Do Hedge Funds Create Value from Liquidity Provision? Russell Jame * August 2015. Abstract

Do Hedge Funds Create Value from Liquidity Provision? Russell Jame * August 2015 Abstract Using transaction data, I examine whether hedge funds profit from liquidity provision. I find hedge funds liquiditysupplying (i.e., contrarian) trades outperform their liquidity-demanding trades by 1.3% over a one-month holding period. The outperformance of contrarian trades is strongest for smaller funds, funds with greater share restrictions, and when funding liquidity is low. Further, funds that engage in greater contrarian trading have persistently higher trading returns over one-month holding periods. The holdings of contrarian funds also outperform, although the outperformance is more modest due to their tendency to hold position long after the abnormal returns from liquidity provision have dissipated. * Gatton College of Business and Economics, University of Kentucky, Lexington, KY, 40506. Email: russell.jame@uky.edu. This paper was previously circulated under the title: How Do Hedge Fund Stars Create Value? Evidence from Their Daily Trades. I would like to thank Allison Keane, Marsha Tracer, and ANcerno Ltd. for providing institutional trading data. I thank Daniel Moevius, Tyson Van Alfen, and Emma Xu for research assistance. I thank Vikas Agarwal, Adam Aiken, Chris Clifford, Steve Dimmock, Jesse Ellis, Will Gerken, Clifton Green, Biliana Guner, Byoung-Hyoun Hwang, Petri Jylhä, Manel Kammoun, Andrew Karoyli, Sugata Ray, Kalle Rinne, Jay Shanken, Ken Singleton, Matti Suominen, Yue Tang, Rob Tumarkin, and seminar participants at the American Finance Association Meetings, the 3 rd Luxembourg Asset Management Summit, the 7 th Paris Hedge Fund Research Conference, Georgia State University, Massey University (Albany), Massey University (Manawatu), Temple University, the University of Georgia, the University of Kentucky, the University of New South Wales, the University of Technology Sydney, and the University of Waterloo for helpful comments. I gratefully acknowledge the financial support from the BNP Paribas Hedge Fund Centre at Singapore Management University.

1. Introduction The hedge fund industry has grown from $38 billion in 1990 to over $2.5 trillion in 2014 (BarclayHedge). 1 Presumably, much of this growth is driven by investors' faith in hedge funds' ability to generate abnormal returns via skilled trading. Consistent with this view, the academic literature generally finds that the average hedge fund delivers net-of-fee alphas of roughly 3-5% (see, e.g., Ibbotson, Chen, and Zhu, 2011; Kosowski, Naik, and Teo, 2007; and Agarwal, Daniel, and Naik, 2009). 2 However, relatively little is known about how hedge funds exploit mispricing. One mechanism through which hedge funds may create value is liquidity provision, i.e., serving as the counterparty to other investors who demand immediacy. The view that hedge funds profit from liquidity provision seems plausible for several reasons. First, it is commonly believed that hedge funds attract the most skilled managers. Thus hedge funds may be particularly adept at minimizing the risks associated with liquidity provision (e.g., adverse selection costs). Second, compared to other institutional investors, hedge funds generally have better liquidity management tools, such as lockups and share restrictions, which likely provide them with a comparative advantage in patient liquidity provision. Lastly, there is ample evidence that liquidity-provision strategies yield abnormal returns (see e.g., Grossman and Miller, 1998; and Nagel, 2012). However, hedge funds need not profit from liquidity provision. Naïve liquidity providers could incur losses by supplying liquidity to informed traders. For example, Barrot, Kaniel and Straer (2015) find that retail traders who provide liquidity are often picked off and experience significant losses on the day of the trade. In addition, hedge funds tend to withdraw from liquidity 1 http://www.barclayhedge.com/research/indices/ghs/mum/hf_money_under_management.html 2 However, not all of the academic literature has been as kind to hedge funds. See, for example, Griffin and Xu (2009), Dichev and Yu (2011), and Aiken, Clifford, and Ellis (2013). 1

provision when funding conditions tighten (Ben-David, Franzoni, and Moussavi, 2012; and Franzoni and Plazzi, 2015), precisely when the profits from liquidity provision are the greatest (Nagel, 2012). Further, as the hedge fund industry continues to grow, decreasing returns to scale may limit hedge funds ability to profit from liquidity provision (Fung et al., 2008; and Pastor, Stambaugh, and Taylor, 2015). Finally, liquidity provision profits tend to be concentrated over short horizons. Thus, the impact of liquidity provision on fund performance is likely small for hedge funds with longer holding periods. The purpose of this paper is to better understand the role of liquidity provision as a source of hedge funds equity trading skill (ETS). Specifically, I ask two related questions. First, does the average hedge fund create significant short-term value through their liquidity-providing trades? Second, do funds that engage in greater amounts of liquidity provision generate superior ETS over both short and long horizons? To answer these questions, I collect equity transaction data from ANcerno Ltd, an execution cost consulting firm. Unfortunately, like 13F holdings, the data do not include nonequity trading. However, over 90% of the funds in the sample are equity-oriented funds (i.e., long/short equity or equity market neutral), suggesting that most of the funds in the sample rely primarily on equity trading to generate abnormal returns. Moreover, the data offer a number of benefits relative to commercial databases and quarterly holdings. First, ANcerno reports the exact date and execution price of each trade. This allows for a more accurate identification of liquiditysupplying and liquidity-demanding trades. It also allows for more precise estimates of trading profits over short horizons (e.g., one month). This is critical as the profits from liquidity provision are likely to be largest in the period immediately following the trade. In addition, unlike quarterly holdings, ANcerno captures all equity trades, including short-sales, confidential filings, and intra- 2

quarter roundtrip trades. Further, the data do not suffer from many of the biases that plague commercial databases (see, e.g., Fung and Hsieh, 2009). I begin by estimating hedge funds equity trading skill (ETS) using transactions-based calendar-time portfolios (see, e.g., Seasholes and Zhu, 2010) with holding periods of one month (ETS1). ETS is computed as the principal-weighted return on the stocks held in the buy portfolio less the principal-weighted return on the stocks held in the sell portfolio. After computing characteristic-adjusted returns based on size, book-to-market, and momentum, I find that the average hedge fund generates a statistically significant ETS1 of 0.39% per month. To test whether hedge funds superior ETS1 stems from liquidity provision, I split hedge fund trading into liquidity-supplying (LS) or liquidity-demanding (LD) trades. Building off Jylhä, Rinne, and Suominen (2014), I define LS trades as purchases (sales) of stocks in the top (bottom) third of expected weekly excess returns, calculated using past estimates of short-term return reversals. LD trades as defined analogously. This measure is strongly correlated with other contrarian-based measures of liquidity provision such as past one-day (Khandani and Lo, 2011), one-week (Lehman, 1990), or one-month (Jegadeesh, 1990) returns. I find that hedge funds ETS1 constructed from LS trades is a statistically significant 1.19%, while hedge funds ETS1 constructed from LD trades is an insignificant -0.10% per month. The ETS associated with LS trades is concentrated in the first-month after the trade, particularly during periods of low funding liquidity. The ETS1 of LS trades also declines after the introduction of the NYSE s autoquote system, which is consistent with increased algorithmic trading reducing the profits from liquidity provision (Hendershott, Jones, and Menkveld, 2011). Lastly, the ETS1 of LS trades is larger for funds with greater share restrictions and for funds with smaller one-month portfolio holdings. These findings 3

are consistent with impatient capital and capacity constraints weakening hedge funds ability to profit from liquidity provision. I next examine the relationship between liquidity provision and ETS1 at the fund level. I find that funds that engage in greater amounts of liquidity provision generate persistently higher ETS1 over the subsequent three years. For example, funds in the top quintile of liquidity provision generate a statistically significant ETS1 of 0.97% per month in the year following the ranking period, while funds in the bottom quintile of liquidity provision exhibit no outperformance. This finding is robust to controlling for a host of fund characteristics including past ETS1, share restrictions, fund size, and asset illiquidity. I also investigate whether liquidity provision is related to the returns on a fund s equity holdings (Equity Holding Skill EHS). I construct holdings by aggregating the net trading of a fund beginning when it first appears in ANcerno. 3 I find that the EHS of the average hedge funds is economically small, and statistically insignificant. However, funds in the top quintile of liquidity provision continue to outperform by a statistically significant 0.23% per month. The difference between LS funds economically large ETS1 (0.97%) and their more modest EHS (0.23%) stems from two factors. First, the profitability of LS trades are concentrated in the first-month after the trade. Second, for the average LS hedge fund, less than 8% of its holdings are due to positions formed in the past month. The above evidence begs the question of whether hedge funds could enhance their EHS by reducing their holding period. However, two factors significantly limit hedge funds ability to fully benefit from their short-term trading skill. First, accounting for trading commissions erodes 3 Note both ETS1 and EHS are constructed from transactions. However, ETS1 only considers transactions made over the prior 21 trading days, while EHS considers all transactions made by the fund since joining ANcerno. 4

hedge funds ETS1 by roughly 0.33% per month. Second, reducing the holding period would result in significantly larger trading volume. 4 Since liquidity provision strategies suffer from decreasing returns to scale, this increased trading would likely further diminish the benefits of moving to a shorter holding period. I estimate that the increased trading associated with moving from a oneyear to a one-month holding period would further erode ETS1 by at least 0.40%. This study relates to several strands of literature. The first is the nascent literature that examines the high-frequency dynamics of hedge fund trading. Patton and Ramadorai (2013) combine monthly returns with higher-frequency conditioning variables, and conclude that shortterm (e.g., daily) dynamics are far more important for hedge funds than mutual funds. In contrast to Patton and Ramadorai (2013), who must infer short-term dynamics from monthly returns, this study offers a direct analysis of high-frequency hedge fund trading. My findings also relate to the literature that explores the role of hedge funds as liquidity providers. Franzoni and Plazzi (2015), who also use ANcerno data, examine how hedge funds liquidity provision and trading costs vary with market conditions. In contrast, my emphasis is on whether liquidity provision is an important source of hedge funds ETS. Jylhä, Rinne, and Suominen, (2014) show that hedge fund returns covary with a liquidity-provision factor. Their results suggest that the average hedge fund follows liquidity-provision strategies, but does not offer any evidence on whether such strategies are profitable. 5 Using transaction data, I directly show that hedge fund create significant short-term value through liquidity provision. I also extend the literature by examining the relationship between liquidity provisions and returns at the fund level. 4 This assumes that hedge funds reinvest the proceeds from their liquidated positions. Hedge funds could simply hold extra cash until another profitably opportunity is discovered. However, holding cash is a zero alpha investment strategy and would also drag portfolio alphas towards zeros. 5 For example, hedge funds could incur significant losses on their liquidity-supplying trades due to adverse selection costs. At the same time, the returns of such funds would likely be higher during periods in which liquidity provision is more profitable, inducing a positive covariance with a liquidity-provision factor. 5

