Does it pay to diversify? U.S. vs. international ETFs Srinidhi Kanuria, Robert W. McLeodb,* aDepartment of Finance Real Estate and Business Law, College of Business, The University of Southern Mississippi, Scianna Hall, 118 College Drive, #5076, Hattiesburg, MS 39406, USA bDepartment of Economics Finance and Legal Studies, Culverhouse College of Commerce, The University of Alabama, Box 870224, Tucaloosa, AL 35487-0224, USA Abstract Individual investors seek diversification in their portfolios using a number of approaches. One approach that is commonly used is to diversify globally. This article evaluates the performance and diversification benefits of international ETFs for U.S. investors during and after the recent financial crisis. Our results show that U.S. ETFs outperform all categories of international ETFs for the period of our study (January 2008 – June 2013); they have higher average monthly returns, lower risk (standard deviation of returns), higher risk-adjusted performance (Sharpe, Sortino, and Treynor ratios) and the highest cumulative returns over the entire period. When we form equally weighted portfolios of each ETF category and compute their risk-adjusted performance, we again find that U.S. ETF portfolios had the best performance for the entire period. We also find that U.S. ETFs have the lowest tracking error during the entire period. Most of these ETFs passively track the benchmark and do not manage for positive �. Previous research has questioned the diversification benefits of international investing during times of financial distress. We find that international ETFs are highly dependent on major U.S. indices during the period of our analysis, and therefore, offered limited diversification benefits for U.S. investors. © 2015 Academy of Financial Services. All rights reserved. JEL classification: G11; G12; G15 Keywords: International ETFs; Diversification; Portfolio; Risk-adjusted performance * Corresponding author. Tel.: �1-205-448-8993; fax: �1-205-348-0590. E-mail address: rmcleod@cba.ua.edu. Financial Services Review 24 (2015) 249–270 1057-0810/15/$ – see front matter © 2015 Academy of Financial Services. All rights reserved. 1. Introduction U. S. investors can achieve global diversification in a number of ways. They can purchase individual securities directly in foreign capital markets or in U.S. markets through American depository receipts (ADRs). Other investors attain international diversification by indirect investments such as mutual funds, closed-end funds, or exchange traded funds (ETFs). Earlier research (e.g., Adler and Dumas, 1983; Black, 1974; Heston and Rouwenhorst,1994; Levy and Sarnat, 1970; Stulz, 1981) supports the importance to investors to allocate some of their funds into foreign investments as a means of reducing portfolio risk because of low correlations among markets or market segmentation that results in barriers to international investment. However, more recent findings bring into question the diversification benefits of international investing especially during times of financial distress. Eun and Shin (1989), King and Wadhwani (1990), and Koch and Koch (1991) show that regional dependencies have increased over time. Longin and Solnik (1995) and Jacquier and Marcus (2001) examine correlations in country portfolio returns during turbulent market conditions and conclude that they increase. Roll (1987) analyzed the crash of October 1987 and reports that all 23 indexes studied declined in a synchronized fashion. Pries, Kenett, Stanley, Helbing, and Ben-Jacob (2012) report that diversification benefits vanish during times of financial distress. Russell (1998) looks at the international diversification benefits of U.S. exchange traded securities such as closed ended funds, ADRs and Multinational Corporation (MNCs) to provide diversification benefits similar to investment in foreign equity. The result indicate that U.S. exchange-listed securities behave more like host exchange than their home ex- change. This results suggests exchange-listed securities on average, do not perform an international diversification role for U.S. investors. Johnson et al. (1999) find that diversi- fication benefits of international mutual funds may be less than what previous studies find. They find that during restrictive U.S. monetary policy periods, international mutual fund indexes provide lower excess returns than domestic counterparts. Additionally, the correla- tions between international mutual funds and domestic mutual funds are higher during restrictive monetary policy periods. This evidence may represent a partial explanation for the home country bias exhibited by United States-based individual and institutional investors. Aiello and Chieffe (1999) compare the performance of international index funds and the S&P 500 from 1989 to 1997 and find that international index funds do not offer superior performance. Ho et al. (1999) find that the United States equity market is a large proportion of the international equity market that is available to individual investors, and United States returns are highly correlated with other markets. Hanna et al. (1999) look at ten years of historical data (January 1988 through December 1997) from the stock markets in the G-7 countries. Across this 10-year period, they find that a portfolio consisting solely of the S&P 500 dominates any portfolio that can be constructed from the S&P 500 and the major market index of the G-7 countries. The growth in the number of international ETFs has been significant, especially imme- diately before the “great recession.” This growth was in response to investor demand for ETFs that provided an opportunity to diversify globally using low cost options. In this article we look at the performance and diversification benefits of international ETFs for U.S. investors from 2008 through June 2013.1 Using ETFs that follow Total World, Total World 250 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Ex U.S., Developed markets, and Emerging markets, we compare their performance to U.S. ETFs that follow the major indices. According to Rompotis (2010), investors choose foreign ETFs for a number of reasons: (1) It was difficult to invest in securities listed on foreign exchanges before the emergence of ETFs because many U.S. brokers were unable to process orders on non-U.S. exchanges and the few good international mutual funds that existed had very high expenses. (2) Apart from difficulties of foreign investing, investors choose international ETFs for broad diversification without having to directly purchase stocks in foreign countries. In addition, investors want to take advantage of specific macroeconomic or microeconomic trends, such as rapid growth in a particular economy or region. International ETFs give U.S. investors a cheaper and less complicated method to invest in foreign stocks rather than direct investment. Our article will test whether international ETFs outperform major U.S. ETFs and whether U.S. investors get diversification benefits by investing in foreign ETFs during the financial crisis and subsequent recovery of the U.S. stock market. 