Who Gains More by Trading Institutions or Individuals?

Transcription

1 Who Gains More by Trading Institutions or Individuals? Granit San Tel-Aviv University First Draft: September 2004 This Version: April 2006 This paper is based on parts of my dissertation. Portions of this research were implemented while I was a Visiting Doctoral Fellow at the Wharton School. I owe special thanks to my advisor, Simon Benninga, for his dedicated support. I am especially indebted to Roni Michaely for his guidance and numerous helpful comments and suggestions. I would also like to thank Avner Kalay, Eugene Kandel, Shmuel Kandel, Pete Kyle, Gideon Saar, and Jacob Sagi for helpful discussions and suggestions. Comments from seminar participants at the Hebrew University, Tel-Aviv University, and the Technion are gratefully acknowledged. This research was supported by a grant from the Ministry of Science and Technology, Israel.

2 Who Gains More by Trading Institutions or Individuals? Abstract We calculate four measures of institutional trading and find significant differences in the return patterns of stocks with different proportions of trading activity by institutions and individuals. In a two-year window around the trading, the lowest returns coincide with intense institutional selling, whereas high returns correspond to intense institutional buying. Our results demonstrate that individuals realize superior gains by selling. In the late 1990s bubble, they also gain about 2% per month more than institutions by buying. They suggest that a possible explanation for the inferior performance of institutions is that institutions hold winners too long and mistime the momentum cycles. Though in line with existing empirical evidence, they do not support the conventional wisdom that individuals are the noise traders who lose money by trading.

3 I. Introduction Noise trading is trading on noise as if it were information Most of the time, the noise traders as a group will lose money by trading, while the information traders as a group will make money Because the actual return on a portfolio is a very noisy estimate of expected return there will always be a lot of ambiguity about who is an information trader and who is a noise trader. Black (1986, p ) Which group of investors, institutions or individuals, are the noise traders who lose money by trading? Financial academics and practitioners tend to view individual investors as the noise traders. This paper demonstrates that this should not be held as a self-evident truth, by presenting evidence that during the period 1986 through 2001 individuals gain more than institutions, thus they are not necessarily the noise traders. We expose a significant difference in the return patters of stocks with different proportions of trading activity by institutions and individuals. These differences indicate that individuals are the ones who buy low and sell high, and that individuals as a group do not lose money by trading, but realize superior gains. Using data on institutional holdings and trading volume for all NYSE and Nasdaq-NM stocks during the period 1986 through 2001, we calculate four measures of institutional trading activity. Our proxies measure the relative trading activity by institutions and individuals at the single stock level. We use these measures to investigate the cross-sectional variations in stocks with different proportions of trading activity by institutions and individuals. Since our measures includes the trading activity of all public participants, a difference between stocks with various amount of trading by institutions and individuals, which emerges through the return patterns, reflects the systematic influence of each group on market prices. 1 In this study, we focus on Black s definition of noise traders. Accordingly, in order to explore which group of investors, institutions or individuals, are the noise traders (who lose money by trading) we use our measures to compare the performance of stocks 1 In that, we are different from most previous research, which focuses on a specific group of investors. For example, Jensen (1968), Malkiel (1995), Grinblatt, Titman and Wermers (1995), Wermers (1999, 2000), and many others, study mutual funds; Lakonishok, Shleifer and Vishny (1992a,b) analyze pension funds; Del Guercio (1996) examines mutual funds and banks; and Odean (1998, 1999) investigates individual investors with accounts in a particular discount brokerage. The extent to which the trading of each group of investors affects market prices also depends on the trading of other market participants, yet this cannot be manifested by these researches. 1

4 with different proportions of institutional and individual trading activity. We implement the four measures of performance that are commonly used in the literature: raw return, CAPM alpha, Fama-French alpha and four-factor alpha. Both, the return patterns and the risk-adjusted returns, are investigated over the two years before and after the trading. Figure 1 graphs the cumulative market-adjusted returns for the top and bottom decile portfolios of institutional net trading. The bottom decile contains the stocks with the most intense institutional net selling, while the top decile contains the stocks with the most intense institutional net buying. Likewise, since individual trading is counter to institutional trading, the bottom decile contains the stocks with the most intense individual net buying, and the top decile contains the stocks with the most intense individual net selling. 2 The return patterns are established by event-times in the two-year window around the trading. The figure summarizes the main results of the paper: the lowest returns coincide with intense institutional net selling (and intense individual net buying), whereas high returns correspond to intense institutional net buying (and intense individual net selling). At first glance, it would seem that our results are puzzling. However, a closer scrutiny reveals that they are, in fact, coincide perfectly with existing empirical evidence. First, consistent with the literature, Figure 1 shows that institutions are momentum investors whereas individuals are contrarian traders. Furthermore, it reveals that one of the implications of this evidence is that individuals time their trades better than institutions and thereby might realize superior gains. Second, numerous studies find that individuals exhibit the disposition effect (i.e. the tendency to sell winners too early). Figure 1 confirms these findings, while exposing that even though individuals sell winners too soon, they sell at a higher price than institutions. Third, our results corroborate the recent findings of Kaniel, Saar and Titman (2004) and Campbell, Ramadorai and Vuolteenaho (2005). Investigating trading at the daily frequency and short-term (up to month) performance, Kaniel, Saar and Titman (2004) find superior performance of individuals, and Campbell, Ramadorai and Vuolteenaho (2005) find inferior performance of institutions. We present similar evidence in the long run. 2 Section III contains the details of this presumption. 2

