Do Hedge Funds Have Information Advantages? Evidence from Hedge Fund Stock Holdings

Transcription

1 Do Hedge Funds Have Information Advantages? Evidence from Hedge Fund Stock Holdings Kee-Hong Bae, Bok Baik, and Jin-Mo Kim * This version: April 2011 * Bae is from the Schulich School of Business, York University, Tel.: , kbae@schulich.yorku.ca; Baik is from the College of Business, Seoul National University, Tel.: , bbaik@snu.ac.kr; and Kim is from the Department of Finance and Economics, Rutgers Business School, Rutgers University, Piscataway, NJ , Tel.: , kimjm@business.rutgers.edu. We are grateful for comments from seminar participants at Concordia University, Hanyang University, Korea Advanced Institute of Science and Technology, Korea University, Rutgers University, and York University. We thank Chris Cappucci at Bloomberg for the discussion on the classification process of the 13F filing institutions in the Bloomberg Financial Markets database. All errors are our own.

2 Do Hedge Funds Have Information Advantages? Evidence from Hedge Fund Stock Holdings Abstract Using quarterly equity holdings of hedge funds, we find that both the level of and change in the stock holdings of hedge funds strongly predict future returns. For instance, an increase of one standard deviation in the change in hedge funds holdings results in a 1.8% increase in annual stock returns. In contrast, we find the holdings of other institutional investors show little such forecasting ability. The return predictability of hedge fund holdings is most pronounced for stocks with high information asymmetry and for funds that are more likely to possess superior information. An arbitrage portfolio that buys and sells stocks in the top and bottom quintiles of the changes in hedge funds holdings generates a statistically significant 6.4% excess return per year. We also find that the stocks that hedge funds buy earn higher abnormal returns around subsequent earnings announcements than those that they sell. Finally, consistent with the model of hedge funds developed by Glode and Green (2010) that rationalizes the superior performance of hedge funds over other investment vehicles, we find that the flow-performance sensitivity of hedge funds is significantly lower than that of non-hedge funds. 1

3 1. Introduction The hedge fund industry has witnessed a tremendous growth over the last two decades. According to Hedge Fund Research, Inc., the total value of assets managed by hedge funds has increased from $50 billion in 1990 to over $1.65 trillion in Moreover, they now represent roughly half of all trading on the New York Stock Exchange (Anderson, 2006). Such rapid growth is often attributed to the superior performance of hedge fund managers exploiting information advantages, and the existence of these information advantages has been the subject of continuing interest for both academics and practitioners. While a large number of studies have examined the informational role of hedge funds by evaluating their performance, the results are not conclusive, probably because this is inherently difficult to do. For example, several studies show that hedge funds deliver measurable abnormal returns (Ackerman, McEnally, and Ravenscraft, 1999; Brown, Goetzmann, and Ibbotson, 1999; Kosowski, Naik, and Teo, 2006), while other studies fail to find positive abnormal returns (Asness, Krail, and Liew, 2001; Amin and Kat, 2003; Malkiel and Saha, 2005; Kat and Palaro, 2006). There are several difficulties with drawing conclusions regarding the informational role of hedge funds by evaluating their performance. It is well known that the hedge fund returns databases used in previous studies are subject to various sample biases including sample selection, survival, and back-fill biases (Fung and Hsieh, 2000, 2001, 2009; Liang, 2000; Stulz, 2007). Another difficulty is that performance evaluation necessarily requires the pricing of risks in hedge fund returns. This is a particularly difficult task since the latter tend to be highly non-linear (Fung and Hsieh, 2001). In this paper, we take an alternative approach and present new evidence on the issue of whether hedge funds have information advantages over other institutional investors. 1 Instead of relying on hedge fund returns data, we make use of their stock holdings. Specifically, we use the Securities and Exchange Commission (SEC) Form 13F filings that impose disclosure requirements on investment managers who manage more than $100 million in equity. Such managers are required to file a quarterly report to the 1 We define other institutional investors as all institutions other than hedge funds. We use the terms other institutional investors and non-hedge funds interchangeably. 2

