Examining the Dark Side of Financial Markets: Who Trades ahead of Major Announcements? November 20, 2009

Transcription

1 Examining the Dark Side of Financial Markets: Who Trades ahead of Major Announcements? JOHN M. GRIFFIN, TAO SHU, AND SELIM TOPALOGLU * November 20, 2009 * Griffin is at the University of Texas at Austin, Shu is at the University of Georgia, and Topaloglu is at Queen s University. Griffin s is john.griffin@mail.utexas.edu. We are very grateful to the Nasdaq stock exchange for their support and data. We thank Kelvin Law, Xin Zhang, and Ligang Zhong for research assistance. Parts of this paper are drawn from the working paper How Informed are the Smart Guys? Evidence from Short-Term Institutional Trading prior to Major Events. For comments on the old paper we are grateful to Bruce Grundy, Marc Lipson, and Alok Kumar for helpful discussion as well as seminar participants at Darden School of Business, the University of Georgia, the University of Texas at Austin, All Georgia Conference, 2008 China International Conference of Finance, and 2 nd Singapore International Conference of Finance.

2 Examining the Dark Side of Financial Markets: Who Trades ahead of Major Announcements? Abstract Institutions often have access to inside corporate information through their connections but little is known about the extent to which institutions might exploit their informational advantage through short-term trading. We first examine daily trading by eight different types of individual and institutional investors ahead of news events and find that prior to takeovers and earnings announcements most types of institutional and individual trading is uninformed. A group of large hedge fund traders consistently sells prior to negative earnings announcements and wealthy individuals at full-service brokerage houses trade in the right direction ahead of takeover announcements. To examine specific trading within the institutional realm, we employ unique broker-level trading data. Despite examining the issue from many facets, we are unable to find consistent evidence that investment banks trade profitably on connections through takeover advisors, IPO, SEO, or lending relationships. We also analyze historical connections between firms and brokerage house trading. Market makers whose trades are profitable with a firm in the past consistently sell prior to impending negative earnings announcements. In contrast to much recent press and literature, our results suggest that institutional investors are reluctant to use their private information or at least cover their tracks extremely well.

3 Institutional investors are in constant and close contact with firms through their investment banking, lending, and asset management arms. At the same time that institutions are afforded with access to information that can potentially be used for extremely profitable trading, they are told not to trade on it. Institutions are quick to emphasize that they would not dare use such information because their firm s integrity is important and future business depends on reputation. Hence, institutions argue that they are extremely diligent to ensure that inside information acquired through connections with firms is not leaked or exploited. Skeptics contend that the short-term profit motive is strong and the SEC enforcement division is relatively lax as evidenced by the small number of prosecutions, at least until recently. Skeptics therefore view the market as consisting of insiders and outsiders where insiders are thought to be large informed institutions that make substantial shortterm trading profits by moving ahead of individual investors prior to impending news events. For example, the Galleon hedge fund has recently been prosecuted by the SEC for profiting through short-term insider trading. Additionally, some skeptics argue that the recent record trading profits of large brokerage houses are evidence for their case. 1 Despite the substantial speculation, evidence for either camp is largely anecdotal or limited to a few prosecuted cases. This paper comprehensively examines the short-term trading activities of different types of institutional and individual traders ahead of the most common stock market events associated with informational asymmetry: takeover and earnings announcements. Specifically, we examine the trading activities of four institution types and four individual types. More importantly, we examine the importance of investment banking relationships such as takeover advising, IPO and SEO underwriting, lending relationships, and firm-to-broker linkages as reflected in past trading profits. We conduct the tests by employing a unique database that allows us to track all Nasdaq trading activity at the brokerage house level. 1 According to Gogoi (2009), Joseph Stiglitz says: Goldman's activity is of negative social value. Its recent profits came from trading, which basically amounts to profiting from insider information at the expense of others. 1

