Momentum Strategies with Stock Index Exchange-Traded Funds

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1 Momentum Strategies with Stock Index Exchange-Traded Funds Yiuman Tse Department of Finance University of Missouri St. Louis St. Louis, MO Tel: Previously reported momentum profits may not be available to individual investors who have more trading constraints. Therefore, I examine the profitability of momentum strategies with international ishares and US sector exchange-traded funds (ETFs) traded on the NYSE. The index ETFs provide individual investors easy access to international stock markets and US sectors for asset allocations. Using both cross-sectional and time series momentum strategies, in contrast to prior research, I find that momentum profits are insignificant for the late 1990s 2013 period. The results indicate that the US and international stock markets are more efficient in recent years. The results are consistent with Chordia, Roll, and Subrahmanyam (2011), who show that the increased trading activity of the US market has been accompanied by improved market efficiency because of lower trading costs and increased sensitivity of arbitrage activity to past returns. January 2014 JEL classifications: G11; G14; 15; Keywords: Momentum; market efficiency; index exchange-traded funds; asset allocation 1

2 Momentum Strategies with Stock Index Exchange-Traded Funds The profitability of momentum strategies (buying past winners and selling past losers) over an intermediate holding period is one of the most resilient market anomalies. Few financial issues have drawn more controversies and interests than these strategies from both academia and the financial industry. Using data from 1965 to 1989, Jegadeesh and Titman (1993) show that US stocks with the best performance over the past 3 12 months continue to outperform the worstperformance stocks over the next few months. Evidence from international stocks and other asset classes is also extensively provided by many papers. Financial theories have not yet conclusively explained why momentum exists, although initial underreaction and delayed overreaction of stock prices are the most cited explanations. Some papers apply momentum strategies across asset classes. Of particular interest, Chan, Hameed, and Tong (2000) report significant momentum profits by reallocating funds across different international stock indices. Doeswijk and van Vliet (2011) find significant momentum profits using global sector indices. In this study, I use ishares exchange-traded funds (ETFs) traded on the New York Stock Exchange (NYSE) of 20 countries of the Asia Pacific region, Europe, and the Americas as well as the US index ETF to examine the momentum profitability. International ishares of Barclay Global Investors have grown in popularity because they allow US investors to diversify their holdings in international markets. The sample countries are similar to those in Chan et al. (2000), but they use stock indices. As they point out, individual investors may not be able to implement their strategies because short selling is restricted and stock index futures are not available in 2

3 some countries. Indeed, individual investors may not be able to exploit the momentum profits reported in prior research. Focusing on domestic sector asset allocation, I also examine momentum profits using US sector ETFs. The Select Sector SPDRs Funds partition the S&P 500 into nine sector index ETFs. I use the terms industry and sector interchangeably in this study, although industry generally describes a much more specific group of companies. Moskowitz and Grinblatt (1999) show that once US stock returns are adjusted for industry effects, momentum profits from individual stocks become insignificant. Buying stocks from past winning industries and selling stocks from past losing industries offer higher profits than individual stock momentum strategies. Related to the industry momentum effect, using monthly returns, Hong, Torous, and Valkanov (2007) find that industries lead stock markets in the US and eight developed countries. An important benefit ETFs provide to individual investors is ease of entry. ETFs can be sold short with no constraints and purchased on margin. This is important for investors hoping for quick entry to capitalize on the market s upward and, particularly, downward momentum. ETFs also avoid the rolling over mechanism for futures contracts. 1 Nevertheless, using onemonth to twelve-month returns over the periods of January 1997 to June 2013 for the international ETFs and January 1999 to June 2013 for the US sector ETFs, I find that the Jegadeesh-Titman momentum strategies do not provide any significant profits either econometrically or statistically. Replicating the approach of Chan et al. (2000) with weekly data using ishares ETFs, I still find no significant profits. The momentum strategies of Jegadeesh and Titman (1993) and all the previously mentioned papers depend on the relative performance of securities or asset classes in the cross- 3

4 section. Moskowitz, Ooi, and Pedersen (2012) show that momentum strategies can be used in the time series based on the past performance of the securities only: buying (selling) if the past 1 12 months offer positive (negative) returns. They report significant time series momentum in the equity index and other futures markets. Although I find significant excess returns from time series momentum of ishares for one-month holding returns, momentum returns generally do not outperform the buy-and-hold returns. The short-term strategies also require high turnover and, accordingly, profits may be eliminated by high transaction costs. The overall results indicate that momentum profits are not readily available to individual investors using stock index ETFs. The results are also consistent with the improved market efficiency and higher correlation between markets in recent years. In particular, Chordia, Roll, and Subrahmanyam (2011) show that the increased trading activity of the US market has been accompanied by improved market efficiency with prices conforming more closely to random walks and declining cross-sectional predictability of returns. The article is organized as follows. In the next section, I summarize the literature of cross-sectional and time series momentum strategies. The subsequent sections describe the ETF data and examine the results of momentum profits. The final section concludes the study and discusses the implications for stock market efficiency. Prior Research Jegadeesh and Titman (1993) offer one of the first extensive studies on the momentum effect. A self-financing strategy that buys the winners and sells the losers of US stocks based on the past 3 12 months yields a 1% monthly return over the next few months. Jegadeesh and Titman (2001) 4

