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

22 buying (selling). We plot Figures 2 and 3 using the same methodology and notations as those we use to graph Figure 1. The only difference is that in Figure 2 (3) stocks are sorted into decile portfolios according to their percentage of institutional buying (selling); hence, stocks in decile 1 are the stocks with the lowest institutional buying (selling) or the highest individual buying (selling), and stocks in decile 10 are the ones with the highest institutional buying (selling). Figures 2 and 3 reveal a dramatic difference between the return patterns of stocks intensively traded by institutions and individuals, as well as superior trading timing for individuals. Figure 2 shows that stocks with intense individual buying significantly outperform, over the following two years, those with intense institutional buying (with the exception of a slight underperformance in the first two quarters following the purchases of NYSE stocks). This means that the gains individuals realize from the stocks they purchase are higher than institutions gains (at least with respect to two out of our three trading horizons). For example, in Nasdaq, the return of stocks with intense individual buying rises, on average, by 42.4% in the two years subsequent to being purchased by individuals, whereas the average return of stocks with intense institutional buying rises only by 8.15% in the same period. Moreover, individuals buy stocks cheaper than institutions: institutions buy stocks after they rise, while individuals buy them after they decline. 18 When an investor sells a stock, the gains he realizes are determined by its past returns; hence, for the selling decision the relevant returns are the past returns. Accordingly, Figure 3 shows that not only do individuals gain more than institutions from their sales, but they also enjoy gains while institutions suffer losses. For example, if both individuals and institutions sold a NYSE stock after holding it for two years, on average, individuals gained 12% while institutions lost 10%. Figure 3 also shows that although the returns of stocks with intense individual selling significantly increase in the two years prior to individuals sales, providing nice gains to their owners, the returns continue to rise after individuals have sold them, suggesting that individuals could have benefited even 18 The only exception is individual buying in Nasdaq, where the decline in stocks return before individuals bought them is less evident. This could reflect high dispersion in individuals opinions, or a high degree of heterogeneity, in those stocks. It could stem, inter alia, from the formidable search problem involved in choosing a stock to buy, when there are well over 10,000 securities available. 20

23 more if they had postponed their sales. These results confirm the disposition effect (i.e. the tendency of investors to sell winners too early (Shefrin and Statman (1985)); documented by Odean (1998) for U.S. individuals, and by many others for U.S. and non- U.S. individuals) for the individuals in our sample. It is worth mentioning briefly some additional implications of our results. First, the institutions in our sample tend to buy recent good performers (Figure 2) and sell off dismal performers (Figure 3). This is in line with the literature on window dressing (i.e. institutions tendency to massage their positions just prior to reporting their holdings so that they will look better in the eyes of investors, e.g. Lakonishok et al. (1991)). Second, a comparison of Figure 1 with Figures 2 and 3 indicates that there are differences between institutional net trading and institutional buying and selling. This reflects a certain degree of heterogeneity (or herding) in both institutional and individual trading activity. Third, our results suggest that if a certain group of investors is among the last buyers to contribute to the rise of overvalued momentum securities and among the first to suffer losses when these securities decline (De Long at al. (1990b)) these investors are institutions, not individuals. V. Empirical Analysis In the previous section, we demonstrate graphically the implications of institutional and individual trading activity for stock prices. Stocks with intense institutional selling have experienced lower past returns than stocks with either intense institutional buying or intense individual selling; and stocks with intense institutional buying realize lower future returns than stocks with intense individual buying. This suggests that individuals are the ones that time their purchases and sales better than institutions, and thereby gain more by trading. In this section, we investigate these results further, by a detailed quantitative analysis of these gains and their sources. A. Trading Style What is the source of the differences in the return patterns of stocks with different rates of institutional trading activity? Do institutions differ from individuals in their trading style? Do the stocks they buy and sell differ in their risk-characteristics? Are 21

24 individuals gains due to high systematic risk? The literature shows that cross-sectional variation in returns can be explained by a compensation for risk, or by systematic risk factors in returns. In line with this, in subsection III.C we study the risk-characteristics of stocks institutions trade by a separate analysis of the four characteristics that are most commonly associated with risk: beta, size, book-to-market ratio, and past returns. To emphasize the robustness of our results, in this section we investigate whether investor trading styles could explain the return differences, by analyzing the risk factor sensitivities on different sets of factors: Sharpe-Lintner CAPM, Fama and French (1993) three-factor model, and Carhart s (1997) four-factor model. At the end of each quarter, stocks are sorted into quintile portfolios according to either their institutional trading (Table II) or their institutional net trading (Table III). The returns on the quintile portfolios are calculated over the three months of institutional trading measure formation, equally weighting the stocks within each quintile. 19 In addition, we calculate the returns on the Q5-Q1 portfolio, constructed by going long the top quintile and short the bottom quintile, to demonstrate clearly the difference between the variables in the extreme quintiles. The three-month return series are linked across quarters to form a monthly series of returns on each quintile portfolio. Using these monthly returns, we estimate three model specifications of the time series regression: R R ( RM, t R f, t ) + s psmbt + hp HMLt + m pumdt e p, t α β (9) p, t f, t = p + p + R p, t is the monthly return for portfolio p. R, is the monthly return on one-month T-bill. f t R, is the monthly return on a value-weighted market portfolio. M t SMB t is the monthly return on a factor-mimicking portfolio for size (i.e. a zero-investment portfolio formed by subtracting the return on a large firm portfolio from the return on a small firm portfolio). HML t is the monthly return on a factor-mimicking portfolio for book-to-market (a portfolio of high book-to-market stocks less a portfolio of low book-to-market stocks). And UMD t is the monthly return on a factor-mimicking portfolio for one-year past return momentum (a zero-investment portfolio formed by subtracting the return on a portfolio of low return stocks over the preceding year from the return on a portfolio of high return 19 We checked the robustness of the results to calculation of the returns over three and twelve months, both following and preceding the trading quarter. The time period over which the returns are calculated has virtually no effect on the risk factors sensitivities results. 22

25 stocks). The three model specifications we use are: the CAPM, that includes only the first factor (market excess return) in equation (9); Fama and French (1993) three-factor model, that extend the CAPM by including also size and book-to-market factors; and Carhart s (1997) four-factor model, that includes all four factors. The slopes are factor loadings that have a clear interpretation as risk factor sensitivities for stocks. In order to compare between the risk-characteristics of stocks traded by institutions and individuals, stocks are sorted into quintile portfolios based on their institutional trading measure. 20 Q1 represents the portfolio with the lowest institutional trading activity (highest individual trading), and Q5 represent the portfolio with the highest institutional trading activity. Table II reports the risk factors sensitivities of two model specifications of equation (9): CAPM beta, and Fama and French three-factor loadings on beta, size, and book-to-market. 21 Stocks with high institutional trading activity have lower beta and higher book-to-market ratio than stocks with low institutional trading. With respect to size, there is a difference between stocks traded on the NYSE and on Nasdaq. On the NYSE, small stocks (high s) are traded more heavily by both institutions and individuals, whereas in Nasdaq small stocks are traded more heavily by individuals. Taking into account the size difference between NYSE and Nasdaq stocks, it seems that both institutions and individuals trade in small stocks, but institutions avoid trading in the smallest ones. In general, despite the differences in institutional and individual trading preferences, the results do not indicate a coherent trend in their attribute toward risk. For example, institutional trading activity decreases with beta, which is associated with a decrease in risk, but at the same time it increase with book-to-market (especially in Nasdaq), which is associated with an increase in risk. In order to compare between the risk-characteristics of stocks purchased and sold by institutions, we sort the stocks into quintile portfolios based on their institutional net trading measure. Q1 represents the portfolio with the lowest, and negative, institutional net trading, thus the portfolio of intense institutional net selling; and Q5 represents the 20 We also sort stocks into quintile portfolios based on their institutional buying and on their institutional selling, and repeat the analysis for these portfolios. The results are qualitatively identical and therefore are not reported. 21 We do not include the past return (momentum) factor here since, in the context of trading activity, this factor contains meaningful information only if we separate between the buying and selling decision. We elaborate on this issue in Table III. 23