4 Our paper adds to a rapidly growing literature that uses new high-frequency data. 2 Kaniel, Liu, Saar, and Titman (2009) find evidence of profitable individual trading ahead of earnings announcements of NYSE firms. In contrast, Campbell, Ramadorai, and Schwartz (2009) infer institutional trading using TAQ data and find that institutions trade profitably prior to earnings announcements. 3 We contribute substantially to this debate by examining both individual and institutional traders and further classifying four main types within each category. We find that the dichotomy within the institutional and individual realm is important, with evidence of informed trading only at the top end of the institutional and individual trading. We also make a significant contribution to the growing literature on connections and trading profitability. Cohen, Frazzini, and Malloy (2008) find that educational affiliations between mutual fund and corporate board managers are associated with more profitable mutual fund trading around corporate news announcements. 4 Acharya and Johnson (2009) find evidence that information leakage prior to buyouts is increasing in the number of private equity participants. Ivashina and Sun (2009) find that access to confidential loan information is related to sizeable informed trading in the month after the loan renegotiation. Bodnaruk, Massa, and Simonov (2009) argue that funds affiliated with takeover advisors take positions in target firms prior to the takeover announcement. Kedia and Zhou (2009) suggest that bond dealers affiliated with takeover target advisors engage in suspicious bond trading prior to takeovers. 2 These studies improve substantially on previous studies that examine small and large trade-size categories ahead of events such as earnings announcements [Lee (1992)]. However, Barclay and Warner (1993) find that medium-size trades (which they hypothesize are from institutions) are the most informative prior to takeovers. With the recent advent of automated orders, institutional traders can often automatically split their trading into small and varying trade sizes, and institutions, if informed, have the incentive to use small trades to hide their information although they generally trade in large sizes. 3 Irvine, Lipson, and Puckett (2007) use a high-frequency database of institutional trades and find that institutions trade in the same direction as impending analyst recommendations. 4 Cohen, Frazzini, and Malloy (2009) find that educational networks are associated with the profitability of analyst forecasts. Coval and Moskowitz (2001) find that funds make much larger profits in local stocks. 2

5 Our data and approach have three potential advantages compared to the papers above: higher frequency data, direct study of brokerage house level trading, and comprehensive analysis through multiple channels of relationships and connections. With the exception of the Kedia and Zhou (2009) s bond analysis, all of the above papers rely on capturing equity trading through quarterly or semiannual filings or indirect measures such as asset returns. If institutions trade on short-term information, or they carefully avoid taking positions at the end of the quarter when they report holdings, studies using reported government filings (13f or N-30D) may understate the importance of connections. We are also able to examine the client trades and proprietary trading of the brokerage house itself rather than relying on filings for various affiliated parts of the bank. Coupled with the data advantage, we also assemble an extensive list of connections through M&A advising, lending, and past profitability. We first examine the trading of four institutional and four individual types for all Nasdaq stocks between January 1997 and December In the two, five, and ten days prior to takeovers, general institutional investors are not net buyers in takeover target firms. Additionally, there is no evidence of abnormal buying activity by the investment banks that prime broker most of the hedge funds, another group of 21 high-frequency hedge funds, and derivative houses. In contrast, all types of individual investors are net buyers prior to the announcements (general individual category, fullservice, discount, and daytraders). For takeovers without any price run-up prior to the announcement (and hence likely no widespread information leakage), buying activity is present from individual full-service and discount brokerage investors, suggesting that these investors may have non-public information. Next, we examine pre-announcement trading for four sub-samples based on earnings announcement returns and find no evidence that general institutional trading is related to future earnings announcement returns, either in small, medium, or large stocks. However, hedge funds, and 3

6 investors trading through the largest I-banks that service hedge funds, are consistently selling stocks prior to negative earnings announcements. We also examine trading prior to days with large positive (>15%) and large negative (<-10%) returns and find that only full-service individual investors are significant buyers ahead of price increases. To summarize, there is little evidence of institutional investors, on average, forecasting future stock returns ahead of major events. This finding, while surprising in light of a large amount of literature finding positive predictability of institutional trading over longer horizons, is consistent with those of studies that find positive profits for individual investors over short horizons. 5 However, this finding is about the average institution and does not mean that there are not some institutions trading on information regarding impending events at the expense of other institutions. Indeed, there is some evidence of disparity within institutional trading, and hedge fund trading is predictive of negative earnings announcement returns. To more thoroughly look for evidence of informed trading within the institutional realm, we further examine the importance of connection through takeover advising, SEO underwriting, IPO underwriting, and lending relationships. We study short-term client and market maker trades for brokerage houses that are also takeover advisors, SEO or IPO underwriters, or lending banks. Throughout all these different relationships (and sub-groups within), we find almost no evidence that these brokerage houses buy prior to takeovers, or trade correctly prior to earnings announcements. These findings are robust to a variety of controls such as distinguishing between sub-groups of investment banks or lenders such as advisors of target firms versus advisors of acquirers, book-runners versus co-managers and syndicate members, underwriters of recent IPOs or SEOs, all lead lenders versus loan participants, lenders of loans syndicated solely to institutions, lenders of newly entered loans, etc. 5 Barber, Odean, and Zhu (2009) and Kaniel, Saar, and Titman (2008) find that unconditional individual trading is profitable in the short-term but disagree on the relative role of liquidity versus information production. 4