5 show that momentum returns persist in the 1990s and they report return reversals over the long run. Several papers report that momentum profits are higher for stocks with certain characteristics, such as small size and low analyst coverage (Hong et al., 2000), high analyst forecast dispersion (Zhang, 2006), and high market-to-book ratios (Daniel and Titman, 1999). However, Bandarchuk and Hilscher (2013) show that such strategies enhance momentum profits simply by trading in stocks with more extreme past returns or higher volatility. More extreme past returns (the only effect) are associated with higher momentum. Avramov et al. (2007) find significant momentum profits in the stocks of low-grade firms, but not in high-grade firms. Jostova et al. (2013) report similar patterns in the US high-grade and low-grade corporate bonds. Conrad and Kaul (1998) suggest that the momentum profits are attributable to rational time variation in expected returns. However, Jegadeesh and Titman (2002) show that the Conrad and Kaul results are driven by small sample bias. The momentum effect can hardly be explained by risk-based models either, including the Fama French three-factor model. In contrast, behavioral models suggest that investors are slow to adjust to new information (underreaction). But once a trend is established, investors will over extrapolate recent results and simply follow the herd (herd behavior and delayed overreaction), resulting in short-term momentum and longterm reversals. 2 Lesmond, Schill, and Zhou (2004) report that the stocks that generate large momentum profits are also the stocks with high trading costs, which will wipe out the profits. Similarly, Korajczyk and Sadka (2004) show that losers are biased to small firms with low liquidity and short sale restrictions. Korajczyk and Sadka also indicate that transaction costs, in the form of 5

6 spreads and price impacts of trades, do not fully explain the return persistence of past winner stocks. Moskowitz and Grinblatt (1999) show that the momentum effects are mainly contributed by industry momentum strategies that buy (sell) stocks from past winning (losing) industries. Buying past winning stocks and selling past losing stocks within the industry offer insignificant profits. O Neal (2000) uses mutual funds to capture industry momentum, but I use sector ETFs. What causes industry momentum? Moskowitz and Grinblatt argue that investors may exhibit overconfidence and self-attribution in certain types of industries over time. Investors may also be more conservative in updating their priors about some industries that they are not familiar with. These behavior biases cause the temporal relation, and therefore momentum effect, between industries. Hong et al. (2007) further suggest that the returns of some industry portfolios that are informative about macroeconomic fundamentals will lead the aggregate market and, accordingly, other industries. Chordia et al. (2011) find that the increased trading activity of the US market has been accompanied by improved market quality (such as lower bid-ask spread) and market efficiency (such as lower return serial correlation). Turnover and information-based trading have been increased more for stocks with higher level of institutional holdings. They point out that increased sensitivity of trading activity to past returns represents arbitrage activity that has reduced the cross-sectional predictability of equity returns. More specifically, the momentum effect (proxies by the cumulative return on the stock over the past six months) significantly influences the current monthly return during the period of , but the effect is 6

7 insignificant during the more recent period of This declining momentum effect reported in Chordia et al. is consistent with the current results also based on recent years. Many papers have examined and confirmed the momentum effect on international stocks, commodities, currencies, and bonds. 3 Some papers, such as Chan, Hameed, and Tong (2000), Blitz and Vliet (2008), and Doeswiijk and van Vliet (2011), apply momentum strategies across asset classes. Using the equity indexes of 23 countries (9 from the Asia Pacific region, 11 from Europe, 2 from North America, and 1 from Africa), Chan et al. report significant momentum profits by the zero-cost strategy of buying the past winning index and selling the losing index for the period Doeswiijk and van Vliet find significant momentum profits with 10 global sector indexes for the period Data It is worth noting that significant results of all previous studies may not be readily available to individual investors because of high transaction costs, product accessibility, short sale constraints, and other market imperfections. The Morgan Stanley Capital International (MSCI) indexes used in Chan et al. (2000) for international asset allocation and Doeswijk and van Vliet (2011) for global sector allocation are not tradable and are costly for investors to replicate all the index constituents. In this paper, I examine the momentum effect with the international ishares exchange-traded funds (ETFs) and the US sector index ETFs. The international and US sector ETFs facilitate investors for international asset allocation and domestic sector allocations, respectively, to fit their particular investment needs or goals. 7

8 In particular, international ETFs of Barclay Global Investors provide US investors with an easy way to diversify their holdings in foreign stocks. US sector ETFs of State Street Bank and Trust Company allow investors to invest the S&P 500 index with the ability to over-weight or under-weight particular sectors. All these international and US sector ETFs are traded on the New York Stock Exchange (NYSE) in US dollars with the same trading hours and mechanisms. The ishares sample countries are similar to those in Chan et al. (2000), but they use foreign local stock indices (with different market structures) and assume that there is no restriction for investors in holding long or short positions in individual markets worldwide. However, indexes are unmanaged and investors cannot invest directly in the index. ETFs provide individual investors with easy access to international markets for both short selling and buying on margin. Investors may favor ETFs for quick entry to capitalize on the market momentum. Table I lists the 21 international ETFs, 20 ishares plus the US ETF represented by SPY: seven from the Asia Pacific region (Australia, Hong Kong, Japan, Malaysia, Singapore, Taiwan, and South Korea), 10 from Europe (Sweden, Germany, Italy, Belgium, Switzerland, the Netherlands, Austria, Spain, France, and the U.K.), and four from the Americas (Canada, Mexico, Brazil, and the U.S.). I collect the daily closing prices and dividends from the NYSE TAQ data. The sample period spans sixteen and a half years from January 1997 through June 2013 (198 months), except for Taiwan, South Korea, and Brazil from January 2001 through 2013 (150 months). These three ishares started trading in May to July 2000 and the other ishares in April 1996, and the US index ETF in January The US index ETF is much more active than the others, distantly followed by Brazil and Japan. 8

