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Examining the Dark Side of Financial Markets: Who Trades ahead of Major Announcements?

JOHN M. GRIFFIN, T AO 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’sUniversity. Griffin’s email is [email protected]. We are very grateful to the Nasdaq stock exchange for theirsupport and data. We thank Kelvin Law, Xin Zhang, and Ligang Zhong for research assistance. Parts of this paper aredrawn 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 2nd Singapore International Conference of Finance.



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 isknown about the extent to which institutions might exploit their informational advantage throughshort-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 earningsannouncements most types of institutional and individual trading is uninformed. A group of largehedge fund traders consistently sells prior to negative earnings announcements and wealthy individuals at full-service brokerage houses trade in the right direction ahead of takeoverannouncements. To examine specific trading within the institutional realm, we employ uniquebroker-level trading data. Despite examining the issue from many facets, we are unable to findconsistent evidence that investment banks trade profitably on connections through takeoveradvisors, IPO, SEO, or lending relationships. We also analyze historical connections between firmsand brokerage house trading. Market makers whose trades are profitable with a firm in the pastconsistently sell prior to impending negative earnings announcements. In contrast to much recentpress and literature, our results suggest that institutional investors are reluctant to use their private

information or at least cover their tracks extremely well.



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 short-

term 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.”

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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, andinstitutions, if informed, have the incentive to use small trades to hide their information although they generally trade inlarge 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.

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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 2002. 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, full-

service, 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

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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 connections 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.

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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 thenrepeatedly 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 post-earnings announcement drift.

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II. Data

A. Investor Types

The primary data set for this paper consists of trading by nine investor groups in all Nasdaq-

listed firms from January 2, 1997, to December 31, 2002. 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

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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 Kelley (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

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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 1996-2003

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 1997-2002 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 2002. We also drop the stocks priced below $5 on

the 21st 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 helpcontrol for these issues. In order to calculate industry/size adjusted imbalances, we drop a firm if no other firm falls inthe same size tercile of the same three-digit industry. This filter eliminates about 0.6% of our sample. We also examineour 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.

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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

1997-2002.

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 1997-2002.

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 themovements being caused by sources that are not informational, we impose the following restrictions: 1) we request thatthe market return on the event day to be between -1% and 1%; 2) in order to exclude the post-IPO period, we drop anevent if the underlying firm has a history of less than 128 days in CRSP; 3) we drop a price movement if the stock’sexcess return is above 10% or below -10% on any of the 20 days prior to the price movement; 4) we drop the pricemovements occurring during the year-end period between December 15 and January 15. In order to exclude the pricemovements 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.

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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 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 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 houses 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 isless than the eleven percent document by Jarrell and Poulsen (1989). Possible reasons for less price run-up in our sampleare that we are extremely conservative and take the earliest date from four sources or information leakage has declinedthrough time. Another possible reason is that we exclude the stocks priced below $5.

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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

(0.155+0.133+0.194+0.060=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 1997-2002

period is $94 million.

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 largeinstitutional trades are typically associated with larger intra-day price impact as well as spreads. Indeed, we do find thatcalculating profits with the actual transaction price rather than the closing price leads to lower institutional profits.

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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 target firms without any price run-up, institutional net buying is negative and

statistically significant, 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 five-day 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-size 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-size 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 1990.

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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 E 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 pre-

announcement 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 housesaccount for only around 17% of trading. If one aggregates the net buying imbalance of all four individual brokeragehouses in the medium-size trade group over the window [-5,-1], then the average net buying of 0.397 is higher than the0.354 net buying coming from the mixed group even though the mixed group has twice as much volume. Thus, themagnitude of mixed buying is consistent with a muted pattern of both individual buying and institution selling in thiscategory. 16 It is important to note again that our announcement date is the first news mention of a possible takeover or rumorand not necessarily the official announcement date. This approach minimizes the amount of trading that is simply due topublic news.

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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 for [0,1] window 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

17 Such a regression is potentially problematic in that institutional trading due to informational leakage could increase thestock price prior to the announcement and lower the announcement return. However, we control for this by using pre-announcement abnormal returns to capture the amount of information leakage.

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earnings 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

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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 andimmediately 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 infuture announcements and on average do not delay their selling activity.

17



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 ownbrokerage house to avoid revealing their trades to other brokers and to keep trading profits within the bank. It ispossible that a hedge fund associated with one brokerage house could execute their trades through another broker but abroker 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



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 1997-2002. 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 -0.0067 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’ connectedbrokers 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 inyear y-1. A connected firm must have at least ten large imbalances. We then keep the top tercile of success ratio andfurther sort into terciles of trading frequency, which is the ratio of the number of large imbalances to the total numberof trades (including zero trading) prior to earnings announcements in y-1. We then identify connected brokers in the toptercile 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-statisticsacross takeovers.

19



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-weightedinvestment returns for our tests on earnings announcements.

20



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 -0.0026 percent (t-statistics -2.89) for large negative announcements but 0.0015

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



particularly informed of earnings announcements. 26 In addition, the market makers trades are

generally uninformed across all the groups and time windows prior to earnings announcements.

E. Trading by Lenders Prior to Takeover and Earnings Announcements

We study client and market maker trading for brokerage houses acting as lenders in Table 8.

We focus on lenders with ongoing loan contracts with target firms during any part of the three-

month period prior to the announcement. 27 Table 8 Panel A shows that none of the client or market

maker imbalances are significantly positive. We further divide lenders into lead lenders and

participants because they can have different roles in information production [Sufi (2007), Acharya

and Johnson (2009), and Bharath, Dahiya, Saunders, and Srinivasan (2007)]. Panel A shows that

none of these groups or ‘dirty’ lenders buy a significant amount prior to takeovers either. Panel B

shows that, consistent with Panel A, lenders do not earn significantly positive returns on their client

or market maker trades prior to takeover announcements.

Table 8 Panel C presents short-term imbalances for lenders prior to earnings

announcements, showing that neither their client trading nor market maker trading is informed.

Panel D presents average investment returns for trades of lenders prior to earnings announcements,

which are not significantly positive except ten-day client trades. The supplemental result (Table S7

Panel A) further shows that the value-weighted investment returns are not significantly positive,

either. Panels E, F, and G further present short-term imbalances for lead lenders, loan participants,

and ‘dirty’ lenders separately. Their client trading and market maker trading are not informed either.

A potential explanation is that lenders might have sold their loans in the secondary market

and therefore stopped acquiring information from the borrowing firm [Ivashina and Sun (2009)].

However, Nandy and Shao (2009) show that only six percent of bank loans, which account for the

26 Our supplemental results (Table S6 Panels C, D, E, and F) also show weak evidence of informed trading in some

circumstances for SEO co-managers, syndicate members, and ‘dirty’ SEO underwriters with alternative constructions. 27 Our results are similar when we use a one-month or six-month period prior to the announcement.

22



vast majority of syndicated loans, are traded in the secondary market. Nevertheless, we examine this

possibility by requiring lenders to have loan contracts that are newly entered during the three-month

period prior to earnings announcements.28 Our supplemental results (Table S7 Panel B shows that

lenders of newly entered loan contracts generally conduct little informed trading. Most of their

imbalances are inconsistent with announcement returns. The five-day client trading imbalances are

significantly negative prior to large and small negative announcements (-0.0005 percent and -0.0016

percent, respectively) but are not positive prior to positive announcements.

Nandy and Shao (2009) claim that compared to bank loans, lenders of loans that are

syndicated only to institutions have a stronger motivation for information acquisition. We therefore

examine trading prior to earnings announcements for lenders of institutional loans. The

supplemental results (Table S7 Panels C, D and E) show little evidence of informed trading for these

lenders as well as ‘dirty’ lenders with alternative constructions.

Our result that lenders generally do not trade in the right direction does not completely rule

out informed trading. For example, an incapable investment manager might lose significantly on

average, but break even on connected firms that he trades on inside information. Therefore we

examine whether lenders earn higher returns from their trading on borrowing firms than on non-

borrowing firms. For each lender we compute the average investment return on borrowing firms

and non-borrowing-firms during 1997-2002.29 We then report the average investment return for

borrowing firms and non-borrowing firms across brokerage houses. The supplemental result (Table

S8 Panel A) reveals that investment returns of lenders are not significantly different between

borrowing firms and non-borrowing firms, suggesting that lenders do not have an informational

advantage on borrowing firms.

28 The results are similar when we use one-month or six-month instead of three-month period.

29 To control for outliers, we require a brokerage house to trade prior to at least fifty earnings announcements of both

borrowing firms and non-borrowing firms during 1997-2002.

23



For an alternative performance measure, we also calculate success ratio for each brokerage

house as the ratio of investments with positive investment returns to the total number of

investments in earnings announcements during 1997-2002. The supplemental results (Table S8 Panel

B) show that, consistent with the lack of informational advantage, lenders’ success ratios in

borrowing firms are not significantly different from non-borrowing firms.

F. Trading by Historically Connected Brokerage Houses Prior to Earnings Announcement

A broker or an investment fund could maintain a consistent relation with a certain set of

firms. For example, hedge funds may culture a long lasting relation at a firm in order to obtain inside

information. Most hedge funds during our sample period have consistent trading relationships with

a main broker. However, if the hedge fund/institution is extremely large they may either have their

own broker code or they may trade with two or three brokers. In the former case, it will be easy to

track down the institution but in the later case the client trades may be more difficult to detect.

We examine whether certain brokers consistently make trading profits on certain firms. Each

year we classify a brokerage house A as historically connected to firm X during the previous year if:

1) Broker A’s total imbalances in the five-day window prior to firm X’s earnings announcements are

always in the same direction as the two-day excess announcement returns; and 2) Broker A trades in

the five-day window prior to at least two earnings announcements of firm X.

Table 9 examines short-term trading prior to earnings announcements for historically

connected brokerage houses identified by client trades and market maker trades, respectively.

Interestingly, Panels A and B reveal that historically connected brokerage houses in both groups are

selling significant amounts prior to large and small negative announcements in terms of market

maker trades. For example, Panel B shows that historically connected brokers are selling 0.0041

percent of shares in the ten-day window prior to large negative announcements (with a t-statistic of -

2.78) in terms of market maker trades. In contrast, they are not trading ahead of positive

24



announcements. Panel C confirms that while client trades of historically connected brokers are not

profitable, their market maker trades prior to earnings announcements earn significantly positive

returns. In unreported results we find that the corresponding value-weighted returns are also

significantly positive. These results suggest that a subset of brokerage houses consistently make

profits with their market maker trades on the negative earnings announcements of a subset of

‘linked’ firms.

G. Persistence in Trading Profits of Brokerage Houses

Some brokerage houses might collect and trade on inside information more aggressively than

others. If so then we would expect trades by these brokerage houses to persistently outperform

trades by other brokerage houses.

We sort brokerage houses into four groups according to their total dollar gain/loss from

trading on earnings announcements in year y-1 and then calculate average gain/loss across brokerage

houses in year y. Table 10 Panel A shows that profitability of client trades in the ten-day and twenty-

day window prior to earnings announcements is increasing in past profitability and differences are

statistically significant (with t-statistics of 2.29 and 3.51). The differences are not significantly

positive for market maker trades except for the ten-day window. We further decompose dollar

gain/loss for each group into three sources: 1) win stocks, on which the broker trades make money

in the previous year; 2) lose stocks, on which the broker trades lose money in the previous year; and

3) neutral stocks with no trades by the broker in the previous year. Panel B shows that generally

there is little persistence in trading profits across stocks for either client trades or market maker

trades.

Table 10 Panel C uses average investment returns as an alternative measure to examine

performance persistence. We examine value-weighted average investment returns for brokerage

houses sorted on average investment returns during the previous year. To control for outliers, we

25



require a brokerage house to trade on at least fifty earnings announcements in both the current year

and previous year. There is little persistence in terms of investment returns for client trades or

market maker trades except for the market-maker trades in the ten-day window. In addition, Panel

D reveals that the trades of winner brokers do not consistently make money from the same group of

firms with the exception of client trading for the two-day window.

V. Conclusion

A large amount of existing finance literature observes that over longer horizons institutional

investors generally make small profits at the expense of individual investors. To our surprise, we find

that on average institutional investors do not exhibit a trading advantage ahead of takeovers,

earnings announcements, or other large price moves. In contrast, wealthy full-service individual

investors buy prior to takeover announcements and hedge funds seem to profit by short-selling

ahead of negative earnings announcements. Hence, the idea that institutions on average make money

by exploiting short-term informational advantages seems incorrect, though there may be substantial

variation in informed trading within the institutional universe. To examine this dichotomy we track

trading through brokerage houses that have investment banking and lending relationships with event

firms.

Even though we examine a host of different levels of connections with both client and

market maker trading, we find little evidence that either brokerages’ internal market maker trading or

their clients’ trading reflects their extensive connections. We do find some evidence that brokerage

houses with historical trading profits around earnings announcements are able to make profitable

trades ahead of negative earnings announcements of the same firms the following year.

Although we believe our testing approach to be more powerful and comprehensive than

those used by previous papers, our findings of little evidence for trading on connections is in direct

26



contrast to a growing literature that finds institutions using their connections. There are several

possible explanations for our findings. First, we should realize that many academics may be trying to

find evidence of insider trading. Second, our findings do not rule out other forms of insiders trading

on connections. Our findings do suggest that it does not appear to be as common as one might

think from reading tabloids and the academic literature. Third, insiders are aware of the possibility of

their trades being monitored and thus may not trade through their own trading desk. Indeed, our

evidence of profitable trading through full-service brokerage houses makes one wonder if connected

individuals choose to use inside information for friends and family instead of for their firms. Also,

given that regulators may be watching for suspicious trading ahead of announcements, informed

investors may forgo some profits and simply trade on or immediately following announcements.

Consistent with this view, we find that institutional trading on and immediately after earnings

announcements is somewhat informative of future prices three months out. Overall, our results

suggest that institutional insider trading is relatively rare or that insiders cover their connection

tracks rather well.

