Do Institutional Investors Have Market Timing Skills? Evidence from ETF Trades

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1 Do Institutional Investors Have Market Timing Skills? Evidence from ETF Trades Biljana Nikolic University of Missouri Columbia, MO Phone: Andy Puckett University of Tennessee Knoxville, TN Phone: Xuemin (Sterling) Yan* University of Missouri Columbia, MO Phone: January, 2012 * We would like to thank Judy Maiorca, Allison Keane, and ANcerno Ltd. (formerly the Abel Noser Corporation) for providing institutional trading data. We thank Jim Cicon, Paul Koch, Phillip Daves, Mike Ehrhardt, Larry Fauver, Grace Hao, John Howe, Andrew Lynch, Tao Shu, and seminar participants at the University of Missouri, University of Tennessee, the 2010 Financial Management Association Conference, and the 2010 China International Conference in Finance for helpful comments.

2 Do Institutional Investors Have Market Timing Skills? Evidence from ETF Trades Abstract Using changes in quarterly institutional holdings of exchange-traded funds (ETFs) and a proprietary database of institutional trades, we investigate whether institutional investors possess market-timing skills. We provide a simple model showing that market timers will trade an ETF instead of a basket of stocks to exploit their market-level information when the ETF is more liquid or exhibits lower tracking error. Consistent with this prediction, we find that quarterly changes in institutional ETF holdings forecast subsequent ETF excess returns. Our results suggest that up to 15% of institutions have significant market-timing skills. 1

3 I. Introduction Market timing refers to the dynamic allocation of capital across broad asset classes such as equities and government bonds, where successful market timers increase their exposure to equities prior to a rise in the market and decrease their exposure to equities prior to a market decline. More than four decades have elapsed since Treynor and Mazuy s (TM 1966) seminal study of market timing, yet the academic literature still has not reached a consensus as to whether institutional investors have superior market-timing skills. Studies that use non-linear regressions to investigate market timing, such as TM (1966) and Henriksson and Merton (HM 1981), generally find little evidence of skill. 1 However, the recent literature is often critical of this regression framework. Several studies suggest that non-linear relations between portfolio returns and market returns may be attributable to factors other than market timing (e.g., Jagannathan and Korajczyk (1986); Ferson and Schadt (1996)) or that these market-timing measures are biased downward when returns are measured at a monthly frequency and institutions engage in active timing and trade more frequently than monthly (Goetzmann, Ingersoll, and Ivkovich (2000)). To overcome these problems, several recent studies use either a novel dataset (e.g., daily data as in Bollen and Busse (2001)) or an innovative methodology (e.g., dynamic Kalman filter models as in Mamaysky, Spiegel, and Zhang (2008) and holdings-based measures as in Jiang, Yao, and Yu (2007)). These studies generally find stronger evidence of market-timing skills. For example, Mamaysky, Spiegel, and Zhang (2008) suggest that almost 20% of all mutual funds exhibit significant market-timing ability. Although these studies represent an important 1 Studies that find insignificant (or even negative) evidence of market-timing skill include Treynor and Mazuy (1966), Henriksson and Merton (1981), Henriksson (1984), Chang and Lewellen (1984), Lehmann and Modest (1987), Chen, Lee, Rahman, and Chan (1992), Grinblatt and Titman (1994), Daniel, Grinblatt, Titman, and Wermers (1997), and Becker, Ferson, Meyers, and Shill (1999), among others. 2

4 contribution to our understanding of market-timing skills, one issue that remains troubling is that none of these studies directly observe funds market-timing decisions and are therefore unable to measure the performance of market-timing strategies. Our study contributes to the market-timing debate by using an innovative approach to examine both the existence of market-timing skills and the ex-post performance of institutional investors market-timing strategies. Specifically, we test whether institutional investors exhibit superior timing skills based on their trades of exchange traded funds (ETFs). According to NYSE-Euronext ETFs offer institutional investors unique opportunities to instantly establish, increase, or decrease exposure to broad U.S. and international equity markets. 2 Our analysis is motivated by Graham and Harvey (1996), who examine changes in asset allocation recommendations by investment newsletters. 3 While Graham and Harvey (1996) find little evidence of superior skill for investment newsletters, their methodology has several appealing characteristics: most notably, it directly measures the subsequent performance of market-timing decisions. 4 Intuitively, the suitability of a particular investment vehicle (e.g. ETF) for implementing market-timing strategies depends on its liquidity and tracking error. To formalize this idea, we provide a simple model where an informed trader chooses between an ETF and a basket of stocks in order to exploit his private information about future market returns. We show that when the ETF is more liquid and exhibits lower tracking error, the market timer will choose to trade the ETF rather than a basket of stocks. Our conversations with several institutional portfolio 2 Quote from NYSE-Euronext website: 3 Chance and Hemler (2001) also directly measure market timing skill using a sample of daily recommendations from 30 professional investment advisors. They find significantly positive unconditional and conditional markettiming skills for their sample of investment advisors. 4 This methodology does not require the simultaneous estimation of stock-selection and market-timing skills and therefore is immune to many of the criticisms of the TM and HM models. 3