9 All these 21 ETFs offered positive returns with a monthly average of 0.48% during the sample period. Mexico has the highest monthly return of 1.05% and a Sharpe ratio of The U.S. has a return of 0.54%, close to the group average. All the Sharpe ratios calculated with the US Treasury bill rates are positive except for Japan. The US market has the lowest volatility with a standard deviation of 4.67%; therefore, the Sharpe ratio of SPY, 0.069, is higher than most countries and the average, The S&P categorizes US stocks into nine sectors as listed in the last panel of Table 1. The Select Sector SPDRs Funds divide the S&P 500 into the following index ETFs: materials (XLB), energy (XLE), financial (XLF), industrial (XLI), technology (XLK), consumer staples (XLP), utilities (XLU), health care (XLV), and consumer discretionary (XLY). The sector ETFs started trading in December 1999 and I use the thirteen-and-a-half-year sample from January 2000 through June I do not examine the ETFs on global sectors because of their short trading history since September Doeswijk and van Vliet (2011) find significant momentum profits using 10 global sector indexes for the period of Although these nine ETFs together comprise all the companies included in the S&P 500, some important sectors may be missed in this study. A notable sector is the real estates, and the first ETF that tracks the U.S. equities in the real estate sector, IYR, is traded in June However, including IYR does not change the results qualitatively. Among the nine US sectors, energy has the highest monthly return (0.815%) and Sharpe ratio (0.098). The next six sectors have similar return characteristics, while the technology and financial sectors give the lowest returns (0.033% and 0.048%), highest volatility (7.61% and 9

10 6.81%), and negative Sharpe ratios. Technology is also the most active sector, closely followed by energy. In the following, I describe the results of international ETFs before the sector ETFs. Relative-Strength Momentum Strategy I examine 25 combinations of five ranking periods denoted by K (one, three, six, nine, and twelve months) and five holding periods denoted by H. For example, with K = 6 and H = 3, I rank the ETFs based on their past six-month returns and form a self-financing investment strategy by buying the winners and selling the losers for the next three months. Previous studies have used two different sets of weights to form the zero-cost portfolios. Jegadeesh and Titman (1993, 2001) consider the top 10% of the winners and the bottom 10% of the losers without using the stocks in the middle range. Some papers also consider the top and bottom 20% or 30%. Another approach employed by Lehmann (1990), Chan et al. (2000), and others holds assets in proportion to their market-adjusted returns. This approach invests in all assets (not just the top and the bottom) and, therefore, incurs more transaction costs. Specifically, for the first approach, based on their past K-month returns, I buy the top three ishares with an equal weight of 1/3 and sell equally in the bottom three ishares, holding this portfolio for H months. 4 For the US sector ETFs, I buy the top ETF and sell the bottom ETF. According to the second approach, the weight of each ETF i in month t equals w it = (r i,t 1 r m,t 1 )/N (1) 10

11 where r i,t 1 is the ETF s past K-month return at time t 1, r m,t 1 is the corresponding return on the equal-weighted average of all the ETFs, and N is the total number of ETFs (21 for ishares and 9 for sector ETFs). I use the same procedures as Jegadeesh and Titman (1993, 2001) and many others to increase the power of test statistics that are based on overlapping holding returns. In any given month t, the strategies hold a series of portfolios that are chosen in the current month as well as the previous H 1 months. These procedures create a time series of monthly returns where each month s return is the average of H strategies with an equal weight of 1/H. I report the results of international ETFs of both weighting approaches in Panel A of Table 2. These two approaches give similar results and show that the momentum profits are insignificant with all the t-statistics being less than 1.40 in magnitude. As in Chan et al. (2000), the t-statistics are calculated using the Newey-West (1987) correction of standard errors for heteroscedasticity and autocorrelation, although the results are almost the same with the conventional t-statistics. All the abnormal monthly returns are also less than 0.5%, except the strategy of K (ranking period) = 6 and H (holding period) = 1 for the first weighting approach, which gives a return of 0.55% with a t-value of Moreover, all the strategies with K = 12 provide insignificantly negative returns. These results are in sharp contrast with the significant results both statistically and economically reported in Jegadeesh and Titman (1993, 2001). The use of the 12-minus-1 month momentum strategy (i.e., a trailing 12-month period, excluding the most recent month, and a one-month holding period) has become popular among practitioners and academics. Novy-Marx (2012) further shows that momentum is primarily driven by the past performance from 12 to seven months prior to portfolio formation and he 11

