Disclosure Quality and Information Asymmetry∗

Stephen Brown#

Stephen A. Hillegeist†

December 2003 Abstract: We examine the association between firms’ disclosure quality and information asymmetry using a three-stage least squares estimation procedure that takes into account the endogeneity between these two variables. Our measure of information asymmetry is the Probability of Informed Trade (Easley, Kiefer and O'Hara [1997]), which measures the probability of information-based trade. Our primary result is that firms’ overall disclosure quality is negatively associated with information asymmetry. This negative association continues to hold for each of the three major components of disclosure quality, although the results are strongest for the quality of investor relations’ activities. Further analyses indicate that the underlying source of this negative association is a negative association between disclosure quality and the relative amount of trading by privately informed investors. Since prior work indicates that the cost of equity capital is increasing with information asymmetry, our results suggest that firms with higher disclosure quality have lower costs of capital. Key Words: Disclosure Quality; Information Asymmetry; Market Microstructure

∗ This paper has benefited from the comments and suggestions of Eli Bartov, Tarun Chordia, Paul Fisher, Simon Gervais, Ole-Kristian Hope, Ravi Jagannathan, Joseph Paperman, Gideon Sarr, Yong-Chul Shin, Sri Sridar, Beverly Walther, Greg Waymire, and seminar participants at University of Chicago, Georgia State University, University of Illinois at Chicago, University of Michigan, University of Minnesota, New York University, and Northwestern University. The authors especially wish to thank Mark Finn for his efforts on an earlier version of this paper. The second author gratefully acknowledges the financial support of the Accounting Research Center at Northwestern University. We also wish to thank Christine Botosan, Marlene Plumlee, and Mark Soszek for supplying the AIMR scores used in this study. We appreciate IBES for providing the analyst forecast data. # Department of Accounting, Goizueta Business School, Emory University † Department of Accounting Information and Management, Kellogg School of Management, Northwestern University. Corresponding author: Kellogg School of Management, 2001 Sheridan Rd., Room 6223, Evanston, IL 60208; Ph.: 847-491-2664; E-mail: s-hillegeist@kellogg.northwestern.edu.

1. Introduction

In this paper, we contribute to the growing literature on the capital market effects of voluntary

disclosure choices. Consistent with economic theory (Diamond [1985], Verrecchia [2001]), we

predict a negative association between a firm’s voluntary disclosure quality and the level of

information asymmetry between investors. Disclosure quality is directly related to asymmetry

because it decreases the amount of private information relative to public information; it is

indirectly related to asymmetry because it reduces incentives to search for private information.

After controlling for the endogenous relation between disclosure quality and asymmetry, we find

that asymmetry is negatively associated with the quality of a firm’s disclosures.

Understanding this association is important because information asymmetry is negatively

related to the cost of equity capital (COC). This negative relation arises because privately-

informed traders profit at the expense of uninformed investors. Rational uninformed investors

anticipate these future losses to informed traders and price protect themselves by demanding an

ex ante risk premium based on the expected level of information asymmetry. The risk is not

diversifiable since uninformed investors are always at a disadvantage compared to informed

investors (O'Hara [2003]). Firms can affect their COC through their choice of disclosure

quality, as disclosure essentially transforms private information into public information.

Empirical evidence in Easley, Hvidkjaer and O'Hara [2002] indicates that less asymmetry is

associated with a lower cost of equity capital.1

We employ a recently-developed model – the EKO model – from the market

microstructure literature (Easley, Kiefer and O'Hara [1997]) to estimate the firm-specific level of

information asymmetry. In the EKO model, large trade order imbalances result from the trading 1 Additionally, there is a widespread belief among accounting regulators, standards setters, and accounting practitioner organizations (AICPA [1994], FASB [2001], FASC [1998], Levitt [1998]) that high quality disclosures reduce a firm’s cost of capital.

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activities of privately-informed traders. We use the daily order flow over an annual period to

estimate the Probability of Informed Trade, PIN.2 The PIN is a firm-specific estimate of the

probability that a particular trade order originates from a privately-informed investor; hence, it

directly captures the extent of information asymmetry among investors in the secondary market.

An important advantage of this approach is that it enables us to analyze the sources of the

underlying relation between disclosure quality and information asymmetry. As Section 2

discusses, higher disclosure quality can either reduce the relative amount of informed trading

and/or decrease the frequency with which certain investors obtain private information. Such

analyses are not possible using spread-based proxies of information asymmetry.

Consistent with many previous studies, including Botosan and Plumlee [2002] and Lang

and Lundholm [1996], we use analysts’ evaluations of disclosure compiled by the Association

for Investment Management and Research (AIMR) as our proxy for disclosure quality. While

they are noisy measures of disclosure quality, the AIMR scores offer several advantages over

alternative proxies. First, they are based on a comprehensive evaluation of the firm’s disclosure

activities. Thus, our study generalizes and complements other studies that focus on a specific

type of disclosure.3 Second, they allow us to gain insights into the relative importance of

different types of disclosures (i.e., those related to the annual report, quarterly reports, and

investor relations activities). Finally, they allow us to examine the effects of disclosure quality

on a relatively large and representative population of firms.4

2 While new to the accounting literature, the PIN methodology has been successfully used in the finance literature to investigate issues such as how informed trading differs across stock exchanges (Easley, Kiefer and O'Hara [1996]) and securities (Easley, O'Hara and Srinivas [1998]), and the effect of stock splits on information asymmetry (Easley, O'Hara and Saar [2001]). 3 For example, Coller and Yohn [1997] and Marquardt and Wiedman [1998] examine management forecasts. 4 In contrast, Lang and Lundholm [2000], Marquardt and Wiedman [1998], Schrand and Verrecchia [2002], and Sengupta [1998] examine the effects of disclosure in the context of raising capital using relatively small samples.

