Investor Trading and Return Patterns around

Earnings Announcements

Ron Kaniel, Shuming Liu, Gideon Saar, and Sheridan Titman

This version: September 2007 Ron Kaniel is from the Fuqua School of Business, One Towerview Drive, Duke University, Durham, NC 27708 (Tel: 919-660-7656, ron.kaniel@duke.edu). Shuming Liu is from the College of Business, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132 (Tel: 415-338-7472, shuming@sfsu.edu). Gideon Saar is from the Johnson Graduate School of Management, Cornell University, 455 Sage Hall, Ithaca, NY 14853 (Tel: 607-255-7484, gs25@cornell.edu). Sheridan Titman is from the McCombs School of Business, University of Texas at Austin, Austin, TX 78712 (Tel: 512-232-2787, Sheridan.Titman@mccombs.utexas.edu). This research began while Saar was on leave from New York University and held the position of Visiting Research Economist at the New York Stock Exchange. The opinions expressed in this paper do not necessarily reflect those of the members or directors of the NYSE.

Investor Trading and Return Patterns around Earnings Announcements

Abstract This paper investigates the behavior of individual and institutional investors around earnings announcements using a unique dataset of NYSE stocks. We find that intense individual buying (selling) prior to the announcement is associated with significant positive (negative) abnormal returns in the three months following the event, with most of the abnormal returns generated by stocks that experience extreme earnings surprises. A similar strategy that follows the trading of institutional investors does not yield significant payoffs. We also examine the behavior of individuals after the earnings announcement and find that they trade in the opposite direction to both pre-event returns (i.e., exhibit “contrarian” behavior), as well as the earnings surprise (i.e., exhibit “news-contrarian” behavior). The latter behavior may contribute to the post-earnings announcement drift, if it slows down the adjustment of prices. While trading against the drift may seem like a dubious strategy, the combination of patterns we observe before and after the events could suggest that individuals are profitably reversing positions which they entered before the announcements.

I. Introduction

There is a growing body of empirical research that documents the extent to which stock

returns are predictable around earnings announcement dates. This research finds that

unconditional expected returns are higher around earnings announcements,1 that

characteristics that predict returns unconditionally tend to more accurately predict returns

around earnings announcements,2 and that returns are abnormally high (low) in the

months following good (bad) earnings announcements’ surprises (the “drift”).3 Although

the higher risk around earnings announcements is likely to generate some abnormal

returns, the degree of return predictability and the magnitude of the post-earnings

announcement drift are likely to be due to trading frictions or other inefficiencies.

Some researchers suggest that since earnings announcements are quite visible,

they are likely to attract the attention of individual investors who are more likely to be

influenced by behavioral biases. As a result, we may see more uninformed or irrational

trading around earnings announcements, which may generate the above mentioned

biases. For example, Hirshleifer, Myres, Myres, and Teoh (2002) posit that if the drift

reflects a profit opportunity due to mispricing, more sophisticated investors should buy

after positive surprises (before an upward drift) and sell after negative surprises (before a

downward drift). Naïve investors would trade in the opposite direction, and their trading

would affect the supply of shares in the market and therefore would slow down the

adjustment of prices to the information, creating the drift. Investigating the trades of

clients of a discount broker, Hirshleifer et al. did not find support for this hypothesis.

Our paper contributes to the literature in a number of ways. First, we examine a

comprehensive dataset that aggregates the trades of all individual or institutional

investors who trade on the NYSE over a four-year period. On this dimension, our 1 See Chari, Jagannathan and Ofer (1988) and Lamont and Frazzini (2006). 2 Jegadeesh and Titman (1993), for example, document high returns around future earnings announcements for stocks that have performed well in the previous six months. 3 The drift was first described in Ball and Brown (1968). See also Foster, Olsen, and Shevlin (1984) and Bernard and Thomas (1989, 1990).

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research extends prior research that either indirectly infers the trades of individuals (or

institutions) based on trade size, or looks at a small subset of the market (either a sample

of individual trades from one discount brokerage house or the TORQ sample of 144

NYSE stocks over a three-month period). Second, we also examine the trading of

institutions around earnings announcement to contrast the individuals’ behavior with that

of (presumably) more sophisticated investors.

Third, we investigate the relation between individual trading prior to earnings

announcements and future returns. This analysis relates to Kaniel, Saar and Titman

(2007), who suggest that by acting as contrarians, individual investors implicitly provide

liquidity to institutions. Kaniel, Saar, and Titman find that unconditionally, the liquidity

provision of individuals is profitable; however, it is possible that conditional on a future

information event, individuals will lose money to the more informed institutions.

Alternatively, it may be the case that because of the uncertainty associated with future

earnings announcements, institutions will have a greater need for liquidity, providing

individuals with greater profit opportunities.

Our results are consistent with the latter hypothesis. Specifically, we find that

stocks that individuals bought in the ten days prior to the event realize abnormal returns

that exceed the abnormal returns of stocks they sold by 1.45% in the two-day event

window and 5.45% in the first three months after the event. Double sorting on net

individual trading prior to the event and earnings surprise (relative to analysts’ forecasts)

reveals that these abnormal returns in the post-event period are strongest for stocks that

experience the most extreme news, both positive and negative. Regression analysis

confirms that the positive relation between net individual trading before the earnings

announcement and returns following the announcement are robust to controlling for past

returns and the contemporaneous relation between returns and individual trading.

If institutions are motivated to sell prior to earnings announcements in order to

reduce the risk of their holdings, then we would expect to see more selling by institutions

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and buying by individuals in stocks with higher earnings uncertainty. Our evidence

suggests that this is indeed the case. When we sort on earnings uncertainty (measured by

the dispersion of analysts’ forecasts) prior to the event, we find that institutions sell and

individuals buy stocks with high earnings uncertainty two and three months before the

announcements. We also find that the post announcement returns associated with net

individual trading before the event are greater for stocks with higher earnings uncertainty.

An additional research question we investigate is whether the trading of

individuals slows down the adjustment of prices and hence contributes to the creation of

the post-earnings announcement drift. The most robust pattern we observe for the

individuals in the post-event period is that they trade in the opposite direction to pre-

event returns (i.e., exhibit “contrarian” behavior), and that their trading is also affected by

the direction of the earnings surprise in a “news-contrarian” fashion. The news-contrarian

pattern means that they buy more (or sell less) of the stocks that experience more

negative earnings surprises. Institutional investors exhibit the opposite patterns in the

post-event period, showing a momentum tendency (trading in the direction of pre-event

returns) that combines with a “news-momentum” pattern (more pronounced trading in the

direction of the earnings surprise).

The “news-contrarian” behavior of individuals is consistent with the hypothesis in

Hirshleifer et al. that individuals are responsible (at least in part) for the post-earnings

announcement drift phenomenon. While trading in the opposite direction of the drift may

in fact slow down the price adjustment process and may not, in isolation, be a good

strategy, combining our findings on individual trading before and after the events may

suggest that individuals are profitably reversing positions to which they have entered

before the announcements.

The rest of this paper proceeds as follows. The next section describes the sample

and the comprehensive dataset we use. Section III investigates the relation between net

imbalances of individuals and institutions prior to the earnings announcements and

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subsequent returns. Section IV examines the behavior of individuals and institutions after

the announcements. Section V contains a detailed discussion of the most related papers in

the literature, comparing and contrasting our results with prior evidence. Section VI

concludes.

II. Sample and Data

II.A. Individual and Institutional Trading Data

We study the trading of individuals and institutions around earnings announcements

using a comprehensive dataset that contains four years of daily buy and sell volume of

executed orders for a large cross section of NYSE stocks. The dataset was constructed

from the NYSE's Consolidated Equity Audit Trail Data (CAUD) files that contain all

orders that execute on the exchange. The CAUD files include a field called Account Type

that specifies for each order whether it originates from an institution or an individual

investor.

Account Type is a mandatory field a broker has to fill for each order that is sent to

the NYSE. The Account Type field is not audited by the NYSE on an order-by-order

basis, but NYSE officials monitor the use of this field by brokers. In particular, any

abnormal use of the individual investor designation in the Account Type field by a

brokerage firm is likely to draw attention, which prevents abuse of the reporting system.

We therefore believe that the Account Type designation of orders is fairly accurate.4

An important advantage of our dataset is that the information about daily buy and

sell volume of individual and institutional investors was created by aggregating executed

orders, rather than trades. In other words, the classification into buy and sell volume in

our dataset is exact, and we do not have to rely on classification algorithms such as the

one proposed by Lee and Ready (1991).

4 Additional information on the Account Type field (and the reporting of individual investor trading) can be found in Lee and Radhakrishna (2000) and Kaniel, Saar, and Titman (2007).

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Most previous work on U.S. markets examined the behavior of institutions and

individuals around earnings announcements using either indirect measures of investor

trading or a very limited sample. For example, researchers have used small and large

trades as proxies for the trades of individual and institutional investors (e.g., Lee (1992),

Shanthikumar (2004), and Frazzini and Lamont (2006)). While such a classification was

shown to be reasonable for a 1990 sample of NYSE stocks (Lee and Radhakrishna

(2000)), recent research casts doubt on its usefulness. For example, Campbell,

Ramadorai, and Schwartz (2007) look at how trades of different sizes relate to changes in

institutional holdings from 1993 through 2000 and conclude that the smallest trades

(below $2,000) are more likely to come from institutions rather than individuals.5 To

examine the behavior of institutional investors, some papers have used changes in

quarterly institutional holdings around earnings announcements (e.g., Ali, Durtschi, Lev,

and Trombley (2004) and Baker, Litov, Wachter, and Wurgler (2005)). This, however,

limits the ability to examine trading over shorter intervals, and has the added

complication that the mapping between institutional trading and changes in institutional

holdings is not one-to-one.

To examine trading by individuals, some researchers (e.g., Hirshleifer, Myers,

Myers, and Teoh (2002)) have used a sample of individuals who traded through one

discount broker from 1991 through 1996. While measuring directly individual trading,

this sample is much smaller than the one we are using, and looks at a subset of

individuals that may or may not be representative of the overall population. Finally, while

Welker and Sparks (2001) and Nofsinger (2001) have used the NYSE’s TORQ database

to look at individual and institutional trading around public announcements, they do not

observe the main results we report (especially the relation between individual trading

prior to the event and subsequent returns). While TORQ also contains Account Type data

(like our dataset), it includes only 144 stocks for a three month period between November

5 Hvidkjaer (2005), who investigates the relation between small trade volume and stock returns, also notes that small trade volume increases markedly in the final years of his sample (that ends in 2004), and it no longer seems to be negatively related to changes in institutional holdings. The bulk of the increase in small trading is probably coming from institutions that split orders into small trades.

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1990 and January 1991. Hence, our different results could be due to the small sample size

in the TORQ database or because it contains much older data.