2. Data To be included in the analysis (for an equal comparison), the ETF should have been created on or before January 2008 and have continuous return and trading history from January 2008 through June 2013. These ETFs where created at different points of time with U.S. ETFs created before Total World, Total World Ex U.S., Developed markets, and Emerging markets ETFs. Table 1a provides a list of each ETF used in our study, its inception date, the underlying benchmark index, and the category to which the ETF belongs. The complete list of different categories of ETFs was obtained from Morningstar Direct. Using data obtained from Morningstar Direct and Bloomberg Terminal we compare the performance of U.S. ETFs following six major U.S. indices (S&P 500, Russell 1000, Russell 3000, Dow Jones Industrial Average, Dow Jones U.S. Total Returns, and NASDAQ 100) to Total World, Total World ex U.S., Emerging markets, and Developed markets ETFs. There are a total of 36 ETFs (6 U.S., 6 Total World, 4 Total World ex U.S., 10 Emerging Markets, and 10 Developed Markets) included in this analysis. We also look at the potential benefits of diversification for American investors from owning international ETFs. Descriptive data are provided in Table 1b that include the average annual net expense and turnover ratios of each ETF from 2008 to 2013 and assets at the end of June 2013. U.S. ETFs have the lowest expense ratios while Developed market and Emerging market ETFs have the highest expense ratios. All these values have also been taken from Morningstar Direct and Bloomberg Terminal. 3. Performance and risk Following Rompotis (2009, 2010) and Shin and Soydemir (2010), ETFs are compared based on their average monthly returns for the entire period (January 2008 through June 2013). We rank ETFs in descending order based on their average returns. The risk of ETFs 251S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 is estimated as the standard deviation of returns. As shown in Table 2, on average, U.S. ETFs and indices have the highest average monthly returns, whereas Emerging (ADRE, EEB, and BKF) markets ETFs and benchmarks have the lowest returns over the entire period. Simi- larly, U.S. ETFs and indices (with the exception of QQQ and its benchmark Nasdaq 100) have the lowest standard deviation of returns over the entire period, whereas Emerging markets ETFs have the highest standard deviation of returns over the entire period. 3.1. Sharpe, Sortino, and Treynor ratios An ETF could have higher returns, but it could have done so by assuming higher risk. To compare risk adjusted returns of ETFs over the same period, Sharpe, Sortino, and Treynor ratios are used. These measures have been widely used in the literature (e.g., Harper, Madura, and Schnusenberg, 2006; Rompotis, 2009, 2010) to compare ETF performance. ETFs are ranked in descending order (from best to worst based on these ratios) for the entire period (January 2008 through June 2013). Table 1a: This table shows the ETF, the benchmark it follows, the inception date and category to which it belongs ETF Name Benchmark Inception Category IVV iShares Core S&P 500 ETF S&P 500 TR USD 5/15/2000 U.S. IWB iShares Russell 1000 Index Russell 1000 TR USD 5/15/2000 U.S. IWV iShares Russell 3000 Index Russell 3000 TR USD 5/22/2000 U.S. DIA SPDR Dow Jones Industrial Average DJ Industrial Average TR USD 1/13/1998 U.S. IYY iShares Dow Jones U.S. Index DJ US TR USD 6/12/2000 U.S. QQQ PowerShares QQQ NASDAQ 100 TR USD 3/10/1999 U.S. DEW WisdomTree Global Equity Income WisdomTree Global Equity Income TR USD 6/16/2006 Total World DGT SPDR Global Dow ETF DJ Global TR USD 9/25/2000 Total World IOO iShares S&P Global 100 Index S&P Global 100 TR 12/5/2000 Total World FGD First Trust DJ Global Select Dividend DJ Global Select Dividend TR USD 11/21/2007 Total World LVL Guggenheim S&P Global Dividend Opps Idx S&P Global Dividend Opport NR USD 6/25/2007 Total World TOK iShares MSCI Kokusai MSCI Kokusai TR USD 12/10/2007 Total World CWI SPDR MSCI ACWI (ex-US) MSCI ACWI Ex USA GR USD 1/10/2007 Total World Ex US DNL WisdomTree Global ex-US Growth WisdomTree Global Ex Us Growth TR USD 6/16/2006 Total World Ex U.S. GWL SPDR S&P World ex-US S&P Developed Ex US BMI TR USD 4/20/2007 Total World Ex U.S. VEU Vanguard FTSE All-World ex-US ETF FTSE AW Ex US TR USD 3/2/2007 Total World Ex U.S. ADRD BLDRS Developed Markets 100 ADR Index BONY Developed Markets 100 ADR TR USD 11/13/2002 Developed DOL WisdomTree International LargeCap Div WisdomTree Intl LargeCap Dividend TR USD 6/16/2006 Developed DOO WisdomTree International Div ex-Finncls WisdomTree Intl Dividend Ex Fincl TR USD 6/16/2006 Developed DTH WisdomTree DEFA Equity Income WisdomTree DEFA Equity Income TR USD 6/16/2006 Developed DWM WisdomTree DEFA WisdomTree DEFA TR USD 6/16/2006 Developed EFA iShares MSCI EAFE MSCI EAFE NR USD 8/14/2001 Developed IDV iShares Dow Jones Intl Select Div Idx DJ EPAC Select Dividend TR USD 6/11/2007 Developed PIZ PowerShares DWA Dev Mkts Technical Ldrs Dorsey Wright Dev Mrkt Tech Ldrs NR USD 12/28/2007 Developed PXF PowerShares FTSE RAFI Dev Mkts ex-US FTSE RAFI Dvlp ex US 1000 TR USD 6/25/2007 Developed VEA Vanguard FTSE Developed Markets ETF FTSE Developed ex North America NR USD 7/20/2007 Developed ADRE BLDRS Emerging Markets 50 ADR Index BONY Emerging Markets 50 ADR TR USD 11/13/2002 Emerging BIK SPDR S&P BRIC 40 S&P BRIC 40 TR 6/19/2007 Emerging BKF iShares MSCI BRIC MSCI BRIC NR USD 11/12/2007 Emerging DEM WisdomTree Emerging Markets Equity Inc WisdomTree EM Equity Income TR USD 7/13/2007 Emerging EEB Guggenheim BRIC BNY/Mellon BRIC TR USD 9/21/2006 Emerging EEM iShares MSCI Emerging Markets MSCI EM NR USD 4/7/2003 Emerging GMM SPDR S&P Emerging Markets S&P Emerging BMI TR USD 3/19/2007 Emerging PIE PowerShares DWA Em Mkts Technical Ldrs Dorsey Wright Em Mrkt Tech Ldrs NR USD 12/28/2007 Emerging PXH PowerShares FTSE RAFI