INDIVIDUAL INVESTORS AND CORPORATE EARNINGS

A DISSERTATION

SUBMITTED TO THE GRADUATE SCHOOL OF BUSINESS

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Daniel Jeffrey Taylor

November 2010

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This dissertation is online at: http://purl.stanford.edu/kc064tf8516

© 2011 by Daniel Taylor. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii

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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

David Larcker, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

William Beaver

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Charles Lee

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iii

iv

ABSTRACT

This dissertation comprises two papers on the trading of individual investors

around earnings announcements:

1. This study examines the effect of earnings announcements on individual

investors’ trading decisions and their trading profits. Consistent with

earnings news informing the trading decisions of individual investors, I

find that earnings announcements are associated with significant increases

in individual investor market participation, and that these increases persist

even after controlling for the information in prices. Moreover, and in

contrast to the conventional wisdom that disclosure benefits

unsophisticated investors at the expense of more sophisticated investors, I

find that individuals’ trades around earnings announcements earn

economically and statistically significant losses, and that these losses are

significantly greater than the losses of non-announcement trades.

Consistent with these losses resulting from inefficient information

processing, I find the higher the information content of the earnings

announcement the greater the loss, and that increased losses around

earnings announcements are concentrated among those individual

investors who are not classified as affluent or active traders. Given the

limited information processing ability of individual investors, the results

suggest a more nuanced view of the welfare effects of disclosure.

v

2. This study examines the effect of contrarian retail trades on the pricing of

earnings information. Consistent with price pressure from contrarian retail

trades delaying the adjustment of prices to earnings information, I find that

the negative price drift accompanying bad news is largest when retail

investors buy on bad news, and that the positive price drift accompanying

good news is largest when retail investors sell on good news. These

findings are consistent with the correlated trading of retail investors

around earnings announcements causing a delayed price adjustment which

manifests as drift.

vi

ACKNOWLEDGMENTS

I thank Maureen McNichols and Bill Beaver—for encouraging me to pursue

research on individual investors early in my doctoral studies, and for detailed comments

on multiple versions of manuscripts, Dave Larcker—for countless hours of advice on

research and life and what must be over a thousand dollars in coffee, Charles Lee—for

exciting me about research in this area and for helping me to understand its implications

and broader contribution, Ro Verrecchia—for helping me to focus what was a descriptive

paper into one more closely tied to theory and the disclosure literature, and Ian Gow—

for providing brutally honest feedback on a timely basis. I also thank Mary Barth, George

Foster, Alan Jagolinzer, Gaizka Ormazabal, and Itamar Simonson for advice and

encouragement, Stefan Nagel for sharing code, and Terry Odean for sharing data. Also, I

am indebted to Jennifer Francis and Richard Willis, for their willingness to advise a

starry-eyed graduate student in economics, and for introducing me to accounting

research.

Finally, I thank my family for all of their support. I thank my wife, Erin, for

tolerating late nights at the office, constant absent-mindedness, and for cheering me up

when times got tough, and I thank my parents, Lou and Terry, and grandparents,

Dorothy, George, and Johanna, for always encouraging me to pursue my passions.

vii

TABLE OF CONTENTS

List of Tables ...................................................................................................................... x

Introduction..........................................................................................................................1

Chapter 1: Individual Investors, Earnings Announcements, and Systematic Mistakes:

Can Trading on Information Make You Worse Off?.....................................................5

1. Introduction..............................................................................................................5

2. Hypothesis Development and Related Literature ..................................................11

2.1. Market Participation around Earnings Announcements ..............................11

2.2. Trade Profitability........................................................................................11

3. Variable Measurement and Sample Selection .......................................................19

3.1. Measures of Market Participation................................................................19

3.2. Measures of Trading Profits.........................................................................22

3.2.1. Implied Profits ....................................................................................22

3.2.2. Realized Trading Profits .....................................................................23

3.3. Sample and Descriptive Statistics................................................................26

3.3.1. Retail Broker Database .......................................................................26

3.3.2. Primary Sample and Descriptive Statistics .........................................29

4. Research Design.....................................................................................................31

4.1. Individual Investor Market Participation .....................................................31

4.1.1. Classic Event Study ............................................................................31

4.1.2. Relative Informativeness of Earnings.................................................32

4.1.3. Do Earnings Inform Individual Investors Incremental to the

Information in Prices..................................................................................34

4.2. Individual Investor Trading Profits..............................................................31

4.2.1. Changes in Trading Profits Around Earnings Announcements..........35

4.2.2. Trade Profitability and the Information Content of Earnings.............37

4.2.3. Who Loses Around Earnings Announcements? .................................38

5. Results....................................................................................................................41

viii

5.1. Individual Investor Market Participation Around Earnings.........................41

5.2. Individual Investor Trading Profits Around Earnings Announcements ......45

5.3. Cross-Sectional Variation in Trading Profits Around Earnings

Announcements................................................................................................47

6. Conclusion .............................................................................................................51

Chapter 2: Retail Investors and the Adjustment of Stock Prices to Earnings

Information ..................................................................................................................52

1. Introduction............................................................................................................52

2. Related Literature...................................................................................................58

2.1. Retail Investors ............................................................................................58

2.2. Post-Earnings Announcement Drift .............................................................61

3. Research Design and Predictions...........................................................................64

3.1. Primary Asset Pricing Tests.........................................................................64

3.1.1. Portfolio Sorts .....................................................................................65

3.1.2. Return Regressions .............................................................................67

3.2. Additional Analyses.....................................................................................68

3.2.1. Speed of Price Adjustment..................................................................68

3.2.2. Additional Determinants of Drift........................................................69

3.2.3. Limited Arbitrage................................................................................71

3.3. Measures of Primary Variables....................................................................73

3.3.1. Measures of Earnings Surprise ...........................................................73

3.3.2. Measures of Contrarian Trade ............................................................73

4. Sample and Descriptive Statistics..........................................................................75

4.1. Retail Investor Database ..............................................................................75

4.2. Primary Sample............................................................................................77

4.3. Descriptive Statistics....................................................................................78

5. Results....................................................................................................................80

5.1. Primary Asset Pricing Tests.........................................................................80

5.1.1. Portfolio Sorts .....................................................................................80

5.1.2. Return Regressions .............................................................................82

ix

5.2. Additional Analyses.....................................................................................83

5.2.1. Speed of Price Adjustment..................................................................83

5.2.2. Additional Determinants of Drift........................................................84

5.2.3. Limited Arbitrage................................................................................86

6. Robustness .............................................................................................................80

6.1. Alternative Measures of Earnings Surprise .................................................87

6.2. Abnormal Return Measurement...................................................................88

6.3. Investor Attention Explanations ..................................................................89

6.4. Omitted Variable and Risk-based Explanations ..........................................91

7. Conclusion .............................................................................................................92

Appendix A: Tables ...........................................................................................................93

Bibliography ....................................................................................................................107

x

LIST OF TABLES

Number Page

Table 1: Descriptive Statistics ...........................................................................................93

Table 2: Market Participation Around Earnings Announcements .....................................95

Table 3: Individual Investor Trading Volume: Controlling for Returns............................97

Table 4: Implied Trading Profits Around Earnings Announcements ................................99

Table 5: Realized Trading Profits Around Earnings Announcements.............................100

Table 6: Trading Profits and Announcement Period Information ...................................102

Table 7: Trading Profits Around Earnings Announcements and Investor

Demographics ................................................................................................104

Table 8: Retail Investor Database ....................................................................................107

Table 9: Descriptive Statistics - Sample Variables..........................................................108

Table 10: Portfolio Sorts..................................................................................................110

Table 11: Future Return Regressions...............................................................................111

Table 12: Future Return Regressions: Multi-Quarter Evidence ......................................112

Table 13: Portfolio Sorts: Controlling for Additional Factors.........................................113

Table 14: Future Return Regressions: Controlling for Additional Variables ..................115

Table 15: Portfolio Sorts: Effects of Limited Arbitrages.................................................116

Table 16: Portfolio Returns: Analyst Forecast Errors......................................................117

1

INTRODUCTION

The primary theme of my work is individual investors. A large and growing

literature examines the trading decisions of individual investors and how such decisions

affect asset prices. Within this literature, one stream of research examines the

performance of individual investors’ stock portfolios. Studies in this stream find that

individual investors’ portfolios consistently underperform the market. A second stream

of research in this literature takes individual investors’ trades as given and examines how

these trades affect asset prices. Studies in this stream conclude that individual investors’

trades negatively predict asset prices. Relatively less research examines the information

individual investors use in their investment decisions and how they use such information.