26 portfolio with the highest, and positive, institutional net trading, thus the portfolio of intense institutional net buying. Table III presents the risk factor sensitivities of Carhart s four-factor loadings on beta, size, book-to-market, and past return (momentum). Since the past return factor might have different implications for the decision to buy or sell, contrary to Table II, Table III adds this factor to the regression. The most salient result in Table III is the difference in past return factor sensitivity between stocks in which institutions are net buyers and stocks in which they are net sellers. Stocks where institutions are net buyers have, on average, a higher past return factor loading than stocks in which institutions are net sellers. This also holds when we compare (not reported here) the quintiles of institutional buying and institutional selling. Thus, in line with the rest of this paper, particularly with the results presented previously, we clearly see that the past returns of stocks heavily bought by institutions are significantly higher than the past returns of stocks heavily sold by them. Moreover, this factor captures most of the difference between the stocks institutions purchase and sell, as the differences with respect to the other variables are negligible. In sum, the results indicate that the differences in risk-characteristics could not be the source of the return differences. While institutions differ from individuals in their trading style, there is no consistent pattern in the risk attributes of the stocks they trade. Moreover, if we take into account the four common risk factors, the sole risk factor that significantly distinguishes stocks with excess institutional net buying from those with excess institutional net selling is the one-year past return momentum. B. Past Returns Are the results that were presented graphically in the previous section significant and robust? In the following two subsections, we address this question and investigate these results further, through a detailed quantitative analysis of the returns to stocks before and after they are sold and purchased by institutions and individuals. At the end of each quarter, stocks are sorted into quintile portfolios according to one of our trading measures. We label this quarter, hereafter the trading or portfolio formation quarter, as Qtr 0, and measure all investment horizons relative to it. Thus, in the tables to follow, Qtr -1 denotes one quarter before the trading quarter, Year 1 one year after it, -2Years the 24

27 two years before it, and so on. As explained in the previous section, we examine three investment horizons: one-quarter, our short-term horizon, and one- and two-year, our long-term horizons. In this subsection, we study these horizons for the periods preceding the trading quarter, and in the next subsection, we analyze them for the periods following it. Monthly returns on the quintile portfolios over each investment horizon are calculated as the rebalanced mean monthly returns for the portfolio, equally weighting the stocks within each quintile. The horizon months return series are linked across quarters, equally weighting the portfolios overlapping returns, to form a monthly series of returns on each quintile portfolio. 22 Panel A of Table IV presents average monthly percentage returns on the quintile portfolios of institutional net trading for the periods preceding the trading quarters. Q1 represents the portfolio with the lowest, and negative, institutional net trading, thus the portfolio of intense institutional net selling; and Q5 represents the portfolio with the highest, and positive, institutional net trading, thus the portfolio of intense institutional net buying. Q5-Q1 represents the abnormal return difference between the quintile of stocks that are most heavily bought (Q5) and the quintile of stocks most heavily sold (Q1) by institutions. Hence, it provides a test of the null hypothesis that the difference in the returns preceding intense institutional net buying and intense institutional net selling is zero. The results reveal a dramatic and significant difference in the past returns to stocks in which institutions are net buyers and those in which they are net sellers. Moreover, the differences persist, and are statistically significant, both for stocks traded on the NYSE and Nasdaq, as well as over each one of the investment horizons (though they are more prominent for Nasdaq stocks and for the shorter horizons). Past returns are decreasing with institutional net selling. Thus, stocks with increasing institutional net selling have experienced lower past returns than stocks with increasing institutional net selling (or, as presented in the table, increasing institutional net buying). For example, in Nasdaq, institutions are intense net buyers (Q5) in stocks with an average monthly return of 2.49% over the previous two years, while they are intense net sellers (Q1) in stocks with a return of 0.98% over the same period. Hence, the returns difference between the 22 Similar to Jegadeesh and Titman (1993), to increase the power of our tests, we include portfolios with overlapping investment period by revising the weights in the portfolio each month. This also allows us to use simple t-statistics while testing the significance of the results. 25

28 stocks institutions buy and sell is 1.51% monthly, or 43.3% over the previous two years, and this difference is statistically significant, with a t-statistic of The evident differences have two important implications. First, the momentum trading style of institutions hurts their performance, while the contrarian trading style of individuals benefits them. Considering that institutional net buying is equivalent to individual net selling, and that past returns reflect the realized gains from sales; our results indicate clearly that the portfolio of intense institutional net selling significantly underperforms, over the previous two years, the portfolio of intense individual net selling. This suggests that institutions gain more than individuals by selling. Second, not only are institutions momentum traders and individuals contrarian traders with respect to the short-term past returns of one-quarter, but they also exhibit the same trading styles with respect to the long-term past returns of two years. Would the evidence hold if we adjust the returns to risk? To answer this question, we use the traditional Jensen s alpha (Jensen (1968)) to measure the risk-adjusted performance of the quintile portfolios. Three versions of alpha, the intercept term in equation (9), are calculated, by estimating equation (9) for three benchmark models. The Sharpe-Lintner CAPM alpha is calculated with respect to the market benchmark, the Fama-French alpha with respect to the market, size, and book-to-market benchmarks of Fama and French (1993), and the four-factor alpha with respect to the market, size, bookto-market, and momentum benchmarks, following Carhart (1997). Panels B, C and D of Table IV reports the alphas, in percentage per month, of the quintile portfolios of institutional net trading for the periods preceding the trading quarters. The clear picture that emerges from these three panels of the table is that the risk differences cannot explain the difference between the past returns of stocks that bought by institutions and those sold by them. For all three benchmark models, the trends and differences in riskadjusted returns (Panels B through D) are similar to those in raw returns (Panel A). In particular, the similarity between the raw returns and the market-adjusted returns (CAPM alpha) indicates that the performance differences are not solely due to market timing; and the similarity between the raw returns and the four-factor adjusted returns indicates that institutions are not properly compensated for buying stocks with high momentum risk. 26

29 Thus, the superior returns that individuals gain from their sales sustain even if we control for systematic risks. In order to compare explicitly between the returns that institutions and individuals realize from their sales, Table V presents the four performance measures (raw returns and alphas) of the quintile portfolios of institutional selling for the periods preceding the trading quarters. Q1 represents the portfolio with the lowest institutional selling (highest individual selling), and Q5 represents the portfolio with the highest institutional selling. Q5-Q1 represents the portfolio that buys the institutional selling portfolio and sells the individual selling portfolio, thus it provides a test of the null hypothesis that the difference in past returns to stocks heavily sold by institutions and to those heavily sold by individuals is zero. The results indicate that past returns decrease with institutional selling, and that stocks with intense individual selling have experienced significantly higher past returns than stocks with intense institutional selling. For example, in the year that precedes the sales, Nasdaq stocks with intense individual selling experienced a statistically significant excess return of 19% (1.46% monthly) relative to stocks with intense institutional selling. Furthermore, as is evident by comparing the raw returns (Panels A of Table V) to the performance measures that take explicit account of the effects of risk on the return (Panels B through D of Table V), adjusting the returns for systematic risks has minor effect on the results. In sum, whether we compare institutional net selling to their net buying or institutional selling to individual selling, and whether we take into account the systematic risks or not; our findings show clearly that the portfolio of intense institutional selling significantly underperform, over the previous two years, the portfolio of intense individual selling. Therefore, our past returns analysis suggests that not only are institutions momentum traders whereas individuals contrarian traders in the short (onequarter) and long (one- and two-year) terms, but also that an important implication of the difference between theirs trading styles is that the gains individuals realize from their sales are higher than those of institutions, independent of risks. 27

30 C. Future Returns In this subsection, we repeat the analysis of the previous subsection for the future returns, using the same methodology and notations. Table VI presents the four performance measures, in percentage per month, of the quintile portfolios of institutional net trading for the periods following the trading quarters. Panel A reports average monthly returns, and Panels B through D report the performance measures that take into account the effect of risk. In general, the results show minor and insignificant differences between the returns (and risk-adjusted returns) to stocks with various levels of institutional net trading. However, in Nasdaq, the future returns to stocks in which institutions are net buyers underperform those of stocks in which they are net sellers in the second year and over the two years following their trades. Table VII compares explicitly between the future returns that institutions and individuals realized from their purchases. The results are quite similar to those of Table VI. In the NYSE, there are insignificant differences between the returns to stocks following institutional and individual buying; and the minor outperformance of institutions in the quarter that follow their purchases eliminates when adjusting for the momentum in stock returns. In Nasdaq, the returns decrease with institutional buying, and the four-factor risk-adjusted returns of the portfolio of intense institutional buying significantly underperform the portfolio of intense individual buying over each one of the trading horizons. A puzzling result, apparent both in tables VI and VII for Nasdaq-traded stocks, is the significant excess future returns of the portfolio of individual buying relative to the portfolio of institutional buying in the second year. This will be cleared through the investigation of the following section. D. Discussion Our findings lead to a coherent picture, with important, though somewhat surprising, implications. The relative trading activity of institutions and individuals conveys relevant information for market prices in a two-year window around their trading. Stocks heavily sold by individuals have experienced significant abnormal excess past returns relative to stocks heavily sold by institutions; indicating that individuals time their exit (sales) from the market better than institutions, and thereby realize higher gains 28

