs from major ETF provider listed in Hong Kong stock exchange and the corresponding closing prices of the tracking underlying indexes. As at end of 1st half of 2012, State Street Global Advisors, iShares and HSBC/ Hang Seng were the top ETF providers in Asia Pacific (exJapan) measured by AUM and percentage of market share, whose ETF funds representing around 80% of the total AUM in Hong Kong and can be considered to be representative of the ETF market in Hong Kong. The data were obtained from the website of the ETF provider or publicity available source such as Hong Kong Exchanges and Clearing Limited. The starting date for the data set is chosen to be on or before 20 July 2010, which covers the period of the European debt crisis that would provide an interesting period of analysis, as well as provide consistent historical data for analysis due to
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data availability. The end date for the data asset is set to be 31 December 2012, when the latest data are made available as the research was conducted. Several ETFs are chosen across the representative emerging markets. This includes Hong Kong, Mainland China, Vietnam, Indonesia, Malaysia, etc. Among these ETFs, ETF in Chinese market are mostly synthetic as the foreign investors do not have direct access to the strictly controlled Chinese market and are forced to resort to the total return swap to construct ETF portfolio. The performance of ETF is evaluated against the major indexes these ETFs are tracking in their respective markets, as well as the benchmark market indexes. The data span through the latest date when data are made available as the research is conducted. The performance is measured using the common return and risk measures. Return is measured with simple return r t+i −Indext t+i −N AVt or rt = IndexIndex calculated as rt = N AVN AVt t where N AV and Index refer to the price of NAV of ETF and the underlying index at different time t. The risk is measured with standard deviation σ calculated as N σ = N1 i=1 (xi −μ)2 . The risk adjusted return is measured m by Sharpe calculated as ri −r σi , which calculates the return per risk, where ri − rm . The tracking error is measure as n ¯i )2 (Di,t −D T Ei = t=1 n−1 , where Di,t is the ith difference between the return of ETF and index at time t, D¯i is the mean of Di,t .
Table I NAME LIST FOR ETF S Stock Code 2836 2846 3001 2841 3050 2829 3006 3039 2823 3010 3032 3004 2801 2802 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3087 3048 3035 3009 3043 3027 3036 3020 3019 3015
Name Listing date iShares BSE SENSEX India Index ETF 2-Nov-06 iShares CSI 300 A-Share Index ETF 18-Nov-09 iShares CSI A-Share Consumer Discretionary Index ETF 20-Jul-10 iShares CSI A-Share Consumer Staples Index ETF 20-Jul-10 iShares CSI A-Share Energy Index ETF 18-Nov-09 iShares CSI A-Share Financials Index ETF 18-Nov-09 iShares CSI A-Share Infrastructure Index ETF 18-Nov-09 iShares CSI A-Share Materials Index ETF 18-Nov-09 iShares FTSE A50 China Index ETF 18-Nov-04 