We start our construction of a daily abnormal net individual trading series by

computing an imbalance measure: subtracting the value of the shares sold by individuals

from the value of shares bought and dividing by the average daily dollar volume (from

CRSP) in the calendar year.6 We then subtract the daily average of that imbalance

measure over the sample period to get an abnormal net individual trading measure, which

we believe is more suitable for examining the patterns of trading around earnings

announcements. Specifically, we define IndNTi,t for stock i on day t as:

, , ,all days in 2000 2003

, ,,

Individual Imbalance Individual Imbalance

where,Individual buy dollar volume Individual sell dollar volume

Individual ImbalanceAverage daily dollar volume

i t i t i t

i t i ti t

IndNT−

= −

−=

∑

,in the calendar yeari t

We define abnormal net institutional trading for stock i on day t, InsNTi,t, in an

analogous fashion (i.e., we compute the daily imbalance of institutional orders, buys

minus sells divided by volume, and subtract from it the average of the institutional

imbalance over the sample period).7

We define cumulative abnormal net individual or institutional trading over a

certain period, [t,T], as:

[ , ] , [ , ] , and T T

i it T i k t T i k

k t k t

IndNT IndNT InsNT InsNT= =

= =∑ ∑

6 Kaniel, Saar, and Titman (2007) note that some trading in NYSE-listed stocks does not take place on the NYSE. For example, some brokers either sell some of their retail order flow to wholesalers for execution or internalize a certain portion of their clients’ orders by trading as principal against them. These trades take place on one of the regional exchanges (or alternatively reported to the NASD) and are therefore not in our sample of NYSE executions. However, these brokers still send a certain portion of their retail order flow to the NYSE, and are more likely to send those orders that create an imbalance not easily matched internally. Therefore, Kaniel, Saar, and Titman argue that net individual trading (i.e., imbalances in individuals’ executed orders on the NYSE) probably reflects, even if not perfectly, the individuals’ imbalances in the market as a whole. 7 We ignore orders marked as “index arbitrage” when constructing our net trading measures because they presumably do not contain information about firm specific events such as earnings announcements. Also, we apply a filter to remove potential reporting errors in the dataset: we take out days where the magnitude of the imbalance in the trading of an investor clientele is more than five times the average daily dollar volume of the stock.

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where the periods are defined relative to the earnings announcement date (day zero). For

example, IndNT[-10,-1] is cumulative abnormal net individual trading from ten days prior

to the earnings announcement to one day prior to the announcement. Similarly,

InsNT[2,61] is cumulative abnormal net institutional trading from two days after the

announcement to (and including) day 61.

Note that on any given period our measures of net individual trading and net

institutional trading need not exhibit mirror image behavior because of the presence of

dealers on the NYSE. More specifically, each NYSE stock is associated with a single

“specialist” who makes a market in the stock, buying and selling for his own account.

Specialists are supposed to ensure price continuity and stability for the stocks, and hence

could absorb to some extent temporary imbalances that a certain investor clientele may

exhibit.

II.B. Sample

Our sample contains all common, domestic stocks that were traded on the NYSE any

time between January 1, 2000 and December 31, 2003. We use the CRSP database to

construct the sample, and match the stocks to the NYSE dataset of individual and

institutional trading by means of ticker symbol and CUSIP. This procedure results in a

sample of 2,034 stocks. We then use IBES and COMPUSTAT to identify all the dates

where stocks in our sample had earnings announcements, and impose two restrictions on

the sample.8 First, we require 60 days of data prior to and after the announcements,

which eliminates most announcements from the first (and last) three months of the

sample period. Second, in order to compute our analysts’ earnings surprise measure we

require that there is an observation in the IBES database for the mean analysts’ forecast

in the month prior to the earnings announcement (i.e., at least one analyst with an

earnings forecast), and also information about the actual earnings number.

8 For each stock on each quarter, we compare the announcement dates from IBES (the REPDATS field) and COMPUSTAT (the RDQE filed) and choose the earlier one if they are different.

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Our screens result in a final sample of 1,821 stocks with 17,564 earnings

announcement events.9 Panel A of Table 1 presents summary statistics from CRSP on the

sample stocks (for the entire sample and for three size groups). Panel B of Table 1 reports

the number of events in each month of the sample period. Table 2 looks at net individual

and institutional trading around earnings announcements. In Panel A we observe that

individuals buy stocks in the month prior to earnings announcement. At the time of the

event itself (days [0,1]) individuals sell (mostly large stocks), and we observe continued

selling in the two weeks after the event. Panel B of Table 2 shows that institutional

investors buy stocks during the event-window, and continue buying in the week after the

event mainly small and mid-cap stocks.

II.C. Abnormal Returns and Earnings Surprises

Throughout the paper we define abnormal returns as market-adjusted returns, and use the

equal-weighted portfolio of all stocks in the sample as a proxy for the market portfolio.

To create the cumulative returns of the market portfolio, say over a 60-day period, we

first compute for each stock the cumulative (raw) return over the relevant 60-day period.

The average of these returns across the stocks in the sample is what we define as the

return on the equal-weighted market portfolio. Our definition of cumulative abnormal

returns for stock i in period [t, T], CARi,[t,T], is the cumulative return on stock i minus the

cumulative return on the market proxy (for period [t,T]). Our results are robust to the use

of size-adjusted returns as an alternative definition of abnormal returns.

Our investigation focuses not on the unconditional relation between investor

trading and returns around earnings announcements but rather on whether investors

anticipate news and how they react to good and bad news. Therefore, we require a

measure of earnings surprise (or the news component of the earnings announcement), and

use analysts’ forecasts to define that surprise. More specifically, we define the

9 This is the number of events we use for analysis of individual investor trading. Due to the filter we discuss in footnote 7 that removes potential reporting errors from our NYSE dataset, the sample we use for analysis of institutional investor trading consists of 17,419 events.

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normalized earnings surprise, ES, as the actual earnings minus the earnings’ forecast,

divided by the price on the forecast day. The earnings’ forecast is the mean of analysts’

forecast one month before the earnings announcements. An earnings surprise measure

using analysts’ forecasts is rather standard in the literature, but we certainly acknowledge

that it is just a proxy for the surprise. There are also papers that use the abnormal return at

the time of the earnings announcement as a proxy for the surprise, and each measure has

its own advantages and disadvantages.10 In our regression analysis explaining post-event

investor trading we include, in addition to the analysts’ earnings surprise measure, the

abnormal return at the time of the announcement as an additional proxy for the news

content of the announcement.

III. Investor Trading before the Event and Return Predictability

Earnings announcements represent the arrival of news to the market. We are interested in

investigating whether a certain investor clientele, individuals or institutions, seems to

have the ability to predict the news or its consequence (in terms of abnormal return).

Therefore, we examine the trading of individuals and institutions in the 10 trading days

(two weeks) prior to the earnings announcement and look at the stocks they intensely buy

or sell. Then, we relate their actions prior to the announcement to the cumulative

abnormal returns on and after the announcement date.

In the first exercise, we sort all stocks each quarter according to net investor

trading prior to the announcement, either by individuals or by institutions, and put the

stocks in five categories (quintile 1 contains the stocks that investors sold the most and

quintile 5 contains the stocks investors bought the most). We compute for each stock the 10 The analysts’ earnings surprise measure presumably reflects the surprise relative to the opinion of well-informed, sophisticated investors. It has the advantage that it does not involve the price level or return at the time of the event that can be affected by liquidity shocks unrelated to the actual updating of beliefs about the stock. On the other hand, it is perfectly conceivable that investors other than sell-side analysts (e.g., skillful individuals, hedge funds, and proprietary trading desks) have information that is relevant to the pricing of the stock that sell-side analysts do not possess. As such, the return at the time of the announcement would aggregate everyone’s opinion, leading to a better measure of surprise than the one that solely considers the information set of the sell-side analysts.

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cumulative market-adjusted return over a certain period, and present in Table 3 the

average of the market-adjusted abnormal returns of all the stock-quarters in each of the

different quintiles (with t-statistics testing the hypothesis of zero abnormal returns). We

use sixty days starting two days after the announcement as the length of our post-event

period to be consistent with the literature that examined the post-earnings announcement

drift.

Panel A of Table 3 presents the results on trading by individuals. We observe a

very strong pattern: the stocks that individuals intensely bought in the two weeks before

the announcements outperform those that they intensely sold, on average, by 1.45%

during the event window (days [0,1]), and an additional 4.13% payoff in the three-month

post-event period (days [2,61]). These excess returns are of comparable magnitudes for

stocks with intense buying and selling. Stocks that individuals intensely sell (quintile 1)

experience a negative abnormal return of –0.7% on the event, and –2.8% in the post-

event period, while those they intensely buy (quintile 5) have 0.80% abnormal return in

the event window, and 1.35% after the event.11

We also sorted the stocks according to size and repeated the analysis separately

for three market-capitalization groups: small, mid-cap, and large stocks.12 For this

analysis, we computed abnormal returns for a stock by subtracting from it the return of

the equal-weighted portfolio of all stocks in its group. In all three size groups the strategy

of buying the stocks that individuals bought before the event and selling those they sold

produces significant profits: 8.03% after three months ([0,61]) for small stocks, 3.51% for

mid-cap stocks, and 3.03% for large stocks.

In Panel B of Table 3 we conduct a similar analysis for institutions. Sorting on

InsNT[-10,-1], we find that the stocks institutions intensely sell realize abnormal returns of 11 We find a similar pattern when we sort on net individual trading in the 20 days prior to the announcement. 12 We sort stocks into deciles by market capitalization and define small stocks as those in deciles 1, 2, 3, and 4, mid-cap stocks as those in deciles 5, 6, and 7, and large stocks are those comprising deciles 8, 9, and 10.

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–1.8% from day zero until day 61, which is statistically different from zero. However,

stocks that institutions intensely buy also realize negative abnormal returns, of –2.3% in

the [0, 61] period. We also conducted a separate analysis of stocks by size categories and

the result was similar to the one we present for the entire sample in the bottom row of the

panel: profit on the zero-investment strategy that is indistinguishable from zero. To

summarize the results in Table 3, we observe that pre-event trading by individuals is

significantly related to abnormal returns at the time of the event and in the post-event

period. On the other hand, there is not a significant difference in the abnormal returns of

stocks that institutions intensely buy and sell.13

In Table 4 we sort stocks independently along two dimensions: the analysts’

earnings surprise measure (ES) and IndNT[-10,-1]. We put the stocks into 25 categories:

five groups of earnings surprise (quintile 1 is the most negative surprise and quintile 5 the

most positive surprise), and five groups of net individual trading (quintile 1 are those

stocks individuals sold the most in the 10 days prior to the announcement and quintile 5

are those they bought the most over that period). We then examine the cumulative

market-adjusted returns in three periods: the event window ([0, 1]), the post-event period

[2, 61]), and the combined return on and after the event ([0, 61]).

Panel A of Table 4 reports the abnormal return during the event window for the

25 categories. Consistent with previous research, stocks that experience a positive

(negative) surprise with respect to the analysts’ forecast have a positive (negative)

abnormal return at the time of the event. As in Table 3, we also observe that stocks

individuals bought have higher abnormal returns than those they sold in each earnings 13 The pattern of significant positive abnormal returns for the strategy that follows individual trading is mirrored by significant abnormal negative returns for the strategy that follows institutional trading during the event window ([0,1]); however, the negative returns for the institutional strategy are insignificant in the post-event period. While many patterns we observe in the trading of institutions are “mirror image” of those exhibited by individuals, this need not always be the case. As we mention in Section II.A., NYSE specialists have a substantial presence in the trading of stocks, and our analysis of institutional trading also intentionally excludes index arbitrage. The more heterogeneous nature of institutional trading strategies coupled with the presence in the market of the specialist (in addition to individual investors) means that we are likely to have less power detecting patterns that are associated with the trading of institutions.