My findings provide new evidence that fund s that engage in greater amounts of liquidity provision tend to earn higher returns on their equity trades and holdings. The study also contributes to the debate over whether skilled hedge funds exist. Recent studies using commercial databases find evidence of star hedge funds (see, e.g., Kosowski, Naik, and Teo, 2007; and Jagannathan, Malakhov, and Novikov, 2010). However, these findings may be a consequence of selection-biases in commercial databases (Aiken, Clifford, and Ellis, 2013). Consistent with this view, Griffin and Xu (2009) examine the quarterly holdings of hedge funds, and find the stock picking ability of the average hedge fund is relatively small. Further, they find only weak evidence of differential ability between hedge funds. My study offers an out-of-sample test that relies on more granular data. Consistent with Griffin and Xu (2009), I find the average ANcerno hedge fund does not earn significant abnormal returns on their equity holdings. However, I also highlight that hedge funds have significant short-term trading skill (i.e., ETS1), and that a subset of funds earn abnormal returns on their equity holdings. My findings suggest that there is differential ability amongst hedge funds and shed light on an underlying mechanism (i.e., liquidity provision) that contributes to this difference. At the same time, I also document that trading costs and capacity constraints limit hedge funds ability to fully take advantage of their significant shortterm trading skill. 2. Data 2.1 Institutional Trading Data I obtain data on institutional equity trading from January 1, 1999 to December 31, 2010 from ANcerno Ltd. (formerly the Abel Noser Corp). 6 ANcerno is a consulting firm that works with 6 Other papers that use ANcerno include Anand et al., (2012), Anand et al., (2013), Green and Jame (2011), Green et al., (2013), Jegadeesh and Tang (2010), and Puckett and Yan (2011). 6

institutional investors to monitor their trading costs. The ANcerno data include the complete equity transaction histories of all of its clients. Each observation corresponds to an executed trade. For each execution, ANcerno reports the date of the trade, the stock traded, whether the trade was a buy or a sell, the number of shares traded, the execution price, the price at the time of placing the trade, the commissions paid, and identity codes for the institution making the trade. For each stock traded in the ANcerno dataset, I collect returns, share price, trading volume, and shares outstanding from CRSP, and I collect book value of equity from Compustat. Each institution in ANcerno has three identifier variables: an institution type identifier, a client identifier, and a manager identifier. The institution identifier distinguishes between clients that are plan sponsors (e.g., CalPERS and United Airlines) and clients that are money managers (e.g., Fidelity and Angelo Gordon). The client identifier corresponds to the plan sponsor or money manager that subscribes to ANcerno. The client identifier is a permanent numeric code. However, the names of the clients are not provided. The manager code corresponds to the management company executing the trades. The manager code, like the client code, is a permanent numeric identifier. However, ANcerno also provides a reference file that links manager codes to money management companies (e.g., manager 3 = 'Acadian Asset Management'). 7 The identification is at the fund-family level, and it is not possible to distinguish different funds within a money management company. 8 7 The reference file linking manager codes to manager names was only available for specific vintages of ANcerno data. It is not possible to link manager codes to manager names after 2010. In addition, in some case ANcerno cannot reliably identify the money manager firm, in which case ANcerno assignement a manager code of either -1 or 0. These observations are excluded from the analysis. 8 ANcerno provides an additional variable, Clientmgrcode, which does vary within a client-manager pair. However, discussions with ANcerno representatives indicate that different Clientmgrcodes within a client-manager generally do not reflect different fund products. For example, if a plan sponsor hires Alliance Bernstein and reports the fund as Alliance Bernstein in one period and Alliance Bern in another period, ANcerno will assign two different Clientmgrcodes. For this reason, ANcerno provides a separate reference file that maps different Clientmgrcodes into one consistent manager code. 7

There are two ways a management company (e.g., Angelo Gordon) can enter ANcerno. First, the management company can invest on behalf of a plan sponsor that subscribes to ANcerno (i.e., client type = plan sponsor). For example, CalPERS may hire Angelo Gordon. If CalPERS subscribes to ANcerno, then ANcerno would include Angelo Gordon s trades on behalf of CalPERS. Alternatively, Angelo Gordon can directly subscribe to ANcerno (i.e., client type = money manager). In this case, ANcerno would report all of Angelo Gordon s trading (i.e., their trading on behalf of all of their investors). 2.2 Descriptive Statistics I use the management company name to distinguish hedge fund clients from other institutional investors. Section IA.1 of the Internet Appendix offers a detailed approach of how I distinguish hedge funds from other institutional investors. 9 As noted earlier, ANcerno only reports equity trading. This could bias estimates of equity trading skill, particularly for funds following strategies that involve both equity and non-equity trading (e.g., convertible arbitrage). To minimize this bias, for each ANcerno hedge fund that appears in TASS, I examine the fraction of funds within the management company that have an investment style of either long/short equity or equity market neutral (hereafter Pct. Equity). If the management company does not offer any equity-oriented funds (i.e., Pct. Equity = 0), I drop the management company from the sample. For management companies that do not report to TASS, I visit the company s website and again exclude companies that do not offer any equity funds. The final sample consists of 71 management companies that offer at least one equity-oriented hedge fund. In the Internet Appendix, I repeat the main analyses after including the 11 management companies with Pct. Equity = 0 and find very similar results. 9 The Internet Appendix can be found here: http://russelljame.com/hf_internet_appendix.pdf 8

Table 1 provides summary statistics. The sample consists of 71 hedge fund management companies that manage money for 324 different ANcerno clients. There are 513 different clientmanager pairs. Hereafter, I will loosely refer to a client/manager pair as a fund. Thus, I classify a money management company s trades on behalf of two different clients as two separate funds, although it may reflect the trading of the same hedge fund product. Of the 513 different hedge funds, 487 enter the sample because they manage money for a plan sponsor who hires ANcerno, while 26 hedge funds enter the sample because they directly hire ANcerno. Thus, tests are skewed toward hedge fund trading on behalf of plan sponsors. Although this may not be representative of aggregate hedge fund trading, plan sponsors hold over 50% of all hedge fund assets. 10 Panel B of Table 1 shows the average number of funds that appear in the sample each quarter across all sample years. In the average quarter in 1999, there are 157 hedge funds. This number is relatively stable until around 2007, at which point the sample of funds steadily decreases. I also examine how long funds remain in the ANcerno sample (unreported). The average hedge fund remains in the sample for just over eight quarters, while funds at the 75 th and 25 th percentiles remain in the sample for 16 quarters and three quarters, respectively. I construct several variables to measure the size and frequency of hedge fund trading. First, for each fund-quarter, I compute the total dollar volume traded by a fund (Volume). I also construct the size of a fund s holdings (Holdings Size), computed as the sum of the fund s long and short holdings. I construct holdings by aggregating the net trading of a fund beginning when it first appears in ANcerno. The Holdings Size variable is only suggestive as it ignores positions formed prior to the fund joining ANcerno. This biases estimates of existing holdings, particularly when 10 http://www.aei-ideas.org/2011/10/who-invests-in-hedge-funds 9

the fund first enters the sample. To reduce this bias, I require a one-year waiting period before computing Holdings Size. Since many tests focus on a one-month holding period, I also compute a fund s holdings based only on the fund s past one-month (21 trading days) of trading (Holding Size1). I then compute the fraction of a fund s total holdings that are due to positions formed in the prior month (i.e., Holdings Size1/Holding Size), which I label Pct_Vol1. Lastly, I compute the ratio of actual quarterly trading volume to implied quarterly trading volume (Actual/Implied). Implied quarterly trading volume is computed as the absolute net dollar volume, buys sells, for a fund-stock-quarter, aggregated across all stocks traded by the fund over the quarter. Thus, the ratio of actual to implied trading volume is a measure of the extent to which missing intra-quarter trading understates trading volume. The Appendix provides an example of the construction of Pct_Vol1 and Actual/Implied to further clarify these measures. Table 2 reports the distribution of the trading variables for hedge funds when trading on behalf of plan sponsors or when trading on their own account (i.e., Money Managers). The unit of observation is a fund-year. Volume and Implied/Actual reflect the average quarterly values, and Holdings Size and Pct_Vol1 reflect the average daily values. Panels A indicates that when trading on behalf of plan sponsors, the average hedge fund executes $40 million of trading per quarter. Naturally, the aggregate trading of hedge fund management companies (i.e., their trading as money management companies) is substantially larger. The average hedge fund management company trades roughly $3 billion per quarter. Similarly, the average Holdings Size is much larger for money managers than plan sponsors ($10.0 billion vs. $123.7 million). Panels C and D of Table 2 report the cross-sectional distribution of Actual/Implied and Pct_Vol1, respectively. I find that the average Actual/Implied is 1.50 for money managers. This suggests that intra-quarter trading accounts for a large fraction of hedge funds total trading. 10

However, intra-quarter' trading may simply reflect two different funds within the same family taking opposing positions during a quarter. For trading on behalf of a specific plan sponsor, the average Actual/Implied is 1.17 and the median value is only 1.09. Thus the typical hedge fund engages in relatively little intra-quarter trading. The results for Pct_Vol1 generally yield similar conclusions. 11 In particular, for the median hedge fund, roughly 8.1% of its total holdings were formed in the last month. This suggests an average holding period of roughly 12 months. This estimate is in line with Griffin and Xu (2009), and Reca, Sias, and Turtle (2015) who, using quarterly holdings, find the median hedge fund has a holding period of roughly one year. These results suggest that the typical hedge fund has a relatively long holding period which contrasts with the relatively short-term nature of liquidity provision profits. 2.3 Database Integrity and the Advantages of Transaction Data Most studies that investigate hedge fund skill focus either on monthly returns from commercial databases (e.g., TASS), or on the long-only equity holdings, reported at a quarterly frequency, from 13F data. In this section, I discuss some of the advantages, as well as the limitations of using transaction data relative to these more commonly used sources. A major advantage of transaction data is that it provides a relatively clean identification of liquidity-supplying and liquidity-demanding trades. This is not possible in commercial databases which only report monthly returns. Further, since the identification of liquidityproviding trades depends critically on the returns in the days prior to the trade, estimating 11 One exception is that money managers appear to be more short-term focused when considering Actual/Implied than when looking at Pct_Vol1. Much of this stems from quick round-trip trades, which have a larger impact on Actual/Implied relative to Pct_Vol1. For example, a purchase of a stock on January 3 rd that is reversed on January 4 th has a smaller effect on Pct_Vol1 than if the position had been reversed on January 30 th. In the former case, the initial purchase only influences a fund s net holdings for one trading day, while in the later it influence the funds net holdings for the entire month. In contrast the Actual/Implied ratio would be the same in both cases. 11

liquidity-providing trades from changes in quarterly holdings would be extremely noisy. Similarly, transaction data allow for more precise estimates of potential trading profits based on the exact date and execution price of the trade. This level of precision is important, as the profits from liquidity provision are likely to be largest in the period immediately following the trade. For example, Suominen and Rinne (2011) estimate that roughly 84% of the profits from liquidity provision accrue in the first week after the trade. Another advantage of ANcerno is that it does not suffer from many of the biases that plague commercial databases such as backfill bias and survivorship bias (Fung and Hsieh, 2009), and unreliable reported returns (Patton, Ramadorai, and Streatfield, 2015). ANcerno collects trading data on a fund only after it has subscribed to ANcerno, which eliminates backfill bias. ANcerno representatives have also confirmed that the data are free of survivorship bias. Moreover, ANcerno provides new data each quarter (with a three-quarter lag), but historical data are not updated. Thus, trades of non-surviving funds remain in the historical data. I also have no reason to doubt the reliability of the reported trades. There is little incentive for institutions to lie about their transactions. Unlike commercial databases, these transactions are not disclosed to potential investors. Moreover, institutions incur a significant expense when hiring ANcerno, and the benefits of ANcerno's services would be significantly reduced if the institution did not provide ANcerno with reliable data. A related concern is that ANcerno captures only a subset of trades. For example, hedge funds may attempt to conceal their most informed trades (Agarwal et al., 2013). However, ANcerno representatives believe it would be very difficult for institutions to conceal trades. Once an institution subscribes to ANcerno, a system is installed through which all trades must be routed. ANcerno representatives have also confirmed that the dataset does include short-sales, although it is not possible to distinguish short-sales from other 12