12 suggests excluding the recent six months for the ranking period. Nevertheless, I find that results using both of these types of momentum strategies are also insignificant. The monthly momentum profits are 0.015% (t = 0.03) and 0.084% (t = 0.19). To compare the current results with those of Chan et al. (2000), who also use international stock indices, on a similar basis, I closely follow their way of forming weekly returns. For the ranking period, weekly returns are taken on Wednesdays. For the holding period, weekly returns are taken on Thursdays. Since the ranking period ends before the holding period starts, the investment strategies do not use price information from the holding period. Similar to Chan et al., I report the results of the strategies of 1, 2, 4, 12, and 26 weeks with the same values of K and H in Panel B. I also include the results of Chan et al. (Table 2 of their paper) with the same weighting approaches of Eq. (1) on the right-hand side of Panel B. Again, all my results are statistically and economically insignificant. For example, the strategy of K = H = 2 weeks yields 0.004% (t = 0.06) per week. In contrast, Chan et al. find significant weekly returns of 0.48% (t = 6.31). As expected, the results of Table 2 are still insignificant if I use a regression analysis with the three well-known factors of Fama French (1993) and the momentum factor of Carhart (1997). The data are obtained from the Kenneth French data library. The results remain the same if I include a dummy variable for the recent financial crisis from October 2007 through March I present the results of US sectors in Panel C of Table 2. Similar to Panels A and B, all the results are statistically insignificant. Only two cases, [K = 12, H = 1] and [K = 6 and H = 6], for the first weighting approach yield a momentum profit greater than 0.5% (0.790% and 0.714%, 12

13 respectively), but the t-statistics are only 1.58 and Neither do the 12-minus-1 month momentum strategy and the Novy-Marx modification provide significant results. All the momentum profits using the proportional weights are insignificant and smaller than 0.3%. In the US stock markets, Bandarachuk and Hilscher (2013) show that firm characteristics (such as size, analyst coverage, and market/book ratios) are irrelevant to momentum profits. The profits are only associated with past returns. Any characteristic that is correlated with volatility will produce more extreme past returns, and, consequently, higher momentum profits. Therefore, I limit the samples to the ETFs with higher volatilities: 12 for the ishares sample and 5 for the US sector sample. The results not reported, nevertheless, are still insignificant. What are the reasons for the failure of the momentum strategies in this study? The stock markets could have been more efficient in recent decades. The sample periods of Jegadeesh and Titman (2001), , and Chan et al. (2000), , for example, are before mine, the late 1990s Doeswijk and van Vliet (2011) do not provide subperiod results of recent years for their 39-year-period ( ) analysis of global sector allocation. Another reason can be the increased correlations of international stock markets. As discussed in Solnik and McLeavey (2008), the diversification benefits of international stocks drop substantially in the recent decade, particularly during market downturns, because of higher correlations between markets. 5 As shown in Sullivan and Xiong (2012), index trading in index mutual funds, particularly ETFs, has caused an increased trading commonality among index constituents through the interactions of market participants, resulting in higher return correlations among stocks. The ishares ETFs are significantly correlated with each other. The average correlation between SPY 13

14 and other ETFs is For highly correlated markets, the relative strength between winners and losers is small. Consider the momentum strategy with ranking and holding periods of six months. The monthly return for the top-performance portfolio is 0.682% (t = 1.31) and that for the bottom-performance portfolio is 0.449% (t = 0.80), while the equal-weighted average index of all the ETFs is 0.517% (t = 1.14). The differential between the best and the worst performers (as well as the average) is small, resulting in minor relative strength profits. Also note that returns of the best and worst performers are not statistically significant. The US sector ETFs are also highly correlated with SPY with an average coefficient of For the momentum strategy of K = H = 6, the monthly return for the top-performance ETF is 0.656% (t = 1.57) and that for the bottom-performance ETF is 0.059% (t = 0.10), while the equal-weighted average index of all the ETFs is 0.366% (t = 1.04). Although the differential between the best and the worst performers is larger than the corresponding results for the ishares ETFs, the returns of the best and worst performers as well as the average index are still insignificant. Time Series Momentum A recent paper by Moskowitz, Ooi, and Pedersen (2012) shows that momentum profits can be derived purely from an asset s own past return, without using the relative performance of other assets. Moskowitz et al. term this strategy time series momentum, distinguishing it from the cross-sectional (or relative-strength) momentum examined in the previous section. They also show that positive auto-covariance in futures returns drives most of the time series and crosssectional momentum effects. This time series momentum strategy is related to other popular 14

15 trend-following technical analyses, such as the moving average and breakout systems. However, time series momentum is likely to be the easiest strategy: buying the asset if its past return is positive and selling if negative (regardless of the ranking of the asset during the past period). It is worth noting that the momentum effect examined in Chordia et al. (2011) is closely related to time series momentum, although the authors describe the results as cross-sectional profitability. Since time series momentum does not depend on the returns of other assets, high crossmarket correlations shown in the ishares and sector ETFs should not weaken the momentum profits. I therefore test whether this strategy works with ETFs. Moskowitz et al. (2012) use 58 futures contracts of different asset classes, including nine international equity index futures for the period of 1975 through Moskowitz et al. aggregate all the scaled futures returns and only report the t-statistics from a pooled panel regression. I do not scale the returns because I want to examine the magnitude of momentum profits and I do not consider other classes (such as commodities and bonds) that have very different volatilities from stock indexes. Moreover, Moskowitz et al. point out that their results are similar if they use OLS without adjusting for each security s volatility. I calculate the excess return of each ETF by subtracting the return from the one-month Treasury bill rate. As noted by Moskowitz et al. (2012), this excess return is virtually the same as the futures return. I report the individual results of each ETF in Table 3. As in the cross-sectional momentum strategy, I examine evaluation and ranking and holding periods of one, three, six, nine, and twelve months. For simplicity, I only report the results of K = H, as well as the case of [K = 3, H = 1]. The last case reported in the last two columns yields the greatest profits both economically and statistically. Although almost all of the individual results are not statistically 15