Emerging Markets FTSE RAFI Emerging TR USD 9/27/2007 Emerging VWO Vanguard FTSE Emerging Markets ETF FTSE Emerging TR USD 3/4/2005 Emerging 252 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Sharpe ratio is calculated as: SR � �RETF � Rf�/�ETF (1) where RETF denotes the monthly returns on the ETF, Rf is the monthly risk free rate, �ETF is the standard deviation of monthly ETF returns. Table 1b: Shows average annual net expense and turnover ratios from 2008 to 2013 and assets at the end of June 2013 ETF Average annual net expense ratio 2008–2013 Average annual turnover ratio 2008–2013 Assets $ (June 2013) Category IVV 0.09% 5.33% 42,573,234,908 U.S. IWB 0.15% 6.67% 7,686,737,763 U.S. IWV 0.20% 6.33% 4,287,062,577 U.S. IYY 0.20% 5.67% 715,567,400 U.S. DIA 0.17% 6.72% 12,568,816,144 U.S. QQQ 0.20% 12.19% 33,645,121,238 U.S. DEW 0.53% 42.33% 98,284,717 Total World DGT 0.51% 27.17% 88,219,982 Total World IOO 0.40% 5.67% 1,256,399,204 Total World FGD 0.60% 36.83% 311,450,253 Total World LVL 0.80% 82.67% 72,989,199 Total World TOK 0.25% 5.67% 576,636,913 Total World CWI 0.34% 6.17% 416,858,949 Total World Ex U.S. DNL 0.58% 54.00% 76,210,727 Total World Ex U.S. GWL 0.35% 5.83% 572,349,639 Total World Ex U.S. VEU 0.19% 7.00% 9,041,860,844 Total World Ex U.S. ADRD 0.28% 10.10% 45,196,595 Developed DOL 0.48% 22.50% 209,073,220 Developed DOO 0.58% 46.33% 322,821,722 Developed DTH 0.58% 31.50% 212,323,318 Developed DWM 0.43% 35.67% 438,785,622 Developed EFA 0.34% 6.33% 40,201,315,518 Developed IDV 0.50% 42.67% 1,969,620,635 Developed PIZ 0.80% 128.17% 246,498,810 Developed PXF 0.71% 21.83% 536,149,000 Developed VEA 0.12% 8.60% 13,152,279,035 Developed ADRE 0.27% 12.04% 235,859,904 Emerging BIK 0.49% 12.33% 226,925,818 Emerging BKF 0.34% 13.33% 491,577,033 Emerging DEM 0.63% 38.50% 4,848,791,521 Emerging EEB 0.63% 11.00% 224,304,688 Emerging EEM 0.69% 14.33% 34,620,518,926 Emerging GMM 0.59% 10.50% 180,418,744 Emerging PIE 0.90% 171.00% 354,721,563 Emerging PXH 0.80% 36.50% 333,461,873 Emerging VWO 0.20% 14.67% 49,355,444,391 Emerging 253S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 The Sharpe ratio evaluates how well an ETF compensates its investor for each unit of risk they incur. The higher the Sharpe ratio, the better is the performance of the ETF. The second measure of risk-adjusted performance is the Sortino ratio expressed as: Sortino � �RETF � Rf�/�d (2) where RETF and Rf are described as above; �d is the standard deviation of ETF’s negative returns. Table 2 Shows average monthly returns and standard deviation of returns (in %) from January 2008 through June 2013 Rank ETF No. of obs Avg. monthly ETF return ETF SD Avg. monthly index return Index SD Category 1 QQQ 66 months 0.7561% 6.1599% 0.7705% 6.1401% US 2 DIA 66 months 0.5148% 4.8122% 0.5279% 4.8237% US 3 IYY 66 months 0.4949% 5.4340% 0.5107% 5.4480% US 4 IWV 66 months 0.4949% 5.4871% 0.5059% 5.5027% US 5 IWB 66 months 0.4810% 5.4020% 0.4896% 5.4150% US 6 IVV 66 months 0.4590% 5.2919% 0.4633% 5.3017% US 7 DEM 66 months 0.4407% 6.7076% 0.5276% 6.7266% Emerging 8 FGD 66 months 0.2923% 7.1894% 0.2776% 7.0462% Total World 9 TOK 66 months 0.2521% 5.9550% 0.2301% 5.9868% Total World 10 DNL 66 months 0.2345% 5.5904% 0.2910% 5.5820% Total World Ex US 11 IDV 66 months 0.1843% 7.6042% 0.1920% 7.7109% Developed 12 PIZ 66 months 0.1534% 7.3502% 0.2334% 7.3026% Developed 13 GMM 66 months 0.1441% 7.8320% 0.1644% 8.0012% Emerging 14 IOO 66 months 0.1387% 5.6871% �0.2219% 6.8277% Total World 15 VWO 66 months 0.0989% 8.1487% 0.1627% 8.0327% Emerging 16 LVL 66 months 0.0824% 7.5689% �0.0142% 7.4818% Total World 17 EEM 66 months 0.0765% 7.9182% 0.1079% 8.0415% Emerging 18 VEU 66 months 0.0473% 6.9711% 0.0683% 6.8047% Total World Ex US 19 VEA 66 months 0.0311% 6.7140% 0.0172% 6.6193% Developed 20 GWL 66 months 0.0252% 6.4864% 0.0837% 6.6414% Total World Ex US 21 CWI 66 months 0.0236% 6.6716% 0.0482% 6.7425% Total World Ex US 22 PXF 66 months 0.0137% 7.4310% 0.1014% 7.3730% Developed 23 PXH 66 months 0.0077% 7.9612% 0.1705% 8.0847% Emerging 24 DGT 66 months �0.0661% 5.5120% 0.2421% 6.0017% Total World 25 DEW 66 months �0.0166% 6.5695% 0.0027% 6.5988% Total World 26 EFA 66 months �0.0112% 6.5123% �0.0064% 6.5437% Developed 27 PIE 66 months �0.0389% 8.4288% 0.3117% 8.1988% Emerging 28 ADRD 66 months �0.0451% 6.7941% �0.0625% 6.8151% Developed 29 DWM 66 months �0.0617% 6.4940% �0.0130% 6.5679% Developed 30 DTH 66 months �0.0930% 6.8944% �0.0602% 6.9748% Developed 31 DOL 66 months �0.0943% 6.4759% �0.0814% 6.5380% Developed 32 BIK 66 months �0.0999% 8.5527% �0.0542% 8.5939% Emerging 33 DOO 66 months �0.1272% 6.9165% �0.1335% 6.9606% Developed 34 ADRE 66 months �0.1960% 7.7663% �0.1874% 7.7789% Emerging 35 EEB 66 months �0.2289% 8.7484% �0.1896% 8.7991% Emerging 36 BKF 66 months �0.2402% 8.9804% �0.2082% 8.9795% Emerging ETFs are ranked in descending order based on average monthly returns. 254 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 The Sortino ratio differentiates between good and bad volatility in the Sharpe ratio. The differentiation of upward and downward volatility allows the calculation of the risk-adjusted return to provide a performance measure of an ETF without penalizing it for positive returns. A large Sortino ratio indicates low risk of large losses occurring. Similar to the Sharpe ratio, the higher the Sortino ratio, the better is the performance of an ETF. The third measure we use is the Treynor ratio that is expressed as: Treynor � �RETF � Rf�/�ETF (3) where RETF and Rf are defined as above, �ETF is the systematic risk of the ETF. Similarly to Sharpe and Sortino ratios, the higher the Treynor ratio, the better is the performance of the ETF. The results shown in Table 3a indicate again that U.S. ETFs have the highest Sharpe and Sortino ratios (the first six ranks are occupied by U.S. ETFs with QQQ and DIA having the best performance out of all ETFs), whereas Emerging (ADRE, BKE, and EEB) and Developed (DTH, DOO, and DOL) market ETFs have the lowest Sharpe and Sortino ratios. The Treynor ratio again indicates that U.S. ETFS had the best performance for the entire period as shown in Table 3b. As a robustness test, Sharpe, Sortino, and Treynor ratios were computed using the three month Interbank Libor rate instead of three month T-Bill rate as many of these ETFs buy international stocks. The results as shown in Tables 3a and b did not change (U.S. ETFs again occupied the first six ranks). 