27



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30

Table 1

Investor Group Trading Prior to Takeover AnnouncementsPanel A reports the average cumulative industry/size adjusted imbalances for nine investor groupsprior to takeover announcements, where day -1 refers to the last trading day before theannouncement. Our sample is comprised of 1,225 takeovers. Imbalance for a stock is the differencebetween buy and sell volumes expressed as a fraction of shares outstanding. We scale the imbalancesby 1,000. We first calculate the cumulative industry/size adjusted imbalances during the [-2,-1], and[-10,-1] windows for each takeover, and then report the cross-sectional means and t-statistics for thecumulative imbalances. Panel B reports the dollar gain/loss from trading prior to theannouncements (in thousand dollars). For each of the [-2,-1], [-10,-1], and [-20,-1] windows, we firstcalculate the dollar gain/loss for each takeover using industry/size adjusted imbalances, and thenreport the average and total dollar gain/loss across all takeovers. The dollar gain/loss spans theperiod from the beginning of the selected window to the end of the announcement day. To beconservative, we assume that buy and sell trades occur at the end of the trading day. In particular, tocalculate total dollar gain/loss for a takeover, we first multiply the daily dollar imbalance for each day with the buy-and-hold excess returns from the next day until the announcement day, and then sumup the products across days. The buy-and-hold excess returns are buy-and-hold returns in excess of

buy-and-hold returns on Nasdaq Composite Index. Panel C reports the sub-sample analysis basedon pre-takeover price run-up. If the twenty-day cumulative return prior to the announcement day ispositive for a takeover, then we treat this takeover as one with price run-up. Otherwise, we treat thetakeover as one without price run-up. Panel D reports the sub-sample analysis based on trade size. We classify the trades before takeovers into three groups: trades for less than 1,000 shares, trades for1,000 to 5,000 shares, and trades for more than 5,000 shares. Panel E reports the sub-sample analysisbased on whether the target firms are traded on the option market. In particular, we obtain monthly total trading volume of options from CBOE. If the target firm’s option is traded in the month(s)covering the twenty days before a takeover, then we treat the takeover as one with options traded.Otherwise, we treat the takeover as one without options traded.

Inst. LargestI-banks HedgeFund Deriv. Indiv.Gen. Indiv.Full. Indiv.Disc. Indiv.Day. Mixed

Panel A: Cumulative Industry/Size Adjusted Imbalances Prior to Takeover Announcements

[-2,-1] 0.001 0.043 0.004 -0.004 0.033 0.099 0.070 0.112 0.031(0.01) (0.79) (0.59) (-0.26) (0.41) (4.23) (2.58) (3.25) (3.89)

[-10,-1] -0.096 -0.087 0.007 -0.041 0.273 0.206 0.161 0.233 0.071(-0.44) (-0.82) (0.52) (-0.64) (1.25) (4.58) (2.57) (2.54) (4.15)

Panel B: Dollar Gain/Loss from Trading Prior to Takeover Announcements (in $1,000)

Avg.[-2,-1] -14.30 12.27 1.69 -0.57 1.83 4.60 2.43 3.77 37.74

Total -17,513 15,036 2,074 -694 2,243 5,636 2,982 4,620 46,234

Avg.[-10,-1] -30.70 -46.01 3.42 2.00 7.44 12.24 9.90 12.10 105.04

Total -37,602 -56,410 4,188 2,450 9,117 15,007 12,138 14,840 128,780

Avg.[-20,-1] -152.86 -65.80 9.83 -8.17 9.04 5.12 -7.84 26.32 170.39

Total -187,257 -80,607 12,045 -10,010 11,073 6,267 -9,609 32,244 208,724



31

Inst.

LargestI-banks

HedgeFund Deriv.

Indiv.Gen.

Indiv.Full.

Indiv.Disc.

Indiv.Day. Mixed

Panel C: Pre-Announcement Price Run-Up

Takeovers with price run-up (N=814) [-5,-1] 0.271 0.000 0.012 -0.060 0.205 0.078 0.150 0.094 0.351

(1.37) (0.00) (0.95) (-1.23) (4.26) (1.40) (1.84) (5.26) (2.03) Takeovers without price run-up (N=411) [-5,-1] -0.411 0.002 -0.016 0.033 0.055 0.243 0.282 -0.006 -0.300

(-2.04) (0.01) (-1.09) (1.38) (1.43) (4.42) (2.70) (-0.54) (-1.12)

Panel D: Trade Size

Below 1,000 Shares (N=1,225) [-5,-1] 0.021 -0.001 0.000 0.001 0.010 0.038 0.030 0.021 0.202

(1.25) (-0.14) (-0.05) (0.11) (1.20) (2.84) (1.02) (2.90) (4.96)1,000-5,000 Shares (N=1,225)

[-5,-1] 0.088 0.018 -0.002 0.008 0.122 0.101 0.136 0.038 0.354(1.76) (0.87) (-0.31) (0.85) (6.02) (4.19) (3.84) (3.59) (5.49)

Above 5,000 shares (N=1,225) [-5,-1] -0.066 -0.017 0.004 -0.037 0.022 -0.006 0.028 0.001 -0.423

(-0.51) (-0.24) (0.95) (-1.30) (1.02) (-0.28) (1.31) (0.58) (-3.01)

Panel E: Options Trading

Without options traded (N=1,090) [-5,-1] 0.108 0.062 0.006 -0.038 0.166 0.116 0.203 0.050 -0.011

(0.70) (1.03) (0.57) (-1.05) (4.43) (2.76) (3.12) (3.88) (-0.07) With options traded (N=135) [-5,-1] -0.489 -0.501 -0.023 0.047 0.062 0.275 0.124 0.143 1.296

(-0.99) (-0.98) (-0.81) (0.64) (0.80) (1.69) (0.48) (3.08) (2.74)



33

Individual full-service 0.010 0.033 0.065 -0.005 -0.011 0.002 -0.001 -0.007 -0.023

(1.34) (2.28) (2.82) (-0.88) (-1.13) (0.15) (-0.17) (-0.73) (-1.39

Individual discount 0.050 0.072 0.093 -0.009 -0.043 -0.083 -0.011 -0.035 -0.079

(4.50) (3.74) (3.16) (-1.53) (-3.43) (-4.40) (-2.09) (-3.89) (-5.19

Individual daytrading 0.018 0.021 0.028 0.000 -0.004 -0.003 0.001 -0.001 -0.004

(4.84) (4.05) (3.74) (0.01) (-1.16) (-0.74) (0.76) (-0.27) (-0.86Mixed 0.072 0.120 0.236 0.012 -0.039 -0.068 -0.003 -0.025 -0.044

(3.12) (2.89) (3.54) (0.79) (-1.36) (-1.49) (-0.20) (-0.77) (-0.92

Number of obs. 13,676 13,676 13,676 18,280 18,280 18,280 17,619 17,619 17,619Panel B: Cumulative Imbalances for the [-5,-1] Window Across Fir

Annc. Ret.<-5% -5%<Annc. Ret.<0% 0%< Annc. Ret.<5% Size group Small Medium Large Small Medium Large Small Medium Larg

Institutional 0.381 0.035 -0.060 0.023 0.115 0.056 0.112 0.036 -0.04

(2.65) (0.50) (-1.02) (0.28) (2.80) (1.25) (1.91) (0.83) (-0.94

Largest I-banks 0.005 -0.017 -0.076 0.011 0.024 -0.013 0.007 0.018 0.055

(0.15) (-0.61) (-2.23) (0.85) (1.79) (-0.57) (0.37) (0.98) (1.89Hedge fund 0.000 -0.009 -0.020 0.009 0.008 -0.011 0.011 0.003 0.004

(-0.01) (-1.38) (-3.56) (1.59) (1.34) (-2.47) (1.63) (0.50) (0.71

Derivatives -0.007 -0.012 -0.003 0.009 0.003 0.006 0.005 -0.023 0.014

(-0.66) (-1.21) (-0.30) (1.31) (0.51) (0.85) (0.63) (-1.02) (2.37)

Individual general 0.006 0.066 0.067 -0.032 -0.006 0.015 -0.004 -0.014 -0.00

(0.18) (3.61) (6.09) (-1.39) (-0.72) (1.69) (-0.27) (-1.81) (-0.80

Individual full-service 0.046 0.016 0.042 -0.005 -0.022 -0.004 0.002 -0.006 -0.01

(0.84) (0.62) (2.33) (-0.17) (-1.34) (-0.28) (0.06) (-0.33) (-1.00



34

Annc. Ret.<-5% -5%<Annc. Ret.<0% 0%< Annc. Ret.<5% Continued Small Medium Large Small Medium Large Small Medium Larg

Individual discount -0.054 0.066 0.101 -0.038 -0.066 -0.027 -0.101 -0.038 -0.01

(-0.90) (1.91) (4.04) (-0.58) (-4.04) (-1.92) (-3.68) (-2.55) (-1.22

Individual daytrading 0.013 0.016 0.025 -0.027 -0.004 0.003 -0.001 -0.001 0.000

(1.10) (1.61) (4.04) (-1.42) (-1.19) (0.76) (-0.28) (-0.27) (-0.01Mixed -0.145 0.002 0.252 0.030 -0.121 0.008 -0.074 -0.119 0.061

(-1.20) (0.04) (4.11) (0.43) (-2.53) (0.18) (-1.25) (-2.14) (1.32)

Number of obs. 1,423 4,957 7,296 2,380 7,037 8,863 2,316 6,622 8,681Panel C: Dollar Gain/Loss from Trading Prior to Earnings Announc

[-2,-1] [-5,-1] [-10,-1]

Average Total Average Total Average

Institutional -1.31 -82,244 -1.58 -98,919 -3.60 -226,383

Largest I-banks -0.16 -9,866 -1.62 -101,856 -2.09 -131,503

Hedge fund 1.10 68,867 1.81 113,686 1.57 98,667

Derivatives 0.28 17,887 0.20 12,726 0.09 5,828 Individual general -0.42 -26,455 -0.95 -59,546 -1.66 -104,214

Individual full-service 0.70 43,798 1.39 87,013 3.70 232,494

Individual discount -0.22 -13,540 -0.70 -43,912 -0.90 -56,496

Individual daytrading -0.31 -19,632 -0.47 -29,572 -0.93 -58,353

Mixed -6.94 -435,980 -7.22 -453,574 -8.29 -520,500



35

Table 3

Panel Regressions of Earnings Announcement Returns on Imbalances This table reports the panel regressions for the earnings announcement sample. The dependent variable is the two-day cumulative excess returns starting on the announcement day, where returnsare in excess of Nasdaq Composite Index returns. The independent variables include cumulativeindustry/size adjusted imbalances for nine investor groups prior to announcements, where day -1refers to the last trading day before the announcement. Imbalance for a stock is the differencebetween buy and sell volumes expressed as a fraction of shares outstanding. Columns 1-3 presentresults for investor group imbalances for the [-2,-1], [-5,-1] and [-10,-1] windows. Excess return[-20,-1] is the cumulative excess return in the 20 days prior to announcement. We present regressionresults for the models with and without controls for firm and year fixed effects.

Imbalance

[-2,-1] [-5,-1] [-10,-1]

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Institutional -0.471 -0.552 -0.139 -0.148 -0.009 -0.017(-2.69) (-2.78) (-1.34) (-1.28) (-0.14) (-0.23)

Largest I-banks 0.168 0.221 0.350 0.259 0.320 0.164(0.48) (0.57) (1.85) (1.24) (2.76) (1.25)

Hedge fund 3.130 2.215 2.736 1.888 1.647 0.819(2.15) (1.33) (3.12) (1.87) (3.01) (1.20)

Derivatives 0.123 0.292 0.055 0.431 0.094 0.272(0.22) (0.48) (0.12) (0.89) (0.31) (0.82)

Individual general -3.070 -2.773 -1.217 -0.782 -0.478 -0.456(-4.05) (-3.29) (-2.56) (-1.47) (-1.50) (-1.26)

Individual full-service -0.314 -0.129 -0.247 -0.166 -0.251 -0.255(-0.68) (-0.26) (-0.93) (-0.57) (-1.57) (-1.43)

Individual discount 0.201 0.330 -0.125 -0.105 -0.136 -0.738

(0.45) (0.67) (-0.51) (-0.38) (-0.85) (-0.41)Individual daytrading -5.219 -5.660 -1.505 -2.330 -0.044 -0.721

(-4.19) (-4.37) (-0.81) (-2.56) (-0.08) (-0.14)

Mixed -0.383 -0.394 -0.080 -0.117 -0.003 -0.033(-2.25) (-2.01) (-0.81) (-1.05) (-0.04) (-0.45)

Excess return [-20,-1] -0.028 -0.030 -0.029 -0.030 -0.031 -0.031(-14.59) (-13.51) (-15.03) (-13.82) (-15.33) (-13.89)

Firm fixed effect No Yes No Yes No Yes

Year fixed effect No Yes No Yes No Yes

Adjusted R 2 0.0047 0.2145 0.0043 0.2142 0.0043 0.2140

Number of obs. 62,804 62,804 62,804 62,804 62,804 62,804



36

Table 4

Market Maker Trading Prior to Takeover and Earnings Announcements: Investor GroupsPanel A reports the average cumulative industry/size adjusted market maker imbalances for nineinvestor groups prior to takeover announcements, where day -1 refers to the last trading day beforethe announcement. Our sample is comprised of 1,239 takeovers. Market maker imbalance for astock is the difference between buy and sell volumes from market maker trades expressed as afraction of shares outstanding. We scale the imbalances by 1,000. We first calculate the cumulativeindustry/size adjusted imbalances during the [-2,-1], [-5,-1], and [-10,-1] windows for each takeover,and then report the cross-sectional means and t-statistics for the cumulative imbalances. Panel Breports the average cumulative industry/size adjusted market maker imbalances for nine investorgroups prior to earnings announcements, where day -1 refers to the last trading day before theannouncement. Earnings announcements are classified into four groups according to two-day excessreturns starting on the announcement day, where returns are in excess of Nasdaq Composite Indexreturns: those with announcement returns below -5%, between -5% and 0%, between 0% and 5%,and greater than 5%. Then we calculate the cumulative industry/size adjusted market makerimbalances during the [-2,-1], [-5,-1], and [-10,-1] windows for each earnings announcement, andreport the cross-sectional means and the associated t-statistics for the cumulative imbalances across

sub-samples.

Inst.Largest

I-banksHedgeFund Deriv.

Indiv.Gen.

Indiv.Full.

Indiv.Disc.