2

In addition to disclosure’s effect on information asymmetry, the concurrent level of

information asymmetry is likely to influence a firm’s choice of disclosure quality. If the current

level of information asymmetry is high, for example, the firm may choose a higher level of

disclosure quality, expecting it to reduce the level of asymmetry. A failure to incorporate this

potential endogeneity into our research design can result in biased coefficients and misleading

inferences (Maddala [1983]). In order to address the endogeneity problem, we employ a three-

stage least squares (3SLS) estimation procedure that models disclosure quality and information

asymmetry simultaneously.

Our primary tests indicate that the overall quality of the firm’s disclosures (Total) is

significantly and negatively related to the level of information asymmetry after controlling for

the endogenous relation between disclosure quality and asymmetry. Separately examining the

three component subscores indicates that this overall negative relation extends to each

component (Annual, Quarterly, and IR); the relation is strongest for the quality of the investor

relations activities. These findings continue to hold in alternative regression specifications.

In addition, we examine the underlying sources of the negative relation between disclosure

quality and information asymmetry. We find that higher quality is negatively associated with the

relative amount of trading by privately-informed investors. This finding is consistent with the

theoretical results in Fishman and Hagerty [1989] and Merton [1987], which indicate that more

informative disclosures increase the amount of uninformed trading. Combined with the results in

Easley, Hvidkjaer and O'Hara [2002], our findings provide some empirical support for

regulators’ beliefs about the capital market benefits of high quality disclosures.

Our use of a direct measure of information asymmetry contrasts with indirect spread-

based proxies of asymmetry used in prior studies, such as Welker [1995] and Leuz and

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Verrecchia [2000]. The spread between the quoted bid and ask prices compensates the market

maker for expected losses to privately-informed traders, in addition to covering her inventory

holding and order processing costs. Relying on the EKO methodology avoids the numerous

econometric problems and interpretation difficulties that occur when using spread-based

measures of information asymmetry (Callahan, Lee and Yohn [1997], Neal and Wheatley

[1998], and O'Hara [1995]). For example, market makers protect themselves from information

asymmetry by simultaneously manipulating both the quoted bid and ask prices along with the

quoted depths associated with those prices. Unless depths are included in research design,

spread-based analyses are incomplete and difficult to interpret (Lee, Mucklow and Ready

[1994]).5

In the next section, we develop our hypothesis about the association between disclosure

quality and information asymmetry. In Section 3, we develop our empirical proxy of

information risk based on the EKO model, and we discuss our proxy for disclosure quality. We

also discuss some of the restrictive assumptions that underlie the PIN measure and their likely

impact on our analyses. In Section 4, we describe how our research methodology takes the

simultaneous relation between disclosure quality and information asymmetry into account.

Section 5 describes the data sources, variable construction, and provides descriptive statistics.

Section 6 presents the results of our empirical tests, and Section 7 summarizes and concludes the

paper.

2. Disclosure Quality and Information Asymmetry

Private information represents information about firm value that has not (yet) been incorporated

into the firm’s stock price. Hasbrouck's [1991] characterization of private information as 5 Furthermore, since spreads and depths are determined simultaneously, this endogeneity should be incorporated into the research design.

4

“essentially prior knowledge of public information” indicates that timing differences account for

much of the distinction between private and public information. Information asymmetry in the

stock market occurs when one or more investors possess private information about the firm’s

value while other investors are uninformed. This dichotomy of information among investors is

consistent with Admati and Pfleiderer [1988], Diamond and Verrecchia [1991], Easley and

O'Hara [1992], Glosten and Milgrom [1985], Kim and Verrecchia [2001], Kyle [1985], and

McNichols and Trueman [1994], among others.6

The level of information asymmetry can be characterized by the risk of trading with a

privately-informed investor. A firm’s choice of disclosure quality--which we define as the

precision, timeliness, and quantity of information provided--affects this information risk by

altering the distribution of public and private information among investors.

Disclosure quality is related to information risk because it affects the incentives to search

for private information. Higher disclosure quality reduces the incentives to search for private

information by reducing the expected benefits from obtaining private information. Diamond

[1985], Hakansson [1977], and Verrecchia [1982] examine settings where public and private

information are substitutes for each other. In these settings, increased public disclosures by firms

generally reduce the incentives to collect costly private information. These findings suggest that

higher disclosure quality leads to less private information being produced.

Higher disclosure quality also leads to less private information search activities, since it

raises the costs of these activities. As timeliness is an important attribute of disclosure quality,

firms with higher disclosure quality are more likely to release material information promptly and

to provide forward-looking information. When search activities take time to complete, fewer

6 This characterization of information asymmetry is, however, distinct from the settings analyzed in Kim and Verrecchia [1991] and Verrecchia [1983], where all investors receive private information, but each private signal contains an idiosyncratic noise component.

5

opportunities arise to discover and trade on private information about those firms with high

disclosure quality. This reduction occurs because less time is available between the

information’s emergence and when potentially informed traders expect the firm to disclose the

information publicly. Less search for private information occurs since the net costs of

discovering private information increase (Grossman and Stiglitz [1980]). Consequently, higher

disclosure quality reduces the frequency with which certain investors obtain private information

(which we call private information events) by reducing the intensity of search activities. Fewer

private information events, ceteris paribus, reduce the risk of trading with a privately-informed

investor, thereby reducing the level of information asymmetry.

In addition to its impact on information search incentives, higher disclosure quality also

reduces information asymmetry by altering the trading behavior of uninformed investors.