iShares MSCI Asia APEX 50 Index ETF 23-Apr-09 iShares MSCI Asia APEX Mid Cap Index ETF 23-Apr-09 iShares MSCI Asia APEX Small Cap Index ETF 23-Apr-09 iShares MSCI China Index ETF 28-Nov-01 iShares MSCI Emerging Asia Index ETF 23-Apr-09 CSI 300 BANKS INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 CONSUMER DISCRETIONARY INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 FINANCIALS INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 INDUSTRIALS INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 MATERIALS INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 REAL ESTATE INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 TRANSPORTATION INDEX ETF* (* This is a synthetic ETF) 3/16/10 CSI 300 UTILITIES INDEX ETF* (* This is a synthetic ETF) 3/16/10 FTSE CHINA 25 ETF* (* This is a synthetic ETF) 6/19/07 FTSE VIETNAM INDEX ETF* (* This is a synthetic ETF) 1/15/08 MSCI BRAZIL TRN INDEX ETF (* This is a synthetic ETF) 12/14/09 MSCI EM ASIA TRN INDEX ETF (* This is a synthetic ETF) 12/14/09 MSCI EMERGING MARKETS TRN INDEX ETF (* This is a synthetic ETF) 12/14/09 MSCI PACIFIC EX JAPAN TRN INDEX ETF (* This is a synthetic ETF) 12/14/09 MSCI RUSSIA CAPPED INDEX ETF (* This is a synthetic ETF) 12/14/09 MSCI TAIWAN TRN INDEX ETF (* This is a synthetic ETF) 6/19/07 MSCI USA TRN INDEX ETF (* This is a synthetic ETF) 1/8/07 MSCI WORLD TRN INDEX ETF (* This is a synthetic ETF) 12/14/09 S&P CNX NIFTY ETF (India) (* This is a synthetic ETF) 7/5/07
Table II P ERFORMANCE COMPARISON OVER LONG - TERM INVESTMENT HORIZON ETF Code 2801 2802 2823 2829 2836 2841 2846 3001 3004 3006 3010 3032 3039 3050 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3048 3035 3009 3043 3027 3036 3020 3019 3015 HSIC MSCI China MSCI CHT
IV. E MPIRICAL S TUDIES In the empirical studies, two sets of experiments are conducted. The performances of different ETFs are tested over both long term and short-term time horizon. The longterm test covers the data from 20 July 2010 to 31 December 2012. The short-term test covers the data from 17 December 2012 to 31 December 2012. The ETFs investigated, their codes and listing dates are listed in Table I. Experiment results for ETFs across different emerging markets over long-term time horizon are shown in table II. Where the last three rows refer to the benchmark indexes including HSIC, MSCI China and MSCI China Hong Kong and Taiwan. rET F and rindex refer to the return of ETF and the underlying index respectively. σET F and σindex refer to the volatility of ETF and the underlying index respectively. SRET F and SRindex refer to the Sharpe Ratio of ETF and the underlying index respectively. T E refers to the tracking errors of different ETFs. The number of date in the same testing period may vary due to recording errors. Experiment results in table II show that in general return of ETF uniformly underperforms the corresponding underlying index. The return of majority of ETFs underperforms the benchmark indexes, but returns of these ETFs also vary. Meanwhile volatility of majority of ETF is lower than those of indexes. Where SRi (i=the underlying index (I), Hangseng index (HSIC), MSCI China index(M China) and MSCI China