Surprisingly, the existing literature remains silent on the accounting information

that individuals use in their investment decisions and whether the use of such information

leads to better investment decisions. Traditional models of disclosure assume

homogenous information processing capabilities, and predict that information events

reduce information asymmetry in the marketplace and benefit unsophisticated, individual

investors. However, more nuanced models of disclosure in which investors have

heterogeneous information processing abilities or heterogeneous levels of

overconfidence, predict that information events may benefit sophisticated investors at the

expense of individual investors—who are limited in their ability to process information or

process it inefficiently (i.e. display overconfidence). While the latter characterization of

individual investors is broadly consistent with evidence in the experimental literature, the

2

implication that trades based on accounting information may lead to worse outcomes for

unsophisticated investors is inconsistent with the conventional wisdom in the disclosure

literature that more information leads to more informed investing decisions.

I investigate these issues in chapter one. In particular, I examine whether

individual investors use earnings information in their trading decisions and whether the

use of such information leads to more informed trading decisions (i.e. whether such

trades perform better). Consistent with individual investors using earning information in

their trading decisions, I find a pronounced spike in individual investor trading volume,

the number of individual investors participating in the equity market, and the number of

first-time individual investors participating in the equity market around earnings

announcements. In relative terms, the increase in volume and market participation

around earnings announcements is greater than the increase in volume and market

participation on days with similar price changes but no earnings announcements. This

suggests that for the same amount of value-relevant information, individuals place

disproportionate weight on earnings information.

However, despite such pronounced increases in trading activity, individual

investors’ trades around earnings announcement not only earn negative market adjusted

returns (even before considering transaction costs), but also earn lower returns than non-

announcement trades. This suggest that individual investors would be better off (from a

wealth standpoint) had they not traded around the information disclosure. Moreover, and

consistent with these losses resulting from inefficient information processing, I find that

announcement period losses are increasing in the amount of value-relevant information

3

released on the announcement day and decreasing in the individuals’ wealth and

historical trading frequency.

The findings of chapter one suggest that individual investors make systematic

mistakes when trading on earnings information, and that earnings announcements

facilitate a wealth transfer from individual investors to more sophisticated investors. This

is in stark contrast to the conventional wisdom in the disclosure literature, which argues

that disclosure of information should level the information playing field. Instead the

results are consistent with more nuanced theories of disclosure, in which the disclosure of

information benefits sophisticated investors at the expense of unsophisticated investors.

At a minimum, the results suggest that individuals should avoid trading around corporate

disclosures because other investors can more efficiently process information.

Having shown a pronounced increase in individual investor trading around

earning announcements, in chapter two, I examine the effect of such trades on asset

prices. A long-enduring anomaly in the capital markets literature is the tendency for

prices to drift in the direction of the earnings surprise up to one year following the

earnings announcement. Two broad classes of explanations for this drift have been

proposed in the literature: risk, and the trading of unsophisticated investors. Building on

this literature, and the results of chapter one, I conjecture that when individuals trade in

aggregate in the direction opposite the earning surprise, their trades exert sufficient price

pressure to delay the adjustment of prices to earnings information, giving rise to price

drift. Consistent with the contrarian trade of individual investors delaying price

adjustment, I find that prices drift in the direction of the earnings surprise only in

4

instances where individuals in net traded in the direction opposite the earnings surprise,

and that these trades delay price adjustment only in those stocks with high limits to

arbitrage. This supports the conjecture that post-earnings announcement drift is related to

the trading of unsophisticated investors, and that, when combined with limits to arbitrage,

the correlated trading of individual investors can cause prices to temporarily deviate from

fundamental values.

Collectively, the evidence in chapter one suggests that individual investors make

systematic mistakes when trading around earnings announcements, and the evidence in

chapter two suggest that the correlated mistakes of individual investors affect asset prices

around earnings announcements.

5

CHAPTER 1: INDIVIDUAL INVESTORS, EARNINGS ANNOUNCEMENTS, AND

SYSTEMATIC MISTAKES: CAN TRADING ON INFORMATION MAKE YOU

WORSE OFF?

1. INTRODUCTION

The SEC is charged with ensuring “fair” disclosure, where “fair” is taken to mean

reducing the extent to which certain investors are at an informational disadvantage, or

“leveling the information playing field.” An integral part of the SEC’s mission is

protecting the welfare of individual investors (Cox, 2006). The FASB, while not

explicitly charged with protecting individual investors, has also shown concern for

individual investors.1 Many SEC and FASB deliberations implicitly or explicitly reflect

concerns about whether individual investors’ trading decisions would be adversely

affected by changes in disclosure policy. For example, in late 1990s, the FASB sought

comments on whether “[g]iven [efficient] markets[,] would any disservice be done to the

interest of individual investors by allowing professional investors access to more

extensive information?” (Bloomfield, Libby, and Nelson, 1999, emphasis added).

Similarly, much of the debate over two-tiered financial reporting reflects concerns that

providing individual investors’ with abbreviated financial statements would adversely

affect their trading decisions (e.g., Bushman, Gigler, and Indejikian, 2002). Relatedly,

Regulation Fair Disclosure (Reg FD) prohibits isolated disclosure of non-public

information to a subset of market participants (e.g., analysts) on the basis of “fairness” to

1 The FASB states that the objectives of financial reporting “stem primarily from the informational needs of external users who lack the authority to prescribe the financial information they want from an enterprise and therefore must use the information that management communicates to them.” (SFAC 1, paragraph 28).

6

individual investors (SEC, 2000). Despite the strong regulatory and standard setting focus

on individual investors, and decades of research on how accounting information affects

the stock market, empirical evidence on how accounting information affects individual

investors’ trading decisions and trading profits is largely absent from the literature (see

Bamber, Barron, and Stevens, 2008 for a review).2

Much of the recent empirical literature focuses on the relation between accounting

information and the cost of capital. However, the effect of accounting information on the

cost of capital can be very different from the effect of accounting information on investor

welfare (e.g., Gao, 2010). While many extant disclosure models make explicit predictions

regarding the effect of accounting disclosures on the trading profits of unsophisticated

investors (see Verrecchia, 2001 for a review), there is scant empirical evidence on this

relation. This is an important gap in the literature, because if regulators require

disclosure under the auspices that such disclosure “levels the information playing field”

and benefits individual investors, it is important to demonstrate that individual investors

use the information in the disclosure and that this information improves their trading

decisions.