4. Cumulative returns and cumulative wealth index Cumulative returns of ETFs for the entire period have been computed. Following Woolridge (2004) we also compute the cumulative wealth index (CWI) for each ETF. The CWI measures the outcome of investing $1,000 in each ETF at the beginning of January 2008, presuming reinvestment of dividends. ETFs are ranked in descending order based on cumulative returns and CWI. U.S. ETFs occupy the top six ranks as shown in Table 4. For example, $1,000 invested in QQQ and DIA in January 2008 would have returned $1,451.14 and $1,300.23 by June 2013, respectively. 5. Tracking error It is important to consider tracking error when analyzing ETF performance. The greater the tracking error the less closely the ETF follows the benchmark. If an investor is considering using an ETF for international diversification and the ETF has a high tracking 255S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 error, the benefit of diversification relative to the benchmark will be lessened. Tracking error is the difference in the performance of ETF and its benchmark. Ideally, the tracking error of an ETF should be zero. However, this is not possible because of expenses; dividends payments arising from stocks of an index; as well as size and timing of index rebalancing (Frino and Gallagher, 2001). Following Frino and Gallagher (2001), tracking error is measured using three different methods. TE1–The first method of estimating tracking error is computed as the average absolute differences between the return on the ETF and its benchmark index. The equation is given as: TE1 � � t � 1 N Abs �Return on ETF � Return on the Benchmark Index�/n (4) TE2–The second method to estimate tracking error is to use standard errors from the regression analysis using monthly returns on each ETF and its benchmark index. The model is: Table 3a: Sharpe and Sortino ratios calculated using three month T-Bill and three month LIBOR rates Rank (T-Bill) ETF Sharpe ratio Sortino ratio Category Rank (libor) ETF Sharpe ratio Sortino ratio Category 1 QQQ 0.1176 0.1684 U.S. 1 QQQ 0.1163 0.1663 US 2 DIA 0.1004 0.1403 U.S. 2 DIA 0.0989 0.1380 US 3 IYY 0.0853 0.1175 U.S. 3 IYY 0.0841 0.1155 US 4 IWV 0.0845 0.1161 U.S. 4 IWV 0.0833 0.1142 US 5 IWB 0.0832 0.1144 U.S. 5 IWB 0.0820 0.1125 US 6 IVV 0.0808 0.1111 U.S. 6 IVV 0.0796 0.1092 US 7 TOK 0.0372 0.0505 Total World 7 DEM 0.0603 0.0860 Emerging 8 DNL 0.0365 0.0516 Total World Ex U.S. 8 DNL 0.0356 0.0502 Total World Ex US 9 FGD 0.0364 0.0514 Total World 9 FGD 0.0356 0.0501 Total World 10 IDV 0.0202 0.0278 Developed 10 TOK 0.0363 0.0492 Total World 11 IOO 0.0191 0.0261 Total World 11 IDV 0.0196 0.0268 Developed 12 PIZ 0.0168 0.0224 Developed 12 IOO 0.0182 0.0248 Total World 13 GMM 0.0145 0.0201 Emerging 13 PIZ 0.0161 0.0214 Developed 14 VWO 0.0084 0.0118 Emerging 14 GMM 0.0139 0.0192 Emerging 15 LVL 0.0069 0.0094 Total World 15 VWO 0.0078 0.0109 Emerging 16 DEM 0.0611 0.0874 Emerging 16 LVL 0.0061 0.0083 Total World 17 EEM 0.0059 0.0082 Emerging 17 EEM 0.0053 0.0073 Emerging 18 VEU 0.0025 0.0034 Total World Ex U.S. 18 VEU 0.0018 0.0024 Total World Ex US 19 VEA 0.0002 0.0002 Developed 19 VEA �0.0005 �0.0007 Developed 20 GWL �0.0007 �0.0010 Total World Ex U.S. 20 GWL �0.0015 �0.0020 Total World Ex US 21 CWI �0.0010 �0.0013 Total World Ex U.S. 21 CWI �0.0017 �0.0023 Total World Ex US 22 PXF �0.0022 �0.0031 Developed 22 PXF �0.0028 �0.0040 Developed 23 PXH �0.0028 �0.0040 Emerging 23 PXH �0.0034 �0.0048 Emerging 24 EFA �0.0063 �0.0084 Developed 24 EFA �0.0070 �0.0093 Developed 25 DEW �0.0071 �0.0093 Total World 25 DEW �0.0078 �0.0102 Total World 26 PIE �0.0082 �0.0103 Emerging 26 PIE �0.0087 �0.0110 Emerging 27 ADRD �0.0110 �0.0151 Developed 27 ADRD �0.0117 �0.0159 Developed 28 DWM �0.0141 �0.0187 Developed 28 DWM �0.0148 �0.0195 Developed 29 BIK �0.0152 �0.0207 Emerging 29 BIK �0.0157 �0.0213 Emerging 30 DGT �0.0174 �0.0231 Total World 30 DGT �0.0182 �0.0240 Total World 31 DTH �0.0178 �0.0236 Developed 31 DTH �0.0184 �0.0244 Developed 32 DOL �0.0191 �0.0253 Developed 32 DOL �0.0198 �0.0261 Developed 33 DOO �0.0227 �0.0298 Developed 33 DOO �0.0233 �0.0305 Developed 34 ADRE �0.0291 �0.0396 Emerging 34 ADRE �0.0296 �0.0403 Emerging 35 EEB �0.0295 �0.0408 Emerging 35 EEB �0.0300 �0.0414 Emerging 36 BKF �0.0300 �0.0410 Emerging 36 BKF �0.0305 �0.0415 Emerging ETFs are ranked in descending order based on Sharpe and Sortino ratios. 256 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 ETFi.t � �i � �i*BRi.t � �i,t (5) where ETF i.t and BR i.t are monthly ETF and benchmark returns, respectively. In this model the standard errors from regressions proxy tracking errors. If the ETF perfectly follows its benchmark, then the standard deviation of residuals from the regression must be zero. TE3–The third method estimates tracking error as the standard deviation of the return difference between an ETF and its benchmark index. This method is the one that is most Table 3b: Treynor ratios calculated using three month T-Bill and three month LIBOR rates Rank (T-Bill) ETF Treynor ratio Category Rank (libor) ETF Treynor ratio Category 1 QQQ 0.7246 US 1 QQQ 0.7199 US 2 DIA 0.4859 US 2 DIA 0.4812 US 3 IWV 0.4662 US 3 IWV 0.4615 US 4 IYY 0.4661 US 4 IYY 0.4614 US 5 IWB 0.4521 US 5 IWB 0.4474 US 6 IVV 0.4299 US 6 IVV 0.4252 US 7 DEM 0.4120 Emerging 7 DEM 0.4073 Emerging 8 FGD 0.2581 Total World 8 FGD 0.2535 Total World 9 TOK 0.2238 Total World 9 TOK 0.1616 Total World 10 DNL 0.2048 Total World Ex US 10 IDV 0.1520 Developed 11 IDV 0.1568 Developed 11 DNL 0.1430 Total World Ex US 12 PIZ 0.1228 Developed 12 PIZ 0.1181 Developed 13 GMM 0.1168 Emerging 13 GMM 0.1120 Emerging 14 IOO 0.1096 Total World 14 IOO 0.1048 Total World 15 VWO 0.0684 Emerging 15 VWO 0.0638 Emerging 16 LVL 0.0532 Total World 16 EEM 0.0426 Emerging 17 EEM 0.0474 Emerging 17 VEU 0.0124 Total World Ex US 18 VEU 0.0170 Total World Ex US 18 VEA �0.0036 Developed 19 VEA 0.0010 Developed 19 CWI �0.0113 Developed 20 GWL �0.0050 Total World Ex US 20 LVL �0.0091 Total World 21 CWI �0.0066 Total World Ex US 21 GWL �0.0098 Total World Ex US 22 PXF �0.0162 Developed 22 PXF �0.0209 Developed 23 PXH �0.0230 Emerging 23 PXH �0.0278 Emerging 24 EFA �0.0415 Developed 24 EFA �0.0462 Developed 25 DEW �0.0452 Total World 25 DEW �0.0497 Total World 26 PIE �0.0675 Emerging 26 PIE �0.0721 Emerging 27 ADRD �0.0753 Developed 27 ADRD �0.0800 Developed 28 DWM �0.0928 Developed 28 DWM �0.0975 Developed 29 DGT �0.1080 Total World 29 DGT �0.1133 Total World 30 DTH �0.1247 Developed 30 DTH �0.1294 Developed 31 BIK �0.1305 Emerging 31 DOL �0.1302 Developed 32 DOL �0.1255 Developed 32 BIK �0.1352 Emerging 33 DOO �0.1578 Developed 33 DOO �0.2201 Developed 34 ADRE �0.2264 Emerging 34 ADRE �0.2311 Emerging 35 EEB �0.2599 Emerging 35 BKF �0.2755 Emerging 36 BKF �0.2708 Emerging 36 EEB �0.3221 Emerging ETFs are ranked in descending order based on Treynor ratios. 