Indiv.Day. Mixed

Panel A: Market Maker Imbalances Prior to Takeover Announcements

[-2,-1] -0.103 -0.027 0.001 -0.018 -0.071 -0.024 0.007 0.000 -0.151

(-1.73) (-0.69) (0.41) (-1.68) (-5.43) (-2.76) (1.30) (-0.11) (-3.48)

[-5,-1] -0.163 -0.040 0.000 -0.006 -0.153 -0.031 0.012 0.000 -0.312

(-1.74) (-0.85) (-0.10) (-0.35) (-5.63) (-2.91) (1.66) (0.07) (-3.57)[-10,-1] -0.100 -0.028 -0.001 0.015 -0.192 -0.039 0.013 -0.004 -0.388 (-0.74) (-0.51) (-0.21) (0.42) (-4.73) (-2.39) (1.56) (-0.97) (-2.95)

Panel B: Market Maker Imbalances in [-5, -1] Window Prior to Earnings Announcements Annc. Ret.<-5% -0.085 0.000 -0.001 -0.003 -0.059 -0.011 -0.003 0.001 -0.099

(-3.34) (-0.01) (-0.57) (-0.64) (-5.69) (-2.15) (-1.64) (0.88) (-3.72)

-5%<Annc.Ret.<0%

-0.004 -0.007 0.001 0.001 0.004 -0.002 -0.001 0.000 0.020

(-0.21) (-1.14) (0.72) (0.38) (0.75) (-0.45) (-0.68) (-0.45) (1.05)

0%<Annc.Ret.<5%

0.004 -0.009 0.000 0.009 0.010 0.001 0.000 -0.001 0.018

(0.21) (-1.22) (0.22) (1.03) (2.22) (0.20) (-0.08) (-1.63) (0.91)

Annc. Ret.>5% -0.068 -0.011 0.001 0.001 -0.015 -0.002 0.000 -0.001 -0.031

(-2.64) (-1.18) (0.45) (0.40) (-1.71) (-0.36) (-0.57) (-0.46) (-1.16)



37

Table 5

Trading by Takeover Advisors Prior to Takeover AnnouncementsPanel A reports average cumulative imbalances prior to takeover announcements for brokeragehouses serving as takeover advisors. Daily imbalance for a stock is the difference between the buy and sell volumes expressed as a fraction of shares outstanding. We scale the imbalances by 1,000. Wefirst calculate the cumulative imbalances during the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows foreach advisor prior to each takeover, and then report the cross-sectional means and t-statistics for thecumulative imbalances. We report results for all advisors, advisors of target firms, advisors of acquirer firms, and ‘dirty’ advisors, respectively. For year y, we identify dirty advisors by client(market maker) trades as the ones that trade at least once in the takeovers they advised from 1997 toy-1 with positive 20-day client (market maker) imbalances prior to all such takeover announcements.Panel B reports equal-weighted average investment returns on trading in target firms prior totakeover announcements for brokerage houses acting as takeover advisors. Investment return is totaldollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for anannouncement, we first multiply the daily dollar imbalance for each day with the buy-and-holdexcess return from the next day until the announcement day (day 0), and then sum up the productsacross days in the selected window. Daily dollar imbalance for a stock is the difference between the

buy and sell volumes multiplied by the closing price for the day. To be conservative, we assume thatbuy and sell trades occur at the end of the trading day. Total dollar investment is the greater of thesum of daily dollar buy imbalances or the sum of daily dollar sell imbalances over the selected window. We calculate investment returns for the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1refers to the last trading day before the announcement. We then take brokerage houses acting astakeover advisors and for each brokerage house calculate investment returns on trading in targetfirms prior to takeover announcements during 1997-2002. We then report the mean investmentreturns and the associated t-statistics across brokerage houses.

Client Trades Market Maker Trades

[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Panel A: Imbalances Prior to Takeover Announcements All Advisors (N=1615) -0.017 -0.028 -0.067 -0.088 -0.010 -0.014 0.016 0.004

(-1.54) (-1.68) (-2.36) (-1.93) (-1.26) (-0.85) (0.64) (0.12) Target Advisors(N=841) -0.030 -0.039 -0.111 -0.152 -0.017 -0.027 0.005 -0.003

(-1.44) (-1.24) (-2.10) (-1.81) (-1.20) (-0.89) (0.11) (-0.05) Acquirer Advisors(N=794) -0.007 -0.025 -0.029 -0.023 -0.002 0.002 0.032 0.020

(-1.20) (-2.01) (-1.62) (-0.83) (-0.31) (0.18) (1.64) (0.77)'Dirty' Advisors byClient Trades(N=140) -0.010 -0.011 -0.020 -0.025 -0.002 -0.003 -0.002 -0.006

(-0.88) (-1.02) (-1.17) (-1.14) (-0.82) (-0.90) (-0.46) (-1.31)'Dirty' Advisors byMM Trades (N=40) -0.172 0.102 -0.216 0.023 0.032 -0.114 0.241 0.202

(-1.09) (0.40) (-0.39) (0.05) (0.53) (-0.78) (0.60) (0.45)

Panel B: Investment Returns (%) Prior to Takeover Announcements

All Advisors (N=1615) 0.12 0.11 0.00 -0.17 -0.42 -0.21 -0.02 -0.17

(1.22) (0.92) (-0.01) (-0.78) (-2.81) (-1.45) (-0.11) (-1.12)



39


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]RecentUnderwriters 0.024 0.082 0.059 0.125 -0.004 -0.024 0.011 0.000

(2.11) (1.75) (1.04) (1.32) (-0.27) (-0.70) (0.18) (0.00)

# Broker Trades 328 328 328 328 753 753 753 753

Book Runners -0.024 -0.071 -0.120 -0.236 0.042 0.010 0.162 0.095

(-1.17) (-1.43) (-1.38) (-1.42) (1.28) (0.14) (1.20) (0.48)

# Broker Trades 218 218 218 218 584 584 584 584'Dirty'Underwriters -0.002 -0.003 0.008 0.036 -0.017 -0.107 -0.102 -0.177

(-0.50) (-0.57) (0.50) (0.82) (-0.56) (-0.69) (-0.76) (-0.93)

# Broker Trades 238 238 238 238 200 200 200 200


All Underwriters 0.20 0.12 0.02 0.01 -0.09 -0.19 -0.14 -0.17

(1.58) (0.70) (0.11) (0.03) (-0.67) (-1.53) (-1.10) (-1.23)

# Broker Trades 778 778 778 778 2,954 2,954 2,954 2,954

Panel C: Imbalances of IPO Underwriters Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] 0.005 -0.005 -0.007 0.004 -0.003 -0.001 0.005 -0.005

(2.44) (-1.94) (-1.21) (2.04) (-1.74) (-0.44) (1.40) (-2.54)

Imbalance [-5,-1] 0.007 -0.007 -0.007 0.006 -0.002 0.004 0.005 -0.007

(1.85) (-1.56) (-1.10) (1.78) (-0.60) (1.29) (1.32) (-2.03)Imbalance [-10,-1] 0.002 -0.012 -0.011 0.003 -0.001 0.004 0.008 -0.003

(0.29) (-1.44) (-1.20) (0.51) (-0.12) (0.69) (1.48) (-0.59)

Imbalance [-20,-1] -0.001 -0.012 -0.016 0.006 0.004 0.007 0.013 0.000(-0.13) (-0.90) (-1.24) (0.58) (0.58) (0.88) (1.72) (0.04)

# Broker Trades 34,323 27,917 27,309 30,382 71,585 56,083 54,288 64,015

Panel D: Investment Returns (%) Prior to Earnings Announcements

[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1] All Underwriters -0.02 0.14 0.33 0.34 -0.03 0.00 0.02 -0.05

(-0.09) (0.76) (2.19) (2.45) (-0.31) (-0.07) (0.26) (-0.79)

# Broker Trades 1,893 3,687 5,899 9,040 14,722 23,829 32,201 41,163

Panel E: Imbalances of Underwriters of Recent IPOs Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] 0.003 0.000 -0.024 0.008 -0.003 -0.004 0.014 -0.008

(0.84) (-0.12) (-1.08) (1.49) (-1.01) (-1.36) (1.18) (-2.22)

Imbalance [-5,-1] 0.008 0.007 -0.030 0.012 0.001 -0.002 0.014 -0.011

(1.01) (0.77) (-1.22) (1.46) (0.17) (-0.29) (1.11) (-1.90)

Imbalance [-10,-1] 0.007 0.006 -0.043 0.011 -0.006 -0.013 0.027 -0.014

(0.46) (0.42) (-1.29) (0.98) (-0.65) (-1.17) (1.55) (-1.35)



40


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-20,-1] 0.005 0.018 -0.034 0.021 0.000 -0.008 0.023 -0.002

(0.23) (0.71) (-0.78) (1.10) (0.01) (-0.47) (1.02) (-0.14)

# Broker Trades 7,890 7,238 6,448 7,325 16,487 14,565 12,690 15,228

Panel F: Imbalances of IPO Book Runners Prior to Earnings Announcements

Imbalance [-2,-1] 0.034 -0.045 -0.074 0.032 -0.012 -0.007 0.020 -0.020

(1.59) (-1.93) (-1.24) (1.52) (-1.20) (-0.45) (0.79) (-1.58)

Imbalance [-5,-1] 0.033 -0.081 -0.081 0.038 -0.005 0.042 0.022 -0.041

(0.91) (-1.69) (-1.19) (1.15) (-0.32) (1.73) (0.74) (-1.61)

Imbalance [-10,-1] -0.038 -0.130 -0.105 -0.013 0.003 0.044 0.038 -0.008

(-0.63) (-1.48) (-1.10) (-0.22) (0.11) (1.20) (0.91) (-0.21)

Imbalance [-20,-1] -0.120 -0.169 -0.115 -0.038 0.012 0.070 0.060 0.003

(-1.20) (-1.29) (-0.91) (-0.37) (0.27) (1.20) (1.10) (0.05)

# Broker Trades 2,911 2,458 2,485 2,311 7,907 6,428 6,329 6,734

Panel G: Imbalances of 'Dirty' IPO Underwriters Defined By Large Trades and Trading FrequencyImbalance [-2,-1] 0.031 0.030 -0.015 0.015 0.009 -0.040 0.049 -0.010

(1.25) (0.94) (-0.99) (0.52) (0.69) (-3.43) (1.95) (-0.87)

Imbalance [-5,-1] 0.030 0.062 -0.060 0.008 0.030 -0.049 0.048 0.001

(0.47) (0.87) (-1.70) (0.26) (1.22) (-2.69) (2.68) (0.06)

Imbalance [-10,-1] 0.115 0.040 -0.115 -0.031 0.008 -0.080 0.081 -0.008

(1.17) (0.36) (-2.09) (-0.91) (0.24) (-2.53) (2.06) (-0.23)

Imbalance [-20,-1] 0.157 0.107 -0.055 -0.066 -0.007 -0.096 0.117 -0.016

(1.03) (0.43) (-0.60) (-1.38) (-0.12) (-1.94) (1.95) (-0.29)

# Broker Trades 831 624 554 692 2,443 1,645 1,544 2,121



41

Table 7 Trading by SEO Underwriters Prior to Takeover and Earnings Announcements

Panel A reports average cumulative imbalances prior to takeover announcements for brokeragehouses serving as SEO underwriters. Daily imbalance for a stock is the difference between the buy and sell volumes expressed as a fraction of shares outstanding. We scale the imbalances by 1,000. We

first calculate the cumulative imbalances during the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows foreach underwriter prior to each takeover, and then report the cross-sectional means and t-statisticsfor the cumulative imbalances. We report results for all SEO underwriters, recent SEO underwriters(SEOs within one year of takeover announcements), SEO book runners, and ‘dirty’ SEOunderwriters. For year y, we identify dirty underwriters by client (market maker) trades as the onesthat trade at least once in the takeovers from 1997 to y-1 where they are SEO advisors to targetfirms and have positive 20-day client (market maker) imbalances prior to all such takeoverannouncements. Panel B reports equal-weighted average investment returns on trading in targetfirms prior to takeover announcements for brokerage houses acting as SEO underwriters of targetfirms. Investment return is total dollar gain/loss divided by total dollar investment. To calculatedollar gain/loss for an announcement, we first multiply the daily dollar imbalance for each day withthe buy-and-hold excess return from the next day until the announcement day (day 0), and then sum

up the products across days in the selected window. Daily dollar imbalance for a stock is thedifference between the buy and sell volumes multiplied by the closing price for the day. To beconservative, we assume that buy and sell trades occur at the end of the trading day. Total dollarinvestment is the greater of the sum of daily dollar buy imbalances or the sum of daily dollar sellimbalances over the selected window. We calculate investment returns for the [-2,-1], [-5,-1], [-10,-1],and [-20,-1] windows. Day -1 refers to the last trading day before the announcement. We then takebrokerage houses acting as SEO underwriters and for each brokerage house calculate investmentreturns on trading in target firms prior to takeover announcements during 1997-2002. We thenreport the mean investment returns and the associated t-statistics across brokerage houses. Panels Creports average cumulative imbalances prior to earnings announcements for SEO underwriters.Panel D presents equal-weighted average investment returns on trading prior to earnings

announcements for SEO underwriters, where investment returns are calculated by assuming thepositions are held until one day after the announcement (day 1). To prevent the average investmentreturns from being dominated by small trades, we drop broker trades with investment below $100,000 in the related windows. Panels E, F, and G report average cumulative imbalances prior toearnings announcements for recent SEO underwriters, SEO book runners, and ‘dirty’ underwriters. To identify ‘dirty’ underwriters for year y, we first sort underwriters into terciles of success ratio(percentage of imbalances in the right direction) for their large 20-day imbalances (dollar imbalancesabove $100,000) prior to earnings announcements in year y-1. We require a broker to have at leastten 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) for underwriters prior to earnings announcements in y-1. We then

identify underwriters in the top tercile of trading frequency as ‘dirty’ underwriters. We exclude top100 brokerage houses for all the results on client trades to control for liquidity trading.