Using data on the actual trades of individual investors, this study examines

whether and how individual investors process earnings information. In particular, this

study focuses on two fundamental, unresolved issues: (i) whether earnings information

informs the trading decisions of individual investors incremental to the (earnings)

2 In fact, the absence of research on individual investors is one reason some argue that market-based studies of accounting information are of limited use to regulators (Holthausen and Watts, 2001, p. 27). With regard to the experimental studies, Kachelmeier and King (2002) suggest research on how individual investors process accounting information has been limited by the perception that such research is not relevant if individuals do not affect asset prices (see Libby, Bloomfield, and Nelson, 2002 for a review).

7

information in prices, and (ii) whether individual investors benefit from trading around

earnings announcements.

To investigate whether individual investors use earnings information in their

trading decisions incremental to the information in prices, I examine whether individual

investor participation in equity markets increases around earnings announcements and

whether this increase persists after controlling for past and contemporaneous returns.

While early studies find increased small trade volume around earnings announcements

(e.g., Lee, 1992), using the actual trades of individual investors, recent behavioral finance

research suggests that individuals chase returns and trade following extreme price

movements (e.g., Grinblatt and Keloharju, 2000; Barber and Odean, 2008). This literature

argues that increased individual trading around earnings announcements is the result of

individuals trading on extreme price changes, and not information in the earnings

announcement per se. While subtle, this is an important distinction with implications for

disclosure regulation. For example, in justifying Reg FD on the basis of “fairness” to

individual investors, regulators implicitly assume that individual investors trade on the

information communicated through disclosure above and beyond the information

impounded in prices. If individual investors trade solely on the information in prices, then

for the purposes of individual decision-making, it does not matter how information is

disseminated, or to whom, only that it is quickly and fully reflected in prices.

To investigate whether individual investors benefit from trading around earnings

announcements, I compare the profitability of individual investors’ trades around

earnings announcements to that of their non-announcement trades. I decompose trading

8

profits into transaction costs (inclusive of the bid/ask spread) and gross returns (i.e.,

trading profits excluding transaction costs) and examine each component separately.3

While Odean (1999) documents that trades by individual investors lose money on

average, how accounting information in general—and earnings information

specifically—affects individual investors’ trading profits remains an open question. The

conventional wisdom in the disclosure literature is that disclosure decreases the

information rents of informed traders and increases liquidity (e.g., Verrecchia, 2001;

Leuz and Wysocki, 2009). In this regard, the conventional wisdom predicts that

accounting information affects individuals’ trading profits via a reduction in trading

frictions. With regard to earnings announcements, this suggests that individuals’

announcement period trades are more profitable than non-announcement trades because

of a reduction in transaction costs.

However, more nuanced models of disclosure suggest two circumstances in which

individuals’ announcement period trades may actually be less profitable than non-

announcement trades. First, if earnings announcements increase the information rents of

more sophisticated traders, then individuals’ announcement period trades will have

higher transaction costs (e.g., Kim and Verrecchia, 1994, 1997). Second, individuals may

process earnings information inefficiently. If efficient and inefficient information

processors trade on information in an earnings announcement, efficient information

processors benefit at the expense of the inefficient information processors (e.g., Fischer

3 Throughout the paper when I use the term “trading frictions” or “transaction costs” I refer not only to the fixed costs of trading (i.e. commission paid to the broker) but also the bid/ask spread implicitly paid by the trader.

9

and Verrecchia, 1999). This suggests announcement period trades are less profitable than

non-announcement trades even after excluding transaction costs. Whether the

conventional wisdom, or these more nuanced models best describe the effect of earnings

announcements on individual investors is an unanswered question.

The findings of this study are as follows. First, I find individual investor

participation in equity markets measured as (i) individual trading volume, (ii) the number

of individual investors trading, and (iii) the number of individuals trading in the firm’s

shares for the first time, increases around earnings announcements. Additional analysis

suggests that individual volume on the day of the announcement accounts for 13.27% of

the variation in individual volume over the quarter, more than twice that of the average

non-announcement day. These findings suggest earnings announcements are associated

with a pronounced increase in individual investor trading activity. In addition, using two

distinct sets of tests, I find that individual investors trade on the information in the

earnings incremental to the information in prices. First, I sort earnings announcements

into quintiles based on both the magnitude of pre-announcement and announcement

period returns. I find significant increases in market participation across all quintiles.

Second, I match each announcement day with a non-announcement day in the same firm,

based on the magnitude of past and contemporaneous returns. Using this within-firm

matched sample design, I find earnings announcements have significantly higher levels of

individual investor market participation than corresponding non-announcement days with

similar past and contemporaneous returns. Taken together, these results suggest

10

individual investors trade on earnings information incremental to the information in

prices.

Second, I find that individuals’ trades around earnings announcements earn

economically and statistically significant losses, and that these losses are significantly

greater than the losses of non-announcement trades. Investigating the source of these

losses, I find evidence that increased losses are the result of both increased transaction

costs and inefficient information processing. In particular, and consistent with earnings

announcements increasing the information rents of more informed traders, I find that

individuals’ announcement period trades incur larger transaction costs than non-

announcements trades. Additionally, I find (1) that announcement period trades earn

larger losses than non-announcement trades even after excluding transaction costs, (2)

that these increased losses are concentrated in firms for which earnings reveal a large

amount of new information, and (3) that these losses are concentrated among individual

investors who are not affluent and are not active traders. These findings suggest losses

around earnings announcements are also attributable to inefficient information

processing.

This study contributes to the literature by providing a detailed examination of the

effect of earnings announcements on individual investors’ equity market participation and

trading profits. Collectively, the results challenge the notion that disclosure necessarily

“levels the information playing field.” Instead, the results suggest that around earnings

announcements sophisticated investors gain at the expense of individual investors. The

results are consistent with extant analytical models in which disclosure increases the

11

information rents of more informed investors, and facilitates a wealth transfer from

inefficient to efficient information processors (e.g., Kim and Verrecchia, 1994, 1997;

Fischer and Verrecchia, 1999). Given the limited information processing ability of

individual investors, the results suggest a more nuanced view of the welfare effects of

disclosure.

The remainder of the paper proceeds as follows. Section 2 discusses the related

literature and develops the hypotheses. Section 3 describes the sample and measurement

of key variables. Section 4 discusses the research design. Section 5 presents the results,

and Section 6 concludes.

2. HYPOTHESIS DEVELOPMENT AND RELATED LITERATURE

2.1. MARKET PARTICIPATION AROUND EARNINGS ANNOUNCEMENTS

Beginning with Beaver (1968), much of the accounting literature examines the

effect of earnings announcements on trading volume (see Bamber, Barron, and Stevens,

2009 for a review). Papers in this literature examine whether the volume reaction to

earnings announcements varies across firms (e.g., Bamber, 1987), across time (e.g.,

Landsman and Maydew, 2002), and test whether these variations are consistent with

investors trading on private information (e.g., Barron Harris and Stanford, 2005).4 Within

this volume literature, one stream of research focuses on how the reaction to earnings

announcements varies across market participants. For example, using TAQ data, Lee

(1992) finds increased small trade volume around earnings announcements. Similarly,

4 For parsimony, I focus only on studies examining volume around earnings announcements rather than other disclosures.

12

Bhattacharya (2001) finds that small trade volume at the time of the earnings

announcement is increasing in the magnitude of forecast errors from a seasonal random

walk model of earnings.5 In a recent study, Dey and Radhakrishna (2007) use data on

actual orders from all individual investors on the NYSE for 144 firms over a three-month

period (i.e., TORQ data) and find a surge in volume across all market participants at the

time of the announcement. They also find that individual investors account for 30.1%

(11.2%) of all trades (volume) around earnings announcements.