sales. Thus, in contrast to quarterly holdings, ANcerno data include intra-quarter roundtrip trades, confidential filings, and short sales. Unfortunately, ANcerno does suffer from a few limitations. First, ANcerno does not provide any information on management or incentive fees of the funds. However, my focus is not on estimating the realized returns that accrue to investors, but rather to test whether liquidity provision is an important mechanism through which hedge funds create value. A second limitation is that ANcerno does not capture non-equity trading. However, after excluding management companies that do not offer any funds following equity-oriented strategies, I find that the average Pct. Equity of ANcerno funds is in excess of 90%. In addition, I find no evidence that a fund s Pct. Equity is correlated with its tendency to engage in liquidity provision or its ETS. Collectively, these results help alleviate the concern that the omission of non-equity trades significantly impacts the main findings. A final concern is that ANcerno hedge funds are not representative of the population of hedge funds. In the Internet Appendix, I compare ANcerno hedge funds to the universe of 13Ffiling hedge funds and hedge funds that report to TASS. I find that ANcerno hedge funds are largely representative of 13F-filing hedge funds with respect to the characteristics of the stocks they hold and trade, as well as the performance of their holdings and trades. ANcerno hedge funds are also similar to TASS funds along a number of dimensions, including performance, incentive fees, leverage, and share restrictions. ANcerno hedge funds do differ from other hedge funds along a few dimensions. First, compared to 13F hedge funds, ANcerno hedge funds are significantly larger. This is consistent with Puckett and Yan (2011), who find that ANcerno institutions are larger than 13F-filing institutions. ANcerno funds are also more likely to have to have lower 13

management fees and more illiquid holdings. In the Internet Appendix, I discuss how the sample s tilt towards funds with lower liquidity and lower management fees may influence my conclusions. Methodology 3.1 Measuring Liquidity Provision I define liquidity provision as acting as the counterparty to an investor who is willing to pay a premium in exchange for immediacy. 12 This notion of liquidity provision is consistent with Franzoni and Plazzi (2015) who define liquidity providers as speculators who absorb temporary order imbalances and profit from the price pressure induced by liquidity-demanding trades. Thus, liquidity providers are similar to the market makers in Grossman and Miller (1988). However, liquidity-providing hedge funds need not act like traditional market makers who tend to hold zero inventory at the end of the day. Instead, hedge funds may benefit from liquidity provision (either intentionally or unintentionally) as part of a more long-term strategy. For example, hedge funds that follow certain quantitative strategies, such as pairs trading, may indirectly benefit from liquidity provision (Kavajecz and Odders-White, 2004; and Engelberg, Gao, and Jagannathan, 2009). Alternatively, funds engaging in fundamental analysis may try to profit from liquidity provision by implementing limit orders or by waiting to submit buy (sell) market orders until stock prices are falling (rising). For example, Fred Fraenkel of Fairholme Hedge Funds describes his investment strategy as: deep-value investing where we do an enormous amount of work on a 12 Note that profits from liquidity provision are conceptually (and empirically) different from profits from liquidity risk. Profits from liquidity risk reflect the excess return from holding stocks that are exposed to aggregate shocks in the level of liquidity (see e.g., Sadka, 2010), while profits from liquidity provision reflect the returns from providing immediacy (see, e.g., Grossman and Miller, 1988). Empirically, the returns from liquidity risk and liquidity provision are negatively correlated (Jylhä, Rinne, and Suominen, 2014). For example, during the financial crisis of 2008-2009, the returns on stocks with high liquidity risk declined (Sadka, 2010), while the returns to liquidity provision increased substantially (Nagel, 2012). 14

small number of names and buy them when they re exceedingly stressed and sell them when everybody wants them. 13 Building off this notion of liquidity provision, I identify liquidity-providing trades as contrarian trades, i.e., buying stocks that are declining in price, and selling stocks that are rising in price. This view of liquidity provision is consistent with Nagel (2012) who constructs a model showing that the returns from contrarian strategies closely track the returns earned by liquidity providers. While it is intuitive that contrarian strategies are related to liquidity provision, the appropriate horizon to measure past returns is less obvious. Prior literature has considered several alternative horizons, including variants of one-day (Khandani and Lo, 2011), one week (Nagel, 2012; and Lehmann, 1990), and one-month (Jegadeesh, 1990; Suominen and Rinne, 2011; Jylhä, Rinne, and Suominen, 2014) contrarian strategies. Suominen and Rinne (2011) and Jylhä, Rinne, and Suominen (2014) show that all daily returns up to twenty lags are significantly negatively related to one-week ahead returns, suggesting that return reversals persist for at least one month. They also show that the ability to forecast future returns improves significantly when you include each of the past twenty days return individually, rather than just the past one-month return. Accordingly, I follow Jylhä, Rinne, and Suominen (2014) and compute the expected return to liquidity provision as: R t= 21 = α + β R + β C + ε (1) i,t, t+ 4 t t, τ it τ tc, it, it,. τ = 1 The dependent variable, Ri,t,t+4, is the stock s next five-day excess return. The choice to forecast five-day returns follows Jylhä, Rinne, and Suominen (2014) who show that the liquidityproviding trading strategy with a one-week trading horizon has a higher Sharpe Ratio than 13 Fund Focus: Fairholme Hedge Fund Builds On Berkowitz 25 Years of Success, FinAlternatives, January 27, 2014. http://www.finalternatives.com/node/25948 15

liquidity-providing strategies with a one-day or one-month horizon. The independent variables include the excess returns of each of the stock s past one-day returns over the prior month (21 trading days), the past one-month return multiplied by the stock s market capitalization at the end of the prior year, and the past one-month return multiplied by the stocks trading volume over the prior month. 14 Excess returns are calculated by deducting from stocks returns the returns to a corresponding equal-weighted Fama-French 48 industry index. The interaction terms control for differences in the degree of return reversal that are related to size and trading volume (Khandani and Lo, 2011; and Campbell, Grossman, and Wang, 1993). I estimate equation (1) each trading day from 1990-2010. I impose the same sample requirements as Jylhä, Rinne, and Suominen (2014) and confirm their main findings. Specifically, I find that the coefficient on all of the lagged return variables are negative, with the largest coefficients (in absolute value), being the lagged one, two, and three day returns. I then compute a stock s expected return due to liquidity provision (RLP) as: where ˆt α, ˆt β, τ, and β, RLP ˆt C t= 21 = ˆ α + ˆ β R ˆ + β C (2) i,t t t, τ it τ t, C i, t τ = 1 represent the estimated values from equation (1). To ensure that RLP could have been estimated by a hedge fund in real time, I use the estimated coefficients from equation (1) that includes all data through the prior year. For example, in estimating RLP in 1999, I use the coefficients from equation (1) estimated using data through 1998. In untabulated analysis, 14 I use the Nasdaq volume adjustment algorithm proposed by Gao and Ritter (2010) to account for institutional differences in the way that Nasdaq and NYSE-Amex volumes are reported. 16

I confirm that the distribution of RLP is similar to the distribution reported in Jylhä, Rinne, and Suominen (2014). 15 I define liquidity-supplying (LS) trades as purchases of stocks with high RLP or sales of stocks with low RLP. Similarly, liquidity-demanding (LD) trades are purchases of stocks with low RLP or sales of stocks with high RLP. In the Internet Appendix, I also consider proxies for liquidity-provision based on a stock s past one-day (Mom1), one-week (Mom5), or one-month (Mom21) return. All three momentum variables are strongly negatively correlated with RLP, and all three typically yield qualitatively similar conclusions. 3.2 Measuring Hedge Funds Equity Trading Skill (ETS) I measure hedge funds equity trading skill (ETS) by examining the extent to which the equity trades of hedge funds forecast returns. I use transactions-based calendar-time portfolios to aggregate the many trades of an individual fund. 16 Specifically, each time a fund buys a stock, I place the same number of shares in the calendar-time buy portfolio. The stock is placed in the portfolio on day 0 (the day of the purchase) and the position is held until either 1) the fund reverses its position, 2) the fund drops out of ANcerno, or 3) until the end of the holding period (whichever comes first). If a buy trade offsets a pre-existing sale (that occurred within the holding period), then the shares are removed from the sell portfolio. Thus, the buy portfolio reflects a fund s net- 15 Specifically, I estimate the returns to a long-short strategy where every day a long (short) position is opened in stocks with positive (negative) expected five-day excess returns. I use the stocks expected five-day excess return as portfolio weights when forming the long and short portfolios. Over the 1999-2010 sample period, after imposing Jylhä, Rinne, and Suominen s (2014) sample requirements, I find this strategy generates an average daily return of 0.10% (t=6.53) and a median daily return of 0.05%. This is similar to Jylhä, Rinne, and Suominen (2014) who estimate an average (median) daily return of 0.06% (0.05%) from 1990-2008 16 Seasholes and Zhu (2010) emphasize the benefits of calendar-time portfolios relative to other approaches. 17

long position, based on its trades over the holding period. I perform an analogous procedure for sales. I consider several holding periods. The first is to not assume any holding period. This is equivalent to computing the performance of hedge funds equity holdings under the assumption that funds have no holdings prior to joining ANcerno. 17 I label this performance metric as Equity Holdings Skill (EHS). While EHS provides insight into whether hedge funds holdings are informed, it may not be well-suited for assessing hedge funds ability to create short-term value via liquidity provisions. For example, hedge funds could create short-term value from liquidity provision, but hold their positons for too long for such ability to significantly influence EHS. To offer a more powerful test of liquidity-provision trading skill, I also consider a holding period of one-month (21-trading days), one-month Equity Trading Skill (ETS1). The one-month holding period amounts to computing a fund s holdings based only on the trades that were executed over the past 21 trading days. The one-month holding period is motivated by the finding that liquidity provision profits persist for at least one month (Jegadeesh, 1990). I also consider several intermediate holding periods such as three month (ETS3) and 12 months (ETS12). The construction of transactions-based calendar time portfolios results in a time-series of daily buy and sell portfolios. I compute the principal-weighted ETS of the buy (or sell) portfolio of fund f on day t as: R n = W R (3) f, t it, 1 it, i= 1 17 In a few cases, managers appear to exit and reenter ANcerno. In cases where a fund does not make any trades in ANcerno for more than two consecutive quarters, I assume the fund has left ANcerno. If the firm later appears, I compute holdings as if it were a new fund. 18

where Wi,t-1 is the weight of stock i in the portfolio of fund f. The weight of stock i is defined as the value of stock i scaled by the aggregate value of all positions in the portfolio. In computing the value of stock i, I distinguish between holdings due to trades made prior to day t and holdings due to trades made on day t. For trades made prior to day t, the value of stock i is computed as the number of shares held of stock i at the end of day t-1, multiplied by the closing price of stock i on day t-1. For trades made on day t, the value of stock i is computed as the number of shares traded of stock i on day t multiplied by the execution price. Ri,t is a measure of the return on stock i on day t. For trades made prior to day t, Ri,t is the benchmark-adjusted return of stock i on day t. For trades made on day t, Ri,t is computed as the closing price on day t divided by the execution price. 18 This approach yields a time-series of daily returns. I convert the daily returns to monthly returns by multiplying the daily return estimate by 21. I follow Daniel, Grinblatt, Titman, and Wermers (hereafter DGTW) (1997) and Wermers (2004) in computing benchmark-adjusted returns. Specifically, excess returns are measured relative to the DGTW 125 size, industry-adjusted book-to-market, and momentum benchmarks. 19 I use DGTW-adjusted returns as the benchmark for adjusting returns for two reasons. First, characteristic matching allows for benchmarks that explain more of the realized variation in returns than those based on factor loadings and thus have greater power to detect abnormal performance (DGTW, 1997). This is likely to be particularly advantageous for studying hedge fund skill, given their greater reliance on dynamic strategies (Patton and Ramadorai, 2013). Second, it allows for greater comparability to the large literature that reports DGTW-adjusted returns as a measure of 18 I include day t transactions since liquidity-providers will likely benefit from intra-day return reversals. Nevertheless, in the Internet Appendix, I confirm that results are similar if I estimate ETS excluding day t transactions. 19 I thank Russ Wermers for providing the benchmark classifications. The DGTW (1997) benchmarks are available at: http://alex2.umd.edu/wermers/ftpsite/dgtw/coverpage.htm 19

fund skill based on a fund s holdings or transactions. 20 In the Internet Appendix, I also compute ETS as the alpha from a five-factor model which includes the three Fama-French (1993) factors, the Carhart (1997) momentum factor, and the Sadka (2006) liquidity factor and find similar results. A few additional clarifications are in order in interpreting ETS. First, ANcerno does not provide any information on management or incentive fees, thus ETS is gross of any fund expenses. As such, the analysis examines whether hedge funds have ETS, but it does not offer any insight into what fraction of the skill is captured by the manager in the form of higher fees. Second, I report results gross of trading commissions. This allows for a more direct comparison to the literature that explores skill using quarterly holdings. Further, if trading commissions primarily reflect a fixed payment for premium services from a broker (e.g., IPO access or invitations to investor conferences), then commissions will largely reflect factors unrelated to trading skill. For example, on average, small institutions need to pay a higher average commission per share than large institutions in order to generate premium status with brokerage firms (Goldstein et al., 2009). On the other hand, if one views trading commissions as the marginal cost of executing a trade, then incorporating trading commissions is a more appropriate metric for assessing the value that hedge funds create through their trading. Thus, in the Internet Appendix, I also compute ETS using returns net of trading commissions. 4. The ETS of Liquidity-Supplying and Liquidity-Demanding Trades 4.1 The ETS of Hedge Funds by Trade Type 20 Examples of such papers include: DGTW (1997), Wermers (2000), Chen, Jegadeesh, and Wermers (2000), Alexander, Cici, and Gibson (2007), Griffin and Xu (2009), and Puckett and Yan (2011). 20