Merton [1987] and Fishman and Hagerty [1989] analyze settings in which firms’ more

informative disclosures reduce the costs associated with processing and assimilating public

information. As a result, greater disclosure induces more investing by uninformed liquidity

traders. Diamond and Verrecchia [1991] find that the amount of uninformed trading by large

investors can increase as the firm discloses more information.7 Assuming that the costs of

processing public information decrease with the quality of the information, we expect that

disclosure quality is positively associated with the amount of uninformed trading. Consistent

with this assumption, accounting regulators and practitioners argue that more and better

disclosure by firms makes the capital markets more attractive to uninformed investors by

“leveling the playing field” (AICPA [1994], FASB [2001], Levitt [1998]).

More uninformed trading decreases the risk of trading against a privately-informed investor,

7 Consistent with these models, Leuz and Verrecchia [2000] find a significant increase in trading volume for German firms committing to higher disclosure levels. Since bid-ask spreads also decrease, their evidence suggests that uninformed investors generate much of the increased trading.

6

ceteris paribus. However, previous research indicates that increases in uninformed trading are

associated with more informed trading. Kyle [1985] demonstrates that if investors are risk

neutral and are not capital constrained, then the amount of informed trading varies

proportionately with the expected amount of uninformed, liquidity-based trading. Informed

traders act strategically to maximize their trading profits by taking advantage of the “noise”

provided by the trading activity of uninformed investors. The end result is that the relative

amount of informed trading remains unchanged when the expected amount of uninformed

trading changes. In practice, however, it is likely that informed traders are risk averse and/or

capital constrained, and thus, there is a less than fully proportional change in the amount of

informed trading. Accordingly, we expect that higher disclosure quality is associated with

relatively less informed trading and, therefore, less information asymmetry.

The above discussion suggests that the level of disclosure quality should be negatively

associated with the level of information asymmetry for two reasons. First, higher disclosure

quality reduces the incentives to search for private information. Second, higher disclosure

quality increases the relative amount of trading by uninformed investors. Accordingly, we test

the following hypothesis, stated in alternate form:

H1: A negative association exists between the levels of disclosure quality and

information asymmetry.

3. Variable Measurement

In this section, we describe our empirical proxies for the theoretical constructs we use to test

hypothesis H1. First, we describe the EKO model and explain how we use it to estimate the

probability of informed trade or PIN, which is our proxy for information asymmetry. Second,

7

we discuss our proxy for disclosure quality, which is based on analysts’ evaluations of firms’

disclosures.

3.1. Proxy for Information Asymmetry

Information asymmetry manifests itself when investors trade on their private information, which

suggests that asymmetry is observable in the form of relatively large imbalances between buy

and sell orders. This observability forms the intuition behind the recent EKO microstructure

model of information risk (Easley, Kiefer and O'Hara [1997]). The model allows us to estimate

the probability of information-based trading (PIN) for a given stock based on the actual order

flow. In the model, the market maker observes the order flow, updates her beliefs about the

probability of information-based trading, and sets trading prices. Over time, the process of

trading and learning from trading results in the convergence of prices to their full information

levels as the private information is revealed through the imbalance between buys and sells in the

order flow.

The EKO model focuses on the role of private information and analyzes how it becomes

impounded into prices through the trading process. The results in French and Roll [1986] and

Barclay, Litzenberger and Warner [1990] indicate that this focus on private information is

warranted, since they find evidence suggesting that return volatility is primarily caused by the

incorporation of information into prices through the trading activities of privately-informed

investors. Additionally, Hasbrouck [1991] shows that the trading process itself is a significant

source of information to the market. Together, these studies suggest that private information is

the dominant cause of trade imbalances and price movements in the market.

The basic structure of the EKO model is shown in the game tree diagram in Figure 1.

The model depicts trading as a game – repeated daily – between the market maker and traders.

8

At the beginning of the trading day, nature determines whether one or more informed investors

observe new private information about a firm. Private information events occur with probability

α. The private information contains “bad” (“good”) news with probability δ (1 - δ). Bad (good)

news indicates that the asset is currently overvalued (undervalued), and hence, the profit

maximizing trade is to sell (buy) the stock. The asset is correctly valued on no news days, which

occur with probability (1 - α).

Trade orders arrive sequentially according to independent Poisson processes. Orders

from informed traders arrive at the daily arrival rate µ on information events days, while orders

from uninformed buyers (sellers) arrive at the daily rate εb (εs) each trading day. The market

maker sets prices to buy or sell one unit of stock at each point in time, and she executes orders as

they randomly arrive. Although unaware whether a private information event has occurred on

any given day, the market maker knows the probability of and the expected order process

associated with each event and uses the actual order flow to update her beliefs throughout the

trading day. Thus, prices evolve in response to the observed order flow on a particular day.

While the occurrence of private information events and the identity of traders are

unobservable, their effects on the order flow are observable. The structure provided by the EKO

model allows us to work backwards and use observable data on the daily number of buy orders

(B) and sell orders (S) to make inferences about unobservable private information events and the

division of trade between informed and uninformed traders. The likelihood function induced by

the EKO model for a trading day, conditional on the parameter vector θ = (α, δ, εb, εs, µ), is

determined by a mixture model in which the weights on the three components (no news, good

news, and bad news) reflect the probabilities of their occurrence in the data. On a no-news day,

the model predicts a roughly equal number of buyer- and seller-initiated trade orders, all of

9

which come from uninformed traders. On a good- (bad-) news day, a relatively large order

imbalance emerges, with buyer-initiated (seller-initiated) trades predominating.