31 by selling. Stocks heavily bought by institutions realize about the same future returns as stocks heavily bought by individuals; indicating that neither institutions nor individuals gain more by buying. Put together, our results cast doubts on the standard view of individuals as the noise traders who lose money by trading. Moreover, the inferior performance of institutions relative to individuals is not due to the lower systematic risks of their portfolios. First, we show that institutions do not consistently trade stocks with risk-characteristics that provide lower returns, thus the abnormal returns that individuals achieve cannot be explained solely by risk compensation. Second, most of the results do not change when we analyze the different measures of performance that take explicit account of the effects of risk on the return of the portfolios. Therefore, our findings suggest that individuals outperformance is not merely a compensation for risks but might reflect stock-picking ability. Additional evidence that emerges from our analysis suggests that a possible explanation for the previous result is that institutions mistime the intermediate momentum in stock returns (Jegadeesh and Titman (1993)). First, in subsection III.C 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; and that they 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, when we compare the trading styles of institutions and individuals, we see that if we consider the four risk factors - market, size, book-to-market, and momentum - the sole risk factor that significantly distinguishes between their trading styles is the momentum risk, which is higher for institutions than for individuals. Third, when we analyze the past returns we see that institutions are momentum traders and individuals are contrarian traders with respect to both the short-term past returns of one-quarter, and the long-terms past returns of one and two years. Finally, when we adjust the returns to the momentum risk, it is apparent that institutions are not properly compensated for taking high momentum risk. In particular, in Nasdaq, adjusting for momentum risk results in a significant abnormal future return of the portfolio of intense individual buying relative to the portfolio of intense institutional buying. Overall, these findings imply that institutions tend to stick to momentum trading style and hold winners too long. In doing so, they mistime the intermediate momentum 29

32 effect and this damages their performance. Alternatively, the reversal that is associated with the relative trading activity of institutions and individuals might not be related to Jegadeesh and Titman s (1993) intermediate momentum effect, but to the long-term reversals documented by De Bondt and Thaler (1985), and the irrational biases accounted for them. VI. The Late 1990s Bubble On January 1995, the Nasdaq Composite Index opened at 751; on March 10, 2000, it closed at 5,048. The unusual rise and fall in the prices of Nasdaq stocks in the late 1990s has led many academics and practitioners to describe this period as a stock price bubble. Who gained more by trading in the late 1990s bubble, institutions or individuals? Were their gains in the bubble different from their gains in the period that precede it? Did they trade differently? In this section, we investigate these questions by a subperiod analysis. We separate our trading sample into two subperiods: 1995 through 2000, the late 1990s bubble period, and 1986 through 1994, the pre-bubble period. 23,24 For each subperiod, we replicate the analysis of the previous sections. We present here only the results that are different from the results in our entire sample period, since these are the results that could shed some light on the distinct characteristics of the bubble. Therefore, results that are not presented here are similar (both in each subperiod and in the entire sample period) to those reported in previous sections. Table VIII repeats the analysis of Table VI, and presents subperiods results of the returns following institutional net trading. Panel A reports the average monthly returns, and Panel B reports the four-factor alphas. The most striking result in this table is the clear difference between the four-factor alphas in the bubble and in the period preceding it, a difference that is most pronounced in Nasdaq stocks. In the pre-bubble period, there are insignificant differences between the returns, and the alphas, of stocks with various levels of institutional net trading, and this holds both for stocks traded on the NYSE and 23 We do not investigate the post-bubble period due to data availability (see footnote 13). 24 Similar results were established when we include within the late 1990s bubble period trading that were implemented in However, since 2001 could be considered as part of the post-bubble period, in order to focus the discussion here on the bubble, we do not present these results here. 30

33 Nasdaq. However, this changes in the late 1990s bubble period, in particular where the bubble was most salient, in Nasdaq. In the late 1990s bubble, Nasdaq-stocks with intense individual net buying (institutional net selling) have significant excess four-factor riskadjusted returns relative to stocks with intense institutional net buying, over each one of the trading horizons. Furthermore, adjusting the returns to the four systematic risk factors has major effect on the short-term results, though minor effect in the long-run. In the quarter and year subsequent to the trading quarter, there is a minor and insignificant difference between the raw returns to stocks with various percentage of institutional net trading; whereas after adjusting the returns to the systematic risks, particularly to the momentum risk, stocks with intense institutional net buying underperform stocks with intense individual net buying. In the long-term (the second year and over the two years following the trades), the underperformance is evident both in the raw returns and alphas. For example, in Nasdaq, the raw returns of stocks in which institutions are net buyers underperform those of stocks in which individuals are net buyers (institutions are net sellers), in the second year after the trades, by 11.6%; and this is statistically significant (at the one-percent level) with a t-statistic of Table IX repeats the analysis of Table VII, and shows subperiods results for the future returns that institutions and individuals realized from their purchases. Panel A reports the average monthly returns, and Panel B reports the four-factor alphas. Similar to Table VIII, the most salient result in this table is the difference between the returns in the bubble and in the period preceding it, particularly for Nasdaq stocks. In the prebubble period, there are insignificant performance differences between stocks with various levels of institutional buying, both for stocks traded on the NYSE and Nasdaq. However, this is changed in the late 1990s bubble period, specifically in Nasdaq. In the late 1990s bubble, Nasdaq-stocks heavily bought by institutions underperform those heavily bought by individuals, and this holds over each trading horizon in the subsequent two years. The underperformance is most prominent in the four-factor risk-adjusted returns. For example, in Nasdaq, the underperformance of the portfolio of intense institutional buying relative to the portfolio of intense individual buying ranges from 2.05% to 2.31% per month (27.57% to 31.53% per year) over the different trading horizons. 31

34 The findings of our subperiod analysis suggest some important implications for the characterization of the trading activity of institutions and individuals in the late 1990s bubble. First, in the late 1990s bubble, not only do individuals gain more than institutions by selling but they also gain more by buying. Contrary to the similar gains of institutions and individuals from their purchases in our entire sample period, in the bubble, the gains individuals realize from their purchases are higher than the gains institutions realize from their purchases. Thus, whereas in the pre-bubble period individuals time their exit (sales) from the market better than institutions, in the bubble they time both, their entry (purchases) and exit (sales) from the market better than institutions. Intriguingly, any doubts, raised previously, about the conventional wisdom of individuals as the noise traders who lose money by trading, become even more serious in the late 1990s bubble. Second, in line with the rest of our findings, the subperiod analysis also indicates that institutions are not properly compensated for taking high momentum risk, thus their tendency to stick to momentum trading style does not benefit them but hurts their performance. However, the subperiod analysis reveals that this is most severe in the late 1990s bubble. In the bubble, when we adjust the future returns to the four-factor risks (and thereby include the risk factor that should compensate institutions for their momentum trading style), the risk-adjusted returns of stocks excessively bought by institutions do not outperform those of stocks excessively bought by individuals, but significantly underperform them by up to 2.31% per month. This supports our previous argument, that a possible explanation for institutions underperformance is their mistiming of the intermediate momentum effect in stock returns. Furthermore, our finding, that this is most distinct in the bubble, might imply that the underperformance of stocks with excess institutional buying may be due to institutions mistiming of the momentum cycle, i.e. buying at the top of it, and increasing the risk. It is possible that institutions, which follow momentum strategies, are among the last buyers that contribute to the rise of overvalued momentum stocks and are among the first to suffer losses when trends reverse and these stocks decline. In this case, the noise traders, in models such as De Long et al. (1990b), are not the individuals. 32

35 Overall, our findings of the inferior performance of institutions relative to individuals, along with it being most pronounced in the late 1990s bubble, suggest that the standard attributes of institutions (sophisticated-informed investors) and individuals (noise-irrational traders) should not be held as a self-evidence truth. Furthermore, they lead to many puzzling implications. For example, since we show that the superior profits, realized by individuals, are not merely a compensation for high systematic risks; this leaves a room for other possible explanations, such as stock-picking ability, trading on different information, agency problems, institutions constraints, etc. VII. Conclusion Black (1986) defines noise traders as traders that as a group, most of the time, lose money by trading. In this paper, we use this definition and test the performance of two groups of investors, institutions and individuals. We calculate four measures of institutional trading, and expose that the relative trading activity by institutions and individuals has important implications for market prices in a two-year window around their trading. Taken as a whole, our evidence indicates that during the period 1986 through 2001 individuals gain more than institutions by trading, thus it should not be taken for granted that individuals are the noise traders. We find that when comparing the two groups of investors, institutions are the ones who realize inferior profits. They buy high and sell low, hence realize inferior gains. Stocks heavily sold by individuals earn significant excess past returns relative to stocks heavily sold by institutions. In the late 1990s bubble, particularly in Nasdaq, the portfolio of intense institutional buying underperforms the portfolio of intense individual buying with respect to future returns. This implies that individuals time their entry and exit from the market better than institutions and gain more. Overall, our findings cast doubts on the common assumption that individuals are the noise traders who lose money by trading while institutions are the information traders who make money; and call for further investigation of possible sources of our results (e.g. agency costs, institutions constraints, fund flows). Interestingly, our evidence suggests a possible explanation for the inferior performance of institutions. First, we find that institutions tend to hold stocks that are 33