4 SEC of all equity holdings greater than 10,000 shares or $200,000 in market value. Using information on the stock holdings of hedge funds as well as those of other institutional investors at the firm level, we investigate whether the level of and the change in hedge fund holdings can predict future stock returns, and also whether this return predictability, if it exists, is superior to that of other institutional investors. The advantage of using hedge funds stock holdings at the firm level is that it allows us to examine whether hedge funds have better return predictability for the firms on which private information is most likely to be profitable, such as those with high information asymmetry. It also allows us to examine whether hedge funds with certain characteristics, such as those having more assets under management, show better return predictability. An examination of these issues can provide more definitive evidence on the existence of information advantages for hedge funds. The approach of using holdings data has been widely used in studies of institutional investors since the pioneering paper by Grinblatt and Titman (1989a). The area of academic research that investigates the stock holdings of institutional investors has developed into two groups of studies. One group of studies examines the abnormal return performance of portfolios constructed from holdings held by institutional investors (Grinblatt and Titman, 1993; Daniel, Grinblatt, Titman, and Wermers, 1997; Wermers, 2000; Ferson and Khang, 2002); the other group examines the return predictability of institutional holdings (Chen, Jegadeesh, and Wermers, 2000; Gompers and Metrick, 2001; Yan and Zhang, 2009; Baik, Kang, and Kim, 2010). Our study makes use of the methodologies developed from this literature and examines the return predictability of hedge funds stock holdings as well as their abnormal return performance. We use the Bloomberg Financial Markets database (hereafter the Bloomberg database) to obtain the list of hedge fund holding companies. 2 The Bloomberg database classifies all institutional investors that file 13F forms by type, such as mutual, hedge, and pension funds, and so on. It provides the most comprehensive list of hedge fund companies that one can identify from the 13F database. For the sample period , we identify 671 hedge fund companies and compute stock holdings for the universe of 2 A hedge fund holding company may have multiple hedge funds under its management. It is the holding company that we must identify in the 13F database. We use the terms hedge fund and hedge fund holding company interchangeably. 3

5 CRSP/Compustat firms held by these hedge fund companies (hereafter, hedge fund ownership or hedge fund holdings) as well as holdings by other institutional investors (hereafter, non-hedge fund ownership or non-hedge fund holdings). Using this ownership data, we present several new findings that are consistent with the view that hedge funds are better informed than other institutional investors. We find that the extent of hedge fund ownership in the US stock market has expanded substantially during our sample period. The mean hedge fund ownership represents only 1.2% of outstanding shares in 2000, but that figure increases to 6.1% in Agarwal and Naik (2005) point out that the hedge fund industry has not only grown tremendously over the years, but has also changed in terms of strategy and importance. They show that the equity hedge strategy had the largest share of the market in the 2000s, while the macro strategy dominated the industry in the 1990s. The substantial expansion in hedge fund ownership suggests that hedge funds now have stronger incentives to acquire private information on the stocks in their portfolio than in the past, underscoring the need to examine a recent sample period to investigate their informational role. Turning to the return predictability of hedge fund ownership, we find that the level of ownership is positively and significantly related to one-quarter-ahead stock returns. Although the level of non-hedge fund ownership also forecasts future returns, the return forecasting power of hedge fund ownership is more significant. More importantly, the change in hedge fund ownership predicts future returns, whereas the change in non-hedge fund ownership does not. The former effect is economically significant. For example, an increase of one standard deviation in the change in hedge fund holdings results in a 1.8% increase in annual stock returns. The return forecasting power of hedge funds is more evident in firms with high information asymmetry. The forecasting ability is also more pronounced for hedge funds that are more likely to possess and exploit superior information, such as domestic as opposed to foreign hedge funds, those with higher turnover rate, and those located in primary metropolitan areas. We also find that when we sort stocks according to the level of hedge and non-hedge fund ownership, stocks in the highest quintile of hedge fund ownership outperform those in the lowest quintile by a significant 5.2% risk-adjusted return per year in which the risk adjustment is made following Daniel, 4

6 et al. (1997). In contrast, the difference in risk-adjusted returns between the highest and lowest quintiles of non-hedge fund ownership is not statistically significant. We find similar evidence when we sort stocks according to changes in hedge and non-hedge fund ownership. Stocks in the highest quintile of the former outperform those in the lowest quintile by a significant 6.4% per year. The difference in risk-adjusted returns between the highest and lowest quintiles of the change in non-hedge fund ownership is, again, not statistically significant. Finally, we find that the stocks that hedge funds buy earn significantly higher abnormal returns around subsequent earnings announcements than those that they sell. An arbitrage portfolio that buys and sells stocks in the top and bottom quintiles of the change in hedge fund ownership generates a significant 51 basis points of abnormal return during the three days around earnings announcements. This result strongly suggests that hedge funds have private information on the earnings fundamentals of their stock holdings. The evidence of better stock-picking ability by hedge fund managers than by other institutional investors raises the natural question of what makes hedge funds distinctive from other investment vehicles. Two theoretical models of active investment managers shed light on this important issue. By assuming decreasing returns to scale for fund managers in deploying their superior ability, Berk and Green (2004) show that a positive relation between fund inflow and fund performance is consistent with the existence of skilled managers, even when the performance of fund managers as a group does not outperform passive benchmarks. This is because skilled fund managers increase the size of their funds to the point at which expected returns to investors are competitive going forward. Building on Berk and Green (2004), Glode and Green (2010) develop a model of hedge funds that rationalizes the superior performance of hedge funds. This model shows that to maintain superior performance, incumbent hedge fund managers and investors need to restrict flows even when expected profitability is high. The reason for this is that without secrecy, the superior information possessed by hedge fund managers can spill over to other fund managers, which attracts imitation and competition and thus reduces the profitability of the incumbent hedge fund going forward. Therefore, hedge fund managers limit the size of their funds to 5