5 We provide a number of new results on the difference between stocks traded by institutions and individuals, and, by implication, on the performance of institutions and individuals and their role in the stock market. First, our results indicate that institutions buy high and sell low, whereas individuals are the ones who buy low and sell high. This is apparent when we examine the return patterns over the four-year period around their trading (two-year before and two-year after). Comparing the event-time market-adjusted returns of stocks heavily traded by individuals and institutions, we find that the trading activity of institutions (or their counterparts, individuals) signals a change in the return trends. For example, the returns of stocks excessively bought by individuals decline in the two-year period preceding individual purchases, while in the two years that follow the purchases the returns increase. Moreover, when we investigate whether our trading measures convey information that is not captured by the holding measure, we find that among the highmomentum stocks (winners) that institutions prefer to hold, institutions are net buyers in stocks whose past returns are significantly higher than the past returns of the stocks in which they are net sellers. Second, our evidence suggests that if a certain group of investors is the noise traders who lose money by trading, this group is not individuals but institutions. We find that individuals time their exit from the market better than institutions and realize superior gains by selling. Independent of the measure of performance we use, stocks heavily sold by individuals, after been held by them over one quarter to two years, have experienced significant abnormal excess past returns relative to stocks heavily sold by institutions. For example, if both institutions and individuals sell a NYSE stock after holding it for one year, the average return of stocks with excess institutional net selling underperforms the average return of stocks with excess individual net selling by 15.7% per year; and this is statistically significant, with a t-statistic of Interestingly, the inferior profits of institutions are salient despite the disposition effect of the individuals in our sample. Our results indicate that although individuals do not realize the highest (optimal) returns from their sales, they do sell at higher price than institutions. We also find that in the pre-bubble period, neither institutions nor individuals realize superior 3

6 gains by buying. Stocks heavily bought by institutions realized about the same future returns as stocks heavily bought by individuals. Third, our subperiod analysis reveals distinct characterizations of the trading activity of institutions and individuals in the late 1990s bubble. In the late 1990s bubble, particularly in Nasdaq, not only do individuals gain more than institutions by selling but they also gain more by buying. In this is period, Nasdaq-stocks excessively bought (and sold) by institutions realize lower returns than Nasdaq-stocks excessively bought (and sold) by individuals; and this holds over any investment horizon of up to two years. Furthermore, when we adjust the returns to the four-factor risk, and include the momentum risk, we find that the four-factor risk-adjusted returns of the portfolio of intense institutional buying significantly underperform the portfolio of intense individual buying. For example, in Nasdaq, the risk-adjusted return earned by the portfolio of intense institutional buying, in the year that follow the purchases, is 28.3% lower than the risk-adjusted return earned by the portfolio of intense individual buying. Fourth, our findings suggest a possible explanation for the inferior performance of institutions. In line with previous evidence, we find that institutions are momentum traders who hold stocks with high past returns. However, our overall results indicate that they tend to stick to momentum trading style and hold winners too long, and in doing so they fail to time their trades to fully exploit the intermediate momentum effect. We find that institutions tend to hold stocks which have high past returns (winners) not only over the previous six-month and one-year but also over the previous two-year. Moreover, they are net sellers in stocks whose past returns are lower than the past returns of the stocks in which they are net buyers, and they are momentum traders with respect to the returns in the previous six-month to two-year. This harms their performance, in particular in light of Jegadeesh and Titman s (1993) results on the intermediate momentum effect in stock returns, suggesting that they might mistime the momentum cycle. Furthermore, the differences between the raw returns and the four-factor risk-adjusted returns indicate that not only are institutions not properly compensated for taking high momentum risk but they also worsen their performance by taking this risk. A possible reason for this evidence is window dressing (e.g., Lakonishok et al. (1991)) by institutions. 4