257S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 commonly used and, according to Pope and Yadav (1994), produces same estimates as Method 1 if � in Method 2 is equal to 1. TE3 � �1 n � 1 � t�1 N �Ri,t � Rj,t� 2 (6) where Table 4 Shows cumulative returns and Cumulative Wealth Index (CWI) over the entire period for each ETF where the CWI measures the outcome of investing $1000 in each ETF at the beginning of January 2008, presuming reinvestment of dividends Rank ETF Cumulative returns (January 2008 through June 2013) Cumulative Wealth in June 2013 ($1000 invested in January 2008) Category 1 QQQ 45.11% $1,451.14 U.S. 2 DIA 30.02% $1,300.23 U.S. 3 IYY 25.60% $1,256.05 U.S. 4 IWV 25.36% $1,253.57 U.S. 5 IWB 24.60% $1,246.02 U.S. 6 IVV 23.31% $1,233.13 U.S. 7 DEM 15.19% $1,151.89 Emerging 8 DNL 5.32% $1,053.16 Total World Ex U.S. 9 TOK 4.92% $1,049.23 Total World 10 FGD 2.04% $1,020.42 Total World 11 IOO �1.52% $ 984.76 Total World 12 IDV �7.12% $ 928.77 Developed 13 PIZ �7.73% $ 922.73 Developed 14 GMM �10.53% $ 894.72 Emerging 15 GWL �11.67% $ 883.34 Total World Ex U.S. 16 VEA �12.19% $ 878.09 Developed 17 VEU �12.30% $ 876.98 Total World Ex U.S. 18 CWI �12.45% $ 875.49 Total World Ex U.S. 19 LVL �13.24% $ 867.60 Total World 20 DGT �13.46% $ 865.40 Total World 21 EFA �13.88% $ 861.15 Developed 22 DEW �14.50% $ 854.97 Total World 23 VWO �14.59% $ 854.13 Emerging 24 EEM �14.70% $ 853.04 Emerging 25 PXF �15.87% $ 841.34 Developed 26 DWM �16.64% $ 833.63 Developed 27 ADRD �16.75% $ 832.47 Developed 28 DOL �18.34% $ 816.65 Developed 29 PXH �18.61% $ 813.95 Emerging 30 DTH �19.86% $ 801.37 Developed 31 DOO �21.83% $ 781.67 Developed 32 PIE �24.14% $ 758.59 Emerging 33 BIK �26.84% $ 731.56 Emerging 34 ADRE �28.36% $ 716.44 Emerging 35 EEB �33.53% $ 664.67 Emerging 36 BKF �34.97% $ 650.33 Emerging ETFs are ranked in descending order based on cumulative returns and CWI. 258 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 R i, t and R j, t are ETF and benchmark returns during month t. The total tracking error is computed as the average of TE1, TE2, and TE3. The results shown in Tables 5a and b indicate that U.S. ETFs (with the exception of QQQ) have the lowest TE among all ETFs. The results show that International ETFs have high tracking errors. This result is not surprising as they face restrictions like time delays or exposure to unsafe market environments, which negatively affects their replication ability (Rompotis, 2009). In addition, international ETFs also have higher expenses compared with U.S. ETFs, which also increases their tracking error as there is a positive relationship between expenses and tracking error (Rompotis, 2009). Table 5a: Shows TE1, TE2, and TE3 by ETF category and the average of TE1, TE2, and TE3 ETF TE1 TE2 TE3 Average TE Category IVV 0.004% 0.016% 0.012% 0.011% US IWB 0.009% 0.023% 0.015% 0.016% US IWV 0.011% 0.023% 0.018% 0.017% US DIA 0.013% 0.031% 0.015% 0.020% US QQQ 0.014% 0.544% 0.296% 0.285% US IYY 0.016% 0.020% 0.019% 0.018% US IOO 0.361% 1.906% 3.350% 1.872% Total World LVL 0.097% 2.118% 1.437% 1.217% Total World TOK 0.022% 0.126% 0.061% 0.070% Total World FGD 0.015% 1.407% 0.678% 0.700% Total World DEW 0.019% 0.378% 0.199% 0.199% Total World DGT 0.308% 4.015% 1.531% 1.952% Total World VEU 0.021% 1.881% 0.891% 0.931% Total World Ex US CWI 0.025% 0.880% 0.265% 0.390% Total World Ex US DNL 0.057% 0.393% 0.186% 0.212% Total World Ex US GWL 0.059% 1.055% 0.334% 0.482% Total World Ex US ADRD 0.017% 0.222% 0.088% 0.109% Developed DOL 0.014% 0.465% 0.941% 0.473% Developed DOO 0.006% 0.388% 0.185% 0.193% Developed DTH 0.013% 0.507% 0.215% 0.245% Developed DWM 0.033% 0.592% 0.235% 0.286% Developed EFA 0.049% 0.138% 0.231% 0.139% Developed IDV 0.005% 1.213% 0.067% 0.428% Developed PIZ 0.008% 1.021% 0.577% 0.535% Developed PXF 0.080% 1.274% 0.403% 0.586% Developed VEA 0.088% 2.045% 0.511% 0.881% Developed ADRE 0.009% 0.337% 0.090% 0.145% Emerging DEM 0.087% 0.405% 0.193% 0.228% Emerging GMM 0.020% 1.161% 0.542% 0.575% Emerging EEM 0.031% 1.580% 0.763% 0.791% Emerging BKF 0.032% 1.035% 0.553% 0.540% Emerging EEB 0.039% 0.118% 0.105% 0.087% Emerging BIK 0.046% 0.205% 0.125% 0.125% Emerging VWO 0.064% 1.577% 0.992% 0.878% Emerging PXH 0.163% 1.816% 1.213% 1.064% Emerging PIE 0.351% 2.429% 1.018% 1.266% Emerging 259S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 6. Alpha and beta We also test to see if the selections of securities within the ETF provide additional value to investors by computing Jensen (1968) � as follows: �RETF, t � Rf, t� � �i � �i*�RBenchmark, t � Rf, t� � �i,t (7) where R ETF, t and R Benchmark, t are monthly returns on the ETF and their benchmark index, respectively. R f, t is the three month T-Bill rate. Table 5b: Shows average of TE1, TE2, and TE3 ranked in ascending order (smaller Average TE is better) Rank ETF Average TE Category 1 IVV 0.011% U.S. 2 IWB 0.016% U.S. 3 IWV 0.017% U.S. 4 IYY 0.018% U.S. 5 DIA 0.020% U.S. 6 TOK 0.070% Total World 7 EEB 0.087% Emerging 8 ADRD 0.109% Developed 9 BIK 0.125% Emerging 10 EFA 0.139% Developed 11 ADRE 0.145% Emerging 12 DOO 0.193% Developed 13 DEW 0.199% Total World 14 DNL 0.212% Total World Ex U.S. 15 DEM 0.228% Emerging 16 DTH 0.245% Developed 17 QQQ 0.285% U.S. 18 DWM 0.286% Developed 19 CWI 0.390% Total World Ex U.S. 20 IDV 0.428% Developed 21 DOL 0.473% Developed 22 GWL 0.482% Total World Ex U.S. 23 PIZ 0.535% Developed 24 BKF 0.540% Emerging 25 GMM 0.575% Emerging 26 PXF 0.586% Developed 27 FGD 0.700% Total World 28 EEM 0.791% Emerging 29 VWO 0.878% Emerging 30 VEA 0.881% Developed 31 VEU 0.931% Total World Ex U.S. 32 PXH 1.064% Emerging 33 LVL 1.217% Total World 34 PIE 1.266% Emerging 35 IOO 1.872% Total World 36 DGT 1.952% Total World 260 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Alpha (�i) represents the return the ETF can achieve above the return of the benchmark. However, as ETFs are passively managed and fully invested in the benchmark index, they are not expected to outperform the benchmark index and generate positive �. On the other hand, ETFs are expected to have slightly negative �s, as they are going to underperform the benchmark by the amount of expenses they charge. The beta (�i) coefficient is the measure of