the effect of earnings announcements on trading …

The Pennsylvania State University

The Graduate School

Smeal College of Business

THE EFFECT OF EARNINGS ANNOUNCEMENTS

ON TRADING OUTCOMES FOR DIFFERENT

INVESTOR CLASSES

A Dissertation in

Business Administration

by

James Dale Vincent

Copyright 2010 by James Dale Vincent

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2010

ii

The dissertation of James Dale Vincent was reviewed and approved* by the following:

Orie E. Barron

Professor of Accounting

Dissertation Adviser

Chair of Committee

Paul Fischer

Professor of Accounting

Karl Muller

Associate Professor of Accounting

Laura C. Field

Associate Professor of Finance

*Signatures are on file in the Graduate School.

iii

Abstract

Theory suggests that earnings announcements can either increase or decrease the level of

information asymmetry between investors. I investigate this by testing the effect of

earnings announcements on the relative ability of small and large investors to trade

advantageously. On average, I find that large traders’ returns increase following earnings

announcements at the expense of small traders, indicating an increase in information

asymmetry following earnings disclosures, at least in the short term. I also find that

several proxies for the availability of private information are associated with the

advantage of large traders following announcements. I also find weak evidence that large

investors are at a greater advantage when earnings surprises are large. However, I find no

evidence to support an effect from the presence of certain types of shareholders at

earnings announcements. Finally, I test the hypothesis of Chae (2005) that volume

patterns around earnings announcement are partially the result of less informed traders

delaying their trades until after the announcement to achieve a more level informational

playing field. Results are inconsistent with this hypothesis.

iv

TABLE OF CONTENTS

List of Figures……………………………………………………………….v

List of Tables………………………………………………………….……vi

Acknowledgements……………………………………………………...…vii

Chapter 1. INTRODUCTION………………………….……………………1

Chapter 2. RELATED LITERATURE AND HYPOTHESIS

DEVELOPMENT……………………………………………………..…….4

Literature Related to Information Asymmetry Following Earnings

Announcements………………………………………………………4

Behavior of Small and Large Traders………….……………………10

Chapter 3. VARIABLES AND RESEARCH

DESIGN……………………………………………………………………14

Chapter 4. DATA AND RESULTS…………..……………………………24

Data Procedures………………………………………..……………24

Descriptive Statistics…………………………………..……………26

Main Results……………………………………...…………………26

Sensitivity Analyses…………………………………………………44

Chapter 5. CONCLUSION……………………………...…………………48

Appendix……………………………………………………………..…….51

References………………………………………………….………………53

v

List of Figures

Figure 1……………………………………………………………….……15

vi

List of Tables

Table 1……………………………………………………………………27

Table 2……………………………………………………..……………..28

Table 3……………………………………………………………………30

Table 4……………………………………………………………..……..32

Table 5……………………………………………………………………35

Table 6……………………………………………………………………37

Table 7…………………………………………………………...……….38

Table 8…………………………………………………………………....40

Table 9………………………………………………………..…………..41

Table 10…………………………………………………………………..45

Table 11…………………………………………………………….…….47

vii

Acknowledgements

I have received an enormous amount of help and advice in the process of

producing this dissertation. I wish to thank my committee: Orie Barron, Paul

Fischer, Karl Muller, and Laura Field, who have been extremely patient and

helpful throughout the process. As an advisor and committee chair, Orie

Barron has been the best a doctoral student could hope for: insightful,

understanding, and supportive. I will always appreciate the work he has done

to help me to become a better thinker and a better researcher.

I also received many helpful comments from students and faculty at the

various schools I visited during the job search process, with Linda Bamber

and Donal Byard providing especially thorough and useful feedback. I

would also like to thank the students and faculty at Penn State, who have

been very helpful and supportive. I would like to give special thanks to

James Sinclair, who has been extremely giving of his time in helping me

through multiple revisions. More so than most, James understands that

support and criticism are complimentary, and I will always appreciate his

well thought out critiques of my work.

Finally, it would not be possible for me to be here without the support of my

family. My mother has been a great inspiration to me throughout my life and

her love and support has been a great help to me during the last few years.

My wife Amanda has also been a tremendous source of love and support

through some difficult times. She was always a wonderful companion and

helped to relieve my stress even as she was working to complete her own

dissertation. Her grace and warmth are an inspiration to me, and her strength

and fortitude throughout her pregnancy and the birth of our son are a

testament to her character. I love her deeply, and I’ll always remember her

support for me during this time. Lastly, I want to thank my father, who

encouraged me in my academic career and always took great interest in my

advancement through the degree. It is my deepest sorrow that he was not

able to see the end of the process. I dedicate this work to him.

1

1. Introduction

The theoretical literature has modeled the effect of earnings disclosures on

information asymmetry between investors in several different ways.1 On the one hand,

disclosures have been seen as reducing or eliminating uncertainty about some future

event, causing the better informed to lose some or all of their advantage over the

uninformed (e.g. Kyle, 1986; Glosten and Milgrom, 1985; Admadi and Pfleiderer, 1988).

On the other hand, disclosures can spur the generation of new private information about a

firm’s future state (Kim and Verrecchia, 1994, 1997). These two ideas are not mutually

exclusive; disclosures can simultaneously resolve uncertainty about some aspect of a

firm’s future earning power while also giving better informed investors additional inputs

from which they can refine their beliefs about future payouts. The purpose of this paper is

to examine how earnings announcements change the relative advantage of the informed

versus the uninformed, and which firm and market attributes serve to change this

dynamic.

Prior studies have examined the effect of earnings announcements on information

asymmetry using bid-ask spreads (Yohn, 1998; Libby, Mathieu and Robb, 2002), the

adverse selection component of spreads (Krinsky and Lee, 1996), the price impact of

trades (Brooks, 1996), and the probability of informed trade (Brown, Hillegeist and Lo,

2008). This paper differs from this prior work in that, rather than measuring a proxy for

the presence of information asymmetry, I measure a likely outcome of that asymmetry.

To examine the effect of information asymmetry on investors, I examine all trades

made in the stock of each firm in my sample, and calculate the three day returns that

1 In this study, I consider information asymmetry between investors, which is distinct from the information

asymmetry between the firm and outsiders. See Agarwal and O’Hara (2007) for a discussion of the

differences between these two forms of information asymmetry.

2

could be earned by the initiator of each trade. I then classify the trade size as small,

medium, and large, with the small group proxying for uninformed investors and the large

group proxying for better informed investors.2 I then compute the overall imputed returns

for each group for each trading day. Finally, I compare the relationship between small

and large returns overall with the small and large returns shortly following earnings

announcements, to see how these disclosures affect the relative returns earned by the two

groups.

Results show that overall, there is an increase in large traders’ profits relative to

small traders’ profits in the period immediately following an earnings announcement. I

also run my tests using control variables for the direction and magnitude of earnings

surprises, to mitigate concerns that results may be related to PEAD trading strategies.

Results remain the same even with the inclusion of these earnings surprise control

variables. I also examine some firm and announcement level characteristics that may be

related to the production of private information. I find that large traders are at a greater

advantage when trading in the shares of firms with large analyst following, suggesting

that large traders are better able to produce private information following earnings

announcements when incentives to produce such information is higher. Furthermore,

reduction of private information, as measured by the change in analysts’ consensus, is

associated with a reduced advantage for large traders following announcements.

However, a similar interaction using firm size, which has been used as a proxy for the

incentive to generate private information in prior literature, does not produce significant

results.

2 Following prior research, I do not use the medium group, as it is more difficult to make inferences about

the composition of this group.

3

Additionally, I test the effect of several features of earnings announcements on

the ability of large traders to achieve better returns following earnings announcements. I

find weak evidence that large earnings surprises (as measured by the difference between

analysts’ expectations and actual earnings) increases large traders’ advantage following

earnings announcements. However, I find no evidence that random walk earnings

surprises affect their advantage, nor any differential effect between positive and negative

surprises, or surprises where the analyst expectation surprise is a different sign than the

seasonal random walk earnings surprise.

I also test the effect of the presence of certain types of institutional traders on

large traders’ advantage. While, generally speaking, the presence of transitory institutions

is associated with better relative returns for small traders, and the presence of dedicated

investors is associated with better relative returns for large traders, there is no incremental

effect following earnings announcements. So while it appears that investor mix does have

a significant effect on the relative returns of more informed and less informed traders, I

find no evidence that features of institutional ownership are associated with differential

returns shortly following earnings announcements.

I also use this data to test the discretionary liquidity trader hypothesis in Chae

(2005) concerning patterns of trading around earnings announcements. This hypothesis

posits that the patterns of trading observed around earnings announcements (decrease in

volume in the period immediately prior to the announcement followed by a large increase

in volume immediately following the announcement) are due, at least in part, to

uninformed traders who wish to time their trades advantageously. According to this

hypothesis, uninformed traders with some discretion in the timing of their exogenously

4

determined liquidity trades will wish to delay trade until after the earnings

announcement, to reduce the adverse selection problem of trading against informed

investors. This implies that uninformed traders who trade immediately following the

announcement should enjoy better returns, relative to informed traders, than uninformed

traders who trade immediately prior to the earnings announcement. I test this proposition

directly by examining the difference in relative trade performance both prior to and

following earnings announcements. While I find that large traders do better than small

traders following earnings announcements, I find no difference in their relative trading

performance immediately prior to the announcement. This evidence suggests that

discretionary liquidity trade timing is unlikely to explain earnings announcement trading

patterns.

The remainder of this paper proceeds as follows. In section 2, I briefly discuss

some of the background literature and the rationale for the tests being performed. I then

discuss the construction of the variables and the research design I use to test the

hypotheses in section 3. In section 4, I discuss the data and results, and I conclude with

section 5.

2. Related Literature and Hypothesis Development

2.1 Literature Related to Information Asymmetry Following Earnings Announcements

Theoretically, there are two distinct approaches to modeling the effect of information

disclosure on information asymmetry between investors. In models such as Kim and

Verrecchia (1991) and Foster and Viswanathan (1990), some investors have private

signals concerning an upcoming announcement. The announcement supplants the private

5

information, making their private signals no longer useful, and information asymmetry is

alleviated. In contrast, in other models (Kim and Verrecchia 1994; Kim and Verrecchia

1997) information disclosures can spur the generation of new, private information, which

increases asymmetry. Although different models emphasize different features of

information announcements, these two effects are not mutually exclusive, and in fact,

complex disclosures such as earnings announcements are likely to substitute for some

privately held information while simultaneously stimulating new private information

among some investors, as in Kim and Verrecchia (1997). These two countervailing

effects make it difficult to predict ex-ante the effect of accounting earnings

announcements on information asymmetry between investors.