rE 0.0034 0.1659 -0.0847 -0.0327 -0.0614 0.1119 -0.1664 -0.1265 0.0231 -0.2032 0.1958 -0.0263 -0.1011 -0.0920 0.0174 -0.0874 0.0435 -0.0370 -0.2029 -0.0694 0.2281 -0.3111 -0.1765 0.0467 -0.1047 0.1752 0.1480 0.3585 0.1434 0.1816 0.3500 0.2717 -0.0453 0.0000 0.0000 0.0000
rI 0.0999 0.1988 0.0390 0.1176 -0.0285 0.2735 0.0155 -0.0307 0.1155 -0.1020 0.3065 0.0634 -0.0011 0.0329 0.0572 -0.0621 0.0704 -0.0162 -0.1763 -0.0188 0.2738 -0.2889 -0.1525 0.0426 -0.0852 0.2023 0.1734 0.3695 0.1654 0.2033 0.3484 0.2797 -0.0221 0.1185 0.0353 0.0559
AREx10−3 0.0178 -0.1190 0.0630 0.1039 0.0839 -0.0954 0.2585 0.3065 0.0114 0.3364 -0.1463 0.0703 0.2268 0.2202 0.0423 0.2471 0.0168 0.1323 0.4224 0.2145 -0.1825 0.6287 0.3609 0.0113 0.2313 -0.1606 -0.1424 -0.3536 -0.0350 -0.0901 -0.1835 -0.2772 0.0900 0.0000 0.0000 0.0000
ARIx10−3 -0.0160 -0.1514 -0.0031 -0.0888 0.0679 -0.3287 0.0243 0.1282 -0.0798 0.1727 -0.2407 -0.0241 0.0847 0.0490 -0.0234 0.1907 -0.0327 0.0776 0.3604 0.1261 -0.2415 0.5609 0.2925 0.0140 0.2021 -0.1901 -0.1685 -0.3680 -0.0589 -0.1037 -0.1828 -0.2816 0.0716 -0.0001 0.0000 -0.0001
σE 0.0147 0.0129 0.0127 0.0140 0.0138 0.0149 0.0137 0.0150 0.0123 0.0121 0.0129 0.0133 0.0183 0.0183 0.0137 0.0157 0.0147 0.0146 0.0167 0.0197 0.0190 0.0173 0.0148 0.0160 0.0170 0.0125 0.0115 0.0129 0.0186 0.0133 0.0115 0.0109 0.0148 0.0000 0.0000 0.0000
σI 0.0147 0.0124 0.0129 0.0142 0.0146 0.0149 0.0138 0.0151 0.0123 0.0120 0.0128 0.0131 0.0185 0.0185 0.0131 0.0152 0.0144 0.0140 0.0154 0.0187 0.0187 0.0136 0.0119 0.0160 0.0169 0.0124 0.0115 0.0128 0.0185 0.0133 0.0115 0.0107 0.0148 0.0197 0.0152 0.0119
T Ex10−3 1.8222 2.1081 1.8329 1.9010 4.5706 1.4140 5.4296 1.4983 1.7756 1.9633 1.7932 1.8019 1.3729 1.7615 4.4095 4.0744 3.7765 5.3341 6.0385 5.2847 3.6069 10.3669 8.7690 0.9029 0.6548 1.1683 1.0384 0.9706 1.3051 0.2451 0.1331 1.3457 0.0609 0.0000 0.0000 0.0000
Hong Kong and Taiwan (M CHT)‘ refers to the Sharpe ratio calculated using the underlying index, Hangseng Index, MSCI China index and MSCI China Hong Kong and Taiwan ¯ I refers to the Sharpe ratio index as the market index. SR using the average returns of daily returns instead. The return adjusted by standard deviation as the risk measure, indicated by the calculated Sharpe ratio vary. However, some of the average return of the ETFs during the tested period may exceed those of the index. This indicates
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Experiment results in table IV provide empirical evidence further supporting the argument aforementioned that the performance of individual ETFs vary across time for both the benchmark index and the underlying index, sometimes exceeding those of index but not otherwise, as indicated by the time varying performance of ETFs in general.