systematic risk. If ��1, the ETF moves more aggressively than the benchmark index, whereas if ��1, the ETF manager is much more conservative than the benchmark index. If � � 1, it indicates that ETF is very consistent with the benchmark index movements. Table 6 Shows � and � for each ETFs ETF � t � t R2 Category QQQ �0.0001592 [�0.48] 1.002071‡ [184.67] 0.9977 US DIA �0.0001197‡ [�9.30] 0.9976226‡ [3193.62] 1.0000 US IYY �0.000146‡ [�8.58] 0.9974398‡ [4991.90] 1.0000 US IWV �0.0000974‡ [�8.63] 0.9971857‡ [4314.20] 1.0000 US IWB �0.0000745‡ [�6.93] 0.9976105‡ [4320.09] 1.0000 US IVV �0.0000346‡ [�4.37] 0.9981557‡ [6374.79] 1.0000 US IOO 0.0029214 [0.96] 0.7275802‡ [3.82] 0.7614 Total World LVL 0.0009631 [0.54] 0.9929058‡ [46.71] 0.9641 Total World TOK 0.0002312‡ [3.57] 0.994646‡ [793.91] 0.9999 Total World FGD 0.0001082 [0.13] 1.01569‡ [72.18] 0.9914 Total World DEW �0.0001941 [�0.80] 0.9951681‡ [265.80] 0.9991 Total World DGT �0.0028468 [�1.65] 0.8893292‡ [22.22] 0.9377 Total World VEU �0.0002154 [�0.20] 1.015923‡ [54.09] 0.9840 Total World Ex US CWI �0.0002438 [�0.77] 0.9887347‡ [112.71] 0.9986 Total World Ex US DNL �0.0005678† [�2.44] 1.000929‡ [255.82] 0.9989 Total World Ex US GWL �0.0005721 [�1.57] 0.9756828‡ [92.78] 0.9980 Total World Ex US ADRD 0.0001712 [1.63] 0.9968484‡ [450.81] 0.9998 Developed DOL 0.0001388 [0.12] 1.00407‡ [49.19] 0.9805 Developed DOO 0.0000518 [0.24] 0.9933894‡ [255.90] 0.9993 Developed DTH �0.0001406 [0.24] 0.9900525‡ [212.84] 0.9990 Developed DWM �0.000339 [�1.25] 0.9880473‡ [195.63] 0.9990 Developed EFA �0.0004915* [�1.82] 0.9883476‡ [167.06] 0.9989 Developed IDV �0.0000502 [�0.69] 0.9951643‡ [723.20] 0.9999 Developed PIZ �0.0000505 [�0.07] 0.9835803‡ [81.64] 0.9945 Developed PXF �0.0008098 [�1.62] 1.004927‡ [98.55] 0.9970 Developed VEA �0.0008811 [�1.40] 1.005444‡ [78.96] 0.9953 Developed ADRE �0.00009 [�0.85] 0.9983069‡ [296.77] 0.9999 Emerging DEM �0.0008532‡ [�3.53] 0.9967554‡ [247.09] 0.9992 Emerging GMM �0.0001712 [�0.27] 0.976705‡ [84.49] 0.9958 Emerging EEM �0.0002985 [�0.32] 0.9801021‡ [62.31] 0.9911 Emerging BKF �0.0003249 [�0.47] 0.9981108‡ [96.91] 0.9962 Emerging EEB �0.0004049 [�3.58] 0.9941872 [847.60] 0.9999 Emerging BIK �0.0004605‡ [�3.19] 0.9951161‡ [487.74] 0.9998 Emerging VWO �0.0006467 [�0.53] 1.006852‡ [64.07] 0.9853 Emerging PXH �0.001591 [�1.08] 0.9734497‡ [53.92] 0.9776 Emerging PIE �0.0035638‡ [�2.80] 1.020725‡ [42.07] 0.9859 Emerging T-stats are heteroskedasticity consistent. * Significant at 10%. † Significant at 5%. ‡ Significant at 1%. 261S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Following Rompotis (2009), � is also a measure of ETFs replication strategy. A � of 1 reflects full replication strategy, whereby the ETF invest all its funds in the benchmark index. On the other hand, � that is significantly different than 1 represents a departure from full replication. In such cases, it is assumed that the manager selected stocks anticipating returns better than the benchmark. As expected and shown in Table 6, most of the ETFs have very small or insignificantly negative �s. U.S. ETFs slightly underperform their benchmark (� is significant, but the magni- tude of annualized � is very small with the range being �0.04% to �0.17%), whereas for international ETFs, � is insignificant in most cases. The � is significantly positive only for one Total World ETF (TOK). Even in this case, the magnitude of outperformance is very small (annualized � of 0.2%). � is positive and significant (at 1%) in all cases. In most cases, � is very close to 1 (�� 0.98), which indicates full replication strategy by the ETF manager. This result clearly indicates that most of these ETFs use passive replication strategies and do not manage for positive �. 7. Diversification We now measure the diversification benefits (if any) of international ETFs for U.S. investors by first computing the average correlation between S&P 500 and other major U.S. indices from January 2008 through June 2013. The Spearman rank correlation test shown in Table 7a indicates that all major U.S. indices are highly correlated with the S&P 500 index. The results are also statistically significant at 1% in all cases. For example, the correlation between S&P 500 and Russell 3000 (that measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market) is 0.9981 (statistically significant at 1%). Similarly, the significance between S&P 500 and DJ U.S. Total Market Index is 0.9987 (again significant at 1%). Table 7a: Spearman rank correlation tests and their significance between S&P 500 and other major U.S. indices Major US indicies S&P 500 Russell 1000 DJIA Russell 3000 DJ U.S. Total Market Index Nasdaq 100 S&P 500 1.0000 Russell 1000 0.9991‡ 1.0000 DJIA 0.9804‡ 0.974‡ 1.0000 Russell 3000 0.9981‡ 0.9996‡ 0.9715‡ 1.0000 DJ US Total Market Index 0.9987‡ 0.9999‡ 0.9724‡ 0.9998‡ 1.0000 Nasdaq 100 0.9232‡ 0.9281‡ 0.8691‡ 0.9283‡ 0.9292‡ 1.0000 * Significant at 10%. † Significant at 5%. ‡ Significant at 1%. 262 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Secondly, we measure the correlation between S&P 500 and international ETFs over the same period. Our results shown in Table 7b indicate that all international ETFs are highly correlated with the S&P 500 (statistically significant at 1% in all cases). For example, for World Ex U.S. ETFs, the correlation varies from 0.7824 to 0.9222 (statistically significant at 1% in all cases). Even in the case of Emerging Market ETFs, correlation with S&P 500 varies from 0.7948 to 0.8583 (significant at 1% in all cases).2 Table 7b: Shows Spearman rank correlation between S&P 500 and international ETFs Total World S&P 500 DEW DGT IOO FGD LVL TOK S&P 500 1 DEW 0.9229‡ 1 DGT 0.9569‡ 0.9438‡ 1 IOO 0.9596‡ 0.9678‡ 0.9867‡ 1 FGD 0.9031‡ 0.9636‡ 0.9202‡ 0.933‡ 1 LVL 0.8937‡ 0.9372‡ 0.8889‡ 0.8998‡ 0.9587‡ 1 TOK 0.9779‡ 0.9699‡ 0.9765‡ 0.9851‡ 0.942‡ 0.9212‡ 1 Total World Ex U.S. S&P 500 CWI DNL GWL VEU S&P 500 1 CWI 0.9176‡ 1 DNL 0.7824‡ 0.8782‡ 1 GWL 0.9222‡ 0.9969‡ 0.8827‡ 1 VEU 0.918‡ 0.9927‡ 0.8711‡ 0.9899‡ 1 Developed Markets S&P 500 ADRD DOL DOO DTH DWM EFA IDV PIZ PXF VEA S&P 500 1 ADRD 0.9189‡ 1 DOL 0.9044‡ 0.9865‡ 1 DOO 0.9077‡ 0.9735‡ 0.9866‡ 1 DTH 0.9022‡ 0.9796‡ 0.9947‡ 0.9928‡ 1 DWM 0.9071‡ 0.9853‡ 0.998‡ 0.987‡ 0.9934‡ 1 EFA 0.915‡ 0.986‡ 0.9911‡ 0.9775‡ 0.9823‡ 0.9957‡ 1 IDV 0.8969‡ 0.9528‡ 0.9655‡ 0.9754‡ 0.9729‡ 0.9714‡ 0.9658‡ 1 PIZ 0.8714‡ 0.9256‡ 0.9258‡ 0.9023‡ 0.9031‡ 0.934‡ 0.9446‡ 0.8982‡ 1 PXF 0.9036‡ 0.9788‡ 0.9768‡ 0.9708‡ 0.9754‡ 0.9817‡ 0.9844‡ 0.9651‡ 0.9187‡ 1 VEA 0.9181‡ 0.9915‡ 0.986‡ 0.9772‡ 0.9784‡ 0.9886‡ 0.991‡ 0.9608‡ 0.9338‡ 0.978‡ 1 Emerging Markets S&P 500 ADRE BIK BKF DEM EEB EEM GMM PIE PXH VWO S&P 500 1 ADRE 0.8459‡ 1 BIK 0.7948‡ 0.962‡ 1 BKF 0.8041‡ 0.971‡ 0.9938‡ 1 DEM 0.8394‡ 0.9302‡ 0.9259‡ 0.933‡ 1 EEB 0.8241‡ 0.9911‡ 0.9731‡ 0.9816‡ 0.9257‡ 1 EEM 0.8583‡ 0.9767‡ 0.9639‡ 0.9754‡ 0.9665‡ 0.968‡ 1 GMM 0.8465‡ 0.9735‡ 0.9786‡ 0.984‡ 0.9686‡ 0.9718‡ 0.992‡ 1 PIE 0.8453‡ 0.9321‡ 0.8994‡ 0.9132‡ 0.9204‡ 0.9164‡ 0.9482‡ 0.943‡ 1 PXH 0.8575‡ 0.9752‡ 0.9568‡ 0.9657‡ 0.9578‡ 0.9649‡ 0.9903‡ 0.9826‡ 0.9362‡ 1 VWO 0.8583‡ 0.978‡ 0.9628‡ 0.973‡ 0.9632‡ 0.9691‡ 0.9945‡ 0.9916‡ 0.9501‡ 0.9897‡ 1 * Significant at 10%. † Significant at 5%. ‡ Significant at 1%. 