7 leave rents to investors, and this makes the flow of money into hedge funds less sensitive to past fund performance than the flow of money into other investment vehicles. We test this prediction and find consistent evidence for it. Specifically, we find a positive and convex performance-flow relation for nonhedge funds, but no such relation for hedge funds. The results on the flow-performance relation of hedge funds and non-hedge funds are consistent with the predictions of Berk and Green (2004) and Glode and Green (2010). Our study contributes and is related to the literature in several ways. First, it provides new evidence on the controversy surrounding the informational role of hedge funds. Previous studies of hedge fund performance mostly use commercial hedge fund return databases, in which hedge funds voluntarily participate and report returns data. The self-reported returns provided by such databases can potentially induce many sample biases. For example, the data may suffer from both self-selection and survival biases since successful hedge funds that have survived tend to self-report voluntarily. When a hedge fund is added to a database for the first time and its historical data is recorded ex-post in the database, it suffers from backfill bias. All these biases will likely introduce the risk of overstating performance. By using equity holdings of hedge funds obtained from mandatory 13F filings, we avoid the issues that arise from using self-reported data. Second, our paper is related to the work of Brunnermeier and Nagel (2004) and Griffin and Xu (2009) who also use the holdings approach to study hedge funds. Brunnermeier and Nagel (2004) examine 13F data for 53 hedge fund holding companies during the technology bubble of They show that hedge funds sold their stock holdings shortly before the price peak, suggesting an information advantage. In contrast, Griffin and Xu (2009) cast doubt on this supposed information advantage. Using data from the sample period , they show that hedge funds exhibit little ability to time sectors or pick better stock styles. Our study expands on their study in at least two important ways. We use a more recent sample period of , during which hedge fund stock holdings have substantially increased, so their informational role is likely to be more important than in the past. Another difference is the way hedge funds are identified. Griffin and Xu (2009) obtain a list of hedge funds from the commercial return 6

8 databases, which may understate the population of hedge funds since they include only voluntarilyparticipating institutions. We use the Bloomberg database, which classifies all institutional investors filing 13F forms, so our list of hedge funds does not suffer from self-reporting bias. In fact, by applying the filtering process suggested by Brunnermeier and Nagel (2004), we are able to obtain 671 hedge fund holding companies during the 10-year period of , whereas Griffin and Xu (2009) identify only 307 hedge fund companies during the 25-year period of Our paper is also closely related to a recent paper by Agarwal, Jiang, Tang, and Yang (2010). They show that the confidential equity holdings of hedge funds exhibit superior performance over the period for which they seek confidentiality. In their study, the focus is on the bias in performance measurement due to the ability of hedge fund managers to hide their holdings temporarily. Our paper focuses on the stock picking ability of hedge fund managers and provides empirical evidence in support of the information advantages of hedge funds from a different perspective. Finally, we provide evidence to support the recent theoretical models of hedge funds (Glode and Green, 2010; Makarov and Plantin, 2010). These models note the difference in organizational structure between hedge funds and other investment vehicles and predict that hedge funds limit fund size to maintain performance persistence. Consistent with the prediction of these models, we find that hedge funds exhibit superior performance and yet fund flows into hedge funds are insensitive to the performance. Several caveats are in order. We examine quarterly long-equity positions to investigate hedge funds informational role in forecasting future stock returns. Thus, our approach ignores intra-quarter trading. Hedge funds are well known to have high turnover. For instance, Agarwal, Fos, and Jiang (2010) find that hedge funds portfolio turnover rate is about twice as high as that of mutual funds, investment advisors, and other institutions, and more than three times that of bank and insurance companies. Examining the intra-quarter trading performance of institutional investors, Puckett and Yan (2010) show that institutional investors have superior intra-quarter trading skills. Therefore, our use of quarterly data may not fully capture the short-term trading of hedge funds. To the extent that this pattern is more 7

9 informative than long-term trading, our results likely underestimate the information advantages of hedge funds. We also note that our analysis is restricted to hedge funds long positions and ignores the many other complex strategies they employ. However, Aragon and Martin (2009) show that common stocks account for 54.7% of the portfolio value of the 250 hedge fund holding companies they examine. Furthermore, since not all long-equity positions are related to information-driven investments, our approach of investigating long-equity positions may understate hedge funds information advantages. Taken together, our findings suggest that despite the limitations of using only quarterly and long-equity positions, at the very least it can be said that some hedge funds have information advantages over other institutional investors. The rest of the paper is organized as follows. Section 2 describes the data and summary statistics. In Section 3, we investigate the predictability of hedge fund ownership in forecasting future stock returns. In section 4, we investigate what might explain the better stock-picking ability of hedge funds. In Section 5, we conduct several robustness tests. Section 6 concludes the paper. 2. Data and summary statistics 2.1. Identification of hedge fund holding companies Our initial sample includes the set of all firm-quarters with institutional ownership from CDA/Spectrum Institutional (13F) Holdings for the period from January 2000 to September The CDA/Spectrum data are based on the SEC s Form 13F, which requires all investment managers managing more than $100 million in equity to file a quarterly report of all equity holdings greater than 10,000 shares or $200,000 in market value. Previous studies use a fund manager number (ID key = MGRNO) in the CDA/Spectrum Institutional (13F) Holdings dataset as the institution identifier. However, we find that this number is reassigned to a different institutional investor if the assigned one disappears. To identify the cases where the same fund manager number is assigned to different institutional investors, we track fund manager numbers and name 8