5 managers confirm that ETFs are indeed a preferred investment vehicle by which managers often implement directional bets on broad market movements. According to Dan Mathisson, managing director and head of electronic trading at Credit Suisse, ETFs are a really convenient and cheap way to implement risk. 5 ETFs allow investors to buy or sell an entire portfolio of stocks or bonds in a single security; moreover, ETFs are traded continuously during market hours, can be sold short, and are considerably more liquid than common stocks. If institutional investors have market-timing skills, we expect to observe a positive relation between changes in institutional ETF holdings and subsequent ETF excess performance. To test this hypothesis, we compute changes in quarterly ETF holdings and calculate subsequent quarter excess ETF returns for all 13F filing institutions during the 1999 to 2008 sample period. Univariate results show that institutional trades have significant predictive power. Specifically, when an institution purchases a broad-market ETF, the subsequent quarter excess return is positive approximately 58% of the time. In contrast, when an institution sells a broad-market ETF, the subsequent quarter excess return is positive only 48% of the time. Multivariate regression analysis confirms the positive and significant relationship between quarterly changes in institutional ETF holdings and subsequent ETF excess returns. Coefficient estimates indicate that subsequent-quarter excess ETF returns are 1.3% higher after an institution increases its holdings than after an institution decreases its holdings. Using a bootstrap approach, we show that this result is statistically significant at the one percent level. In addition, our analysis suggests that up to 15% of institutions in our sample have significant market-timing skills. Our results are robust to controls for macroeconomic variables known to capture variation in expected market returns (Fama and French (1989); Harvey (1989)). In 5 Quote taken from Markets Continue Functioning as ETFs Rise in Volume Traders Magazine Online News, August 11,

6 addition, we find that results hold for both broad-market and sector ETFs, although markettiming skill is stronger for the broad market ETF sample. A somewhat different variant of market-timing skill is the ability of institutional investors to predict future stock market volatility. Institutional investors that are able to forecast future stock market volatility can potentially increase the Sharpe ratio of their portfolios by decreasing their exposure to equities prior to an increase in stock market volatility. We investigate whether changes in quarterly ETF holdings contain information about future realized market volatility (volatility timing) by regressing subsequent quarter ETF volatility on institutional ETF trading. We find that institutional ETF purchases predict lower subsequent quarter volatility. Our results support the conclusion that a significant number of institutions in our sample possess volatility timing skills. Portfolio managers who experience unexpected fund flows might trade ETFs in order to quickly increase or decrease equity exposure in their portfolios. If ETF trades are primarily motivated by fund flows, then the market-timing skill that we document should be attributed to retail investors. To test this possibility, we control for fund flows in our multivariate analysis of market-timing skill. Our results continue to show evidence of significant market-timing skill for institutional investors. More generally, we recognize that institutional investors might hold and trade ETF shares for a variety of reasons. However, the presence of non-information-motivated ETF trading (e.g., hedging and equitizing) should bias against us finding significant timing skills. As such, one should interpret our analyses as providing a lower bound on the estimates of timing coefficients. 6 6 We acknowledge that institutions might use other investment vehicles such as a basket of equities to implement their market timing strategies (Jiang, Yao, and Yu (2007)). This possibility should work against us finding significant timing skills through ETFs. 5

7 To gain a better understanding of institutional market timers, we next examine the characteristics of institutions that exhibit significant market-timing skills. These institutions are on average older and larger, which is consistent with Berk and Green (2004), who show that fund size and age are positively associated with skill. In addition, skilled institutions tilt their portfolio holdings towards smaller stocks and stocks with high book-to-market ratios, consistent with findings by Kacperczyk, Sialm, and Zheng (2005) and Jiang, Yao, and Yu (2007). One potential limitation of our analysis is that changes in quarterly ETF holdings are a proxy for actual institutional trading activity. Such a proxy is not able to capture intra-quarter round-trip transactions, nor can it identify the exact timing of ETF trades within the quarter. We overcome the limitations of quarterly institutional holdings data by using a proprietary database of actual institutional trades provided by ANcerno Ltd. (formerly the Abel Noser Corporation) for the period 1999 to Overall, we find only weak evidence that ANcerno institutions have market-timing skills at the daily or weekly horizon. There are several potential reasons why results for this sample are weaker than those for the 13F sample. First, ANcerno data contains only a subset of 13F institutions and therefore might omit institutions that have market timing skills. Second, institutions have incentives to enter into short-term ETF positions that are unrelated to market-timing strategies. 7 As such, this type of short-term ETF trading will add significant noise to our tests of market timing skill over daily or weekly horizons. Our paper makes several distinct contributions to the market timing literature. First, we employ a unique methodology that allows us to directly identify institutional investors that are increasing or decreasing their exposure to U.S. equity markets. Second, our analysis of ETF trades permits us to measure the subsequent performance of market timing strategies. Finally, to 7 For instance, institutional trading desks often take short-term positions in ETFs to hedge market exposure while implementing a large trading decision (see Keim and Madhavan (1995)). 6