Table III S HARPE RATIO AGAINST DIFFERENT MARKET INDEXES LONG - TERM INVESTMENT HORIZON ETF Code 2801 2802 2823 2829 2836 2841 2846 3001 3004 3006 3010 3032 3039 3050 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3048 3035 3009 3043 3027 3036 3020 54 3019 3015
SRI -6.5576 -2.5479 -9.7257 -10.7296 -2.3820 -10.8532 -13.3230 -6.3644 -7.5274 -8.3798 -8.6109 -6.7457 -5.4532 -6.8191 -2.9064 -1.6070 -1.8267 -1.4228 -1.5940 -2.5689 -2.4072 -1.2888 -1.6243 0.2534 -1.1496 -2.1681 -2.2109 -0.8510 -1.1837 -1.6271 0.1325 -0.7334 -1.5718
SRHSIC -7.8255 3.6761 -15.9766 -10.7978 -13.0336 -0.4435 -20.8687 -16.2835 -7.7751 -26.6314 6.0048 -10.8929 -11.9757 -11.4911 -7.3934 -13.1150 -5.0977 -10.6209 -19.2610 -9.5319 5.7639 -24.8970 -19.9745 -4.4966 -13.1513 4.5379 2.5707 18.6411 1.3388 4.7426 20.1345 14.1129 -11.0731
SRMC hina -2.1709 10.1312 -9.4347 -4.8558 -7.0059 5.1467 -14.7734 -10.7538 -0.9932 -19.7436 12.4751 -4.6341 -7.4389 -6.9493 -1.3107 -7.8150 0.5541 -4.9400 -14.2748 -5.3125 10.1424 -20.0751 -14.3403 0.7116 -8.2484 11.1982 9.8187 25.1039 5.8216 10.9956 27.3740 21.7801 -5.4506
¯I SR 0.0023 0.0025 0.0052 0.0138 0.0012 0.0157 0.0172 0.0119 0.0074 0.0135 0.0073 0.0071 0.0077 0.0093 0.0048 0.0036 0.0034 0.0037 0.0037 0.0045 0.0031 0.0039 0.0046 -0.0002 0.0017 0.0024 0.0023 0.0011 0013 0010 -0.0001 0.0004 0.0012
SRMC HT -3.5663 8.5383 -11.0490 -6.3221 -8.4934 3.7672 -16.2776 -12.1184 -2.6668 -21.4433 10.8784 -6.1786 -8.5585 -8.0701 -2.8117 -9.1229 -0.8406 -6.3419 -15.5052 -6.3537 9.0619 -21.2650 -15.7306 -0.5736 -9.4583 9.5546 8.0301 23.5091 4.7154 0. 9.4526 0. 25.5875 19.8880 -6.8381
¯ SRHSIC -8.0537 -9.2044 -9.3138 -8.4568 -8.5801 -7.9694 -8.6635 -7.8566 -9.6597 -9.7837 -9.2281 -8.9102 -6.4501 -6.4576 -8.6617 -7.5339 -8.0497 -8.0833 -7.0775 -5.9997 -6.2467 -6.8323 -8.0013 -7.4182 -6.9704 -9.5003 -10.3370 -9.2335 -6.3876 -8.9140 -10.3284 -10.9472 -8.0029
SRM¯C hina -2.3990 -2.7493 -2.7719 -2.5148 -2.5525 -2.3793 -2.5683 -2.3268 -2.8778 -2.8959 -2.7578 -2.6514 -1.9134 -1.9158 -2.5789 -2.2339 -2.3979 -2.4024 -2.0912 -1.7802 -1.8682 -2.0103 -2.3671 -2.2100 -2.0675 -2.8400 -3.0890 -2.7707 -1.9047 -2.6610 -3.088 -3.2800 -2.3805
SRM¯C HT -3.7944 -4.3422 -4.3862 -3.9811 -4.0399 -3.7588 -4.0724 -3.6914 -4.5514 -4.5956 -4.3545 -4.1959 -3.0329 -3.0366 -4.0799 -3.5418 -3.7926 -3.8043 -3.3217 -2.8214 -2.9487 -3.2002 -3.7575 -3.4952 -3.2774 -4.4835 -4.8775 -4.3656 -3.0110 -4.2041 9 -4.87 -5.1721 -3.7679
Table V S HARPE RATIO AGAINST DIFFERENT MARKET INDEXES OVER SHORT- TERM INVESTMENT HORIZON ETF Code 2801 2802 2823 2829 2836 2841 2846 3001 3004 3006 3010 3032 3039 3050 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3048 3035 3009 3043 3027 3036 3020 3019 3015
that the performance of ETF may exceed that of index. More importantly, it’s clear that different ETFs have different tracking errors, which are closely related to the regions it focuses on. ETFs tracking indexes in China demonstrate significantly larger level of tracking error than for those tracking India and Asia in general. This further implies that experiment results for ETFs across different emerging markets over short-term time horizon from to are shown in table IV.