Researchers have used a number of proxies for information asymmetry in

studying its relationship with earnings disclosures, with bid-ask spreads, or a spread-

based construct being the most common. Other proxies include analyst consensus,

probability of informed trade (PIN), and the price impact of block trades. No general

consensus has emerged in this literature as to how the announcements affect asymmetry.

One of the first papers to study the issue of information asymmetry around

earnings announcements is Morse and Ushman (1983). This study examines bid-ask

spreads around quarterly earnings announcements, looking at each day from ten days

prior to the announcement to ten days following the announcement. These days are

individually compared to days that are further from the announcement date. No

significant differences are found for any date in this window, although this non-result

may be an artifact of the sample: 378 quarterly announcements for 25 firms over four

years. It’s also questionable how applicable this sample would be to the present day, as

6

trading volume has increased dramatically, and spreads fallen greatly in the years since

their sample ends in 1976.

Lee, Mucklow, and Ready (1994) provide a more modern and thorough

examination of the behavior of spreads (and market depth) around earnings

announcements. They show that liquidity decreases prior to earnings announcements

(and prior to large surprises especially) and remains low in the wake of the announcement

for a full day (i.e. spreads are higher than normal and quoted depth is lower than normal.)

This effect is greatly mitigated, however, when volume is controlled for, in this case

being limited to a liquidity decrease for only the first half hour following the

announcement. The authors note, however, that the effect of controlling for volume is not

perfectly clear theoretically.

Brooks (1996) approaches the question using the Hasbrouck (1991)

decomposition of price variance. Price movements are decomposed by vector

autoregression into a random walk component and a stationary component, and a

measure of the proportion of private to public information is created. The author shows

that this measure is significantly lower on earnings announcement days, although it oddly

seems to be lower on the two previous days as well. Overall, Brooks (1996) interprets his

results as being consistent with earnings announcements having an information leveling

effect on asymmetry.

Krinsky and Lee (1996) examine the issue by using a decomposition of bid-ask

spreads into its component parts. They note that prior literature examining spreads around

announcements is difficult to interpret as components of the spread other than adverse

selection may be changing in announcement periods as well. Upon decomposition, they

7

find that different spread components tend to move in different directions simultaneously,

and they attribute the previously mixed results in the literature to this fact. They find that

while the adverse selection component of the spread increases following (and prior to) an

announcement, while the inventory holding cost and order processing cost components

decrease. They interpret their results as being consistent with information asymmetry

between investors increasing following earnings announcements. While this is an

interesting result which addresses some of the difficulties of prior research with spreads,

there have recently been a number of papers critical of the many various methods used to

decompose elements of the spread. Van Ness, Van Ness and Warr (2001) examine five

different measures of adverse selection, and find that they are uncorrelated with other

measures of information asymmetry such as analyst forecast error and market to book.

The measures are also only weakly correlated with each other and with measures of the

presence of informed trade such as analyst following and institutional ownership. Neal

and Wheatley (1998) document the puzzling result that different estimations of adverse

selection components of the spread show consistently high values of adverse selection in

closed-end mutual funds, which by their nature have little to no information asymmetry

problems. These studies suggest that interpretations of decomposition based research

remain difficult.

Barclay and Dunbar (1996) use yet another proxy for information asymmetry, the

price impact of block trades, in addition to examining effective spreads (and a spread

decomposition) around announcements. While they expect that prices will be more

sensitive to block trades at times of higher asymmetry, they find no evidence that any

window around the announcement has a different sensitivity than their baseline period.

8

Similarly, they show no significant difference between their measure of spreads prior to

and following the announcement. They conclude that they find “no evidence to support

the hypothesis that the uninformed can trade on more favorable terms by varying the

timing of their trades in relation to a quarterly earnings announcement.”

Yohn (1998) reaches a different conclusion. Testing a sample of 1,989 quarterly

announcements between 1988 and 1990, she finds that spreads remain higher than normal

for both the day of the announcement and the day following the announcement. She

further documents a positive relationship between this reaction and the size of the price

reaction to the earnings announcement. She also notes that the size of the spread increase

prior to the announcement is increasing in both firm size and analyst following.

Analyst forecast information is used by Barron, Byard and Kim (2002) to

examine the issue from a different angle. Using the Barron et. al. (1998) estimation of the

ratio of analysts’ private information to analysts’ common information, they show that,

on average, earnings announcements tend to increase the proportion of idiosyncratic,

private information in analysts’ forecasts about future earnings. This suggests that

earnings announcements can be used by sophisticated investors to increase their private

information about future firm performance.

While the studies mentioned above have focused on U.S. equity markets, Libby,

Mathieu, and Robb (2002) examine earnings announcements on the Toronto Stock

Exchange. They examine spreads and quoted depth simultaneously, noting that both are

methods used by market makers to provide or withdraw liquidity. They find that liquidity

seems to increase following the announcements in their sample, although the window

being tested is large (thirty days prior to and following the announcement.) As a result, it

9

is difficult to make inferences about the short term effect of earnings announcements as a

result of this study.

Prior research relating to information asymmetry following earnings

announcements has also been used to make inferences about trading volume patterns at

announcement time. In traditional models of trading in the presence of information

asymmetry, such as Kyle (1985) and Glosten and Milgrom (1985), uninformed traders

have exogenous liquidity needs, and are unable to choose the timing of their trades. Other

models examine the effect of timing on trading outcomes for relatively uninformed

investors. In Admati and Pfleiderer (1988), some uninformed liquidity traders can

exercise discretion over the timing of their trades. This leads to trade clustering, as the

uninformed benefit from the lower sensitivity of price to trading when volume is high.

The informed traders likewise benefit from the presence of the uninformed liquidity

traders, which drives volume even higher. Chae (2005) hypothesizes that discretionary

liquidity trading may explain some of the patterns that are observed around earnings

announcements: slightly lower volume immediately prior to the announcement and very

high volume immediately following it. Examining the differences between scheduled and

unscheduled (e.g. merger) announcements, Chae (2005) notes that scheduled

announcements have greater volume reductions prior to the announcement and larger

increases in volume following the announcement than unscheduled announcements do.

They conclude that this is consistent with uninformed traders delaying their trades until

after the announcement, leading to pent-up demand that is released after the

announcement. Additionally, Chae (2005) finds stronger results when some proxies for

information asymmetry are high, implying that less informed investors are more likely to

10

delay their trades until after the announcement, when asymmetry attenuates. If, however,

information asymmetry tends to increase following earnings announcements, this calls

into question the discretionary liquidity trader hypothesis.

2.2 Behavior of Small and Large Traders

In operationalizing my tests of information asymmetry, I divide investors between

small and large traders, a proxy for the less informed and better informed investor

classes. Two streams of literature examine the behavior of smaller, less sophisticated

investors versus larger, more sophisticated ones. The first looks directly at the long term

performance of trades made by different investor classes or of stocks that are heavily

purchased or sold by individuals. The second uses the inferred net buying or selling

activities of investors to see how large and small traders react differently to various

events. Among the first stream, Barber and Odean (2000) use data from a discount

brokerage to examine the performance of individual investors and find that they

underperform the market as a whole, and that frequent traders incur large transaction

costs without any appreciable increase in performance. Hvidkjaer (2008) finds that shares

favored by small investors experience declines in both the near and long term. Barber et.

al. (2008) show that individuals trading in the Taiwan market lose systematically to

institutions and foreign players, and that their losses stem largely from active trading (i.e.,

buy or sell orders rather than limit orders). In contrast, Kaniel, Saar, and Titman (2008)

use NYSE data to examine the performance of stocks that are heavily bought or sold by

individual investors, and finds that stocks that are heavily purchased by individuals have

higher future returns. Kaniel et. al. (2009) do a similar analysis around earnings

11

announcements and find that stocks that are heavily purchased by individuals prior to an

announcement experience better returns in the coming days and months. They attribute

this to both individuals’ provision of liquidity to institutions, and superior private

information possessed by individuals.

Asthana, Balsam and Sankaraguruswamy (2004, hereafter ABS) examine

the issue of relative trade profitability of small and large traders around 10-K filing dates.

They find that small traders’ trades tend to be less profitable in the period around 10-K

filings, relative to other times, and that large traders’ trades tend to be more profitable

around these filings, though this effect is mitigated when firms file on EDGAR. There are

several ways in which the results in this paper differ from the findings in ABS. First of

all, the profitability measure in ABS is calculated as the product of net-buying of a

particular size group and the five day price change in days -1 to +4 surrounding the filing.

As a result, each buy (or sell) in the event period is taken equally, regardless of the actual

executed trading price. Thus, a trade made in anticipation of the 10-K filing is treated the

same as a trade made several days following the filing, after which market price may

have partially or fully incorporated the information in the filing.3

Secondly, the trading window examined in ABS incorporates the day prior

to the 10-K filing in addition to the period immediately following the filing. We are

therefore unable to assert whether the difference demonstrated in event versus non-event

periods shown in ABS is due to trading in anticipation of the filing or due to reaction to

3 To examine the impact of these differing methods on results, I also run my main test using a modified

version of the ABS method. In this untabulated test, I calculate my dependent variable by multiplying the

netbuying activity of the small and large groups by the overall three day price swing following the day of

calculation. For both the LG variable and the LG*EAday interaction term, this modified version of the

ABS test yields no statistically significant coefficients. This demonstrates how the increased precision of

the research design in this paper can yield different conclusions from those based on aggregations of

buying and selling activity.

12

information in the filing itself. As my research question relates to the differential use of

accounting disclosures by small and large traders, I limit my sample to the period after

the earnings announcement.