I begin by reporting the ETS1 for the average hedge fund. First, using the methodology outlined in Section 3.2, I calculate the average ETS1 for each fund-day (ETS1f,t). I then compute the mean of ETSf,t across all funds in the sample on day t (ETS1t). Finally, I compute the mean of ETS1t across the 3,018 trading days in the sample (ETS1). Thus, ETS1 captures the time-series average of daily returns, expressed as a monthly return in percent. I compute standard errors from the time-series standard deviation of ETS1t. 21 Table 3 reports the results. The ETS1 of the buy minus sell portfolio, using DGTW-adjusted returns, is a statistically significant 0.39% per month. This suggests that the average hedge fund has significant short-term trading skill. To test whether this trading skill is concentrated in liquidity-supplying trades, I compute an ETS1 constructed exclusively from liquidity-supplying (LS) trades. I also compute an ETS1 constructed from liquidity-neutral (LN) trades, and liquiditydemanding (LD) trades. I define LS trades as purchases of stocks in the top third of RLP (as defined in Section 3.1) and sales of stocks in the bottom third of RLP. Similarly, LD trades are purchases of stocks in the bottom third of RLP and sales of stocks in the top third of RLP. All remaining trades are defined as LN. I find that the average hedge fund generates a statistically significant ETS1 of 1.19% per month from their LS trades. These findings suggest that hedge funds are creating significant shortterm value from their liquidity-supplying trades. This results is in stark contrast to the Barrot, Kaniel, and Sraer (2014) who find that retail investors lose money on their liquidity-supplying 21 I find that ETS1 does not exhibit significant serial correlation. For example, the first-order autocorrelation is -0.01 (t=-1.28). Thus computing standard errors from the time-series standard deviation should lead to unbiased standard errors (Petersen, 2009) 21

trades because they are picked off and earn significant negative returns on day 0. Thus, hedge funds appear to be particularly adept at minimizing adverse selection costs. In contrast to hedge funds significant ETS1 in their LS trades, their LD trades earn a statistically insignificant -0.10%. The difference between ETS1 in LS and LD trades, 1.29%, is also highly significant. In the Internet Appendix, I confirm the main finding from Table 3 are robust to alternative proxies for liquidity provision, alternative risk-adjustments, and alternative sample filters. I also show that the ETS1 for LS trades is positive for all 12 years in the sample, indicating that the superior ETS1 of LS trades is not driven by a few special time periods (e.g., the tech bubble or the financial crisis). Further, I show that LS trades continue to generate significantly positive ETS1 even after accounting for trading commissions. I next examine the ETS of LS and LD trades over longer horizons. Specifically, I compute the ETS of LS and LD trades by calculating holdings based on trading ranging from the prior one month (i.e., ETS1) to the prior 12 months (i.e., ETS12). Figure 1A plots the average ETS of the buy minus sell portfolio constructed from LS and LD trades. The average profitability of LS trades is strongest over a one-month holding period and quickly declines. The decline in the profitability of LS trades is not surprising since liquidity-provision profits are concentrated in the first month after the trade. It is worth emphasizing that the decline in average monthly ETS does not necessarily imply that LS holdings earn negative returns after the first month; rather it simply suggests that the profitability of LS holdings is largest in the first month after the trade. To obtain a better sense for whether LS trades earn positive or negative returns after the first month of the trade, Figure 1B plots the cumulative ETS over different holding periods. I compute cumulative ETS as the average ETS multiplied by the monthly holding period. For 22

example, if an LS trade was held for two months and earned a return of 2% in the first month and 0.50% in the second month, the cumulative ETS (Figure 1B) would be 2.5%, while the average ETS (Figure 1A) would be 1.25%. Figure 1B indicates that LS trades earn positive returns over longer horizons. Specifically, the cumulative ETS of LS trades is 1.19% for a one-month holding period versus 2.92% for a one-year holding period. The positive drift in LS trades suggests that the ETS1 associated with LS trades does not reverse over the subsequent year. Figures 1A and 1B also show that, regardless of the investment horizon, LD trades earn returns that do not substantially differ from zero. 4.2 Determinants of the ETS1 of LS and LD Trades The prior results show that the ETS1 of hedge funds is greatest in their liquidity-supplying trades, particularly over a one-month holding period. In this section, I explore time-series and cross-sectional factors that may impact a fund s ability to profit from liquidity provision. In the time series, I examine whether the ETS1 of hedge funds LS trades are larger during periods of low funding liquidity, when the expected returns to liquidity provision are the greatest. Following Nagel (2012), I use the VIX Index (VIX) as a proxy for funding liquidity. Since VIX may not capture the most dramatic declines in funding liquidity, I also create a Financial Crisis dummy which equals one during the second half of 2008, and zero otherwise (Ben-David, Franzoni, and Moussavi, 2012). Hendershott, Jones, and Menkveld (2011) find that the introduction of NYSE s autoquote system increased algorithmic trading and improved liquidity, 23

suggesting the profits from liquidity provision likely declined following the introduction of autoquote. I thus include a Post Autoquote dummy which equals one starting in February, 2003. 22 In the cross section, I test whether the ETS1 of LS trades is weaker for funds with more assets in their ETS1 portfolio (Holdings Size1). Such a relationship would be consistent with capacity constraints limiting hedge funds ability to generate short-term returns due to liquidity provision. 23 I also examine whether the ETS1 associated with LS trades is larger for funds with greater share restrictions (Restrictions). Such funds are likely to have more patient capital, and may be better able to take advantage of price-pressure induced mispricing. For example, during periods where most hedge funds experience significant outflows (e.g., the financial crisis), fund with strict share restrictions are in the best position to purchase stocks at a significant discount. To measure Restrictions, I match the ANcerno management company to the company name in TASS. If the management company reports to TASS, I estimate Restrictions of the management company, as the average Restrictions across all funds offered by the management company, excluding funds of funds. I measure Restrictions of a fund as the sum of the notice period and the redemption period. I exclude funds of funds since such funds would not appear in ANcerno. If the ANcerno management company does not report to TASS (roughly half the sample), I set Restrictions equal to zero and include a corresponding TASS Dummy (not reported). I estimate a panel regression of the ETS1 of fund f on day t on: Log (VIX), Financial Crisis, Post Autoquote, Restrictions, Log (Holding Size1), Money Manager, and TASS Dummy. I report 22 Autoquote was introduced in phases with the first stock being added on 1/29/2003 and the last stock added on 5/27/03. Setting Post Autoquote equal to one starting in June, 2003 yields very similar results. 23 Fung et al., (2008) and Ramadorai (2013) find evidence of capacity constraints in the hedge fund industry. There is also evidence of decreasing returns to scale outside of the hedge fund industry, including mutual funds (Chen et al., 2004), closed-end funds (Wermers, Wu, and Zechner, 2013), and venture capital (Kaplan and Schoar, 2005). However, recent work by Pastor, Stambaugh, and Taylor (2015) finds that decreasing returns to scale for mutual funds is stronger at the industry level than the fund level. 24

results for ETS1 constructed from both LS and LD trades. I standardize continuous variables to have mean zero and variance one. Since Holdings Size1 has a different interpretation for plan sponsors and money managers (see Table 2), I standardize Holding Size1 separately for the plan sponsor and money manager sample. I compute standard errors by double clustering by day and management company. Specifications 1 through 3 of Table 4 examine the determinants of the ETS1 of LS trades. Specification 1 omits Financial Crisis due to its high correlation with VIX. I find that LS trades are more profitable during periods when VIX is high, which is consistent with liquidity provision being more profitable during periods of low funding liquidity. The profitability of LS trades also declines by 0.66% after the introduction of autoquote, which is consistent with increased algorithmic trading reducing hedge funds liquidity-provision profits. Funds with greater share restrictions and smaller funds also benefit more from liquidity provision. This suggests that impatient capital and capacity constraints reduce hedge funds ability to profit from liquidity provision. Specification 2 confirms that the ETS1 of hedge funds trades were substantially larger during the financial crisis. Controlling for the financial crisis also strengthens the conclusion that the introduction of autoquote reduced the ETS1 associated with hedge funds LS trades. One concern is that some omitted variable is driving the time-series patterns. For example, it is possible that the hedge funds in the ANcerno sample after 2003 are simply less skilled than funds in the sample prior to 2003, creating a spurious coefficient on Post Autuquote. To address this concern, in Specification 3 I include fund fixed effects. This approach also helps control for the possible endogoneity between fund size and trading skill (Pastor, Stambaugh, and Taylor, 2015). I find that the coefficients on the time series variables (VIX, Financial Crisis, and Post- Autoquote) are virtually unchanged after the inclusion of fund fixed effects. Further, the coefficient 25

on Holdings Size1 becomes stronger, further supporting the view liquidity-provision strategies suffer from decreasing returns to scale. 24 I also find (unreported) that the fund fixed effects are jointly significant. This is consistent with other unobservable fund characteristics, such as trading skill, contributing to a fund s ability to generate short-term returns from liquidity provision. For comparison, Specifications 4 through 6 repeat the analysis for LD trades. There is no evidence that the ETS1 of LD trades varies with any of the time-series variables. The coefficients on Restrictions remains significant, suggesting Restrictions are valuable for both LS and LD trades. There is also some evidence that larger funds generate smaller ETS1 on their LD trades, although this finding is not robust to the inclusion of fund fixed effects. 5. The ETS of Liquidity-Supplying and Liquidity-Demanding Funds 5.1 Characteristics of Liquidity-Supplying and Liquidity-Demanding Funds In the previous section, I document that hedge funds ETS1 is significantly larger in their liquidity-supplying trades relative to their liquidity-demanding trades. All else equal, this finding suggests that funds that engage in greater amounts of liquidity provision should earn higher ETS1. However, a fund s tendency to engage in liquidity provision may be correlated with other sources of trading skill. For example, funds that engage in greater amounts of liquidity-demanding trades may do so because they have significant amounts of short-lived private information. If so, LS funds need not outperform LD funds. Alternatively, funds that take advantage of profits from liquidity provision may be more likely to take advantage of other sources of mispricing. This argument suggests that LS funds will outperform LD funds both because they place more weight on more profitable LS trades and because they are generally more skilled at identifying mispriced stocks. 24 I exclude Restrictions since this variable has virtually no time-series variation within a fund. 26