263S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 8. Single factor model Following Pennathur, Delcoure, and Anderson (2002), we use the following single factor model to estimate the diversification benefits of international ETFs for U.S. investors. They used this model to estimate the diversification of international closed-end country funds relative to the S&P 500. RETF,t � �i � �i* RS&P 500, t � ei, t (8) where RETF,t and R S&P 500,t are monthly returns for international ETFs and the S&P 500 index, respectively. Table 8 The regression of monthly international ETF returns on monthly S&P 500 returns for the entire period (January 2008 through June 2013) following Pennathur et al. (2002) ETF � t S&P 500 t R2 Category DEW �0.005464* [�1.75] 1.143574‡ [17.90] 0.8517 Total World DGT �0.0052699† [�2.61] 0.9948454‡ [26.41] 0.9157 Total World IOO �0.0033817* [�1.68] 1.029334‡ [26.76] 0.9208 Total World FGD �0.0027508 [�0.73] 1.224583‡ [14.22] 0.8155 Total World LVL �0.005087 [�1.19] 1.275853‡ [10.91] 0.7987 Total World TOK �0.0025677 [�1.63] 1.098427‡ [38.61] 0.9563 Total World CWI �0.0051134 [�1.54] 1.154643‡ [19.73] 0.8419 Total World Ex US DNL �0.0014771 [�0.34] 0.8249698‡ [7.41] 0.6121 Total World Ex US GWL �0.0049754 [�1.59] 1.12824‡ [20.96] 0.8504 Total World Ex US VEU �0.0051187 [�1.49] 1.207013‡ [20.79] 0.8427 Total World Ex US ADRD �0.0059064* [�1.79] 1.177599‡ [21.48] 0.8444 Developed DOL �0.0060609* [�1.77] 1.10468‡ [20.49] 0.8179 Developed DOO �0.0067583* [�1.85] 1.18415‡ [16.39] 0.8239 Developed DTH �0.0063656* [�1.73] 1.173226‡ [18.85] 0.8140 Developed DWM �0.0057644* [�1.70] 1.111143‡ [20.12] 0.8229 Developed EFA �0.0053195 [�1.62] 1.123948‡ [20.20] 0.8373 Developed IDV �.0041174 [�0.97] 1.286446‡ [12.96] 0.8045 Developed PIZ �0.0040626 [�0.87] 1.208036‡ [12.95] 0.7593 Developed PXF �0.0057306 [�1.49] 1.266507‡ [17.28] 0.8165 Developed VEA �0.0050758 [�1.54] 1.162653‡ [22.44] 0.8429 Developed ADRE �0.0077003 [�1.46] 1.239071‡ [11.31] 0.7155 Emerging BIK �0.0069385 [�1.05] 1.282092‡ [9.21] 0.6316 Emerging BKF �0.0087125 [�1.29] 1.362032‡ [10.18] 0.6466 Emerging DEM �0.0005135 [�0.11] 1.061991‡ [12.12] 0.7046 Emerging EEB �0.0085882 [�1.37] 1.359787‡ [10.79] 0.6791 Emerging EEM �0.0051737 [�1.01] 1.28185‡ [13.43] 0.7366 Emerging GMM �0.0043526 [�0.82] 1.250542‡ [10.95] 0.7166 Emerging PIE �0.0066144 [�1.12] 1.343829‡ [9.85] 0.7145 Emerging PXH �0.005889 [�1.16] 1.287644‡ [13.18] 0.7353 Emerging VWO �0.0051226 [�0.97] 1.31925‡ [12.52] 0.7367 Emerging T-stats are heteroskedasticity consistent. * Significant at 10%. † Significant at 5%. ‡ Significant at 1%. 264 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Here we regress monthly international ETF returns on monthly S&P 500 returns. A � close to or higher than 1 would indicate that international ETF return mimics the S&P 500, whereas R2 provides information on tracking effectiveness of the ETFs. Our results shown in Table 8 indicate that the coefficient for the S&P 500 is close to or greater than 1 and statistically significant at 1% in all cases. For example, for the four Total World Ex U.S. ETFs, the coefficient for the S&P 500 varies from 0.83 to 1.21 (statistically significant at 1% in all cases). The R2 is also high and varies from 0.6121 to 0.8504. Similarly, for Total World, Developed, and Emerging Market ETFs, coefficient for the S&P 500 is very close to or much greater than 1 in all cases (results are statistically significant at 1% in all cases). R2 is also high in all cases that indicate that international ETFs closely track the S&P 500. The results are similar for other major U.S. indices (not reported but available upon request). Pennathur et al. (2002) found similar results for international closed end country funds. These results indicate that international ETFs closely follow U.S. indices and there are not many diversification benefits from investing in international ETFs for U.S. investors. 9. Principal component analysis We also use Principal Component Analysis (PCA) analysis to compute diversification benefits of international ETFs for U.S. investors. This method groups international ETFs and S&P 500 returns into principal components in terms of similarities in their return movement patterns. If international ETFs and the S&P 500 have high factor loadings in the same principal component, they are highly correlated, and, hence, there is limited diversification benefit international ETFs for U.S. investors. If the S&P 500 has low factor loadings in the same principal loadings (than international ETFs), then there are significant benefits of diversification. Therefore, investors should invest in ETFs that have high factor loadings in different principal components than S&P 500 to get benefits of diversification. In this method, the correlation matrix of monthly returns for international ETFs and S&P 500 is used as the input for the entire period. The Eigen value reported in Table 9 for only the first common factor is greater than 1 and explains more than 90% of the variation in all cases. Hence, only the first common factor is important and is reported for this analysis (Eigen value 2 and its variation are also shown for comparison purposes. Detailed results are available upon request.) Results again indicate that international ETFs are highly correlated to U.S. markets as the factor loadings of international ETFs for component 1 are very close to factor loadings of the S&P 500 for component 1. These results hold for other U.S. indices too. 10. Risk adjusted performance and CWI of equally weighted portfolios We form equally weighted portfolios of U.S., Total World, Total World Ex U.S., Developed, and Emerging market ETFs and compute their risk adjusted performance (Sharpe and Sortino ratios) for the entire period. The results from Table 10a