Collectively, the results of this literature suggest that individual investor volume

increases around earnings announcements. However, it is unclear why. On the one hand,

if individuals have (or perceive that they have) private information not impounded in

prices, trading around the announcement may reflect speculation. On the other hand, if

individuals view the market as fully reflecting all information, trading activity may be

explained by risk preferences. For example, if earnings contain information about a

firm’s risk (e.g., Beaver, Kettler, and Scholes, 1970; Fama and French, 1993), then trades

around the announcement may reflect timely decisions with regard to long-term asset

allocation (i.e. portfolio rebalancing based on new information about risk). Still another

reason why individuals may trade around earnings announcement is that trading during

such periods minimizes the rents paid to more informed traders (e.g., Admati and

Pfleiderer, 1988).

5 Additionally, Battalio and Mendenhall (2005) find the direction of small trades around the earnings announcement has a higher correlation with seasonal random walk forecast errors than analyst forecast errors, Shanthikumar (2004) shows this correlation is increasing in the forecast error from prior quarters, and Bhattacharya et al. (2007) show this correlation is larger for pro-forma earnings rather than GAAP earnings.

13

Recent behavioral finance research suggests increased individual investor trading

activity around earnings announcements may not be the result of individuals trading on

the information in earnings per se. For example, using the actual trades of individual

investors, prior work finds that individuals tend to trade more in stocks with extreme

returns over the past three months (e.g., Grinblatt and Keloharju, 2000). Similarly, prior

work also finds that individuals display an “attention effect” buying on days immediately

following extreme price changes (e.g., Barber and Odean, 2008). Because many stocks

experience extreme price changes prior to the earnings announcement (e.g., Ball and

Brown, 1968), these findings raise the possibility that increased individual trading around

earnings announcements may be the result of individuals trading on information in prices

(i.e. extreme returns), rather than the information in the earnings announcement. If

individuals do not trade on information in accounting disclosures incremental to the

information in prices, then the disclosure itself is not relevant for their decision making,

only the information impounded in prices. In contrast, if earnings inform the trading

decisions of individuals incremental to prices, it suggests that individuals base their

trading decisions on information other than price.

I extend this literature by testing various explanations for increased individual

investor trade around earnings announcements. First, I test whether individuals trade

around earnings announcements after controlling for the information in prices. If

individuals trade on earnings incremental to the information in prices, then I expect to

find increased individual trading volume around the earnings announcement even after

controlling for the information in pre-announcement and announcement period returns.

14

Additionally, I expect to find that individual volume around earnings announcements is

greater than the individual volume around non-announcement days with similar past and

contemporaneous returns. Second, I test whether individual trades around earnings

announcements are motivated by a reduction in trading frictions. If increased individual

trade around earnings announcements is motivated by reduced trading frictions then I

expect to find transaction costs (inclusive of the bid-ask spread) are lower around

earnings announcements.

2.2. TRADE PROFITABILITY

A growing literature in behavioral finance examines the portfolio choices and

investment performance of individual investors (see Campbell, 2006 for a review).

Barber and Odean (2000) find that investors at a large discount broker tilt their portfolios

toward small value stocks, pay 3% in commissions and 1% in bid-ask spread, and that

their portfolios earn significant abnormal returns of –3.7% net of transaction costs.6

Odean (1999) examines the profitability of individual investor trades (as opposed to

portfolio positions) and finds that stocks sold by individuals earn higher subsequent

returns than stocks bought by individuals. Examining cross-sectional variation in trading

profits, Seru, Shumway, and Stoffman (2009) find that more experienced investors are

more profitable. Linnainmaa (2009) finds that the market orders of individual investors

earn higher returns than limit orders, consistent with traders that demand immediacy

6 Prior to including transaction costs, Barber and Odean (2000) find that market-adjusted and Fama-French adjusted portfolio returns of individual investors is insignificantly different from zero (Table 2, p. 787). Using a comprehensive dataset of all individual investors in Taiwan, Barber et al. (2009) find that the annual net losses of individual investors is equivalent to 2.2% of Taiwan’s GDP.

15

having superior information or skill, and Grinblatt, Keloharju, and Linnainmaa (2009)

show that the trader’s intelligence quotient (IQ) is positively related to trading profits.

However, what effect (if any) accounting information has on individuals’ trading

profits is unclear. Some argue that if markets are informationally efficient, then investors

who rely on public accounting information are “price protected,” in which case

accounting information—so long as it is priced efficiently—will have no effect on trading

profits (e.g., Kothari, Ramanna, and Skinner, 2009).7 However, this “price protection”

argument takes prices as given and ignores the effect of accounting information on

trading frictions and the formation of equilibrium prices (e.g., Grossman and Stiglitz,

1980). In this regard, the extant disclosure literature suggests a more nuanced view of

capital markets, in which accounting information can affect individuals’ trading profits.

The conventional wisdom is that disclosure “benefits the uninformed at the

expense of the informed” and results in a decrease in the rents to private information and

an increase in liquidity.8 Under this view, earnings announcements reduce the

information rents of informed investors, such that trades of individual investors around

earnings announcements should be more profitable than their non-announcement trades.

In contrast to the conventional wisdom, several studies suggest the possibility that

earnings announcements facilitate a wealth transfer from individual investors to more

sophisticated investors.

7 This argument dates back to the early literature on market efficiency and its implications for accounting standard setters (e.g., Beaver and Demski, 1974; Gonedes and Dopuch, 1974). 8 See Leuz and Wysocki (2009) for a review of the disclosure literature and its effect on market liquidity and adverse selection.

16

There are two non-mutually exclusive reasons to suspect individual investors’

trades around earnings announcements may actually be less profitable than their non-

announcements trades. First, the conventional wisdom is predicated on the notion that

disclosure reduces private information. However, Indejikian (1991) shows that when

information processing is costly, disclosure is associated with an increase in private

information collection.9 Similarly, Kim and Verrecchia (1994, 1997) show that if

investors have private contextual information useful in conjunction with the information

in the disclosure (e.g., private information about the persistence of earnings), then

disclosure, and earnings announcements specifically, will increase the information rents

of sophisticated investors. Consistent with increased information rents at the time of the

announcement, Lee, Mucklow, and Ready (1993) find that the bid/ask spread increases

on the day of and the day following the announcement, and Krinsky and Lee (1996) find

a significant increase in the adverse selection component of the spread around earnings

announcements, but no change in total bid/ask spread.

Second, the conventional wisdom is predicated on the notion that unsophisticated

investors are efficient information processors. Even in the absence of information

asymmetry, if individual investors process information inefficiently, then their

information-based trades may be less profitable then their non-information based trades.

Considering public disclosure in the context of efficient (rational) and inefficient

(overconfident) traders, Fischer and Verrecchia (1999) show that if the market is

composed of a sufficient number of rational traders, earnings announcements will

9 Indjejikian (1991) suggests that investors who face higher information processing costs will rely more on commonly observed information sources (e.g., prices).

17

facilitate a wealth transfer from inefficient information processors (i.e., non-Bayesians) to

the efficient information processors (i.e., Bayesians). While individuals are known to

inefficiently process earnings information in laboratory settings (e.g., Maines and Hand,

1996) it remains unclear what effect (if any) this has on their trading profits. Whether the

“price protection” argument, the conventional wisdom, or these more nuanced models

best describe the effect of earnings announcements on individual investors is an

unanswered question.