To examine the relationship between a fund s tendency to provide liquidity and their ETS1, each year I sort funds into quintiles based on the magnitude of their liquidity provision. Specifically, for each fund-year, I compute the principal-weighted average RLP of stocks purchased less the principal-weighted average RLP of stocks sold. Thus, a high RLP indicates that the fund is more likely to be buying stocks with high expected returns due to liquidity provision and selling stocks with low expected returns due to liquidity provision. I define funds in the top quintile of RLP as liquidity-supplying (LS) funds and funds in the bottom quintile as liquiditydemanding (LD) funds. I begin by presenting summary statistics on the characteristics of LS and LD funds. For each fund-year, I compute the principal-weighted average characteristic of stocks purchased less the principal-weighted average characteristic of stocks sold. I consider the following characteristics: market capitalization (Size), the book-to-market ratio (BM), two measures of the level of liquidity (Turnover and Amihud Illiquidity), idiosyncratic volatility (IVOL), and the return on the stock in the prior day, week, or month (Mom1, Mom5, Mom21, respectively). Each trading day, all stock characteristics are standardized to have mean zero and variance one. The Appendix provides a more detailed description of the construction of the stock characteristics. I then merge annual averages for each fund-year with each of the fund s one-year ahead daily estimates of ETS1. I report the average value across all funds in the portfolios. I also test whether the average value for LS funds is significantly different from the average value for LD funds. Significance is computed from standard errors double clustered by management company and year. 27

Not surprisingly, LS funds are significantly more contrarian as measured by Mom1, Mom5, and Mom21. 25 LS funds also have significantly higher levels of RLP in the year following the ranking period, which confirms that a fund s tendency to engage in liquidity provision is a persistent fund characteristic. However, LS funds did not provide liquidity during the financial crisis which suggests that LS funds ability to provide liquidity is sensitive to funding conditions. Panel B of Table 5 reports fund-level characteristics. For each fund-year, I compute the number of days the fund has appeared in the sample (Age), the natural log of average quarterly trading volume (Volume), and Actual/Implied and Pct_Vol1, as defined in Section 2.2. Each day, I also compute the size of the fund s holdings constructed from its trading over the past 21 trading days (Holding Size1) as well as its trading over the entire sample period (Holding Size). Finally, I report summary statistics for the money manager dummy (Money Manager), the average commission per share (Com_Share) and the average execution shortfall (Shortfall), defined as the difference between the execution price and the price at the time the order was placed (Anand et al., 2012). LS funds are significantly smaller as measured by either Holdings Size1 or Volume. This suggests that smaller funds are more likely to follow liquidity provision strategies. This is consistent with the findings in Table 4 which suggest that liquidity provision strategies suffer from decreasing returns to scale. LS funds also have longer holding periods, as measured by both Actual/Implied and Pct_Vol1. Finally, LS funds have significantly lower Shortfall. The negative 25 LS funds also tend to tilt their net purchases towards large stocks and growth stocks. This is largely driven by the fact that LS funds, by construction, are net buyers of stocks with low industry-adjusted returns, and large stocks and growth stocks are more likely to have low industry-adjusted returns (Fama and French, 1992). 28

shortfall of LS funds suggests they obtain better execution prices than their pre-trade benchmarks, which is consistent with LS funds profiting from liquidity provision. I also collect the following fund characteristics from TASS: Management Fee, Incentive Fee, the sum of the notice and redemption period (Restrictions), the first-order serial correlation of the fund s returns (Asset Illiquidity), a fund s beta with respect to the Sadka (2006) liquidity risk factor (Liquidity Risk), and a dummy for whether the fund s primary category is either equity market neutral or long-short equity (Pct. Equity). The Appendix provides further details on the definitions and construction of the fund characteristics. I next merge the ANcerno and TASS data at the management company level. Of the 57 hedge fund management companies that appear in Table 5, 23 of the management companies report to TASS. 26 For each management company, I report the equal-weighted average characteristic across all fund products offered by the management company during the ranking year. 27 If the management company does not report to TASS during the ranking year, I use information from the most recent year prior to the ranking period. In computing the averages across all funds within the management company, I exclude funds of funds, since such funds would not appear in ANcerno. The final sample includes 599 fund-year observations with non-missing TASS data. However, for a given year, there is no variation in the TASS characteristics for funds that belong to the same management company. Further, the fund characteristics in TASS reflect the most recent values for the fund. As a result, the only source of time-series variation in characteristics for a management company is when a fund enters or exits TASS. Given the 26 Although the initial sample consists of 71 hedge fund management companies (see Table 1), requiring the fund to stay in the sample for two consecutive years reduces the sample to 57. 27 I report equal-weighted averages rather than assets-under-management (AUM) weighted averages, because many funds are missing information on AUM. However, results are similar using AUM-weighted averages. 29

relatively small number of unique observations, the results based on TASS characteristics should be interpreted with caution. Panel C of Table 5 compares LS and LD funds across the fund characteristics computed from TASS. Overall, there is no evidence that LS and LD funds differ significantly across any dimension. The results also highlight that the Pct. Equity for LS and LD funds is very high (0.97 and 0.90, respectively). This suggests that most ANcerno funds (or at least those that report to TASS), follow equity-oriented strategies. This alleviates the concern that non-equity trading results in biased estimates of ETS. 5.2 The ETS1 of Liquidity-Supplying and Liquidity-Demanding Funds Univariate Sorts I now examine whether LS funds earn superior ETS1. Specifically, at the end of each calendar year, I sort funds into quintiles based on their RLP. I compute the ETS1 of each fund (as described in Section 4.1) and then compute the ETS1 of each quintile as the average ETS1 across all funds that belong to the quintile. I examine the ETS1 of each quintile in the one, two, and three years following the ranking period. The results, presented in Table 6, indicate that LS funds generate persistently positive ETS1. For example, in the year following the ranking period, LS funds earn an ETS1 of 0.97% per month. Similarly, in the 2 nd and 3 rd year following the ranking period, LS funds earn an ETS1 of 1.53% and 1.06%, respectively. All three estimates are significant at a 1% level. In contrast, the ETS1 of LD funds is not significantly different from zero in each of the three years following the ranking period. A natural question is whether the superior performance of LS funds is simply a consequence of placing more weights on LS trades, which on average are more profitable, or whether some of the performance difference comes from more subtle forms of trading skill. In the 30

year after the ranking period, I find that relative to LD funds, LS funds overweight LS trades by roughly 5% and underweight LD trades by roughly 5%. Given the evidence in Table 3 that LS trades outperform LD trades by 1.3%, differences in portfolio weights on LS vs LD trades accounts for roughly 0.13% of the outperformance. 28 This suggests that much of LS funds superior ETS1 stems from factors beyond simply placing more weight on LS trades. For example, LS funds may be particularly adept at identifying profitable liquidity-provision opportunities that are not captured by the RLP measure. These less mechanical sources of trading skill are likely to be less easily replicable and help explain why significant differences in ETS1 can persist in equilibrium. In the Internet Appendix, I also confirm that the Table 6 results are robust to a variety of methodological choices. First, I repeat the analysis using DGTW-adjusted returns net of trading commissions. I find that the ETS1 of LS funds in the year after the ranking period falls to 0.59% per month, but the estimate remains statistically significant. I also confirm that the results are robust to using alternative proxies for liquidity provision, alternative risk adjustments, including funds with Pct. Equity = 0, excluding Day 0 returns from the analysis, excluding money managers from the sample, and correcting for potential look-ahead bias due to fund attrition within the ANcerno sample (see e.g., ter Horst, Nijman, and Verbeek (2001) and Baquero, ter Horst, and Verbeek (2005). Lastly, I find that a similar, but slightly weaker pattern emerges amongst nonhedge fund institutions (e.g., mutual funds, banks, insurance companies, etc.). 5.3 The ETS1 of Liquidity-Supplying and Liquidity-Demanding Funds Regression Evidence The results from Table 5 indicate that LS and LD funds differ along several dimensions. To examine whether the superior ETS1 of LS funds is robust to controlling for these differences, I 28 In the internet Appendix, I more formally decompose ETS1 into stock picking, style timing, and average style effects. I find that the average style difference between LS and LD funds is a statistically significant 0.14%. 31

estimate daily cross-sectional regressions of a fund s ETS1 on a fund s RLP and other controls. The controls include the stock and fund characteristics, reported in Panels A and B of Table 5. However, I omit some variables due to high correlations. First, I omit Amihud Illiquidity due to its high correlation with Size (ρ=-0.82). In addition, since RLP is, by construction, highly negatively correlated with past returns, I exclude the Mom1, Mom5, and Mom21 variables. 29 Similarly, since Shortfall is related to a fund s tendency to provide liquidity, in many specifications I also omit Shortfall. I also drop Log (Volume) due to its strong correlation with Log (Holding Size1) (ρ=0.93), and I drop Pct_Vol1 because it is strongly related to Log (Actual/Implied). To explore whether RLP has predictive ability after controlling for past performance, I include a fund s t-statistics of their ETS1 in the prior year [t(ets1)]. 30 I standardize all continuous control variables to have mean zero and variance one on each day, and Log (Holding Size1) and Log (Actual/Implied) are standardized separately by institution type (i.e., plan sponsor vs. money manager). Specification 1 of Table 7 reports the average coefficients from the 2,767 daily crosssectional regressions. I find that a one-standard deviation increase in RLP is associated with a 0.46% per month increase in ETS1 over the subsequent year. The estimate is significant at a 1% level. Further, the point estimate is larger than the estimates on any of the other control variables. The only other variable that is significant at a 5% level is Log (Holding Size1). This finding is consistent with capacity constraints limiting a fund s ability to generate short-term trading returns. Specification 2 replaces RLP with Shortfall, both of which can be viewed as proxies for a fund s tendency to supply liquidity [ρ(rlp, Shortfall) = -0.64]. The significantly negative coefficient on Shortfall further supports the view that liquidity-supplying funds have greater ETS1. 29 In the Internet Appendix, I confirm that replacing RLP with any of the Mom variables leads to similar conclusions. 30 Using t(ets1) rather than ETS1 helps reduce the downward bias in persistence due to measurement error in alphas (Jagannathan, Malakhov, and Novikov, 2010). Results are qualitatively similar if I replace t(ets1) with ETS1. 32

However, the coefficient on Shortfall is smaller, in absolute value, than RLP (0.35 vs. 0.46), which suggest that RLP is a better predictor of a fund s future ETS1. To more directly test this conjecture, in Specification 3, I include both RLP and Shortfall. I find that RLP wins the horse race. Specifically, the coefficient on RLP remains significantly positive (at a 10% level), while the coefficient on Shortfall is no longer statistically significant. Specifications 4 and 5 augment Specification 1 by including Restrictions and Asset Illiquidity as controls, respectively. I only consider two TASS variables because the TASS sample contains only 23 unique management companies, and many of the variables are highly correlated. Further, Restrictions and Asset Illiquidity have a correlation of 0.71, so I estimate each variable separately. If the fund does not have TASS data, I set Restrictions (or Asset Illiquidity) to zero and include a corresponding TASS dummy which equals one when TASS data is available and zero otherwise (not reported). Consistent with Aragon (2007), the results from Specifications 4 and 5 indicate that funds with greater share restrictions and higher levels of asset illiquidity generate superior ETS1. However, controlling for these effects has little impact on the coefficient on RLP. 5.4 The EHS of Liquidity-Supplying and Liquidity-Demanding Funds The analysis thus far suggests that LS funds generate persistently higher ETS in the month following the trade. The one-month holding period offers a powerful test of whether liquiditysupplying funds are able to create short-term value through liquidity provision. However, it does not offer any insight into how the short-term value actually impacts the performance of a fund s holdings. The evidence in Figure 1 indicates that most of the abnormal returns associated with LS trades are concentrated over one-month holding periods. However, the summary statistics in Tables 2 and 5 suggest that hedge funds have average holding periods of roughly one year. Taken 33