7 We also investigate historical connections between brokerages and firms. The motivation is similar to that employed by the Galleon hedge fund where connections at firms are garnered and used repeatedly. 6 We do find evidence that proprietary trading is profitable for brokerage houses that have traded profitably at a firm in the past. Specifically, historically connected brokerage houses sell in large and significant amounts prior to both large and small negative earnings announcement but not positive announcements. Lastly, we look for dirty brokerage houses by asking if brokerage houses that made an aggregate profit on trading prior to earnings announcements in one year continue to make an aggregate profit the next year. We do not find evidence that the brokerage firm itself can consistently make profits. Our findings do not contradict the large literature showing that over longer horizons institutional trades are informative, but suggest a more nuanced understanding of it. The fact that institutions do not possess short-term informational advantages during periods of heightened informational asymmetry implies that institutions make money primarily by mechanisms other than acquiring short-term private information. 7 Overall, our evidence suggests that on average the aggregate institutional trading advantages are not based on hot phone calls and tips. To the extent that tips are traded on, it seems that wealthy individuals are more apt to utilize brazen information like that prior to takeovers to enrich themselves rather than their firm. Institutional investment banking and lenders seem to be extremely careful to not utilize the valuable information, at least in a way that might be detectable. Overall, our findings suggest that recent press of brazen insider trading by institutions is the exception rather than the norm. 6 In October 2009, the SEC filed a complaint alleging that Raj Rajaratnam obtained non-public information about corporate earnings, takeover activity etc. at several companies including Google, Hilton, Intel, and IBM. He then repeatedly traded on these tips on behalf of his hedge fund Galleon. 7 Toward this end, we examine institutional trading during the two days starting from the earnings announcement date and find that institutional trading on and after the announcement day is indeed profitable even after controlling for postearnings announcement drift. 5

8 II. Data A. Investor Types The primary data set for this paper consists of trading by nine investor groups in all Nasdaqlisted firms from January 2, 1997, to December 31, Griffin, Harris, Shu, and Topaloglu (2009) use sub-samples of this data for technology and Nasdaq 100 stocks. The data is derived from Nasdaq clearing records that include the date, time, ticker symbol, trade size, and price of each transaction for each stock. These clearing records also include additional identifying fields from the settlement process that allow the volume to be assigned to various investor groups. Hence, each trade can be linked to the parties on both sides of the trade, and each side of every trade is classified as to whether the parties are trading for their own account (as a market maker) or for a client (agency trading). Additionally, each trade is marked as to which party is buying and selling. This feature of the data is advantageous in that it avoids problems that may arise through commonly applied tick-test rules. Primarily based on a rigorous classification of over 500 major brokerage houses, the data is assigned to one of either four institutional investor groups, four individual investor groups, or a mixed group that handles trades from both institutions and individuals. The nine categories are institutional, large investment banks, (21) hedge funds, derivatives traders, individual full service, discount, day trading, general individual, and brokerage houses that handle a mix of individual and institutional clients. The largest three investment banks account for more than 60% of the prime brokerage business for hedge funds, and, thus, their trading volume is likely to represent a mix of hedge funds and other large, highly sophisticated investors. A more accurate yet cumbersome description of institutional trades would be transactions through brokerage houses dealing primarily with institutional investors. We acknowledge that each 6

9 group might include a few traders that do not belong. For example, most institutional brokerage houses also have a private wealth management business that manages capital for extremely wealthy individuals, and these individuals may occasionally make their own trading decisions. However, since our trading focuses on net activity, the impact of any misclassified individuals is likely to be miniscule and swamped by the general activity of other types of large institutional traders. This data captures almost all of the activity on Nasdaq except for a small fraction of ECN trades with reporting issues. All of our calculations are relative to this identified trading volume. Griffin, Harris, Shu, and Topaloglu (2009) describe many additional details of the data and show that the Nasdaq data correlates better with quarterly 13f filings than high-frequency alternatives. Namely, the data compares favorably to either the high frequency NYSE data [Boehmer and Kelly (2009)] or the method of extracting institutional trades from TAQ by Campbell, Ramadorai, and Schwartz (2009). For most of our analysis, we use imbalances, which are defined as the difference between buy and sell volumes expressed as a fraction of shares outstanding. The concept of imbalances is similar to turnover (which dominates the volume literature) and relies on standardizing volume by a variable that is fairly representative across firms. If one believes that a move of a given percentage of shares in a certain direction may influence the price, then net buy-sell imbalances are the proper indicator for measuring net activity. We generally adjust our investor category imbalances by measuring the imbalance for each firm in excess of a benchmark imbalance. The default benchmark in this paper is based on the average investor-type imbalance of other firms that are within the same three-digit SIC code industry and then the same size tercile within the industry. The purpose of benchmarking is similar to a return benchmark where we are seeking to control for abnormal buying 7