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announcement quintile. It seems as if the performance of the zero-investment strategy

that follows individual trading (in the bottom row of the panel) is better for stocks that

experience extreme earnings surprises (1.91% for bad news in quintile 1 and 1.85% for

good news in quintile 5) than for no-news stocks (1.17% in quintile 3), but the

differences are not statistically significant.

The performance of this zero-investment strategy differs much more between

news and no-news events when we examine the post-event period ([2, 61]) in the last row

of Panel B. The most prominent pattern we observe is that buying the stocks individuals

bought and selling those they sold is profitable mainly in the extreme surprise quintiles

(e.g., quintile 1 and quintile 5). For example, the payoff to this strategy for good-news

stocks is 5.50% while the payoff for the “no-news” stocks (quintile 3) is only 2.13%.14

Panel C of Table 4 provides evidence on returns in the combined period that includes the

event window and the post-event period. We observe that the abnormal return in the three

months following the announcement is the most negative for stocks that experience both

individual selling prior to the event and negative news (-6.80% abnormal return over

[0,61]), while the abnormal return is most positive for those stocks that experience both

individual buying prior to the event and positive news (7.57% over that period).

One interpretation of the results in Table 4 is that individuals have useful

information about the forthcoming earnings announcements. Alternatively, as discussed

in Kaniel, Saar, and Titman (2007), individuals may profit by providing liquidity to

institutions that may have an incentive to exit positions prior to earnings announcement

dates. However, the magnitude of the returns observed in this study suggests that

liquidity provision is unlikely to provide the sole explanation. In comparison to Kaniel,

Saar, and Titman (2007), who find cumulative abnormal returns of about -0.5% (0.8%) in

the two to three months after a week of intense selling (buying) by individuals, the three

14 The difference between them is marginally statistically significant (p-value = 0.0775) using an F-test.

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month returns associated with individual trading prior to earnings announcements are

four to five times larger.

In Table 5 we present the same analysis for institutional investors. Panel A

provides no further insights relative to Table 3: we observe that the strategy that mimics

institutional buying and selling is unprofitable. Similarly, conditioning on news does not

help us observe any predictability with respect to post-event abnormal returns: Panel B

shows that abnormal returns in the post-event period are generally negative for both the

stocks institutions intensely sell and those that they buy. A similar picture is observed

when we consider abnormal returns over the combined event window and post-event

period. Overall, it does not seem as if conditioning on the earnings surprise enables us to

better uncover any predictive ability by institutions with respect to returns on and after

the event.

We proceed to investigate the ability of net individual trading prior to

announcements to predict cumulative abnormal returns on and after the announcements

(CAR[0,61]) using multivariate regressions that control for mean-reversion in returns and

the contemporaneous relation between net individual trading and returns. For robustness,

we use models where pre-event abnormal returns and net individual trading are measured

over either 10 days or 60 days before the announcements.15 In order to overcome

potential econometric problems associated with contemporaneously correlated errors for

earnings announcements that are clustered in time (i.e., in the same quarter), we use a

methodology in the spirit of Fama and MacBeth (1973). We first run a cross-sectional

regression separately for each quarter. We then use time-series variability in the 16

estimates we have for each coefficient (one from each quarterly regression) to get the

standard error for the mean coefficient.

15 The reason we consider both specifications is that while in Table 4 and Table 5 we focus on net investor trading in the 10 days before the event, our choice for a three-month post-event period follows other papers in the “drift” literature, and therefore we also look at a pre-event period of 60 days to have equal periods before and after the announcements.

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In Panel A of Table 6 we observe that the coefficient on pre-event net individual

trading is positive and statistically significant in the regressions using the entire sample.

This means that more intense individual buying before the earnings announcement is

associated with higher market-adjusted abnormal returns on and after the event.16 We

also ran separate regressions predicting the abnormal return at the time of the event

(CAR[0,1]) and in the post-event period (CAR[2,61]). The results were similar: the

coefficient on net individual trading before the event was statistically significant in all

specifications. Panel A of Table 6, which reports separate regressions for small, mid-cap

and large stocks, reveals similar results across the size groups.

In contrast, the coefficient on pre-event net institutional trading in Panel B of

Table 6 is statistically significant in only two of the eight regression specifications, and it

has the “wrong” sign (i.e., it is negative). In other words, there is no evidence of

institutional ability to predict excess return associated with the event.17

III.A. Earnings Uncertainty

Up to this point we have shown that net individual investor trading prior to earnings

announcements is associated with abnormal returns on and after the event. The evidence

of a relation between excess returns and individual trading is stronger when following

extreme earnings surprises. Since more extreme surprises (either good or bad) could be

the result of greater uncertainty about earnings prior to the announcement, we proceed by

examining the relation between the returns on the individuals’ strategy and earnings

uncertainty.

To measure earnings uncertainty (EU) prior to the announcement we use the

dispersion in analysts’ forecasts: the high forecast minus the low forecast divided by the

price on the forecast day. The high and low forecasts are from one month prior to the 16 We include IndNT[0,61] in the regression to see whether the apparent ability to predict return is simply due to a contemporaneous relation between net individual trading and returns after the event coupled with serial correlation in individual trading. We observe that the predictive ability of net individual investor trading in the pre-event period is not subsumed by the inclusion of this variable. 17 Similar results are obtained when we use either CAR[0,1] or CAR[2,61] as the dependent variable.

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announcement, and we only use stocks with at least three analysts providing forecasts.18

Stocks are sorted every quarter according to EU and put into five groups: quintile 1 is the

one with the least uncertainty (smallest forecast range) and quintile 5 is the one with the

most uncertainty (largest forecast range).

Table 7 presents the returns associated with net individual and institutional

trading prior to the earnings announcement conditional on the level of uncertainty. We

observe an interesting pattern: institutions sell stocks with high uncertainty of earnings

prior to the announcement, and their intensity of selling is significantly different from

zero in the three months and one month periods prior to the event. Individuals, on the

other hand, buy stocks with high earnings uncertainty prior to the announcement, and

their buying is significantly different from zero in the three months, one month, and two

weeks periods prior to the event. They also buy stocks with low uncertainty about

earnings, but at least over the entire three-month period prior to the announcement, their

buying of high uncertainty stocks is greater than their accumulation of low uncertainty

stocks (i.e., the number in the last row of the table, Q5−Q1, is statistically different from

zero).

The evidence in Table 7 points to a potential explanation for the abnormal return

we document for the strategy that follows individual buying. It is possible that when

institutions dispose of high earnings uncertainty stocks prior to the announcements,

individuals end up buying the high uncertainty stocks, and are compensated by realizing

higher returns on average during the event window and in the post-event period. In Panel

A of Table 8 we double sort independently on earnings uncertainty and net individual

investor trading in the 60 days prior to the event.19 We then look at abnormal returns in

18 This reduces the number of earnings announcement events in the analysis of individual (institutional) trading to 14,149 (14,118). 19 We use 60 days in this case rather then 10 days as in the rest of the analysis because the evidence in Table 8 is that individual buying of stocks with high uncertainty is significantly greater than their selling of these stocks when we consider a three-month period prior to the event, but not in the two weeks prior to the event.

15

the three months after the event (CAR[0,61]). We observe that the excess returns associated

with individual accumulation are much stronger for high earnings uncertainty stocks: the

difference in returns is 7.39% in EU quintile 5 versus 2.04% in EU quintile 1 (and the

difference is statistically significant).20 It is interesting to note, though that we cannot

detect the opposite pattern for the institutional investors in Panel B of Table 8. In other

words, it does not seem as if their selling of high uncertainty stocks prior to the event

means that they earn more or less return after the event.21

IV. Investor Trading after the Event

While the previous section focused on the behavior of individuals and institutions prior to

earnings announcements, in this section we look at their trading during and after the

event. The reason to focus on their behavior after the event is that one of the puzzles

associated with earnings announcements is the “drift,” which is the phenomenon that

stocks with negative earnings surprises experience negative abnormal returns in the post-

event period and stocks with positive earnings surprises experience positive abnormal

returns in the post-event period.

Some authors have conjectured that the behavior of individuals is responsible for

the slow adjustment of prices to information in earnings announcements, which manifests

itself as the drift. Indirect evidence for this effect is found in Bartov, Krinsky, and

Radhakrishnan (2000), who document that the drift is negatively related to the extent of

20 We use an F-test to statistically verify this effect. 21 Varian (1985) provides a theoretical model where a greater dispersion of beliefs is associated with lower asset prices, which would imply that when earnings uncertainty is reduced (after the announcement) we should observe higher prices. In Miller (1977), on the other hand, divergence of opinions along with short selling restrictions lead to overpricing (because prices would reflect the more optimistic valuation of the assets), and the resolution of uncertainty should therefore lead to a downward revision in prices. Garfinkel and Sokobin (2006) use unexpected volume at the earnings announcement as a measure of investors’ opinion divergence and find that higher opinion divergence is positively correlated with future returns in the three months after the announcements, which they interpret as supporting the Varian (1985) risk story. Our finding of higher abnormal returns after individuals buy stocks with higher earnings uncertainty seems consistent with the results of Garfinkel and Sokobin. Diether, Malloy, and Scherbina (2002) and Dimitrov, Jain, and Tice (2006) provide evidence consistent with the Miller prediction.

16

institutional holdings. So far, however, there has been no direct evidence using individual

trading data in the U.S. that is consistent with this idea. In particular, Hirshleifer, Myers,

Myers, and Teoh (2002) hypothesize that if the drift reflects mispricing, then more

sophisticated investors (i.e., institutions) should buy immediately after good news (before

an upward drift) and vice versa after bad news. They conjecture that naïve individual

investors would take the opposite side of these transactions, and their trading would slow

down the adjustment of prices to the information. Hirshleifer et al. investigate this idea

using a sample of retail clients of a discount broker, but conclude that their data does not

support it.

In Table 9 we sort all stocks each quarter according to the analysts’ earnings

surprise measure and put the stocks in five categories (quintile 1 is the most negative

surprise and quintile 5 the most positive surprise). We look at net individual trading for

each stock over a certain period, and present in Panel A the average of the net individual

trading measure of all the stock-quarters in each of the different quintiles (with t-statistics

testing the hypothesis of zero net individual trading). We observe a “news-contrarian”

pattern in the post-event period: individuals buy those that experience bad news (quintile

1) and sell those that experience good news (quintile 5). In Panel B we observe an

opposite pattern for institutions in that they behave in a “news-momentum” manner: they

sell after the event those stocks that experience bad news and buy those stocks that

experience good news.

The results in Table 9 seem consistent with the idea that individuals trade in the

direction that would slow down the adjustment of prices to the news after earnings

announcements. However, Kaniel, Saar, and Titman (2007) show that individual

investors generally trade in a contrarian fashion. If prices move prior to the earnings

announcements to reflect information that would only later be announced publicly, it is

possible that the results in Table 9 simply show the tendency of individuals to trade in

response to price patterns as opposed to trading in response to the public release of news.

17

To differentiate between these two potential effects, we look at net trading by

individuals and institutions during the event window and in the post-event period

conditional on two variables: earnings surprise and abnormal return prior to the earnings

announcement. Conditioning on return before the event is meant to enable us to discern

whether investor behavior is motivated by past prices (e.g., the contrarian tendency of

individual investors documented in Kaniel, Saar, and Titman (2007)) or whether it is

motivated by the news itself, where we use the earnings surprise measure as a proxy for

the news content of the announcement.