10 changes for all institutional investors during our sample period. We use the Bloomberg Financial Markets database to obtain the list of hedge fund holding companies filing SEC Form 13F in June and September The Bloomberg database contains all investment company names filing the 13F by type such as hedge, mutual, and pension funds, and so on. Since most institutional investors, including hedge funds, are customers of Bloomberg and maintain business relationships, Bloomberg is better able to classify their types. In particular, Bloomberg follows each hedge fund and assigns it a Bloomberg Ticker at the fund level. Using an internal hedge fund database, Bloomberg links each fund to the fund holding company and determines if a 13F filing institution is a hedge fund holding company. 3 Initially, we obtain a sample of 973 hedge fund companies from the Bloomberg database. To make sure that these companies main business is hedge fund management, we apply the filtering process of Brunnermeier and Nagel (2004). We first check whether a candidate hedge fund company is registered as an investment advisor with the SEC. Unlike hedge funds, all non-hedge fund companies, such as mutual funds and pension plans, are required to register with the SEC. If an investment company is not registered, we include the company in our sample of hedge fund companies. If it is registered, then it is required to complete a Form ADV. 4 We manually check the ADV forms and require the following two criteria for the registered investment company to be eligible for our sample of hedge fund companies: (1) at least 50% of its clients are other pooled investment vehicles (such as private equity and hedge funds) or high net worth individuals, and (2) it charges performance-based fees. This process results in a final sample of 671 hedge fund companies. Since our sample funds include passive traders who simply replicate the movements of the market index with little input to portfolio decisions and our objective is to evaluate the performance of active institutional investment managers, we exclude such passive funds and examine only active investment 3 Unlike the Bloomberg Financial Markets database, the internal hedge fund database in the Bloomberg is not publicly available. We thank Chris Cappucci of Bloomberg for providing the detailed information on the 13F institution classification process in the Bloomberg database. 4 Form ADV is a required submission to the SEC by a professional investment advisor that specifies the investment style, assets under management, and key officers of the firm. It must be updated annually and be made available for companies managing in excess of $25 million. 9

11 funds. Following Yan and Zhang (2009), we define passive institutional investors as those that either invest more than $1 billion, or have more than 50% of their total net assets, in index funds. 5 We obtain information on stock holdings held by hedge fund companies as well as by non-hedge fund companies from January 2000 to September Stock returns and financial data are obtained from CRSP and Compustat, respectively. The resulting sample comprises 153,153 firm-quarter observations Summary statistics Table 1 shows the mean and median of total institutional ownership, hedge fund ownership, and nonhedge fund ownership aggregated at the firm level during the sample period by year. Hedge fund ownership is computed as the number of shares held by hedge funds by total shares outstanding, and non-hedge fund ownership as the difference between total institutional ownership and hedge fund ownership. The total institutional ownership has increased over the sample period. Its mean value is 30.8% in 2000 and reaches 39.9% by Hedge funds long-equity position has also grown significantly through 2008, but then declines in 2009, due to the credit crisis that started in The average hedge fund ownership is only 1.2% in 2000, going up to 6.1% by While non-hedge fund ownership has also increased during the same period, hedge fund ownership shows a much larger percentage increase. Hedge fund ownership represents only 4.0% of total institutional ownership in 2000, and that figure increases to 13.8% in The mean hedge fund ownership over the whole sample period is 3.4%, while the mean non-hedge fund ownership is 35.3%. Table 2 provides descriptive statistics on the variables we use in the empirical analyses. In each of the 39 quarters from January 2000 to September 2009, we compute cross-sectional averages of the variables in the sample, and the table reports the time-series mean, median, standard deviation, first quartile, and third quartile of the quarterly cross-sectional averages. We partition the sample firms by the median of hedge fund ownership into sub-samples of high and low ownership, and report summary statistics for the firms of sub-samples. The mean (median) hedge fund ownership is 6.5% (6.1%) for firms 5 We find no passive funds among our sample of hedge funds. 10