Hints of the relation between individual investors’ trading profits and earnings

information can be seen in a few recent studies. Elliott, Hodge, and Jackson (2008)

examine survey data from 339 non-professional investors, and find that investors’ self-

reported trading profits are decreasing in the ratio of the number of times the investor

used information in SEC filings to the number of times the investor used information

reported by Market Guide, Value Line, and Standard and Poor’s. Although not focusing

on earnings announcements, Asthana et al. (2004) use the five-day buy-and-hold return to

small trades on TAQ as a measure of the trading profits of unsophisticated investors, and

find 10-K filings on EDGAR are associated with increased profits. Also using this

measure of trading profits, Miller (2008) finds the profitability of small trades around 10-

K dates is negatively related to measures of 10-K “readability,” and Mikhail et al. (2007)

find that small trades placed around analyst recommendation earn losses. Each of these

studies is careful to acknowledge the limitations of using TAQ data to infer trade

direction and sophistication of traders, and computing profits assuming a five-day

holding period.

18

More recently, Hirshleifer et al. (2009) and Kaniel et al. (2009) find that

individual investors’ net purchases around earnings announcements negatively predict

asset prices. While primarily interested in the relation to post-earnings announcement

drift, the results of these studies suggest that individual investors’ trades around earnings

announcements earn losses. However, given that prior work documents a negative

correlation between individual investor trades and returns in general (e.g., Odean, 1999;

Barber, Odean, and Zhu, 2009), it is unclear whether the results of Hirshleifer et al.

(2009) and Kaniel et al. (2009) are unique to trades around earnings announcements.

While the results in prior work may hint that trades around earnings announcements lose

money, relevant to this study is whether the trades around earnings announcements are

more or less profitable than non-announcement trades, and whether changes in

profitability are due to changes in trading frictions or inefficient information processing.

To the best of my knowledge this is the first study to address these questions.

This study extends this literature by examining the effect of earnings

announcements on the trading profits of individual investors using data on the actual

trades of individual investors, with a particular focus on whether differences in trade

profitability results from trading frictions or inefficient information processing. If

changes in profits around earnings announcements are due to changes in trading frictions

(e.g., Kim and Verrecchia, 1994, 1997), I expect to find earnings announcements are

associated with the amount individual investors pay in bid/ask spread, and unrelated to

trade profitability after excluding such transaction costs. If changes in profits around

earnings announcement are associated with inefficient information processing (e.g.,

19

Fischer and Verrecchia, 1999), I expect to find announcement period trades earn lower

profits (larger losses) than non-announcement trades even after excluding transaction

costs, that the higher the information content of the earnings announcement the larger the

loss, and that changes in profits are most pronounced among those investors least likely

to understand the valuation implications of earnings news.

3. VARIABLE MEASUREMENT AND SAMPLE SELECTION

3.1. MEASURES OF MARKET PARTICIPATION

In this section I develop a model that yields testable predictions regarding the

relationship between debt contracting, monitoring of the financial reporting process and

properties of the reported financial numbers, such as conservatism. While, as discussed

above, much of the literature on debt covenants has focused on signaling and

renegotiation, I focus on a setting with symmetric information at the time of contracting

and essentially no renegotiation. While these features are undoubtedly important in

practice (Dichev and Skinner, 2002; Gârleanu and Zwiebel, 2008; Nini, Smith, and Sufi,

2008), the imperfection of accounting information, incomplete contracting and costly

renegotiation are also likely to be important. With regard to renegotiation, the model I use

here might be viewed as a limiting case in which renegotiation is sufficiently costly to

effectively rule it out.

My primary measure of individual investor market participation is individual

investor trading volume. Similar to prior research on total volume (e.g., Landsman and

20

Maydew, 2002), I compute abnormal individual investor volume (AbIV) for firm i in

quarter q on date t as

qi

tqitqi

tqi

IVEIVAbIV

,

,,,,

,,

][

σ

−

= (1)

where IVi,q,t is individual trading volume for firm i, in quarter q on date t scaled by shares

outstanding, E[IVi,q,t] is the predicted value from a modified market model of individual

volume estimated for each firm-quarter over the sixty-one trading days surrounding the

earnings announcement and excluding the five trading days surrounding the

announcement (i.e. t = -30…-6 , 6…30), and σi,q is the standard deviation of market

model residuals for firm i in quarter q. Specifically I estimate

IVi,q,t = αi,q + β1i,q Vm

t + β2i,q IVm

t + ε i,q,t t = –30…–6 , 6…30 (2)

Vm is market-wide total volume scaled by shares outstanding, and IVm is market-wide

individual volume scaled by shares outstanding. Market-wide total volume (Vm) controls

for market-wide factors that affect volume (e.g. macro-economic news), and market-wide

individual investor volume (IVm ) controls for changes in individual investor volume that

are the result of correlated individual biases (e.g., individual investor sentiment),

individual specific news events (e.g., the release of quarterly account statements), or a

difference in the relevance of macro-economic news between individual and non-

individual investors (e.g., a change in the discount rates). Since the regression is

estimated for every firm-quarter, the intercept serves as a firm-quarter fixed effect and

filters out any unobserved cross-sectional variation either across firms or across quarters,

21

leaving only intra-quarter temporal variation.10 These modifications to the standard

market model (e.g., Beaver, 1968) ensure that market-wide variation in individual

volume is filtered out and that abnormal individual volume is cross-sectionally

comparable (i.e., similar mean and variance). For comparison, I use the same procedure

to compute abnormal total volume (AbV) but omit the market-wide individual volume

(IVm) term in equation (2).

In order to address concerns that changes in individual volume around earnings

announcements may be driven by a small subset of individual investors, I also use two

non-volume based measures of market participation. The first, IndInv, is the number of

individual investors trading in firm i, in quarter q on date t, scaled by the number of

individuals on the dataset. The second, NewIndInv, is based on the number of new

individual investors trading in the stock (i.e. those individuals who were not previous

shareholders) scaled by the number of individuals on the dataset. As with individual

volume, I compute the abnormal number of individual investors (AbII) and abnormal

number of new individual investors (AbNII) relative to a modified market model. For the

former, the market model includes market-wide total volume (Vm) on day t and the sum

of IndInv across all firms on day t. For the latter, the market model includes market-wide

total volume (Vm) on day t and the sum of NewIndInv across all firms on day t.

10 The difference in average individual volume for across firms will be reflected in the intercepts. For example, if individual volume in Firm A is on average higher than Firm B, Firm A’s intercept will be larger than Firm B’s. Similarly, within-firm differences in average individual volume will also be reflected in the intercept. For example, if individual volume in Firm A in quarter q is higher than that in quarter r, Firm A’s quarter q intercept will be higher than quarter r.

22

3.2. MEASURES OF TRADING PROFITS

Much of the prior research on individuals investors focuses on the performance of

their portfolio holdings rather than the profitability of specific trades (e.g., Barber and

Odean, 2000; Barber and Odean, 2001; Barber et al., 2009). In contrast, my hypotheses

are in regard to the profitability of individuals’ trades around earnings announcements,

rather than the overall performance of their portfolios. Thus, testing my hypotheses

requires a trade-specific measure of risk-adjusted trading profits. I use two

complementary methods to estimate risk-adjusted trading profits.11 The first method uses

no information about the trade other than the trade date and trade direction, and estimates

trading profits as the abnormal return in the direction of the trade assuming a constant

holding period. The second method is more elaborate and takes advantage of detailed

trade-specific information. The second method uses the time-series of trades, trade

quantities, trade prices, and trade dates, and computes actual holding periods and profits

assuming trades are allocated on a first-in-first-out basis.

3.2.1. Implied Profits

The first method I use to estimate risk-adjusted trading profits is a direct

application of techniques in the insider trading literature (e.g., Jagolinzer et al., 2009).

For each individual j, I net all trades in firm i on date t.12 I then calculate abnormal

returns to each net trade as the intercept from the three factor Fama-French (1993) model

11 Profits are denominated in returns rather than dollars, as returns are cross-sectionally comparable and allow for the application of standard risk adjustment techniques. The disadvantage of using a return measure is that it does not take into account variation in the amount invested. However, I adjust for this in my analyses by weighting observations by the dollar value of the trade. 12 This analysis ignores trades that are opened and closed within the same day. Such trades account for less than 0.5% of the sample.