We sort all events according to earnings surprise and put them in five groups:

quintile 1 is the most negative surprise and quintile 5 is the most positive surprise. We

also independently sort on cumulative abnormal return in the three months prior to the

event.22 Panel A of Table 10 shows a very clear picture: individuals trade during the

event window predominantly in response to prior price patterns, not the earnings surprise.

IndNT[0,1] is positive and significant (i.e., individuals buy) across the first line of the

panel that corresponds to the quintile of stocks that experienced the most negative return

before the event, but there is not much difference between net individual trading of bad

news and good news stocks. Similarly, individuals sell irrespective of the earnings

surprise if the return before the event was positive (i.e., abnormal return quintile 5). In

other words, individuals simply behave as contrarians.

Panel B of Table 10 looks at IndNT[2,61], or the trading of individuals during the

post-event period. Here we observe a more complex behavior. It is still the case the

individuals behave as contrarians: sell (buy) stocks that went up (down) in price before

the event. However, there is also a “news-contrarian” effect whereby individuals buy

more of the stocks that went down in price and had bad news than stocks that went down 22 The period over which we consider return prior to the event is somewhat arbitrary, but we present the analysis using three months of return before the event because we are also using three months of return after the event. We repeated the analysis conditioning on 20-day and 10-day returns prior to the events, and our conclusions did not change: the same (statistically significant) patterns were found conditioning on these two shorter periods.

18

in price but had good news. Similarly, for stocks that had the highest return before the

event, individuals sell less of those stocks with bad news than those with good news. The

statistical significance of the “news-contrarian” effect is demonstrated in the last column

of the table (Q5−Q1).

It is interesting to note that individuals seem to be much more active in the cells

(Q1,Q1) and (Q5,Q5) of the table: the “dogs” and “angels” cells. The dogs had both the

most negative return before the event and bad news, and individuals buy them almost

twice as much as they buy stocks in other cells of the table (that either experience

negative return or bad news). The angels had both the most positive return before the

event and good news, in which case individual sell them almost twice as much as they

sell the stocks in any other cell in the table.23 Intense individual buying or selling

therefore seems to be shaped by both past return and news in a “contrarian” fashion.

The trading of institutions is reported in Table 11. Panel B shows two significant

patterns in the behavior of institutions in the post-event period. The first is a momentum

effect, where they buy stocks that experience positive returns before the event and sell

stocks that experience negative returns. The second effect is a “news-momentum” effect:

they buy (sell) much more of the stocks that both went up (down) in price prior to the

event and had good (bad) news than those that went up (down) in price and had bad

(good) news. The analysis in the Q5−Q1 column shows that the news-momentum pattern

is statistically significant. The behavior of institutions in the post-event period therefore

seems to mirror that of the individuals.

As we mention in Section II.C., the magnitude of the change in prices at the time

of the event could be viewed as an alternative measure of earnings surprise. In particular,

the sell-side analysts’ forecasts that we use to construct the ES measure can be non-

representative of the entire market’s expectations, biasing the surprise measure.

23 We have verified that the differences between these cells two cells and other cells in the table are statistically significant.

19

Therefore, the news-contrarian or news-momentum patterns we observe in the post-event

period could also be responses to price changes during the event window itself.24 We

believe that conceptually, however, a post-event trading pattern that goes in opposite

direction to returns at the time of the announcement should not be simply labeled as

“contrarian” or “momentum” (i.e., as a response to prices rather than news) because at

the time of the announcement both the price adjustment and the analysts’ earnings

surprise measure are proxies for the same thing—the change in beliefs of market

participants.

To use both proxies for earnings surprise in a single framework we regress net

investor trading in the post-event period ([2,61]) on abnormal returns before the event (to

control for contrarian tendencies), and both the ES measure and abnormal returns during

the event window (as proxies for surprise). As in Table 6, we use a methodology in the

spirit of Fama and MacBeth (1973) whereby we run a cross-sectional regression

separately for each quarter, and then use time-series variability in the 16 estimates we

have for each coefficient to compute standard errors. We present models where pre-event

abnormal returns and net individual trading are measured over either 10 days or 60 days

before the announcements, and show the results of the regressions for the entire sample

and separately for three size groups (small, mid-cap, and large stocks).

The results for the entire sample in Panel A of Table 12 indicate that net

individual trading after the event is negatively related to both proxies for earnings

surprise (ES and CAR[0,1]) in the specification with a 60-day pre-event period. Looking at

separate regressions by size groups, the analysts’ earnings surprise is not significantly

related to post-event net individual trading, while the other proxy for surprise, the

abnormal return at the time of the event, is significant in all size groups.25 The contrarian

24 We find similar patterns of trading in the post-event period when we double-sort on CAR[0,1] (as an alternative measure of earnings surprise) and CAR[-60,-1]. 25 The results of the regressions suggest that the while individual investor behavior in the post-event period seems to respond to news released during the event, it is conceivable that part of the reaction is to the

20

pattern (negative relation between post-event net individual trading and pre-event returns)

is observed in mid-cap and large stocks.

The pattern we observe whereby individuals trade after the event in opposite

direction to the news has the potential to impede the adjustment of prices to the news. In

fact, combining the results in sections III and IV could suggest that individual investors

prior to the event buy (sell) the stocks that will experience high (low) abnormal returns

following the event, and then reverse their positions in the post-event period. Such a

trading strategy could potentially be profitable, and at the same time it could also slow

down the adjustment of prices after the event and give rise to (or sustain) the drift.

Unfortunately, our data do not enable us to identify specific individual investors and

observe their strategies. Our net individual trading measure represents a fictitious

“aggregate” or representative individual investor, and therefore we cannot say for sure

that the profitable strategy above is actually pursued by certain traders. It is, however,

consistent with the relationships between return and trading that we observe.

While our results suggest that individual investor trading could in fact contribute

to the formation of a drift, the interpretation of the results seems very different from the

hypothesis discussed by Hirshleifer et al (2002). They propose that individuals are naïve

investors who lose money by trading in opposite direction to the drift, while institutions

trade as if they know a drift exists and are taking advantage of it. We, on the other hand,

show evidence consistent with the idea that while individuals on the NYSE trade in a

direction that slows down the adjustment of prices after earnings announcements, they

could be reversing potentially profitable positions to which they have entered before the

events, a behavior that could hardly be characterized as “naïve.”

pattern of price adjustment to the news at the time of the event. We do not make an attempt here to try to separate these two (potentially inseparable) effects.

21

V. Relating our Findings to the Extant Literature

In this section we compare our results to the extant literature on the buying and selling of

individuals and institutions around earnings announcements.26 It is noteworthy that our

unconditional results concerning the trading of individuals on and after earnings

announcements differ from those in papers that utilize small trades as a proxy for

individual investor trading. Lee (1992) and Frazzini and Lamont (2006) document net

small trade buying on the announcement date and in the immediate aftermath of the

event, which they argue is consistent with the “attention-grabbing” hypothesis, i.e., that

individuals are more likely to initiate purchases of stocks that grab their attention (e.g.,

due to an earnings announcement). This is in direct contrast to our findings that

individuals buy stocks in the month prior to earnings announcement and are net sellers at

the time of the announcement (mainly for larger firms) and in the several days following

the event.27 One way to reconcile these findings is to note that since institutions often

break up their orders, small trades may come from institutions rather than individuals. As

we mention in Section II, Campbell, Ramadorai, and Schwartz (2007) examine data on

trades and changes in institutional holdings and argue that the smallest trades (below

$2,000) are more likely to come from institutions rather than individuals.

We should also note that our evidence that individual trading prior to the event

predicts abnormal returns both during and after the event is in contrast to Welker and

Sparks (2001), who investigate the behavior of individuals and institutions around

various public announcements from November 1990 through January 1991 using the

TORQ database.28 They do not find a consistent relation between the market reaction at

26 While our focus is on the directional trading (buying and selling) of individuals and institutions around earnings announcements, there are papers that look at volume (i.e., non-directional trading) of different investors around these events. See, for example, Bhattacharya (2001) and Dey and Radhakrishna (2007). 27 Lee (1992) finds that small trade buying starts one day prior to the event, which is consistent with our results of individual buying before the announcements, though Frazzini and Lamont (2006) find no small trade buying before the announcement date itself. 28 Nofsinger (2001) also uses TORQ to investigate individual and institutional trading around a variety of firm-specific and macro-economic announcements.

22

the time of the announcement and the direction of trading by investors before the news

release. The TORQ database they examine contains data from the NYSE’s CAUD files

that is similar to the data we use here. However, they examine a substantially smaller data

set (there are 144 stocks in the TORQ database that are observed for only three months

with only 82 earnings announcements) that is not likely to provide enough power to

detect the abnormal returns.

Campbell, Ramadorai, and Schwartz (2007) develop a methodology where they

regress quarterly changes in institutional holdings on trade imbalances for different sizes

of trades (from the TAQ database) to infer trading by institutions. Using their

methodology, they report that institutions buy (sell) stocks before positive (negative)

earnings surprises and that stocks they buy (sell) tend to experience positive (negative)

drift after the event. We, on the other hand, do not find a significant predictive ability

when it comes to institutions. When Campbell et al. use the more standard methodology

that attributes large trades to institutions, the predictive ability of institutions for the

earnings surprise and the drift disappears.29 While they find that net small trades

positively forecast the drift, this result does not obtain using their more complex

methodology (i.e., the one utilizing institutional holdings data) to identify the imbalances

of individuals.

Vieru, Perttunen, and Schadewitz (2004) investigate the trading of individual and

institutional investors on the Helsinki Stock Exchange around interim earnings

announcements. They document that net trading by the very active individual traders in

the three days prior to the event is positively related to abnormal returns in the five days

29 It is interesting to note that Campbell et al.’s finding of predictive power by institutions comes out of a methodology that involves changes in quarterly institutional ownership. Their result may therefore be related to the results of both Ali, Durtschi, Lev, and Trombley (2004) and Baker, Litov, Wachter, and Wurgler (2005) that changes in institutional ownership (or specifically mutual fund ownership in the case of the latter paper) are positively related to the abnormal return at the time of the subsequent earnings announcement. Seppi (1992) finds that price impacts of block trades prior to earnings announcements are correlated with the earnings surprise, which is also consistent with the idea that institutions may trade on useful information prior to the event.

23

that start with the event day. This, however, does not hold for all other individuals, and

the opposite result is observed for institutions. Their result on individual trading in

Finland could be consistent with our findings in the U.S. in the sense that while we

observe net individual trading in the aggregate (without the ability to separate different

classes of individual investors), it is possible that the intense net imbalances we observe

are being driven by more active individual traders.

On the question of whether the behavior of individuals or institutions after the

announcement is related to the drift, our results seem stronger than those previously

documented in the U.S. In particular, Hirshleifer, Myers, Myres, and Teoh (2002) use a

sample of clients of one discount broker from 1991 through 1996 to test the hypothesis

that naïve individual investors would trade in opposite direction to the news following

earnings announcements, and their trading would slow down the adjustment of prices to

the information. They find that individual investors are net buyers after negative earnings

surprises, consistent with helping create a drift by slowing down the adjustment of prices.