8 our knowledge, we are the first to examine market-timing skills of a comprehensive sample of institutional investors. Prior studies, by contrast, typically focus on a subset of institutions (e.g., mutual funds, pension funds, or proprietary samples). Our paper also adds to the literature on ETFs. As one of the fastest growing classes of financial products, ETFs have attracted considerable attention from the academic literature during the past decade. However, much of this literature focuses on the pricing efficiency, liquidity, or tax efficiency of ETFs. For example, Hasbrouck (2003) examines the relative informational efficiency among equity index products and finds that ETFs contribute significantly to price discovery. Few papers have examined the role of ETFs in asset allocation decisions. Our paper is the first to investigate a long standing issue market timing skills using ETFs. The remainder of the paper proceeds as follows. Section II presents a simple model of market timing with ETFs. Section III discusses our data and sample. Section IV presents our methodology and main empirical results. Section V presents robustness tests and additional analyses, and Section VI concludes. II. A Simple Model of Market Timing We present a simple model to explain why institutional investors prefer to trade ETFs when implementing a market-timing strategy. In our model, a market timer chooses between an ETF and a basket of stocks to exploit his superior information about future market returns. We show that when the ETF is more liquid than stocks, the market timer will trade the ETF rather than a basket of stocks. Alternatively, even when stocks are more liquid, the market timer might still choose to trade the ETF because it entails lower tracking error. 7

9 We consider a two-period model in which a single trader forms his portfolio today and the assets in the portfolio pay off tomorrow. There is a risk-free bond that yields gross return R. In addition, there are n stocks and an ETF. The payoff of the ETF is u, and one can think of the i ETF as representing the market portfolio. The payoff of stock i isυ = u + e, where e i is the i stock-specific return. We assume that corr( u, ei ) = 0 i and corr( e, e ) = ρ 0 for i j. For i j 1 simplicity, we assume all stocks have a beta of 1. The prior distributions of u and e i are Ν ( u, λ ) and Ν ( 0, η 1 ), respectively. The prior distribution of υ i 1 1 is then Ν ( u, λ + η ). Without loss of generality, we normalize the market prices of all securities to 1. There is one informed trader (market timer) who receives a private signal, θ, about the market return, but not about stock-specific returns. The signal θ is equal to u + δ, where 1 ( 0, s ) δ ~ Ν and corr( δ, ei ) = 0 i. We assume the equilibrium asset prices are unaffected by this private information. Since the market timer has no private information about firm-specific returns, we assume that when he trades stocks, he will trade an equal-weighted portfolio of n stocks. Trading the ETF will incur a trading cost + 0 a Q where a, a1 > and Q >. Here, Q is the dollar amount of a 1 trading, a 0 represents the fixed cost and a 1 is the proportional trading cost. 8 The trading cost for each stock is a Q where a > 0 and Q 0. The cost of trading the equal-weighted stock a > portfolio is then na0 + a2q. Here, the total fixed cost is n times that of trading one stock. In practice, institutions can engage in a program trade so that the fixed cost may be smaller than na 0. In this case, one can alternatively interpret na 0 as capturing the inconvenience of trading n 8 The model is still tractable with a quadratic trading cost function. The basic results of the model are similar if one makes suitable assumptions about the convexity of the trading cost function. 8

10 different stocks. 9 The proportional trading cost is a fraction of the dollar amount of trading and therefore is unrelated to n. The market timer has an initial wealth of W 0, which is entirely invested in the risk-free bond. The market timer has a mean-variance utility over his terminal wealth with a constant coefficient of absolute risk aversion A. Let x denote the amount invested in the bond; y denote the amount invested in the ETF; and z denote the amount invested in the equal-weighted stock portfolio. The market timer maximizes his utility: max y, z U ( y, z) = W R + y A 2 0 ( E( u θ ) R) + z( E( u θ ) R) 2 2 p [ y Var( u θ ) + z Var( υ θ ) + 2yzVar( u θ )] ( a + a y ) ( na + a z ) (1) For brevity, we present the detailed solutions to the above problem in Appendix A and briefly discuss only the qualitative results here. The key finding of the model is that when the ETF is more liquid than the stocks, i.e., a 2 > a 1, the market timer will trade the ETF to exploit his superior information about the market. On the other hand, even if the basket of stocks is more liquid than the ETF, the market timer might still trade the ETF because the ETF offers a lower tracking error. The basic intuitions of the model carry over when we allow n to be endogenous. III. Data and Sample We construct our initial sample by first identifying all ETFs in the CRSP database. 10 The first ETF, the SPDR S&P 500 index, was listed on the AMEX in 1993; and since then, the number and market value of ETFs have increased dramatically. This increase is quite apparent when illustrated graphically, as in Figure I. According to the Investment Company Institute, the 9 Most of our results require only that the fixed cost of trading the stock portfolio is larger than that of the ETF, not necessarily n times as large. 10 We identify ETFs in two ways: first, we require that a security has a CRSP sharecode equal to 73. Second, we require that a security has an ETF/ETN flag value equal to F in the CRSP mutual fund database. 9