HORIZON rE 0.0109 0.0074 0.0442 0.0512 0.0068 0.0306 0.0470 0.0639 0.0149 0.0336 0.0039 0.0103 0.0355 0.0445 0.0446 0.0682 0.0526 0.0541 0.0377 0.0308 0.0687 0.0333 0.0244 0.0091 0.0335 0.0104 0.0148 0.0041 0.0230 0.0060 -0.0183 -0.0059 0.0084 0.0000 0.0000 0.0000
rI 0.0110 0.0096 0.0454 0.0532 0.0109 0.0317 0.0484 0.0650 0.0157 0.0354 0.0041 0.0108 0.0367 0.0462 0.0423 0.0648 0.0530 0.0482 0.0449 0.0364 0.0708 0.0366 0.0314 0.0094 0.0340 0.0094 0.0153 0.0056 0.0241 0.0060 -0.0183 -0.0057 0.0086 0.0068 0.0110 0.0213
AREx10−3 -0.0028 -0.0011 -0.0257 -0.0705 0.0012 -0.0776 -0.0680 -0.1145 -0.0081 -0.0440 0.0023 -0.0043 -0.0604 -0.0864 -0.0652 -0.1000 -0.0767 -0.0795 -0.0549 -0.0689 -0.0735 -0.1000 -0.0363 -0.0043 -0.0364 -0.0032 -0.0158 0.0000 -0.0211 0.0046 0.0047 -0.0037 0.0001 0.0000 0.0000 0.0000
ARIx10−3 -0.0028 -0.0043 -0.0267 -0.0740 -0.0017 -0.0803 -0.0704 -0.1163 -0.0089 -0.0461 0.0023 -0.0046 -0.0638 -0.0899 -0.0676 -0.1030 -0.0832 -0.0794 -0.0742 -0.0721 -0.0793 -0.0736 -0.0344 -0.0043 -0.0378 -0.0050 -0.0163 -0.0026 -0.0233 0.0044 0.0049 -0.0020 -0.0001 -0.0003 -0.0008 -0.0019
σE 0.0051 0.0059 0.0101 0.0118 0.0083 0.0170 0.0097 0.0058 0.0050 0.0112 0.0057 0.0047 0.0114 0.0112 0.0166 0.0075 0.0159 0.0102 0.0175 0.0120 0.0308 0.0160 0.0090 0.0073 0.0100 0.0072 0.0063 0.0032 0.0095 0.0108 0.0101 0.0069 0.0104 0.0000 0.0000 0.0000
σI 0.0052 0.0061 0.0101 0.0117 0.0092 0.0170 0.0095 0.0057 0.0051 0.0110 0.0058 0.0048 0.0114 0.0109 0.0147 0.0064 0.0153 0.0111 0.0110 0.0115 0.0306 0.0124 0.0126 0.0072 0.0100 0.0068 0.0061 0.0034 0.0102 0.0108 0.0101 0.0067 0.0104 0.0197 0.0152 0.0119
SRHSIC 0.8010 0.1037 3.6953 3.7538 0.0006 1.3993 4.1587 9.7677 1.6196 2.4008 -0.5055 0.7518 2.5137 3.3789 2.2862 8.2326 2.8892 4.6171 1.7688 1.9990 2.0070 1.6581 1.9552 0.3113 2.6615 0.4963 1.2750 -0.8406 1.7135 -0.0727 -2.4946 -1.8439 0.1493
SRMC hina -0.0100 -0.6024 3.2827 3.4006 -0.5017 1.1540 3.7274 9.0531 0.7877 2.0269 -1.2331 -0.1374 2.1478 3.0047 2.0340 7.6727 2.6261 4.2093 1.5301 1.6509 1.8717 1.3972 1.4913 -0.2601 2.2447 -0.0801 0.6113 -2.1628 1.2728 -0.4595 -2.9099 -2.4503 -0.2514
SRMC HT -2.0118 -2.3454 2.2643 2.5288 -1.7417 0.5486 2.6627 7.2891 -1.2659 1.1037 -3.0293 -2.3324 1.2446 2.0807 1.4115 6.2906 1.9765 3.2024 0.9410 0.7916 1.5376 0.7534 0.3461 -1.6705 1.2160 -1.5031 -1.0271 -5.4267 0.1849 -1.4145 -3.9351 -3.9471 -1.2406