7 Our fifth finding is that our trading measures convey information that cannot be captured by the holding measure. We find that stocks with high level of institutional holdings have distinct characteristics from stocks with intense institutional trading. Large stocks, with high beta and low book-to-market ratio have large institutional holdings; while among the stocks with high institutional holdings, the stock with frequent institutional trading are smaller, and have lower beta and higher book-to-market ratio than the stocks with thin institutional trading. Finally, our results indicate that the superior performance of individual is not merely a compensation for high systematic risks. We find that though institutions and individuals differ in their trading style, institutions do not consistently trade stocks with risk-characteristics that provide lower returns. The rest of the paper is organized as follows. Section II reviews related literature. Section III presents the measures of institutional trading. We first describe the datasets and methodology used to calculate them, and then demonstrate that it contains significant information that cannot be captured by the holding measure. Section IV presents our main results graphically, and section V investigates them further by a detailed empirical analysis of the trading style, the past returns, and the future returns. We end this section by a discussion of the complement implications of the empirical analysis. In Section VI we look into the late 1990s bubble by a subperiod analysis. Section VII concludes. II. Related Literature Prior empirical works examine aspects of the relation between institutions and individuals and stock returns. The evidence on the preferences of institutions and individuals is rather conclusive. On one hand, institutional studies find a positive relation between past returns and net change in institutional holdings. For example, Grinblatt, Titman and Wermers (1995), and Sias, Starks, and Titman (2001) document this relation for one quarter; Wermers (1999), and Chen, Jegadeesh and Wermers (2000), document it for one and two quarters; and Nofsinger and Sias (1999) for one year. On the other hand, individual studies find that individuals tend to be contrarians. For example, Odean (1998, 1999) finds that U.S. individuals tend to hold on to their losers and sell their winners; and Hvidkjaer (2005a) finds small-trade buying pressure for loser stocks. Individuals outside 5

8 the U.S. also tend to be contrarians. For example, Grinblatt and Keloharju (2000) document this for Finish individuals, Choe, Kho, and Stulz (1999) for individuals in Korean, Shapira and Venezia (2001) for Israeli amateurs, Jackson (2003) for Australian individuals, and Richards (2005) for individuals in six Asian markets. Our results, that institutions are momentum traders and individuals are contrarians, are in line with these works. We add to them by exploring that these results are not limited to few quarters but persist over the two years prior to the trading; and by presenting their implications for the realized profits of individuals and institutions and for their long-term performance. The evidence on the performance of institutions is mixed. For example, Jensen (1968), and Malkiel (1995) find that mutual funds underperform relevant market indices over horizons of one year; and Lakonishok, Shleifer and Vishny (1992b) find that active money managers of pension funds underperform the market index. In contrast, Wermers (2000) finds that mutual funds hold stocks that outperform a broad market index by 1.3% per year over one quarter, and Nofsinger and Sias (1999) find that following large changes in institutional ownership stocks institutions purchase subsequently outperform those they sell. Gompers and Metrick (2001) find that the aggregate institutional portfolio outperforms the aggregate individual portfolio by 0.67% per annum. The disparity in these findings corresponds to our results, which indicate that during the prebubble period the differences between the future raw returns of stocks with various levels of institutions and individuals buying are insignificant, particularly in the quarter and year subsequent to their purchases. We add to these papers by comparing the performance of institutions and individuals; and by suggesting that the tendency of institutions to hold on to winners might drive the results, thus the performance measure that should be applied is the four-factor alpha. Carhart (1997) finds that some apparent persistent in the performance of mutual funds is due to momentum in stock returns, hence it is not extended beyond a year. His results indicate that active managers fail to outperform passive benchmark portfolio. Similarly, Grinblatt, Titman and Wermers (1995), and Daniel et al. (1997) attribute much of mutual funds outperformance to the high average returns of the stocks they hold, thus to the momentum effect. Chen, Jegadeesh and Wermers (2000), like us, examine trades of mutual funds rather than holdings. They show that funds tend to buy stocks that 6

9 outperform the stocks they sell, but only in the first year following the trades. They also find that the persistence in performance is mostly due to the momentum effect in stock returns. These findings are in line with our argument that the performance measure that should be applied is the four-factor alpha. We add to them by presenting evidence that institutions tend to stick to momentum trading style too long, thus they are not properly compensated for taking high momentum risk; and by demonstrating that this was particularly pronounced in the late 1990s bubble. Furthermore, unlike these papers, which focus on mutual funds, we take a broader approach and investigate all institutions and individuals. 3 It is this extension that enables us to expose market effects. The evidence on the performance of individuals in the U.S. is also mixed. Odean (1999) investigates individual investors with U.S. discount brokerage accounts in the period 1987 to He finds that individual buying portfolio underperform their selling portfolio over the following two years. Hvidkjaer (2005b) studies small-trade volume to infer retail trading, and finds that stocks with intense sell-initiated small-trade volume outperform those with intense buy-initiated small-trade volume. Barber, Odean and Zhu (2003) analyze trading records of individuals with discount broker accounts between 1991 and 1996, as well as larger sample of investors with retail broker accounts between 1997 and They find no convincing evidence that stocks heavily purchased by these clients outperform those heavily sold by them. In fact, in their second sample, they find outperformance (though not reliable). Possible reasons for the differences between these findings and ours are that our group of individuals is broader and includes investors that are not included in the particular group of individuals that these studies examine, and their sample periods. 4 Barber, Odean and Zhu s (2003) results support this conjecture. Several recent papers investigate the short-term dynamic relation between institutions and individuals and stock returns. Kaniel, Saar and Titman (2005) use a unique NYSE dataset for the period 2000 through 2002 to examine the relation between daily individual investor trading and short horizons (up to a month) returns. They find that individuals tend to be contrarian and that the stocks that individuals buy exhibit 3 It is worth noting that contrary to the extensive research on mutual funds, mutual funds constitute only a fraction of all institutions. For example, in December 1986 their holdings represent only 5.7% of the value of total institutional holdings; and in December 1995 their holdings represent only 22.2% of the value of total institutional holdings (Gompers and Metrick (2001)). 4 We show that the significant outperformance of individuals is unique to the late 1990s bubble. 7