together, these findings suggest that the superior ETS1 of LS funds may not translate into an economically large outperformance of their holdings. To more directly examine this issue, in Table 8 I repeat the analysis in Table 6, except I now examine the average equity holding skill (EHS), as defined in section 3.2, of each portfolio. The All Funds row indicates that the average hedge fund has economically small and statistically insignificant EHS. This finding provides an out-of-sample confirmation of Griffin and Xu (2009), who using 13F data, also find little evidence that the equity holdings of hedge funds outperform. While there is some evidence that hedge funds create value in non-equity investments (Aragon and Martin, 2012), the growing evidence that the equity holdings of hedge funds do not outperform casts doubt on the value of equity-oriented hedge funds. 31 The remaining rows report the EHS of funds sorted by RLP quintiles. The only evidence of significant outperformance is amongst the funds in the top quintile of liquidity-provision who outperform by 0.23% per month in the year after the ranking period. 32 While this estimate is statistically significant, the magnitude is much smaller than the ETS1 estimate of 0.97% per month. Figure 2 plots the ETS of all hedge funds (All), LS hedge funds, and LD hedge funds using calendar-time transaction portfolios with holding periods of one month, three months, six months, and 12 months (ETS1, ETS3, ETS6, and ETS12). Figure 2 also reports EHS and ETS>1, where ETS>1 captures a fund s EHS after excluding positions formed in the past month. Consistent with 31 There is some evidence that equity-oriented hedge funds are becoming less attractive to investors as well. According to Barclay s Hedge, the fraction of the hedge fund industry dedicated to equity-oriented strategies has declined from a high of 39% in 2002 to a low of 22% at the end of 2012 (http://www.barclayhedge.com/research/indices/ghs/mum/hf_money_under_management.html). 32 Given the point estimate is barely significant at a 5% level one may wonder how sensitive this conclusion is to different methodological choices. In the Internet Appendix, I consider 11 different specifications including different liquidity provision proxies, different risk adjustments, and alternative sample filters. In all 11 cases I find that LS hedge funds earn positive EHS, and in 8 of the 11 specifications the estimate is significant at a 5% level. I also examine the results for non-hedge institutions (e.g. mutual funds), and I find no evidence of outperformance. 34

longer holding periods reducing the value of liquidity provision, the ETS of LS funds (and All funds) falls monotonically as the holding period increases. Even if LS funds reversed all positions after one-year (i.e., ETS12), the average returns for LS funds would be 0.29%, roughly 1/3 of their ETS1. In addition, the EHS of LS funds is similar to their ETS>1 (0.23% vs. 0.19%), suggesting that LS funds impressive ETS1 has a relatively modest impact on their EHS. Table 9 also examines the relationship between RLP and EHS in a regression framework. The analysis is analogous to Table 7, but I replace ETS1 with EHS. Since I now examine the performance of a fund s holdings, I also replace Log (Holding Size1) with Log (Holding Size). In addition, I include the t-statistics of the EHS of the fund in the prior year as a control for a fund s longer-horizon trading skill. Specification1 of Table 9 indicates that RLP is significant predictor of EHS. In particular, a one-standard deviation increase in RLP is associated with a 0.19% increase in EHS. This estimate is roughly 40% of the magnitude found in Table 7 (0.19 vs. 0.46). This is consistent with the findings in Table 8 and Figure 2 and further highlights that the impact of RLP on ETS declines at the holding period increases. I also find that the other variables that were significant predictors of ETS1 (e.g., Shortfall, Restrictions, and Asset Illiquidity) fail to predict EHS. This suggests that many of these variables give funds a significant advantage in short-term trading but don t materially affect the performance of the fund s holdings. 5.5 Why Don t Hedge Funds Unwind Their Trades in the Short-Term? The results suggest that LS funds create significant short-term value from liquidity provision, but due to their relatively long holding periods, the impact of liquidity provision on the performance of their holdings is more modest. This finding raises the questions of why LS hedge funds don t unwind their positions more quickly. Trading costs are one factor that limits the 35

benefits of moving to a shorter holding period. In particular, results from the Internet Appendix indicate that the ETS1 of LS funds falls from 0.97% to 0.64% after incorporating trading commissions. However, 0.64% is still more than double the EHS (or even the ETS12) of LS funds, suggesting that trading costs are only a partial explanation. Another potential factor is that liquidity-provision strategies likely suffer from decreasing returns to scale. For example, a hedge fund that moves from a one-year holding period to a onemonth holding period must invest 12 times as much in each position or spread its principal across other (presumably less profitable) investment opportunities. 33 In the absence of other profitable investment opportunities, it may be optimal for a fund to simply hold its existing positions and avoid additional transaction costs. This explanation is consistent with the Table 4 findings that show that funds with greater Holding Size1 benefit less from liquidity provision, as well as the results in Table 5 which show that larger funds (as measured by Holding Size1 or Volume) are less likely to follow liquidity-provision strategies. To estimate how much the increase trading volume could erode fund performance, I consider a fund with a one-year holding period, roughly the median holding period for funds in the sample. Such a fund would have to increase its trading volume 12-fold, resulting in a 2.49 [Ln (12)] increase in Log (Holding Size1). The cross-sectional standard deviation of Log (Holding Size1) is 1.51, so this increased trading volume amounts to a 1.65 standard deviation increase. The estimates from Specification 1 of Table 7 indicate that a one-standard deviation increase in Log (Holding Size1) is associated with -0.26 decline in ETS1. Thus, the estimated impact of increasing trading volume 12 fold on ETS1 is -0.43% (-0.26% * 1.65). One concern is that the Log (Holding 33 Alternatively, a fund could sell the position after one-month and hold the proceeds in cash or reinvest in passive funds to gain beta exposure (e.g., index ETFs). However, these investment options are associated with zero alpha and would drag abnormal performance towards zero. 36

Size1) coefficient is not a causal estimate of capacity constraints, but instead reflects unobservable factors (e.g., fund skill) that are correlated with fund size. Following, Pastor, Stambaugh, and Taylor (2015), I address this concern by estimating the impact of Log (Holding Size1) using a panel regression with fund fixed effects. Using this alternative approach, I estimate the impact of increasing trading volume 12 fold on ETS1 to be 0.80%. While the above estimates are admittedly noisy, the results are consistent with trading costs and capacity constraints largely eroding the benefits of moving to a shorter holding period. Specifically, after accounting for transaction costs, and using the more conservative capacity constraint estimate of -0.43%, an LS fund that moved to a one-month holding period would have an EHS of 0.21% (0.97% - 0.33% 0.43%), which is very close to the EHS estimate of 0.23% for LS funds found in Table 8. 34 6. Conclusion This paper exploits the granularity of transaction data to better understand the role of liquidity provision as a source of hedge funds equity trading skill. The use of transaction data offers many advantages. First, given the short-lived nature of liquidity provision profits, it is critical to have information on the exact date and execution price of a trade. Further, transaction data avoid many of the biases associated with commercial databases (e.g., unreliable returns, backfill bias, survivorship bias, etc.) and provide a more comprehensive view of hedge funds equity trading than quarterly holdings (e.g., transaction data captures intra-quarter trading, shortselling, and confidential fillings). 34 The results in Table 8 are reported gross of trading commissions. After incorporating trading commissions the EHS of LS funds falls to 0.20%. 37

My findings provide new evidence that liquidity provision is an important mechanism through which hedge funds create short-term value. Specifically, I find that hedge funds ETS is largest in their contrarian trades over a one-month holding period. This effect is particularly large during periods of low funding liquidity and for funds with greater share restrictions and smaller one-month portfolio holdings (i.e., Holding Size1). Hedge funds ability to create short-term value from liquidity provision suggests that there are significant incentives for hedge funds to continue providing liquidity to other market participants. I also find that funds that engage in greater amounts of liquidity provision generate persistently higher ETS1 over the subsequent three years. While some of this outperformance stems directly from placing more weight on liquidity-providing trades, much of LS funds superior ETS1 stems from factors beyond this mechanical liquidity-provision strategy. Further, funds that engage in greater amount of liquidity provision also exhibit greater performance in their equity holdings. However, the impact of liquidity provision on EHS is significantly attenuated by the fact that hedge funds tend to hold their positions long after the abnormal returns from liquidity provision have dissipated. This finding raises the question of whether hedge funds could enhance their performance by unwinding their positions more quickly. However, rough estimates suggest that transaction costs and capacity constraints would likely erode most, if not all, of the benefits of moving to a shorter holding period. While the primary goal of this paper is to examine whether liquidity provision is an important source of hedge funds equity trading skill, my findings also contribute to the broader debate on whether skilled hedge funds exist. I find no evidence that the equity holdings of the average ANcerno hedge funds earn significant abnormal returns, even before accounting for management and incentive fees. However, I do find that the average hedge fund exhibits significant 38

short-term trading skill (i.e., ETS1). I also document that a fund s tendency to provide liquidity helps explain cross-sectional differences in the returns earned on their equity trades and holdings. These findings suggest that there is significant differential ability amongst hedge funds and highlight that a fund s tendency to provide liquidity may be a useful indicator for identifying higher ability hedge fund managers. 39

Appendix: Variable Definitions Stock Characteristics: RLP: the expected return due to liquidity provision calculated using past estimates of shortterm return reversals. A more detailed definition is available in Section 3.1. Mom1: the return on the stock on the trading day prior to the day of the trade. Mom5: the return on the stock in the five trading days prior to the day of the trade. Mom21: the return on the stock in the 21 trading days prior to the day of the trade. Size: market capitalization (share price * total shares outstanding) at the end of the year prior to the year of the trade. BM: book-to-market ratio computed as the book value of equity for the fiscal year ending before the most recent June 30 th divided by market capitalization on December 31 st of the same fiscal year. Estimated for the fiscal year prior to the year of the trade. Turnover: the average daily turnover (i.e., share volume scaled by shares outstanding) over all trading days in the year prior to the year of the trade. Amihud Illiquidity: The Amihud (2002) measure computed using all daily data available for the year prior to the year of the trade. IVOL: the square root of the mean squared residual from an annual regression of a firm s daily returns on market (value-weighted CRSP index) returns. Computed in the year prior to the year of the trade. Fund Characteristics obtained from ANcerno Data: ETS1 (One-Month Equity Trading Skill): the DGTW-adjusted returns on the fund s long holdings less the DGTW-adjusted returns on the funds short holdings, where both long and short holdings are estimated based on a fund s trading over the prior 21 trading days (including the current trading day). A more detailed definition is provided in Section 3.2 t(ets1): ETS1 scaled by the standard error of ETS1. EHS (Equity Holding Skill): the DGTW-adjusted returns on the fund s long holdings less the DGTW-adjusted returns on the funds short holdings, where both long and short holdings are estimated based on all of a fund s historical trading since entering ANcerno (including the current trading day). A more detailed definition is provided in Section 3.2. t(ehs): EHS scaled by the standard error of EHS. 40

Holding Size1: the total value of a fund s long holdings and short holdings where both long and short holdings are estimated based on a fund s trading over the prior 21 trading days (including holdings established on the current trading day). Holding Size: the total value of a fund s long holdings and short holdings where both long and short holdings are estimated based on all of a fund s historical trading since entering ANcerno (including the current trading day). This measure is computed for each fund-day, for all funddays in which the fund has been in the ANcerno sample for at least one year. Volume: the average quarterly trading volume of a fund. Actual/Implied Trading: the ratio of actual quarterly trading volume to implied quarterly trading volume. Actual trading reflects the aggregate quarterly trading of a fund. Implied quarterly trading volume is computed as the absolute net dollar volume, buys sells, for a fund-stockquarter, aggregated across all stocks traded by the fund over the quarter. o If a fund purchased $50,000 of Microsoft and $100,000 of Apple in January 2008 and sold $20,000 of Microsoft in February 2008, the fund s total trading volume in quarter 1 of 2008 would be $170,000, while its implied trading volume would be $130,000. Actual/Implied would thus be 1.31 ($170,000/$130,000). Pct_Vol1: Holding Size1/ Holding Size. This measure is computed for each fund-day, for all fund-days in which the fund has been in the ANcerno sample for at least one year. o Suppose a fund purchased 200 shares of Microsoft on January 30 th and sold 100 of Microsoft on June 30 th, and sold 50 shares of Apple on July 27 th. If the price of Apple on July 30 th is $100 and the price of Microsoft is $50, then the total value of the fund s long holdings is $500 ((200-100) *50) and the total value of its short holdings is $500 (50 * 100), resulting in total holdings value (i.e., Holding Size) of $1000. As the positions in Microsoft were established more than 21 trading days ago, the total value of the fund s holdings due to trades in the prior month is $500 (i.e. Holding Size1). This would result in a Pct_Vol1 of 50% ($500/$1000). Plan Sponsor: a dummy variable equal to one if the hedge fund enters the ANcerno sample because it manages money on behalf of a plan sponsor client that has hired ANcerno. This value is set to zero for hedge funds that enter the sample because they directly hire ANcerno (i.e., Money Managers). Money Manager: a dummy variable equal to one if the hedge fund directly subscribes to ANcerno sample. This value is set to zero for hedge funds that enter the sample because they manager money on behalf of a plan sponsor who subscribes to ANcerno (i.e., Plan Sponsors). Com/Share: the dollar volume paid in commissions scaled by total share volume traded (reported in cents). 41