!!)()1(

!)(

!!!)1()|,(

)(

)(

Se

Be

Se

Be

Se

BeSBL

Ss

Bb

Ss

Bb

Ss

Bb

sb

sbsb

εεµδα

εµεαδεεαθ

εεµ

εµεεε

−+−

+−−−−

+−+

++−=

(1)

Inspection of equation (1) reveals that the daily number of buys and sells is a sufficient statistic

for the data. The parameter vector θ can be estimated via maximum likelihood with increasing

precision as the daily order flow is observed over an increasing number of days. The empirical

implementation of the EKO model implicitly assumes that a firm’s underlying information

process – and hence the PIN parameters – are stationary over the one-year estimation period.

Evidence in Easley, Hvidkjaer and O'Hara [2002] indicates that this assumption is reasonable.

Once the model’s parameters (α, δ, εb, εs, µ) have been estimated, the PIN is calculated

as follows:

sb

PINεεαµ

αµ++

= (2)

Equation (2) shows that information risk increases with more frequent information events (α)

and more informed trading (µ), and decreases with the amount of trading by uninformed

investors (εb and εs). PIN represents the percentage of trades that are expected to be information

based each day, since αµ is the expected number of orders from privately-informed investors and

αµ + εb + εs is the expected total number of orders. Thus, the ratio of the two is the ex ante

probability that the first trade of the day is information based. For example, consider a stock for

which on 60% of days there are 50 buys and 50 sells, on 20% of the days there are 80 buys and

50 sells; and on 20% of the days, there are 50 buys and 80 sells. The EKO model parameters

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would be identified as εb = εs = 50, µ = 30, α = .40, and δ = .50. The corresponding PIN is

10.7%.

One concern with using the PIN as a proxy for the level of information asymmetry is that

by assuming all orders are the same size (or at least have the same information content), the EKO

model ignores an important source of information since traders potentially reveal information by

their choice of trade order size. However, privately-informed investors will disguise their

presence by mimicking the trade sizes of uninformed traders (Barclay and Warner [1993],

Chakravarty [2001]); otherwise, the market maker could infer their identity from their order size,

which would significantly reduce the expected profitability of trading on their private

information. Jones, Kaul and Lipson [1994] find that the relation between the volume of trade

and the volatility of stock price changes virtually disappears when the number of trade orders is

controlled for. These results suggest that the loss of information due to ignoring trade size will

be small. To the extent that order size provides additional information about the identity of

investors, it will add noise to our estimates.

Two other important restrictions in the EKO model are: (1) each trading day is assumed

to be independent, and (2) privately-informed buy and sell orders are not allowed to occur on the

same day. As Easley, Kiefer and O'Hara [1997] discuss, the main impact of dependence across

days is to change the interpretation of the α and δ parameters to unconditional probabilities. For

example, αδ would represent the unconditional probability of a bad news day and correspond to

the fraction of abnormally large, sell-led order imbalances over the sample period. Since our

empirical tests and interpretations correspond to this interpretation, the effects of dependence in

our study should be small. Thus, as long as information event days are classified correctly,

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dependence should not affect the estimation of the PIN parameters (Easley, Kiefer and O'Hara

[1997]).

Violation of the second restriction may not be as benign. To the extent that informed

investors initiate buy and sell orders on the same day, as in Kim and Verrecchia [1991], the

estimation and interpretation of the PIN parameters will be affected. Specifically, we expect that

“balanced” informed trading will tend to bias our estimates of ε upwards and µ downwards,

which in turn will bias PIN downwards. However, since these trades offset one another, the

market maker need not concern herself with balanced informed trades when setting prices.

Furthermore, as long as the measurement errors in ε and µ are not correlated with our disclosure

quality variables, then these errors should not result in misleading inferences. Nevertheless, our

results should be interpreted with these caveats in mind.

3.2. Proxy for Disclosure Quality

Disclosure quality reflects the overall informativeness of a firm’s disclosures and depends on the

amount, timeliness, and precision of the disclosed information. We use the Association of

Investment Management and Research (AIMR) disclosure scores as our empirical proxy for a

firm’s disclosure quality.8 According to the AIMR, the scores are intended to evaluate a “firm’s

effectiveness in communicating with investors” and the extent to which a firm’s aggregate

disclosures ensure that “investors have the information necessary to make informed judgments.”

Based on a review of the specific reasons cited for large disclosure score increases, Healy,

Hutton and Palepu [1999] find that firms exhibit a great deal of discretion when determining the

8 Prior studies that have used the AIMR disclosure scores include Botosan and Plumlee [2002], Gelb and Zarowin [2002], Healy, Hutton and Palepu [1999], Lang and Lundholm [1993], Lang and Lundholm [1996], Lundholm and Myers [2002], Sengupta [1998], and Welker [1995].

12

amount of information to disclose, the level of detail to provide, and the timeliness with which to

convey information, for both mandatory reports and purely voluntary disclosures.

Each year during our sample period, the AIMR forms industry-based committees

composed of leading analysts to undertake a comprehensive evaluation of disclosure quality for a

subset of firms in a select number of industries. “To allow efficient and convenient comparisons

among industries,” (1994-95 AIMR Report, p. 1) the committees use a common checklist to

guide their evaluations, although they can modify or augment this checklist as they see fit. The

industries selected for evaluation, the firms within an industry, the analyst committee

composition, and the checklists change from year to year. In most cases, the end result of the

evaluation process is a numerical score representing the overall quality of the firm’s disclosures

throughout the year. We refer to this result as the Total score. Additionally, most committees

also report separate subscores that reflect the three major categories of disclosure: (1) the annual

report and other required published information (Annual), (2) the quarterly report and non-

required published information (Quarterly), and (3) investor relations and related activities (IR).9

Consistent with most prior research, we convert each score into a percentile score based

on the maximum possible score for each disclosure category. While the scores for a single

industry-year are directly comparable, the scores across industries in a given year or across years

for a given industry may not be comparable since each analyst committee might be using a

different rating scales and criteria. In order to avoid industry- confounding effects, we make the

conservative assumption that all industry-year differences are induced by different evaluation

criteria. Accordingly, we subtract the industry-year mean percentile disclosure score from the

unadjusted percentile score, and use this value in our empirical tests. To make the analysis

9 Detailed discussions of the AIMR rating process and the disclosure scores can be found in Lang and Lundholm [1993] and Healy, Hutton and Palepu [1999].