36 winners over the previous two-year, but, among the high-momentum stocks they hold they are net sellers in stocks whose past returns are lower than the past returns of the stocks in which they are net buyers. Second, we find that institutions are momentum traders with respect to the past two-year returns. Third, when we adjust the returns to the four-factor risks, it is apparent that institutions are not properly compensated for taking high momentum risk but lose from it, particularly in the late 1990s bubble. These findings imply that institutions might gain less than individuals due to holding winners too long. In so doing, they mistime the intermediate momentum effect (Jegadeesh and Titman (1993)), and increase their risk without exploit its profit. This has important implications for both practitioners and academics. For instance, it seems that some practitioners do in fact recognize this: Before I look at a stock, I take a look at the (SEC) filings to see who the major shareholders are. If you see a large amount of momentum money in there, you have to accept that there s a high risk 25 Our results also indicate that the superior performance of individuals is not merely a compensation for high systematic risks. Individual outperformance could arise from better timing the momentum cycle, but also from numerous other reasons. For example, institutions might underperform due to their structural constrains; individuals and institutions might possess different information or interpret it differently; individuals might have superior stock selection skills or forecasting ability. What are the reasons that explain the differences in return patterns is an open question. What are the implications of our evidence to existing theories? They support models of noise trading by institutions, in particular when a bubble exists (e.g. Allen and Gorton (1993)). They could be explained differently by models of the interaction between noise traders and informed traders. For example, if noise traders mistime the momentum cycle (e.g. De Long at al. (1990b)) institutions are the noise traders, and this corresponds to our pronounced results in the bubble; however, if noise traders gain due to the noise they create (e.g. De Long at al. (1990a)) individuals are the noise trader. Clearly, further research is required in order to apply existing theories to our findings. 25 Quoted from money manager in an article by Greg, 1997, Red flag on wall street: Momentum investors, Wall Street Journal, February

37 We suggest two avenues for future research. First, in this study we focus on the performance of stocks with different proportions of institutions and individuals trading activity. In order to explore the dynamics that derives our results we plan to investigate additional characteristics of stocks that differ in the relative trading activity by institutions and individuals. Second, our results reveal that the trading volume by institutions and individuals contains important information for market prices. Why is trading volume by institutions and individuals able to predict the magnitude and persistence of future movements is an open question for further research. We end with some reservations. Although the evidence suggests inferior performance of stocks with more institutional trading, for a variety of reasons investors should interpret them with caution. First, individuals may have reasons, other than institutions performance or stock-picking ability, to invest through institutions: saving time and effort, piece of mind, and others. Second, our evidence applies on average, only for stocks, and to the market as a whole. It does not suggest that the portfolios of institutions perform worse than the portfolios of individuals. Third, our results might be unique to our study period. Appendix. Intermediary Trading Main issue that has to be considered while estimating the fraction of intermediary trading is that the trading mechanisms are different for the NYSE and Nasdaq. Thus, we discuss intermediary trading separately for each exchange. A1. NYSE The NYSE reports its member trading in the NYSE Fact book. The report includes monthly and annual data of the aggregate total member purchases and sales. In addition to the total member trades, it also specifies separately specialists purchases and sales, as well as non-specialists members purchases and sales that originated on the floor and off the floor. According to these data, about half of the member purchases and sales are by specialists, about half of them are by non-specialist off-floor members, while nonspecialist on-floor trades constitute only negligible fraction of the total member purchases and sales. 35

38 Hasbrouck and Sofianos (1993) use data from November 1988 through August 1990 and find that the specialists participation rate is higher for stocks in the lowest trade freqency decile. Madhavan and Sofianos (1998) use data from July 1993 and find that specialist trading varies across stocks and is inversely related to trading frequency. Sofianos and Werner (2000) use data of common stocks for January and February 1997 and find that specialists participation rate decreases with trading volume, however the only distinct difference is in the lowest trading volume decile; the differences between the other deciles are not substantial. The results of this paper are most relevant for us since, like us, it includes only common stocks. 26 Based on this evidence we should use different estimates for intermediary trading in stocks with different trading volume, specifically for the most illiquid stocks. However, as was emphasized above, besides specialists trading, non-specialist off-floor members trading contributes equally to the NYSE intermediary trading volume. Several papers suggest that the trades of non-specialists off-floor members decrease with trading volume. First, Hasbrouck, Sofianos and Sosebee (1993), and Madhavan and Cheng (1997) provide evidence that non-specialists member trades that originate off the floor tend to be very large block trades, and that the percentage of upstairs-facilitated block volume increases with block size. Sofianos and Werner (2000) find that the average block size increases with trading volume and that this pattern is most apparent for upstairs-facilitated block trades. Madhavan and Sofianos (1998) find that specialist trading is inversely related to trading frequency and proxies for off-exchange competition, and that specialists participate less in stocks for which block trading volume is significant and trade size in larger, because larger trades are more likely to be directed to upstairs brokers. Madhavan and Cheng (1997) remark that small capitalization stocks have only a few upstairs transactions in a month. Taking all of these together, it is apparent that in the less liquid and smaller capitalization stocks there are fewer block trades, specifically larger-block trades that are originated off the floor. Second, Madhavan and Cheng (1997) report that floor brokers play an important role in the execution of upstairs-facilitated trades, and Sofianos and Werner (2000) find that when 26 Madhavan and Sofianos (1993) include in their sample, not only common stocks but also preferred stock and closed-end fund. For these issues they find lower specialist participation, evidence that might bias their findings relative to a sample of common stocks. 36

39 trading volume decreases, floor broker participation decreases, specifically for the upstairs-facilitated block trades. Overall, the evidence shows that low trading volume enhances the need for the specialist to bridge gaps in natural liquidity, but also reduces the trading by non-specialist off-floor members. Therefore, the differences in the fraction of intermediary trading between NYSE stocks should not be substantial. Moreover, the only distinct difference in specialists trading is in the lowest trading decile. Most of the stocks in the illiquid decile are small capitalization stocks, which are excluded from our sample (see A.3). Thus, the differences in the percentages intermediary trading across stocks should not be significant, particularly for the stocks in our sample. A2. NASDAQ Nasdaq is largely a dealer market, where market makers are on the opposite side of most trades. Ellis, Michaely and O'Hara (2002) analyze the mechanisms of market making of dealers for 313 stocks in the first seven months after their IPO. Although their sample might seem specific, their evidence shows that the data of stable market makers participation is similar for different stocks. First, they find that markets for newly issued stocks adjust rapidly to a permanent equilibrium and are stable after twenty trading days. Second, they show that the mean trading volume in months two through seven for their sample firms is identical to the average turnover of Nasdaq stocks; and that despite the differences in sample stocks, trading and dealer participations is consistent across stocks. Griffin, Harris and Topaloglu (2003a,b) use proprietary data and qualitative analysis to identify the parties involved in each trade, and classify both sides of all trades as originating from an individual, an institution or a market maker. Their findings indicate that the percentages of the different investors type participation are stable. Put together, despite the variation in the stocks, time periods, and methodologies, the differences in the resulting percentages of intermediary trading are not significant. 27 Evidently, market makers constitute a significant percentage of total trading for Nasdaq stocks, and only a small fraction of the trading volume is executed directly by brokers. 27 Moreover, since we are interested in the cross-sectional variations of different stocks, throughout the analysis, we sort the stocks into portfolios at the end of each quarter. Therefore, the time series differences in intermediary trading do not affect our results. 37

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44 Figure 1 Returns Patterns around Intense Institutional Net Trading This figure presents event time cumulative market-adjusted returns for the top and bottom decile portfolios of institutional net trading. At the end of each quarter (date 0), stocks are sorted into decile portfolios according to their percentage of institutional net trading in this quarter. The figure plots the cumulative market-adjusted returns for stocks in decile 1, with the highest institutional net selling, and for stocks in decile 10, with the highest institutional net buying. Cumulative market-adjusted return is the time-series average of cross-sectional means of the cumulative, buy-and-hold, market-adjusted return (in excess of CRSP value-weighted index), in event time. The event times are τ = -18, -12, -6, -3, 0, 3, 6, 12, 18, and 24 months. The trading sample period is January 1986 through December 2001 (hence the returns are from 1984 to 2003). Panel A shows the results for stocks traded on the NYSE and Panel B for stocks traded on Nasdaq. Panel A. NYSE 28 Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutionl net buying intense institutionl net selling Panel B. Nasdaq 80 Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutionl net buying intense institutionl net selling 42