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]Panel A: Imbalances Prior to Takeover Announcements

All Underwriters -0.001 0.008 0.010 0.002 0.006 -0.027 0.000 0.005

(-0.16) (0.51) (0.40) (0.05) (0.17) (-0.74) (-0.01) (0.09)

# Broker Trades 1,431 1,431 1,431 1,431 3,070 3,070 3,070 3,070



42


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]RecentUnderwriters 0.002 0.037 0.052 0.102 0.007 0.002 -0.005 0.028

(0.11) (0.66) (0.62) (0.91) (0.28) (0.07) (-0.07) (0.23)

# Broker Trades 323 323 323 323 664 664 664 664Book Runners -0.057 -0.156 -0.214 -0.460 -0.020 0.014 0.137 0.356

(-2.13) (-2.99) (-2.15) (-2.81) (-0.63) (0.27) (1.13) (1.85)

# Broker Trades 184 184 184 184 518 518 518 518'Dirty'Underwriters 0.002 0.004 0.005 0.018 -0.028 0.016 0.002 0.179

(1.11) (1.37) (0.56) (0.88) (-0.89) (0.31) (0.02) (0.92)

# Broker Trades 156 156 156 156 126 126 126 126Panel B: Investment Returns (%) Prior to Takeover Announcements

All Underwriters -0.19 -0.16 -0.07 -0.07 -0.40 -0.21 -0.28 -0.29

(-1.13) (-0.73) (-0.28) (-0.22) (-2.21) (-1.14) (-1.58) (-1.55)

# Broker Trades 1,431 1,431 1,431 1,431 3,070 3,070 3,070 3,070Panel C: Imbalances of SEO Underwriters Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] -0.004 -0.002 -0.002 0.002 0.001 0.001 -0.002 -0.006

(-1.17) (-0.85) (-0.59) (0.72) (0.26) (0.21) (-0.73) (-2.23)Imbalance [-5,-1] -0.009 0.004 -0.003 0.011 0.004 0.008 -0.012 -0.013

(-1.67) (0.45) (-0.27) (2.18) (0.94) (1.42) (-2.02) (-2.47)Imbalance[-10,-1] -0.026 -0.002 -0.012 0.015 0.010 0.006 -0.012 -0.017

(-2.89) (-0.11) (-0.98) (1.74) (1.28) (0.73) (-1.49) (-2.21)Imbalance

[-20,-1] -0.011 -0.005 -0.010 0.028 -0.001 0.006 -0.012 -0.025(-0.54) (-0.25) (-0.57) (1.86) (-0.08) (0.47) (-0.95) (-2.01)

# Broker Trades 19,120 18,186 17,816 18,371 41,245 37,665 37,387 39,688


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

All Underwriters 0.769 0.590 0.725 0.872 -0.033 0.028 0.053 0.074

(3.83) (3.96) (5.68) (7.26) (-0.45) (0.50) (1.06) (1.48)

# Broker Trades 2,417 4,555 6,979 10,085 19,377 29,742 38,180 46,326

Panel E: Imbalances of Underwriters of Recent SEOs Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] 0.005 -0.014 0.008 0.004 -0.001 -0.001 0.000 -0.013

(0.47) (-1.57) (0.94) (0.61) (-0.06) (-0.13) (-0.06) (-1.86)

Imbalance [-5,-1] -0.001 0.024 0.037 0.028 0.015 0.021 -0.039 -0.036

(-0.06) (0.73) (1.32) (1.89) (1.17) (1.28) (-2.05) (-2.32)Imbalance[-10,-1] -0.024 0.018 0.023 0.027 0.037 0.018 -0.038 -0.044

(-0.90) (0.37) (0.68) (1.00) (1.70) (0.76) (-1.58) (-2.07)



43


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance[-20,-1] 0.061 0.037 0.063 0.076 -0.012 0.026 -0.027 -0.053

(0.95) (0.66) (1.47) (1.50) (-0.31) (0.75) (-0.77) (-1.45)

# Broker Trades 5,088 5,058 4,536 4,465 10,962 10,308 9,573 9,519

Panel F: Imbalances of SEO Book Runners Prior to Earnings Announcements

Imbalance [-2,-1] -0.020 -0.016 -0.016 0.010 0.013 0.014 0.005 -0.036

(-1.27) (-1.26) (-1.25) (1.27) (1.08) (1.20) (0.42) (-3.24)

Imbalance [-5,-1] -0.048 0.030 -0.051 0.034 0.035 0.040 -0.030 -0.035

(-1.92) (0.54) (-0.86) (1.41) (1.85) (1.64) (-1.11) (-1.48)Imbalance[-10,-1] -0.132 -0.031 -0.078 0.043 0.071 0.030 -0.020 -0.050

(-3.33) (-0.35) (-1.15) (1.02) (2.18) (0.85) (-0.56) (-1.58)Imbalance[-20,-1] -0.059 -0.067 -0.135 0.080 0.056 0.044 0.008 -0.063

(-0.57) (-0.60) (-1.44) (1.03) (0.93) (0.82) (0.16) (-1.21)

# Broker Trades 2,965 2,804 2,647 2,544 7,721 6,953 6,879 6,933

Panel G: Imbalances 'Dirty' SEO Underwriters Defined By Large Trades and Trading Frequency

Imbalance [-2,-1] -0.044 0.024 0.018 0.030 0.024 0.017 0.025 0.004

(-1.87) (1.09) (0.35) (1.44) (0.93) (0.75) (1.12) (0.26)

Imbalance [-5,-1] -0.062 -0.008 0.152 0.019 0.047 0.010 -0.034 -0.014

(-1.49) (-0.24) (0.72) (0.45) (1.37) (0.33) (-1.39) (-0.63)Imbalance[-10,-1] -0.095 -0.029 0.140 0.013 0.075 0.013 -0.060 -0.029

(-1.86) (-0.46) (0.64) (0.24) (1.66) (0.26) (-1.58) (-0.76)

Imbalance[-20,-1] -0.127 -0.124 0.008 -0.011 -0.008 -0.044 -0.061 -0.031

(-1.33) (-1.12) (0.03) (-0.12) (-0.11) (-0.69) (-0.97) (-0.49)

# Broker Trades 438 461 497 486 2,451 2,010 2,015 2,371



44

Table 8: Trading by Lenders Prior to Takeover and Earnings Announcements

Panel A reports average cumulative imbalances prior to takeover announcements for brokeragehouses serving as lenders of ongoing loans during the three-month period prior to theannouncement date. Daily imbalance for a stock is the difference between the buy and sell volumes

expressed as a fraction of shares outstanding. We scale the imbalances by 1,000. We first calculatethe cumulative imbalances during the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows for each lenderprior to each takeover, and then report the cross-sectional means and t-statistics for the cumulativeimbalances. We report results for all lenders (of ongoing loans during the three-month period priorto the announcement date), lead lenders, participating lenders, and ‘dirty’ lenders. For year y, weidentify dirty lenders by client (market maker) trades as the ones that trade at least once in thetakeovers from 1997 to y-1 where they are lenders to target firm and have positive 20-day client(market maker) imbalances prior to all such takeover announcements. Panel B reportsequal-weighted average investment returns on trading in target firms prior to takeoverannouncements for brokerage houses acting as lenders of target firms. Investment return is totaldollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for anannouncement, we first multiply the daily dollar imbalance for each day with the buy-and-hold

excess return from the next day until the announcement day (day 0), and then sum up the productsacross days in the selected window. Daily dollar imbalance for a stock is the difference between thebuy and sell volumes multiplied by the closing price for the day. To be conservative, we assume thatbuy and sell trades occur at the end of the trading day. Total dollar investment is the greater of thesum of daily dollar buy imbalances or the sum of daily dollar sell imbalances over the selected window. We calculate investment returns for the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1refers to the last trading day before the announcement. We then take brokerage houses acting aslenders of ongoing loans during the three-month period prior to the announcement date and foreach brokerage house calculate investment returns on trading in target firms prior to takeoverannouncements during 1997-2002. We then report the mean investment returns and the associatedt-statistics across brokerage houses. Panels C reports average cumulative imbalances prior to

earnings announcements for lenders. Panel D presents equal-weighted average investment returnson trading prior to earnings announcements for lenders, where investment returns are calculated by assuming the positions are held until one day after the announcement (day 1). To prevent theaverage investment returns from being dominated by small trades, we drop broker trades withinvestment below $100,000 in the related windows. Panels E, F, and G report average cumulativeimbalances prior to earnings announcements for lead lenders, participating lenders, and ‘dirty’lenders. To identify ‘dirty’ lenders for year y, we first sort lenders into terciles of success ratio(percentage of imbalances in the right direction) for their large 20-day imbalances (dollar imbalancesabove $100,000) prior to earnings announcements in year y-1. We require a broker to have at leastten 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) for lenders prior to earnings announcements in y-1. We then identify lenders in the top tercile of trading frequency as ‘dirty’ lenders. We exclude top 100 brokeragehouses for all the results on client trades to control for liquidity trading.


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Panel A: Imbalances Prior to Takeover Announcements All Lenders -0.001 0.000 0.002 0.016 -0.004 -0.017 -0.020 -0.048



45


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

(-1.34) (-0.09) (1.06) (0.62) (-0.95) (-1.86) (-1.01) (-1.78)

# Broker Trades 778 778 778 778 1,533 1,533 1,533 1,533Lead Lenders -0.012 -0.007 0.013 0.005 -0.005 -0.018 -0.015 -0.051

(-1.37) (-0.49) (0.42) (0.14) (-0.85) (-1.28) (-0.56) (-1.35)

# Broker Trades 468 468 468 468 978 978 978 978LoanParticipants 0.000 0.009 0.039 0.032 -0.002 -0.017 -0.029 -0.041

(0.37) (0.78) (1.28) (1.00) (-0.46) (-1.85) (-1.06) (-1.30)

# Broker Trades 310 310 310 310 555 555 555 555

'Dirty' Lenders 0.003 0.013 0.059 0.033 -0.028 -0.118 0.011 -0.050

(1.15) (1.46) (1.09) (0.47) (-0.75) (-1.42) (0.08) (-0.32)

# Broker Trades 66 66 66 66 67 67 67 67


All Lenders 0.08 -0.21 0.24 0.37 -0.32 -0.23 -0.45 -0.32(0.51) (-0.79) (0.74) (0.97) (-1.95) (-1.39) (-2.38) (-1.78)

# Broker Trades 778 778 778 778 1,533 1,533 1,533 1,533

Panel C: Imbalances of Lenders Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] -0.001 0.000 0.000 0.001 -0.003 0.001 -0.001 -0.004

(-0.83) (-0.49) (0.49) (0.80) (-1.94) (0.38) (-0.83) (-2.18)

Imbalance [-5,-1] -0.002 -0.002 0.000 0.002 -0.002 0.002 -0.005 -0.005

(-0.92) (-0.69) (0.27) (0.64) (-0.61) (0.74) (-1.91) (-1.73)Imbalance[-10,-1] 0.000 -0.012 0.001 0.002 0.000 0.004 -0.010 -0.004

(-0.12) (-1.31) (0.54) (0.63) (0.04) (0.79) (-2.33) (-0.83)Imbalance[-20,-1] 0.001 -0.016 0.005 0.002 0.000 0.000 -0.017 -0.006

(0.18) (-1.53) (1.23) (0.43) (0.00) (0.01) (-2.09) (-0.88)

# Broker Trades 10,735 13,620 13,627 12,243 20,267 26,560 26,496 23,481


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

All Lenders 0.108 0.308 0.414 -0.074 0.072 -0.012 0.046 -0.083

(0.46) (1.78) (2.67) (-0.49) (0.64) (-0.14) (0.56) (-1.00)

# Broker Trades 1,189 2,183 3,272 4,732 5,689 8,414 10,686 13,010

Panel E: Imbalances of Lead Lenders Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] -0.001 -0.001 0.000 0.000 -0.004 0.001 -0.003 -0.004

(-1.14) (-0.39) (0.10) (0.07) (-1.90) (0.51) (-1.17) (-1.53)

Imbalance [-5,-1] -0.003 -0.003 -0.001 0.000 -0.001 0.003 -0.007 -0.006

(-0.98) (-0.61) (-0.51) (0.11) (-0.32) (0.57) (-1.88) (-1.58)



46


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance[-10,-1] 0.000 -0.015 -0.001 0.002 0.005 0.003 -0.012 -0.007

(-0.13) (-0.95) (-0.30) (0.51) (0.61) (0.40) (-2.00) (-1.06)Imbalance[-20,-1] -0.003 -0.021 -0.001 0.004 0.010 0.002 -0.022 -0.008

(-0.36) (-1.21) (-0.20) (0.70) (0.77) (0.21) (-1.85) (-0.81)

# Broker Trades 6,619 8,002 7,783 7,285 13,619 16,828 16,463 14,981

Panel F: Imbalances of Participating Lenders Prior to Earnings Announcements

Imbalance [-2,-1] 0.000 0.000 0.001 0.002 -0.001 -0.001 0.001 -0.004

(0.10) (-0.31) (0.88) (1.26) (-0.48) (-0.34) (0.85) (-2.16)

Imbalance [-5,-1] 0.000 -0.001 0.002 0.004 -0.003 0.002 -0.001 -0.003

(-0.12) (-0.37) (1.22) (0.70) (-0.80) (0.68) (-0.45) (-0.73)Imbalance

[-10,-1] 0.000 -0.008 0.004 0.003 -0.009 0.006 -0.006 0.001(-0.03) (-1.73) (1.28) (0.41) (-1.65) (1.31) (-1.22) (0.20)

Imbalance[-20,-1] 0.006 -0.009 0.013 0.000 -0.020 -0.003 -0.008 -0.003

(0.93) (-1.45) (1.70) (-0.05) (-1.85) (-0.49) (-1.01) (-0.34)

# Broker Trades 4,116 5,617 5,844 4,954 7,006 9,729 10,032 8,491

Panel G: Imbalances of 'Dirty' Lenders Defined By Large Trades and Trading Frequency

Imbalance [-2,-1] -0.002 0.002 0.002 0.004 -0.009 -0.006 -0.014 -0.001

(-0.33) (0.33) (1.14) (0.95) (-0.36) (-0.33) (-0.81) (-0.02)

Imbalance [-5,-1] -0.008 0.020 0.002 0.016 0.070 -0.008 -0.023 0.002

(-0.73) (1.87) (0.43) (1.59) (1.28) (-0.27) (-0.60) (0.05)Imbalance[-10,-1] -0.011 0.034 -0.015 0.022 0.155 0.002 -0.032 0.063

(-0.64) (1.75) (-1.09) (1.37) (1.10) (0.04) (-0.52) (0.92)Imbalance[-20,-1] -0.018 0.018 -0.008 0.017 0.321 -0.037 -0.022 -0.030

(-0.74) (0.56) (-0.72) (0.69) (2.16) (-0.45) (-0.11) (-0.28)

# Broker Trades 355 454 456 400 508 697 606 586



47

Table 9: Trading by Historically Connected Brokerage Houses Prior to Earnings Announcements

Panel A (Panel B) reports average cumulative imbalances prior to earnings announcements forhistorically connected brokerage houses identified by client (market maker trades). Daily imbalancefor a stock is the difference between the buy and sell volumes expressed as a fraction of sharesoutstanding. We scale the imbalances by 1,000. We first calculate the cumulative imbalances during the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows for each brokerage house prior to eachannouncement, and then report the cross-sectional means and t-statistics for the cumulativeimbalances. Panel C reports equal-weighted average investment returns on trading prior to earningsannouncements for historically connected brokerage houses. A brokerage house is classified ashistorically connected to a firm if that broker trades at least twice prior to the firm’s earningsannouncements in the previous year and trades in the same direction as announcement returns foreach announcement. Investment return is total dollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for an announcement, we first multiply the daily dollar imbalance foreach day with the buy-and-hold excess return from the next day until one day after theannouncement day (day 1), and then sum up the products across days in the selected window. Daily dollar imbalance for a stock is the difference between the buy and sell volumes multiplied by the

closing price for the day. To be conservative, we assume that buy and sell trades occur at the end of the trading day. Total dollar investment is the greater of the sum of daily dollar buy imbalances orthe sum of daily dollar sell imbalances over the selected window. We calculate investment returns forthe [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1 refers to the last trading day before theannouncement. We then take historically connected brokerage houses and for each brokerage housecalculate investment returns on trading prior to earnings announcements during 1997-2002. We thenreport the mean investment returns and the associated t-statistics across brokerage houses. Resultsare reported for historically connected brokerage houses classified using client trades and marketmaker trades, respectively. To prevent the average investment returns from being dominated by smalltrades, we drop broker trades with investment below $100,000 in the related windows. We excludetop 100 brokerage houses for all the results on client trades to control for liquidity trading.