Finally, my study differs from ABS in that 10-K filings are fundamentally

different from earnings announcements. Except in the unusual cases where 10-K’s are

filed prior to an earnings announcement (Stice, 1991), 10-K filings take place after a

large portion of the financial information contained in the 10-K has already been

disseminated. Strong market reactions to 10-K filings are rare (Easton and Zmijewski,

1993) and more likely to be the result of a discrepancy with previously disclosed

financials or information contained in a footnote disclosure which was not discussed at

the earnings announcement. Since the relevant disclosures are relatively obscure, this

type of environment is likelier to favor sophisticated traders than the relatively more

straightforward earnings announcements. In other words, it is reasonable to expect, ex-

ante, that trading following earnings announcements would be a more favorable

environment for small investors than trading following 10-K filings.

The second stream of literature uses the imbalance in trade orders to make

inferences about the differing behavior of small and large investors. Lee (1992) found

that whereas large traders tend to buy on good surprises around earnings announcements

and sell on downside surprises, small traders buy on either good or bad news.

Bhattacharya (2001) shows that small traders’ abnormal trading activity is associated

with a random walk model of earnings expectation, while the same does not hold true for

large traders. Battalio and Mendenhall (2005) extend Bhattacharya (2001) by using the

inferred sign of the trade to establish a net buying/selling position for small and large

13

investors. They find similar results: small traders seem to respond to earnings

announcements based on a random walk model, while large traders respond based on

analyst expectations. Other papers have looked at the role of small investors in causing

PEAD (Hirshleifer et. al. 2008; Ayers, Li, Yeung 2009) and their interpretation of analyst

recommendations (Malmendier and Shanthikumar, 2007). While these studies document

interesting regularities in the behavior of different types of investors, they do not speak

directly to the relative trading performance of these investors following earnings

announcements.

The discussion above shows that small and large investors tend to demonstrate

systematically different trading behaviors. And while there are several different notions

about the way that disclosures of important financial information affects information

asymmetry, we know very little about how earnings announcements translate into trading

outcomes for different investor classes. This paper is an attempt to understand how

earnings announcements affect the actual trading performance of different sized

investors. Do smaller investors benefit, on average, from the revelation of formerly

private information that takes place at earnings announcements? Or does the advantage

that accrues to larger, more sophisticated investors who are able to generate private

event-period information in the run-up to earnings announcements overwhelm any

leveling effect that the announcement might have? As it is possible that either effect may

predominate, I do not form a directional hypothesis. Instead, I proceed by first testing the

sample as a whole to identify any general tendency in one particular direction.

As theory demonstrates, private information production following earnings

announcement is likely to be due to private information gathered prior to the

14

announcement in anticipation of it. I therefore also gather a number of firm and

announcement characteristics that are likely related to private information production,

and interact these variables with my measure of information asymmetry. I follow Yohn

(1998) in examining the effect of both firm size and analyst following on my main

variable of interest. The first variable I examine is firm size. Atiase (1985) proposes that

market participants will generate more private information before earnings

announcements when firm size is large, suggesting that large investors will benefit more

from earnings announcements of larger firms. By conditioning on size, I test the extent to

which the earnings announcement effect is greater or smaller for large versus small firms.

I also test the effect of analyst coverage on the relative performance of small

versus large investors. Prior literature has seen analysts as either exacerbating (Brennan

and Subrahmanyam, 1995) or alleviating (Roulstone, 2003) information asymmetry

problems. I examine the effect of analyst coverage on my variables of interest, as the

information that analysts generate may affect the ability of different investor groups to

interpret earnings disclosures.

Finally, change in analysts’ consensus (as defined in Barron et. al. 1998) has been

shown in several studies to be associated with private information production (Barron,

Byard and Kim, 2002; Botosan, Plumlee and Xie, 2004; Barron Harris and Stanford,

2005). The consensus variable measures the ratio of common to private information

available to analysts in making earnings forecasts. If consensus about future earnings

decreases around an earnings announcement, this suggests that the announcement has

stimulated the production of new private information.

3. Variables and Research Design

15

To investigate my research question, I use a measure of imputed short term

profitability of trade, PROF. For each actual trade made for firms in my sample, I

compare the actual transaction price with closing price three days later.4 This serves as a

proxy for the ability of the investor initiating the trade to anticipate short term price

movement. This is similar to the method Asthana, Balsam and Sankaragurswamy (2004)

use to establish the potential short term profitability of trades. In their paper, they assess

the potential profitability of buying or selling a specific security, and multiply this return

by the net-buying or net-selling of the small investor group. This method assigns small

investors, as a whole, a certain short-term potential profitability, with the intention

Trader

Category

Trade

Type

Execution

Price

Number

of Shares

3-Day

Forward Price

Imputed

Return

Small Buy 11.51 100 11.45 -0.0052

Large Buy 11.53 5,800 11.45 -0.0070

Small Sell 11.53 200 11.45 0.0070

Large Sell 11.50 8,000 11.45 0.0044

PROF for small investors for firm-day: 0.0029

PROF for large investors for firm-day: -0.0004

Figure 1: Simplified example of construction of PROF for a single firm-day. Each actual trade execution price is

compared to the closing price 3 days forward to construct an imputed return for that trade. Then, the trades are value-

weighted within the trader category to arrive at a PROF for each category for the day.

of comparing their investment performance across disclosure regimes. In this paper, I

construct a relative measure of profitability between the large and small groups by

comparing each signed order (inferred using the Lee and Ready (1991) algorithm5) to the

closing price three trading days later. I then value-weight the trades within the group to

4 I use the midpoint of closing bid and ask quotes as closing price, to avoid the “bid-ask bounce” problem.

5 I use a one second rule to alleviate the concerns raised by Henker and Wang (2006).

16

construct a return, and compare the returns that could potentially be earned by large

versus small traders, and how that relationship changes in the wake of earnings

announcements. This process is illustrated in a simplified example in Figure 1.

My measure of trade profitability has several advantages. Like the measure in

Asthana, Balsam and Sankaragurswamy (2004), it gives a sense of the ability of the

different groups of investors to assess the impact of earnings news in the short-term. But

it also adds an additional layer of precision by examining each trade individually. 6

By

comparing the short-term price benchmark to actual trading prices, PROF also measures

the ability of investors to buy and sell at advantageous prices.

In order to partition trades into informed and uninformed, I use a trade size cutoff.

Although many different cutoff points (and methods) have been used in the literature, I

follow Bhattacharya (2001) in using a $5,000 cutoff for small trades and a $50,000 cutoff

for large trades. I use these cutoffs because the rather conservative dollar values provide

the best assurance that trades in each category truly represent trades from informed or

uninformed investors. All trades between $5,000 and $50,000 are eliminated as

ambiguous. If a stock’s price, however, is above $50, then single lot trades are still

considered to be small trades, since trades of under $5,000 would not be possible without

trading odd lots. Stocks priced above $100, however, are thrown out of my sample, as it

no longer becomes possible to transact in those stocks at prices that unambiguously

represent uninformed investors. I also perform a sensitivity test, described in section 4.4,

to address concerns about changes in small and large trade patterns over time.

6 This allows the measure to capture not only the effect of intraday variation in prices, which can be large

around earnings announcements, but also some of the transactions costs incurred by traders (via the bid-ask

spread and price concessions.)

17

My main question of interest is in the effect of earnings announcements on the

relative ability of small and large investors to trade advantageously. Prior research has

shown that abnormal returns occur around earnings announcements; this effect has been

attributed to both behavioral bias (DeBondt and Thaler, 1985) and risk shifting (Ball and

Kothari, 1991). Due to the known patterns of abnormal returns around earnings

announcements, my tests do not focus on the effect of the earnings announcement on

each investor class, but rather on the relative impact of earnings announcements on the

two groups. In other words, there are many reasons why the short term returns following

an earnings announcement might vary, but the differential impact on small versus large

traders should best capture the effect of the announcement on the change in informedness

between the two groups. To investigate the effects of the difference between the two

investor groups, I use a stacked regression with one observation for each imputed three

day return for each investor type. I estimate the following equation for my main test:

(1)

Where:

PROFij = The imputed 3 day return on firm-day i of trades initiated by trader category j,

expressed in percentage terms.

EAdayi = An indicator variable equal to 1 if firm-day i is the day of or the day following

an earnings announcement, and 0 otherwise.

LGj = An indicator variable equal to 1 if trader category j is large, 0 otherwise.

This specification allows me to directly compare the differential effect of earnings

announcements on the trading of large versus small investors. A positive estimation of α3

would indicate that large traders improve their trading performance during the EAday

period relative to small traders. This result would be consistent with earnings

announcements, on average, causing an increase in information asymmetry between

18

investors. If α3 is negative, on the other hand, this would be indicative of an improvement

in the performance of small traders relative to large, consistent with earnings

announcements serving primarily to reduce information asymmetry between investors.

I include a number of controls for other features of the trading environment which

may cause differences in returns around earnings announcements. RET3i measures the

decile ranked magnitude of the three day return after the observation day (firm day i to

firm day i+3). Since absolute returns following earnings announcements are larger, on

average, than returns on non-announcement days, this control picks up the portion of

variation in PROF that is simply a result of the magnitude of the price movement.

Without the inclusion of this control, any results could be attributable to changes in return

magnitudes, rather than in the underlying abilities of different investor classes.

NB3ij is the net-buying activity for investor class j in the three days prior to the

observation (firm day i-3 to firm day i). This controls for the possibility that trading

outcomes may be partially the result of short term reversals of prior trading positions.

Kaniel et al. (2009) examine the behavior of individual investors using a proprietary data

set that identifies trade initiators and conclude that some individuals tend to unwind their

positions following the announcement. This control will help to alleviate concerns that

patterns in the data may be the result of prior trading rather than earnings announcement

information.

Other controls include Q4i, a dummy which equals 1 if the earnings

announcement is a fourth quarter and zero otherwise, and BA%i, a measure of the daily

bid-ask spread, scaled by closing price. I also include a number of controls designed to

describe the mix of traders, which could potentially affect the performance of the two

19

different groups. LPROPi and SPROPi describe the proportion of total trades on firm-day

i made by large and small investors, respectively. LVOLPROPi and SVOLPROPi

measure the proportion of trades by the two groups, and LAVGi and SAVGi is the

average number of shares traded by each group on firm-day i. These variables control for

changes in investor mix and trading activity around earnings announcements.7

One potentially confounding effect is post earnings announcement drift. If

different investor types are differentially using PEAD-related trading strategies, their

returns following earnings announcements may not reflect the degree to which they are

using the announcement to generate private information. This presents a difficulty

because the research question of this paper centers on the level of private information

about a firm that different investor groups possess. If some traders are better able to

understand and exploit PEAD, then results may pick up this difference rather than

differences due to information based trading. As there is some evidence that institutional

investors exploit PEAD (Ke and Ramalingegowda 2005) I control for the magnitude of

the earnings surprise. But since earnings surprise is also information that informed

investors can use to make investment decisions in conjunction with their private

information, I estimate model (1) both with and without the surprise controls.