11 total value of ETF assets under management reached $531 billion by the end of During the 1993 to 2008 sample period, we find a total of 820 ETFs that have been traded in U.S. markets. For each ETF, we collect its name, Lipper classification, and portfolio allocation (i.e., percentage of funds invested in common stocks, preferred stocks, and other assets). Based on these three variables, we classify each ETF into one of six mutually exclusive categories: U.S. broad market, U.S. sector, international equity, fixed income, commodities, and bear market. The first two categories identify ETFs that invest exclusively in U.S. equities. U.S broadmarket ETFs invest in a diversified portfolio of stocks (e.g., S&P500 or S&P1500), whereas U.S sector ETFs invest in stocks in a particular industry (e.g., financials) or style (e.g., small stocks or growth stocks). The final four categories hold stocks outside of the U.S. (international equity), fixed income investments (fixed income), commodity futures (commodities), or short positions (bear market). Because we are primarily concerned with investigating market-timing skill in the U.S. stock market, we restrict our ETF sample to the first two categories: U.S. broad market and U.S. sector. This restriction leaves us with 359 ETFs. We collect returns, trading volume, and shares outstanding for all ETFs in our sample from the CRSP database and obtain quarterly institutional ownership data from 13F filings provided by Thomson Reuters. 11 Although the first U.S. broad-market ETF was introduced in 1993, initial growth of the new security progressed slowly until the turn of the century. For this reason, we begin our sample period in 1999, at which time there were 12 U.S. equity ETFs. Summary statistics presented in Panel A of Table I show that the number of U.S. equity ETFs increased from 12 in 1999 to 333 in Total ETF market capitalization increased from $ The Securities Act Amendment of 1975 requires that institutional investors managing more than $100 million report their portfolio holdings to the Securities and Exchange Commission (SEC) on a quarterly basis (13F filings). Institutions are required to disclose an equity holding if their stock position is greater than $200,000 or 10,000 shares. 10

12 billion to $304.2 billion over the same period, and the increasing trend is evident for both U.S. broad-market and U.S. sector ETFs. Specifically, the number of broad-market (sector) ETFs increases from 2 to 18 (10 to 315) and market capitalization increases from $5.1 billion to $145 billion ($5.8 billion to $159.2 billion). The breadth of institutional ownership in ETFs also increased dramatically during our sample period. In 1999, 15.94% of 13F reporting institutions (300 institutions) owned shares of U.S. equity ETFs; and by 2008, 48.74% of institutions (1,533 institutions) held ETFs in their portfolios. Over the same time period, the market value of institutional ETF ownership increased from $9.33 to $ billion. We compare the characteristics of ETFs in our sample to all common stocks in the CRSP database during the 1999 to 2008 sample period. Results presented in Panel B of Table I show that, on average, ETFs are smaller and younger than the average common stock. However, ETFs are considerably more liquid when compared to common stocks. Amihud s (2002) illiquidity measure for ETFs is only one fiftieth of that for common stocks, and quoted spreads for ETFs (0.044%) are approximately half the size of those for common stocks (0.076%). The liquidity of ETFs is likely to be particularly important to institutions that are concerned about the implementation costs of market timing strategies. IV. Methodology and Empirical results Successful market timers increase their portfolio weight in equities prior to a rise in the market and decrease their weight in equities prior to a fall in the market. Thus, if institutional investors have market-timing skills we expect the ETFs that they purchase (sell) to perform well 11

13 (poorly) during the subsequent period. We test this relationship in both univariate and multivariate settings. A. Univariate Analysis We measure ETF trades for each institution using the methodology of Chen, Jegadeesh and Wermers (2000) and Kacperczyk, Sialm, and Zhang (2005). Specifically, we calculate fractional holdings in each quarter for each ETF and institution (w i,j,t ) as the ratio between the number of shares of ETF i, held by institution j, at the end of quarter t and the number of shares outstanding for ETF i at the end of quarter t. The institutional trade is then the quarterly change in fractional ETF holdings (Δw i,j,t ). 12 We separate all ETF trades into buys ( w 0) and sells ( w<0) and track the excess return for each ETF trade during the subsequent quarter t+1. Excess returns are calculated as the difference between the ETF return and the return on the 90-day Treasury bill. We first investigate whether institutions anticipate the direction of market movements. Results in Panel A of Table II indicate that excess returns are greater than zero following 50.11% of ETF buy trades, compared to 45.92% of sell trades. The difference of 4.19% (p-value=0.03) suggests that institutions have positive market-timing ability. In addition, evidence of markettiming skill is stronger for broad-market ETFs than for sector ETFs. Specifically, 57.88% (47.37%) of next quarter s excess returns are greater than zero for buy trades in broad-market (sector) ETFs, whereas 47.81% (45.91%) of next quarter s excess returns are greater than zero for sell trades in broad-market (sector) ETFs. The difference in the fraction of positive excess returns between buy and sell trades is 10.07% (p-value=0.04) for the broad-market ETF sample and 1.46% (p-value=0.22) for the sector ETF sample. 12 Changes in fractional ETF holdings may reflect either changes in shares held or changes in the number of shares outstanding. As a robustness check, we retain only those observations where the number of shares outstanding of an ETF does not change. Our main empirical results are robust to this alternative approach. 12