23

estimated over each of three different horizons: the 50, 100, and 150 trading days

following the trade.

(Ri - Rf) = α + β1 (Rmkt - Rf) + β2 SMB + β3 HML + ε (3)

Ri is the daily return for firm i, Rf is the daily risk-free rate; Rmkt is the CRSP value-

weighted market return, and SMB and HML are the size and book-to-market factors

(Fama and French, 1993). Implied profits for purchases (sales) are calculated as the

estimated α (-α) multiplied by the number of days used to estimate the model (i.e., 50,

100, or 150). Computing trade-specific abnormal returns in this manner controls for

differences in risk across trades (i.e. trade-specific factor loadings) and controls for the

tendency of individuals to tilt their portfolios toward smaller value-oriented firms (e.g.,

Barber and Odean, 2000). This procedure results in an estimate of the risk-adjusted

trading profits to the net trade of individual j in firm i on day t over the 50 trading days

(ImpProfit50), 100 trading days (ImpProfit100), and 150 trading days (Improfit150)

following the trade.

3.2.2. Realized Profits

The second method I use to estimate trading profits, uses data on actual trade

prices, quantities, and holding periods. As before, for each individual j, I net all trades in

firm i on date t. If individual j places multiple trades in firm i on date t, I calculate the

price of the net trade (POpen) as the weighted average of trade prices on that day. For

example, on day t if individual j buys 1 share at $1 and then buys 2 shares at $2.50, the

net trade will be 3 (1+2) shares at $2 (1/3 x $1 + 2/3 x $2.50). I then calculate the first

date in which the position taken by individual j on date t is fully offset, using first-in-

24

first-out (FIFO) to match trades and compute realized holding periods. Any trades not

fully offset over the sample period (e.g., an investor buys 1 share but does not sell it prior

to the end of the sample) are closed out on the last day of the sample (i.e. profits are

measured over the sample period). I estimate risk-adjusted trading profits, net of

transaction costs (ProfitNeti,j,t), according to the formula:

ProfitNet IP

PP

P

PPSZBM

t

SZBM

t

SZBM

T

Open

t

Open

t

Closed

T

−−

−= – Commission (4)

t is the day of the trade, or the day the trade is “opened.” T is the day the trade is fully

offset, or “closed”, computed according to FIFO. POpen is the price of the trade on date t

defined above, PClosed is the price at which the trade is closed. If the trade on date t is

closed out over a number of subsequent trades, PClosed is the weighted average price of the

subsequent trades. For example, if the investor buys 100 shares at $1 on date t, sells 50

shares at $1.10 on date t+1, and sells 50 shares at $1.20 on date t+2, the holding period is

two days (T – t = 2) and the closing price is $1.15 (50/100 x $1.1 + 50/100 x $1.20).

Computing closing price in this way, accounts for any partial “cashing out”. PSZBM is the

price of the respective (5x5) size and book-to-market portfolio on day t based on the size

and book-to-market ratio of the firm measured as of the prior quarter-end. I is an

indicator variables equal –1 if the trade is a sale and 1 if it is a purchase. Commission is

the total amount paid to the broker in trading commission divided by the dollar value of

the trade (i.e. measured in percent).

In addition to ProfitNet, I also construct an alternative measure of realized trading

profits, Profit, which excludes trading commission paid to the broker (Commission).

25

Because realized holding periods vary by trade, and can be as short as a day, estimating

risk-adjusted trading profits over the holding period using the Fama and French (1993)

model, as in equation (3), is not practicable (i.e. estimation of equation (3) requires at

minimum four observations). Instead, equation (4) measures trading profits relative to an

equivalent investment in the respective (5x5) size and book-to-market portfolio.

Computing trade-specific abnormal returns in this manner controls for differences in

holding periods and controls for the tendency of individuals to tilt their portfolios toward

smaller value-oriented stocks.

Following prior literature (e.g., Barber and Odean, 2000, 2001) I decompose net

trading profits (ProfitNet) into three components: the amount paid in trading commission

(Commission), the amount paid in bid-ask spread (Sprd), and gross trading profits

(ProfitGross). Commission measures the trading commission paid to the broker as a

percent of the dollar value of the trade and is defined as above. Sprd measures the

amount of bid-ask spread paid by investor as a percent of the dollar value of the trade and

is defined as in Barber and Odean (2000, 2001) as

Sprd IP

POpen

t

t

−= 1 (5)

where Pt is the closing price on date t, POpen is the price of the trade on date t, and I is an

indicator variables equal –1 if the trade is a sale and 1 if it is a purchase.13 Sprd includes

any intraday return of the trade. If the closing price is the equilibrium price, then Sprd

measures the bid-ask spread paid by the individual without error. For example, if the

13 The literature on large block trades uses a similar measure, replacing trade price with price immediate prior to the trade (e.g., Holthausen, Leftwich, and Mayers, 1987; LaPlante and Muscarella, 1997).

26

equilibrium price is $4.90 with a posted ask of $5.00 and the investor buys at the

prevailing ask, the investor effectively paid 2% (1 – $4.90/$5.00) in spread. Note that if

the investor subsequently sells at $4.90 he would realize a profit of –2%. However, in this

case the loss is purely the result of the prevailing bid-ask spread at the time of the trade.

ProfitGross measures the gross trading profit and excludes the amount paid in

commission to the broker and the amount paid in bid-ask spread. Gross trading profits are

computed as in equation (4) using the closing price on the day of the trade, rather than the

actual trade price (i.e., substituting Pt for POpen).

3.3. SAMPLE AND DESCRIPTIVE STATISTICS

3.3.1. Retail Broker Database

This study uses a database of individual investor trades and demographic data

provided by a popular retail broker. The database covers the daily trades of 158,006

accounts from January 1991 through November 1996, inclusive. Of these, 126,460

accounts placed trades over the period, 101,581 placed trades in U.S. common stock

(SHRCD 10, 11, or 12 on CRSP), and the remaining 24,879 traded exclusively in mutual

funds, close-end funds, bonds, or other securities. The accounts in this database were

randomly selected from the broker’s total client-base of 1.25 million households, which

represents approximately 4% of the population of individual investors over the period.14

The total value of accounts in this database is approximately $8.83 billion, and that the

14 The retail broker does not provide equity research services. See Barber and Odean (2000) and Kumar and Lee (2006) for more details on this database.

27

average account value is just over $55,000. In total, French (2008) reports that U.S

households directly held 27.2% of all public equity at the end of 1996.15

For each trade the sample contains the account number, date, trade price, quantity

of shares traded, trade direction (i.e. buy or sell), and trading commission paid to the

broker. Prior work on individual investors uses trade size observed on the TAQ tapes to

infer the trades of individuals, deeming “small trades” (

28

Ramadorai, and Schwartz, 2007).18 Third, this study documents a pronounced increase in

individual investor trade size around earnings announcements. In this regard, using a

simple cutoff rule (e.g.,

29

3.3.2. Primary Sample and Descriptive Statistics

I construct my primary sample using the retail broker database, CRSP, and

Compustat Quarterly. To be included in the sample, a firm must appear on the retail

broker database, Compustat Quarterly, and have common stock on CRSP (SHRCD equal

to 10, 11, or 12). Additionally, in each quarter, I require net income, an earnings

announcement date on Compustat within three months of the quarter-end, and market

value and book value of equity at the end of the prior quarter. Finally, I require positive

individual investor trading volume on at least one day over the quarter, and trading

volume and returns on CRSP over the thirty trading days prior to the earnings

announcement. The resulting sample contains 63,250 firm-quarters and 1,224,218 daily

trades for 91,066 accounts.19

Panel A of Table 1 reports descriptive statistics for firms in the sample.