12 with high, and 0.5% (0.4%) for firms with low, hedge fund ownership. Firms with the former also have higher institutional ownership (mean 50.4% and median 50.0%) than firms with the latter (mean 32.2% and median 34.7%). Firm characteristic variables include size, market-to-book ratio, return volatility, turnover, price, age, dividend yield, accruals, and R&D. Firm size is measured as market capitalization; market-to-book ratio is computed as the ratio of market capitalization to book value of equity; return volatility is estimated as the standard deviation of monthly returns over the past six months; turnover is the average monthly volume to number of shares outstanding over the past six months; stock price is the share price from CRSP; S&P 500 dummy is a dummy variable that equals one if the firm is included in the S&P 500 index; age is calculated as the number of months since a firm s first stock price appeared in CRSP; dividend yield is cash dividend divided by share price; accruals are net income minus cash from operations scaled by assets; and R&D is research and development expense (zero for missing values) divided by sales. All variables are estimated at the same quarter-end. The comparison of firm characteristics of the two groups of firms with high and low hedge fund ownership shows that the former tend to be smaller, more actively traded, and younger than the latter. They also pay lower dividends, have larger accruals in absolute value, and have higher R&D expenses. Firms with high hedge fund ownership exhibit characteristics that are typical of high information asymmetry. We do not see significant differences in the return volatility, price level, and S&P 500 membership between the two groups. 6 6 In unreported tests, we examine the determinants of hedge fund holdings using the Fama and MacBeth (1973) approach. We find that while non-hedge funds tend to hold old firms and firms with low return volatility, hedge funds tend to hold young firms, low-priced stocks, firms with high return volatility, and non-s&p 500 stocks. Hedge funds appear to select stocks with higher information asymmetry. These findings are consistent with Griffin and Xu (2009), who show that hedge funds are likely to hold more opaque firms that may be more costly to trade and are less analyzed. The results are also consistent with the findings of Fung and Hsieh (1997) that the factor exposures of the returns of hedge funds and mutual funds are dramatically different. 11

13 3. Hedge Fund Holdings and Future Stock Returns In this section, we conduct several tests that examine the stock-picking ability of hedge funds and non-hedge funds and compare their performances. We first examine the return predictability of the level of and change in hedge and non-hedge fund ownership. After documenting the superior stock-picking ability of hedge funds, we then examine whether return predictability is stronger for stocks with a high degree of information asymmetry and for certain groups of hedge funds that are more likely to possess superior information. We also examine whether the stock-picking ability of hedge funds translates into better investment performance. Finally, we test if hedge funds can predict earnings-related fundamentals Return predictability of hedge fund ownership Table 3 presents the results for the return predictability of the level of hedge and non-hedge fund ownership. For each quarter, we estimate a cross-sectional regression of one-quarter-ahead returns on hedge and non-hedge fund ownership and stock characteristics variables. Using the approach of Fama and MacBeth (1973), we compute the time-series average coefficients from 39 cross-sectional regressions along with their t-statistics. We include stock characteristics variables that might affect institutional ownership as control variables. If stock characteristics explain average returns across stocks and also affect institutional ownership, then the return predictability could be a result of institutional ownership proxying for stock characteristics variables. Thus, to capture the marginal impact of institutional ownership in forecasting future returns, one has to control stock characteristics that explain returns across stocks. Following Falkenstein (1996) and Gompers and Metrick (2001), we include three sets of stock characteristics. The first set of variables includes firm size (log of market capitalization), past stock returns (cumulative market-adjusted return for the preceding six months and cumulative market-adjusted return for the penultimate six months), and market-to-book ratio. These variables are all related to historical return patterns. Small stocks, stocks with low market-to-book ratio, and stocks with high momentum are shown 12

14 to realize higher returns than stocks without such characteristics (Fama and French, 1992; Jegadeesh and Titman, 1993). 7 The second set of stock characteristics is concerned with liquidity and/or transaction costs. Institutional investors tend to hold liquid stocks, and they are more sensitive to transactions costs since they trade more often than individual investors (Gomers and Metrick, 2001; Schwartz and Shapiro, 1992). We include stock price and stock turnover in addition to firm size as proxies for liquidity and transactions costs. The third set of control variables includes age, dividend yield, S&P 500 index membership, and stock volatility. These variables are intended to consider the prudence characteristics of stocks, since institutional investors are often constrained by prudence restrictions when they select stocks (Del Guercio, 1996; Longstreth, 1986). We also include accruals as a control variable, since prior research suggests that accruals capture future returns (Sloan, 1996). In column (1), we regress quarterly returns at quarter t+1 on the total institutional ownership and other control variables at quarter t. Consistent with Gompers and Metrick (2001), we find a positive and significant relationship between institutional ownership and future returns. There are two possible explanations for this. To the extent that the level of institutional ownership reflects the accumulated purchase of undervalued firms by investors, its positive relationship with future returns suggests that institutional investors are informed investors. Alternatively, the increase in institutional ownership generates a large demand for the stocks among institutional investors, so that the positive relation between institutional ownership and future returns arises from institutional demand shocks. In columns (2) and (3), we partition total institutional ownership into hedge and non-hedge fund ownership and re-estimate the regression in column (1) using each ownership type as separate explanatory variable. We find that the coefficient estimates on the levels of both hedge and non-hedge fund ownership are positive and significant, but the economic significance of the ownership effect is much stronger with 7 Market-to-book ratio is winsorized at the 1 st and 99 th percentiles. Using cumulative raw returns for the preceding and penultimate six months instead of cumulative market-adjusted returns for the same time intervals does not change the results. 13