Shortfall: the principal-weighted execution shortfall of a fund. Following Anand et al., (2012), I measure execution shortfall as: P 1t P 0t D t P 0t where P1t measures the value-weighted execution price of ticket t, P0t is the price at the time when the broker receives the ticket, and Dt is an indicator variable that equals one for a buy ticket and minus one for a sell ticket. Time Series Variables: VIX: the CBOE S&P 500 implied volatility index Post Autoquote: a dummy variable equal to after (and including) February, 2003 and zero prior to February 2003. Financial Crisis: a dummy variable equal to one for the 2 nd half of 2008 and zero otherwise. Fund Characteristics obtained from TASS Data: Note: All TASS fund characteristics are aggregated at the management company level and reflect the equal-weighted average across all funds that belong to the management company (excluding funds of funds). Management Fee: the management fee charged by the fund. Incentive Fee: the incentive fee charged by the fund. Dlockup: a dummy variable equal to one if the fund has a lockup, and zero otherwise. Restrictions: the sum of the notice period and the redemption period. Asset Illiquidity: the first-order serial correlation of a fund s returns. Liquidity Risk: the fund s beta with respect to the liquidity risk factor of Sadka (2006). The beta is estimated over three-year rolling windows and also includes the Fung and Hsieh (2004) seven factors. Pct. Equity: The fraction of funds within the money management company who have investment objectives (as reported by the primary category variable in TASS) of either equity market neutral or long-short equity. TASS Dummy: A dummy variable equal to one if the management company in ANcerno also reports to TASS and zero otherwise. 42

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Table 1: Summary Statistics This table presents descriptive statistics for hedge fund trading data obtained from ANcerno. Panel A reports the total number of hedge fund managers (i.e., hedge fund management companies), clients (i.e., plan sponsors or money managers), and manager-client pairs (i.e., funds) during the full sample period from 1999 to 2010. I report the results for all ANcerno clients. I also separately report the results for plan sponsor and money manager clients. Panel B reports the number of managers and manager-client pairs (funds) that report to ANcerno each quarter, averaged across the four quarters in each year. Panel A: Aggregate Sample Size Client Type Managers Clients Man-Clients Clients Per Manager All 71 324 513 7.23 Plan Sponsor 61 300 487 7.98 Money Manager 23 24 26 1.13 Panel B: Time-Series of Quarterly Averages Year Managers Clients Man-Clients Clients Per Manager 1999 42 112 157 3.74 2000 39 111 145 3.72 2001 37 112 144 3.89 2002 39 124 155 3.97 2003 39 121 154 3.95 2004 40 119 152 3.80 2005 42 118 155 3.69 2006 40 110 142 3.55 2007 39 104 134 3.44 2008 35 53 105 3.00 2009 30 60 74 2.47 2010 26 42 50 1.92 Average 37 99 131 3.50 47

Table 2: The Cross-Sectional Distribution of Hedge Fund Trading This table reports summary statistics of hedge fund trading. The unit of observation is a fund-year. Panel A reports the cross-sectional distribution of quarterly trading volume of hedge funds, averaged across all quarters the fund that was in the sample during the calendar year. I report the results separately for plan sponsor clients and money manager clients. Panel B report the size of a fund s holding (Holding Size), computed as the sum of the long and short holdings. Panel C reports the ratio of actual to implied quarterly trading. Actual trading is based on actual transaction data. Implied quarterly trading is computed as the net dollar volume (buys-sells) of a stock, aggregated across all stocks traded by the fund over a quarter. Panel D reports summary statistics for Pct_Vol1 which measures the fraction of hedge funds holdings that are due to trading in the prior month. In computing Holding Size and Pct_Vol1, I exclude the first year in which the fund reports to the sample. Additional details of the construction of the trading variables are available in the Appendix. Panel A: Ave Quarterly Trading Volume ( Volume, $Mil) Obs. Mean Std. Dev 95 75 50 25 5 Plan Sponsors 1814 40.28 69.87 159.81 45.10 15.55 4.97 0.51 Money Managers 106 3,012.21 6,909.00 18,146.81 1,928.08 651.80 171.35 28.86 Panel B: Size of Total Holdings (Holding Size $Mil) Mean Std. Dev 95 75 50 25 5 Plan Sponsors 1281 123.65 206.56 428.67 140.29 57.60 26.40 6.46 Money Managers 87 9,957.23 18,319.40 56,201.55 7,765.50 2,628.37 674.15 201.51 Panel C: Actual to Implied Quarter Trading (Actual/Implied) Mean Std. Dev 95 75 50 25 5 Plan Sponsors 1814 1.17 0.48 1.54 1.18 1.09 1.01 1.00 Money Managers 106 1.50 0.34 2.14 1.66 1.43 1.24 1.08 Panel D: Pct_Vol1 Mean Std. Dev 95 75 50 25 5 Plan Sponsors 1281 9.25% 6.71% 22.00% 12.59% 8.10% 4.53% 0.20% Money Managers 87 8.76% 5.87% 19.67% 13.04% 7.78% 4.09% 0.78% 48

Table 3: One-Month Equity Trading Skill (ETS1) of Hedge Funds by Trade Type This table reports the average one-month equity trading skill (ETS1) of hedge funds. The construction of ETS1 is explained in Section 3.2 For each trading day in the sample, I compute the equal-weighted average ETS1 across all hedge funds in the sample. I report the time-series average of ETS1, expressed as a monthly (DGTW-adjusted) returns, in percent. I report the results separately, for a fund s net long positions (i.e., Buys), net short positions (i.e., Sells) and the difference between the long and short positions (i.e., Buys Sells). I also report the results separately for all executed trades, as well as for liquidity-supplying (LS), liquidity-neutral (LN), and liquidity-demanding (LD) trades. I define LS trades as purchased of stocks of stocks in the top third of expected returns due to liquidity provision (RLP) and sales of stocks in the bottom third or RLP. Similarly, LD trades as purchased of stocks of stocks in the bottom third of RLP and sales of stocks in the top third or RLP. The construction of RLP in explained in Section 3.1. T-statistics, based on standard errors computed from the time-series standard deviation are reported in parentheses. I also report the number of hedge funds that appear in the portfolio, averaged across all trading days in the sample. The sample includes 3019 trading days from 1999-2010. Trade Type Buys Sells Buys -Sells All Trades 0.38-0.01 0.39 (4.04) (-0.11) (3.73) [105] [105] [105] Momentum Trades (LD) 0.17 0.28-0.10 (1.41) (1.96) (-0.67) [94] [94] [95] Other Trades (LN) 0.35 0.03 0.32 (3.53) (0.31) (2.69) [93] [93] [93] Contarian Trades (LS) 0.80-0.39 1.19 (6.45) (-2.95) (7.71) [92] [92] [92] LS - LD 0.63-0.66 1.29 (4.94) (-4.43) (6.14) 49

Table 4: Determinants of ETS1 by Trade Type This table reports the results from panel regressions. In Specifications 1 through 3, the dependent variable is the one-month equity trading skill (ETS1) from liquidity-supplying (LS) trades for a fund-day. Specifications4 through 6 present analogous results for liquidity-demanding (LD) trades. ETS1, LS trades, and LD trades are defined as in Table 3. All independent variables are defined in the Appendix. T-statistics, based on standard errors double clustered by management company and by day are reported in parentheses. ETS1 of LS Trades ETS1 of LD Trades [1] [2] [3] [4] [5] [6] Intercept 1.56 1.63-0.50-0.50 (4.60) (4.75) (-1.18) (-1.21) Log (VIX) 0.49 0.32 0.32-0.09-0.09-0.04 (3.24) (2.05) (1.87) (-0.55) (-0.55) (-0.25) Financial Crisis 2.48 2.68 0.04 0.17 (2.79) (2.96) (0.03) (0.12) Post Autoquote -0.66-0.94-1.05 0.16 0.15 0.17 (-1.93) (-2.71) (-2.16) (0.36) (0.41) (0.44) Restrictions 0.30 0.31 0.48 0.48 (2.21) (2.26) (3.14) (3.13) Log (Holding Size1) -0.36-0.38-0.76-0.25-0.25 0.05 (-1.91) (-1.98) (-2.48) (-2.25) (-2.32) (0.19) Money Manager -0.31-0.33-0.16-0.16 (-1.47) (-1.54) (-0.63) (-0.64) Fund Fixed Effects No No Yes No No Yes Observations 265,097 265,097 265,097 274,034 274,034 274,034 R-squared 0.03% 0.03% 0.18% 0.01% 0.01% 0.16% 50

Table 5: The Characteristics of Liquidity-Supplying and Liquidity-Demanding Hedge Funds This table compares the characteristics of liquidity-supplying (LS) hedge funds and liquidity-demanding (LD) hedge funds. For each fund-year, I compute the principal-weighted average RLP of stocks purchased less the principalweighted average RLP of stock stocks (where RLP is defined as in Section 3.1). Funds in the top (bottom) quintile of RLP are classified as LS (LD) funds. The remaining 60% of funds are classified as liquidity neutral (LN). Panel A reports the principal-weighted net purchases (i.e., buys sells) of other stock characteristics during the year of the ranking period (or if labelled t+1 in the year after the ranking period). I then report the equallyweighted average across all LS, LN, and LD hedge funds. I also report the difference between LS and LD funds. Panels B and C conduct a similar analysis for fund-level characteristics estimated from ANcerno and Tass, respectively. Panel C is limited to the sample of management companies that appear in both ANcerno and TASS. Additional details of merging the ANcerno and TASS data are available in Section 4.1. The stock and fund characteristics are defined in the Appendix. T-statistics, reported in parentheses, are based on standard errors double clustered by management company and day. Liquidity Suppliers (LS) Liquidity Neutral (LN) Liquidity Demanders (LD) LS -LD t (LS - LD) Panel A: Stock Characteristics of Net Purchases Fund-Day Obs. 41,050 125,698 40,986 RLP(t) 0.22-0.02-0.28 0.49 (17.52) Mom21 (t) -0.51-0.08 0.31-0.82 (-11.84) Mom5 (t) -0.34-0.01 0.34-0.68 (-15.29) Mom1 (t) -0.20 0.02 0.26-0.46 (-13.35) Size (t) 0.00-0.03-0.13 0.13 (4.68) BM (t) -0.08-0.01 0.06-0.14 (-8.69) Turn (t) 0.02 0.01-0.02 0.04 (1.21) Amihud Illiquidity (t) 0.00 0.02 0.07-0.07 (-3.21) IVOL (t) 0.02 0.04 0.06-0.04 (-1.21) RLP (t+1) 0.10-0.01-0.14 0.24 (8.23) RLP(t+1) - High VIX 0.02-0.03-0.17 0.19 (5.67) RLP (t+1) - Fin. Crisis -0.05 0.00-0.05 0.00 (-0.07) Panel B: Fund Characteristics from ANcerno Fund-Day Obs. 41,050 125,698 40,986 Age (Days) 865.24 833.60 793.36 71.88 (1.02) Log (Holdings Size1) 15.13 16.03 15.99-0.87 (-3.49) Log (Holdings Size) 17.91 18.50 18.28-0.37 (-1.56) Log (Volume) 16.39 17.31 17.32-0.92 (-3.53) Log (Actual/Implied) (%) 9.24 14.71 16.86-7.62 (-2.32) Pct_Vol1 7.62 10.05 12.71-5.09 (-3.68) Money Manager 5.50 8.81 4.46 1.04 (0.25) Com/Share 3.92 3.94 4.15-0.23 (-0.52) Shortfall -0.25 0.30 1.08-1.33 (-7.01) Panel C: Fund Characteristics from TASS Fund-Day Obs. 17,122 67,906 25,468 Management Fee 1.35 1.34 1.23 0.13 (0.80) Incentive Fee 16.42 17.51 17.76-1.34 (-0.49) Dlockup 0.59 0.68 0.37 0.22 (0.90) Restrictions 233.18 192.59 131.11 102.07 (1.38) Asset Illiquidity 0.07 0.10 0.08-0.01 (-0.06) Liquidity Risk 0.58 0.90 1.20-0.62 (-1.23) Pct. Equity 0.97 0.91 0.90 0.07 (1.16) 51