I use four different controls for earnings surprise. Given that small and large

traders have been shown to respond differently to surprises calculated using the random

walk or analyst forecast method (Bhattacharya, 2001), I include both as controls. I also

partition the variables into negative and positive surprises, as the effect of PEAD can be

different for negative versus positive surprises. SURP_RW_POSi represents the positive

random walk earnings surprise of the earnings announcement closest to firm-day i, with

7 Results are similar whether these variables are included or not.

20

SURP_RW_NEG representing negative random walk earnings surprises.

SURP_AN_POS and SURP_AN_NEG represent the corresponding variables for earnings

surprises calculated using analyst forecasts.

I also examine the effect of two firm-level characteristics on the relationship

explored in formula (1), firm size and analyst following. I do this by interacting these

variables with my main variables of interest:

(2)

Where PI is one of three variables designed to proxy for the amount of private

information held by investors. MVE_RANK is the decile rank of firm size, measured as

market value of equity. NUMAN is the number of analysts following the firm prior to the

earnings announcement. And C_CONS is the change in analyst consensus, as defined in

Barron et. al. (1998). Appendix A provides detailed variable definitions.

Finally, I test the discretionary liquidity trader hypothesis of Chae (2005). While

the results of model (1) have some implications for this hypothesis, (1) by construction

compares the post-earnings announcement period to all other periods. It is possible,

however, that the period immediately prior to the announcement may also be

fundamentally different from other periods. If large investors had a greater advantage in

the pre-announcement period than in the post-announcement period, it would still be

advantageous for uninformed investors with a short discretionary window to delay their

trade until after an earnings announcement. I test the pre announcement period with the

following model:

(3)

21

This is identical to model (1), except for the substitution of EApre for EAday. EAprei is

an indicator variable which is equal to 1 if firm-day i is one of the two days prior to the

earnings announcement (days -1 or -2). A positive coefficient estimate for α3 would

indicate that the pre announcement period is more advantageous to large traders than

other periods. A negative coefficient would indicate that this period is more advantageous

for small traders.

Next, I test my sample to examine the effect of certain earnings features on the

ability of better informed traders to achieve higher returns following an earnings

announcement. The first attribute of earnings that I test is the absolute magnitude of the

surprise. Yohn (1998) finds that this is positively associated with spread following

earnings announcements, suggesting that large surprises are more likely to result in

information asymmetries between investors. This notion also has intuitive appeal, as

more sophisticated investors are more likely to be in a position to correctly interpret the

meaning of an earnings surprise. For this test, and other tests of earnings attribute effects,

I restrict my sample to days of, or days following (if the day of the announcement is not

available) the earnings announcement. This is due to the fact that these tests are designed

to test the difference between different types of announcements. As a result, I test the

following model:

….(4)

where ABS_EARN is the measure of absolute earnings surprise, based on either analysts’

expectations or a seasonal random walk. In addition to this continuous measure, I also

22

test to see if there are differences between announcements where earnings surprises are

extreme, versus announcements with zero surprise, or very little surprise:

(5)

where EXTREME = 1 if the absolute earnings surprise is in the 5th

quintile among my

sample, and EXTREME = 0 if the absolute earnings surprise is in the 1st quintile among

my sample. All other observations are omitted to afford a direct comparison between the

highest and lowest earnings surprise quintiles.

Next, I examine whether the profitability of trade for larger investors is greater

following positive versus negative earnings surprises. Brown, Hillegeist, and Lo (2009)

provide some evidence that positive and negative earnings surprises may affect the arrival

rate of private information differently, thus having a differing effect on the profitability of

different types of traders. Furthermore, prior studies have shown that investors react

differently to negative versus positive surprises (Hayn, 1995; Matsumoto 2002) I

therefore test the following model:

(6)

Where POS = 1 if the earnings surprise is positive and POS = 0 if it is negative. Due to

the fact that I use two different types of earnings surprise in this paper, and they are not

always in concordance (i.e. one will be negative while the other is positive, on occasion),

I only use observations for (6) where both estimations of earnings surprise are in

agreement. All other observations, including those with zero earnings surprise, are

omitted from the estimation of (6).

Next, I examine the case where there is disagreement between the two measures

of earnings surprise. This is potentially interesting because Battalio and Mendenhall

23

(2005) and Bhattacharya (2001) both provide evidence that small traders are more likely

to anchor off seasonal random walk expectations of earnings, while large traders appear

to react more to analysts’ expectations of earnings. Thus, when one construct of earnings

surprise is positive and the other negative, the reactions of the two groups may be most

different for these announcements, and this may be a time at which small traders’

information (i.e. prior year’s quarterly earnings) is most out of date. I examine this

question by testing the following model:

(7)

Where DISAGREE = 1 if the two measures of earnings surprise have opposite signs, and

DISAGREE = 0 if the two measures have the same sign. Other observations are omitted.

Next, I examine the effect of shareholder attributes on large traders’ advantage.

Since large trades are likely to be institutional, it is plausible that the types of institutions

which trade in a firm are likely to affect the relative profitability of large trades. Ali et. al.

(2004) find that patterns of changes in institutional holdings have some predictive power

for returns around earnings announcements, and Matsumoto (2002) finds that greater

ownership by transient institutional investors increases management’s incentives to meet

earnings forecasts. I use the classification scheme developed in Bushee (2001) to classify

13f holdings of firms in my sample as held by transient institutions, quasi-indexing

institutions, and dedicated institutions.8 For each observation in my sample, I then use the

proportional holdings of each type of institution for the quarter prior to the nearest

earnings announcement to estimate the following:

8 Institutions are classified based on portfolio turnover and diversity. Transient investors, for example,

display a large amount of turnover and do not tend to own similar clusters of stocks.

24

(8)

where INV = the proportion of outstanding shares owned by a type of institution in the

quarter prior to the nearest earnings announcement. I expect that the presence of both

dedicated and transient ownership will be positively associated with large traders’

profitability following earnings announcements, as these are the types of institutions

which are more likely to seek an advantage from firms’ accounting statements. Quasi-

indexers, on the other hand, are more likely to employ informationless trading strategies,

and I form no prediction regarding their presence on my main effect of interest.

4. Data and Results

4.1 Data Procedures

My sample includes all ordinary equity securities for domestic, non-ADR firms

(CRSP shrcd 10 or 11) for the years 1994 to 2007 inclusive. I further restrict my sample

to only firms listed on the NYSE to avoid difficulties in comparing microstructure

techniques such as the Lee-Ready algorithm across different exchanges.9 For each

security in my sample, I use the TAQ database to infer the direction of trade initiation. I

eliminate all trades that are not routed through the NYSE (i.e. regional exchanges) as

variations in the timing of quotes from these exchanges can make comparison with

NYSE quotes and trades problematic. Trades with a negative price or a price more than

$5 different from the previous price are eliminated.10

I further eliminate quotes where the

bid is equal to or greater than the ask price, where the spread is greater than $5, where

depth is negative, or where changes in the bid or ask are more than $5 from the previous

9 Additionally, as noted by Atkins and Dyl (1997) and Anderson and Dyl (2005), double counting of trades

makes work with NASDAQ at the transaction level difficult. And Ellis, Michaely, and O’Hara (2000) point

out issues related to using the Lee-Ready algorithm with NASDAQ. 10

These observations are likely errors in the database.

25

bid or ask. I then match the trades with the quotes, using the Lee-Ready (1991) algorithm

(with the one second delay suggested by Henker and Wang (2006)) and infer the

direction of the trade initiator by comparing executed price with the prevailing quote.

I then compute the short term imputed profit for each trade in my sample by

comparing the actual execution price to the midpoint of the closing bid and ask quotes

three days hence. Trade direction is used to determine the sign of the imputed return.

Investors are also classified as small, medium, or large at this point based on the dollar

value of the trade. All medium-sized trades are discarded. For the small and large traders,

PROFij is computed by taking the value-weighted average of imputed three-day return for

that day within that investor class.

The resulting dataset is then merged with Compustat to obtain earnings

announcement dates and some firm variables. Firms without a minimum of five earnings

announcement dates are dropped from the sample. To prevent serial correlation due to

overlapping return periods, I drop two thirds of my observations. To maximize the

number of earnings announcement observations, I anchor on earnings announcement

days, using the following procedure. For each observation with two earnings

announcement days (i.e. day 0 and day +1), I randomly choose one day to begin the

process of culling overlapping return periods.11

After this, I begin dropping observations

around the anchor date. For example, if day +1 relative to the earnings announcement is

my anchor date, I would delete days +2 and + 3, as well as days 0 and -1. I then continue

through the days that have the relevant earnings announcement date as the nearest

earnings announcement date, keeping days +4 and -2 in this example, and deleting days

11

In the case where only one of these two days is available, I use that day as the anchor.

26

+5 and +6, -3 and -4, and so forth. This ensures that there are no overlaps between the

many different calculated return periods in my sample.

4.2 Descriptive Statistics

A total of 2,883 unique firms representing 1,594,692 non-overlapping trading

days are in the sample. 156,239 sample days are in the post-earnings announcement

period, i.e. they are days 0 or 1 relative to the earnings announcement. Almost all of these

days have some small trader activity, and 87% of days have some larger trade activity as

well. Both mean and median PROF is negative for both large and small traders, although

the mean is somewhat lower for small traders. Negative PROF is intuitive, as most of the

three-day windows involve little price movement, but the entry or exit price of the

security includes the cost of the spread.

Table 2 shows a correlation matrix consisting of the main variables used in the

tests. PROFij is positively correlated with EAdayi, NB3i, MV_RANKi and NUMANALi.

As expected, PROFij is significantly negatively correlated with BA%, indicating that

higher spreads reduce short term returns.