indicate that U.S. ETF portfolio has the best performance (both absolute and risk-adjusted performance) for the entire period. Similarly, U.S. ETFs portfolios have the highest cumulative returns and CWI. 265S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Table 10b shows the Spearman-rank correlation test between S&P 500 and equally weighted ETF portfolios. Results again indicate that all international ETF portfolios are highly correlated with S&P 500 (all the results are statistically significant at 1%). Results Table 9 Eigen values for Component 1 and 2 and the principal factor loadings for component1 for international ETFs and S&P 500 Total World Entire period Eigen value Proportion Cumulative Component 1 6.66062 0.9515 0.9515 Component 2 0.187901 0.0268 0.9784 Variable (entire period) Factor loading Component 1 TOK 0.3844 IOO 0.3821 DEW 0.3805 DGT 0.3788 FGD 0.3756 S&P 500 0.3754 LVL 0.3687 Total World Ex U.S. Entire period Eigen value Proportion Cumulative Component 1 4.66593 0.9332 0.9332 Component 2 0.225322 0.0451 0.9783 Variable (entire period) Factor loading Component 1 GWL 0.4598 CWI 0.4592 VEU 0.4579 S&P 500 0.4354 DNL 0.4224 Developed Markets Entire period Eigen value Proportion Cumulative Component 1 10.5741 0.9613 0.9613 Component 2 0.153925 0.014 0.9753 Variable (entire period) Factor loading Component 1 DWM 0.3064 EFA 0.3061 DOL 0.3057 VEA 0.3053 DTH 0.3045 ADRD 0.3046 DOO 0.3038 PXF 0.3033 IDV 0.3002 PIZ 0.2896 S&P 500 0.2863 (continued on next page) 266 S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 (not reported) are similar when we regress equally weighted portfolio returns on S&P 500 as well as the PCA. These results again indicate that these international ETFs are highly dependent on U.S. indices and there were limited benefits of diversification in these ETFs for U.S. investors during the period of our analysis. 11. Conclusions Our results indicate that U.S. ETFs outperform international ETFs during the period beginning January 2008 through June 2013. U.S. ETFs have higher average returns and lower risk (standard deviation of returns) than international ETFs. Risk adjusted measures of performances (Sharpe, Sortino, and Treynor ratios) also confirm that U.S. ETFs outperform international ETFs. Table 9 (continued) Emerging Markets Entire period Eigen value Proportion Cumulative Component 1 10.4036 0.9458 0.9458 Component 2 0.284646 0.0259 0.9717 Variable (entire period) Factor loading Component 1 EEM 0.3084 GMM 0.3084 VWO 0.3083 PXH 0.3067 ADRE 0.3056 BKF 0.3045 EEB 0.3042 BIK 0.3021 DEM 0.2996 PIE 0.2958 S&P 500 0.2711 Because the Eigen value only for Component 1 is greater than 1, only factor loading for Component 1 are reported. If factor loadings for Component 1 are close to each other, there are limited benefits of diversification. Table 10a: Shows equally weighted portfolios of U.S., Total World, Total World Ex U.S., Developed, and Emerging market ETFs and their risk adjusted performance (Sharpe, Sortino and Treynor ratios), cumulative returns, and cumulative wealth index (CWI) the portfolios Rank Equally weighted portfolio Time period (January 2008 through June 2013) Average monthly return SD of monthly returns Sharpe ratio Sortino ratio Cumulative Returns Cumulative wealth (initial wealth - $1,000 in January 2008) Number of ETFs 1 U.S. 66 months 0.53% 5.35% 0.0939 0.1307 29.26% $1,292.63 6 2 Total World 66 months 0.11% 6.26% 0.0134 0.0182 �5.46% $ 945.40 6 3 Total World Ex U.S. 66 months 0.08% 6.28% 0.0084 0.0115 �7.36% $ 926.37 4 4 Emerging 66 months �0.0036% 7.96% �0.0042 �0.0057 �19.45% $ 805.45 10 5 Developed 66 months �0.005% 6.82% �0.0051 �0.0068 �14.68% $ 853.19 10 267S. Kanuri, R.W. McLeod / Financial Services Review 24 (2015) 249–270 Jensen’s � indicates that most of these ETFs have negative or insignificant �s. These results are expected as these ETFs are passively managed and closely follow their bench- mark, but underperform the benchmark by the amount of expenses they charge. Alpha is positive in only one instance (TOK), however, even in cases where ETFs have significantly positive or negative �s, the amount of out or under performance is very small. � is positive and significant (at 1%) in all cases. In most cases, � is very close to 1 (�� 0.98), which indicates full replication strategy by the ETF manager. This clearly indicates passive replication instead of active management for positive �. Diversification benefits of international ETFs-Results indicate that international ETFs are highly correlated with major U.S. indices during the entire period. Spearman rank correlation tests find that all international ETFs are highly correlated with the S&P 500 during the entire period (results are significant at 1%). Results are similar for other major U.S. indices (DJIA, Nasdaq 100, Russell 1000, Russell 3000, and Dow Jones U.S. Total Return index). We find similar results with PCA. The second model we use (following Pennathur, Delcoure, and Anderson, 2002), where we regress monthly returns of international ETFs against S&P 500 returns indicates that all international ETFs are highly dependent on S&P 500. These results are statistically signif- icant at 1% or 5% in all cases. We find similar results between international ETFs and other major U.S. indices. In conclusion our results indicate that during the financial crisis and the ensuing recovery, U.S. ETFs provided superior performance relative to international ETFs on both an absolute and risk-adjusted basis. In addition, during this period, international ETFs exhibit high correlation with U.S. markets that eliminates most, if not all, of their global diversification benefits. As such, individual investors should be aware that global diversification using ETFs may not provide them with any benefits especially during times of extreme financial distress. Table 10b: Shows the Spreaman-rank correlation between equally weighted ETF portfolios and S&P 500 for the period of our study (January 2008-June 2013) S&P 500 U.S. ETF portfolio Total World ETF portfolio Total World Ex U.S. ETF portfolio Emerging ETF portfolio Developed ETF portfolio S&P 500 1 U.S. ETF portfolio 0.9968‡ 1 Total World ETF portfolio 0.9408‡ 0.9376‡ 1 Total World Ex U.S. ETF portfolio 0.8770‡ 0.8791‡ 0.9651‡ 1 Developed ETF portfolio 0.8896‡ 0.8876‡ 0.9790‡ 0.9794‡ 1 Emerging ETF portfolio 0.8091‡ 0.8183‡ 0.8868‡ 0.9519‡ 0.8999‡ 1 * Significant at 10%. † Significant at 5%. ‡ Significant at 1%. 268 S. Kanuri, R.W. 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