15 hedge funds. The magnitude of the coefficient estimate on hedge fund ownership (0.096) is more than four times as large as that on non-hedge fund ownership (0.021). In column (4), we include both ownership types as explanatory variables and find similar results to those in columns (2) and (3). The null hypothesis of equal coefficients on the hedge and non-hedge fund ownership is rejected at the 1% significance level. To the extent that the level of institutional ownership reflects the accumulated purchases of undervalued firms, the results support the view that hedge funds have information advantages over other institutional investors. On the other hand, to the extent that the rapid growth in equity investments by hedge funds generates a large demand for the stocks they purchase, demand shocks from hedge funds could have a stronger effect on the relation between hedge fund ownership and future returns (Gompers and Metrick, 2001). We distinguish between these two possibilities in the next section Return predictability of hedge fund trading In this section, we use the change in hedge fund ownership as a measure of informed trading. If a certain group of investors is able to trade on private information about firms future prospects, and markets are semi-strong form efficient, then the change in their stock holdings should be better able to forecast future returns than the levels of stock holdings since their abnormal performance will be shortlived (Coval and Moskowitz, 2001). Gompers and Metrick (2001) find a strong and positive relation between the level of lagged institutional ownership and future returns, but a weak relation between the change in institutional ownership and future returns. Using the level of lagged institutional ownership as a measure for future institutional demand and the change in institutional ownership as a measure for institutional information advantage, they interpret this finding as evidence that the return forecasting power of institutional ownership comes from demand shocks rather than the informed trading of institutional investors. Following Gompers and Metrick (2001), we decompose the current level of institutional ownership (Institutional ownership t ) as the sum of the one-quarter lagged institutional ownership (Institutional ownership t-1 ) plus the change in institutional ownership during the quarter t 14

16 (ΔInstitutional ownership t ). We then regress quarterly returns at quarter t+1 on the institutional ownership at quarter t-1 and the change in institutional ownership during quarter t, controlling for other explanatory variables. Table 4 reports the regression results. Column (1) shows that future returns are significantly and positively related to Institutional ownership t-1 but are insignificantly related to ΔInstitutional ownership t. This result is consistent with that of Gompers and Metrick (2001). In column (2), we regress quarterly returns at quarter t+1 on the level of hedge fund ownership at quarter t-1 and the change in ownership during quarter t. In column (3), we repeat the regression using non-hedge fund ownership at quarter t-1 and the change in ownership during quarter t. We find that the estimates on the levels of both types of ownership are positively and significantly related to future returns. The effects of demand shocks on stock returns appear to exist for both. We also find that the coefficient estimate on the change in hedge fund ownership is positive and statistically significant at the 1% level, whereas the coefficient estimate on the change in non-hedge fund ownership is negative, but statistically insignificant. More importantly, the effect of informed trading by hedge funds on future returns is economically large. The coefficient estimate on the change in hedge fund ownership is 0.22, which indicates that all else being constant, a 1% increase in the change in hedge fund ownership leads to a 0.22% increase in one-quarter-ahead stock returns. 8 A comparison with Yan and Zhang (2009) and Baik et al. (2010) puts this figure into perspective. In a study of the informational role of short-term institutional investors, Yan and Zhang (2009) find that a 1% increase in the change in shortterm institutional ownership is associated with a 0.06% increase in one-quarter-ahead stock returns. Baik et al. (2010) find that a 1% increase in the change in local institutional ownership results in a 0.06% increase in one-quarter-ahead stock returns. The return forecasting power of hedge funds appears to be larger than that of institutional investors known to have information advantages. In column (4), we include both the levels of, and changes in hedge and non-hedge fund ownership as explanatory variables 8 Put another way, since the standard deviation of the change in hedge fund ownership for our sample firms is 2.05%, an increase of one standard deviation in the change in hedge fund ownership is associated with a 1.8% (0.22 x 2.05 x 4) increase in annual returns. 15

17 and find that the level of and change in hedge fund ownership are strong predictors of future returns, while the level of and change in non-hedge fund ownership do not predict future returns. The null hypothesis of equal coefficients on the level of hedge and non-hedge fund ownership is rejected at the 5% significance level, whereas the null hypothesis of equal coefficients on the changes in hedge and nonhedge fund ownership is strongly rejected, with a p-value less than 1%. These results strongly support the view that hedge funds have information on future stock returns and significant advantages over other institutional investors. One may argue that hedge fund ownership provides short-run price support for a stock, but triggers a drop in prices at longer horizons. To investigate the possibility of price reversal, in an unreported analysis, we examine the impact of hedge fund ownership and its change on cumulative returns two, three, and four quarters ahead. While the coefficient estimates on the level of hedge fund ownership are all insignificant, the coefficient estimates on the change in hedge fund ownership up to a year ahead are consistently positive and significant. When we examine the quarterly returns instead of cumulative returns for two, three, and four quarters ahead, we find that the coefficient estimate on the change in hedge fund ownership two quarters ahead is positive and marginally significant, while the estimates three and four quarters ahead are all insignificant. This finding suggests that the return predictability of hedge fund ownership is not driven by short-term price pressure. It is noteworthy that our finding is in sharp contrast to that of Griffin and Xu (2009), who find that hedge fund trades are predictive of future returns without controlling past returns. However, they also find that hedge fund trades do not predict future returns once past returns are controlled, concluding that hedge funds are no better at predicting returns than investors using simple momentum strategies. In contrast, our results show that hedge funds stock holdings retain forecasting power even after controlling for past returns and other stock characteristics that are known to affect future returns. While our work and that of Griffin and Xu (2009) employ the same approach of using equity holdings by hedge funds to examine their information role, the conflicting results can be attributed to at least two important differences between the studies. 16