16 significant, the excess returns are generally positive. Indeed, all the ETFs offer positive returns for the [3, 1] case. The numbers of ETFs that yield positive returns are 20, 19, 19, 12, and 9 for the cases of [1, 1], [3, 3], [6, 6], [9, 9] and [12, 12], respectively. When I stack all the ETFs with a sample size of about 4,000, the pooled excess return of the [3, 1] case is 0.733% (t = 6.10). The cases of [1, 1], [3, 3], and [6, 6] also offer significant returns, 0.547% (t = 4.44), 0.332% (3.32), and 0.371% (3.50), but the returns of longer-term strategies of [9, 9] and [12, 12] are insignificant, 0.088% (0.82) and 0.034% ( 0.34). In contrast, Moskowitz et al. (2012) report significant results for all cases up to 12 months, except [1, 1] and [3, 1], even after being adjusted for the Fama French and other factors. It will be interesting to investigate whether the results remain the same for the recent decade. As noted by Handley (2012), however, Moskowitz et al. do not break down their 25-year-long sample period into subperiods. To further investigate the economic significance of the results, I report the pooled returns in excess of the buy-and-hold returns. The [3, 1] strategy offers higher returns than the buy-andhold by 0.451% (t = 2.27). The next highest return is given by the [1, 1] case with 0.292% (t = 1.47) in excess of buy-and-hold. Both the 12-minus-1 month and the Novy-Marx strategies give insignificant results, 0.093% (0.43) and 0.056% ( 0.26), respectively. I notice that time series momentum profits reported in this study are only obtained from strategies with a holding period of one month. This short-term strategy requires more turnover and, accordingly, transaction costs that may erode profits. Including a one-way transaction cost of 0.5% as in Jegadeesh and Titman (1993), I find that the [3, 1] strategy gives a pooled return of 16

17 0.329% (t = 1.65) more than the buy-and-hold. If I consider a 5% significance level or higher because of a large sample size, the result is not statistically significant. For the US sector ETFs, the excess profits presented in Panel B of Table 3 are generally lower than the results of ishares presented in Panel A. Similar to the ishares, most of the sector ETFs yield positive, albeit insignificant, excess returns. All sector ETFs offer positive returns for the 12-minus-1 month strategy as reported in the last two columns of Panel B. This strategy also gives higher returns than other cases. When I stack all the sectors, the pooled excess return of this strategy is (t = 2.72), but the return is only 0.249% (t = 0.97) more than the buy-andhold. The next highest pooled return in excess of buy-and-hold is given by the [12, 1] case with 0.233% (t = 0.89). The Novy-Marx strategy gives much smaller returns, 0.031% (t = 0.12). For completeness, I also include the 0.5% transaction cost in the 12-minus-1 month strategy, giving a pooled return of 0.186% (t = 0.73) more than the buy-and-hold. In sum, all the profits from cross-sectional momentum are insignificant for both the ishares and sector ETFs. The time series momentum strategy works better than the crosssectional momentum strategy. However, excess profits from time series momentum of the ishares only happen for short-term holding periods with higher transaction costs. The excess profits are even smaller for the sector ETFs. Conclusions Momentum investing remains one of the most puzzling market anomalies since the extensive analysis by Jegadeesh and Titman (1993). Although institutional investors may be able to use momentum strategies to earn excess profits, individual investors have more trading constraints 17

18 and higher transaction costs. I examine the profitability of momentum strategies by using international ishares exchange-traded funds (ETFs) and US sector ETFs traded on the NYSE. The 21 ETFs across the Asia Pacific region, Europe, and America and the nine US sector ETFs provide individual investors with easy access to international and domestic asset allocations. The ETFs allow individual investors quick entry to capitalize on the market momentum in both directions with short selling and buying on margin. I use more recent periods, namely January 1997 June 2013 for ishares and January 1999 June 2013 for US sectors, than prior studies. I do not find significant momentum profits using the relative-strength performance strategies across these ETFs for all the combinations of ranking and holding periods of one to 12 months. Relative-strength momentum focuses on the relative returns of the ETFs in the cross section. Following the time series momentum strategy of Moskowitz et al. (2012), I investigate the momentum profits based only on the past returns of an ETF. The results are more significant than the cross-sectional momentum profits. Nevertheless, the time series momentum profits are only obtained from strategies with short-term one-month holding periods. This short-term strategy incurs higher transaction costs because of higher turnover. My results are different from prior studies using stock indexes in earlier decades, including Chan et al. (2000), Moskowitz et al., and Doeswijk and van Vliet (2011). Although a detailed analysis will shed more light on the impact of transaction costs on momentum strategy, my results show that momentum profits are not readily available to individual investors with the ETFs. The results based on more recent years suggest that the US and international stock markets are efficient. The results are consistent with Chordia et al. (2011), 18