10 or selling of a particular group of stocks for extraneous reasons, for example, institutions moving into or out of small internet stocks. 8 B. Brokerage Level Data and Mapping For our analysis on connections, we use the same data but at the brokerage level as has been done for IPOs and described with more details by Griffin, Harris, and Topaloglu (2007). We match brokerage houses with takeover advisors, IPO underwriters, SEO underwriters, and lenders. We obtain data on takeover advisors from SDC and complement that with Mergerstat and Corpfin Worldwide databases. We obtain data on IPO underwriters and SEO underwriters during from SDC. Takeover advisors, IPO underwriters, and SEO underwriters are then manually matched with brokerage houses by name. We carefully address investment bank mergers using the list of mergers from Corwin and Schultz (2005). Data on lenders are obtained from Loan Pricing Corporation (LPC) DealScan database. Due to the large number of lenders, we pick the top 500 brokerage houses in terms of total Nasdaq trading volume during and then match with lenders by name. We address bank mergers following Sufi (2007). 9 C. Takeovers, Earnings Announcements, and Large Event Samples The samples used in this paper consist of Nasdaq firms from January 1997 to December 2002 with a) takeovers/mergers over the period, b) earnings announcements, c) large price moves, and d) the whole Nasdaq sample from 1997 to We also drop the stocks priced below $5 on the 21 st day prior to any of our announcements. For our takeover sample, we obtain information from the Securities Data Corporation Mergers and Acquisitions Database for all US targets listed on Nasdaq over our sample period. We exclude LBOs, spinoffs, recapitalizations, self-tenders, 8 Additionally, if any systematic classification errors exist in the reporting of ECN trades [as discussed in Griffin, Harris, and Topaloglu (2007)], then to the extent that these errors are similar across similar stocks, benchmarking should help control for these issues. In order to calculate industry/size adjusted imbalances, we drop a firm if no other firm falls in the same size tercile of the same three-digit industry. This filter eliminates about 0.6% of our sample. We also examine our key findings without benchmarking and with other benchmarks such as past turnover and return momentum. 9 We thank Amir Sufi for providing the list of bank mergers. 8

11 exchange offers, repurchases, minority stake purchases, acquisitions of remaining interest, and privatizations. In addition to the date announced and original date announced variables from SDC, we also searched Mergerstat, Corpfin worldwide, and Lexis/Nexis for the first news item we could obtain about the target firm potentially being a takeover target. Because we want to focus on trading before news on a merger has been publicly announced, we take a conservative approach and take the earliest of the four sources. Thus, some of our dates are rumor dates, as they occur prior to the official announcement dates. Our final sample of takeovers/mergers contains 1,225 events during Our earnings announcements sample is the intersection of CompuStat quarterly accounting data and CRSP stock data. In particular, we obtain the dates of quarterly earnings announcements from CompuStat quarterly data file. We exclude a firm if it is not on CRSP or if its share code is not 10 or 11 (ordinary common shares). Our final sample contains 62,804 earnings announcements during We also look for other large stock price movements either above 15% or below -10% as a large price move that do not coincide with our takeover/merger and earnings announcements samples. 10 Our final sample of other large price movements contains 754 positive and 939 negative price moves. 10 We choose below -10% because there are many more positive price moves than negative ones. In order to avoid the movements being caused by informational sources that are not informational, we impose the following restrictions: 1) we request that the market return on the event day to be between -1% and 1%; 2) in order to exclude the post-ipo period, we drop an event if the underlying firm has a history of less than 128 days in CRSP; 3) we drop a price movement if the stock s excess return is above 10% or below -10% on any day of the 20 days prior to the price movement; 4) we drop the price movements occurring during the year-end period between December 15 and January 15. In order to exclude the price movements associated with our takeover/merger and earnings announcements samples, we drop movements if they occur during either a [-20,1] takeover announcement window or a [-2,1] earnings announcement window. 9

12 III. Trading by Investor Groups Ahead of Events A. Takeovers Takeovers are interesting because they are events that are hard to predict unless one is privy to information. As described in the data section, we are extremely conservative and take the earliest date from four sources so that our first announcement on information leakage has declined through time. Despite this caution, in unreported results we find that volume begins to pick up beginning seven days prior to the announcement. 11 All of our results will be reported in terms of the magnitude of imbalances but it is interesting to note that these magnitudes will be influenced by the size of the investor trading groups. Figure 1 examines the cumulative net (buy-sell) imbalances for the institutional and individual investor groups (benchmarked relative to industry/size imbalances) in the fifteen trading days prior to first news of a takeover. Surprisingly, Figure 1 shows that the general institutional category is a net seller prior to the takeovers. Clients of the three largest investment banks are also net sellers and the 21 hedge funds and the derivative traders both have relatively small net activity. Interestingly, all four of the individual investor groups are net buyers, with the largest increase coming through the full-service brokerage house. The large amount of net activity at the full-service brokerage house is particularly surprising in light of the fact that normally it is only responsible for a small fraction of volume (only 3.72% in the benchmark period). Table 1 summarizes abnormal trading prior to takeover announcements for the nine investor groups. The statistical significance of the cumulative imbalances over the three different windows is computed using the cross-section of abnormal imbalances. Panel A of Table 1 shows that all four individual investor groups are significant in the five- and ten-day windows prior to the 11 Our pre-announcement buy-and-hold return in the twenty days prior to the announcement is seven percent, which is less than the eleven percent document by Jarrell and Poulsen (1989). Possible reasons for less price run-up in our sample are that we are extremely conservative and take the earliest date from four sources or information leakage has declined through time. Another possible reason is that we exclude the stocks priced below $5. 10