14 Panel B of Table II presents average subsequent quarter excess returns following ETF trades. Across all ETFs, buy trades outperform sell trades by 1.27% (p-value<0.01). Again, we find that the magnitude of our result is larger for broad-market ETFs (2.02%, p-value=0.01) than it is for sector ETFs (1.17%, p-value=0.01). We note that excess returns following both ETF buy and sell trades are negative. For example, we find excess returns of -0.35% following broadmarket ETF buys and excess returns of -2.37% following broad-market ETF sells. One explanation for negative average excess returns following both buy and sell trades is that our sample period is characterized by a generally declining market (i.e. the burst of the Internet bubble during and the financial crisis of ). 13 B. Multivariate Analysis Our multivariate tests of market timing closely follow the methodology of Graham and Harvey (1996). We use the following regression model: R i,t+1 = δ i,1 + δ i,2 Δw i,j,t +δ i,3 R i,t + ε i,t+1, (2) Where, R i,t+1 is the one-quarter ahead excess return on ETF i; Δw i,j,t is (as previously described) the quarterly change in ETF i holdings for institution j; and R i,t is the excess return for ETF i during the current quarter. We estimate regression (2) separately for each institution and require a minimum of 24 valid observations for an institution to be included in our regression analysis. We then calculate the average regression coefficient across all institutions and use a bootstrapping procedure to conduct statistical inferences. 14 Our bootstrap procedure is similar to that used by Kosowski, Timmermann, Wermers, and White (2006), Jiang, Yao, and Yu (2007), and Fama and French 13 Unreported results indicate that both the percentage of positive market excess returns and the average market excess return are substantially higher when we exclude from our sample. 14 In untabulated robustness tests, we also use the cross-sectional variation in coefficients to compute statistical significance. Our methodology is similar to that used by Fama and MacBeth (1973) and produces significance levels that are similar to those reported. 13

15 (2010). Specifically, we resample the data under the null hypothesis of no market timing while maintaining the correlation structure across institutional ETF trading and across ETF returns. The bootstrap approach helps alleviate concerns related to the cross-sectional correlations of market-timing coefficients. In particular, the assumption of identical and independent distributions across institutions is not likely to hold. Further, many sample institutions exist only for a short-period of time and the finite sample properties of the cross-section of timing coefficients may differ significantly from asymptotic distributions. Details of our bootstrap analysis are described in Appendix B. The bootstrapped p-values are reported in squared brackets in each of the tables. A potential concern is that outliers for the independent variable of interest (Δw) might add significant noise to our inferences. To address this concern, we construct two alternate independent variables. The first, Δw +, is an indicator variable that is equal to 1 if Δw is positive and is equal to zero otherwise. The second, Δw RANK, is a decile rank value that is assigned to each ETF-institution observation. Specifically, for each institution we rank all Δw s into ten groups and assign a decile rank value of 10 to the highest-value group and a value of 1 to the lowestvalue group. These alternate measures are appealing because they reduce the influence of extreme outliers in our coefficient estimates. We present the regression results in Table III. Columns 1-3 present results for each of our three measures of ETF trading. Coefficient estimates for Δw, Δw +, and Δw RANK are 0.57, 1.29, and 0.21, and all are statistically significant at the 1% level using bootstrapped p-values. These estimates are also economically significant. For example, the coefficient estimate on Δw + indicates that subsequent quarter ETF excess returns are 1.29% higher when institutions buy an ETF compared to when institutions sell an ETF. 14

16 In addition to point estimates, we include statistics for the percentage of coefficient estimates that are positive (negative) and the percentage that are positive (negative) and significant at the 5% level. We compare the coefficient estimates to the bootstrapped distribution in order to determine whether our results are statistically different from those that would have been attained by chance. The percentage of institutions with positive coefficients for Δw, Δw +, and Δw RANK are 58.31%, 72.20%, and 69.15%, respectively, and the percentage that are positive and significant are 10.06%, 15.14%, and 15.48%, respectively. This proportion of positive and statistically significant coefficients is significantly different from 2.5%, which we would expect by chance. Furthermore, the bootstrap analysis suggests that the percentage of negative and significant coefficients does not differ from that expected by chance for regressions using Δw + and Δw RANK. Taken together, our results suggest that between 10 and 15 percent of institutions are skilled market timers. 15 One possible explanation for our results is that institutional investors exploit the information contained in macroeconomic variables that predict future expected market returns. To explore this possibility, we re-estimate our predictive regressions using the following augmented equation: R i,t+1 = δ i,1 + δ i,2 Δw i,j,t +δ i,3 R i,t + δ i Z t + ε i,t+1, (3) Where, R i,t+1, Δw i,j,t and R i,t are as previously defined; and Z t is a vector of macroeconomic control variables. The vector Z t includes the Treasury bill rate (TB), the difference between the long-term yield and treasury bill rate (TERM), the Standard and Poor BBB-AAA yield spread (DEF), and the market dividend yield (DP). These variables have been shown to capture 15 In an un-tabulated analysis we find that ETFs that institutions buy outperform those they sell by 90 basis points in the first month after the trades. In addition, our results using one-month ahead ETF excess return as a dependent variable suggest that between 5% and 13% of institutions are skilled market timers. 15