10 positive excess returns in the following month. Griffin, Harris and Topaloglu (2003a) use the type of brokerage house to identify individual and institutional trading in Nasdaq- 100 stocks for May 2000 through February They find positive relation between daily institutional trading and past daily and intra-daily returns, but no significant relation to future daily returns. Campbell, Ramadorai and Vuolteenaho (2005) apply a sophisticated method to infer daily institutional trading from 13F filling and TAQ. They find that institutions are momentum traders at the daily frequency, and that daily institutional sales strongly predict positive return while institutional purchases only weakly predict negative returns. Our study complements these studies by analyzing longer horizons. We find similar results at the quarterly frequency. While these papers focus on daily trades and their relatively short-term (days to a month) dynamics, we investigate quarterly trades and their longer-term (a quarter to two-year) influences; thus, we evaluate noise at different resolutions. In addition, our result expose that the relative trading activity by institutions and individuals has different implications for the future returns in the late 1990s bubble and in the period preceding it; suggesting that the validity of their results to other periods should be considered with caution. Our evidence is related to existing theories in two ways. First, some researchers (e.g., Trueman (1988), Allen and Gorton (1993), and Dow and Gorton (1997)) recognize that institutions might be the noise traders and provide various explanations of why they engage in noise trading. Our findings support the premise of these models. Furthermore, recent studies support our results and provide alternative explanations for them. Dasgupta, Prat and Verardo (2005) model the conformist tendency of institutions. They show that conformism could cause institutions to herd and present empirical evidence that institutions lose from their trades in stocks that have been persistently bought (sold) by them. Frazzini and Lamont (2005) evidence suggest that institutional flows could also derive our results. They find that high mutual funds flows predict low future returns. Second, many studies use the interaction between noise traders and information traders in modeling the way in which information is incorporated into the markets and affects prices. Despite the wide use of the distinction between trader types, the various models are hard to verify since the identity, role and impact of the different traders are largely unexplored empirically. Our results have implication for these theories, in 8

11 specifying the identity of the traders in the models. We briefly give two examples. De Long at al. (1990b) suggest that rational speculators might move ahead of noise traders, in order to push up prices and trigger behavioral feedback traders to buy, so that they profit from driving stock price movements. In this case, our results imply that institutions are the noise traders, particularly in the late 1990s bubble. Alternatively, De Long at al. (1990a), among others, recognize that noise traders can lead prices away from fundamental values by creating noise trader risk which information traders cannot arbitrage, and thereby affect prices and earn high returns. In this case, our evidence might imply that individuals are the noise traders and they gain due to the noise they create. Obviously, further empirical study is required in order to derive the validity of the various models. III. The Measures of Individual Investor Trading A. Data We calculate our institutional trading measures using two databases: institutional holdings and trading volume. In this subsection, we describe each database, the way we use it to calculate our measures, and our sample selection. A.1 Institutional Holdings A 1978 amendment to the Securities and Exchange Act of 1934 requires all institutions with greater than $100 million of securities under discretionary management to report their holdings to the SEC. Holdings are reported quarterly on the SEC s form 13F, where all common stock positions greater than 10,000 shares or $200,000 must be disclosed. 5 13F filings were drawn from CDA/Spectrum Institutional Holdings database, currently distributed by Thomson Financial. We use the quarterly holdings from this database to calculate institutional trading, defined as the total institutional dollar trading (i.e. buying and selling) in a 5 Other types of securities holdings (e.g. convertible bonds, stock options, preferred stock) are also required to be disclosed and count toward the $100 million limit, but only common stocks are included in our study. 9