13

consistent, we also industry-adjust all of the variables in our empirical analyses. While some

studies have converted the industry-adjusted scores into ranks (Lang and Lundholm [1993], Gelb

and Zarowin [2002]), we choose not to do so because converting cardinal scores into ranks

results in a substantial decrease in power.10 Additionally, converting the industry-adjusted scores

into ranks can “artificially” increase or decrease the variation in the scores, which can bias our

results unpredictably.

4. Methodology

We are interested in analyzing the association between disclosure quality and information

asymmetry. The potentially endogenous relation between these two attributes makes this task

more difficult. In addition to disclosure quality’s effect on information asymmetry, a manager’s

choice of disclosure quality is likely to be influenced by the concurrent level of information

asymmetry. This potential endogeneity will cause the association between disclosure quality and

asymmetry to be less negative since firms with higher asymmetry are more likely to choose

higher disclosure quality because the expected benefits are higher. Failure to incorporate this

endogeneity into our research design could result in misleading inferences.11 Accordingly, we

estimate the association between disclosure quality and information asymmetry using a three-

stage, instrumental variables procedure (Maddala [1983]), whereby the AIMR disclosure quality

scores, Score, are modeled as a function of PIN and one set of exogenous variables and PIN is

modeled as a function of Score and another set of exogenous variables. The disclosure quality

and information asymmetry models are as follows, where firm and year subscripts are

understood:

10 The industry committee reports clearly indicate that the disclosure scores are meant to be cardinal representations. 11 While most studies examining the effects of disclosure quality have not addressed this potential endogeneity, notable exceptions include Leuz and Verrecchia [2000], Marquardt and Wiedman [1998], and Welker [1995].

14

0 1 2 3 4 5 6

7 8 9 10

Score PIN Size Return Surprise Correlation CapitalInstOwn Analysts Owners EarnVol

β β β β β β ββ β β β ε

= + + + + + ++ + + + +

(3)

0 1 2 3 4 5

6 7

PIN Score Size InstOwn Analysts DispersionLeverage EarnVol

γ γ γ γ γ γγ γ ψ

= + + + + ++ + +

(4)

Depending on the specification, Score refers to either the Total, Annual, Quarterly, or IR score.

As we discuss in Section 3.2, all variables are industry-adjusted by subtracting the industry-year

mean. We discuss these models in more detail below.

The analysis proceeds as follows: In the first stage, Score is modeled as a function of all

the exogenous variables in both equations (3) and (4), and the endogenous variable PIN is

excluded from the regression. Predicted values for Score, , are calculated using the

estimated coefficients from this regression and the firm specific values for the exogenous

variables. Likewise, the predicted values for PIN, , are the fitted values from a regression

of PIN on the all of the exogenous variables in equations (3) and (4), but excluding Score. In the

second stage of the analysis, we estimate consistent estimates of the β coefficients in equation (3)

by regressing Score on and the exogenous variables in the disclosure model. Similarly, we

obtain consistent estimates of the γ coefficients in equation (4) by regression PIN on and

the exogenous variables in the information asymmetry model.

Score

PIN

PIN

Score

4.1. Disclosure Quality Model

As discussed above, we expect that managers take the level of information asymmetry into

account when they choose the quality of their disclosures. The previous literature identifies a

number of other factors associated with firms’ disclosure quality choices. Lang and Lundholm

[1993] examine the cross-sectional determinants of the AIMR disclosure quality scores. Based

on their findings, we include the following variables in the disclosure quality model: (1) Size -

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the natural log of the firm’s market value of equity measured at the end of the firm’s fiscal

period; (2) Return - the market-adjusted stock return of the firm’s equity measured over the fiscal

period; (3) Surprise - the difference between the firm’s actual per share earnings and the

consensus analyst forecast (scaled by price) eight months prior to fiscal year end and winsorized

at the 1% level; (4) Correlation - the correlation between annual stock returns and annual

earnings measured over the ten years prior to the current fiscal period; and (5) Capital – an

indicator variable that equals one if the firm issues public debt or equity during the current and

following two-year period, and zero otherwise. Based on the results in Lang and Lundholm

[1993], we expect the coefficients on Size, Return, and Capital to be positive and the coefficients

on Surprise and Correlation to be negative.

We include four additional variables that we expect to affect disclosure quality. The first

three are designed to capture differences in shareholders’ demands for disclosure quality. We

anticipate that managers take the demand for disclosure quality by shareholders into account

when they make their disclosure quality choices. Botosan and Harris [2000] and Bushee,

Matsumoto and Miller [2003] find evidence indicating that firms respond to investor demands

for increased disclosure. Accordingly, we expect the following three variables to have positive

coefficients in the disclosure quality model: (6) InstOwn - the percentage of shares owned by

institutional shareholders; (7) Analysts - the number of analysts covering the firm; and (8)

Owners - the natural log of the number of shareholders. We include the standard deviation of

earnings (scaled by assets) measured over the previous ten years, EarnVol, as an additional

explanatory variable. Zhang [2001] shows that the equilibrium level of disclosure quality

(measured by the precision of the disclosure) increases with the volatility of earnings. Firms

increase their disclosure quality to (partially) counteract the higher level of private information-

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based trading that is induced by high earnings volatility. Thus, we expect EarnVol to have a

positive coefficient in the disclosure quality model.