17 variation in expected market returns (Fama and French (1989); Harvey (1989)) and are obtained from Amit Goyal s website. Columns 4-6 of Table III present regression results for equation (3). Both the magnitude and significance of institutional trading variables are consistent with earlier results. Coefficient estimates for Δw, Δw +, and Δw RANK are 0.66, 1.27, and 0.20 and all are statistically significant. In addition, the percentage of institutions with positive and significant coefficients for Δw, Δw +, and Δw RANK are 9.94%, 14.46%, and 13.90%, respectively. Again, our results suggest that between 10 and 15 percent of institutions are skilled market timers. In summary, our multivariate results support the hypothesis that some institutional investors have market timing skill. The coefficient estimates for Δw + suggest that ETFs in which an institution increases its holdings outperform ETFs in which an institution decreases (or does not change) its holdings by between 1.27% and 1.29% in the subsequent quarter. These estimates are economically significant and suggest an excess performance difference of more than 5% per year. The coefficient estimates for Δw RANK suggest that a one decile increase (move to a higher buying decile) is associated with a 0.2% increase in excess returns during the subsequent quarter. By comparing extreme decile ranks, our estimate suggests a 1.8% subsequent return difference between the extreme buy and extreme sell ETF portfolio. C. Multivariate Analysis by ETF Type To explore the robustness of our results and to gain insights into whether market-timing skills are evident for both the broader market and sectors, we repeat the multivariate analysis presented in Table III for broad-market and sector ETFs separately. On the one hand, we might expect institutional investors to be more skilled in their broad-market ETF trades if they concentrate their information collection and processing efforts on aggregate equity movements. 16

18 Furthermore, broad-market ETFs tend to be larger and more liquid than sector ETFs and therefore may be more attractive to institutional investors. On the other hand, market timing might be restricted to particular segments of the market in which portfolio managers specialize (Kacperczyk, Sialm, and Zheng (2005)). For the broad-market ETF sample, we find that coefficient estimates for Δw, Δw +, and Δw RANK are 4.67, 1.74, and 0.29 when we do not control for macroeconomic variables. 16 After including the vector of macroeconomic control variables, both the magnitude and significance of institutional trading variables are consistent with earlier results. Looking at the coefficient estimate on Δw +, subsequent quarter excess returns following institutional buying are 1.74% higher than those following institutional selling. Similar to results presented in Table III, we find the percentage of positive and significant coefficients for Δw, Δw +, and Δw RANK to be more than four times larger than what we expect by chance, which suggests that more than 10% of institutions in our sample are skilled market timers. Our results presented in Panel B of Table IV show that the regression coefficients on institutional trading variables are generally lower for the sector ETF sample. Coefficient estimates for Δw, Δw + and Δw RANK (in columns 1-3) are 1.15, 1.24, and 0.l9, respectively. When we include the vector (Z t ) of macro-economic control variables, our regression coefficients of interest become insignificant at the 5% level. Although our results provide little support, on average, for sector-timing skills, we continue to find evidence of sector-timing skills for some institutions. The percentage of institutions with positive and significant coefficients for Δw, Δw +, 16 A potential concern is that the predictive ability of institutions for future ETF returns is due to price impact. To address this concern, we use CRSP value-weighted market excess return as the dependent variable in regression equation (3) for our broad-market ETF sample. Unreported results indicate that institutional ETF trading significantly predicts subsequent market excess returns, suggesting that our results are not driven by price pressure associated with individual ETFs. 17

19 and Δw RANK are 9.24%, 9.24%, and 10.05%, respectively. Overall, we find much stronger evidence of timing skills using broad-market ETFs than using sector ETFs. D. Volatility Timing Broadly speaking, market-timing skills may also include the ability of investors to predict market volatility. Institutional investors who are able to forecast future stock market volatility can potentially increase the Sharpe ratio of their portfolios by decreasing their exposure to equities prior to increases in stock market volatility. We use the following regression to test whether quarterly changes in ETF holdings predict subsequent quarter volatility: Vol i,t+1 = δ i,1 + δ i,2 Δw i,j,t +δ i,3 Vol i,t + δ i,3 Neg i,t+1 + ε i,t+1, (4) Where Vol i,t+1 is the logarithm of one-quarter ahead realized volatility for ETF i estimated using daily return data; Δw i,j,t is (as previously described) the quarterly change in ETF i holdings for institution j; Vol i,t is the logarithm of lagged return volatility for ETF i; and Neg i,t+1 is an indicator variable that equals 1 if the one-quarter ahead excess return for ETF i is negative and zero otherwise. We include lagged volatility (Vol i,t ) to control for volatility persistence and the negative ETF return dummy (Neg i,t+1 ) to control for higher volatility in declining markets (i.e., leverage effects ). Table V presents our results for regression equation (4). After controlling for persistence and leverage effects, we find modest evidence of volatility timing skills. Specifically, coefficient estimates for Δw + and Δw RANK are negative and significant in most specifications. In addition, the percentage of institutions with negative and significant coefficients for Δw, Δw +, and Δw RANK are 5.42%, 6.55%, and 5.76%, respectively. The percentage of negative and significant coefficients is more than twice what we would expect by chance and suggests that more than 5% of institutions are successful in timing future stock market volatility. 18