12 specific stock, i, during a given quarter, t. 6 Letting j, i t denote institution j s holdings of h, stock i at the end of quarter t, and p i, t denote the average daily price (taken from CRSP daily stock file) of stock i in quarter t; 7 we define i, t = 0 j (1) institutio nal selling i, t = Max h j, i, t 1 h j, i, t, 0 pi, j (2) ( institutio nal buying) Max( h j, i, t h j, i, t 1, ) pi, t ( ) ( ) t ( institutional trading) i, t ( institutional buying) i, t + ( institutional selling) i, t = (3) To uniquely define each stock, we match each cusip code (which is used in the Spectrum 13F file to identify a security) to its CRSP permanent number (permno). To uniquely define each institution, we correct for reused institution s identification numbers by assigning the reused number a different institution number. 8 Quarters without any holding report are considered as having the same holding as in the previous quarter. The use of 13F filings yields a good, though not perfect, proxy for institutional trading. The main limitation of this proxy is that the quarterly snapshots of institutional positions do not measure intraquarter roundtrip transactions. However, existing evidence supports the assumption that such transactions are infrequent and should have a minor impact on the results. First, institutions turnover is about 80 percent per year, which corresponds to a holding period of fifteen months, well above a quarter. 9 Second, until 1997 the so-called short-short rule of the IRS imposed tax penalties on funds that derive more than 30 percent of their profits from holding periods of 91 or fewer days; this ruling discourages funds from turning over stocks during short time periods. Third, both, Lakonishok, Shleifer and Vishny (1992a), who analyze pension funds, and Wermers 6 We calculated trading in both dollars and number of shares (adjusted to distribution events). The results established with the two measures are qualitatively identical. Hence, we report only the results based on dollar volume and interchangeably use the two definitions. 7 Since institutions could have transacted any time during the quarter, we do not use the end-of-quarter prices imputed in the 13F reports, but the average price for the quarter (calculated using CRSP daily prices). 8 Spectrum reused institution s identification numbers. A gap of more than one year in the reported holdings for the same institution s number typically reflects a different and unrelated institution. 9 80% per year is actually an upper estimate. For example, for mutual funds, which are among the most active institutional traders, Carhart (1997) finds a turnover of 77.3% per year, Wermers (2000) documents that the turnover is 59% per year, and Jin (2004) finds a minimum holding period of four months. 10

13 (1999), who examines mutual funds, indicate that roundtrip intraquarter trades are infrequent and represent a small minority of all fund trades. Fourth, Griffin, Harris, and Topaloglu (2003a), who use proprietary data to distinguish between individual and institutional trading on Nasdaq, find a strong relation between their proxy for institutional trading and the proxy that measure institutional trading by quarterly changes in institutional ownership from Spectrum. A.2 Trading Volume We use volume traded and prices from CRSP daily file to calculate total trading, defined as the total dollar trading (i.e. buying and selling) in a specific stock during a given quarter. While using CRSP volume to calculate total trading, it is important to notice that there are two different reporting conventions. In one, a transaction is reported by each side of a trade, hence buy and sell are counted as two separate transactions. We use this convention, along with the term trading (defined as the sum of buying and selling) to indicate a transaction that is reported by the two parties involved in it. In the other convention a transaction is reported only by one side of the trade (either the buyer or the seller), and hence buy and sell are considered as one transaction. Volume traded, reported on CRSP, use this convention. Therefore, to compute total buying (selling), we first multiply the daily volume traded in a stock by its daily price, and then aggregate this over the quarters trading days to get total buying (selling) in this stock during the quarter. The sum of total buying and selling (i.e. twice CRSP volume) is total trading. A.3 Sample Selection We merge the three trading datasets (described above) to create our trading sample. Our study covers all equity securities traded on the NYSE and Nasdaq with available data from CRSP. The trading period is from the beginning of 1986 through the end We only include ordinary common shares of firms incorporated in the United States (share code 10 or 11). If a firm is delisted, we exclude the delisting quarter (due to lack of holding data at the end of this quarter). Since we analyze the NYSE and 10 The trading period was set by data availability. When we implemented our empirical study, the data of institutional holdings were available from the beginning of 1986, and the return data (from CRSP) were available through the end of Since we are interested in the return patterns in the two years following the trades our trading sample ends in

14 Nasdaq stocks separately, we only include quarters in which the stock is traded during the whole quarter in the same exchange. To ensure that the results are not driven primarily by small and illiquid stocks, we exclude Nasdaq Small Cap stocks and stocks whose average market capitalization is lower than $100 million. We do not drop from our sample observations with either no institutional trading, or institutional trading greater than public trading (defined below). If a stock in CRSP is not traded by any institution (insider) in a given quarter, we set institutional (insider) trading to zero. If institutional buying (selling) in a given stock-quarter is greater than public buying (selling), we restrict it to be equal to public buying (selling). Our final trading sample consists of 166,976 stock-quarter observations, with 7,245 stocks (2,858 traded in the NYSE and 4,387 in Nasdaq-NM) and 64 quarters. B. Methodology We use our proxies of institutional buying, selling and trading, defined in equations (1), (2), and (3), to calculate four trading measures: %( institutio nal buying) = institutional buying total buying (4) %( institutio nal selling) = institutional selling total selling (5) institutional trading %( institutio nal trading) = total trading (6) % ( institutional net trading) = ( institutio nal buying) %( institutional selling ) % (7) Our analysis combines the information in each of these measures, in order to provide a comprehensive picture of stocks with different proportions of institutional and individual trading activity. The net trading measure (equation 7) provides information on the direction and magnitude of imbalances in institutional investor trading. When purchases among institutions exceed their sales, it is positive and institutions are the net buyers (i.e. there is a positive trade imbalance where institutions are the net buyers). When institutional selling exceeds their buying, it is negative and institutions are the net sellers. Thus, the measure of institutional net trading quantifies the relation between net 12