18 First, Griffin and Xu (2009) cover the period , while our sample period covers , during which time there is a significant increase in hedge funds stock holdings. Our evidence indicates that hedge fund ownership more than doubled between 2004 and 2008 (Table 1), suggesting that their informational role in stock markets has become more important in the more recent sample period. Another factor that may contribute to the informativeness of hedge fund holdings is the SEC s change in the regulations regarding confidential 13F filings. Hedge fund companies can request secrecy from the SEC to delay revealing their quarter-end positions. If the SEC agrees, hedge funds can delay disclosure of their holdings for up to one year. Such confidential holdings are not included in the CDA/Spectrum data. In recent years, the SEC has increasingly tightened up the conditions for accepting requests to file confidential 13Fs and hedge funds are finding it more difficult to meet these conditions. 9 Therefore, hedge funds stock holdings based on the 13F filings are likely to have become more complete and informative in the more recent sample period compared to the previous one. In fact, Agarwal et al. (2010) show that the confidential holdings of money managers exhibit superior risk-adjusted returns for up to four months after the quarter end. Second, we use a different hedge fund identification strategy. We rely on the Bloomberg database, which provides the most comprehensive list of hedge funds as it covers all institutions that file a 13F form with the SEC, whereas Griffin and Xu (2009) employ several commercial databases that may understate the population of hedge funds as they only include voluntarily participating hedge funds. Several studies show that the information advantages of institutional and individual investors and analysts are particularly pronounced in stocks with greater information asymmetry for which valuerelevant private information is relatively difficult to obtain (Coval and Moskowitz, 1999; Ivkovic and Weisbenner, 2005; Malloy, 2005; Kang and Kim, 2008; Bae, Stulz, and Tan, 2008). Accordingly, we examine whether the return forecasting power of hedge funds is stronger for stocks with high information 9 In 1998, following a controversy over the confidential treatment of investor Warren Buffett s stock holdings that caused a significant decline in the share price of Wells Fargo & Co, the SEC announced that it would tighten its surveillance of 13F filings by institutional investors (Buckett, 1998). In 2004, the SEC rejected hedge fund manager D.E. Shaw s request for continued confidential treatment of their holdings. Cauchi (2005) argues that this is part of an overall effort by the SEC to increase transparency in the hedge fund industry. 17

19 asymmetry than for those with low information asymmetry. Specifically, using information asymmetry variables such as return volatility, R&D, S&P 500 membership, and accounting quality, we partition the sample firms into those with high and low information asymmetry based on the top and bottom tercile of each information asymmetry variable with the exception of S&P 500 membership. We then re-estimate column (4) in Table 4 separately for these two groups. Table 5 reports the regression results. They show that the positive relation between the change in hedge fund ownership and future returns is statistically significant only for the stocks that have high return volatility and high R&D intensity, do not belong to S&P 500, and have lower accounting quality. 10 While the coefficient estimates on the change in hedge fund ownership are positive and significant for stocks with high information asymmetry, none of the corresponding estimates on the change in non-hedge fund ownership are significant. These findings are consistent with the view that hedge funds superior return forecasting ability is attributable to their informational advantages over non-hedge funds. In sum, the results in Tables 4 and 5 suggest that the informed trading of hedge funds predicts future returns and that this forecasting power is particularly pronounced in stocks with high information asymmetry Hedge fund characteristics and return predictability In this section, we examine whether certain groups of hedge funds that are more likely to possess and exploit information have better return predictability. We divide hedge funds into sub-sets using several fund characteristics and examine whether the return forecasting ability is more pronounced in certain of these sub-sets. Table 6 presents the regression results. First, in every quarter we measure the size of hedge funds by the value of the portfolio holdings they hold and divide them into large and small. Large (small) hedge funds are those in the top (bottom) tercile of fund sizes for a given quarter. Then, for each sample firm, 10 We measure accounting quality following Dechow and Dichev (2002) and define low accounting quality as the extent to which accruals do not map into cash flow realizations. 18