4.3 Main Results

Results of estimating (1) are shown in Table 3. The coefficients for both LGj and

EAdayi*LGj are positive and significant at conventional levels. This indicates that large

traders enjoy an advantage in their short term returns compared to small traders in both

the period following earnings announcements and at other times as well. The 0.022

coefficient for EAdayi*LGj indicates that earnings announcements cause large traders’

advantage over small traders to more than double from the 0.014 percent advantage

implied by the coefficient for LGj. An increase of 0.022% over a three day window is an

27

TABLE 1

Descriptive Statistics for a Sample of NYSE firm trading days for calendar years

1994 - 2007

PROF is defined as the value weighted imputed 3-day return for each investor class for the firm-day.

LPROP and SPROP are the proportion of the total number of large trades and small trades, respectively, to

the total number of trades in the day. LVOLPROP and SVOLPROP are the proportion of the total volume

composed of large trades and small trades in a day, respectively. LAVG and SAVG are the average number

of shares per large trade and per small trade, respectively. SURP is the absolute value of the earnings

surprise, defined as the difference between the analyst consensus (defined as the average of forecasts

initiated or reiterated in the 45 days prior to the announcement) and IBES actuals. If this number is

unavailable, I use the difference between reported earnings and the seasonal random walk. EAday is an

indicator variable equal to 1 if the day is an earnings announcement day (or the day following an

announcement) and zero otherwise. LG is an indicator variable equal to 1 if the investor group is large, and

zero otherwise.

Panel A:

Sample Description

Total Number of Firms in Sample: 2,883

Total Number of Earnings Announcement Firm-Days 156,239

Total Number of Firm-Days 1,594,692

Firm-Days with Calculable Large Trader Profit 1,388,730

Firm-Days with Calculable Small Trader Profit 1,578,662

Panel B:

Variable Descriptives

Variable Name N Mean Median Std 25th

75th

PROF (LG=1) 1 1,388,730 -0.0365 -0.0565 1.652 -0.5734 0.4630

PROF (LG=0) 1,578,662 -0.0818 -0.0517 1.388 -0.4564 0.2865

RET3 1,577,698 0.0336 0.0203 1.024 0.0087 0.0400

NB3 1,570,341 0.0611 0.0857 0.276 -0.0667 0.2174

SURP_RW_POS 1,546,347 0.0098 0.0012 0.033 0 0.0057

SURP_RW_NEG 1,547,578 -0.0093 0 0.036 -0.0051 0

SURP_AN_POS 1,225,012 0.0013 0.0002 0.003 0 0.0012

SURP_AN_NEG 1,225,021 -0.0017 0 0.009 -0.0002 0

LPROP 1,355,122 0.2494 0.1786 0.229 0.0426 0.4219

SPROP 1,355,122 0.3632 0.3262 0.202 0.1964 0.5000

LVOLPROP 1,355,122 0.6054 0.6636 0.304 0.3377 0.8948

SVOLPROP 1,355,122 0.0927 0.0456 0.112 0.0146 0.1304

LAVG 1,388,730 5,391 3,566 9,452 2,201 5,942

SAVG 1,372,700 145 100 113.5 100 150

28

TABLE 2

Correlation Matrix of Selected Variables

Variables are defined in Appendix A. Pearson correlations are on the top right, Spearman correlation on the bottom left. P-values are displayed below

correlations.

PROF EAday LG RET3 NB3 BA% SURP_AN

_POS

SURP_AN

_NEG

MV_RANK NUMAN C_CONS

PROF - 0.0102

0.000

0.0149

0.000

0.0012

0.038

0.0228

0.000

-0.0619

0.000

-0.0099

0.000

0.0266

0.000

0.0456

0.000

0.0092

0.000

0.0021

0.013

EADay 0.0104

0.000 -

0.0048

0.000

0.0019

0.001

0.0011

0.071

0.0081

0.000

0.0101

0.000

-0.0053

0.000

-0.0087

0.000

-0.0029

0.001

0.0000

0.972

LG -0.0078

0.000

0.0023

0.009 -

-0.0023

0.000

0.0313

0.000

-0.0468

0.000

-0.0238

0.000

0.0565

0.000

0.0899

0.000

0.0116

0.000

0.0002

0.826

RET3 -0.0012

0.1923

0.0484

0.000

-0.0023

0.009 -

-0.0048

0.000

0.0257

0.000

0.0035

0.000

-0.0155

0.000

-0.0127

0.000

0.189

0.000

-0.0153

0.000

NB3 0.0161

0.000

0.0018

0.038

0.0638

0.000

-0.0174

0.000 -

-0.1818

0.000

-0.0585

0.000

0.0883

0.000

0.2157

0.000

0.0902

0.000

0.0036

0.000

BA% -0.0624

0.000

0.0113

0.000

-0.0039

0.000

0.1678

0.000

-0.1139

0.000 -

0.0999

0.000

-0.2655

0.000

-0.4797

0.000

-0.1593

0.000

-0.0195

0.000

SURP_AN_POS 0.0057

0.000

0.0168

0.000

-0.0063

0.000

0.0163

0.000

-0.0169

0.000

-0.0498

0.000 -

0.0811

0.000

-0.1667

0.000

-0.0742

0.000

0.0007

0.000

SURP_AN_NEG 0.0099

0.000

-0.0000

0.964

0.0035

0.000

0.0025

0.0042

0.0477

0.000

-0.0223

0.000

0.5775

0.000 -

0.2604

0.000

0.0503

0.000

0.0197

0.000

MV_RANK 0.0409

0.000

-0.0057

0.000

0.0207

0.000

-0.1240

0.000

0.1625

0.000

-0.3822

0.000

-0.0602

0.000

0.0760

0.000

- 0.5256

0.000

0.0028

0.001

NUMAN 0.0208

0.000

-0.0053

0.000

0.0126

0.000

0.0169

0.000

0.0873

0.000

-0.1936

0.000

-0.0486

0.000

0.0674

0.000

0.5323

0.000

- 0.028

0.000

C_CONS 0.0034

0.000

0.0004

0.635

0.0003

0.6895

-0.0113

0.000

0.0025

0.004

-0.0204

0.000

0.0119

0.000

0.0359

0.000

0.0036

0.000

0.0333

0.000 -

29

economically significant excess return for such a small period of time. Assuming 252

trading days in a year, excess return of 0.022% in each 3 day period would lead to an

annual excess return of 1.87%. These results are consistent with earnings announcements

on average causing an increase in short-term information asymmetry that benefits better-

informed investors. If we set all control variables to their median values, the difference

between small and large profitability following earnings announcements is 0.034% over

three days, equivalent to a 2.90% annual return.

The second column of Table 3 shows results with the inclusion of controls for the

magnitude and direction of the earnings surprise (each variable is included individually,

as well as interacted with EAdayi and LGj individually, although only the three way

interaction term is shown for brevity.) Interestingly, while none of the surprise interaction

terms are statistically significant, their inclusion increases the magnitude of the

coefficient for EAdayi*LGj and decreases its standard error. Thus, to the extent that

investors do trade strictly on PEAD following an earnings announcement, such trading

the short term returns of investors, as expected. Higher levels of price movement are

associated with higher individual trading returns, and this relationship becomes stronger

on earnings announcement days. Prior net-buying seems to also affect individual short

term returns, indicating that unwinding of prior positions does seem to affect the

profitability of trade, although it is interesting to note that this relationship does not

change in the period following earnings announcements. As expected, bid ask spreads

have a negative impact on short-term returns.

Results for the incremental effect of information variables are shown in Table 4.

The first column of panel A shows the results of inclusion of a firm size interaction with

30

TABLE 3

Results of Regressions of Short Term Trader Profitability on Trader Characteristics

and Information Variables

Variables are defined in Appendix A. Stacked regression consists of one observation per trader type (small

or large) per trading day (only one out of each three potential trading days is used, to avoid overlapping

return periods.) Some interactions are omitted for brevity (all earnings surprise variables are also interacted

with EAday and LG individually). Firm fixed effects are included. All standard errors are clustered by date

using the Rogers (1993) method. Coefficients in italics are statistically significant at the 5% level. All tests

are two-tailed

Variable Name Estimate T-Stat Estimate T-Stat

Intercept 0.00941 0.96 0.01605 1.57

EAday -0.01217 -1.65 -0.02208 -2.78

LG 0.01436 4.94 0.00396 3.22

EAday*LG 0.02184 2.38 0.03425 3.22

RET3 0.00744 7.53 0.00677 6.61

RET3*LG 0.00098 1.08 0.00084 0.91

RET3*EAday 0.00990 4.82 0.01068 5.00

RET3*EAday*LG 0.00038 0.16 -0.00144 -0.58

NB3 0.02302 3.82 0.02071 3.22

NB3*EAday 0.00501 0.20 0.02131 0.82

NB3*EAday*LG -0.04209 -1.44 -0.04430 -1.43

Q4*EAday -0.00724 -0.74 -0.00893 -0.87

BA% -2.22494 -13.05 -2.15328 -12.02

SURP_RW_POS*EAday*LG -0.67895 -1.89

SURP_RW_NEG*EAday*LG 0.30154 0.96

SURP_AN_POS*EAday*LG 1.32583 0.48

SURP_AN_NEG*EAday*LG -0.85744 -0.63

LPROP 0.06914 3.22 0.07499 3.35

SPROP -0.04726 -2.45 -0.02859 -1.42

LVOLPROP -0.07251 -5.19 -0.07436 -5.11

SVOLPROP -0.03219 -0.87 -0.04992 -1.32

LAVG 0.00000 -0.05 0.00000 -0.06

SAVG -0.00022 -6.51 -0.00024 -6.35

Number of observations: 2,558,530 2,277,370

31

the main variables of interest. The coefficient for MV_RANK is positive and significant,

indicating that trading profits are higher overall for large firms, although the negative

does not appear to substantially explain the relative improvement in trading performance

that large traders experience following an earnings announcement.

Regarding the control variables, the magnitude of the price change seems to affect

coefficient for MV_RANK*LG indicates that increases in size tend to benefit smaller

traders. The coefficient for the three way interaction term MV_RANK*LG*EAday is not

statistically significant, nor is the coefficient for MV_RANK*EAday, indicating, contrary

to expectations, that firm size does not seem to affect the difference between small and

large traders’ profits following earnings announcements.