45 Figure 2 Returns Patterns around Intense Purchases by Institutions and Individuals This figure presents event time cumulative market-adjusted returns for the top and bottom decile portfolios of institutional buying. At the end of each quarter (date 0), stocks are sorted into decile portfolios according to their percentage of institutional buying in this quarter. The figure plots the cumulative market-adjusted returns for stocks in decile 1, with the lowest institutional buying (highest individual buying), and for stocks in decile 10, with the highest institutional buying. Cumulative market-adjusted return is the time-series average of cross-sectional means of the cumulative, buy-and-hold, market-adjusted return (in excess of CRSP value-weighted index), in event time. The event times are τ = -18, -12, -6, -3, 0, 3, 6, 12, 18, and 24 months. The trading sample period is January 1986 through December 2001 (hence the returns are from 1984 to 2003). Panel A shows the results for stocks traded on the NYSE and Panel B for stocks traded on Nasdaq. Panel A. NYSE 17 Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutional buying intense individual buying Panel B. Nasdaq 60 Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutional buying intense individual buying 43

46 Figure 3 Returns Patterns around Intense Sales by Institutions and Individuals This figure presents event time cumulative market-adjusted returns for the top and bottom decile portfolios of institutional selling. At the end of each quarter (date 0), stocks are sorted into decile portfolios according to the percentage of institutional selling in this quarter. The figure plots the cumulative marketadjusted returns for stocks in decile 1, with the lowest institutional selling (highest individual selling), and for stocks in decile 10, with the highest institutional selling. Cumulative market-adjusted return is the timeseries average of cross-sectional means of the cumulative, buy-and-hold, market-adjusted return (in excess of CRSP value-weighted index), in event time. The event times are τ = -18, -12, -6, -3, 0, 3, 6, 12, 18, and 24 months. The trading sample period is January 1986 through December 2001 (hence the returns are from 1984 to 2003). Panel A shows the results for stocks traded on the NYSE and Panel B for stocks traded on Nasdaq. Panel A. NYSE 23 Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutional selling intense individual selling Panel B. Nasdaq Cumulative Market-Adjusted Returns (%) Event Time (months) intense institutional selling intense individual selling 44

47 Table I Stocks Characteristics, Comparing our Trading Measures and the Holding Measure At the end of each quarter, stocks are sorted into quintiles portfolios according to institutional holdings at the beginning of the quarter, and then sorted within the quintiles according to institutional net trading (Panel A) or institutional trading (Panels B, C, and D). Stocks characteristics are averaged within each of the 25 portfolios for this quarter, and then the time series average over all quarters is calculated. All characteristics are measured at the beginning of the quarter. Panel A reports six-month, one-year and twoyear past returns, calculated as the compounded return over these time periods. Panel B reports the average size decile, where size decile is the rank of the market capitalization of equity, based on NYSE size decile cutoff. Panel C reports the average beta, where beta is estimated for each stock by the market model, using monthly returns in the months prior to the trading quarter. Panel D reports the average natural log of the book-to-market ratio, computed as the book value of the stock for the calendar quarter, lagged by sixmonth, divided by its market capitalization at the beginning of the quarter. The sample s trading period is January 1986 through December 2001, and the results are shown separately for stocks traded on the NYSE and Nasdaq. Quintiles of Institutional Quintiles of Institutional Holdings Quintiles of Institutional Holdings Net Trading low high low high Panel A. Past six-month, one-year, and two-year Returns (Momentum) NYSE Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) All

48 Quintiles of Institutional Quintiles of Institutional Holdings Quintiles of Institutional Holdings Trading low high low high Panel B. Size Decile NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) All Panel C. Beta NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) All Panel D. ln(b/m) NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) All

49 Table II Comparing between the Risk-Characteristics of Stocks Traded by Institutions and Individuals At the end of each quarter, stocks are sorted into quintiles portfolios according to their institutional trading measure. Stocks in quintile 1 (Q1) are the stocks with the lowest institutional trading (highest individual trading), stocks in quintile 5 (Q5) are the ones with the highest institutional trading, etc. The returns on the quintile portfolios are calculated over the three months of institutional trading proxy formation, equal weighting the stocks within each quintile. The three-month return series are linked across quarters to form a monthly series of returns on each quintile portfolio, which are used to estimate the time series regressions of Sharpe-Lintner CAPM model, and Fama and French (1993) three-factor model. The table reports the risk factors sensitivities of CAPM on beta, and of Fama and French three-factor model on beta, size (s), and book-to-market (h). The factors sensitivities are reported for each quintile portfolio as well as for the Q5-Q1 portfolio, constructed by going long quintile 5 and short quintile 1. The results are shown separately for stocks traded on the NYSE and Nasdaq. The sample period is January 1986 through December We report the statistical significance of the results (*, ** denote significant at the 5-percent and 1-percent levels, respectively) only for the Q5-Q1 portfolio. Quintiles of Institutional Trading CAPM FF Factors Sensitivities β β s h CAPM FF Factors Sensitivities β β s h NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) Q5-Q ** * ** ** ** ** 1.04 ** 47

50 Table III Comparing between the Risk-Characteristics of Stocks in which Institutions are Net Sellers and Net Buyers At the end of each quarter, stocks are sorted into quintiles portfolios according to their institutional net trading measure. Stocks in quintile 1 (Q1) are the stocks with the highest institutional net selling, and stocks in quintile 5 (Q5) are the ones with the highest institutional net buying. The returns on the quintile portfolios are calculated over the three months of institutional net trading proxy formation, equal weighting the stocks within each quintile. The three-month return series are linked across quarters to form a monthly series of returns on each quintile portfolio, which are used to estimate the time series regressions of Carhart s (1997) four-factor model. The table reports the risk factors sensitivities of the market (β), size (s), book-to-market (h) and one-year past return momentum. The factors sensitivities are reported for each quintile portfolio as well as for the Q5-Q1 portfolio, constructed by going long quintile 5 and short quintile 1. The results are shown separately for stocks traded on the NYSE and Nasdaq. The sample period is January 1986 through December We report the statistical significance of the results (** denote significant at the 1-percent level) only for the Q5-Q1 portfolio. Quintiles of Institutional Net Trading Four-Factor Sensitivities β s h m Four-Factor Sensitivities β s h m NYSE Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) Q5-Q ** ** 48

51 Table IV Returns Preceding Institutional Net Buying and Net Selling This table reports four performance measures, in percentage per month, of institutional net trading quintile portfolios, for the periods preceding the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional net trading measure. Stocks in quintile 1 (Q1) are the stocks with the highest institutional net selling, stocks in quintile 5 (Q5) are the ones with the highest institutional net buying (individual net selling), and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintile portfolios are calculated over one quarter (Qtr -1), one year (Year -1), year two (Year -2) and two years (-2Years) periods preceding the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns (raw returns), as well as the risk-adjusted returns, estimated from the time series regressions of the CAPM model, Fama and French (1993) three-factor model and Carhart s (1997) fourfactor model. The table reports the raw returns (Panel A), and alphas in each model, as well as their t- statistics (in parentheses). The CAPM alpha (Panel B) is defined with respect to the market benchmark, the Fama-French alpha (Panel C) with respect to the market, size, and book-to-market benchmarks, and the four-factor alpha (Panel D) with respect to Fama and French and momentum benchmarks. The sample s trading period is January 1986 through December 2001 (hence the returns are from 1984 to 2001), and the results are shown separately for stocks traded on the NYSE and Nasdaq. Quintiles of Institutional Time Period Time Period Net Trading Qtr -1 Year -1 Year -2-2Years Qtr -1 Year -1 Year -2-2Years NYSE Panel A. Raw Returns Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) Q5-Q (16.78) 1.22 (17.63) 0.26 (4.65) 0.78 (13.42) 3.85 (14.29) 2.46 (14.07) 0.31 (2.22) 1.51 (9.48) Panel B. CAPM Alphas NYSE Nasdaq Q1 (net sellers) (-4.07) (-2.65) (-1.04) (-1.53) (-5.25) (-2.94) 0.37 (1.50) (-1.11) Q (-1.33) (-0.11) 0.10 (0.75) 0.12 (0.85) (-1.62) 0.01 (0.02) 0.98 (3.32) 0.40 (1.30) Q ( (1.59) 0.11 (0.89) 0.26 (1.94) 0.74 (2.22) 0.87 (2.77) 1.03 (3.46) 0.86 (2.88) Q (2.36) 0.31 (2.00) 0.06 (0.43) 0.28 (1.95) 1.52 (5.29) 1.22 (4.58) 0.79 (3.22) 1.02 (4.05) Q5 (net buyers) 1.09 (6.48) 0.75 (4.80) 0.11 (0.74) 0.55 (3.61) 2.26 (8.49) 1.73 (7.81) 0.84 (4.57) 1.34 (6.66) Q5-Q (16.64) 1.21 (17.34) 0.26 (4.61) 0.79 (13.46) 3.85 (14.10) 2.55 (14.79) 0.47 (3.66) 1.64 (11.02) 49