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Panel A: Imbalances of Connected Houses (Identified by Client Trades) Prior to Earnings Announcements

Imbalance [-2,-1] 0.001 0.000 -0.001 0.002 -0.004 -0.003 0.000 0.000

(0.50) (-0.22) (-0.82) (1.41) (-2.98) (-1.46) (-0.16) (0.07)

Imbalance [-5,-1] 0.000 0.001 -0.003 0.001 -0.008 -0.006 -0.001 -0.001

(0.10) (0.28) (-0.96) (0.35) (-3.59) (-2.28) (-0.24) (-0.40)Imbalance[-10,-1] -0.003 0.004 -0.002 0.003 -0.012 -0.008 0.000 -0.005

(-0.70) (1.00) (-0.40) (0.81) (-3.63) (-2.00) (-0.03) (-1.38)

Imbalance[-20,-1] -0.003 0.005 0.002 -0.006 -0.018 -0.012 0.002 -0.007

(-0.42) (0.88) (0.23) (-0.92) (-3.37) (-2.07) (0.35) (-1.18)

# Broker Trades 16,346 11,707 10,672 15,022 32,177 24,152 22,661 30,238

Panel B: Imbalances of Connected Houses (Identified by MM Trades) Prior to Earnings Announcements

Imbalance [-2,-1] 0.002 0.007 0.003 0.003 -0.016 -0.012 0.006 0.000

(0.26) (0.71) (0.42) (0.62) (-2.72) (-2.00) (1.26) (-0.09)



48


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-5,-1] 0.003 0.015 -0.009 -0.011 -0.032 -0.016 -0.003 0.004

(0.19) (0.78) (-0.54) (-0.75) (-3.74) (-1.49) (-0.36) (0.55)

Imbalance[-10,-1] -0.005 0.025 0.013 0.004 -0.041 -0.016 -0.007 0.011

(-0.25) (0.92) (0.47) (0.23) (-2.78) (-0.90) (-0.52) (0.81)Imbalance[-20,-1] 0.040 0.062 0.039 0.004 -0.074 -0.024 -0.018 0.020

(0.86) (1.25) (0.91) (0.13) (-3.35) (-0.85) (-0.81) (0.82)

# Broker Trades 2,687 2,139 1,842 2,357 8,485 6,582 6,182 8,042

Panel C: Investment Returns (%) Prior to Earnings Announcements

[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Houses Identified byClient Trades 0.157 0.061 0.334 0.106 0.235 0.125 0.159 0.091

(0.90) (0.49) (3.20) (1.11) (1.72) (1.22) (1.76) (1.03)

# Broker Trades 3,547 6,587 10,055 14,876 6,080 9,626 12,930 16,676

Houses Identified byMM Trades 0.022 0.049 0.035 0.074 0.302 0.230 0.246 0.180

(0.06) (0.18) (0.14) (0.32) (2.06) (2.11) (2.56) (1.96)

# Broker Trades 758 1,355 2,009 2,880 5,088 8,252 11,190 14,506



50

Client Trades Market MLoser

Brokers 2 3 WinnerBrokers W - L t-stat

LoserBrokers 2 3

Trading[-20,-1] ($1000) -1389.33 -103.33 6.57 701.95 2091.28 (3.51) -261.37 6.24 5.84 Average #

Brokers 355 355 368 369 249 250 243

Panel B: Decomposition of Dollar Profits for Brokers: Stocks Classified by Past PerformaLoser

Brokers 2 3 WinnerBrokers

LoserBrokers 2 3

Trading [-2,-1]($1000)

Win Stocks -21.98 0.48 -2.42 -92.71 148.12 -0.95

Neutral Stocks -31.25 -5.63 3.83 1.40 87.05 3.55 9.46

Lose Stocks -37.30 -2.37 4.05 -72.84 128.05 1.29 1.72

Win - Lose 15.31 2.86 -6.46 -19.87 20.06 -2.24 -

t-stat (0.13) (0.79) (-2.37) (-0.29) (0.31) (-1.16) (-1.18) Trading [-5,-1]($1000)

Win Stocks -134.68 2.89 9.31 22.10 25.21 0.26

Neutral Stocks -64.44 5.56 4.92 17.09 107.27 4.19 -1.77

Lose Stocks -59.00 -2.24 3.20 -24.19 190.00 0.56 0.98

Win - Lose -75.68 5.13 6.11 46.29 -164.79 -0.31 -

t-stat (-0.37) (1.23) (1.09) (0.25) (-0.91) (-0.15) (-1.23)

Trading[-10,-1] ($1000)

Win Stocks -317.68 -4.26 28.64 312.67 310.84 -3.76

Neutral Stocks -204.08 4.49 0.76 159.49 28.93 4.39 13.57 Lose Stocks -212.15 -8.17 28.34 -278.99 -221.00 3.87 -0.16

Win - Lose -105.53 3.92 0.30 591.66 531.85 -7.63 5

t-stat (-1.39) (0.71) (0.01) (2.34) (2.91) (-0.83) (0.76)



51

Client Trades Market MLoser


LoserBrokers 2 3

Trading[-20,-1] ($1000)

Win Stocks -596.49 -18.95 2.21 559.27 -219.85 -6.26 Neutral Stocks 100.61 13.97 -21.36 16.17 -55.21 7.91 -7.25

Lose Stocks -893.45 -98.35 25.71 126.51 13.69 4.59 10.71

Win - Lose 296.96 79.40 -23.50 432.77 -233.53 -10.85 -

t-stat (0.64) (1.65) (-1.87) (0.53) (-0.31) (-1.52) (-0.98)

Panel C: Average Investment Returns for Brokers Sorted on Past Performance

Client Trades Market MLoserBroker 2 3

WinnerBroker W - L t-stat

LoserBroker 2 3

Trading [-2,-1](%) 0.07 -0.06 0.01 0.10 0.03 (0.32) 0.22 0.03 0.24 Average #Brokers 118 119 119 118 47 47 47 Trading [-5,-1](%) 0.10 0.05 0.08 0.04 -0.06 (-0.54) 0.24 0.08 0.05 Average #Brokers 155 155 156 155 61 61 62 Trading[-10,-1] (%) 0.02 0.08 0.05 0.10 0.08 (0.43) -0.10 -0.01 0.02 Average #Brokers 184 185 185 184 75 76 76 Trading[-20,-1] (%) 0.05 -0.02 0.14 0.17 0.12 (0.68) -0.22 -0.16 0.02 Average #

Brokers 215 215 216 215 93 93 94



52

Panel D: Decomposition of Investment Returns for Brokers: Stocks Classified by Past PerfoLoser


LoserBrokers 2 3

Trading [-2,-1](%)

Win Stocks 0.13 -0.08 0.04 0.20 0.50 -0.02 Neutral Stocks 0.16 -0.08 0.12 -0.03 -0.06 0.20 0.32

Lose Stocks -0.04 -0.03 -0.04 0.19 -0.23 0.07 0.21

Win - Lose 0.16 -0.05 0.08 0.01 0.73 -0.08

t-stat (0.71) (-0.34) (0.49) (0.04) (2.82) (-0.46) (-0.16) Trading [-5,-1](%)

Win Stocks 0.12 0.01 0.12 0.02 0.19 0.11

Neutral Stocks 0.14 0.15 0.05 0.11 0.10 -0.08 0.03

Lose Stocks 0.17 0.07 0.14 -0.08 -0.43 0.01 -0.24

Win - Lose -0.05 -0.06 -0.02 0.10 0.61 0.10

t-stat (-0.26) (-0.31) (-0.16) (0.52) (1.80) (0.54) (2.12)

Trading[-10,-1] (%)

Win Stocks -0.01 0.05 0.24 0.17 -0.19 0.05

Neutral Stocks 0.05 0.16 0.08 0.05 -0.08 0.16 -0.17

Lose Stocks 0.26 -0.04 -0.07 0.13 0.06 -0.21 0.08

Win - Lose -0.27 0.08 0.31 0.04 -0.25 0.26

t-stat (-1.54) (0.56) (2.19) (0.48) (-0.64) (3.28) (-0.23)

Trading[-20,-1] (%)

Win Stocks 0.10 0.00 0.17 0.14 -0.13 -0.10 Neutral Stocks 0.00 0.12 0.25 0.10 -0.10 -0.35 -0.15

Lose Stocks 0.33 0.03 0.13 0.11 -0.45 -0.08 -0.05

Win - Lose -0.23 -0.02 0.04 0.03 0.32 -0.02

t-stat (-1.37) (-0.23) (0.31) (0.14) (0.93) (-0.14) (0.97)



53

Figure 1

Net Buying by Investor Groups Prior to Takeover Announcements This figure plots the average cumulative industry/size adjusted imbalances for investor groups priorto takeover announcements. Day -1 refers to the last trading day before the takeover announcement.Imbalance for a stock is the difference between buy and sell volumes expressed as a percentage of shares outstanding. For each day during the (-15,-1) window, we plot the average cumulativeimbalances across takeovers. The left axis has the scale for the cumulative imbalances in percent, andthe right axis has the scale for the cumulative excess stock returns, where returns are in excess of Nasdaq Composite Index returns.

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Individual Daytrading Institutional Largest I-banks

Hedge Fund Derivatives Average Stock Return



54

Figure 2

Net Buying by Investor Groups Prior to Earnings Announcements This figure plots the average cumulative industry/size adjusted imbalances for investor groups priorto earnings announcements. We classify earnings announcements into four groups according to theirtwo-day excess returns starting on the announcement day, where returns are in excess of Nasdaq Composite Index returns: those with announcement returns below -5% (Panel A), between -5% and0% (Panel B), between 0% and 5% (Panel C), and greater than 5% (Panel D). Day -1 refers to thelast trading day before the earnings announcement. Imbalance for a stock is the difference betweenbuy and sell volumes expressed as a percentage of shares outstanding. For each day during the(-15,-1) window, we plot the average cumulative imbalances for each earnings announcement group. The left axis has the scale for the cumulative imbalances in percent, and the right axis has the scalefor the cumulative excess stock returns, where returns are in excess of Nasdaq Composite Indexreturns.

Panel A: Announcement Returns<-5% Panel B: -5%<Announcement Returns<0%

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Individual Daytrading Institutional Largest I-banks

Hedge Fund Derivatives Average Stock Return



55

Appendix: Supplemental Tables and Figures

Table S1

Investor Group Trading Prior to Takeover Announcements for Various Sub-SamplesPanels A and B report average cumulative imbalances for nine investor groups prior to takeoverannouncements for various sub-samples, and Panel C reports the average cumulative imbalances fornine investor groups prior to takeover announcements using different benchmarks. Imbalance for astock is the difference between buy and sell volumes expressed as a fraction of shares outstanding. We scale the imbalances by 1,000. We first calculate the cumulative imbalances during the [-2,-1],[-5,-1], and [-10,-1] windows for each takeover, and then report the cross-sectional means and theassociated t-statistics for the cumulative imbalances. Industry/size adjusted imbalances are used inPanels A and B. Panel A reports the sub-sample analysis based on firm size. We classify thetakeovers into three size groups according to the size of target firms. In particular, we assign atakeover into the large size group if the target firm’s market capitalization on the 21st day before thetakeover falls in the top Nasdaq market capitalization tercile. Similarly, we assign a takeover intomedium (small) size group if the target firm’s market capitalization falls in the middle (bottom)

Nasdaq market capitalization tercile. Panel B reports the sub-sample analysis based on trade size. Weclassify the trades before takeovers into three groups: trades for less than 1,000 shares, trades for1,000 to 5,000 shares, and trades for more than 5,000 shares. Panel C reports the pre-takeoverimbalances adjusted using different benchmarks. In particular, for a given day, we first sort all theNasdaq firms into deciles based on their turnover (stock returns) in the previous 20 days, and thensubtract the average imbalance of its corresponding turnover (momentum) decile from theimbalance of a firm to obtain the takeover (momentum) adjusted imbalance. Raw imbalance is theimbalance without any adjustments.

Inst.LargestI-banks

HedgeFund Deriv.

Indiv.Gen.

Indiv.Full.

Indiv.Disc.

Indiv.Day. Mixed

Panel A: Firm Size

Small size group (N=148) [-2,-1] 0.210 -0.077 0.005 0.008 0.012 0.063 0.130 -0.001 -0.288

(0.82) (-0.89) (1.00) (1.13) (0.36) (0.82) (1.25) (-0.10) (-1.39)[-5,-1] 0.375 -0.098 -0.018 0.010 0.018 0.068 0.217 -0.021 -0.496

(0.89) (-1.08) (-1.03) (0.61) (0.25) (0.65) (0.77) (-1.27) (-1.36)[-10,-1] 0.314 -0.169 -0.011 0.023 0.032 0.177 0.189 -0.015 -0.672

(0.57) (-1.30) (-0.61) (0.97) (0.24) (1.16) (0.49) (-1.01) (-1.06)Medium size group (N=483) [-2,-1] 0.013 0.068 0.011 -0.039 0.121 0.039 0.148 0.023 -0.081

(0.11) (1.60) (1.11) (-1.07) (2.43) (0.77) (2.99) (2.17) (-0.65)[-5,-1] 0.006 0.073 0.028 -0.100 0.160 0.159 0.303 0.035 -0.025

(0.03) (0.91) (1.60) (-1.37) (2.46) (2.12) (3.48) (2.15) (-0.12)[-10,-1] -0.058 0.023 0.031 -0.184 0.197 0.226 0.357 0.030 0.030

(-0.19) (0.23) (1.19) (-1.23) (2.38) (2.00) (2.75) (1.28) (0.10)Large size group (N=594) [-2,-1] -0.062 0.053 -0.003 0.021 0.103 0.098 0.078 0.046 0.205

(-0.37) (0.50) (-0.29) (1.13) (4.09) (2.91) (1.49) (3.30) (1.73)[-5,-1] -0.011 -0.035 -0.013 0.019 0.184 0.129 0.100 0.100 0.417

(-0.05) (-0.24) (-1.02) (0.53) (4.18) (2.40) (1.13) (4.63) (1.80)[-10,-1] -0.229 -0.156 -0.008 0.059 0.256 0.104 0.143 0.126 0.706



56

Inst.