20 we compute ownership by large and small hedge funds respectively and separately regress one-quarterahead returns on these two ownership measures. The first set of columns indicates that the positive relation between hedge fund holdings and future returns is evident for large, but not for small, hedge funds. One interpretation of this result is that the return forecasting power of hedge funds is stronger when they possess more resources and hence have better access to expertise and talent. Second, we calculate each fund manager s average churn rate, measured according to the method of Gasper, Massa, and Matos (2005), over the past four quarters (see Appendix for definition). Then, we regress one-quarter-ahead returns on ownership by high and low churn rate hedge funds separately. High (low) churn rate hedge funds are those in the top (bottom) tercile of the churn rate for a given quarter. The results show that the coefficient estimates on the level of and the change in hedge fund holdings are significant only for the former, suggesting that they have an informational advantage over low-turnover funds. This result is consistent with Yan and Zhang (2009) who show that only short-term institutional ownership forecasts future stock returns. The next set of regressions uses the location of hedge funds to distinguish domestic from foreign. Several studies show that domestic investors and analysts have a significant information advantage over their foreign counterparts (Choe, Kho, and Stultz, 2005; Bae, Stulz, and Tan, 2008; Teo, 2009). In particular, using data on Asia-focused hedge funds, Teo (2009) finds that those with a physical presence in their investment region outperform other funds by 3.7% per year and that the local information advantage is pervasive across all major geographical regions. Consistent with these studies, we find that the forecasting ability of domestic hedge funds is superior to that of their foreign rivals. Both the level of and the change in hedge fund ownership are only significant for the domestic sub-sample. In the fourth set of columns, we decompose hedge fund ownership into those located inside and outside metropolitan areas. We define the former as funds whose headquarters are within a Primary Metropolitan Statistical Area (PMSA) according to the US Census Bureau. Hong, Kubik, and Stein (2005) show that information about stocks is transferred between mutual fund managers within a city, and Christoffersen and Sarkissian (2009) show that this transfer of information has a positive effect on 19

21 investment performance, particularly among those located in large cities. We find that the coefficient estimates on the level of and the change in ownership held by metropolitan hedge funds are positive and significant, whereas those by funds located outside metropolitan areas are insignificant. The results in Table 6 indicate that certain types of hedge funds, namely those that are more likely to possess and exploit information, show stronger return forecasting power. Large funds, funds with a high churn rate, domestic funds, and funds with headquarters in a metropolitan area outperform other hedge funds in return predictability Hedge funds portfolio performance at the firm level So far, we have examined the stock-picking ability of hedge funds and have shown that their stock holdings and trades predict future stock returns, suggesting that hedge funds possess an ability to select stocks. In this section, to examine whether such stock-picking abilities translate into better investment performance, we measure the performance of the underlying stocks in hedge fund portfolios by computing benchmark-adjusted returns (BARs) using the approach by Daniel et al. (1997). Evaluating the performance of stocks held by hedge funds has one important advantage over evaluating performance in terms of hedge fund returns. Traditional linear factor models can control for the risks of underlying stocks reasonably well, but may not be appropriate for describing the risk-return profiles of hedge fund returns, which tend to be highly nonlinear due to the heavy use of derivatives. We form quintile portfolios based on the level of and the change in hedge fund ownership in each quarter and calculate the BARs for these portfolios; the benchmark returns are drawn from 125 portfolios formed on the basis of size, book-to-market, and price momentum following Daniel et al. s (1997) procedure. We also form an arbitrage portfolio that buys stocks in the top quintile and sells stocks in the bottom quintile and compute its average return. If hedge funds possess private information about firms and trade based on this information, then the stocks they hold more of, or increase their holdings in, should significantly outperform the stocks they hold less of, or decrease their holdings in. Accordingly, the arbitrage portfolio should generate positive abnormal returns. 20

22 Panel A of Table 7 presents the time-series averages of the annualized quarterly returns on the portfolios sorted by levels of hedge and non-hedge fund ownership. For the sort based on the level of hedge fund ownership, the average annualized risk-adjusted return on the arbitrage portfolio is 5.2% and is significant at the 5% level. The corresponding return of the arbitrage portfolio based on the level of non-hedge fund ownership is smaller as 2.2% and is insignificant. In Panel B of Table 7, we report the time-series averages of the annualized quarterly returns on the portfolios sorted by changes in hedge and non-hedge fund ownership. We find that the risk-adjusted return for stocks in the highest quintile of the change in hedge fund ownership is 6.4% higher than the corresponding figure in the lowest quintile. The difference is significant at the 1% level. In contrast, the arbitrage portfolio return based on the change in non-hedge fund ownership generates a statistically insignificant return. Overall, the results in Table 7 suggest that stocks with the highest hedge fund ownership or those which are purchased predominantly by hedge funds consistently outperform stocks with the smallest hedge fund ownership or stocks which such funds primarily sell Hedge funds trading and earnings information To further provide evidence of hedge funds predictive ability, we ask if they can predict earningsrelated fundamentals. In a recent paper, Baker, Litov, Wachter, and Wurgler (2010) construct measures of trading skills based on how the stocks held and traded by fund managers perform at subsequent corporate earnings announcements. Using the measure of trading skills developed by Baker et al. (2010), we examine whether hedge funds have superior information on earnings fundamentals and are thus better able to forecast future earnings. Specifically, for each fund-date-stock holding observation, we calculate the BARs over the three-day window around the subsequent earnings announcement dates. The BAR is estimated as the difference between the raw return and the average earnings announcement return over the three-day window on stocks of similar book-to-market, size, and momentum that have also announced 21