52 Quintiles of Institutional Time Period Time Period Net Trading Qtr -1 Year -1 Year -2-2Years Qtr -1 Year -1 Year -2-2Years Panel C. Fama-French Alphas NYSE Nasdaq Q1 (net sellers) (-7.61) (-5.64) (-2.48) (-4.12) (-6.01) (-3.14) 0.67 (6.77) 0.01 (0.08) Q (-4.01) (-2.59) (-0.45) (-1.36) (-0.83) 0.40 (2.53) 1.39 (11.08) 0.86 (5.74) Q (-0.53) (-0.28) (-0.20) 0.01 (0.11) 1.15 (6.85) 1.27 (8.69) 1.45 (11.99) 1.31 (9.78) Q (1.06) 0.05 (0.46) (-0.61) 0.03 (0.29) 1.82 (13.07) 1.50 (12.85) 1.08 (10.70) 1.32 (11.75) Q5 (net buyers) 0.86 (7.13) 0.52 (4.47) (-0.24) 0.30 (2.64) 2.53 (18.64) 1.91 (18.32) 0.97 (10.20) 1.49 (15.21) Q5-Q (16.96) 1.24 (17.51) 0.27 (4.70) 0.81 (13.41) 3.93 (14.17) 2.49 (14.22) 0.30 (3.00) 1.48 (10.42) Panel D. Four-Factor Alphas NYSE Nasdaq Q1 (net sellers) (-6.47) (-4.21) (-0.07) (-2.40) (-4.60) (-1.21) 0.71 (6.87) 0.35 (2.40) Q (-2.41) (-0.90) 0.24 (2.55) 0.05 (0.47) 0.25 (1.47) 0.69 (4.78) 1.45 (11.02) 1.12 (8.00) Q (0.97) 0.12 (1.24) 0.26 (2.81) 0.17 (1.67) 1.43 (9.38) 1.49 (10.85) 1.48 (11.66) 1.52 (11.84) Q (2.73) 0.27 (2.49) 0.24 (2.57) 0.24 (2.33) 1.95 (13.97) 1.65 (14.69) 1.15 (10.85) 1.47 (13.52) Q5 (net buyers) 0.97 (7.94) 0.67 (6.01) 0.25 (2.37) 0.47 (4.24) 2.45 (17.63) 1.95 (18.12) 1.05 (10.60) 1.57 (15.72) Q5-Q (16.39) 1.15 (16.75) 0.26 (4.27) 0.73 (12.37) 3.33 (14.38) 2.14 (14.11) 0.33 (3.17) 1.21 (9.39) 50

53 Table V Returns Preceding Institutional and Individual Sales This table reports four performance measures, in percentage per month, of institutional selling quintile portfolios, for the periods preceding the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional selling measure. Stocks in quintile 1 (Q1) are the stocks with the lowest institutional selling (highest individual selling), stocks in quintile 5 (Q5) are the ones with the highest institutional selling, and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintile portfolios are calculated over one quarter (Qtr -1), one year (Year -1), year two (Year -2) and two years (-2Years) periods preceding the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns (raw returns), as well as the risk-adjusted returns, estimated from the time series regressions of the CAPM model, Fama and French (1993) three-factor model and Carhart s (1997) fourfactor model. The table reports the raw returns (Panel A), and alphas in each model, as well as their t- statistics (in parentheses). The CAPM alpha (Panel B) is defined with respect to the market benchmark, the Fama-French alpha (Panel C) with respect to the market, size, and book-to-market benchmarks, and the four-factor alpha (Panel D) with respect to Fama and French and momentum benchmarks. The sample s trading period is January 1986 through December 2001 (hence the returns are from 1984 to 2001), and the results are shown separately for stocks traded on the NYSE and Nasdaq. Quintiles of Institutional Time Period Time Period Selling Qtr -1 Year -1 Year -2-2Years Qtr -1 Year -1 Year -2-2Years NYSE Panel A. Raw Returns Nasdaq Q1 (low) Q Q Q Q5 (high) Q5-Q (-4.72) (-2.67) (-1.19) (-1.87) (-5.33) (-4.03) (-2.68) (-3.07) Panel B. CAPM Alphas NYSE Nasdaq Q1 (low) 0.53 (2.80) 0.25 (1.44) (-0.33) 0.18 (1.13) 1.66 (3.50) 1.10 (2.55) 0.90 (2.46) 0.91 (2.27) Q (0.97) 0.25 (1.65) 0.16 (1.26) 0.29 (2.10) 0.88 (2.51) 0.95 (2.86) 1.09 (3.48) 0.93 (2.94) Q (0.44) 0.19 (1.22) 0.15 (1.12) 0.26 (1.78) 0.27 (0.93) 0.69 (2.52) 0.93 (3.63) 0.78 (3.03) Q (0.14) 0.14 (0.84) 0.07 (0.50) 0.20 (1.32) (-0.84) 0.32 (1.48) 0.79 (3.84) 0.56 (2.72) Q5 (high) (-0.74) (-0.14) (-0.78) 0.02 (0.14) (-1.36) (-0.27) 0.36 (2.07) 0.17 (0.92) Q5-Q (-4.20) (-2.04) (-0.57) (-1.31) (-4.83) (-3.40) (-1.98) (-2.46) 51

54 Quintiles of Institutional Time Period Time Period Selling Qtr -1 Year -1 Year -2-2Years Qtr -1 Year -1 Year -2-2Years Panel C. Fama-French Alphas NYSE Nasdaq Q1 (low) 0.35 (2.70) 0.05 (0.44) (-1.36) (-0.13) 2.28 (8.41) 1.65 (6.49) 1.37 (7.54) 1.50 (6.34) Q (-0.93) (-0.12) 0.02 (0.16) 0.03 (0.32) 1.36 (8.35) 1.40 (9.57) 1.53 (11.66) 1.43 (10.12) Q (-1.64) (-0.79) (-0.06) (-0.20) 0.55 (3.88) 1.00 (8.64) 1.27 (11.95) 1.13 (10.42) Q (-1.94) (-1.13) (-0.64) (-0.58) (-0.16) 0.50 (4.92) 1.02 (11.38) 0.77 (8.23) Q5 (high) (-2.97) (-2.22) (-1.88) (-1.79) (-1.82) (-0.22) 0.47 (5.33) 0.23 (2.07) Q5-Q (-5.05) (-2.74) (-0.73) (-1.84) (-8.98) (-6.78) (-4.56) (-5.66) Panel D. Four-Factor Alphas NYSE Nasdaq Q1 (low) 0.58 (5.02) 0.31 (3.08) 0.15 (1.53) 0.25 (2.49) 2.60 (9.81) 1.99 (8.13) 1.38 (7.19) 1.82 (7.95) Q (0.63) 0.17 (1.63) 0.29 (3.07) 0.21 (2.12) 1.60 (10.40) 1.60 (11.37) 1.56 (11.28) 7.95 (11.67) Q (-0.34) 0.07 (0.66) 0.27 (2.74) 0.14 (1.33) 0.76 (5.61) 1.16 (10.35) 1.35 (12.30) 1.29 (12.27) Q (-0.50) 0.02 (0.20) 0.21 (1.89) 0.10 (0.80) 0.18 (1.63) 0.64 (6.72) 1.10 (11.93) 0.92 (10.34) Q5 (high) (-1.66) (-0.79) 0.05 (0.43) (-0.33) 0.04 (0.37) 0.22 (2.15) 0.55 (6.16) 0.45 (4.55) Q5-Q (-5.20) (-3.15) (-0.82) (-2.33) (-8.73) (-6.94) (-3.96) (-5.95) 52