15 buying and net selling, and enables to compare stocks with buying and selling pressures by institutions. 11 The buying, selling and trading measures (equations 4, 5 and 6) provide information on the magnitude and intensity of buying, selling and trading by institutions; thus on the impact of their trading activity itself. In this paper, we are interested in the cross-sectional variations of different stocks. Hence, the four trading measures are normalized so that they measure the percentage of institutional trading. To ease the terminology, we henceforth use the terms institutional trading and percentage of institutional trading interchangeably. Main issue that has to be considered while calculating the percentage of institutional trading is that the trading mechanisms are different for the NYSE and Nasdaq stocks. The NYSE is primarily an auction market whereas Nasdaq is a dealer market. Consequentially, total trading reported for securities listed on the NYSE is not directly comparable to total trading reported for securities listed on Nasdaq. To address this, throughout the study, we separate between the two exchanges. For simplicity, we refer to investors that file 13F as institutions and to all other investors as individuals, even though this group also includes very small institutions and short-term (intraquarter) institutional traders, since they make up only a tiny percentage of the category. In addition, while the trades of individuals are likely to be the counterparties to institutional trades, the complement of the percentage of institutional trading is only an approximate estimate of the percentage of individual trading. Besides individual trading, the complement of institutional trading includes insider trading, and intermediary trading. Insider trading is smaller than institutional trading by an order of magnitude, thus constitutes only a negligible fraction of the total trading. 12 With the exception of small, illiquid stocks (which are not included in our sample), the fraction of intermediary trading is not substantially different for stocks that are traded in the same 11 In order to exclude the possibility that this measure is strongly dependent on the amount of trading (e.g., it is close to zero mainly due to either negligible trading or intense trading (with negligible difference between buying and selling)), we also normalized the net trading measure by institutional trading (instead of public investor trading). This has insignificant effect on the results (if anything, it reinforces them) since the two measures are highly correlated (the correlation is about 0.9). 12 In previous version of this paper, we calculate insider trading using insider transactions information from Forms 3, 4 and 5 fillings with the SEC, and incorporated it with institutional trading. This has no material effect on the results. 13

16 exchange. 13 Hence, the fraction of a stock traded by institutions (i.e. our institutional trading measures) should be negatively correlated with the fraction of a stock traded by individuals. Although our method is not perfect, it does have a meaningful ability to detect significant differences in the levels of trading activity by institutions and individuals, thus provide information on the trading activity of all public participants. Since the extent to which the trading of each group of investors affects market prices also depends on the trading of other market participants, comparing the trading activity of the two groups provides an opportunity to identify their systematic influences and measure the performance of their underling stocks. C. Are Our Trading Measures Different from the Holding Measure? A prerequisite to an understanding of the dynamics of stock prices is an understanding of the trading activity of the various market participants and their effects on market prices. But, does trading activity by different investors convey information that cannot be captured by their holdings? Could the return patterns of stocks with intense institutional trading be different from those of stocks with high level of institutional holdings? Do stocks preferably held by institutions have distinct characteristics from stocks frequently traded by them? Are the trading strategies of institutions important only in the stocks they prefer to hold? Since trading involves transaction costs and tax concerns, the decision to actively trade a stock is likely to reflect stronger views, or at least different considerations, about value than the decision to passively hold it. Since trading reflects realized gains and losses whereas holdings reflect paper (or unrealized) gains and losses, any evidence of stock selection ability would be more discernible in trading rather than holdings. Furthermore, institutions face a variety of constraints on their investment decisions (e.g. different regulation rules, prudence restrictions), which required them to hold stocks with certain characteristics. Hence, it is possible that the stocks institutions heavily hold are different from the stocks they frequently trade. All of the above lead us to hypothesize 13 The Appendix provides supporting evidence of this presumption for intermediary trading in each exchange. 14