Table 6: Liquidity Provision and ETS1 - Univariate Sorts This table reports the ETS1 of all hedge funds (All Funds) and portfolios of hedge funds sorted on RLP. For each fund-year, I compute the principal-weighted average RLP of stocks purchased less the principal-weighted average RLP of stock stocks (where RLP is defined as in Section 3.1). I then sort funds into quintiles, with quintile 5 being liquidity-supplying (LS) funds and quintile 1 being liquidity-demanding (LD) funds. I report the average ETS1, across all trade types, of each portfolio in the one (Year 1), two (Year 2), or three years (Year 3) after the ranking period. The ETS1 of each portfolio is computed as in Table 3. T-statistics, based on standard errors computed from the time-series standard deviation are reported in parentheses. I also report the number of hedge funds that appear in the portfolio, averaged across all trading days in the sample. The Year 1 sample includes all trading days from 2000-2010. The Year 2 and 3 sample include all trading days from 2001-2010, and 2002-2010, respectively. Year 1 Year 2 Year 3 ALL Funds 0.45 0.46 0.41 (3.64) (3.62) (3.11) [73] [58] [47] Sorts by RLP 1 (LD) -0.04-0.39 0.02 (-0.17) (-1.48) (0.10) [14] [12] [0] 2 0.12 0.25 0.25 (0.56) (1.04) (0.92) [15] [12] [9] 3 0.58 0.50 0.16 (2.79) (1.99) (0.68) [15] [12] [9] 4 0.68 0.50 0.60 (3.06) (2.08) (2.38) [15] [11] [9] 5 (LS) 0.97 1.53 1.06 (3.73) (5.50) (3.49) [14] [12] [10] LS - LD 1.02 1.91 1.04 (2.77) (5.16) (2.64) 52

Table 7: Predicting ETS1 This table reports the estimates from daily Fama-Macbeth regression. The dependent variable is the ETS1 of a hedge fund. All of the independent variable are defined in the Appendix and are computed in the period prior to computing ETS1. All continuous variables are standardized to have mean 0 and variance 1 on each trading day. Log (Holding Size1) and Log (Actual/Implied) are standardized separately for plan sponsors and money managers. T- statistics, based on standard errors computed from the time-series standard deviation, are reported in parentheses. [1] [2] [3] [4] [5] Intercept 0.49 0.49 0.50 0.31 0.46 (3.71) (3.67) (3.75) (1.64) (2.77) RLP 0.46 0.39 0.52 0.51 (2.75) (1.88) (3.00) (2.92) Shortfall -0.36-0.12 (-2.33) (-0.61) Size 0.42 0.39 0.39 0.41 0.38 (1.89) (1.77) (1.76) (1.83) (1.74) BM 0.24 0.18 0.22 0.36 0.29 (1.23) (0.92) (1.09) (1.81) (1.45) Turn 0.14 0.14 0.16 0.07 0.08 (0.55) (0.55) (0.63) (0.27) (0.31) Ivol 0.16 0.12 0.12 0.15 0.12 (0.63) (0.46) (0.46) (0.59) (0.47) Com/Share -0.14-0.11-0.08-0.10-0.17 (-1.16) (-0.87) (-0.68) (-0.82) (-1.38) Log (Actual/Implied Trading) 0.13 0.19 0.19 0.09 0.03 (1.15) (1.57) (1.54) (0.74) (0.21) Log (Holding Size1) -0.26-0.29-0.24-0.22-0.25 (-2.15) (-2.46) (-1.98) (-1.73) (-2.02) Log(Age) 0.05 0.09 0.07 0.04 0.04 (0.62) (1.20) (0.82) (0.50) (0.52) t(ets1) 0.11 0.13 0.17 0.05 0.04 (1.04) (1.29) (1.62) (0.47) (0.35) Money Manager -0.42-0.48-0.61-0.26-0.14 (-1.34) (-1.51) (-1.93) (-0.82) (-0.44) Restrictions 0.40 (2.66) Asset Illiquidity 0.37 Average Obs. (Per Day) 73 73 73 73 73 (3.35) Number of Days 2,767 2,767 2,767 2,767 2,767 R-squared 23.99% 23.83% 26.17% 28.43% 28.23% 53

Table 8: Liquidity Provision and EHS - Univariate Sorts This table reports the EHS (i.e., the return on a fund s equity holdings) of all hedge funds (All Funds) and portfolios of hedge funds sorted on RLP. For each fund-year, I compute the principal-weighted average RLP of stocks purchased less the principal-weighted average RLP of stock stocks (where RLP is defined as in Section 3.1). I then sort funds into quintiles, with quintile 5 being liquidity-supplying (LS) funds and quintile 1 being liquiditydemanding (LD) funds. I report the average EHS, across all trade types, of each portfolio in the one (Year 1), two (Year 2), or three years (Year 3) after the ranking period. The EHS of each portfolio is computed as defined in Section 3.2. T-statistics, based on standard errors computed from the time-series standard deviation are reported in parentheses. I also report the number of hedge funds that appear in the portfolio, averaged across all trading days in the sample. The Year 1 sample includes all trading days from 2000-2010. The Year 2 and 3 sample include all trading days from 2001-2010, and 2002-2010, respectively. Year 1 Year 2 Year 3 [1] [2] [3] ALL Funds 0.05 0.01-0.02 (1.04) (0.27) (-0.41) [77] [62] [51] Sorts by RLP 1 (LD) -0.06-0.14 0.00 (-0.42) (-1.25) (0.02) [15] [12] [10] 2-0.03 0.03-0.05 (-0.34) (0.39) (-0.65) [16] [12] [10] 3 0.06 0.08 0.02 (0.73) (1.08) (0.23) [15] [13] [10] 4 0.03 0.00-0.03 (0.42) (-0.01) (-0.40) [15] [13] [10] 5 (LS) 0.23 0.14 0.03 (2.00) (1.41) (0.30) [15] [13] [10] LS - LD 0.29 0.28 0.03 (1.52) (1.77) (0.17) 54

Table 9: Predicting EHS This table reports the estimates from daily Fama-Macbeth regression. The dependent variable is the EHS of a hedge fund (i.e., the return on funds equity holdings). All of the independent variable are defined in the Appendix and are computed in the period prior to computing EHS. All continuous variables are standardized to have mean 0 and variance 1 on each trading day. Log (Holding Size1) and Log (Actual/Implied) are standardized separately for plan sponsors and money managers. T-statistics, based on standard errors computed from the time-series standard deviation, are reported in parentheses. [1] [2] [3] [4] [5] Intercept 0.07 0.06 0.07 0.05 0.09 (1.30) (1.13) (1.34) (0.64) (1.31) RLP 0.19 0.20 0.23 0.24 (2.78) (2.30) (3.23) (3.23) Shortfall -0.09 0.04 (-1.27) (0.40) Size -0.05-0.04-0.07-0.07-0.07 (-0.58) (-0.54) (-0.97) (-0.91) (-0.84) BM 0.08 0.06 0.07 0.13 0.13 (1.11) (0.86) (0.98) (1.67) (1.70) Turn 0.17 0.19 0.19 0.16 0.17 (1.76) (1.95) (2.04) (1.68) (1.77) Ivol -0.05-0.10-0.10-0.08-0.08 (-0.49) (-0.98) (-0.97) (-0.78) (-0.80) Com/Share 0.06 0.07 0.08 0.06 0.05 (1.10) (1.16) (1.34) (0.98) (0.80) Log (Actual/Implied Trading) 0.06 0.07 0.05 0.04 0.03 (0.97) (1.24) (0.98) (0.65) (0.51) Log (Holding Size) -0.03-0.06-0.04-0.02-0.02 (-0.66) (-1.51) (-0.95) (-0.45) (-0.43) Log(Age) 0.04 0.05 0.04 0.04 0.05 (1.09) (1.45) (1.16) (1.23) (1.30) t(ets1) 0.02 0.03 0.03 0.03 0.02 (0.56) (0.66) (0.78) (0.64) (0.56) t(ehs) 0.00 0.04 0.02-0.02-0.02 (-0.01) (0.71) (0.35) (-0.32) (-0.37) Money Manager -0.19-0.22-0.23-0.13-0.09 (-1.15) (-1.36) (-1.42) (-0.77) (-0.55) Restrictions 0.08 (1.35) Asset Illiquidity 0.05 Average Obs. (Per Day) 77 77 77 77 77 (1.18) Number of Days 2,767 2,767 2,767 2,767 2,767 R-squared 26.05% 26.86% 28.75% 29.46% 29.35% 55

Figure1: ETS by Trade Type across Different Holding Periods This figure plots the average equity trading skill (ETS) of hedge funds across different holding periods. The construction of ETS is explained in Section 3.2. For each trading day in the sample, I compute the equal-weighted average ETS across all hedge funds in the sample. I report the time-series average of ETS, expressed as a monthly (DGTW-adjusted) return, in percent. I report the results for liquidity-supplying (LS) and liquidity-demanding (LD) trades. I define LS trades as purchased of stocks in the top third of expected returns due to liquidity provision (RLP) and sales of stocks in the bottom third or RLP. Similarly, LD trades are purchases of stocks of stocks in the bottom third of RLP and sales of stocks in the top third or RLP. The construction of RLP in explained in Section 3.1. Panel A plots the average ETS. Panel B plots the cumulative ETS which is computed as the average ETS multiplied by the holding period (in months). The sample includes 3019 trading days from 1999-2010. Figure 1A: Average Monthly ETS by Trade Type ETS (%) 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% -0.20% 0 1 2 3 4 5 6 7 8 9 10 11 12 Holding Period Liquidity Supplying (LS) Liquidity Demanding (LD) Figure 1B: Cumulative ETS by Trade Type 3.00% 2.50% 2.00% ETS (%) 1.50% 1.00% 0.50% 0.00% -0.50% 0 1 2 3 4 5 6 7 8 9 10 11 12 Holding Period Liquidity Supplying (LS) Liquidity Demanding (LD) 56

Figure 2: ETS of LS and LD Hedge Funds by Holding Period This figure plots the average equity trading skill (ETS) of all hedge funds (All Funds), liquidity-supplying hedge funds (LS Funds) and liquidity-demanding hedge funds (LD funds) across different holding periods. For each fund-year, I compute the principal-weighted average RLP of stocks purchased less the principal-weighted average RLP of stock stocks (where RLP is defined as in Section 3.1). Funds in the top quintile of RLP are defined as LS funds and funds in the bottom quintile of RLP are defined as LD funds. I construct holdings for each fund using calendar time transaction-portfolios with holding periods of one month, three months, six months, and 12 months (ETS1, ETS3, ETS6, and ETS12). I also report the results assuming no holding period (EHS) and assuming no holding period after excluding trades made in the prior one month (ETS >1). The figure reports the average ETS across all funds in the portfolio. 1.2 1 0.8 ETS of LS and LD Funds Across Different Holding Periods ETS (%) 0.6 0.4 0.2 0-0.2 All Funds LS Funds LD Funds ETS1 ETS3 ETS 6 ETS12 EHS ETS >1 57