55 Table VI Returns Following Institutional Net Buying and Net Selling This table reports four performance measures, in percentage per month, of institutional net trading quintile portfolios, for the periods following the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional net trading measure. Stocks in quintile 1 (Q1) are the stocks with the highest institutional net selling (individual net buying), stocks in quintile 5 (Q5) are the ones with the highest institutional net buying, and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintile portfolios are calculated over one quarter (Qtr 1), one year (Year 1), year two (Year 2) and two years (2Years) periods following the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns (raw returns), as well as the risk-adjusted returns, estimated from the time series regressions of the CAPM model, Fama and French (1993) three-factor model and Carhart s (1997) fourfactor model. The table reports the raw returns (Panel A), and alphas in each model, as well as their t- statistics (in parentheses). The CAPM alpha (Panel B) is defined with respect to the market benchmark, the Fama-French alpha (Panel C) with respect to the market, size, and book-to-market benchmarks, and the four-factor alpha (Panel D) with respect to Fama and French and momentum benchmarks. The sample s trading period is January 1986 through December 2001 (hence the returns are from 1986 to 2003), and the results are shown separately for stocks traded on the NYSE and Nasdaq. Quintiles of Institutional Time Period Time Period Net Trading Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years NYSE Panel A. Raw Returns Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) Q5-Q (0.47) 0.06 (0.52) 0.01 (0.13) 0.02 (0.20) 0.20 (0.78) 0.07 (0.38) (-3.50) (-2.11) Panel B. CAPM Alphas NYSE Nasdaq Q1 (net sellers) 0.30 (1.39) 0.24 (1.14) 0.29 (1.44) 0.30 (1.56) 0.42 (1.24) 0.39 (1.36) 0.88 (3.14) 0.72 (2.64) Q (0.77) 0.17 (1.02) 0.22 (1.34) 0.22 (1.39) 0.07 (0.20) 0.25 (0.73) 0.66 (1.98) 0.52 (1.61) Q (0.95) 0.16 (1.05) 0.25 (1.57) 0.21 (1.43) 0.51 (1.51) 0.39 (1.18) 0.47 (1.49) 0.46 (1.49) Q (1.07) 0.22 (1.42) 0.28 (1.66) 0.26 (1.67) 0.56 (1.98) 0.50 (1.87) 0.48 (1.83) 0.52 (2.05) Q5 (net buyers) 0.36 (2.24) 0.28 (1.69) 0.28 (1.65) 0.30 (1.84) 0.61 (2.56) 0.44 (1.94) 0.30 (1.33) 0.41 (1.91) Q5-Q (0.47) 0.04 (0.41) (-0.08) (-0.01) 0.19 (0.72) 0.05 (0.27) (-3.66) (-2.32) 53

56 Quintiles of Institutional Time Period Time Period Net Trading Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years Panel C. Fama-French Alphas NYSE Nasdaq Q1 (net sellers) (-0.26) (-0.57) (-1.11) (-0.72) 0.47 (1.76) 0.38 (1.81) 0.71 (4.46) 0.60 (3.52) Q (-1.24) (-0.97) (-1.13) (-1.00) 0.36 (1.65) 0.44 (1.99) 0.66 (3.21) 0.59 (2.93) Q (-1.18) (-1.00) (-0.79) (-0.97) 0.82 (5.01) 0.66 (3.57) 0.55 (2.75) 0.60 (3.37) Q (-0.93) (-0.35) (-0.56) (-0.52) 0.80 (7.65) 0.67 (5.20) 0.49 (3.32) 0.59 (4.53) Q5 (net buyers) 0.09 (0.82) 0.02 (0.19) (-0.47) (-0.12) 0.75 (6.21) 0.54 (4.85) 0.22 (1.53) 0.40 (3.38) Q5-Q (1.00) 0.10 (0.97) 0.08 (0.92) 0.07 (0.92) 0.28 (1.03) 0.16 (0.87) (-3.28) (-1.59) Panel D. Four-Factor Alphas NYSE Nasdaq Q1 (net sellers) 0.27 (2.09) 0.17 (1.39) 0.04 (0.35) 0.10 (0.97) 1.10 (5.83) 0.91 (6.28) 0.96 (6.77) 0.96 (7.37) Q (0.87) 0.10 (1.08) 0.06 (0.59) 0.08 (0.87) 0.83 (4.91) 0.97 (5.90) 1.04 (6.08) 1.01 (6.43) Q (0.23) 0.07 (0.72) 0.09 (0.96) 0.06 (0.68) 1.08 (7.33) 1.05 (7.01) 1.00 (6.90) 0.99 (7.20) Q (0.04) 0.10 (0.92) 0.09 (0.83) 0.07 (0.71) 0.90 (8.75) 0.93 (8.58) 0.82 (7.69) 0.86 (8.60) Q5 (net buyers) 0.14 (1.21) 0.11 (0.92) 0.09 (0.80) 0.09 (0.86) 0.77 (6.21) 0.68 (6.36) 0.53 (4.98) 0.62 (6.33) Q5-Q (-1.24) (-0.69) 0.05 (0.54) (-0.17) (-1.66) (-1.61) (-2.80) (-2.81) 54

57 Table VII Returns Following Institutional and Individual Purchases This table reports four performance measures, in percentage per month, of institutional buying quintile portfolios, for the periods following the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional buying measure. Stocks in quintile 1 (Q1) are the stocks with the lowest institutional buying (highest individual buying), stocks in quintile 5 (Q5) are the ones with the highest institutional buying, and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintile portfolios are calculated over one quarter (Qtr 1), one year (Year 1), year two (Year 2) and two years (2Years) periods following the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns, as well as the risk-adjusted returns, estimated from the time series regressions of the CAPM model, Fama and French (1993) three-factor model and Carhart s (1997) four-factor model. The table reports the raw returns (Panel A), and alphas in each model, as well as their t-statistics (in parentheses). The CAPM alpha (Panel B) is defined with respect to the market benchmark, the Fama- French alpha (Panel C) with respect to the market, size, and book-to-market benchmarks, and the fourfactor alpha (Panel D) with respect to Fama and French and momentum benchmarks. The sample s trading period is January 1986 through December 2001 (hence the returns are from 1986 to 2003), and the results are shown separately for stocks traded on the NYSE and Nasdaq. Quintiles of Institutional Time Period Time Period Buying Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years NYSE Panel A. Raw Returns Nasdaq Q1 (low) Q Q Q Q5 (high) Q5-Q (1.45) 0.15 (0.80) 0.01 (0.09) 0.05 (0.30) (-0.15) (-0.90) (-2.01) (-1.78) Panel B. CAPM Alphas NYSE Nasdaq Q1 (low) 0.00 (0.01) 0.09 (0.39) 0.25 (1.21) 0.20 (1.01) 0.30 (0.55) 0.50 (0.97) 0.97 (1.99) 0.85 (1.78) Q (1.52) 0.22 (1.44) 0.26 (1.64) 0.25 (1.75) 0.39 (1.10) 0.39 (1.13) 0.52 (1.62) 0.49 (1.52) Q (1.53) 0.23 (1.38) 0.22 (1.32) 0.23 (1.47) 0.56 (2.10) 0.38 (1.52) 0.46 (1.81) 0.45 (1.87) Q (1.21) 0.24 (1.33) 0.25 (1.41) 0.27 (1.55) 0.39 (1.74) 0.37 (1.73) 0.44 (2.03) 0.43 (2.10) Q5 (high) 0.39 (2.24) 0.29 (1.62) 0.32 (1.78) 0.31 (1.84) 0.54 (2.84) 0.34 (1.77) 0.39 (1.90) 0.40 (2.16) Q5-Q (1.76) 0.20 (1.13) 0.07 (0.46) 0.11 (0.70) 0.24 (0.51) (-0.38) (-1.46) (-1.16) 55

58 Quintiles of Institutional Time Period Time Period Buying Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years Panel C. Fama-French Alphas NYSE Nasdaq Q1 (low) (-1.30) (-0.98) (-0.69) (-0.81) 0.78 (2.03) 0.88 (2.48) 1.14 (3.54) 1.09 (3.36) Q (-0.44) (-0.40) (-0.78) (-0.54) 0.75 (3.95) 0.66 (3.13) 0.60 (2.95) 0.63 (3.23) Q (-0.42) (-0.54) (-1.13) (-0.88) 0.75 (5.89) 0.51 (3.56) 0.41 (2.80) 0.47 (3.41) Q (-0.75) (-0.50) (-0.98) (-0.67) 0.45 (3.81) 0.39 (3.38) 0.30 (2.38) 0.36 (3.10) Q5 (high) 0.10 (0.78) 0.01 (0.08) (-0.25) (-0.12) 0.52 (4.41) 0.28 (2.74) 0.19 (1.59) 0.26 (2.44) Q5-Q (1.63) 0.17 (0.98) 0.07 (0.45) 0.10 (0.66) (-0.67) (-1.71) (-3.07) (-2.68) Panel D. Four-Factor Alphas NYSE Nasdaq Q1 (low) 0.18 (1.24) 0.21 (1.68) 0.18 (1.57) 0.18 (1.63) 1.47 (4.55) 1.59 (5.29) 1.67 (5.95) 1.66 (5.98) Q (0.97) 0.13 (1.39) 0.09 (1.00) 0.10 (1.18) 1.10 (6.95) 1.13 (7.11) 1.03 (6.76) 1.06 (7.19) Q (0.63) 0.07 (0.60) 0.01 (0.08) 0.02 (0.23) 0.95 (8.37) 0.83 (7.57) 0.74 (6.68) 0.77 (7.40) Q (0.07) 0.04 (0.30) 0.01 (0.05) 0.02 (0.21) 0.59 (5.18) 0.60 (6.15) 0.55 (5.53) 0.58 (6.18) Q5 (high) 0.17 (1.32) 0.11 (0.85) 0.08 (0.68) 0.08 (0.69) 0.59 (4.98) 0.41 (4.23) 0.43 (4.34) 0.44 (4.69) Q5-Q (-0.04) (-0.65) (-0.66) (-0.69) (-2.58) (-3.66) (-4.14) (-4.20) 56