19 who show that the US stock market has been more efficient with higher turnover because of lower trading costs and increased sensitivity of arbitrage activity to past returns. They show that the momentum effect (proxies by the impact of the past six-month returns on the current monthly returns) is significant in the 1990s, but not during the recent period of With improved market efficiency over the years, a simple momentum investing rule should not produce abnormal returns. Future research may examine how the momentum strategy interacts with the value strategy and other trading rules in the ETFs. 6 19

20 Notes 1. Because futures contracts expire in a year and only the nearby contracts are liquid, investors must rollover to the nearby contracts every three months for index futures. 2. See the behavioral models of Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and Hong and Stein (1999). 3. See Rouwenhorst (1998, 1999) for international stocks and Asness, Moskowitz, and Pedersen (2013) and Novy-Marx (2012) for various asset classes such as commodities and currencies. 4. Moskowitz and Grinblatt (1999) choose the top three and the bottom three among 20 industries. I also use the top two and four and the bottom two and four ETFs: the results are similar. These and other robust testing results are not reported but are available upon request. 5. Asness, Israelov, and Liew (2011) find that international diversification can disappoint over the shorter term because international stock markets have a tendency to crash together. However, they show that international diversification is still beneficial over the long term. 6. Asness (1997) published one of the first studies on the interaction of value and momentum strategies in the US stocks. Asness, Moskowitz, and Pedersen (2013) examine the returns to value and momentum strategies jointly across different asset classes. 20

21 References Asness, Cliff S The Interaction of Value and Momentum Strategies. Financial Analyst Journal, vol. 53, no. 2 (April): Asness, Cliff S., Roni Israelov, and John M. Liew International diversification works (eventually). Financial Analysts Journal, vol. 67, no. 3 (June): Asness, Cliff S., Tobias J. Moskowitz, and Lasse Heje Pederson Value and Momentum Everywhere. Journal of Finance, vol. 68, no. 3 (June): Avramov, Doron, Tarun Chordia, Gergana Jostova, and Alexander Philipov. Momentum and Credit Rating. Journal of Finance, vol. 62, no. 5 (October): Bandarchuk, Pavel, and Jens Hilscher Sources of Momentum Profits: Evidence on the Irrelevance of Characteristics. Review of Finance, vol. 17, no. 2 (April): Barberis, Nicholas, Andrei Shleifer, and Robert Vishny A Model of Investor Sentiment. Journal of Financial Economics, vol. 49: Blitz, David C., and Pim van Vliet Global Tactical Cross-Asset Allocation: Applying Value and Momentum across Asset Classes. Journal of Portfolio Management, vol. 34, no. 1: Carhart, Mark M On Persistence in Mutual Fund Performance. Journal of Finance, vol. 51, no. 5: Chan, Kalok, Hameed Allaudeen, and Wilson Tong Profitability of Momentum Strategies in the International Equity Markets. Journal of Financial and Quantitative Analysis, vol. 35, no. 3: Chordia, Tarun, Richard Roll, and Avanidhar Subrahmanyam Recent Trends in Trading Activity and Market Quality. Journal of Financial Economics, vol. 101, no. 3: Conrad, Jennifer, and G. Kaul An Anatomy of Trading Strategies. Review of Financial Studies, vol. 11, no.3: Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam Investor Psychology and Security Market Under- and Overreaction. Journal of Finance, vol. 53: Daniel, Kent, and Sheridan Titman Market Efficiency in an Irrational World. Financial Analysis Journal, 55, Doeswijk, Ronald, and Pim van Vliet Global Tactical Sector Allocation: A Quantitative Approach. Journal of Portfolio Management, vol. 38, no. 1:

22 Fama, Eugene F., and Kenneth R. French Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, vol. 22, no. 1: Handley, Nicholas J Time Series Momentum (Digest Summary). CFA Digest, vol. 42, no.3 (August): Hong, Harrison, and Jeremy C. Stein A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets. Journal of Finance, vol. 54: Hong, Harrison, T. Lim, and Jeremy C. Stein Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. Journal of Finance, 55: Hong, Harrison, Walter Torous, and Rossen Valkanov Do Industries Lead Stock Markets? Journal of Financial Economics, vol. 83: Jegadeesh, Narasimhan, and Sheridan Titman Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, vol. 48, no. 1: Jegadeesh, Narasimhan, and Sheridan Titman Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, vol. 56, no. 2: Jegadeesh, Narashimhan, and Sheridan Titman Cross-Sectional and Time-Series Determinants of Momentum Returns. Review of Financial Studies, vol. 15, no. 1: Jostova, Gergana, Stanislava Nikolova, Alexander Philipov, and Christof W. Stahel Momentum in Corporate Bond Returns. Review of Financial Studies, vol. 26, no. 7 (July): Korajczyk, Robert A., and Ronnie Sadka Are Momentum Profits Robust to Trading Costs? Journal of Finance, vol. 59: Lehmann, Bruce N Fads, Martingales, and Market Efficiency. Quarterly Journal of Economics, vol. 105, no. 1: Lesmond, David A., Michael J. Schill, and Chunsheng Zhou The Illusory Nature of Momentum Profits. Journal of Financial Economics, vol. 71: Moskowitz, Tobias J., and Mark Grinblatt Do Industries Explain Momentum? Journal of Finance, vol. 54: Moskowitz, Tobias J., Yao Hua Ooi, and Lasse Heje Pedersen Time Series Momentum. Journal of Fiancial Economics, vol. 104: Newey, Whitney K, and Kenneth D. West A Simple Positive Definite, Heterosdasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, vol. 55:

23 Novy-Marx, Robert Is Momentum Really Momentum? Journal of Financial Economics, vol. 103: O Neal, Edward S Industry Momentum and Sector Mutual Funds. Financial Analysts Journal, vol. 56, no. 4: Rouwenhorst, K. Geert International Momentum Strategies. Journal of Finance, vol. 53, no. 1: Rouwenhorst, K. Geert, Local Return Factors and Turnover in Emerging Market Stocks. Journal of Finance, vol. 54, no. 4 (August): Solnik, Bruno, and McLeavey, Dennis International Investments. Pearson Addison Wesley, Boston. Sullivan, Rodney N., and James X. Xiong How Index Trading Increases Market Vulnerability. Financial Analysts Journal, vol. 68, no. 2: Zhang, Frank X Information Uncertainty and Stock Returns. Journal of Finance, vol. 61:

24 Table 1. Exchange-Traded Funds: Countries and US Sectors Country/ Industry Symbol Sample period Mean (%) Std dev (%) Sharpe ratio Volume (m $) Asia-Pacific Australia EWA Jan Jun Hong Kong EWH Jan Jun Japan EWJ Jan Jun Malaysia EWM Jan Jun Singapore EWS Jan Jun Taiwan EWT Jan Jun S. Korea EWY Jan Jun Europe Sweden EWD Jan Jun Germany EWG Jan Jun Italy EWI Jan Jun Belgium EWK Jan Jun Switzerland EWL Jan Jun Netherlands EWN Jan Jun Austria EWO Jan Jun Spain EWP Jan Jun France EWQ Jan Jun U.K. EWU Jan Jun America Canada EWC Jan Jun Mexico EWW Jan Jun ,528 Brazil EWZ Jan Jun ,023 U.S. SPY Jan Jun ,227 US Sector Materials XLB Jan Jun ,741 Energy XLE Jan Jun ,696 Financial XLF Jan Jun ,779 Industrial XLI Jan Jun ,940 Technology XLK Jan Jun ,184 Cons. staples XLP Jan Jun ,812 Utilities XLU Jan Jun ,590 Health care XLV Jan Jun ,063 Cons. discretion. XLY Jan Jun ,234 Note: The table reports the summary monthly statistics of 20 ishares ETFs, the S&P500 index ETF, and nine US sector ETFs. All these ETFs are traded on the New York Stock Exchange (NYSE) in US dollars with the same trading hours and mechanisms. 24

25 Table 2. Returns of Relative-Strength Momentum Panel A: Momentum returns per month (%): Country ETFs or ishares Equal weights of top- and bottom-three ETFs Proportional weights. K H = K H = ( 0.16) (1.34) (0.80) (0.70) (0.13) ( 0.14) (1.37) (0.92) (0.88) (0.29) (0.71) (0.50) (0.32) (0.34) (0.14) (1.24) (1.01) (0.83) (0.63) (0.48) (1.09) (0.81) (0.58) (0.46) ( 0.29) (0.88) (0.83) (0.46) (0.49) ( 0.23) (0.44) (0.27) (0.09) ( 0.41) ( 0.90) (0.76) (0.35) (0.15) ( 0.38) ( 0.99) ( 0.44) ( 0.37) ( 0.72) ( 0.89) ( 0.24) ( 0.46) ( 0.20) ( 0.64) ( 1.00) ( 0.37) Panel B: Momentum returns per week (%): Country ETFs or ishares Equal weights of topand bottom-three ETFs Proportional weights K = H = ( 0.69) ( 1.00) Chan et al (2.88) K = H = ( 0.49) Chan et al (6.31) K = H = (0.68) Chan et al (3.35) K = H = (0.03) Chan et al (1.31) K = H = (0.93) Chan et al (2.35) Panel C: Momentum returns per month (%): US sector ETFs Equal weights of top and bottom ETFs Proportional weights. K H = K H = (0.28) (0.27) (0.27) (0.21) (0.33) ( 0.12) ( 0.11) (0.56) (0.50) (0.48) (0.28) (0.13) (0.85) (0.75) (0.07) (0.214) (0.36) (0.65) (0.78) (0.40) (0.61) (0.93) (1.58) (1.01) (0.94) (0.70) (0.83) (1.21) (0.62) (0.71) (0.69) (0.91) (0.36) (0.32) ( 0.11) (0.57) (1.20) (0.55) (0.54) (0.20) (1.53) (0.72) (0.85) (0.31) (0.87) (0.72) (0.57) (0.56) (0.17) (0.77) 25