4.2. Information Asymmetry Model

As Section 2 discusses, we expect the level of disclosure quality to be negatively related to

information asymmetry. In addition, we expect several other factors associated with the firm’s

information environment to be associated with the level of information asymmetry. The first

variable we include is Size. Previous research indicates that stock prices incorporate information

about large firms earlier than information about small firms. Based on the results in Atiase

[1985], Bamber [1987], and Diamond and Verrecchia [1991], we expect a negative association

between Size and PIN.

Ayers and Freeman [2001] and Jiambalvo, Rajgopal, and Venkatachalam [2002] find

evidence that current returns reflect future earnings to a greater extent when institutional

ownership (InstOwn) is higher. These findings suggest that sophisticated investors are more

actively trading on private information relating to future earnings, and current prices thus reflect

future earnings information to a greater extent. If the trading activities of sophisticated investors

are more frequently based on private information, then we expect PIN to be positively associated

with InstOwn. While Ayers and Freeman [2001] find that analyst following plays a similar role

to institutional ownership, Jiambalvo, Rajgopal and Venkatachalam [2002] find that the analyst

following is negatively associated with the extent that prices lead future earnings. The analysis

in Easley, O’Hare and Paperman [1998] suggests that the role of analysts is more complex since

the number of analysts cannot simply be used as a proxy for informed trade. They find that

analyst following is positively associated with both the amount of informed and uninformed

trading; with the net effect that information asymmetry is negatively associated with analyst

17

following. Therefore, we expect a negative coefficient on Analysts.

The next variable included in the information asymmetry model is the dispersion of

analyst forecasts, Dispersion, which is measured as ln((standard deviation of forecast earnings

per share in the 4th month of fiscal period/stock price) + 0.001). We expect that when more

uncertainty exists regarding expected earnings, more potential private information can be

discovered and traded upon. In this case, private information search incentives are increasing

with the amount of earnings uncertainty. Therefore, we expect a positive association between

Dispersion and PIN.

The amount of private information search activities will be positively associated with the

expected benefits of obtaining the information. Boot and Thakor [1993] demonstrate that the

incentives for private information acquisition are increasing with a firm’s debt-to-assets ratio

(Leverage). This result occurs because, for a given amount of private information about the

value of a firm’s assets, the expected profits from trading on that information in the equity

market increase with the firm’s leverage, ceteris paribus. Additionally, Zhang [2001]

demonstrates that the endogenously-determined level of private information production increases

with the volatility of earnings. When earnings volatility is higher, greater benefits arise from

obtaining private information about future earnings. Assuming that the amount of information-

based trade increases with the amount of private information search activities, we expect

Leverage and EarnVol to be positively associated with PIN.

5. Sample Description

Our sample is based on firms that were evaluated in the 1986 through 1996 editions of the

Annual Review of Corporate Reporting Practices by the AIMR. Our final sample consists of

2,432 firm-year observations representing 444 individual firms that have Total scores alone and

18

1,951 firm-year observations representing 350 individual firms that also have Annual, Quarterly,

and IR scores. Our sample comprises 41 industries.12

For each firm-year observation, we collect trade data from the ISSM Transactions File

database and the Trades and Quotes (TAQ) database over the twelve-month period beginning

eight months prior to the firm’s fiscal year end. This time period likely corresponds to the AIMR

evaluation period.13 We match fiscal years to report years as follows: disclosure scores in the

1996 AIMR report correspond to fiscal-year ends that fall between 4/1/95 and 3/31/96. The

other reports are matched similarly.

For each firm, we gather data on every trade during the twelve-month sample period and

classify it as either a buyer- or seller-initiated trade using the standard Lee-Ready algorithm (Lee

and Ready [1991]). The algorithm classifies a trade that takes place above (below) the midpoint

of the current quoted spread as a buy (sell). For trades taking place at the midpoint, we use a

“tick test” based on the most recent transaction price to classify the trade.14 When a sample firm

has two or more classes of common stock, we only use the class of shares that has the highest

trading volume during the sample period. We require firms to have at least one hundred days on

which shares were actually traded in order to reduce estimation errors. Given the number of

daily buys and sells for each trading day during our sample period, we use equation (1) to

compute the maximum likelihood estimates for the PIN parameters (θ = {α, δ, εb, εs, µ}) for each

firm-year in our sample. Given θ, we calculate the PIN for each firm using equation (2). In our

sample, the number of trading days ranges from 107 to 254, with a mean (median) of 249 (252). 12 For approximately five industry-years, the committees reported only ranks - not Scores. We therefore estimate the value of Score based on the reported ranks and the range of scores in other years. 13 This information is based on conversations that Mark Soczek had with Patricia McQueen, Vice President of Advocacy Programs, AIMR, who headed the evaluation program in 1995-96. 14 Following standard practice, we use a five-second lag on reported quote times to adjust for differences in reporting times between quotes and trades. Additionally, large trades are often broken down and matched against multiple investors. Reporting conventions will often classify such a transaction as multiple trades. Following Hasbrouck [1988], we classify all trades occurring within five seconds of each other as a single trade.

19

Data for the control variables come from a variety of sources. Accounting data is

obtained from COMPUSTAT and market prices and return data come from CRSP. Institutional

ownership data is derived from the CDA/Spectrum 13F Institutional Holdings database, and

SDC Platinum is the source of our data on capital issuance. Analyst forecast data is obtained

from IBES.

Table 1 provides descriptive statistics for our final sample prior to industry adjusting.