Column 2 of Panel A shows the results of interactions with the number of analysts

following a firm prior to the earnings announcement. Here results show that more analyst

following does affect trading around earnings announcements, with large traders

benefiting from an increase in analyst coverage, relative to small traders. Interestingly,

however, the opposite effect obtains in non-earnings announcement periods, although the

magnitude of the effect is smaller. This indicates that, while heavily followed stocks are,

ceteris paribus, a worse trading environment for large traders, these stocks are

nonetheless more advantageous for large traders following earnings announcements.

Finally, in Panel B, we see the results of interactions with C_CONS, or change in

analyst consensus. If analyst consensus decreases following an earnings announcement,

this implies that the ratio of private information to common information has increased,

which would tend to benefit sophisticated investors who can generate private

information. Results are consistent with this notion, as the coefficient on

32

TABLE 4

Effect of Firm-Level Variables on the Relative Advantage of Large Traders

Following Earnings Announcements




with EAday and LG individually). All standard errors are clustered by date using the Rogers (1993)

method. Coefficients in italics are statistically significant at the 5% level. All tests are two-tailed.

Panel A:

Incremental effect of firm size and analyst following

PI = MV_RANK PI = NUMAN


Intercept 0.1477 1.42 0.0248 1.67

EAday -0.0203 -2.45 -0.0212 -1.41

LG 0.0057 1.78 0.0170 3.56

EAday*LG 0.0334 3.10 0.0097 0.57

PI 0.0000 1.05 -0.0002 -0.33

PI*LG -0.0000 -5.00 -0.0011 -2.81

PI*EAday -0.0000 -1.24 -0.0009 -0.76

PI*EADay*LG 0.0000 0.67 0.0027 2.09

RET3 0.0068 6.64 0.0058 4.45

RET3*LG 0.0008 0.86 -0.0004 -0.40

RET3*EAday 0.0106 4.85 0.0123 4.53

RET3*EAday*LG -0.0014 -0.57 -0.0015 -0.51

NB3 0.0207 3.23 0.0152 1.64

NB3*EAday 0.0217 0.84 0.0661 1.56

NB3*EAday*LG -0.0439 -1.42 -0.0501 -1.00

Q4*EAday -0.0092 -0.89 -0.0054 -0.43

BA% -2.1529 -12.01 -1.4961 -6.00

SURP_RW_POS*EAday*LG -0.6768 -1.88 -0.3407 -0.64

SURP_RW_NEG*EAday*LG 0.2999 0.96 0.0091 0.02

SURP_AN_POS*EAday*LG 1.3578 0.49 -1.7486 -0.42

SURP_AN_NEG*EAday*LG -0.8674 -0.63 -0.7284 -0.30

LPROP 0.0753 3.28 0.0789 2.76

SPROP -0.0284 -1.40 0.0057 0.20

LVOLPROP -0.0744 -5.08 -0.0857 -4.43

SVOLPROP -0.0499 -1.32 -0.0821 -1.55

LAVG -0.0000 -0.05 0.0000 1.19

SAVG -0.0002 -6.35 -0.0003 -4.63


33

Panel B:

Incremental effect of change in analyst consensus

PI = C_CONS

Variable Name Estimate T-Stat

Intercept 0.0202 1.45

EAday -0.0485 -3.67

LG 0.0087 1.80

EAday*LG 0.0584 3.37

PI 0.0003 0.83

PI*LG -0.0003 0.83

PI*EAday 0.0042 2.45

PI*EADay*LG -0.0059 -2.48

RET3 0.0057 4.45

RET3*LG -0.0005 -0.47

RET3*EAday 0.0124 4.55

RET3*EAday*LG -0.0015 -0.52

NB3 0.0147 1.59

NB3*EAday 0.0659 1.54

NB3*EAday*LG -0.0475 -0.95

Q4*EAday -0.0053 -0.43

BA% -1.4881 -5.97



SURP_AN_POS*EAday*LG -2.1094 -0.51


LPROP 0.0816 2.88

SPROP 0.0073 0.25

LVOLPROP -0.0859 -4.45

SVOLPROP -0.0825 -1.56

LAVG 0.0000 -4.63

SAVG -0.0071 -0.07

Number of observations: 1,300,461

34

C_CONS*EAday*LG is negative. This indicates that if an earnings announcement

produces information that increases the amount of private, idiosyncratic information in

analysts’ forecasts about future earnings (a decrease in consensus), large investors’

advantage over small investors following the announcement is greater. This is consistent

with the proposition of this paper that the relative profitability of trade between large and

small traders following earnings announcements is driven by differential levels of private

information.

Table 5 details the effect of earnings surprises on the profitability of large traders.

As the sample from which this is drawn includes only post earnings announcement days,

the coefficient of interest is the interaction between the surprise term and the LG

indicator. When absolute surprise is measured using analysts’ expectations, large trader

profitability is positive, but only significant at the 10% level. This is weak evidence that

large traders’ advantages increase when earnings surprise, as measured by analysts’

expectations, is high. When earnings is measured as a seasonal random walk, the effect

on large trade profitability not statistically significant. Note that, consistent with prior

results, LG is positive and significant, indicating that large traders are more profitable

than small traders on post earnings announcement days. I also test both types of earnings

surprise simultaneously to see if the effect persists in the presence of both types of

earnings surprise. Interestingly, the results stay the same when both types of surprise are

present, consistent with the notion in Battalio and Mendenhall (2005) extend

Bhattacharya (2001) that the different investor types are focused on different types of

earnings surprise.

35

TABLE 5

Effect of Earnings Surprises on Relative Advantage of Large Traders


or large) per trading day; in these tests only earnings announcement days (day 0 or day +1 relative to the

earnings announcement) are used. Firm fixed effects are included. All standard errors are clustered by date

using the Rogers (1993) method. Coefficients in italics are statistically significant at the 5% level. All tests

are two-tailed.

Using Analysts’

Expectations Surprise Using Random Walk

Surprise


Intercept -0.0971 -11.38 -0.0973 -11.38

LG 0.0349 3.38 0.0363 3.48

ABSOLUTE_ANAL -0.0041 -0.56

ABSOLUTE_ANAL*LG 0.0156 1.65

ABSOLUTE_RAND 0.0345 0.61

ABSOLUTE_RAND*LG -.14532 -1.45

RET3 0.0163 6.80 0.0162 6.74

RET3*LG -0.0006 -0.22 -0.0002 -0.07

NB3 0.0741 2.65 0.0757 2.71

NB3*LG -0.0533 -1.59 -0.0562 -1.67

Q4*LG 0.0061 0.33 0.0066 0.36

Number of observations: 217,632 217,632

Both Types of Surprise


Intercept -0.097 -11.37

LG 0.036 3.51

ABSOLUTE_ANAL -0.004 -0.58

ABSOLUTE_ANAL*LG 0.016 1.70

ABSOLUTE_RAND 0.036 0.64

ABSOLUTE_RAND*LG -0.153 -1.53

RET3 0.016 6.74

RET3*LG -0.000 -0.09

NB3 0.075 2.70

NB3*LG -0.056 -1.67

Q4*LG 0.006 0.34

Number of observations: 217,632

36

In Table 6, I turn to the effect of extreme earnings surprises. This sample includes

only observations that are in the highest and lowest absolute earnings surprise quintiles in

my sample. While there is weak evidence (at the 10% level) that the profitability of trade

initiators in general increases when analysts’ expectation earnings surprises are extreme,

there is no evidence that this gain is captured mostly by large traders (the coefficient for

EXTREME*LG is not significant). This suggests that the effect documented in Table 5 is

not confined to the most extreme earnings surprises. Again, when surprise is defined

using the seasonal random walk, no effect is discernable.

The difference between positive and negative earnings surprises is documented in Table

7. Interestingly, trade initiator profitability in general is significantly higher when

earnings surprises are positive, but again there is no incremental effect for large traders

specifically. One difficulty of this specification used in this paper is that earnings

surprises will effect short term security returns. Therefore, it is likely that the inclusion of

the RET3*LG control variable in this estimation, and in all the earnings characteristics

estimations in Tables 5-8, may take away from the main effect of interest. But given that

large security returns are likely to be mechanically associated with higher profitability for

large traders, I continue to include RET3 as a control variable.

My final test of the effect of earnings characteristics on short term trade

profitability is in Table 8. The term DISAGREE measures instances in which the

analysts’ forecasts measure of earnings surprise is a different sign than the random walk

measure of earnings surprise. The coefficient for DISAGREE is negative and significant,

indicating that overall short term returns for trade initiators are lower when these surprise

measures yield different conclusions. Again, however, the interaction term

37

TABLE 6

Effect of Extreme Earnings Surprises on Relative Advantage of Large Traders



earnings announcement) are used. Firm fixed effects are included. Sample consists of observations that are

in either the first or fifth quintile of absolute earnings surprises. Observations in the fifth (highest) earnings

surprise quintile are coded EXTREME=1, whereas those in the first quintile are coded EXTREME=0. All

standard errors are clustered by date using the Rogers (1993) method. Coefficients in italics are statistically

significant at the 5% level. All tests are two-tailed.

Analysts’ Expectation

Surprise Random Walk Surprise


Intercept -0.1252 -7.83 -0.0956 -6.44

LG 0.0372 1.77 0.0562 2.99

EXTREME 0.0407 1.93 -0.0188 -1.03

EXTREME*LG 0.0192 0.81 0.0094 0.43

RET3 0.0140 4.50 0.0139 4.45

RET3*LG -0.0034 -0.82 -0.0059 -1.45

NB3 0.0563 1.37 0.0692 1.60

NB3*LG -0.0653 -1.24 -0.0479 -0.90

Q4*LG 0.0196 0.61 0.0089 0.29

Number of observations: 87,055 87,064

38

TABLE 7

Effect of Positive versus Negative Earnings Surprises on Relative Advantage of

Large Traders



earnings announcement) are used. Firm fixed effects are included. Sample consists of observations in

which there is concordance between analysts’ expectations earnings surprise and random walk earnings

surprise. If both measures of surprise are positive, then POS=1. If both are negative then POS=0. All other

observations are discarded. All standard errors are clustered by date using the Rogers (1993) method.

Coefficients in italics are statistically significant at the 5% level. All tests are two-tailed.