However, this is not mirrored by individual selling after positive earnings surprises, and

they end up concluding that their evidence does not support the hypothesis that individual

investors drive the post-earnings announcement drift.30 Lee (1992) and Shanthikumar

(2004) find positive small trade imbalances, which they attribute to individual investors,

after both good and bad surprises.31

All these results contrast with the clear evidence of “news-contrarian” behavior

we document for individual investors following earnings announcements. Since we

observe this behavior after both good and bad news, these results are more consistent 30 Battalio and Mendenhall (2005) reach the conclusion that individual investors contribute to the drift using a different exercise. They find that large trade imbalances are correlated with analysts’ forecasts errors, while small trade imbalances are correlated with forecast errors from a naïve time-series model. They claim that their results are consistent with the idea that individuals display behavior that causes the post-earnings announcement drift because small trade imbalances reflect beliefs that significantly underestimate the implications of current earnings innovations for future earnings levels. 31 Shanthikumar (2004) also finds that while large trade imbalances are indeed in the direction of the surprise in the first month after the announcement, starting from the second month small trade imbalances can be found in the direction of the announcements.

24

with the hypothesis that individual investor trading could be contributing to the drift

phenomenon. Our results on the behavior of individuals and institutions in the post-event

period in the U.S. are therefore consistent with findings from Finland, where Vieru,

Perttunen, and Schadewitz (2005) document that individuals (especially those trading

infrequently) exhibit news-contrarian behavior while institutions exhibit news-

momentum behavior.32

VI. Conclusions

In this paper we investigate the relationship between net trading of individual and

institutional investors around earnings announcements and return patterns. Our results

provide answers to some questions, but in turn raise additional questions that we hope

future work will address. The two main insights that arise from our work is that (i) net

individual trading prior to earnings announcements has predictive power with respect to

abnormal returns at the time of the announcement and in the post-event period, and (ii)

individuals in the U.S. exhibit news-contrarian trading following earnings

announcements that could inhibit the adjustment of prices to information.

Starting with the behavior of individuals before the events, there are at least a

couple of explanations that are consistent with our results. First, it is possible that

individuals simply buy (sell) when other traders push prices down (up), and hence benefit

from the subsequent reversion in prices. This is essentially the argument made by Kaniel,

Saar, and Titman (2007) who find that net individual trading has predictive power with

respect to short-horizon return, and who interpret this return pattern as arising from risk

averse liquidity provision on the part of individuals (in the spirit of the theoretical models

of Grossman and Miller (1988) and Campbell, Grossman, and Wang (1993)). An

32 The result that institutions trade in a news-momentum fashion has also been documented by Welkers and Sparks (2001) using TORQ and by Ke and Ramalingegowda (2005) using data on quarterly institutional holdings. Cohen, Gompers, and Vuolteenaho (2002) show that institutions buy stocks following positive cash-flow news using a measure of cash-flow news derived from a vector autoregression.

25

alternative explanation is that individual investors who trade on the NYSE have useful

information about stocks, and are therefore able to forecast the response of the stocks’

prices to the new information that is revealed in the earnings announcements.

After the announcements, individuals exhibit both contrarian and news-contrarian

behavior. Both of these tendencies can be driven by liquidity provision, because we show

that institutions pursue momentum and news-momentum trading. Hence, individuals

could be responding to the institutions’ need, and as such their news-contrarian behavior

may seem a bit naïve. This is the argument that Hirshleifer et al. use when they discuss

the hypothesis that individual investors generate the drift by trading in opposite direction

to the institutions. Their characterization as naïve is based on the fact that stocks exhibit a

drift, and hence trading in opposite direction would imply that individuals must lose to

institutions.

However, it could be that the individuals’ trading pattern after the announcement

is evidence of profit-taking behavior. This would tie their predictive ability before the

events to their contrarian trading after the event. In other words, they could simply be

profitably reversing positions to which they have entered prior to the announcements,

hence reducing their risk after the events. Such behavior is consistent with the theoretical

work of Hirshleifer, Subrahmanyam, and Titman (1994). In their model, lucky or skillful

investors uncover valuable information early, while unlucky or low-ability investors

uncover it only later. In equilibrium, investors who discover the information early trade

aggressively initially and then partially reverse their trades. Prices continue to adjust to

information in the late trading round (hence creating a drift) due to the trading of those

investors who become informed late. The strategy of the early-informed traders appears

to exhibit a “profit-taking” property.

Could individual investors be these early-informed traders? It is hard to say. Their

trading strategy would suggest it, but the economics of information gathering and the vast

resources institutions devote to generating excess returns seem to make this a somewhat

26

counter-intuitive conclusion. On the other hand, institutions may be averse to trading too

aggressively immediately before an announcement for fear of litigation. Furthermore,

Coval, Hirshleifer, and Shumway (2002) claim that individuals are better positioned to

exploit a given informational advantage. This, because it is easier to create and unwind

small positions profitably than larger ones, and individuals are also less constrained than

a typical mutual funds (at least with respect to diversification requirements or short-

selling).

While our comprehensive dataset of individual and institutional trading enables us

to document these interesting patterns and provide new insights, it does have some

limitations. Most notably, we do not observe the strategies of specific individuals and

institutions, and hence are unable to definitively answer the question whether trading by

individuals after the event is naïve or rather it is part of a profit-taking strategy. It is likely

that there is some heterogeneity among individual investors (and definitely among

institutions), and hence more fine-tuned conclusions would need to await additional

datasets.

27

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31

32

Table 1 Summary Statistics

The sample of stocks for the study consists of all common, domestic stocks that were traded on the NYSE at any time between January 1, 2000 and December 31, 2003 with records in the CRSP database. We use ticker symbol and CUSIP to match the stocks to a special dataset containing daily aggregated buying and selling volume of individuals and institutions that was provided to us by the NYSE. We then use IBES and COMPUSTAT to identify all the dates where stocks in our sample had earnings announcements, and impose two restrictions on the sample. First, we require 60 days of data prior to and after the announcements. Second, we require that there is an observation in the IBES database of mean analysts’ forecast in the month prior to the earnings announcement (and also the actual earnings number). Our screens result in a final sample of 1,821 stocks with 17,564 earnings announcement events. In Panel A we provide summary statistics from the CRSP database. For each stock we compute the following time-series measures: AvgCap is the average monthly market capitalization over the sample period; AvgPrc is the average daily closing price; AvgTurn is the average weekly turnover (number of shares traded divided by the number of shares outstanding); AvgVol is the average weekly dollar volume; and StdRet is the standard deviation of weekly returns. We then sort the stocks by market capitalization into ten deciles, and form three size groups: small stocks (deciles 1, 2, 3, and 4), mid-cap stocks (deciles 5, 6, and 7), and large stocks (deciles 8, 9, and 10). The cross-sectional mean and median of these measures are presented for the entire sample and separately for the three size groups. In Panel B we provide the number of earnings announcement events used in the analysis for each month during the sample period. Panel A: Summary Statistics of Sample Stocks (from CRSP) AvgCap

(in million $)AvgPrc

(in $) AvgTurn

(in %) AvgVol

(in million $) StdRet (in %)

All stocks Mean 5,783.5 64.16 2.67 125.00 0.0726 Median 1,049.8 22.87 2.19 27.06 0.0611Small stocks Mean 354.5 15.49 2.65 11.34 0.0884 Median 353.2 12.40 1.83 5.86 0.0736Mid-Cap stocks Mean 1,367.5 27.28 3.29 45.74 0.0676 Median 1,279.6 24.37 2.62 34.15 0.0601Large stocks Mean 14,652.0 140.38 3.25 321.40 0.0607 Median 5,314.5 37.59 2.61 170.62 0.0532

Panel B: Number of Earnings Announcement Events in our Sample Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 0 0 17 949 343 84 929 345 90 786 288 862001 638 488 160 852 283 82 829 338 71 866 289 782002 626 456 120 843 304 73 879 282 78 903 272 872003 589 510 148 851 318 75 879 290 80 10 0 0All years 1853 1454 445 3495 1248 314 3516 1255 319 2565 849 251

33

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29.8

Table 3 Predicting Returns using Investor Trading before the Announcements

This table presents analysis of market-adjusted returns on and after earnings announcements conditional on different levels of net individual or institutional trading before the event. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year. We then subtract from the imbalance measure the average of individual imbalances over the sample period to get the net individual trading measure. We follow a similar procedure to construct the net institutional trading measure. In Panel A, we sort all stocks each quarter according to net individual trading in the 10 trading days prior to the announcement (IndNT[-10,-1]), and put the stocks in five categories (quintile 1 contains the stocks that individuals sold the most and quintile 5 contains the stocks individuals bought the most). We compute for each stock the cumulative market-adjusted return over a certain period, and present in the table the average of the market-adjusted abnormal returns of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero abnormal returns). Panel B presents a similar analysis conditional on net institutional trading before the announcements (InsNT[-10,-1]). We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Abnormal Returns Conditional on Net Individual Trading before the Event Time Periods IndNT[-10,-1] [0,1] [2,6] [2,11] [2,21] [2,61] [0,61] Q1 Mean -0.0070** -0.0040** -0.0050** -0.0100** -0.0280** -0.0330** (Selling) t-stat. (-5.35) (-3.82) (-2.99) (-5.14) (-8.25) (-9.19) Q2 Mean 0.0002 0.0003 -0.0009 -0.0020 -0.0200** -0.0190** t-stat. (0.16) (0.31) (-0.69) (-1.31) (-6.41) (-5.50) Q3 Mean 0.0041** 0.0039** 0.0043** 0.0071** -0.0010 0.0031 t-stat. (3.46) (3.95) (3.33) (3.85) (-0.33) (0.85) Q4 Mean 0.0084** 0.0074** 0.0098** 0.0129** 0.0102** 0.0191** t-stat. (6.48) (6.63) (6.40) (6.35) (2.85) (4.80) Q5 Mean 0.0080** 0.0031* 0.0055** 0.0089** 0.0135** 0.0212** (Buying) t-stat. (5.18) (2.28) (3.10) (3.68) (3.30) (4.78)

Mean 0.0145** 0.0072** 0.0100** 0.0189** 0.0413** 0.0545** Q5–Q1 t-stat. (7.38) (4.16) (4.31) (6.09) (7.79) (9.53) Panel B: Abnormal Returns Conditional on Net Institutional Trading before the Event Time Periods InsNT[-10,-1] [0,1] [2,6] [2,11] [2,21] [2,61] [0,61] Q1 Mean 0.0025 -0.0000 -0.0010 -0.0040* -0.0210** -0.0180** (Selling) t-stat. (1.93) (-0.04) (-0.97) (-2.10) (-6.28) (-5.13) Q2 Mean 0.0039** 0.0038** 0.0065** 0.0119** 0.0112** 0.0150** t-stat. (3.07) (3.41) (4.13) (5.30) (2.85) (3.61) Q3 Mean 0.0055** 0.0030* 0.0056** 0.0098** 0.0095* 0.0152** t-stat. (4.05) (2.48) (3.48) (4.51) (2.38) (3.55) Q4 Mean 0.0034* 0.0038** 0.0051** 0.0028 -0.0010 0.0034 t-stat. (2.53) (3.44) (3.55) (1.44) (-0.42) (0.87) Q5 Mean -0.0010 0.0002 -0.0010 -0.0030 -0.0220** -0.0230** (Buying) t-stat. (-0.89) (0.17) (-0.90) (-1.79) (-7.38) (-6.87)

Mean -0.0040* 0.0002 0.0001 0.0006 -0.0020 -0.0050 Q5–Q1 t-stat. (-2.01) (0.15) (0.07) (0.23) (-0.37) (-0.93)