26 Note: This table reports the returns of relative-strength momentum. The zero-cost portfolios are formed based on K-month past returns and held for H months. Panels A and C uses monthly returns for the country and sector ETFs, respectively. Panel B uses weekly returns as in Chan et al. (2000) for the country ETFs. On the right-hand side of Panel B, results given by Chan et al. are included for comparison. For the first weighting approach presented in the left-hand side of Panel A, based on their past K-month returns, the top three ishares are purchased with an equal weight of 1/3 and the bottom three ishares are sold equally, holding this portfolio for H months. For the US sector ETFs in Panel C, the top ETF is purchased and the bottom ETF is sold. According to the proportional weights reported in the right-hand side of each panel, the weight of each ETF i in month (week) t equals w it = (r i,t 1 r m,t 1 )/N, where r i,t 1 is the ETF s past K-month (week) return at time t 1 and r m,t 1 is the corresponding return on the equal-weighted average of all the ETFs. N = 21 (9) is the total number of country (US sector) ETFs. The t-statistics calculated by the Newey-West heteroscedasticity and autocorrelation consistent errors are reported in parentheses. 26

27 Table 3. Excess Returns of Time-Series Momentum Panel A: Country ETFs or ishares K = H = 1 K = H = 3 K = H = 6 K = H = 9 K = H = 12 K =3, H = 1 EWA (0.29) (0.49) (1.22) ( 0.09) ( 0.18) (0.74) EWH (1.94) (0.10) (1.15) ( 0.77) ( 0.88) (0.79) EWJ (1.63) (1.42) (1.13) (0.56) ( 0.15) (2.94) EWM (0.68) (1.10) (1.07) ( 0.50) ( 1.23) (0.45) EWS (0.84) (0.70) (1.24) ( 0.06) ( 0.47) (1.22) EWT (0.74) ( 0.88) ( 0.99) ( 1.40) ( 1.47) (1.22) EWY (0.21) (0.57) (0.26) ( 0.29) ( 0.38) (1.91) EWD (1.35) (1.30) (1.43) (0.46) ( 0.05) (1.54) EWG (1.19) (0.64) (0.93) ( 0.02) (0.02) (1.37) EWI (0.52) (0.47) (0.60) (0.07) (0.03) (0.94) EWK (1.54) ( 0.06) (1.49) (0.93) (0.61) (0.54) EWL (1.42) (0.13) (0.80) (0.37) (0.66) (0.70) EWN ( 0.02) (1.09) (0.60) (0.33) (0.18) (0.85) EWO (1.28) (0.89) (0.28) (0.69) (1.23) (1.80) EWP (1.46) (0.05) (0.35) ( 0.30) ( 0.43) (0.79) EWQ (1.45) (0.52) (1.12) (0.46) (0.33) (1.49) EWU (1.17) (1.00) (1.32) (1.08) (0.85) (1.35) EWC (1.08) (1.17) (0.32) ( 0.24) ( 0.73) (2.24) EWW (0.57) (0.97) (0.54) (0.54) ( 0.48) (2.19) EWZ (0.97) (2.74) ( 0.32) (0.52) ( 0.02) (3.16) SPY (0.82) (0.57) (1.67) (1.48) (1.14) (1.28) Pooled returns (4.44) (3.32) (3.50) (0.82) ( 0.34) (6.10) Pooled returns in excess of buy-and-hold (1.47) (0.38) (0.20) ( 0.95) ( 2.17) (2.27) 27

28 Panel B: US Sector ETFs K = H = 1 K = H = 3 K = H = 6 K = H = 9 K = H = 12 K = 12 1, H = 1 XLB (0.08) (0.82) ( 0.80) ( 0.36) ( 1.02) (0.47) XLE (0.01) (0.94) (0.28) (0.38) (0.61) (2.46) XLF (1.27) (0.78) (0.70) (0.66) (0.32) (0.52) XLI (1.46) (1.27) (0.67) (1.19) (0.14) (1.17) XLK ( 0.22) (0.74) (1.36) (0.44) (0.45) (0.44) XLP ( 0.49) (1.05) (0.88) (1.25) (1.30) (1.67) XLU (3.04) (0.90) (2.24) (1.37) (1.63) (1.28) XLV (1.29) ( 0.94) (0.60) (0.70) (0.75) (0.91) XLY (0.65) (0.19) (0.61) (0.80) ( 0.11) (0.34) Pooled returns (1.98) (2.00) (2.12) (1.95) (0.98) (2.72) Pooled returns (0.26) (0.29) (0.29) (0.22) ( 0.53) (0.97) in excess of buy-and-hold Note: This table reports the excess return (of the one-month Treasury bill rate) of the time series momentum. This strategy requires buying the asset if its past return is positive and selling if negative (regardless of the ranking of the asset during the past period). Evaluation and holding periods of 1 12 twelve months are examined. For simplicity, only the results of K = H, as well as the case of [K = 3, H = 1] in Panel A and the case of 12-minus-1 month in Panel B, are reported. The last case presented in the last two columns of each panel yields the greatest profits both economically and statistically. The pooled returns are calculated by stacking all ETF returns in each panel. The t-statistics calculated by the Newey-West heteroscedasticity and autocorrelation consistent errors are reported in parentheses. 28

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