Intercept -0.1639 -13.64

LG 0.0359 2.21

POS 0.0871 6.81

POS*LG -0.0101 -0.57

RET3 0.0204 7.72

RET3*LG 0.0012 0.37

NB3 0.0712 2.36

NB3*LG -0.0718 -1.84

Q4*LG 0.0031 0.14


39

DISAGREE*LG yields no firm results. So while disagreement between measures of

earnings surprise seems to affect the trading environment following earnings

announcements, I cannot conclude that it affects the relative advantage of the better

informed over the less informed. Overall, the evidence on the effect of earnings attributes

on large trader advantage indicates there is some advantage to large traders when

absolute analysts’ forecast earnings surprise is high, but other aspects of earnings

examined here do not seem to affect this advantage. This may be due to the inability of

this research design to separate the effect of earnings announcements while

simultaneously controlling for the price effect of that announcement.

I examine the effect of the presence of different types of institutional traders in

Table 9. Contrary to my expectations, transient institutional ownership is positively

associated with profitability in general, but negatively associated with large trader

profitability on all days. This indicates that the profitability of large trades tends to be

lower when transient institutional ownership is high. It’s unknown whether this is due to

the trading activities of the transient institutions themselves or some other associated

attribute of their ownership. However, transient ownership seems to have no particular

effect on earnings announcement days as opposed to other days. So the effect of

ownership appears to be a general one, and not necessarily related to interpretation of

financial statements following earnings disclosures.

The second column of Table 9 shows the results for dedicated institutional

ownership. Contrary to the results for transient ownership, greater dedicated ownership is

associated with better short term trade profitability for large traders. This is in line with

expectations, as dedicated investors have invested heavily in understanding a particular

40

TABLE 8

Effect of Differing Earnings Surprise Signals on the Relative Advantage of Large

Traders



earnings announcement) are used. Firm fixed effects are included. Sample consists of observations in

which there are differences in earnings surprise signals between analysts’ expectations earnings surprise

and random walk earnings surprise. If one measure of surprise is positive and the other is negative, then

DISAGREE=1. If both are either positive or negative, then DISAGREE=0. All other observations are

discarded. All standard errors are clustered by date using the Rogers (1993) method. Coefficients in italics

are statistically significant at the 5% level. All tests are two-tailed.


Intercept -0.0847 -8.65

LG 0.0296 2.32

DISAGREE -0.0325 -3.34

DISAGREE*LG -0.0010 -0.07

RET3 0.0175 6.95

RET3*LG 0.0005 0.18

NB3 0.0909 3.08

NB3*LG -0.0493 -1.35

Q4*LG 0.0105 0.53


41

TABLE 9

Effect of Investor Mix on the Relative Advantage of Large Traders



return periods.) INV represents the percentage of shares outstanding by three different types of 13f

reporting institutions in the quarter prior to the nearest earnings announcement. All standard errors are

clustered by firm using the Rogers (1993) method. Coefficients in italics are statistically significant at the

5% level. All tests are two-tailed.

INV = Transient Investor

Percentage INV = Dedicated

Investor Percentage


Intercept .0161 1.95 .0456 5.59

EAday -0.163 -1.69 -0.014 -1.48

LG 0.031 7.81 0.002 0.42

EAday*LG 0.018 1.42 0.019 1.40

INV 0.151 11.96 -0.062 -4.13

INV*LG -0.125 -6.98 0.117 4.72

INV*EAday 0.003 0.09 -0.013 -0.29

INV*EADay*LG 0.015 0.25 0.014 0.23

RET3 0.007 12.38 0.008 14.40

RET3*LG 0.000 0.50 -0.000 -0.44

RET3*EAday 0.011 5.78 0.011 5.82

RET3*EAday*LG 0.001 0.59 0.002 0.67

NB3 0.082 11.78 0.089 12.70

NB3*EAday -0.021 -0.85 -0.022 -0.88

NB3*LG -0.072 -7.85 -0.079 -8.52

NB3*EAday*LG 0.001 0.05 0.003 0.08

Q4*EAday -0.012 -1.09 -0.012 -1.03

BA% -2.553 -16.32 -2.581 -16.37

SURP_RW_POS*EAday*LG -0.567 -1.41 -0.564 -1.40

SURP_RW_NEG*EAday*LG 0.281 0.76 0.280 0.76

SURP_AN_POS*EAday*LG 2.261 0.70 2.306 0.71

SURP_AN_NEG*EAday*LG -1.124 -0.73 -1.116 -0.73

LPROP 0.109 8.08 0.104 7.68

SPROP -0.063 -3.09 -0.053 -2.61

LVOLPROP -0.097 -7.33 -0.099 -7.48

SVOLPROP -0.008 -0.19 -0.035 -0.87

LAVG 0.000 1.72 0.000 1.92

SAVG -.0003 -9.34 -0.000 -9.73


42

INV = Quasi-Indexer Investor

Percentage


Intercept -0.001 -0.16

EAday -0.001 -0.11

LG 0.040 7.41

EAday*LG 0.026 1.45

INV 0.092 10.49

INV*LG -0.067 -5.86

INV*EAday -0.040 -1.63

INV*EADay*LG -0.015 -0.40

RET3 0.009 14.85

RET3*LG -0.001 -0.73

RET3*EAday 0.011 5.77

RET3*EAday*LG 0.002 0.69

NB3 0.087 12.40

NB3*EAday -0.020 -0.80

NB3*LG -0.076 -8.28

NB3*EAday*LG 0.003 0.10

Q4*EAday -0.012 -1.05

BA% -2.411 -14.83



SURP_AN_POS*EAday*LG 2.229 0.69


LPROP 0.101 7.51

SPROP -0.047 -2.33

LVOLPROP -0.092 -6.94

SVOLPROP -0.050 -1.27

LAVG 0.000 1.76

SAVG -0.000 -9.57


43

firm. Again, however, there is no discernable effect on post earnings announcement days.

The final column in Table 9 shows the effect of quasi-indexer ownership on large trader

advantage. Similarly to transient ownership, large trades tend to be relatively less

profitable as quasi-indexer ownership increases. This may be due to the tendency of these

institutions to make large, informationless trades. As with the other institutional types,

however, this test shows no significant effect of ownership mix on earnings

announcement days, or large trader advantage on those days.

Next, I turn to the discretionary liquidity trader hypothesis of Chae (2005). This

argues that patterns in trading volume around earnings announcements can be explained

by the “pent up demand” of liquidity traders who wish to delay their trade until after the

earnings announcement, as that will reduce their chances of trading against informed

traders and cause them to experience lower average returns. While the evidence presented

in table 3 would seem to be inconsistent with this notion, it is possible that the conditions

for small traders are even worse immediately prior to an earnings announcement than

immediately following the announcement. If this is the case, it still may be rational for

uninformed traders who have exogenous liquidity needs to delay their trade until the

announcement has been made. I test this proposition by running an estimation comparing

small and large trade performance in the period immediately prior to the earnings

announcement. Results of this test are presented in table 10.

If conditions for small traders are poor in this period, we would expect that the

coefficient for EApre*LG would be positive and significant. Instead, the coefficient is

not statistically significant, nor is the coefficient for EApre alone, indicating that there is

no evidence to support the notion that the period prior to the announcement is a worse

44

trading environment for small traders. Combined with the results in table 3, these results

are consistent with the view that uninformed investors would put themselves at a

disadvantage by waiting for the post-announcement period to execute their trades, the

opposite of the discretionary liquidity trader hypothesis.

4.4 Sensitivity Analyses

While the use of small and large trade size to proxy for less and more

sophisticated investors is well established in the literature, it has also been noted that the

proxy may be becoming less reliable over time, as electronic trading innovations make

order splitting by institutions easier and less costly (Kaniel, Saar, Titman, 2008). While

order splitting has been a potential strategy for masking informed trade for many years

(Barclay and Warner, 1993) there is some evidence that its use has increased over time.

Puckett and Yan (2008), using a large database of actual trades from 840 different

institutions show a steady decline in median trade size from 60,030 in 1999 to 14,232 in

2005. And Campbell et. al. (2009) show that very small trades are correlated with

changes in institutional holdings, suggesting that some institutions are splitting their

trades into very small portions. Finally, Hvidkaajer (2008) finds that in his sample,

correlation between small trading direction and institutional holdings is negative until

2002, at which point it turns slightly positive.

I do two additional analyses to address these concerns. First, I partition my

sample into an early and a late period. The early period (1994 – 2001) should have fewer

concerns about proxy noise due to excessive institutional order splitting, compared to the

later period (2002 – 2007). I rerun my tests on both periods in order to examine any

45

TABLE 10

Relative Performance of Small and Large Traders Prior to the Earnings

Announcement




with EAday and LG individually). All standard errors are clustered by date using the Rogers (1993)

method. Coefficients in italics are statistically significant at the 5% level. All tests are two-tailed.


Intercept 0.0277 2.51

EApre 0.0078 0.71

LG 0.0133 4.60

EApre*LG 0.0117 0.81

RET3 0.2738 3.21

RET3*LG -0.1258 -1.73

RET3*EApre 0.0775 0.31

RET3*EApre*LG -0.0370 -0.12

NB3 0.0199 2.88

NB3*EApre 0.1253 3.33

NB3*EApre*LG -0.0792 -1.76

Q4*EApre -0.0283 -1.92

BA% -2.0311 -10.79

LPROP 0.0798 3.31

SPROP 0.0166 0.76

LVOLPROP -0.0654 -4.25

SVOLPROP -0.0898 -2.30

LAVG -0.0000 -0.14

SAVG -0.0003 -7.10


46

differences. Abbreviated results of estimating (1) for these two time periods are shown in

Panel A of Table 11. For the early period, results are consistent with the entire sample

results reported in Table 3. The coefficients for LG and LG*EAday are both positive and

significant, and both are larger in magnitude than the combined results. For the latter

period, however, the coefficient on LG becomes negative and significant, with the

coefficient for EAday*LG remaining positive but only at a marginal level of significance.

These results are consistent with a change in trading patterns of informed and uninformed

investors over time, although I cannot rule out the possibility of other influencing trends

over time.