13 announcement. If one adds up the individual investor imbalances in the five-day window prior to the announcement, then it is clear that individuals purchase a little more than ( =0.542/1000) one-twentieth of a percent of the shares outstanding. To gauge the dollar magnitude of the institutional losses, Panel B computes the average (and total) gain/loss by computing the excess returns on the days subsequent to the trade up and through the announcement day, where returns are in excess of the Nasdaq Composite Index returns. 12 Combining the institutional and I-bank group, we see that up to the end of the announcement day, institutions lost on average $76,710 per takeover from their trading during the ten days prior to the announcement. Because there are 1,225 takeovers in the sample, the total loss over the period is $94 million for their trading during the ten days prior to the announcement. B. Differentiating Potential Explanations Institutional investors are mainly selling prior to takeovers, but why? Here we investigate several possible explanations. B.1. Pre-announcement run-up If news is leaking out publicly prior to our first news date, then it is likely to be disseminated to a wide audience and cause an upward price movement prior to the takeover. If the news is mostly private then one might not expect it to cause much price run-up. Panel C of Table 1 reports the net buying activity for the investor groups for sub-samples with and without a positive abnormal cumulative return in the 20 days prior to the announcement. For the firms with a positive abnormal return prior to the announcement, the daytraders have the most statistically significant buy increase with largely significant increases for the individual general and discount groups as well. Interestingly, 12 We choose to focus on returns benchmarked from closing prices so that we can focus on the informational value of trading rather than differences between institutions and individuals due to price impact and intra-day trading patterns. By calculating the returns on subsequent days the measure is likely to underestimate institutional losses because large institutional trades are typically associated with larger intra-day price impact as well as spreads that all participants incurred. Indeed, we do find that calculating profits with the actual price where the transaction occurred rather than the closing price leads to lower institution profits. 11

14 the full-service individual investors do not have statistically significant positive net buy imbalances over any window. Additionally, institutional investors have positive (though not statistically so) net imbalances. For the target firms without any price run-up, the institutional buying is statistically negative, as is the case for individual daytraders. The derivative trading activity is positive but statistically insignificant, indicating that not much net buying activity is spilling over from the stock market. The full-service and discount individual investors exhibit statistically significant positive imbalances that amount to one-twentieth of one percent of the shares outstanding during the fiveday window prior to the announcement. Given that institutions have large price impact and that their trades are more noticeable in small stocks, institutional investors may only pay attention to medium or large takeover targets where they can profit in a relatively liquid environment. Inconsistent with this explanation, Panel A of Table S1 shows that institutional imbalances are more negative in the largest size group. 13 B.2. Trade size Barclay and Warner (1993) find that medium-size stealth trades are responsible for moving prices prior to takeovers. Panel D of Table 1 (with more details in Panel B of Table S1) shows some marginal evidence of medium-sized institutional trading in the ten days prior to announcements. This trading activity is not significant at the two or five-day frequency. Interestingly, the strongest evidence of net buying activity comes from medium-size individual trades. With an average price of $20.45, these medium-sized trades are in the $20,450 to $102,250 range, suggesting that the individuals making the trades prior to takeovers are relatively wealthy. 14 For the mixed trading category, the small- and medium-size mixed groups are dominated by buying consistent with the individual categories. It is possible that institutions are targeting their 13 Additionally, in Panel C of Table S1 we find that benchmarking of imbalances relative to stocks of similar short-term momentum, turnover, or just using raw imbalances does not affect inferences. 14 These results contrast with Chakravarty (2001), who finds that it is medium-size institutional trades that are the most informative using a 63-day sample of NYSE firms beginning in November

15 stealth trades specifically at these brokerage houses to camouflage their trades. However, the patterns observed in the mixed category are less prominent than those in the individual categories once the size of the category is controlled for. 15 B.3. Option trading Another possibility is that sophisticated institutional investors use options to benefit from takeovers and this is why we do not observe net buying activity through the stock market. For stocks with options, Cao, Chen, and Griffin (2005) observe that informed trading prior to takeovers is more likely to occur in the option market, but they do not have information on the composition of the option investors. Panel D suggests that when options of the target firms are available for trade, informed individual traders divert their trading to options markets. However, the option findings indicate that the lack of institutional buying prior to takeovers cannot be explained by trading through the option market, since institutional imbalances are negative even for targets that do not have options traded. B.4. Speculation or informed trading? An alternative way to investigate the informedness of institutional trading is to examine whether trading predicts the announcement day abnormal return. 16 We estimate a cross-sectional regression where the dependent variable is the cumulative two-day [0,1] announcement return for the target and explanatory variables are net buying by various investor groups in the preannouncement period. Additionally, we include takeover characteristics that may be related to the 15 The buy imbalance is extremely large in this medium group but it is important to note that the volume coming through the mixed brokerage house is typically over 33% of trading while all four of the individual brokerage houses account for only around 17% of trading. If one aggregates the net buying imbalance of all four individual brokerage houses in the medium-sized trade group over the window [-5,-1], then the average net buying is is higher than the net buying coming from the mixed group even though the mixed group has twice as much volume. Thus, the magnitude of mixed buying is consistent with a muted pattern of both individual buying and institution selling in this category. 16 It is important to again note that our announcement date is the first news mention of a possible takeover or rumor and not necessarily the official announcement date. This approach minimizes the amount of trading that is simply due to public news. 13