34

Table 4 Returns Conditional on Net Individual Trading before Event and Earnings Surprise

This table presents analysis of market-adjusted returns on and after earnings announcements conditional on both different levels of net individual trading before the event (IndNT[-10,-1]) and the extent of earnings surprise. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. Earnings surprise (ES) is defined as the actual earnings minus the earnings’ forecast one month before the announcement, divided by the price on the forecast day. For the analysis in the table, we sort stocks independently along two dimensions: ES and IndNT[-10,-1]. We put the stocks into 25 categories: five groups of earnings surprise and five groups of net individual trading. We compute for each stock the cumulative market-adjusted return over a certain period, and present in the table the average of the market-adjusted abnormal returns of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero abnormal returns). We examine the cumulative market-adjusted returns over three periods: the event window ([0,1]) in Panel A, the post-event period [2,61]) in Panel B, and the combined return on and after the event ([0,61]) in Panel C. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Abnormal Return on [0,1] Conditional on Individual Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) IndNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0300** -0.0170** -0.0008 0.0036 0.0148** 0.0446** (Selling) t-stat. (-9.72) (-7.49) (-0.27) (1.57) (5.92) (11.35) Q2 Mean -0.0200** -0.0100** 0.0029 0.0096** 0.0216** 0.0419** t-stat. (-5.74) (-4.83) (1.12) (4.25) (7.59) (9.32) Q3 Mean -0.0170** -0.0060** 0.0097** 0.0140** 0.0221** 0.0396** t-stat. (-4.61) (-3.09) (4.31) (6.00) (7.09) (8.08) Q4 Mean -0.0190** -0.0040 0.0124** 0.0240** 0.0339** 0.0526** t-stat. (-4.83) (-1.87) (4.79) (9.48) (11.36) (10.77) Q5 Mean -0.0110** -0.0030 0.0109** 0.0162** 0.0333** 0.0441** (Buying) t-stat. (-2.92) (-1.28) (2.82) (5.91) (10.51) (8.89)

Mean 0.0191** 0.0138** 0.0117* 0.0126** 0.0185** Q5–Q1 t-stat. (3.85) (4.17) (2.47) (3.58) (4.61) Panel B: Abnormal Return on [2,61] Conditional on Individual Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) IndNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0410** -0.0350** -0.0240** -0.0200** -0.0150* 0.0252* (Selling) t-stat. (-4.48) (-5.92) (-3.32) (-3.18) (-2.09) (2.18) Q2 Mean -0.0140 -0.0200** -0.0300** -0.0280** -0.0080 0.0063 t-stat. (-1.39) (-3.71) (-4.70) (-4.46) (-0.93) (0.49) Q3 Mean 0.0096 -0.0100 -0.0090 0.0049 0.0060 -0.0040 t-stat. (0.69) (-1.91) (-1.51) (0.70) (0.68) (-0.22) Q4 Mean 0.0249* -0.0009 -0.0006 -0.0020 0.0359** 0.0110 t-stat. (2.19) (-0.015) (-0.09) (-0.34) (3.97) (0.76) Q5 Mean 0.0073 0.0150* -0.0030 -0.0060 0.0397** 0.0324* (Buying) t-stat. (0.76) (1.98) (-0.34) (-0.79) (4.53) (2.45)

Mean 0.0479** 0.0496** 0.0213 0.0144 0.0550** Q5–Q1 t-stat. (3.55) (5.21) (1.86) (1.47) (4.83)

35

Panel C: Abnormal Return on [0,61] Conditional on Individual Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) IndNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0680** -0.0510** -0.0250** -0.0160* 0.0005 0.0687** (Selling) t-stat. (-7.21) (-8.12) (-3.11) (-2.38) (0.06) (5.57) Q2 Mean -0.0320** -0.0290** -0.0270** -0.0180* 0.0145 0.0461** t-stat. (-2.83) (-5.06) (-3.88) (-2.51) (1.61) (3.25) Q3 Mean -0.0120 -0.0170** 0.0017 0.0198** 0.0305** 0.0427** t-stat. (-1.00) (-2.97) (0.25) (2.60) (2.99) (2.69) Q4 Mean 0.0019 -0.0040 0.0123 0.0229** 0.0739** 0.0721** t-stat. (0.15) (-0.65) (1.63) (3.06) (7.26) (4.49) Q5 Mean -0.0070 0.0116 0.0078 0.0116 0.0757** 0.0831** (Buying) t-stat. (-0.74) (1.44) (0.80) (1.41) (7.66) (5.86)

Mean 0.0608** 0.0625** 0.0331** 0.0277** 0.0752** Q5–Q1 t-stat. (4.34) (6.15) (2.64) (2.63) (5.93)

36

Table 5 Returns Conditional on Net Institutional Trading before Event and Earnings Surprise

This table presents analysis of market-adjusted returns on and after earnings announcements conditional on both different levels of net institutional trading before the event (InsNT[-10,-1]) and the extent of earnings surprise. We construct the net institutional trading measure by first computing an imbalance measure: subtracting the value of the shares sold by institutions from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. Earnings surprise (ES) is defined as the actual earnings minus the earnings’ forecast one month before the announcement, divided by the price on the forecast day. For the analysis in the table, we sort stocks independently along two dimensions: ES and InsNT[-10,-1]. We put the stocks into 25 categories: five groups of earnings surprise and five groups of net institutional trading. We compute for each stock the cumulative market-adjusted return over a certain period, and present in the table the average of the market-adjusted abnormal returns of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero abnormal returns). We examine the cumulative market-adjusted returns over three periods: the event window ([0,1]) in Panel A, the post-event period [2,61]) in Panel B, and the combined return on and after the event ([0,61]) in Panel C. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Abnormal Return on [0,1] Conditional on Institutional Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) InsNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0120** -0.0060** 0.0086** 0.0076** 0.0216** 0.0340** (Selling) t-stat. (-3.72) (-3.08) (2.85) (3.05) (7.93) (7.74) Q2 Mean -0.0220** -0.0070** 0.0093** 0.0159** 0.0250** 0.0467** t-stat. (-6.11) (-3.03) (3.38) (7.04) (8.36) (10.07) Q3 Mean -0.0230** -0.0030 0.0071** 0.0197** 0.0311** 0.0541** t-stat. (-5.46) (-1.56) (2.96) (8.03) (9.82) (10.23) Q4 Mean -0.0180** -0.0120** 0.0081** 0.0146** 0.0268** 0.0445** t-stat. (-4.19) (-5.27) (2.68) (5.66) (8.82) (8.75) Q5 Mean -0.0210** -0.0110** 0.0049 0.0079** 0.0200** 0.0412** (Buying) t-stat. (-6.61) (-4.82) (1.86) (3.45) (6.94) (9.53)

Mean -0.0090 -0.0050 -0.0040 0.0003 -0.0020 Q5–Q1 t-stat. (-1.88) (-1.47) (-0.93) (0.09) (-0.40) Panel B: Abnormal Return on [2,61] Conditional on Institutional Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) InsNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0380** -0.0080 -0.0120 -0.0300** -0.0120 0.0257* (Selling) t-stat. (-4.82) (-1.41) (-1.65) (-4.36) (-1.51) (2.26) Q2 Mean 0.0218 0.0060 -0.0100 -0.0110 0.0474** 0.0256 t-stat. (1.72) (0.97) (-1.17) (-1.61) (4.88) (1.61) Q3 Mean 0.0446** -0.0070 -0.0150* 0.0034 0.0231* -0.0220 t-stat. (3.26) (-1.13) (-2.29) (0.47) (2.47) (-1.30) Q4 Mean 0.0060 -0.0180** -0.0170* 0.0034 0.0221** 0.0161 t-stat. (0.56) (-2.88) (-2.42) (0.46) (2.68) (1.21) Q5 Mean -0.0340** -0.0260** -0.0150* -0.0180** -0.0150* 0.0192 (Buying) t-stat. (-4.06) (-4.70) (-2.50) (-2.99) (-2.06) (1.73)

Mean 0.0039 -0.0180* -0.0030 0.0119 -0.0030 Q5–Q1 t-stat. (0.34) (-2.17) (-0.31) (1.29) (-0.24)

37

Panel C: Abnormal Return on [0,61] Conditional on Institutional Trading in [-10,-1] and Earnings Surprise (Negative) Earnings Surprise (Positive) InsNT[-10,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0510** -0.0160* -0.0030 -0.0220** 0.0099 0.0610** (Selling) t-stat. (-6.14) (-2.52) (-0.38) (-2.87) (1.10) (4.99) Q2 Mean -0.0050 -0.0001 -0.0001 0.0059 0.0754** 0.0801** t-stat. (-0.38) (-0.02) (-0.01) (0.83) (7.10) (4.87) Q3 Mean 0.0171 -0.0100 -0.0080 0.0256** 0.0567** 0.0396* t-stat. (1.21) (-1.61) (-1.04) (3.16) (5.42) (2.24) Q3 Mean -0.0100 -0.0290** -0.0080 0.0183* 0.0521** 0.0620** t-stat. (-0.86) (-4.24) (-1.01) (2.36) (5.47) (4.19) Q5 Mean -0.0550** -0.0360** -0.0100 -0.0100 0.0066 0.0614** (Buying) t-stat. (-6.32) (-5.93) (-1.55) (-1.45) (0.80) (5.13)

Mean -0.0040 -0.0210* -0.0070 0.0120 -0.0030 Q5–Q1 t-stat. (-0.31) (-2.37) (-0.69) (1.20) (-0.27)

38

Table 6 Regressions Relating Pre-Event Trading of Investors to Future Abnormal Returns

This table presents regression analysis relating abnormal returns on and after the event to pre-event trading by individuals or institutions. In order to overcome potential econometric problems associated with contemporaneously correlated errors for earnings announcements that are clustered in time (i.e., in the same quarter), we use a methodology in the spirit of Fama and MacBeth (1973). We first run a cross-sectional regression separately for each quarter. We then use time-series variability in the 16 estimates we have for each coefficient (one from each quarterly regression) to get the standard error for the mean coefficient. In Panel A the dependent variables is cumulative abnormal returns (CAR[0,61]) and the regressors include the earnings surprise measure (ES), net individual trading before the event (IndNT[-10,-1] or IndNT[-60,-1]), and additional control variables (contemporaneous net individual trading and past abnormal return). We present the results of the regressions for the entire sample and separately for three size groups. In Panel B we use analogous specifications to relate abnormal returns during and after the event to pre-event net institutional trading. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Using Pre-Event Net Individual Trading to predict CAR[0,61]

Intercept ES IndNT[-60,-1] IndNT[-10,-1] IndNT[0,61] CAR[-60,-1] CAR[-10,-1]

-0.0151 2.3244 0.0833** -0.0379** 0.3430 (-1.85) (0.93) (6.29) (-9.08) (0.74)

-0.0167 3.1586 0.0275** -0.0322** -0.0401 All

Stoc

ks

(-1.60) (0.95) (2.86) (-13.62) (-0.88) -0.0233** 2.2006 0.0444 -0.0310** 0.4837

(-3.04) (0.94) (1.56) (-6.72) (0.88) -0.0233* 2.0227 0.0275* -0.0241** -0.0573 Sm

all

Stoc

ks

(-2.46) (0.94) (2.28) (-11.73) (-1.06) -0.0344 0.9427 0.0965** -0.0447** -0.1013 (-1.10) (1.21) (3.40) (-7.45) (-1.60)