LargestI-banks

HedgeFund Deriv.

Indiv.Gen.

Indiv.Full.

Indiv.Disc.

Indiv.Day. Mixed

(-0.64) (-0.79) (-0.41) (1.11) (4.71) (1.26) (1.16) (4.29) (2.02)

Panel B: Trade Size

Below 1,000 Shares (N=1,225)

[-2,-1] 0.009 -0.002 0.000 -0.001 0.009 0.027 0.015 0.015 0.088(0.98) (-0.31) (0.09) (-0.34) (1.88) (3.09) (0.83) (3.96) (3.73)

[-10,-1] 0.031 -0.003 0.002 0.004 0.015 0.038 0.029 0.014 0.304(1.34) (-0.26) (0.68) (0.49) (1.26) (2.09) (0.75) (1.32) (4.57)

1,000-5,000 Shares (N=1,225) [-2,-1] 0.043 0.012 0.001 0.006 0.066 0.045 0.077 0.017 0.141

(1.35) (1.00) (0.34) (0.96) (5.21) (2.99) (4.20) (2.69) (3.59)[-10,-1] 0.145 -0.002 -0.001 0.019 0.167 0.107 0.180 0.052 0.584

(2.03) (-0.06) (-0.09) (1.29) (5.98) (3.04) (3.54) (3.48) (6.17) Above 5,000 shares (N=1,225) [-2,-1] -0.052 0.033 0.002 -0.009 0.025 -0.002 0.019 -0.001 -0.196

(-0.60) (0.61) (0.82) (-0.70) (1.46) (-0.11) (1.58) (-0.36) (-2.97)

[-10,-1] -0.272 -0.082 0.006 -0.063 0.024 0.016 0.024 0.004 -0.615(-1.42) (-0.91) (1.01) (-1.09) (0.95) (0.44) (0.75) (1.23) (-3.02)

Panel C: Benchmarking with Different Firm Characteristics

Turnover benchmark (N=1,225) [-2,-1] 0.032 0.047 0.002 -0.005 0.089 0.064 0.070 0.031 0.001

(0.33) (0.86) (0.29) (-0.30) (3.90) (2.39) (2.11) (3.90) (0.02)[-5,-1] 0.045 0.008 0.001 -0.026 0.129 0.122 0.098 0.059 0.099

(0.31) (0.11) (0.13) (-0.80) (3.88) (3.02) (1.56) (4.81) (0.69)[-10,-1] -0.060 -0.095 0.007 -0.035 0.155 0.143 0.051 0.073 0.216

(-0.27) (-0.91) (0.49) (-0.54) (3.62) (2.29) (0.57) (4.36) (1.01)Momentum benchmark (N=1,225)

[-2,-1] -0.006 0.039 0.001 -0.006 0.106 0.080 0.116 0.033 0.047(-0.07) (0.72) (0.11) (-0.36) (4.56) (3.02) (3.47) (4.16) (0.60)[-5,-1] -0.021 -0.013 -0.001 -0.030 0.164 0.156 0.200 0.065 0.199

(-0.15) (-0.17) (-0.07) (-0.91) (4.88) (3.89) (3.19) (5.15) (1.38)[-10,-1] -0.183 -0.127 0.003 -0.043 0.215 0.201 0.224 0.082 0.390

(-0.85) (-1.23) (0.19) (-0.68) (4.91) (3.24) (2.56) (4.69) (1.84)Raw imbalance (N=1,225) [-2,-1] 0.068 0.033 0.002 -0.010 0.132 0.069 0.150 0.046 0.066

(0.71) (0.61) (0.34) (-0.60) (5.66) (2.60) (4.44) (5.76) (0.85)[-5,-1] 0.141 -0.026 0.002 -0.039 0.233 0.130 0.293 0.097 0.244

(0.98) (-0.34) (0.23) (-1.19) (6.81) (3.22) (4.61) (7.60) (1.70)[-10,-1] 0.142 -0.156 0.006 -0.060 0.353 0.151 0.417 0.146 0.483

(0.66) (-1.50) (0.44) (-0.94) (7.83) (2.43) (4.65) (8.19) (2.27)



57

Table S2

Cross-Sectional Regressions of Target Announcement Returns on Imbalances This table reports the cross-sectional regressions for the takeover sample. The dependent variable isthe two-day cumulative excess returns on the target firm starting on the announcement day, wherereturns are in excess of Nasdaq Composite Index returns. The independent variables includecumulative industry/size adjusted imbalances for nine investor groups prior to announcements, where day -1 refers to the last trading day before the announcement. Imbalance for a stock is thedifference between buy and sell volumes expressed as a fraction of shares outstanding. Columns 1-3present results for investor group imbalances for the [-2,-1], [-5,-1] and [-10,-1] windows. For thecontrol variables, excess stock return [-20,-1] is the cumulative excess return in the 20 days prior toannouncement. Hostile is a dummy variable which is one if the takeover is hostile, and zerootherwise. Takeover is a dummy variable which is one if the deal is a takeover, and zero if it is amerger. Successful is a dummy variable which is one if the deal is completed, and zero otherwise. The t-statistics are White robust t-statistics adjusted for heteroskedasticity.

Imbalance

[-2,-1] [-5,-1] [-10,-1]

Constant 0.113 0.113 0.114 (8.36) (8.39) (8.41)

Institutional -1.992 -1.414 -0.608 (-1.01) (-1.08) (-0.70)

Largest I-banks -4.388 -4.505 -2.340 (-1.26) (-1.60) (-1.19)

Hedge fund 19.032 1.727 17.634 (0.71) (0.09) (1.19)

Derivatives 0.259 -0.708 -0.366 (0.03) (-0.15) (-0.15)

Individual general -4.417 -6.943 -6.536

(-0.60) (-1.29) (-1.44)Individual full-service 5.378 5.247 4.766

(0.80) (1.27) (1.91)

Individual discount -3.486 -1.427 -1.926 (-0.51) (-0.35) (-0.83)

Individual daytrading -8.945 -12.668 -9.090 (-0.29) (-0.86) (-0.89)

Mixed -0.128 -0.270 0.276 (-0.06) (-0.23) (0.31)

Excess stock return [-20,-1] -0.064 -0.062 -0.062 (-1.82) (-1.75) (-1.76)

Hostile 0.057 0.057 0.053

(0.93) (0.95) (0.88) Takeover -0.093 -0.091 -0.093

(-3.20) (-3.13) (-3.20)

Successful 0.068 0.069 0.067 (4.55) (4.57) (4.43)

Adjusted R 2 0.0177 0.0211 0.0233 Number of observations 1,225 1,225 1,225



58

Table S3

Investor Group Trading Prior to Price MovementsPanel A reports the average cumulative industry/size adjusted imbalances for nine investor groupsprior to price movements, where day -1 refers to the last trading day before the price movement.Imbalance for a stock is the difference between buy and sell volumes expressed as a fraction of shares outstanding. We scale the imbalances by 1,000. We first calculate the cumulative industry/sizeadjusted imbalances during the [-2,-1], [-5,-1], and [-10,-1] windows for each price jump (drop), andthen report the cross-sectional means and t-statistics for the cumulative imbalances for the pricejump (drop) sub-samples. Panel B reports imbalances adjusted using momentum benchmarks. Inparticular, for a given day, we first sort all the Nasdaq firms into deciles based on their stock returnsin the previous 20 days, and then subtract the average imbalance for the corresponding momentumdecile from the imbalance of a firm to obtain the momentum-adjusted imbalance.

Inst.LargestI-banks

HedgeFund Deriv.

Indiv.Gen.

Indiv.Full.

Indiv.Disc.

Indiv.Day. Mixed

Panel A: Cumulative Industry/Size Adjusted Imbalances Prior to Price Movements

Price jumps (N=752)

[-2,-1] -0.198 -0.025 0.004 -0.013 -0.015 0.092 0.004 -0.004 0.255(-2.01) (-0.48) (0.38) (-0.57) (-1.00) (3.11) (0.11) (-0.73) (2.99)

[-5,-1] -0.352 0.040 0.007 -0.039 -0.024 0.197 -0.054 -0.017 0.290(-1.76) (0.52) (0.58) (-1.00) (-0.87) (2.84) (-0.88) (-2.03) (1.95)

[-10,-1] -0.570 0.038 0.004 -0.010 -0.058 0.326 -0.168 -0.054 0.244(-2.09) (0.29) (0.20) (-0.20) (-1.55) (2.62) (-1.93) (-4.07) (1.17)

Price drops (N=965) [-2,-1] -0.097 0.007 0.030 0.005 0.017 -0.022 0.004 -0.013 0.026

(-1.16) (0.19) (2.65) (0.50) (1.32) (-0.78) (0.19) (-2.49) (0.22)[-5,-1] -0.031 -0.025 0.047 0.012 0.030 -0.043 0.025 -0.039 -0.068

(-0.20) (-0.46) (2.00) (0.71) (1.28) (-1.00) (0.63) (-4.37) (-0.45)[-10,-1] -0.138 0.069 0.044 0.035 0.013 -0.041 -0.038 -0.044 -0.080

(-0.68) (0.91) (1.43) (1.34) (0.35) (-0.63) (-0.56) (-2.62) (-0.38)

Panel B: Cumulative Momentum-Adjusted Imbalances Prior to Price Movements

Price jumps (N=752) [-2,-1] -0.176 -0.028 0.007 -0.017 -0.007 0.078 -0.003 0.003 0.269

(-1.84) (-0.55) (0.63) (-0.80) (-0.44) (2.66) (-0.09) (0.48) (3.23)[-5,-1] -0.352 0.014 0.012 -0.045 -0.015 0.186 -0.056 -0.002 0.311

(-1.80) (0.19) (0.92) (-1.16) (-0.60) (2.74) (-0.97) (-0.24) (2.16)[-10,-1] -0.615 0.017 0.009 -0.028 -0.028 0.304 -0.174 -0.013 0.295

(-2.31) (0.13) (0.44) (-0.56) (-0.80) (2.47) (-2.17) (-1.03) (1.44)Price drops (N=965) [-2,-1] -0.124 0.016 0.025 0.007 0.029 -0.025 -0.001 0.000 0.029

(-1.51) (0.47) (2.26) (0.72) (2.23) (-0.88) (-0.05) (0.01) (0.25)[-5,-1] -0.063 -0.009 0.041 0.011 0.058 -0.040 0.007 -0.009 -0.080(-0.41) (-0.18) (1.74) (0.72) (2.48) (-0.94) (0.20) (-1.12) (-0.55)

[-10,-1] -0.201 0.107 0.032 0.044 0.061 -0.037 -0.061 0.010 -0.088(-1.00) (1.44) (1.06) (1.70) (1.68) (-0.59) (-0.92) (0.63) (-0.45)



59

Table S4

Institutional Trading and Returns after Earnings AnnouncementsPanel A reports the panel regressions, where the dependent variable is the buy-and-hold excessreturn from day 2 (second trading day after the earnings announcement) to day 66 (three monthsafter the announcement). The buy-and-hold excess return is equal to the difference between thebuy-and-hold return on the stock and the buy-and-hold return on the Nasdaq Composite Index. The independent variables include Inst. Imbalance, the cumulative industry/size adjusted imbalancesfor institutional investors for the [0,1] window, where day 0 refers to the announcement day.Imbalance for a stock is the difference between buy and sell volumes expressed as a fraction of shares outstanding. Return[0,1] is the two-day excess return from day 0 to day 1. Past Six-MonthReturn are the excess returns in the six months prior to the month of earnings announcement date.Ln(ME) is the natural log of the firm’s market capitalization on day 1. Ln(ME)2 is the square of Ln(ME). Book-to-market is the book-to-market ratio of the firm calculated as the sum of marketequity and deferred tax divided by book equity. Book-to-market ratio of the fiscal year ending inyear y is used for the one-year period from July of year y+1 to June of year y+2. Return[0,1] x Inst.Imb is the product of Return[0,1] and Inst. Imbalance. We present regression results for the models with and without controls for firm and year-month fixed effects. Panel B reports the dollar gain/loss

from institutional trading after earnings announcements (in thousand dollars). For each of the [0,1]and [2,11] windows, we first calculate the dollar gain/loss for each earnings announcement using industry/size adjusted imbalances, and then report the average dollar gain/loss across allannouncements. The dollar gain/loss spans the period from the beginning of the selected window to day 11, 1 month after the announcement (day 22), 3 months after the announcement (day 66),and 6 months after the announcement (day 132). To be conservative, we assume that buy and selltrades occur at the end of the trading day. In particular, to calculate total dollar gain/loss for anearnings announcement, we first multiply the daily dollar imbalance for each day with thebuy-and-hold excess returns from the next day until the end of the holding period, and then sum upthe products across days. The buy-and-hold excess return raw is equal to the difference between thebuy-and-hold return on the stock and the buy-and-hold return on the Nasdaq Composite Index.