¯I SR 0.0000 0.0005 0.0001 0.0003 0.0003 0.0002 0.0002 0.0003 0.0002 0.0002 0.0000 0.0000 0.0003 0.0003 0.0001 0.0004 0.0004 0.0000 0.0011 0.0003 0.0002 -0.0016 -0.0002 0.0000 0.0001 0.0003 0.0001 0.0008 0.0002 0.0000 0.0000 -0.0003 0.0000
¯ SRHSIC -1.3205 -1.1494 -0.6741 -0.5808 -0.8175 -0.4037 -0.7090 -1.1827 -1.3557 -0.6126 -1.1839 -1.4482 -0.6008 -0.6169 -0.4144 -0.9247 -0.4331 -0.6716 -0.3916 -0.5723 -0.2227 -0.4308 -0.7591 -0.9306 -0.6819 -0.9387 -1.0828 -2.1520 -0.7196 -0.6293 -0.6755 -0.9875 -0.6522
SRM¯C hina -2.1314 -1.8555 -1.0866 -0.9340 -1.3198 -0.6490 -1.1403 -1.8972 -2.1876 -0.9866 -1.9115 -2.3374 -0.9667 -0.9912 -0.6666 -1.4846 -0.6963 -1.0795 -0.6302 -0.9204 -0.3580 -0.6916 -1.2230 -1.5019 -1.0986 -1.5151 -1.7465 -3.4742 -1.1603 -1.0161 -1.0908 -1.5939 -1.0529
SRM¯C HT -4.1333 -3.5985 -2.1051 -1.8058 -2.5598 -1.2544 -2.2050 -3.6612 -4.2412 -1.9097 -3.7076 -4.5324 -1.8699 -1.9151 -1.2891 -2.8666 -1.3459 -2.0863 -1.2194 -1.7797 -0.6921 -1.3355 -2.3682 -2.9123 -2.1274 -2.9381 -3.3850 -6.7381 -2.2482 -1.9711 -2.1160 -3.0908 -2.0421
This result remains the same for Sharpe ratio as the risk adjusted return measure. Other results remain the same as those from experiments over long-term investment horizon. Over short-term investment horizon, different ETFs also have different tracking errors, which are closely related to the region. ETFs tracking indexes in China demonstrate significantly larger level of tracking error than for those tracking India and Asia in general. Correlation between each ETFs and different market indexes are listed in Table VI. Where ρL,HSIC , ρL,M CHS and ρL,M CHT refer to the correlation between each ETF and three market indexes including Hangseng index (HSIC), MSCI China index(MCHS) and MSCI China Hong Kong and Taiwan (MCHT) over the long term. ρS,HSIC , ρS,M CHS and ρS,M CHT refer to the corresponding correlation in the short term. Majority of ETFs have high degree of correlation to the market indexes, which confirms that ETFs offering significant diversification benefits. In the meantime there are also significant portion of ETFs that have lower level of correlation to the market index while provide improved performance. This indicates that the performance of ETFs can be further improved by diversifying away those diversifiable risks.