16 target firm s announcement return. 17 Interestingly, Supplemental Table S2 shows that most of the investor group imbalances are not positively related to the size of the announcement day return. The daytrader and individual general categories have negative coefficients, which suggest that their trading is simply speculation on a takeover occurring and not truly informative of deal profitability. Interestingly, the full-service individual trading for the [-10,-1] window is the only group with a positive and weakly (t-statistic=1.91) statistically significant relation to the size of the announcement day return. Overall, after examining the trading by investor groups prior to takeovers with alternative test designs, sub-sample analysis, and different measures, the evidence consistently shows little evidence of buying by institutional investors ahead of takeovers. However, there is evidence that full-service individuals seem informed. C. Earnings Announcements Unlike takeovers, earnings announcements are for the most part scheduled corporate events. If investors are using information from past earnings and public reports to predict the direction of future earnings announcements, then they have the incentive to trade early before analysts and other investors find out their information. However, if they are trading on direct information about the exact size of the earnings estimate, these may only be known to corporate insiders after the financial statements are in and directly prior to the announcement. We divide earnings announcements into four groups based on the abnormal announcement return [0,1] and display the trading activity for each group in Figure 2. Individual investors and institutions are net buyers prior to earnings announcements with large negative (<-5%) returns. Clients of large investment banks and hedge funds tend to be selling. In addition, for earnings 17 Such a regression is potentially problematic in that institutional trading due to informational leakage could increase the stock price prior to the announcement and lower the announcement return. However, we control for this by using preannouncement abnormal returns to capture the amount of information leakage that has made it to the market. 14

17 announcements with small negative returns both the clients of the large investment banks and institutions are net buyers. Panel A of Table 2 summarizes the trading behavior over three different windows prior to earnings announcements for each return group. Institutions are statistically significant net buyers prior to earnings announcements with small negative returns. Hedge funds and the largest Investment bank clients are statistically significant net sellers prior to big negative earnings announcements only. The general individuals are large net buyers ahead of earnings announcements with both large positive and large negative returns, suggesting that they increase their trading ahead of uncertain events. To summarize, hedge funds and the brokerages that prime broker for hedge funds seem to have some ability to short stocks prior to negative earnings events but not on the positive side. Investors may have relatively more private information in small and, therefore, less analyzed securities. In Panel B of Table 2, we examine abnormal trading in the five days prior to the announcement for small, medium, and large stocks. The most significant result is that the general institutional group is a statistically significant net buyer in small stocks just prior to these stocks experiencing large negative returns. There is little consistent evidence to indicate that any of the investor groups is systematically trading in the correct direction in front of both positive and negative return events in either small, medium, or large stocks. Panel C calculates the average and total dollar gain or loss through the one day after earnings announcement day from trading in the selected period. Panel C shows that institutions and clients at the large investment banks on average lose $5,690 in their trading during the 10-day window prior to earnings announcements. Because we have 62,804 earnings announcements in the sample, this amounts to about 358 million dollars in institutional losses from trading around the earnings announcement over our period. To investigate the explanatory power of imbalances in predicting announcement returns, we estimate a panel regression of announcement returns for the [0,1] window on past imbalances and 15

18 returns, with and without firm and year fixed effects (Table 3). Interestingly, institutional imbalances are generally not related to the announcement return except for trading in the two-day window where the relation is actually negative. One possible explanation for why many of the imbalances as well as the past returns are all systematically negatively related to the announcement returns is as follows. Perhaps announcements are only largely surprising to investors if they turn out in the opposite direction to that forecasted. So, if investors anticipate a positive announcement and purchase large amounts of shares the stock will go up. When the eventual earnings report is good, investors are not surprised and stock prices do not move much. However, if a negative earnings figure is released (contrary to expectations), then the firm is punished with a large negative price reaction. An opposite story could be told for positive earnings reactions. For this story to explain the patterns in the data, earnings that are in the same direction as expectations (either more positive or more negative than expected) would need to systematically receive less price response than news that is in the opposite direction from expectations. The end result is that the trading behavior of investors in many categories systematically moves in the wrong direction prior to the announcement. The coefficients of hedge fund trading prior to the announcement indicates that it is the only group that systematically moves in the correct direction but this result is weaker for two-day and ten-day windows after controlling for firm and year fix effects. D. Large Events and Trading on Public Announcements Other than takeovers and earnings announcements, there are also many random events (restructurings, accounting scandals, patent information, sales agreements, new products, etc.) that widely impact firm price. For most of these events, there will be investors holding valuable information at least several days prior to a public news release. Excluding takeover and earnings announcements, we take events of excess daily returns above 15% or below -10% and examine prior trading patterns in Figure S1 and Table S3. For large price increases, institutional investors are 16