20 V. Additional Analyses A. Fund Flows It is possible that our results are driven by fund flows from retail investors rather than the market-timing skill of institutions. If institutions respond to fund inflows (outflows) by buying (selling) ETF shares, then the market-timing skills that we document could be attributed to retail investors. Alternatively, if as prior literature suggests (e.g., Frazzini and Lamont (2008)), retail investor flows are negatively related to future stock market performance, then the presence of fund flows might inhibit the ability of institutional investors to successfully time the market. To test these propositions, we augment regression equation (2) to explicitly control for fund flows in our multivariate analysis: R i,t+1 = δ i,1 + δ i,2 Δw i,j,t +δ i,3 R i,t + δ i,4 Flow j,t + ε i,t+1, (5) Where Flow j,t is the percentage change in the total assets under management for institution j minus the portfolio return (i.e., weighted average return of all stocks held by institution j) during quarter t. All other variables are as defined previously. Table VI presents our results for regression (5). After controlling for fund flows, both the magnitude and statistical significance of institutional trading coefficients are similar to those presented in Table III. Coefficient estimates for Δw, Δw +, and Δw RANK are 0.67, 1.14, and 0.18, and all are statistically significant at the 1% level. Consistent with Frazzini and Lamont (2008), we find that fund flows are negatively related to subsequent quarter excess returns. As such, it appears that retail fund flows inhibit the ability of institutional investors to time the market. B. Characteristics of Skilled Managers In order to gain a better understanding of market timing, we investigate the characteristics of institutions that are successful market timers. Because our regression analysis is performed 19

21 institution-by-institution, we are able to examine the characteristics of institutions that have a positive and significant regression coefficient in our primary regression (regression specification 3 in Table III). 17 Our regressions involve 885 different institutions, of which 128 have positive and significant coefficients on the institutional trading variable Δw +. We compare institutional characteristics such as size, age, number of equity holdings, size of ETF holdings, number of ETFs held, asset growth, and legal type for the 128 skilled institutions in our sample to the remaining 757 unskilled institutions. 18 We present the average characteristics of skilled and unskilled institutions in Panel A of Table VII. Skilled institutions are significantly larger ($14.8 billion versus $9.2 billion) and older (59 quarters versus 20 quarters) than unskilled institutions. Both of these characteristic differences are consistent with the propositions of Berk and Green (2004), who show that fund size and age are positively associated with skill. Skilled institutions also hold a greater number of equity securities in their portfolios (575 versus 530) and own more ETFs ($178 million versus $93 million) than unskilled institutions. We also find disproportionately greater representation of banks among skilled institutions. This evidence is consistent with Lewellen (2010), who finds that banks are marginally more skilled than insurance companies and other institutions. Finally, we report differences in the characteristics of equity holdings for skilled and unskilled institutions in Panel B of Table VII. Our findings suggest that skilled institutions tilt their portfolios towards smaller stocks and stocks with higher book-to-market ratios, which is consistent with evidence provided in Kacperczyk, Sialm, and Zheng (2005) and Jiang, Yao, and Yu (2007). 17 We perform the same analysis using regression specification 5 from the Table VI and find similar results. 18 The classification of institutions by legal type in the Thomson-Reuters database is problematic after 1997, and we follow the approach proposed by Lewellen (2010) to classify all institutions into three categories, banks, insurance companies, and others. 20

22 C. Actual Institutional Trades Our final robustness test addresses potential measurement error in our institutional trading variables. Specifically, changes in quarterly ETF holdings cannot identify intra-quarter transactions where institutions purchase and sell or sell and re-purchase the same ETF within the quarter, nor can they identify the exact timing and execution price of trades (see Puckett and Yan (2010); Elton, Gruber, Blake, Krasny, and Ozelge (2010)). If institutions engage in active market timing by trading ETFs more frequently than quarterly, this measurement error will reduce the power of our quarterly tests (Kothari and Warner (2001)) and may lead to incorrect inference (Goetzmann, Ingersoll, and Ivkovich (2000)). Even though we find significant evidence of market-timing skill using quarterly data, we attempt to overcome the limitations of quarterly institutional holdings data by using a large proprietary database of actual institutional trades provided by ANcerno Ltd. (formerly the Abel Noser Corporation). We obtain data on institutional trades for the period from January 1, 1999 to December 31, 2008 from ANcerno Ltd. ANcerno is a widely recognized consulting firm that works with institutional investors to monitor their equity trading costs. ANcerno clients include pension plan sponsors such as CalPERS, the Commonwealth of Virginia, and the YMCA retirement fund, as well as money managers such as MFS (Massachusetts Financial Services), Putman Investments, and Lazard Asset Management. We refer readers to Puckett and Yan (2010) for a complete and detailed description of the ANcerno trading database. 19 We identify more than 1.18 million ETF trades in the ANcerno database that are made by 486 different institutions. 20 For each trade, the database provides an identity code for the 19 Previous academic studies that have used ANcerno data also include Goldstein, Irvine, Kandel and Wiener (2009), Chemmanur, He, and Hu (2008), Puckett and Yan (2009), and Goldstein, Irvine, and Puckett (2010). 20 We note that the sample of institutions for which we have transactions data is not the same as the sample of institutions for which we observe quarterly holdings. 21