-0.0336 1.3731 0.0278** -0.0449** -0.0514

Mid

-Cap

St

ocks

(-1.07) (1.70) (2.70) (-7.42) (-1.43) -0.0173 42.356 0.3743 -0.0928** -0.1608* (-1.06) (1.15) (1.94) (-8.01) (-2.21)

-0.0168 41.663 0.0645* -0.0908** -0.0440 Larg

e St

ocks

(-1.01) (1.13) (2.46) (-8.06) (-1.49)

39

Panel B: Using Pre-Event Net Institutional Trading to predict CAR[0,61]

Intercept ES IndNT[-60,-1] IndNT[-10,-1] IndNT[0,61] CAR[-60,-1] CAR[-10,-1]

-0.0142 2.2025 -0.0210 0.0031 0.4040 (-1.75) (0.93) (-1.58) (0.89) (0.86)

-0.0151 2.4482 -0.0038** 0.0005 0.0093 All

Stoc

ks

(-1.61) (0.94) (-2.69) (0.23) (0.20) -0.0223* 2.0190 -0.0194 0.0053 0.4332

(-2.42) (0.94) (-1.69) (1.95) (0.95) -0.0234* 2.3152 -0.0046* 0.0035* 0.0464 Sm

all

Stoc

ks

(-2.35) (0.95) (-2.52) (1.99) (0.98) -0.0247 1.4895 -0.0262 -0.0049 -0.0379 (-1.11) (1.85) (-1.19) (-1.80) (-0.60)

-0.0256 1.8019* -0.0061 -0.0050* -0.0145

Mid

-Cap

St

ocks

(-1.09) (2.23) (-1.16) (-2.01) (-0.41) -0.1014 67.176 -0.2211 -0.0025 -0.1274 (-1.14) (1.47) (-1.06) (-0.67) (-1.59)

-0.0057 40.306 -0.0197 -0.0030 -0.0011 Larg

e St

ocks

(-0.33) (1.10) (-1.17) (-0.82) (-0.04)

40

Table 7 Investor Trading before the Announcement and Earnings Uncertainty

This table presents net individual and institutional trading before the earnings announcement conditional on different levels of uncertainty about the upcoming earnings release. As a measure of earnings uncertainty (EU) prior to the announcement we use dispersion in analysts’ forecasts: the high forecast minus the low forecast divided by the price on the forecast day. The high and low forecasts are from one month prior to the announcement, and we only use stocks with at least three analysts providing forecasts. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. We follow a similar procedure to construct the net institutional trading measure. In Panel A, we sort all stocks each quarter according the earnings uncertainty measure and put the stocks in five categories. We compute for each stock the net individual trading measure over a certain period before the announcement, and present in the table the average of net individual trading of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero net individual trading). Panel B presents a similar analysis for net institutional trading. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Individual Trading before the Announcement by Earnings Uncertainty Quintiles Time Periods EU [-60,-1] [-20,-1] [-10,-1] Q1 Mean -0.016 0.029** 0.021** (lowest) t-stat. (-0.88) (3.69) (4.50) Q2 Mean -0.063** 0.009 0.008 t-stat. (-2.94) (1.03) (1.53) Q3 Mean 0.000 0.017 0.016** t-stat. (-0.01) (1.72) (2.64) Q4 Mean 0.055* 0.048** 0.031** t-stat. (2.22) (4.63) (5.10) Q5 Mean 0.140** 0.053** 0.027** (highest) t-stat. (4.81) (4.23) (3.83)

Mean 0.156** 0.024 0.006 Q5–Q1 t-stat. (4.53) (1.64) (0.74) Panel B: Institutional Trading before the Announcement by Earnings Uncertainty Quintiles Time Periods EU [-60,-1] [-20,-1] [-10,-1] Q1 Mean 0.031 0.046* 0.024 (lowest) t-stat. (0.73) (1.99) (1.59) Q2 Mean 0.014 0.039 0.041** t-stat. (0.32) (1.73) (2.78) Q3 Mean -0.007 0.034 0.009 t-stat. (-0.16) (1.45) (0.58) Q4 Mean -0.074 0.006 -0.003 t-stat. (-1.63) (0.27) (-0.20) Q5 Mean -0.208** -0.066** 0.020 (highest) t-stat. (-4.32) (-2.80) (-1.37)

Mean -0.238** -0.112** -0.044* Q5–Q1 t-stat. (-3.75) (-3.39) (-2.09)

41

Table 8 Returns after Events Conditional on Pre-Event Investor Trading and Earnings Uncertainty

This table presents cumulative abnormal returns after the earnings announcement conditional on different levels of uncertainty about the upcoming earnings release and on net investor trading before the event. As a measure of earnings uncertainty (EU) prior to the announcement we use dispersion in analysts’ forecasts one month prior to the announcement (high minus low) divided by the price on the forecast day. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. We follow a similar procedure to construct the net institutional trading measure. For the analysis in Panel A, we sort stocks independently along two dimensions: EU and IndNT[-60,-1]. We put the stocks into 25 categories: five groups of earnings surprise and five groups of net individual trading. We compute for each stock the cumulative market-adjusted return (CAR[0,61]), and present in the table the average of the market-adjusted abnormal returns of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero abnormal returns). Panel B presents a similar analysis sorting on earnings uncertainty and net institutional trading before the event. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Abnormal Return on [0,61] by Individual Trading before the Event and Earnings Uncertainty (lowest) Earnings Uncertainty (highest) IndNT[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0170* -0.0310** -0.0110 -0.0340** -0.0450** -0.0280* (Selling) t-stat. (-2.46) (-4.34) (-1.54) (-3.80) (-3.99) (-2.01) Q2 Mean 0.0055 -0.0170* -0.0290** -0.0090 -0.0180 -0.0240 t-stat. (0.73) (-2.19) (-4.00) (-1.03) (-1.57) (-1.78) Q3 Mean -0.0150* 0.0027 0.0000 0.0000 -0.0070 0.0084 t-stat. (-2.10) (0.33) (0.01) (0.00) (-0.57) (0.64) Q3 Mean 0.0094 -0.0020 0.0015 -0.0008 0.0401* 0.0307 t-stat. (1.28) (-0.20) (0.18) (-0.09) (2.42) (1.81) Q5 Mean 0.0033 -0.0002 0.0145 0.0199* 0.0288* 0.0255 (Buying) t-stat. (0.35) (-0.02) (1.69) (2.19) (2.22) (1.40)

Mean 0.0204 0.0309** 0.0259* 0.0538** 0.0739** Q5–Q1 t-stat. (1.77) (2.86) (2.30) (4.21) (4.20) Panel B: Abnormal Return on [0,61] by Institutional Trading before the Event and Earnings Uncertainty (lowest) Earnings Uncertainty (highest) InsNT[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0190* -0.0110 0.0099 -0.0130 -0.0170 0.0021 (Selling) t-stat. (-2.40) (-1.41) (1.11) (-1.51) (-1.65) (0.15) Q2 Mean 0.0121 -0.0110 -0.0170* 0.0032 0.0340* 0.0219 t-stat. (1.58) (-1.37) (-2.10) (0.35) (2.19) (1.26) Q3 Mean -0.0050 0.0071 -0.0050 0.0005 0.0250 0.0298 t-stat. (-0.60) (0.83) (-0.70) (0.05) (1.51) (1.71) Q3 Mean -0.0002 -0.0160* -0.0070 -0.0001 -0.0110 -0.0100 t-stat. (-0.02) (-2.07) (-1.01) (-0.01) (-0.88) (-0.76) Q5 Mean -0.0020 -0.0180* -0.0050 -0.0130 -0.0250* -0.0230 (Buying) t-stat. (-0.33) (-2.49) (-0.66) (-1.51) (-2.32) (-1.78)

Mean 0.0171 -0.0070 -0.0150 -0.0007 -0.0080 Q5–Q1 t-stat. (1.59) (-0.67) (-1.29) (-0.06) (-0.54)

42

Table 9 Net Investor Trading Conditional on Earnings Surprise

This table presents analysis of net individual and institutional trading on and after earnings announcements conditional on different levels of the analysts’ earnings surprise measure. Earnings surprise (ES) is defined as the actual earnings minus the earnings’ forecast one month before the announcement, divided by the price on the forecast day. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. We follow a similar procedure to construct the net institutional trading measure. In Panel A, we sort all stocks each quarter according the earnings surprise measure and put the stocks in five categories. We compute for each stock the net individual trading measure over a certain period before the announcement, and present in the table the average of net individual trading of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero net individual trading). Panel B presents a similar analysis for net institutional trading. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Net Individual Trading on and after Earnings Announcements Time Periods ES [0,1] [2,6] [2,11] [2,21] [2,61]

Mean 0.005 0.020** 0.057** 0.113** 0.224** Q1 (negative) t-stat. (1.17) (3.22) (5.62) (6.61) (6.48) Q2 Mean 0.000 0.001 0.008 0.008 0.025 t-stat. (-0.08) (0.31) (1.28) (0.85) (1.21) Q3 Mean 0.001 -0.019** -0.024** -0.017 0.028 t-stat. (0.40) (-4.51) (-3.54) (-1.72) (1.38)

Mean -0.006** -0.027** -0.037** -0.050** -0.018 Q4 t-stat. (-2.65) (-6.11) (-5.49) (-4.61) (-0.89)

Mean -0.015** -0.042** -0.074** -0.115** -0.218** Q5 (positive) t-stat. (-3.42) (-6.21) (-6.82) (-6.82) (-6.55)

Mean -0.020** -0.062** -0.130** -0.228** -0.443** Q5–Q1 t-stat. (-3.28) (-6.75) (-8.82) (-9.49) (-9.22) Panel B: Net Institutional Trading on and after Earnings Announcements Time Periods ES [0,1] [2,6] [2,11] [2,21] [2,61]

Mean 0.013 -0.021 -0.085** -0.188** -0.437** Q1 (negative) t-stat. (1.61) (-1.84) (-4.92) (-7.24) (-8.93) Q2 Mean 0.024** -0.022* -0.061** -0.109** -0.223** t-stat. (4.07) (-2.36) (-4.56) (-5.64) (-6.08) Q3 Mean 0.009 0.019 0.011 0.001 0.017 t-stat. (1.19) (1.79) (0.72) (0.05) (0.40)

Mean 0.016* 0.051** 0.071** 0.104** 0.143** Q4 t-stat. (2.44) (5.00) (4.49) (4.51) (3.48)

Mean 0.026** 0.072** 0.113** 0.175** 0.325** Q5 (positive) t-stat. (3.20) (5.85) (6.31) (6.54) (6.28)

Mean 0.013 0.093** 0.198** 0.363** 0.762** Q5–Q1 t-stat. (1.16) (5.51) (7.95) (9.74) (10.70)

43

Table 10 Individual Trading after Event Conditional on Earnings Surprise and Pre-Event Return