19 fundamentally surprised as they are statistically significant net sellers prior to the announcements (as are daytraders). Full-service individual investors are purchasing shares in the two, five, and ten days prior to the move. For price drops, we find only small and insignificant net selling by institutions prior to the drop, whereas hedge funds trade in the wrong direction. An interesting issue is whether our findings are consistent with the larger literature [like DGTW (1997)] which observes that institutional trading is profitable prior to transaction costs. We differ from most of the prior literature in two aspects. While most of the prior literature uses quarterly and annual filings and implicitly ignores short-term intra-quarter trading, our analysis does not examine the interpretation of public news. To partially address this issue, we present in the supplemental result (Figure S2) that stocks that general institutional group buy on the earnings announcement day outperforms those that institutions sell by 2.4 percent (imbalance weighted returns) in the three months after the announcement. The difference for market capitalization weighted returns is also 2.4%. 18 The evidence suggests that institutions profit from better analyzing public information in earnings announcements or institutions with private information wait to trade until the announcement but they would lose much of the information value. 19 E. Informed Market-maker Activity? All of our current analysis has been at the client level. Brokerage houses could apply information to their internal market maker trades prior to major events. We therefore present in Table 4 the market maker imbalances prior to takeover announcements (Panel A) and earnings announcements (Panel B). None of the market maker trades from the nine brokerage house groups are significantly positive prior to takeover announcements. Their trades are not in the right direction 18 Table S4 further confirms through regressions with controls that the positions taken by institutions on and immediately after the announcement day earn them positive returns for the next three months. 19 If institutions with inside information of impending good news, held stocks for longer than they would otherwise, we would observe less selling than normal and hence more net buy-sell imbalances ahead of future good news. The fact that we do not find such evidence suggests that the average institutional seller is unaware of private information contained in future announcements and on average do not delay their selling activity. 17

20 for earnings announcements either. Institutional houses sell significant amounts prior to both large positive and large negative announcements. IV. Connections Although we find little evidence of informed trading by institutions in aggregate, this does not rule out the possibility that some institutions are informed. In fact, there is a fast growing literature about information leakages or informed trading by connected institutions such as takeover advisors and lenders [Cohen, Frazzini, and Malloy (2008), Acharya and Johnson (2009), Bodnaruk, Massa, and Simonov (2009), Ivashina and Sun (2009), Kedia and Zhou (2009), Nandy and Shao (2009)]. While the papers above either examine low-frequency trading or employ indirect measures of information leakage such as stock returns, we directly examine short-term trading of brokerage houses with connections through their investment banking or lending relationships. For most of our analysis, we explore the connections by looking at the trading activity of brokerage house clients as well as the brokerage houses own market maker trading. We also examine historical links between firms and brokerage houses through past profitable trading. A. Investment Banking Connections We analyze the proprietary (market maker) trading of brokerage houses in case they have bridged the firewall and are leaking information from the investment banking arm to the trading arm. Alternatively, the trades might be made on behalf of a hedge fund within the investment bank and here it is unclear if it would be cleared as a market maker or client trade. 20 For this reason we 20 It is our understanding that most hedge funds associated with investment banks would trade through their own brokerage house to avoid revealing their trades to other brokers and to keep trading profits within the bank. It is possible that a hedge fund associated with one brokerage house could execute their trades through another broker but a broker might instantly be aware that they are facing adverse selection if they rarely trade with a group and know that they clear most of their trades internally. 18