59 Table VIII Returns Following Institutional Net Buying and Net Selling: Subperiod Analysis This table reports subperiod results of two performance measures, in percentage per month, for institutional net trading quintile portfolios in the periods following the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional net trading measure. Stocks in quintile 1 (Q1) are the stocks with the highest institutional net selling (individual net buying), stocks in quintile 5 (Q5) are the ones with the highest institutional net buying, and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintiles portfolios are calculated over one quarter (Qtr 1), one year (Year 1), year two (Year 2) and two years (2Years) periods following the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns (raw returns), as well as the four-factor risk-adjusted returns, estimated from the time series regression of the Carhart s (1997) four-factor model with respect to the market, size, book-to-market and momentum benchmarks. The table reports the raw returns (Panel A), and the four-factor alphas (Panel B), as well as their t-statistics (in parentheses). This table reports the returns within two subperiods: the late 1990s bubble period, where the trading period is , and the prebubble period, where the trading period is The results are shown separately for stocks traded on the NYSE and Nasdaq. Panel A. Raw Returns Quintiles of Institutional Net Trading Time Period Time Period Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years The Late 1990s Bubble Period: NYSE Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) Q5-Q (0.31) 0.06 (0.30) (-1.13) (-0.46) 0.29 (0.51) (-0.21) (-3.04) (-1.90) The Pre-Bubble Period: NYSE Nasdaq Q1 (net sellers) Q Q Q Q5 (net buyers) Q5-Q (0.35) 0.09 (0.69) 0.15 (1.23) 0.09 (0.88) 0.29 (1.50) 0.26 (2.04) (-0.99) 0.04 (0.37) 57

60 Panel B. Four-Factor Alphas Quintiles of Institutional Time Period Time Period Net Trading Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years The Late 1990s Bubble Period: NYSE Nasdaq Q1 (net sellers) 0.31 (1.69) 0.18 (1.03) 0.27 (1.46) 0.27 (1.59) 1.57 (4.02) 1.45 (4.80) 1.35 (5.38) 1.39 (6.04) Q (0.64) 0.11 (0.67) 0.20 (1.22) 0.21 (1.40) 1.39 (3.76) 1.56 (4.45) 1.74 (5.48) 1.64 (5.71) Q (-0.88) 0.05 (0.31) 0.23 (1.42) 0.16 (1.16) 1.57 (5.14) 1.45 (5.34) 1.58 (5.74) 1.53 (6.20) Q (-0.85) (-0.20) 0.11 (0.67) 0.09 (0.60) 0.74 (3.37) 1.09 (5.51) 1.15 (5.39) 1.12 (5.75) Q5 (net buyers) (-0.03) 0.08 (0.42) 0.13 (0.79) 0.15 (0.98) 0.73 (2.98) 0.89 (4.08) 0.62 (3.00) 0.78 (4.01) Q5-Q (-1.71) (-0.63) (-0.98) (-0.86) (-2.18) (-1.77) (-2.74) (-2.42) The Pre-Bubble Period: NYSE Nasdaq Q1 (net sellers) 0.40 (3.19) 0.21 (1.92) 0.02 (0.15) 0.13 (1.46) 0.60 (4.21) 0.48 (4.63) 0.48 (3.90) 0.44 (4.37) Q (1.07) 0.08 (1.51) 0.03 (0.43) 0.06 (1.09) 0.47 (3.45) 0.56 (4.39) 0.41 (2.84) 0.45 (3.65) Q (1.57) 0.07 (1.17) 0.03 (0.64) 0.03 (0.56) 0.76 (4.95) 0.66 (5.29) 0.46 (3.90) 0.51 (4.54) Q (1.55) 0.13 (2.02) 0.18 (2.60) 0.14 (2.41) 0.98 (8.58) 0.81 (7.78) 0.49 (5.35) 0.60 (6.77) Q5 (net buyers) 0.23 (2.69) 0.16 (2.24) 0.18 (2.33) 0.16 (2.34) 0.76 (5.66) 0.64 (6.45) 0.40 (3.93) 0.48 (5.24) Q5-Q (-1.17) (-0.42) 0.16 (1.44) 0.04 (0.42) 0.16 (0.92) 0.16 (1.48) (-0.59) 0.04 (0.35) 58

61 Table IX Returns Following Institutional and Individual Purchases: Subperiod Analysis This table reports subperiod results of two performance measures, in percentage per month, for institutional buying quintile portfolios in the periods following the trading quarters. At the end of each quarter, stocks are sorted into quintile portfolios according to their institutional buying measure. Stocks in quintile 1 (Q1) are the stocks with the lowest institutional buying (highest individual buying), stocks in quintile 5 (Q5) are the ones with the highest institutional buying, and the portfolio Q5-Q1 is constructed by going long Q5 and short Q1. The returns on the quintiles portfolios are calculated over one quarter (Qtr 1), one year (Year 1), year two (Year 2) and two years (2Years) periods following the trading quarter, equal weighting the stocks within each quintile. The monthly returns series on each quintile portfolio are used to calculate the average monthly returns (raw returns), as well as the four-factor risk-adjusted returns, estimated from the time series regression of the Carhart s (1997) four-factor model with respect to the market, size, book-to-market and momentum benchmarks. The table reports the raw returns (Panel A), and the four-factor alphas (Panel B), as well as their t-statistics (in parentheses). This table reports the returns within two subperiods: the late 1990s bubble period, where the trading period is , and the pre-bubble period, where the trading period is The results are shown separately for stocks traded on the NYSE and Nasdaq. Panel A. Raw Returns Quintiles of Time Period Time Period Institutional Buying Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years The Late 1990s Bubble Period: NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) Q5-Q (0.24) 0.00 (0.00) 0.02 (0.06) 0.02 (0.09) (-0.61) (-1.14) (-1.36) (-1.41) The Pre-Bubble Period: NYSE Nasdaq Q1 (low) Q Q Q Q5 (high) Q5-Q (1.43) 0.20 (1.04) 0.15 (0.79) 0.16 (0.91) 0.10 (0.33) (-0.95) (-0.85) (-0.92) 59

62 Panel B. Four-Factor Alphas Quintiles of Institutional Time Period Time Period Buying Qtr 1 Year 1 Year 2 2Years Qtr 1 Year 1 Year 2 2Years The Late 1990s Bubble Period: NYSE Nasdaq Q1 (low) 0.52 (2.13) 0.37 (1.60) 0.43 (2.15) 0.44 (2.23) 2.86 (4.08) 2.68 (4.29) 2.57 (4.85) 2.60 (5.09) Q (0.12) 0.07 (0.45) 0.16 (1.02) 0.15 (1.08) 1.48 (4.79) 1.44 (4.71) 1.63 (5.55) 1.55 (5.78) Q (-1.22) (-0.41) 0.11 (0.66) 0.08 (0.49) 0.75 (3.53) 1.10 (5.57) 0.99 (4.84) 1.01 (5.59) Q (-0.75) (-0.10) 0.05 (0.30) 0.06 (0.36) 0.41 (1.83) 0.72 (3.85) 0.81 (4.09) 0.80 (4.56) Q5 (high) (-0.12) 0.04 (0.23) 0.20 (1.10) 0.17 (1.03) 0.55 (2.48) 0.57 (3.03) 0.51 (3.01) 0.56 (3.53) Q5-Q (-1.82) (-1.19) (-1.14) (-1.24) (-3.25) (-3.18) (-3.71) (-3.82) The Pre-Bubble Period: NYSE Nasdaq Q1 (low) 0.10 (0.56) 0.07 (0.49) 0.02 (0.16) 0.05 (0.39) 0.67 (2.79) 0.82 (3.93) 0.65 (2.83) 0.68 (3.29) Q (2.88) 0.14 (2.46) 0.09 (1.61) 0.10 (1.91) 0.78 (4.70) 0.83 (6.23) 0.52 (4.10) 0.62 (5.13) Q (3.55) 0.15 (2.38) 0.08 (1.27) 0.11 (1.79) 1.03 (7.73) 0.63 (5.91) 0.46 (4.65) 0.49 (5.07) Q (1.08) 0.12 (1.54) 0.08 (1.03) 0.11 (1.43) 0.59 (5.40) 0.51 (5.52) 0.32 (3.49) 0.39 (4.41) Q5 (high) 0.27 (2.84) 0.16 (1.98) 0.16 (1.78) 0.15 (1.90) 0.50 (4.31) 0.37 (3.89) 0.30 (3.02) 0.32 (3.53) Q5-Q (0.77) 0.10 (0.56) 0.14 (0.81) 0.11 (0.69) (-0.63) (-2.02) (-1.31) (-1.59) 60