This table presents analysis of net individual trading in the post-event period conditional on both different levels of abnormal returns before the announcement and the extent of earnings surprise. We construct the net individual trading measure by first computing an imbalance measure: subtracting the value of the shares sold by individuals from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. Earnings surprise (ES) is defined as the actual earnings minus the earnings’ forecast one month before the announcement, divided by the price on the forecast day. For the analysis in the table, we sort stocks independently along two dimensions: market-adjusted returns in the three months prior to the announcement (CAR[-60,-1]) and ES. We put the stocks into 25 categories: five groups of earnings surprise and five groups of cumulative abnormal returns. We compute for each stock the net individual trading measure over a certain period, and present in the table the average of net individual trading of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero net individual trading). We examine net individual trading over two periods: the event window ([0,1]) in Panel A, the post-event period [2,61]) in Panel B. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Net Individual Trading in [0,1] by Earnings Surprise and Abnormal Return on [-60,-1] (lowest) Earnings Surprise (highest) CAR[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean 0.0520** 0.0242** 0.0171** 0.0207** 0.0338** -0.0182 (negative) t-stat. (6.71) (5.82) (3.99) (4.34) (4.12) (-1.46) Q2 Mean 0.0106 0.0085 0.0118** 0.0053 0.0106 -0.0001 t-stat. (1.67) (1.89) (3.25) (1.03) (1.07) (0.00) Q3 Mean 0.0015 0.0009 -0.0016 -0.0009 -0.0058 -0.0074 t-stat. (0.21) (0.26) (-0.40) (-0.17) (-0.64) (-0.62) Q4 Mean -0.0456** -0.0077* -0.0006 -0.0130** -0.0166 0.0290* t-stat. (-4.30) (-2.04) (-0.15) (-3.03) (-1.89) (2.09) Q5 Mean -0.0760** -0.0314** -0.0178** -0.0262** -0.0627** 0.0133 (positive) t-stat. (-5.08) (-3.74) (-3.63) (-6.15) (-6.27) (0.75)

Mean -0.1280** -0.0556** -0.0349** -0.0469** -0.0965** Q5–Q1 t-stat. (-8.24) (-6.13) (-5.05) (-7.10) (-6.75) Panel B: Net Individual Trading in [2,61] by Earnings Surprise and Abnormal Return on [-60,-1] (lowest) Earnings Surprise (highest) CAR[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean 0.5990** 0.1983** 0.2973** 0.3234** 0.2691** -0.3299** (negative) t-stat. (10.19) (4.56) (6.02) (7.07) (3.15) (-3.20) Q2 Mean 0.2441** 0.1188* 0.1083** 0.0672 -0.0416 -0.2857** t-stat. (3.69) (2.49) (2.62) (1.46) (-0.52) (-2.76) Q3 Mean 0.0797 0.0269 -0.0117 0.0022 -0.2260** -0.3056** t-stat. (1.15) (0.65) (-0.30) (0.05) (-3.10) (-3.02) Q4 Mean -0.0579 -0.0243 -0.0195 -0.0909* -0.2227** -0.1648 t-stat. (-0.61) (-0.60) (-0.44) (-2.12) (-3.10) (-1.40) Q5 Mean -0.3290** -0.2572** -0.1693** -0.2372** -0.6104** -0.2815* (positive) t-stat. (-2.86) (-4.79) (-3.43) (-4.96) (-9.74) (-2.34)

Mean -0.9280** -0.4555** -0.4666** -0.5607** -0.8796** Q5–Q1 t-stat. (-7.84) (-6.65) (-6.45) (-7.89) (-8.43)

44

Table 11 Institutional Trading after Event Conditional on Earnings Surprise and Pre-Event Return

This table presents analysis of net institutional trading in the post-event period conditional on both different levels of abnormal returns before the announcement and the extent of earnings surprise. We construct the net institutional trading measure by first computing an imbalance measure: subtracting the value of the shares sold by institutions from the value of shares bought and dividing by the average daily dollar volume (from CRSP) in the calendar year, and then subtracting the mean imbalance over the sample period. Earnings surprise (ES) is defined as the actual earnings minus the earnings’ forecast one month before the announcement, divided by the price on the forecast day. For the analysis in the table, we sort stocks independently along two dimensions: market-adjusted returns in the three months prior to the announcement (CAR[-60,-1]) and ES. We put the stocks into 25 categories: five groups of earnings surprise and five groups of cumulative abnormal returns. We compute for each stock the net institutional trading measure over a certain period, and present in the table the average of net institutional trading of all the stock-quarters in each of the different quintiles (with t-statistics testing the hypothesis of zero net institutional trading). We examine net individual trading over two periods: the event window ([0,1]) in Panel A, the post-event period [2,61]) in Panel B. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Net Institutional Trading in [0,1] by Earnings Surprise and Abnormal Return on [-60,-1] (lowest) Earnings Surprise (highest) CAR[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.0108 -0.0233 -0.0144 -0.0299 0.0122 0.0229 (negative) t-stat. (-0.86) (-1.79) (-0.72) (-1.72) (0.70) (1.06) Q2 Mean 0.0371* 0.0081 -0.0409* 0.0108 0.0121 -0.0250 t-stat. (2.06) (0.65) (-2.51) (0.73) (0.53) (-0.87) Q3 Mean 0.0381* 0.0406** 0.0084 0.0114 0.0049 -0.0332 t-stat. (2.14) (3.41) (0.57) (0.87) (0.28) (-1.33) Q4 Mean 0.0166 0.0389** 0.0319* 0.0406** 0.0335 0.0170 t-stat. (0.85) (2.82) (1.96) (2.88) (1.92) (0.63) Q5 Mean -0.0011 0.0615** 0.0541** 0.0326* 0.0469** 0.0480 (positive) t-stat. (-0.04) (3.74) (2.65) (2.25) (2.97) (1.69)

Mean 0.0097 0.0847** 0.0685* 0.0625** 0.0348 Q5–Q1 t-stat. (0.38) (4.08) (2.30) (2.72) (1.43) Panel B: Net Institutional Trading in [2,61] by Earnings Surprise and Abnormal Return on [-60,-1] (lowest) Earnings Surprise (highest) CAR[-60,-1] Q1 Q2 Q3 Q4 Q5 Q5–Q1 Q1 Mean -0.7676** -0.6618** -0.6816** -0.7126** -0.3300** 0.4376** (negative) t-stat. (-9.78) (-8.87) (-6.67) (-7.04) (-2.75) (3.12) Q2 Mean -0.5972** -0.5974** -0.2759** -0.1895* 0.0149 0.6121** t-stat. (-5.46) (-7.30) (-2.96) (-2.16) (0.13) (3.85) Q3 Mean -0.3499** -0.1323 0.0100 0.1726* 0.0644 0.4143* t-stat. (-3.17) (-1.76) (0.11) (2.01) (0.51) (2.45) Q4 Mean -0.1394 0.0125 0.2764** 0.2788** 0.5493** 0.6887** t-stat. (-0.95) (0.16) (3.23) (3.41) (4.74) (3.70) Q5 Mean 0.2485 0.3295** 0.5198** 0.7777** 0.9171** 0.6686** (positive) t-stat. (1.74) (3.56) (5.22) (8.40) (8.98) (3.77)

Mean 1.0161** 0.9913** 1.2014** 1.4903** 1.2471** Q5–Q1 t-stat. (6.58) (8.41) (8.16) (10.45) (7.75)

45

Table 12 Regressions Explaining Post-Event Net Investor Trading

This table presents regression analysis relating post-event net investor trading to past trading, past returns, and the earnings surprise. In order to overcome potential econometric problems associated with contemporaneously correlated errors for earnings announcements that are clustered in time (i.e., in the same quarter), we use a methodology in the spirit of Fama and MacBeth (1973). We first run a cross-sectional regression separately for each quarter. We then use time-series variability in the 16 estimates we have for each coefficient (one from each quarterly regression) to get the standard error for the mean coefficient. In Panel A the dependent variable is net individual trading in the post-event period (IndNT[2,61]), and the regressors include the earnings surprise measure (ES), net individual trading before (IndNT[-10,-1] or IndNT[-

60,-1]) and during (IndNT[0,1]) the announcement, and market-adjusted cumulative abnormal returns before (CAR[-10,-1] or CAR[-60,-1]) and during (CAR[0,1]) the event. We present the results of the regressions for the entire sample and separately for three size groups. In Panel B we use an analogous specification with net institutional trading (InsNT[2,61]) as the dependent variable. We use “**” to indicate significance at the 1% level and “*” to indicate significance at the 5% level (both against a two-sided alternative). Panel A: Net Individual Trading in Post-Event Period (IndNT[2,61]) as Dependent Variable

Intercept ES IndNT [-60,-1]

IndNT [-10,-1]

IndNT [0,1]

CAR[-60,-1] CAR[-10,-1] CAR[0,1]

0.0990 -33.7278 0.1517** 1.4891** -0.9558 -0.6039 (0.72) (-1.01) (3.61) (4.66) (-1.59) (-0.51)

0.1176 -0.9965* 0.3959 1.4901** 1.4420 -1.7996** All

Stoc

ks

(0.80) (-2.21) (1.69) (5.43) (0.50) (-4.60) 0.1389 -33.1066 0.1307** 1.4989** -1.1319 -1.7395 (0.60) (-1.02) (2.91) (3.55) (-1.59) (-1.39)

0.2013 -2.4452 0.4861* 1.4304** 0.1605 -3.3320** Smal

l St

ocks

(0.71) (-1.47) (2.34) (4.47) (0.06) (-3.38) -0.0206 -1.4307 0.2158** 1.5228** -0.4267** -1.2146** (-0.30) (-0.77) (4.89) (7.64) (-4.54) (-6.71)

-0.0197 -3.3711 0.7884** 1.4206** -0.7074** -1.2847**

Mid

-Cap

St

ocks

(-0.26) (-1.73) (5.54) (5.38) (-3.53) (-6.05) 0.0154 4.5938 0.1952** 1.4602** -0.2066** -0.7952** (0.37) (0.55) (3.56) (6.48) (-3.01) (-5.76)

0.0012 3.4255 0.7900** 1.4305** -0.2547 -0.8128** Larg

e St

ocks

(0.03) (0.42) (2.58) (6.55) (-1.91) (-5.65)

46

Panel B: Net Institutional Trading in Post-Event Period (InsNT[2,61]) as Dependent Variable Intercept ES InsNT

[-60,-1]

InsNT [-10,-1]

InsNT [0,1]

CAR[-60,-1] CAR[-10,-1] CAR[0,1]

0.0404 113.280 0.0734 0.2292 3.0429* 0.5346 (0.37) (1.00) (1.66) (0.33) (1.98) (0.31)

0.0419 25.0375 0.2166 0.3845 -3.1251 2.9217** All

Stoc

ks

(0.30) (1.00) (1.12) (0.66) (-0.61) (3.00) 0.3521 63.3158 0.0558 -0.3692 0.6388 1.0148 (0.79) (0.98) (0.61) (-0.26) (0.73) (0.77)

0.1134 60.1985 0.1232 -0.2456 1.0251 1.9532** Smal

l St

ocks

(0.36) (0.99) (0.41) (-0.22) (1.73) (3.25) 0.0963 8.9331 0.0575* 0.4791** 1.7707** 2.2739** (0.29) (1.15) (2.20) (3.18) (5.31) (5.33)

0.0890 11.0802 0.2465** 0.5357** 3.6115** 2.4471**

Mid

-Cap

St

ocks

(0.27) (1.56) (2.74) (3.60) (5.24) (5.35) 0.1321 -102.433 0.0237 0.5710** 1.9219** 2.4158** (1.04) (-1.13) (0.68) (2.91) (6.00) (4.83)

0.0472 -72.9481 0.0343 0.6524** 3.0996** 2.3873** Larg

e St

ocks

(0.81) (-0.83) (0.16) (3.39) (6.21) (4.77)

47