21 also examine client trading. If the brokerage house handles diversified order flow from many clients it would be difficult to detect informed trading from a particular group trading through the brokerage house. Therefore, we exclude the top 100 brokerage houses (of 2904) in terms of total Nasdaq trading volume during However, our results including the top 100 brokers are similar. We detect abnormal trading by looking directly at abnormal imbalances as well as computing brokerage level investment returns. The intensity of insider trading could vary among connected brokerage houses. Therefore we further identify a group of dirty connected brokers within each connection type as brokerage houses that seemingly profited from their connections in the previous year. 21 B. Trading by Takeover Advisors Prior to Takeovers We examine both client and market maker trades prior to takeover announcements for brokerage houses acting as takeover advisors. Specifically, we calculate both trading imbalances and investment returns prior to takeover announcements, treating each broker-takeover pair as one event and reporting averages and t-statistics across events. 22 Table 5 Panel A shows that neither client nor market maker imbalances are significantly positive prior to takeover announcements. In fact, the average ten-day client imbalance is a significantly negative percent. Panel A also presents trading for target advisors, acquirer advisors, and dirty advisors separately which shows little evidence of informed trading for these groups. Table 5 Panel B further confirms that client and market maker investment returns are not significantly positive for takeover advisors. 21 Specifically, for our tests on takeovers, in year y we identify dirty connected brokers as the ones that: 1) trade at least once from 1997 to y-1 in their connected takeover target firms, and 2) have positive imbalances over the 20-day window prior to takeover announcements. For our tests on earnings announcements, in year y we identify dirty connected brokers by first sorting connected brokers into terciles of success ratio (percentage of imbalances in the right direction) for their large 20-day imbalances (total 20-day dollar imbalances above $100,000) prior to earnings announcements in year y-1. A connected firm must have at least ten large imbalances. We then keep the top tercile of success ratio and further sort into terciles of trading frequency, which is the ratio of the number of large imbalances to the total number of trades (including zero trading) prior to earnings announcements in y-1. We then identify connected brokers in the top tercile of trading frequency as dirty connected brokers. 22 The results are similar when we sum up trading for each takeover first and then calculate averages and t-statistics across takeovers. 19

22 C. Trading by IPO Underwriters Prior to Takeover and Earnings Announcements We examine whether the client and market maker trades of IPO underwriters are informed prior to takeovers and earnings announcements. Table 6 Panel A shows that IPO underwriters are not significant buyers prior to takeover announcements. Since book runners of IPOs have much more access to corporate insiders and information than co-managers or syndicate members, we examine their imbalances separately in Panel A as well as underwriters of recent IPOs (within one year of announcement) and dirty IPO underwriters which are simply those with past profits in connected firms. They exhibit no significant buying prior to takeover announcements. 23 Table 6 Panel B further confirms that none of the client or market maker investment returns are significantly positive for IPO underwriters. We then investigate the trades of IPO underwriters prior to earnings announcements. Table 6 Panel C presents imbalances prior to four categories of earnings announcement returns for IPO underwriters, which show little evidence that they are informed prior to earnings announcements. Table 6 Panel D shows that their average investment returns are generally not significantly positive except for the client trades over ten-day and twenty-day windows. 24 In addition, the supplemental results (Table S5 Panel B) show that none of the value-weighted investment returns is significantly positive. Table 6 Panels E through G present imbalances prior to four categories of earnings announcement returns for underwriters of recent IPOs (one year within earnings announcement), IPO book runners, and dirty IPO underwriters. In supplemental results (Table S5 Panels C through F) we further examine imbalances for IPO co-managers, IPO syndicate members, and dirty IPO 23 In supplemental results (Table S5 Panel A) we present imbalances for co-managers and syndicate members separately and they are not significantly positive, either. 24 To prevent the average investment returns from being dominated by small trades, we drop broker trades with total investment below $100,000 in the related windows. We apply this filter when calculating all the equal-weighted investment returns for out tests on earnings announcements. 20

23 underwriters with alternative constructions. None of these groups trade correctly prior to earnings announcements. D. Trading by SEO Underwriters Prior to Takeover and Earnings Announcements Table 7 investigates whether underwriters of earlier SEOs are informed of takeovers or earnings announcements. Panel A (average imbalances) and Panel B (average investment returns) show that SEO underwriters are not significant buyers prior to takeovers, nor do they earn significant returns for their trades. In addition, Panel A shows that underwriters of recent SEOs (within one year of takeovers), SEO book runners, and dirty underwriters are not informed of takeovers either. 25 In contrast, Table 7 Panel C examines the imbalances and presents mixed evidence as to whether the client trades of SEO underwriters are informed prior to large negative and large positive announcements. For example, the strongest evidence is at the ten-day frequency where client imbalances are percent (t-statistics -2.29) for large negative announcements but percent (t-statistics 1.74) for large positive announcements. Other horizons are largely insignificant. Table 7 Panel D shows that investment returns of client trades from SEO underwriters are significantly positive across all time windows prior to earnings announcements. For example, the average investment return for ten-day client imbalances is 72.5 basis points. While these numbers are highly significant, the supplemental results on value-weighted returns (Table S6 Panel B) are positive but insignificant. Overall, while SEO underwriters might be informed of earnings announcements of their issuing firms, the evidence is not striking. We further examine imbalances separately for underwriters of recent SEOs, SEO book runners, and dirty underwriters in Table 7 Panels E through G. None of these sub-groups is 25 Our supplemental results (Table S6 Panel A) show that SEO co-managers and syndicate members are not informed prior to takeovers, either. 21