Consistent with Barber and Odean (2000), Panel A shows that individual investors tend

to transact in small stocks (median market value of $139 million) and value stocks (mean

and median book-to-market ratio of 0.5 and 0.47). Over the sixty-one day period

centered on the earnings announcement, trading volume is on average 31% of shares

outstanding, and trading volume for individuals in the sample is on average 0.06% of

shares outstanding. On average 19.36 individual accounts trade in the firm’s shares over

the period, and of those 7.37 accounts did not previously hold the firm’s shares.

Consistent with returns leading earnings, over the sixty trading days prior to the earnings

announcement the average (unsigned) abnormal return is 1.28% (14.50%). With regard

19 Measures of trade profitability are computed prior to imposing these sample requirements. In this manner, sample requirement do not affect the matching of trades using FIFO.

30

to earnings announcement, the average (median) announcement contains good (bad)

news. The mean (median) announcement period return is 0.77% (-0.19%). Consistent

with earnings containing significant information, the average (median) unsigned

announcement period return is 8.65% (5.66%).

Panel B of Table 1 reports descriptive statistics for the trades in the sample.

Similar to Barber and Odean (2000), most of the trades in the sample are buys (54%), the

average (median) trade size is $12,471 ($5,375), and the average holding period is 180

(91) trading days.20 Panel C of Table 1 reports descriptive statistics on the trading profits

for each account in the sample. Similar to Barber and Odean (2000), the average account

pays 1.87% in commission to the broker and 0.51% in bid/ask spread.21 To put these

costs in context, French (2008) suggests the cost of investing in an active managed

mutual fund over the period is 1.46% and the cost of investing in a passive index fund

over the period is 0.15%. This suggests individuals incur a significant cost to trading,

and that this cost even exceeds that of investing in an actively managed mutual fund.

Consistent with prior work, and the average individual investor being uninformed,

Panel C shows that individual investors consistently lose money. After including

transaction costs (trading commission and bid/ask spread), the average (median) account

earns –6.91% (–1.55%) in trading profits After excluding trading commission, the

average (median) account earns –5.04% (–0.10%), and after excluding trading

commission and the amount paid in bid/ask spread, the average (median) account earns –

20 Barber and Odean (2000) report 54.94% of trade in their sample are buys and the average trade size is $12,332.46. They do not calculate holding periods. 21 Barber and Odean (2000) report that the average trade costs 1.50% in commission (3% round-trip) and 0.5% in spread (1% round trip).

31

4.33% (0.34%). Notably, even after including transaction costs, the 75th percentile of

trading profits exceeds 20% (75th percentile of ProfitNet is 21.03%). This suggests that,

when ranked on trading profits, the top quartile of accounts earn very large profits. This

is consistent with prior work that finds the performance of individual investors

significantly with characteristics of the investor (i.e., gender, experience, wealth, etc.),

and suggests the need to control for investor-specific effects in subsequent analysis.

4. RESEARCH DESIGN

4.1. INDIVIDUAL INVESTOR MARKET PARTICIPATION

4.1.1. Classic Event Study

To test whether earnings announcements are associated with an increase in

individual investor market participation, I regress each measure of abnormal market

participation on three announcement period indicator variables. Pooling across all firms

(i) and the sixty-one trading days centered on the earnings announcement (i.e., ∈t [–

30,+30]), I estimate the regression

Xi,t = θ0+ θ1 ANNCi,t + θ2 POSTANNCi,t + θ3 PREANNCi,t + ηi,t (6)

where X is one of the three measures of abnormal market participation (either AbIV, AbII,

or AbNII), ANNC is an indicator variable equal one on the day of and the day following

the announcement (i.e. ∈t [0,+1]) and zero otherwise, POSTANNC is an indicator

variable equal one for ∈t [+2,+5] and zero otherwise, and PREANNC is an indicator

32

variable equal one for ∈t [–1,–5] and zero otherwise.22 If earnings announcements

inform the trading decisions of individual investors I expect θ1 and θ2 are positive. Since

each of the measures of market participation is standardized by the mean of the non-

announcement period, θ0 equals zero by construction. While equation (3) may seem

parsimonious, recall that the measures of abnormal market participation filter out market-

wide movements, and include firm-quarter fixed effects to filter out the effects of cross-

sectional determinants of market participation that do not vary within a firm-quarter (e.g.,

book-to-market).

4.1.2. Relative In formativeness of Earnings

While the classic event-study analysis is used throughout the trading volume

literature, Ball and Shivakumar (2008, BS) argue that it can provide a “misleading

impression” about the relative importance of earnings for the overall informational

environment of the firm. While BS examine the relative importance of earnings for the

overall equity market, I next examine the relative importance of earnings for individual

investors. To do so, I adapt the research design of BS to my setting. In particular, rather

than focus on equity returns and total trading volume, as BS do, I adapt their tests to

individual investor trading volume.

22 While Compustat reports the date of the earnings announcement, it does not report whether the announcement occurs after the market closed, in which case the next trading day after the announcement would be “day zero”. Prior work (e.g., Patell and Wolfson, 1982; Francis Pagach and Stephan, 1992; Berkman and Truong, 2009) suggests a significant number of earnings announcements occur after the market has closed. Notably, Berkman and Truong (2009) show that failure to correct for such announcements can lead to significant downward bias in calculating announcement day volume and returns. To overcome this bias, they suggest defining the announcement day (the ANNC indicator) to include both the day of and the day after the announcement. Inferences are unchanged if ANNC is defined relative to the day of the announcement. In subsequent analyses I calculate market participation and trading profits separately for each event day.

33

Fraction of Volume. First, I measure the relative importance of earnings for individual

investors by calculating individual trading volume on the earnings announcement as a

fraction of total individual trading volume over the sixty-one day period centered on the

earnings announcement. I then test whether the fraction of volume on the announcement

day is significantly different from what one would expect on a non-announcement day. I

use a simple Monte Carlo simulation to provide an estimate of the appropriate null.23 In

particular, for each firm-quarter I randomly draw a day outside the five- day

announcement window and compute the fraction of volume over the sixty-one day period

that occurs on that day. I repeat this procedure 1,000 times and use the mean value as the

null hypothesis. I then test whether the fraction of volume occurring on the

announcement day is significantly different from the null using the empirical distribution

of values.

Explanatory Power. I also examine the fraction of the variation in volume explained by

announcement period volume. In particular I regress total volume over the sixty-one day

period on volume on the announcement day (scaling both variables by average shares

outstanding). The adjusted-R2 from this regression measures the fraction of volume over

the period explained by volume on the announcement day.24 Again, I use a simulation to

provide an estimate of the appropriate null. In particular, I re-estimate this regression but

using volume on a randomly selected day outside five-day announcement window as the

independent variable. I repeat this procedure 1,000 times to get the empirical distribution

23 BS test whether the fraction of trading volume on the announcement day is significantly different from what one would expect if trading volume is uniformly distributed over all days (in this setting 1/61). 24 The adjusted-R2 from this regression measure the extent to which volume on the announcement day explains volume over sixty-one day period. The higher the R2, the higher the relative importance of earnings for the overall information environment of the firm.

34

of R2 under the null hypothesis that the announcement day has similar explanatory power

to a non-announcement day, and use the mean value as the null hypothesis. I then test

whether the explanatory power of the earnings announcement is significantly different

from the null using the empirical distribution of values.