The mean (median) PIN is 17.6 (17.4), which indicates roughly a 17% - 18% chance that the

opening trade on any given day is based on private information. The mean and median values of

α demonstrate that private information events occur on almost half of trading days. The average

value of ln(µ/ε) corresponds to a value of µ/ε of 0.45, where ε = εb + εs. This value indicates that

informed trades are less than half the level of uninformed trades and represent about 30% of total

trades on information-event days. Thus, our sample is characterized by firms with frequent

information events and high levels of uninformed trading.

The AIMR scores presented in Table 1 represent the reported score as a percentage of the

maximum possible score. The mean Total score is 73.1, and the average subscore ranges from

72.3 to 74.9. Considerable variation emerges within each disclosure category as the standard

deviation ranges from 13.0 for the Annual score to 16.4 for the IR score. Table 1 also indicates

that the firms rated by the AIMR tend to be large firms with large analyst followings (median =

19) and institutions generally holding over half of all shares outstanding. Ownership in these

firms also tends to be widespread, with the median firm having over 18,000 owners.

Table 2 presents the Spearman correlations for our sample. (All figures in Tables 2 to 4

are based on the industry-adjusted figures.) While ln(µ/ε) has a strong positive correlation with

PIN (0.84) as expected, no significant correlation exists between PIN and α (0.00). This

20

unexpected result likely arises from the strong negative correlation between ln(µ/ε) and α (-

0.48). Untabulated results show that after controlling for the correlation between ln(µ/ε) and

PIN, the partial correlation between PIN and α is significantly positive. As expected, the

correlations between PIN and the disclosure score variables are all significantly negative,

although somewhat moderate in magnitude (ranging from -0.07 to -0.14).

We find that the correlations between the four disclosure scores and Size, Return,

Correlation, Capital, InstOwn, Analysts, and Owners all have the expected signs. While the

correlations between the Scores and Surprise and EarnVol have unexpected signs, the

magnitudes of the correlations are generally small, with the eight correlations ranging between

0.06 and 0.14 in absolute magnitude. The correlations between PIN and the exogenous variables

in the information asymmetry model have the expected signs, with the exceptions of InstOwn (-

0.10) and Leverage (-0.04).

6. Analysis and Results

In this section, we report the results of analyses that examine the relation between the quality of a

firm’s disclosures and the level of information asymmetry. In addition to examining the relation

between information asymmetry and the Total disclosure quality score, we examine the relation

separately for each of its three components: Annual, Quarterly, and IR. We extend our analysis

by examining the relation between the disclosure scores and the underlying parameters on which

PIN is based: α and ln(µ/ε).

6.1 Association between Disclosure Quality and Information Asymmetry

Our primary analysis involves estimating the disclosure quality model (equation (3)) and

information asymmetry model (equation (4)) simultaneously. Untabulated Hausman [1978] tests

21

for endogeneity reject the null hypothesis of “no simultaneity” for all the models on which we

report. We present the results of this analysis in Table 3. The upper half of Column 1 presents

the results for the information asymmetry model where PIN is the dependent variable and Total

is the measure of disclosure quality. The Total coefficient is significantly negative (t = -2.8).

This result supports our primary hypothesis that a firm’s overall disclosure quality is negatively

associated with the level of information asymmetry among investors.

The magnitude of the coefficient, -0.12, indicates that an increase of 13% (the standard

deviation of Total) in the total disclosure score is associated with a 1.6% decline in PIN. Based

on the results in Easley, Hvidkjaer and O’Hara (2002), a 1.6% reduction in PIN is associated

with a reduction in the cost of capital of 41 basis points. We view this magnitude as a moderate

and economically plausible effect of disclosure quality on the cost of capital. Since the median

firm in our sample has an equity capitalization of $2.2 billion, this reduction in cost of capital

translates into an annual savings of $9 million.

Columns 2 – 4 present the results where Annual, Quarterly, and IR are the respective

measures of disclosure quality. Each of the three coefficients is significantly negative, as

expected (t-statistics vary from -2.3 to -4.0). The IR coefficient is the most significantly negative

and is also the most robust to the alternate specifications discussed in Section 6.2. The IR results

are consistent with the claims in Mahoney [1991] and Marcus and Wallace [1991] that reduced

uncertainty and lower information asymmetry among stock participants are among the benefits

of high quality IR activities. However, the results also suggest that Annual and Quarterly also

contribute to the overall effect of disclosure quality. The magnitudes of their coefficients are in

fact larger than those on IR, and a direct comparison of the magnitudes is valid since the standard

deviations of the industry-adjusted Scores are similar.

22

One reason that IR has a higher level of significance may be that many regulatory

requirements (including those of the SEC and FASB) govern the form and content of quarterly

and annual reports, setting a lower bound on the quality of these disclosures. In contrast,

investor relations activity is less regulated, and firms have a greater ability to distinguish

themselves in this regard. Consequently, the variation in the underlying quality of the annual and

quarterly reports for firms complying with the regulations is likely less than the underlying

variation in the quality of the firm’s IR activities. For this reason, we expect the extent of

measurement errors in the IR scores to be relatively smaller than the errors in the Annual and

Quarterly scores. Accordingly, the power of tests based on IR will be higher than for tests based

on Annual and Quarterly.

The coefficients on Size and Analysts in the information asymmetry model are significant

and have the predicted signs for each of the four Score measures. However, the results for the

other control variables do not coincide with our expectations. While InstOwn is positive and

moderately significant in the Total regression (t = 1.8) as predicted, it is insignificantly negative

in each of the three subscore regressions. Likewise contrary to our expectations, the coefficients

on Dispersion and Leverage are generally significantly negative in each of the four regressions,

and EarnVol is negative (though insignificantly so) in each model. In untabulated tests, we use

several different combinations and specifications of these variables to investigate why the

contrary results may have arisen. In all specifications, the contradictions remain – offering an

opportunity for future research.