17 that trading conveys additional and important information that is not captured by holdings. To test whether our trading measures contain information that is not captured by the holding measure, we perform conditional sorts. Each quarter, we conduct doublequintile sorts, sorting first by institutional holdings and then by our institutional trading measures. We then calculate the time series average of the cross-sectional means of stocks characteristics in the resulting 25 portfolios. Institutional holding measure is calculated as in previous studies (e.g., Gompers and Metrick (2001), Sias, Starks and Titman (2001), and Campbell, Ramadorai and Vuolteenaho (2005), among other). Using 13F filings, we compute institutional holdings in a stock-quarter, as the sum of shares held by all institutions divided by the total shares outstanding. In the current study, we focus on the cross-sectional variation in the return patterns of stocks with different proportions of trading by institutions and individuals. Academic research has shown that stocks with high past returns ( momentum ), small stocks, stocks with high beta, and stocks with high book-to-market ratio, have higher returns than stocks without those characteristics, and attribute this to risk differences. We therefore investigate these characteristics for possible differences in the returns and the risks associated with them. All variables are measured at the beginning of the trading quarter. We compute the characteristics using the four trading measures. For simplicity of presentation, we only report one trading measures for each characteristic, but the results using all other trading measures were very similar. The results, presented in Table I, conclusively confirm our hypothesis. Stocks with high institutional holdings differ significantly from stocks with intense institutional trading, and this is true with respect to each one of the above characteristics, as well as for stocks traded both on the NYSE and on Nasdaq. In Panel A of Table I, we report the average past returns ( momentum ) for stocks in the double-quintiles of holding and net trading. We present past returns for three time periods: six-month, one-year and two-year, calculated as the compounded return over these time terms. The double-sort is by holdings and then by net trading. We present the results for the net trading measure, since it enables to test explicitly whether institutions realize their predicted momentum gains. The results are quite striking. Not 15

18 only do institutions prefer to hold stocks with high past returns in the previous six and twelve months (previous studies), but also the stocks with high institutional holdings have high returns in the preceding six months to two years. Moreover, they are net buyers in stocks whose past returns, over all three time periods, are significantly higher than the past returns of the stocks in which they are net sellers. For example, among the stocks with the highest percentage of institutional holdings in the NYSE, institutions are net buyers in stocks with an average past two-year return of 61.58% while the respective return of the stocks where they are net sellers is 33.04%. In other words, institutions do not seem to realize optimally their expected gains from holding winners. This establishes our explanation that institutions might lose due to misting the intermediate momentum in stock returns (Jegadeesh and Titman (1993)). Panel B of Table I presents the average size decile of the stocks in each portfolio. The size decile is the rank of the market capitalization of equity, based on NYSE size decile cutoff, with the size rank of one being the smallest and the size rank of ten being the largest. As apparent from the last row, and consistent with previous research (Daniel, Grinblatt, Titman and Wermers (1997); Chen, Jegadeesh and Wermers (2000); and Gompers and Metrick (2001)), institutions prefer to hold large capitalization stocks, and in particular have an aversion to small stocks (Falkenstein (1996)). However, our evidence reveals that not only do institutions prefer large stocks, but also that the stocks with high institutional holdings are larger than stocks with low institutional holdings. Furthermore, among the stocks with high institutional holdings, the stocks with intense institutional trading are smaller than the stocks with thin institutional trading. For example, among the stocks with the highest proportion of institutional holdings in the NYSE, the average size decile of stocks with the highest institutional trading activity is 6.19 versus 7.38 for stocks with the lowest institutional trading activity. 14 This indicates that though the preferred holdings of institutions are large stocks, it is not necessarily the case that this is also where their trading strategies are most important. 14 The results persist both in the NYSE and Nasdaq, and along the different holdings quintiles. The only exception is very small stocks. According to our results, those stocks are rarely either held or traded by institutions. However, this could be an artifact of our proxy. The threshold reporting levels required to fill in the 13F form will import a small bias to both the holdings and trading proxies, which will be more pronounced in the smaller stocks. 16

19 Panel C of Table I reports the average beta within each portfolio. Beta is estimated for each stock by the market model, using monthly returns in the months prior to the trading quarter. 15 The results show a clear difference between institutions preference to hold more stocks with higher beta (beta increase with institutional holdings), and their tendency to trade more often in the stocks with the lower beta (controlling for holdings, beta decreases with trading). Panel D of Table I presents the natural log of the book-to-market ratio of the stocks in each portfolio. The book-tomarket ratio is the book value for the calendar quarter, lagged by six-month, divided by market capitalization at the beginning of the trading quarter. As can be seen, stocks with high level of institutional holdings have lower book-to-market ratio than stocks with low holdings, whereas, controlling for holdings, stocks with frequent institutional trading have higher book-to-market ratio than stocks with low institutional trading. In sum, in light of our results, it is clear that stocks with high level of institutional holdings have distinct characteristics from stocks with intense institutional trading. This demonstrates that our measures contain significant information, conveyed in investors trading activity, which cannot be captured by the holdings measure. IV. Graphical Presentation of the Main Results Figure 1 presents graphically the principal results of this paper. The figure graphs event-time, cumulative market-adjusted returns for the top and bottom decile portfolios of institutional net trading. It shows the return patterns to stocks before and after they are intensively purchased and sold by institutions. At the end of each quarter, hereafter date 0, stocks are sorted into decile portfolios according to their percentage of institutional net trading during that quarter. Cumulative buy-and-hold, market-adjusted returns for each portfolio, p, for a period of τ trading months relative to date -24 (two-year before the end of trading quarter), are calculated as: R τ τ ( 1+ Ri, t ) ( + RM, t ) = (8) N τ 1 p, = 1 N i= 1 t 24 t= We also test Scholes-Williams beta, estimated from the return data in the calendar year prior to the trading quarter. Results are essentially identical to those reported herein. 17