4.1.3. Do Earnings Inform Individual Investors Incremental to the Information

in Prices?

I use two complementary research designs to test whether earnings

announcements inform individual investors incremental to the information in prices.

These two research designs test whether earnings inform individual investors holding

constant the information in past and contemporaneous returns. In other words, whether

increased individual investor market participation around earnings announcements is

simply the result of investors trading on extreme returns.

In the first design, I compute buy-and-hold abnormal returns relative to the

respective (5x5) size and book-to-market portfolio over the sixty trading days prior to the

announcement (PastRet) and over the eleven trading days centered on the announcement

(AnncRet).25 I then sort each earnings announcement into quintiles based on the

respective unsigned abnormal return (AbsPreRet and AbsAnncRet respectively).26 For

each sample partition, I re-estimate equation (6). If individual investors trade on earnings

25 Stocks are matched to each of the twenty five (5x5) Fama and French (1993) book-to-market portfolios based on market value and book-to-market value at the end of the prior quarter. 26 Buy-and-hold abnormal returns capture the net revision in the market’s prior about the value of the firm over the period of interest. I use unsigned abnormal returns because my predictions pertain to the total amount of information incorporated into prices, not whether that information was good or bad news. For example, the attention effect documented by Barber and Odean (2008) is in regards to extreme returns, regardless of sign.

35

information incremental to the information in prices, I predict θ1 and θ2 are positive in

every sample partition.

In the second design, I employ a within-firm matched pair design. In particular, I

calculate the equivalent of AbsPastRet and AbsAnncRet for every non-announcement

date. I then match each announcement day (treatment sample) with a corresponding non-

announcement day, excluding the five trading days before and after the announcement,

for the same firm based on values of AbsPastRet and AbsAnncRet (control sample).27

This procedure yields a one-to-one, within-firm match between announcement days and

non-announcement days based on the magnitude of past and contemporaneous returns. I

then test for a difference in abnormal market participation between the treatment and the

control samples. If earnings announcements inform individual investors incremental to

the information in price (i.e. incremental to the extreme returns around earnings

announcements), I expect earning announcements are associated with higher levels of

individual investor equity market participation even when compared to days with a

similar amount of return news.

4.2. INDIVIDUAL INVESTOR TRADING PROFITS

4.2.1. Changes in Trading Profits Around Earnings Announcements?

To test for a systematic change in individual investors’ trading profits around

earnings announcements, I regress estimated trading profits on three announcement

period indicator variables. In particular, I estimate the regression

27 The within-firm matched pair design is implemented by selecting the non-announcement day that minimizing the Euclidean distance between the announcement day and all other non-announcement days within a given firm. Using a within-firm matched pair design control for the effect of any firm-specific determinants of abnormal volume.

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Xi,j,t = θ0+ θ1 ANNCi,t + θ2 POSTANNCi,t + θ3 PREANNCi,t + ηi,j,t (7)

where X is a measure of the risk-adjusted trading profit to the trade of individual j in firm

i on day t, and ANNC, POSTANNC, and PREANNC are as previously defined. Similar to

Barber and Odean (2000, 2001), I estimate equation (7) using weighted-least-squares,

where each trade is weighted by its size (in dollars).28 Additionally, to control for cross-

sectional variation in trading profits attributable to unobserved individual investor

characteristics (i.e. skill, risk aversion, etc.), I also estimate equation (6) including

investor-specific fixed effects. For example, if investor j’s non-announcement trades

earn below (above) average returns and investor j also trades more around earnings

announcements than other individuals, then we might expect trades around earnings

announcements to earn below (above) average returns. If this is the case, then after

including investor-specific fixed effects, the coefficient on the announcement period

indicators will be insignificant. In the investor-specific fixed effect model, the

announcement period indicators test whether the profitability of announcement period

trades is different from the profitably of non-announcement trades after adjusting for the

expected profitability of the specific investor who placed the trade.

I estimate two versions of equation (7) and the investor-specific fixed effect

model. In the first version, X is one of three measures of implied trading profits (i.e.

ImpProfit50, ImpProfit100, and ImpProfit150). In the second version, X is one of three

measures of realized trading profits or transaction costs: net profits including commission

28 Because the analysis is conducted at the individual-level, and profits are denominated in returns, it is important to control for differences in the dollar value of the trade. For example, if a $500 trade earns a 10% return and a $2000 trade earns a 1% return, then the net (or total) return is 2.8% (1/5 x 10% + 4/5 x 1%). The announcement period indicators represent the difference in net return between announcement and non-announcement period trade.

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and bid/ask spread (ProfitNet), profits excluding commission (Profit), gross profits

excluding commission and the bid/ask spread (ProfitGross), and the amount of bid/ask

spread paid by the individual (Sprd). If disclosure “benefits the uninformed at the

expense of the informed” via a reduction in private information, then I expect

individuals’ announcement period trades are more profitable than their non-

announcement trades, and thus θ1 and θ2 are positive when the dependent variable

measures trading profits (e.g., ProfitNet) and negative when the dependent variable is the

amount of bid/ask spread paid by the individual (Sprd). In contrast, if earnings

announcements are associated with an increase in private information and/or individuals

inefficiently process earnings information, then I expect individual investors’

announcement period trades are less profitable that their non-announcement trades, and

thus θ1 and θ2 are negative (positive) when the dependent variable measures trading

profits (amount of bid/ask spread paid by the individual). Moreover, if earnings

announcements are associated with changes in trade profitability simply because of a

change in transaction costs (i.e. a change in the bid/ask spread), then I expect: (1) the

amount of bid/ask spread paid by individual investors, Sprd, changes around earnings

announcements, and (2) trading profits excluding such transaction costs, ProfitGross, do

not change around earnings announcements.

4.2.2. Trade Profitability and the Information Content of Earnings?

If individual investors’ announcement period trades are less profitable than their

non-announcement trades, and the decline in trade profitability is incremental to changes

in transaction costs, it raises the possibility that increased losses could be the result of

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inefficient information processing (e.g., Maines and Hand, 1996; Fischer and Verrecchia,

1999). To investigate whether inefficient information processing contributes to changes

in individual investors’ trading profits around earnings announcements, I sort earnings

announcements into quintiles based on the amount of information priced around the

earnings announcement. I use the absolute value of returns in excess of the respective

(5x5) size and book-to-market portfolio over the eleven day window centered on the

earnings announcement (AbsAnncRet) to measure the amount of information impounded

in prices over the announcement period.29 I then re-estimate equation (7) for each

quintile. If increased losses around earnings announcements are the result of inefficient

information processing, then the larger the amount of information, the greater should be

the loss. Moreover, if individual’s non-announcement trades are also correlated with the

information in earnings, then the greater the announcement return, lower the profitability

of non-announcement trades.

4.2.3. Who Loses Around Earnings Announcements?

An alternative approach to testing whether increased losses around earnings

announcements are the result of inefficient information processing, is to investigate

whether these losses vary in a predicted manner with the characteristics of the individual

investor. Prior work suggests that wealthy individuals outperform the market (e.g.,

Yitzhaki, 1987), that experienced investors outperform inexperienced investors (e.g.,

29 By “the amount of information” I mean the extent to which the market revises its priors. Using announcement period returns to measure the information in earnings allows for heterogeneity in model of expected earnings and heterogeneity in the quality of reported earnings (e.g., Ecker et al., 2006, Ball and Shivakumar, 2008). For example, despite a large forecast error, reported earnings might be of such low quality that the earnings surprise actually contains very little information. Using the announcement period return controls for this possibility, and is consistent with prior research that shows announcement period returns are a function of both the magni