Panel A: Panel Regression of Excess Return [2,66] on Institutional Imbalance [0,1]Model 1 Model 2 Model 3 Model 4

Inst. imbalance 0.507 0.741 0.876 0.791(1.13) (1.48) (1.97) (1.60)

Return [0,1] 0.140 0.141 0.133 0.133(7.32) (7.36) (6.86) (6.84)

Past Six-Month Return 0.004 0.004 0.019 0.019(1.91) (1.91) (7.16) (7.16)

Ln(ME) 0.071 0.071 -0.145 -0.145(3.34) (3.33) (-2.91) (-2.91)

Ln(ME)2 -0.002 -0.002 -0.001 -0.001

(-3.09) (-3.07) (-0.54) (-0.55)Book-to-market 0.029 0.029 0.041 0.041

(7.05) (7.06) (6.02) (6.02)

Return [0,1] × Inst. imb. 1.682 -0.169

(1.04) (-0.39)

Firm fixed effect No No Yes YesMonthly Dummy No No Yes Yes



60

Model 1 Model 2 Model 3 Model 4 Adjusted R 2 0.0150 0.0150 0.2309 0.2309Number of observations 39,882 39,882 39,882 39,882

Panel B: Long Term Gain/Loss For Institutional Trading after Earnings AnnouncementsEnd of Holding Period

Dollar gain / loss fromtrading (in $1,000) Day 11

1 Month(Day 22)

3 Months(Day 66)

6 Months(Day 132)

[0,1] 6.55 17.76 19.20 10.67

[2,11] -2.88 3.89 27.98 65.67



61

Table S5: Trading by IPO Underwriters Prior to Takeover and Earnings Announcements

Panel A reports average cumulative imbalances prior to takeover announcements for brokeragehouses serving as IPO co-managers and syndicate members. Daily imbalance for a stock is thedifference between the buy and sell volumes expressed as a fraction of shares outstanding. We scalethe imbalances by 1,000. We first calculate the cumulative imbalances during the [-2,-1], [-5,-1],[-10,-1], and [-20,-1] windows for each underwriter prior to each takeover, and then report thecross-sectional means and t-statistics for the cumulative imbalances. Panel B reports value-weightedaverage investment returns on trading prior to earnings announcements for brokerage houses acting as IPO co-managers and syndicate members. Investment return is total dollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for an announcement, we first multiply the daily dollar imbalance for each day with the buy-and-hold excess return from the next day until theannouncement day (day 0), and then sum up the products across days in the selected window. Daily dollar imbalance for a stock is the difference between the buy and sell volumes multiplied by theclosing price for the day. To be conservative, we assume that buy and sell trades occur at the end of the trading day. Total dollar investment is the greater of the sum of daily dollar buy imbalances orthe sum of daily dollar sell imbalances over the selected window. We calculate investment returns for

the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1 refers to the last trading day before theannouncement. We then take brokerage houses acting as IPO co-managers and syndicate membersand for each brokerage house calculate investment returns on trading prior to earningsannouncements during 1997-2002. We first calculate value-weighted investment returns for eachmonth where weights are total dollar investment, and then report time-series mean investmentreturns and the associated t-statistics across months. Panels C and D report average cumulativeimbalances prior to earnings announcements for IPO co-managers and syndicate members. Panel Ereports imbalances for ‘dirty’ underwriters identified by all previous trades. Specifically, we identify ‘dirty’ underwriters for year y as the top quartile of success ratio (percentage of imbalances in theright direction) for their non-zero 20-day imbalances prior to earnings announcements in year y-1. We require a broker to have at least ten non-zero imbalances in y-1. Panel F reports imbalances for

‘dirty’ underwriters identified by large/directional trades and trading frequency. To identify ‘dirty’underwriters, we first sort underwriters into terciles of success ratio (percentage of imbalances inthe right direction) for their 20-day imbalances of large/directional trades (dollar imbalances above$100,000 and 10 percent of total dollar trading volume) prior to earnings announcements in year y-1. We require a broker to have at least ten large/directional trades. 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/directional trades to the total number of trades (including zero trading) for underwriters priorto earnings announcements in y-1. We then identify underwriters in the top tercile of trading frequency as ‘dirty’ underwriters. We exclude top 100 brokerage houses for all the results on clienttrades to control for liquidity trading.

Client Trades Market Maker Trades[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Panel A: Imbalances Prior to Takeover Announcements

Co-Managers -0.001 0.010 -0.035 -0.107 -0.020 -0.037 -0.042 -0.054

(-0.06) (0.21) (-0.63) (-1.09) (-0.94) (-0.99) (-0.87) (-0.76)

# Broker Trades 351 351 351 351 869 869 869 869



62


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Syndicate Members -0.001 0.003 0.001 0.008 0.000 0.006 0.005 0.009

(-0.24) (0.98) (0.19) (1.04) (0.11) (0.79) (0.53) (0.74)

# Broker Trades 1,806 1,806 1,806 1,806 3,559 3,559 3,559 3,559Panel B: Value-Weighted Investment Returns (%) Prior to Earnings Announcements

All Underwriters -0.15 0.36 0.35 -0.12 -0.13 -0.11 0.15 -0.37

(-0.33) (0.87) (1.01) (-0.36) (-0.40) (-0.33) (0.46) (-1.20)

# Broker Trades 10,104 15,167 20,093 26,145 44,263 48,882 52,117 56,056

Panel C: Imbalances of IPO Co-Managers Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] 0.009 -0.002 0.000 0.005 -0.008 -0.004 0.005 -0.008

(1.42) (-0.21) (0.06) (0.67) (-1.89) (-0.71) (0.92) (-1.16)

Imbalance [-5,-1] 0.014 -0.008 0.002 0.006 -0.007 -0.003 0.010 -0.012(1.06) (-0.64) (0.15) (0.51) (-0.79) (-0.31) (0.93) (-1.13)

Imbalance [-10,-1] 0.022 -0.020 -0.015 -0.007 -0.014 -0.014 0.019 -0.013

(1.03) (-0.93) (-0.70) (-0.39) (-0.89) (-0.85) (1.23) (-0.82)

Imbalance [-20,-1] 0.059 -0.029 -0.039 0.012 -0.019 -0.019 0.026 -0.024

(1.97) (-0.87) (-1.20) (0.37) (-0.83) (-0.73) (1.13) (-0.88)

# Broker Trades 5,027 3,989 3,841 4,335 12,802 9,587 9,278 11,306

Panel D: Imbalances of IPO Syndicate Members Prior to Earnings Announcements

Imbalance [-2,-1] 0.001 -0.001 0.000 0.001 0.000 0.001 0.002 -0.002

(1.62) (-1.34) (0.11) (1.33) (0.01) (1.46) (1.52) (-1.72)Imbalance [-5,-1] 0.003 0.001 0.000 0.003 0.000 0.000 0.002 -0.001

(1.95) (0.55) (0.00) (1.43) (-0.02) (0.26) (1.27) (-0.39)

Imbalance [-10,-1] 0.002 0.002 0.001 0.006 0.001 0.001 0.001 0.000

(1.02) (0.87) (0.26) (1.82) (0.69) (0.52) (0.72) (0.01)

Imbalance [-20,-1] 0.000 0.008 0.000 0.008 0.007 0.002 0.003 0.003

(-0.02) (1.79) (0.06) (1.91) (2.11) (0.69) (1.07) (1.01)

# Broker Trades 26,596 21,675 21,187 23,916 51,577 40,678 39,267 46,691

Panel E: Imbalances of 'Dirty' IPO Advisors Defined By Previous Trades

Imbalance [-2,-1] 0.006 0.009 -0.008 -0.005 0.004 -0.006 0.003 0.004

(1.34) (0.84) (-1.85) (-1.72) (1.18) (-0.57) (0.65) (0.93)

Imbalance [-5,-1] 0.001 0.020 -0.026 -0.008 0.006 0.008 0.004 0.014

(0.04) (0.90) (-2.56) (-1.35) (1.00) (0.46) (0.63) (1.48)

Imbalance [-10,-1] 0.009 0.026 -0.044 -0.021 0.006 0.007 -0.004 0.024

(0.28) (0.71) (-2.86) (-2.06) (0.48) (0.37) (-0.40) (1.55)

Imbalance [-20,-1] -0.014 0.051 -0.067 -0.040 -0.012 -0.014 -0.013 -0.007

(-0.27) (0.65) (-2.62) (-2.34) (-0.77) (-0.50) (-0.71) (-0.34)



63


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

# Broker Trades 2,596 2,014 2,026 2,311 2,790 2,330 2,368 2,620

Panel F: Imbalances of 'Dirty' IPO Advisors Defined By Large/Directional Trades and Trading Frequency

Imbalance [-2,-1] -0.006 0.026 -0.019 0.017 -0.006 -0.023 0.023 -0.010

(-0.41) (1.10) (-1.32) (1.24) (-0.67) (-1.55) (1.29) (-0.78)

Imbalance [-5,-1] -0.026 0.025 -0.072 0.015 -0.023 -0.021 0.035 -0.009

(-0.27) (0.75) (-1.55) (0.54) (-1.23) (-0.76) (1.54) (-0.41)

Imbalance [-10,-1] 0.089 0.003 -0.088 -0.008 -0.025 -0.062 0.069 -0.007

(0.58) (0.04) (-1.23) (-0.20) (-0.70) (-1.49) (1.44) (-0.18)Imbalance [-20,-1] 0.056 -0.093 -0.052 -0.015 -0.021 -0.090 0.069 0.012

(0.24) (-0.87) (-0.48) (-0.21) (-0.36) (-1.57) (1.09) (0.20)# Broker Trades 521 409 377 439 2,707 1,790 1,737 2,374



64

Table S6: Trading by SEO Underwriters Prior to Takeover and Earnings Announcements

Panel A reports average cumulative imbalances prior to takeover announcements for brokeragehouses serving as SEO co-managers and syndicate members. Daily imbalance for a stock is thedifference between the buy and sell volumes expressed as a fraction of shares outstanding. We scalethe imbalances by 1,000. We first calculate the cumulative imbalances during the [-2,-1], [-5,-1],[-10,-1], and [-20,-1] windows for each underwriter prior to each takeover, and then report thecross-sectional means and t-statistics for the cumulative imbalances. Panel B reports value-weightedaverage investment returns on trading prior to earnings announcements for brokerage houses acting as SEO co-managers and syndicate members. Investment return is total dollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for an announcement, we first multiply the daily dollar imbalance for each day with the buy-and-hold excess return from the next day until theannouncement day (day 0), and then sum up the products across days in the selected window. Daily dollar imbalance for a stock is the difference between the buy and sell volumes multiplied by theclosing price for the day. To be conservative, we assume that buy and sell trades occur at the end of the trading day. Total dollar investment is the greater of the sum of daily dollar buy imbalances orthe sum of daily dollar sell imbalances over the selected window. We calculate investment returns for

the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1 refers to the last trading day before theannouncement. We then take brokerage houses acting as SEO co-managers and syndicate membersand for each brokerage house calculate investment returns on trading prior to earningsannouncements during 1997-2002. We first calculate value-weighted investment returns for eachmonth where weights are total dollar investment, and then report time-series mean investmentreturns and the associated t-statistics across months. Panels C and D report average cumulativeimbalances prior to earnings announcements for SEO co-managers and syndicate members. Panel Ereports imbalances for ‘dirty’ underwriters identified by all previous trades. Specifically, we identify ‘dirty’ underwriters for year y as the top quartile of success ratio (percentage of imbalances in theright direction) for their non-zero 20-day imbalances prior to earnings announcements in year y-1. We require a broker to have at least ten non-zero imbalances in y-1. Panel F reports imbalances for

‘dirty’ underwriters identified by large/directional trades and trading frequency. To identify ‘dirty’underwriters, we first sort underwriters into terciles of success ratio (percentage of imbalances inthe right direction) for their 20-day imbalances of large/directional trades (dollar imbalances above$100,000 and 10 percent of total dollar trading volume) prior to earnings announcements in year y-1. We require a broker to have at least ten large/directional trades. 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/directional trades to the total number of trades (including zero trading) for underwriters priorto earnings announcements in y-1. We then identify underwriters in the top tercile of trading frequency as ‘dirty’ underwriters. We exclude top 100 brokerage houses for all the results on clienttrades to control for liquidity trading.


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Panel A: Imbalances Prior to Takeover Announcements

Co-Managers 0.009 0.066 0.091 0.136 0.018 0.024 0.016 0.054

(0.70) (1.70) (1.38) (1.53) (0.76) (0.62) (0.28) (0.70)

# Broker Trades 437 437 437 437 1,039 1,039 1,039 1,039SyndicateMembers 0.001 0.001 0.002 0.004 0.001 -0.007 -0.013 0.000



65


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

(0.98) (1.00) (1.06) (1.39) (0.19) (-0.89) (-0.97) (-0.02)

# Broker Trades 847 847 847 847 1,616 1,616 1,616 1,616

Panel B: Value-Weighted Investment Returns (%) Prior to Earnings Announcements

All Underwriters 0.40 0.36 0.14 0.48 -0.22 -0.05 0.27 0.13

(1.21) (1.16) (0.42) (1.47) (-1.00) (-0.24) (1.12) (0.59)

# Broker Trades 9,259 13,390 17,147 21,437 47,153 51,284 54,072 57,511

Panel C: Imbalances of SEO Co-Managers Prior to Earnings Announcements

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Imbalance [-2,-1] -0.002 -0.001 0.001 -0.002 -0.005 -0.003 -0.005 -0.002

(-0.38) (-0.13) (0.09) (-0.20) (-1.02) (-0.67) (-1.05) (-0.35)

Imbalance [-5,-1] -0.004 -0.004 0.016 0.013 -0.001 0.006 -0.013 -0.016

(-0.41) (-0.30) (1.30) (1.19) (-0.17) (0.57) (-1.56) (-1.66)

Imbalance [-10,-1] -0.015 0.003 0.008 0.032 0.001 0.001 -0.018 -0.022

(-0.92) (0.12) (0.44) (1.74) (0.10) (0.07) (-1.44) (-1.40)

Imbalance [-20,-1] -0.013 -0.011 0.050 0.070 -0.013 -0.008 -0.015 -0.032

(-0.47) (-0.31) (1.42) (2.44) (-0.70) (-0.37) (-0.64) (-1.40)

# Broker Trades 5,945 5,135 5,320 5,514 14,638 12,214 12,774 13,715

Panel D: Imbalances of SEO Syndicate Members Prior to Earnings Announcements

Imbalance [-2,-1] 0.000 0.001 0.001 0.002 -0.001 -0.001 -0.003 0.000

(0.05) (0.48) (0.53) (1.17) (-0.28) (-0.32) (-1.57) (-0.10)

Imbalance [-5,-1] -0.001 0.001 0.001 0.006 -0.002 0.001 -0.002 -0.004

(-0.23) (0.53) (0.21) (1.57) (-0.59) (0.50) (-0.67) (-1.27)

Imbalance [-10,-1] -0.003 0.005 -0.003 0.002 -0.006 0.002 -0.001 -0.003

(-0.42) (1.24) (-0.53) (0.34) (-1.08) (0.36) (-0.31) (-0.67)

Imbalance [-20,-1] 0.004 0.013 -0.008 -0.004 -0.011 -0.001 -0.011 -0.006

(0.31) (1.67) (-0.86) (-0.32) (-1.25) (-0.14) (-1.62) (-0.79)