To better understand the changing nature of trade over time, I present trends in

order size for my sample in Panel B. In examining the proportion of large and small

trades both by number and by trading volume, a clear trend emerges away from large

trades and towards small ones. While there are likely other causes for this trend besides

order splitting (such as the emergence of online brokerages and day trading) the

magnitude of the change points towards wide-spread changes in institutional trading

habits. This is consistent with the data presented in Chordia, Roll and Subrahmanyam

(2008) who find a similar trend, and conclude that institutional order splitting is the most

likely cause.

Secondly, I adopt a different technique for categorizing large and small traders

based on evidence presented in Campbell et. al. (2009). This paper examines the

correlation between different trade sizes and changes in reported institutional

shareholdings. While large trades tend to be positively associated with institutional

ownership, and most small trades negatively associated with institutional ownership, very

47

TABLE 11

Sensitivity Test and Trends in Small/Large Trading Patterns

Variables are defined in Appendix A. Selected estimates from estimation of equation (1) for different

sample periods.

Panel A:

1994-2001 2002-2007


EAday -0.0184 -1.43 -0.0048 -0.46

LG 0.0393 10.64 -0.0261 -8.13

EAday*LG 0.0508 2.91 0.0280 1.89

Panel B:

Average values by year of selected variables

Year LPROP SPROP LVOLPROP SVOLPROP

1994 0.390 0.270 0.808 0.040

1995 0.400 0.264 0.811 0.039

1996 0.414 0.252 0.816 0.036

1997 0.426 0.238 0.812 0.035

1998 0.415 0.244 0.800 0.038

1999 0.365 0.292 0.779 0.047

2000 0.359 0.307 0.771 0.052

2001 0.133 0.362 0.553 0.066

2002 0.086 0.450 0.553 0.108

2003 0.058 0.500 0.379 0.148

2004 0.056 0.482 0.348 0.152

2005 0.053 0.483 0.317 0.163

2006 0.042 0.517 0.269 0.194

2007 0.022 0.646 0.156 0.317

48

small trades (below $2,000) show a positive association, suggesting that many of these

trades represent institutional order splitting. I therefore re-run my main analyses using

negatively associated with institutional ownership, respectively. I classify trades between

$2,000 and $10,000 as small12

, and trades greater than or equal to $40,000 as large.

Untabulated results from this alternate specification are nearly identical to the previously

reported results. This suggests that the reported results are robust to alternate

specifications designed to reduce the amount of investor type misclassification.

Additionally, I test my data for the presence of multicollinearity. Computing

variance inflation factors for all the variables used in my main tests shows that none have

a VIF of over 10. Several, however, are relatively high, with SPROP, LPROP,

SVOLPROP, and LVOLPROP all being greater than seven. This is intuitive, as the

proportion of small traders is highly negatively correlated with the proportion of large

traders. As these are control variables, the relatively high VIF’s are not a concern, but in

any case removing them from the tests does not change the results. All other variables

have VIF scores under 3.5.

5. Conclusion

In this paper I test the effect of earnings announcements on the relative ability of

small and large investors to trade advantageously. While many studies have examined the

issue of the effect of earnings announcements on information asymmetry, using various

proxies for asymmetry, a clear consensus has yet to emerge. I measure information

12

Since the great majority of trades take place in round lots of 100 shares, eliminating trades under $2,000

will only eliminate trades for shares with a price of $20 or less. To address the possibility that very small

order splitting takes place for shares with a price above $20, I eliminate all single lot trades from this

analysis.

49

asymmetry between large and small investors (a proxy for informed and uninformed

investors) by examining how well those investors’ actual trades predict short-term price

movement. Results show that, on average, large traders tend to improve their trading

performance following an earnings announcement more than small traders. This effect is

strongest for firms which are very large and have large analyst followings, and for

earnings announcements which produce more private information about future earnings.

There is some evidence that large traders improve their relative performance when

earnings surprises (as measured by analysts’ forecasts) are high. Other attributes of

earnings surprises, however, fail to yield significant results. Similarly, examination of

attributes of intuitional ownership fails to find evidence that certain types of institutions

are more likely to increase the advantage of large trading on post earnings announcement

days.

This paper contributes to two different streams of literature. The first is the broad

category of research on the effect of earnings announcements on information asymmetry.

This paper differs from prior work in that it examines the consequences of information

asymmetry, rather than relying on estimations of the probability of informed trade.

Regulatory concern relating to accounting disclosures is focused on the consequences of

accounting disclosures on the trading behavior of investors. This paper examines this

issue directly by analyzing the short-term returns of actual trades.

Secondly, this paper contributes to the literature on volume patterns around

earnings announcements. Prior literature has suggested that one possible explanation for

the large amount of trading volume following earnings announcements is the “pent up

demand” of uninformed traders who were waiting for an information-leveling event

50

before executing their trades. Results in this paper suggest, however, that informed

traders increase their advantage following earnings announcements, while no such

advantage can be demonstrated in the period immediately preceding the announcement.

Collectively, these results are inconsistent with the idea that earnings announcement

trading volume patters are caused by the type of trade-shifting described in Chae (2005).

51

Appendix – Variable Definitions

PROFij The three day imputed profit earned by investor group j on

firm-day i. For group j (large or small), PROF is the value

weighted average of returns earned by buying or selling at

each actual transaction price, and reversing this position three

days later (the midpoint of daily close bid-ask three trading

days forward.) PROF is then multiplied by 100 to express it in

percentage terms, and winsorized at the 1% and 99% levels.

RET3i The decile ranked absolute value of the security’s return over

the three trading day period from i to i+3

NB3ij The net-buying activity of investor group j in the three days

prior to day i. Net buying is computed as the number of shares

bought by group j on firm-day i less the number of shares

sold, divided by the total number of shares transacted by

group j on firm-day i.

BA%i Bid-ask spread for firm-day i, expressed in percentage terms.

Computed as the closing ask less the closing bid, divided by

closing price on firm-day i.

LGij An indicator variable equaling 1 if investor group j is the large

investor group on firm-day i, and 0 otherwise.

EAdayi An indicator variable equaling 1 if firm-day i is in the post

earnings announcement period (days 0 or 1 relative to the

earnings announcement.)

EAprei An indicator variable equaling 1 if firm-day i is in the pre

earnings announcement period (days -1 or -2 relative to the

earnings announcement.)

SURP_RW_POSi Positive seasonal random walk earnings surprise. Seasonal

random walk is computed as the difference between earnings

per share in the earnings announcement closest to firm-day i

less earnings per share four quarters prior (adjusted for stock

transactions). If the surprise is positive, SURP_RW_POS

takes its value. Otherwise, it takes a zero value.

SURP_RW_NEGi Negative seasonal random walk earnings surprise. The

random walk earnings surprise is calculated as above, if it is

negative, then SURP_RW_NEGi takes its value. Otherwise, it

takes a zero value.

SURP_AN_POSi Positive analyst expectation earnings surprise. Surprise is

defined as the difference between actual EPS (as defined by

IBES) and the most recent forecast or forecast update prior to

the earnings announcement closest to day i. If an

announcement has no forecasts or updates within the 30 days

prior to the announcement, it is not assigned a value. If the

surprise is positive, SURP_AN_POS takes its value.

Otherwise, it takes a zero value.

SURP_AN_NEGi Negative analyst expectation earnings surprise. The analyst

52

expectation earnings surprise is calculated as above, if it is

negative, then SURP_AN_NEGi takes its value. Otherwise, it

takes a zero value.

MV_RANKi Decile rank of market value (defined as market price at the

end of the prior quarter multiplied by shares outstanding, as

defined by CRSP.

NUMANi Number of analysts following a firm. Defined as the number

of unique analyst codes in IBES (excluding 0000) identified

as making earnings forecasts for the announcement closest to

firm-day i.

Q4i An indicator variable that takes the value 1 if the earnings

announcement closest to firm-day i is for the firm’s fourth

fiscal quarter, and 0 otherwise.

C_CONSi Decile ranked change in analyst consensus for one quarter

ahead earnings. Consensus is defined as in Barron, Harris and

Stanford (2005) where Consensus = 1 – D/V. D is defined as

analyst forecast dispersion, and V is defined as the squared

differences in analysts’ forecasts:

Where FCi is analyst i’s earnings forecast, EPS is actual

earnings per share, and n is the number of forecasting

analysts. The measure represents the ratio of commonly held

information to private information. An increase (positive

C_CONS) represents a decrease in the proportion of private

information due to information released in the earnings

announcement closest to day i.

LPROPi The proportion of trades made by large traders to total trades

on firm-day i.

SPROPi The proportion of trades made by small traders to total trades

on firm-day i.

LVOLPROPi The proportion of trading volume generated by large traders

to total trading volume on firm-day i.

SVOLPROPi The proportion of trading volume generated by small traders

to total trading volume on firm-day i.

LAVGi The average number of shares traded per large trader

transaction on firm-day i.

SAVGi The average number of shares traded per small trader

transaction on firm-day i.

53

References

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Quantitative Finance and Accounting. 11: 165-182.

James D. Vincent Vita Pennsylvania State University WK: (814)-865-0573

Department of Accounting Cell: (607)-727-8904

371B Business Building Email: [email protected]

University Park, PA 16802

Education

Ph.D. Accounting, Pennsylvania State University Expected 2010

M.S. Accounting, Binghamton University 2005

B.A. History, Grinnell College 1997

Research Interests

Voluntary disclosure, Investor behavior, Trading volume, Information Economics,

Liquidity.

Research

Working Papers

“The Effect of Earnings Announcements on Trading Outcomes for Different Investor

Classes” Job market paper.

“Large and Small Investors’ Reactions to the Change in the Degree of Private

Information in the Market” with O. Barron and S. Chung. Currently under review at

Journal of Accounting Research

“Market Sentiment and the Reaction to Analyst Recommendation Changes” with O.

Barron and S. Chung.

Works in Progress

“Information Dissemination in Earnings Press Releases Versus Conference Calls”

“Liquidity and Disclosure Levels in Earnings Press Releases” with O. Barron and D.

Byard.

“Pro-Forma Earnings Disclosures and Information Quality”

Related Work Experience

Research Assistant and Instructor, Penn State University 2005-Present

Program Accountant, BAE Systems 2004-2005

Cost Accountant, BAE Systems 2003-2004

the effect of earnings announcements on trading …

Documents