20 Where R, is the CRSP monthly return for stock i on month t, R, is month t return on the i t CRSP value-weighted index including distributions, and τ = -18, -12, -6, -3, 0, 3, 6, 12, 18, and 24 months. 16 Negative dates are τ months before the end of the trading quarter, corresponding to the period over which returns are calculated to characterize investor preferences. Positive dates are τ months after the portfolio formation date, the period over which return performance is evaluated. The time-series average of the event-time cumulative returns for each portfolio is its cumulative market-adjusted return in event time. The returns are depicted for stocks in decile 1, which have the highest institutional net selling (i.e. institutional net trading is the lowest and negative); and for stocks in decile 10, which have the highest institutional net buying (i.e. institutional net trading is the highest and positive). Since the complement of institutional trading is individual trading, stocks in decile 1 have the highest individual net buying and stocks in decile 10 have the highest individual net selling. Panel A shows the results for stocks traded on the NYSE and Panel B for stocks traded on Nasdaq. The figure shows the return patterns from two years before the trading quarter until two years after it; hereby includes the three investment horizons we examine in detail in this study: one-quarter, one-year and two-year. We focus on these horizons for the following reasons. One-quarter is the most frequently studied period in the various institutional researches and is also close to Odean s (1998) approximate median of individual holding period for stocks. One-year is Benartzi and Thaler s (1995) estimate of the average investor s investment horizon, and it is close to Carhart s (1997) mean holding period of mutual fund. Two-year is the average turnover of NYSE securities during this period. We present the market-adjusted returns, not the raw returns, in order to remove the effect that market timing might have on our results; particularly since part of our research period was characterized by high returns and was highly volatile. The most striking results in Figure 1 are the evident difference between the return patterns of stocks with intense institutional purchases and sales, and the trend change that is associated with the imbalances in their trading activity. Moreover, taking into account the fact that intense institutional purchases coincide with intense individual sales (and 16 We checked the robustness of the results to calculations that are done for the CRSP equal-weighted or exchange equal-weighted indices, and found that the change in indices has virtually no effect on the market-adjusted returns of the portfolios relative to each other. M t 18

21 vise versa for institutional sales), it is apparent that if a certain group of traders better time its exit and entry from a stock, this group is individuals. This highlights the question: Are individuals indeed the noise traders who lose money by trading? Figure 1 shows that institutions buy high and sell low. Furthermore, it reveals that the trading activity of institutions signals a change in trend. In the stocks with the highest percentage of institutional net selling, selling pressure by institutions is associated with a reversal in the return patterns: in the two-year period preceding institutional sales the return of stocks with the highest percentage of institutional net selling declines, whereas following the sales the return increases. In stocks with the highest percentage of institutional net buying the return rises steeply in the two-year period preceding institutional purchases, while following the purchases the increase in return is mild. The graphs also demonstrate that institutions are momentum investors and individuals are contrarian traders. Moreover, the graphs show that not only do they exhibit these trading styles with respect to the short-term (one-quarter), but also with respect to the long-term (one- and two-year). Stocks with intense institutional net selling have experienced at least two years of a decrease in return before being sold by institutions, while stocks with intense institutional net buying have experienced an increase in return over the same period. Contrary to the distinct difference in the returns preceding institutional trading, which reflect superior timing of individuals relative to institutions, the implications of the returns following institutional trading are not as conclusive. While Nasdaq stocks with intense institutional net buying underperformance (by 10.34% within two years) those with intense individuals net buying, the return differences following institutional trades in the NYSE are insignificant. 17 In Figure 1 we present the first-order results, namely institutional net buying and net selling, and compare between institutional net purchases and sales. This obscures possible impact differences of the trading activity by institutions and individuals. To investigate further these differences, Figures 2 (3) present the second-order results for institutional and individual buying (selling). It enables us to explicitly characterize and compare stocks with intense institutional buying (selling) to those with intense individual 17 The overall increase in returns stems from our sample selection (see III.A.3 for details). The average return of the stocks in our sample is higher than CRSP value-weighted return. 19