Table IV P ERFORMANCE COMPARISON OVER SHORT- TERM INVESTMENT
ETF Code 2801 2802 2823 2829 2836 2841 2846 3001 3004 3006 3010 3032 3039 3050 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3048 3035 3009 3043 3027 3036 3020 3019 3015 HSIC MSCI China MSCI CHT
SRI -0.0194 -0.3745 -0.1166 -0.1727 -0.4967 -0.0631 -0.1370 -0.1940 -0.1515 -0.1642 -0.0353 -0.0966 -0.1053 -0.1570 0.1404 0.4574 -0.0224 0.5760 -0.4077 -0.4729 -0.0678 -0.2010 -0.7790 -0.0413 -0.0560 0.1390 -0.0845 -0.4690 -0.1088 0.0017 0.0063 -0.0276 -0.0228
T Ex10−3 0.0406 0.8907 0.6333 0.5218 2.9870 0.3693 0.2631 1.0840 0.2587 0.8901 0.2298 0.3427 0.7844 0.6272 4.1444 2.5387 2.9762 6.3721 10.8206 8.3817 1.6976 5.9268 6.5664 0.1724 1.1796 1.0496 0.7373 0.7496 1.0179 0.2238 0.1712 0.8390 0.0491 0.0000 0.0000 0.0000
V. C ONCLUSIONS In this paper, we have conducted empirical studies on the performance of ETF across different markets worldwide, against the benchmark indexes. In terms of return, risk and tracking errors measures, we found that the performance
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Table VI C ORRELATION BETWEEN ETF S AND MARKET INDEXES ETF Code 2801 2802 2823 2829 2836 2841 2846 3001 3004 3006 3010 3032 3039 3050 3061 3025 2844 3049 3005 3062 2816 3063 3052 3007 3048 3035 3009 3043 3027 3036 3020 8 3019 0 3015
ρL,HSIC 0.9485 0.8838 0.1101 0.0823 0.3542 -0.0353 0.5491 0.2931 0.8885 0.1404 0.8800 0.9023 0.2303 0.1260 0.7684 0.6996 0.7577 0.7024 0.6616 0.6614 0.6155 0.5472 0.5586 0.8879 0.1935 0.5064 0.5902 0.5607 0.6311 0.3565 0.0248 0.0188 0.3837
ρL,M CHS 0.9936 0.8474 0.2185 0.1832 0.4509 0.0665 0.7007 0.4288 0.9300 0.2556 0.7832 0.9396 0.3539 0.2456 0.6743 0.8033 0.8038 0.7935 0.7858 0.7602 0.4918 0.7842 0.8182 0.9906 0.8898 0.8802 0.9086 0.5004 0.7412 0.2836 -0.1401 0.2548 0.4420
ρL,M CHT 0.9294 0.8568 0.1084 0.0622 0.3297 0.0665 0.7124 0.3665 0.9678 0.1484 0.7510 0.9456 0.2947 0.1739 0.6953 0.9061 0.8392 0.9232 0.9124 0.9171 0.5457 0.8581 0.8299 0.9235 0.8969 0.8656 0.9037 0.4669 0.8151 0.4342 -0.0482 0.2464 0.5316
ρS,HSIC 0.9600 0.7336 0.6877 0.6125 0.6981 0.7067 0.6574 0.5311 0.7327 0.6474 0.6161 0.9362 0.6872 0.7138 0.6973 0.5962 0.6087 0.7002 0.7300 0.4030 0.5704 0.5393 0.5393 0.8950 0.7074 0.7978 0.9343 0.8310 0.7275 0.3678 -0.4158 -0.0871 0.6116
ρS,M CHS 0.9999 0.8475 0.6703 0.5943 0.8112 0.7828 0.6250 0.4469 0.7552 0.5943 0.7299 0.9200 0.6384 0.6524 0.7282 0.5241 0.6101 0.6674 0.6374 0.3512 0.4806 0.4980 0.4980 0.9495 0.7080 0.9161 0.9854 0.8261 0.6942 0.5099 -0.3691 -0.0282 0.7550
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ρS,M CHT 0.7857 0.7193 0.9762 0.9555 0.5265 0.8067 0.9602 0.7850 0.9759 0.9520 0.5804 0.8121 0.8822 0.9383 0.9733 0.8334 0.9674 0.9461 0.9113 0.7218 0.9051 0.9141 0.9141 0.7361 0.4356 0.6572 0.7677 0.5546 0.6939 0.5368 -0.811 -0.596 0.3300
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ACKNOWLEDGEMENT This work is supported by the Strategic Research Grant of City University of Hong Kong (No. 7004135), the National Natural Science Foundation of China (NSFC No. 71201054), and the Fundamental Research Funds for the Central Universities (No. ZZ1315).
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