# Broker Trades 10,831 10,652 10,375 10,899 20,461 19,659 19,158 20,514

Panel E: Imbalances of 'Dirty' SEO Advisors Defined By Previous Trades

Imbalance [-2,-1] -0.014 0.002 0.001 0.009 -0.011 -0.005 -0.003 -0.013

(-1.78) (0.27) (0.13) (1.57) (-1.44) (-0.40) (-0.46) (-1.44)

Imbalance [-5,-1] -0.017 0.013 0.010 0.022 -0.008 0.013 -0.017 -0.041

(-1.38) (0.86) (0.97) (0.94) (-0.47) (0.80) (-1.33) (-2.48)

Imbalance [-10,-1] -0.027 0.044 0.015 0.034 -0.042 0.015 0.018 -0.047

(-1.53) (1.03) (0.77) (1.30) (-1.67) (0.45) (1.06) (-1.95)

Imbalance [-20,-1] -0.056 0.041 0.000 0.045 -0.077 0.098 -0.001 -0.056

(-1.55) (0.81) (0.01) (1.29) (-1.88) (1.37) (-0.05) (-1.32)



66


Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

# Broker Trades 1,958 1,957 1,941 2,034 1,989 1,907 1,848 1,792

Panel F: Imbalances of 'Dirty' SEO Advisors Defined By Large/Directional Trades and Trading Frequency

Imbalance [-2,-1] -0.045 0.007 -0.014 0.016 0.022 -0.011 0.007 0.003

(-1.73) (0.35) (-0.25) (0.96) (0.82) (-0.81) (0.26) (0.20)

Imbalance [-5,-1] -0.059 -0.046 0.076 -0.018 0.019 -0.015 -0.008 -0.040

(-1.49) (-1.32) (0.38) (-0.44) (0.60) (-0.63) (-0.25) (-1.69)

Imbalance [-10,-1] -0.081 0.020 0.072 -0.097 -0.028 0.024 -0.032 -0.029

(-1.56) (0.26) (0.34) (-1.23) (-0.57) (0.51) (-0.71) (-0.81)

Imbalance [-20,-1] -0.008 -0.069 -0.052 -0.095 -0.059 0.037 -0.037 -0.005

(-0.07) (-0.62) (-0.23) (-0.85) (-0.88) (0.42) (-0.58) (-0.09)

# Broker Trades 529 524 527 532 2,244 1,813 1,834 2,152



67

Table S7: Trading by Lenders Prior to Major Events

Panel A reports value-weighted average investment returns on trading prior to earningsannouncements for brokerage houses acting as lenders of target firms. Investment return is totaldollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for anannouncement, we first multiply the daily dollar imbalance for each day with the buy-and-holdexcess return from the next day until the announcement day (day 0), and then sum up the productsacross days in the selected window. Daily dollar imbalance for a stock is the difference between thebuy and sell volumes multiplied by the closing price for the day. To be conservative, we assume thatbuy and sell trades occur at the end of the trading day. Total dollar investment is the greater of thesum of daily dollar buy imbalances or the sum of daily dollar sell imbalances over the selected window. We calculate investment returns for the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1refers to the last trading day before the announcement. We then take brokerage houses acting aslenders and for each brokerage house calculate investment returns on trading prior to earningsannouncements during 1997-2002. We first calculate value-weighted investment returns for eachmonth where weights are total dollar investment, and then report time-series mean investmentreturns and the associated t-statistics across months. Panels B and C report average cumulative

imbalances prior to earnings announcements for lenders of newly entered loans in the three-monthperiod prior to the announcement and lenders of ongoing institutional loans during the three-monthperiod prior to the announcement. Panel D reports imbalances for ‘dirty’ lenders identified by allprevious trades. Specifically, we identify ‘dirty’ lenders for year y as the top quartile of success ratio(percentage of imbalances in the right direction) for their non-zero 20-day imbalances prior toearnings announcements in year y-1. We require a broker to have at least ten non-zero imbalances iny-1. Panel E reports imbalances for ‘dirty’ lenders identified by large/directional trades and trading frequency. To identify ‘dirty’ lenders, we first sort lenders into terciles of success ratio (percentage of imbalances in the right direction) for their 20-day imbalances of large/directional trades (dollarimbalances above $100,000 and 10 percent of total dollar trading volume) prior to earningsannouncements in year y-1. We require a broker to have at least ten large/directional trades. We then

keep the top tercile of success ratio and further sort into terciles of trading frequency, which is theratio of the number of large/directional trades to the total number of trades (including zero trading)for lenders prior to earnings announcements in y-1. We then identify lenders in the top tercile of trading frequency as ‘dirty’ lenders. We exclude top 100 brokerage houses for all the results on clienttrades to control for liquidity trading.


[-2,-1] [-5,-1] [-10,-1] [-20,-1] [-2,-1] [-5,-1] [-10,-1] [-20,-1]

Panel A: Value-Weighted Investment Returns (%) Prior to Earnings Announcements

All Lenders -0.20 -0.22 -0.10 -0.45 -0.17 0.30 0.19 0.11

(-0.52) (-0.66) (-0.28) (-1.31) (-0.45) (1.39) (0.94) (0.57)

# Broker Trades 4,771 6,963 9,104 11,717 13,275 14,723 15,823 17,184

Ret<-5%-5<Ret<0%

0%<Ret<5% Ret>5% Ret<-5%

-5<Ret<0%

0%<Ret<5% Ret>5%

Panel B: Imbalances of Lenders of Newly Entered Loans Prior to Earnings Announcements

Imbalance [-2,-1] 0.000 -0.002 -0.005 -0.007 0.003 -0.001 -0.016 -0.004

(-0.32) (-0.60) (-1.67) (-1.17) (0.42) (-0.18) (-1.34) (-0.55)

Imbalance [-5,-1] -0.005 -0.016 -0.002 -0.001 0.003 0.000 -0.044 -0.023

(-1.98) (-1.75) (-0.43) (-0.22) (0.22) (0.03) (-2.64) (-1.46)



68

Imbalance [-10,-1] -0.003 -0.020 -0.006 -0.002 -0.006 0.007 -0.077 -0.020

(-0.28) (-1.63) (-1.05) (-0.21) (-0.34) (0.48) (-3.08) (-0.76)

Imbalance [-20,-1] -0.003 -0.044 0.012 -0.011 -0.027 -0.027 -0.150 -0.025

(-0.17) (-2.01) (0.92) (-0.92) (-0.95) (-1.18) (-2.72) (-0.53)

# Broker Trades 830 949 952 702 1,611 1,893 1,876 1,405

Panel C: Imbalances of Lenders of Institutional Loans Prior to Earnings Announcements

Imbalance [-2,-1] 0.001 0.001 0.002 -0.002 -0.002 -0.003 0.000 -0.008

(1.29) (1.10) (1.28) (-1.38) (-0.44) (-0.86) (0.03) (-1.35)

Imbalance [-5,-1] 0.002 0.000 0.003 -0.004 0.008 0.003 -0.003 -0.011

(0.92) (-0.31) (1.37) (-1.54) (0.83) (0.61) (-0.39) (-1.47)

Imbalance [-10,-1] 0.006 0.003 0.009 -0.008 0.022 0.009 -0.008 -0.006

(1.29) (0.54) (2.06) (-1.82) (0.85) (1.16) (-0.73) (-0.33)

Imbalance [-20,-1] 0.002 0.000 0.008 -0.008 0.007 -0.009 -0.031 -0.021

(0.42) (0.03) (1.77) (-1.84) (0.28) (-0.66) (-1.91) (-0.89)

# Broker Trades 1,382 1,819 1,551 1,492 2,978 3,953 3,404 3,219

Panel D: Imbalances of 'Dirty' Lenders Defined By Previous Trades

Imbalance [-2,-1] -0.003 0.002 0.002 0.001 -0.008 0.000 0.002 -0.003

(-2.01) (1.57) (1.54) (1.86) (-1.01) (-0.07) (0.24) (-0.65)

Imbalance [-5,-1] -0.001 0.003 0.001 0.002 -0.012 0.004 0.002 0.014

(-0.59) (1.28) (1.01) (0.65) (-1.16) (0.56) (0.17) (1.40)

Imbalance [-10,-1] 0.000 0.006 -0.002 0.002 -0.016 -0.003 0.009 0.004

(-0.08) (1.53) (-0.73) (0.35) (-1.22) (-0.25) (0.50) (0.30)

Imbalance [-20,-1] 0.000 0.012 0.004 -0.004 -0.019 -0.014 0.009 0.022

(-0.07) (1.67) (0.65) (-0.46) (-0.71) (-0.83) (0.33) (1.02)# Broker Trades 877 1,120 1,133 1,117 2,040 2,490 2,535 2,349

Panel E: Imbalances of 'Dirty' Lenders Defined By Large/Directional Trades and Trading Frequency

Imbalance [-2,-1] 0.009 0.005 0.006 0.009 -0.007 -0.004 -0.005 -0.024

(1.64) (0.58) (1.83) (1.51) (-0.26) (-0.17) (-0.25) (-0.79)

Imbalance [-5,-1] 0.016 0.016 0.009 0.014 0.038 -0.015 -0.034 -0.039

(2.38) (1.33) (1.64) (1.13) (0.74) (-0.43) (-0.82) (-0.83)

Imbalance [-10,-1] 0.017 0.027 0.009 0.021 0.223 -0.056 -0.070 0.054

(2.40) (1.49) (1.16) (1.06) (1.23) (-1.06) (-0.98) (0.65)

Imbalance [-20,-1] 0.008 0.034 0.012 0.027 0.227 -0.172 -0.090 -0.002(0.30) (1.08) (1.59) (0.82) (1.27) (-2.02) (-0.36) (-0.01)

# Broker Trades 395 533 480 454 395 533 480 454



69

Table S8:Performance of Lenders’ Trading Prior to Earnings Announcements

Panel A presents investment returns on trading in borrowing firms versus non-borrowing-firmsprior to earnings announcements for brokerage houses acting as lenders. Investment return is totaldollar gain/loss divided by total dollar investment. To calculate dollar gain/loss for anannouncement, we first multiply the daily dollar imbalance for each day with the buy-and-holdexcess return from the next day until one day after the announcement day (day 1), and then sum upthe products across days in the selected window. Daily dollar imbalance for a stock is the differencebetween the buy and sell volumes multiplied by the closing price for the day. To be conservative, weassume that buy and sell trades occur at the end of the trading day. Total dollar investment is thegreater of the sum of daily dollar buy imbalances or the sum of daily dollar sell imbalances over theselected window. We calculate investment returns for the [-2,-1], [-5,-1], [-10,-1], and [-20,-1] windows. Day -1 refers to the last trading day before the announcement. We then take brokeragehouses acting as lenders of ongoing loans during the three-month period prior to earningsannouncements. For each brokerage house, we then calculate the average investment return ontrading in borrowing firms and non-borrowing firms prior to earnings announcements during 1997-2002. We then report the mean investment returns on borrowing firms, non-borrowing firms,

the difference, and the associated t-statistics across brokerage houses. Panel B repeats the tests inPanel A, but with investment success ratio instead of investment returns. Specifically, for eachbrokerage house, we examine investment returns on trading prior to earnings announcements during 1997-2002 and calculate percentage of positive investment returns (investment success ratio) forborrowing firms and non-borrowing firms, respectively. We then report the mean investmentsuccess ratio on borrowing firms, non-borrowing firms, the difference, and the associated t-statisticsacross brokerage houses. To control for outliers, we require a brokerage house to trade prior to atleast 50 earnings announcements for borrowing and non-borrowing firms during 1997-2002.

Panel A: Lenders’ Investment Returns on Borrowing Firms versus Non-Borrowing Firms

Client Trades Market Maker TradesNon-

BorrowingFirms

BorrowingFirms Diff t-stat

#Broker

Non-

BorrowingFirms

BorrowingFirms Diff t-stat

#Broker

Trading [-2,-1](%) 0.18 0.07 -0.11 (-1.28) 61 0.07 0.08 0.01 (0.10) 45

Trading [-5,-1](%) 0.25 0.22 -0.02 (-0.39) 66 0.09 -0.03 -0.12 (-1.21) 46

Trading[-10,-1] (%) 0.26 0.19 -0.06 (-0.77) 68 0.07 0.00 -0.07 (-0.74) 47

Trading[-20,-1] (%) 0.27 0.32 0.05 (0.53) 70 0.07 -0.17 -0.24 (-2.05) 51

Panel B: Lenders’ Investment Success Ratios on Borrowing Firms versus Non-Borrowing Firms

Client Trades Market Maker TradesNon-

BorrowingFirms BorrowingFirms Diff t-stat#

Broker

Non-

BorrowingFirms BorrowingFirms Diff t-stat#

Broker

Trading [-2,-1] 51.09% 50.96% -0.13% (-0.27) 61 50.44% 50.98% 0.54% (0.76) 45

Trading [-5,-1] 51.36% 51.76% 0.40% (0.88) 66 50.99% 50.67% -0.32% (-0.57) 46 Trading[-10,-1] 51.33% 51.44% 0.10% (0.24) 68 50.86% 50.90% 0.04% (0.06) 47

Trading[-20,-1] 51.45% 51.57% 0.12% (0.30) 70 50.66% 50.01% -0.65% (-1.37) 51



Figure S2

Returns after Earnings Announcements for Sub-Samples based on Past InstitutionalImbalances

This figure plots the buy-and-hold excess returns from day 2 (second trading day after the earningsannouncement) to day 66 (3 months after the announcement) for two groups based on pastinstitutional imbalances. Imbalance for a stock is the difference between buy and sell volumesexpressed as a percentage of shares outstanding. We classify earnings announcements into twogroups according to their industry/size adjusted institutional imbalances during the [0,1] window. The announcements with positive (negative) institutional imbalances are assigned to institutional buy (sell) group. For each announcement, we calculate the buy-and-hold excess returns for the [2,66] window, where returns are in excess of Nasdaq Composite Index returns. We then plot theimbalance-weighted and value-weighted buy and hold excess returns for each group. Forimbalance-weighted returns, the weights are equal to the absolute value of industry/size adjustedinstitutional imbalances during the [0,1] window. For value weighted returns, the weights are equal tothe market capitalization on day 1.

-0.03

-0.02

-0.02

-0.01

-0.01

0.00

0.01

0.01

0.02

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64

Days after Announcements

B u y - a n d - H o l d E x e s s R e t u r n s

Bought by Inst., Imbalance-weighted Bought by Inst., Value-weighted

Sold by Inst., Imbalance-weighted Sold by Inst., Value-weighted

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