23 institution, date of execution, ETF traded, number of shares executed, execution price, commissions paid, and whether the trade is a buy or a sell. We aggregate trading on each day for each institution and ETF and then regress subsequent daily (or weekly) excess returns on daily ETF trading imbalances. Our methodology closely corresponds to regression equation (2) and results are presented in Table VIII. Panel A presents regression estimates where the dependent variable is the one-day-ahead excess ETF return. We find insignificant coefficient estimates for Δw, and positive and significant coefficient estimates for Δw + and Δw RANK. Specifically, after controlling for lagged returns, the coefficient estimate on Δw + is 0.04 (bootstrapped p- value=0.01) and the coefficient estimate on Δw RANK is 0.01 (bootstrapped p-value=0.01). In Panel B of Table VIII we present regression results where the dependent variable is the one-week-ahead excess ETF return. The magnitude of our regression coefficients in Panel B is generally larger than those in Panel A; however, we now find that the regression coefficients on all three institutional trading variables are statistically insignificant. For example, the regression coefficient on Δw + is 0.09 (versus 0.04 in Panel A), but the bootstrapped p-value is Our results provide mixed evidence that daily ETF trades reflect market-timing skill. However, it is possible that certain types of institutional investors have market-timing skills while other types do not. The ANcerno trading data allow us to classify each institution as either a money manager or pension plan sponsor. We investigate whether market-timing skill differs across institution types by repeating our regression analysis separately for these two subsamples of institutions. Panel A of Table IX presents results of our regression for pension plan sponsors. Coefficients are insignificant for all three independent variables of interest, Δw, Δw +, and Δw RANK for the daily horizon; however, they are 0.27, 0.23% and 0.03%, respectively and statistically significant for 22

24 one-week-ahead returns. Extrapolating the weekly results to the quarterly frequency, we find that institutional buying is associated with subsequent quarter excess returns of 2.76%. Panel B of Table IX presents results for the money manager subsample. We find insignificant coefficient estimates for Δw, and positive and significant coefficient estimates for Δw + and Δw RANK at the daily horizon. Specifically, the coefficient estimate on Δw + is 0.04 and for Δw RANK is 0.01; both coefficients are significant at the 5% level. However, none of the coefficients are significant for the weekly horizon. Overall, we find only weak evidence of market timing skills at the daily and weekly horizons. We conjecture that institutions have many potential reasons to take short-term positions in ETFs that are unrelated to market timing strategies. For example, institutional trading desks often take short-term positions in ETFs or futures contracts in order to hedge market exposure while implementing a large trading decision (see Keim and Madhavan (1995)). As such, this type of short-term ETF trading will add significant noise to our tests of market timing skill over short-term horizons. VI. Conclusions The question of whether institutional investors have superior market-timing skills is essential to our understanding of efficient capital markets. In this paper, we contribute to the market-timing literature by examining institutional investors ETF trades. We first provide a theoretical model where a market timer chooses between an ETF and a basket of stocks in order to exploit his private information about future market returns. We show that when the ETF is more liquid or exhibits lower tracking error, the market timer will choose to trade the ETF rather than a basket of stocks. 23

25 We test whether institutional investors exhibit superior broad-market or sector timing skills based on their trades of ETFs. In univariate tests, we find that excess returns are more likely to be positive after an institution purchases an ETF than after an institution sells an ETF. For the broad-market ETF sample, the excess return is positive 57.88% of the time after an institution purchases an ETF, compared to only 47.81% of the time after an institution sells an ETF. The difference in average excess return between buys and sells is 2.02% over the next quarter. Using a multivariate framework, we continue to find evidence of superior market timing when investigating institutional ETF trading. Specifically, we find a significant positive relationship between institutional ETF trading and subsequent ETF returns. Our coefficient estimates suggest that ETFs in which an institution increases its holdings outperform ETFs in which an institution decreases (or does not change) its holdings by about 1.3% in the subsequent quarter. Our results are significantly stronger for broad-market ETFs when compared to sector ETFs; however, we find evidence of significant market timing skill in both samples. Furthermore, we find that these results are robust even after controlling for fund flows, suggesting that our results are not attributable to individual investor demand. Overall, we find that institutional ETF trades predict subsequent ETF excess returns, and our results suggest that up to 15% of institutions have significant market-timing skills. 24

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