In the lower half of Table 3, we present the results from estimating the disclosure quality

model. We find consistent evidence that the quality of disclosures chosen by managers is

increasing in the level of information asymmetry. The coefficient on PIN is significantly

23

positive for all measures of disclosure quality. The positive coefficients on PIN in the Score

regressions contrast strongly with the negative coefficients on Score in the PIN regressions and

are consistent with the endogeneity of the two variables, as is strongly confirmed by untabulated

Hausman tests.

The coefficients on the control variables in the Score equations are generally consistent

with our hypotheses. The coefficients on Size, Capital, InstOwn, Analysts, and Owners are

significantly positive in general, while the Correlation coefficients are all negative and

significantly so in the Total and IR specifications. As in Lang and Lundholm (1993), the

coefficients on Surprise are not significantly negative and for Total are significantly positive.15

Additionally, EarnVol is not significantly positive in any specification but is in fact significantly

negative in the Annual equation.

Having found evidence that disclosure quality is negatively associated with the level of

information asymmetry, we now investigate the sources of this association. As we discuss in

Section 2, we expect disclosure quality to be related to information asymmetry through two

channels: a reduction in the frequency with which investors become aware of private information

and a reduction in the relative trading intensity of informed investors. Therefore, we examine

the relation between disclosure quality and the frequency of private information events, α, and

the relative amount of uninformed trading, ln(µ/ε). The two component regressions are similar

to equation (4), except that the dependent variable is based on the underlying PIN parameters

rather than PIN itself.

Panel A of Table 4 presents the results for the α-based regressions. The results show that the

IR coefficient is significantly positive, while the Total, Annual, and Quarterly coefficients are

15 We also measure Surprise by reference to a random walk model for earnings. Surprise loses its significance in the Total equation, but otherwise the results are not materially different.

24

not significantly different from zero. The positive coefficient is unexpected since PIN is an

increasing function of α. Thus, the negative associations between disclosure quality and

information asymmetry documented in Table 3 are not driven by a negative association between

disclosure quality and the frequency of information events.

However, this unexpected result could be explained if the assessments of disclosure quality

reflect the precision of the disclosed information. In this case, the results in Demski and Feltham

[1994], Kim and Verrecchia [1994], and McNichols and Trueman [1994] suggest that higher

disclosure quality will increase the incentives to acquire private information about upcoming

firm disclosures. Higher search incentives are expected to lead to more frequent information

events.

The analyses reported in Panel B of Table 4 examine the net effect of disclosure frequency

on the relative arrival rates of informed to uninformed traders, ln(µ/ε). The results indicate that

the coefficients for all four Scores are negative and highly significant (t-stats between -2.2 and -

4.8). These results indicate that higher disclosure quality is associated with relatively less

trading by privately-informed investors. This finding is consistent with our arguments in

Section 2 that uninformed investors trade more frequently in firms with higher quality

disclosures and that any increases in informed trading are not fully offsetting. Thus, the results

in Panel B indicate that higher disclosure quality is associated with less information asymmetry

via reductions in the relative amount of informed trading. Furthermore, this effect more than

overcomes the corresponding increase in the frequency of information events for the IR

specification.

6.2 Robustness Tests

25

We run alternate specifications of the models to test the robustness of the results reported above.

As previously noted, the model specification used above is very conservative insofar as all

variables are measured within industry-year groups. Accordingly, all inter-industry and inter-

year variation is removed from the data. We adopt this conservative approach in order to be

consistent with prior studies and because it is not clear that the scores awarded by different

groups of industry analysts are fully comparable between industries. However, it is clear from

reading the narrative of the AIMR reports that the analyst committees who award the scores are

aware of the disclosure practices in other industries and other time periods and they take this

knowledge into account when awarding the scores. In Table 5, we report the results of

estimating the model using the pre-industry-adjusted variables and including indicator variables

for each of the 41 industries and 11 years.

The results in Table 5 are very similar to those in Table 3. Score is significantly negative in

each of the PIN equations, and IR is once again the most significant disclosure variable.

Similarly, PIN is significantly positive in each of the Score equations. The coefficients on the

control variables have similar magnitudes and levels of significance – including those that are

contrary to our expectations. We also test the model using different specifications of the

variables, different groups of explanatory variables for the system of equations, and allowing

Analysts to be endogenously determined.16 Overall, the results from these alternate

specifications are consistent with the results discussed above; PIN is consistently negatively

associated with the Score variables, more significantly so for IR than for Annual and Quarter.

6.3 Relation to Disclosure Quality and Cost of Capital Literature

16 The variables used to explain analyst coverage are based on those in Lang and Lundholm (1996) – Score, Size, EarnVol and Correlation.

26

Lang and Lundholm [2000], Marquardt and Wiedman [1998], and Schrand and Verrecchia

[2002] examine the relation between disclosure frequency and the COC in the context of initial

and seasoned equity offerings. Collectively, they find evidence consistent with a negative

relation between disclosure quality and the COC if more frequent disclosures correspond to

higher disclosure quality. The studies generally interpret their results as indicating that a firm’s

disclosures are effective in reducing the level of information asymmetry at least between the firm

and investors and possibly among investors as well.17

The types of disclosures examined by these studies would for the most part fall within the

category of investor relations activities using the AIMR’s classifications and are thus most

comparable to our IR results. Using a much larger and broader sample of firms, we find that the

level of information asymmetry is negatively related to the quality of IR activities. Taken

together, these results are consistent with a link between disclosure quality (at least with respect

to IR activities) and the COC, and furthermore, they indicate that this link is driven by a negative

association between disclosure quality and information asymmetry. The evidence in Easley,

Hvidkjaer and O'Hara [2002] of a negative association between information asymmetry and the

COC further establishes the link between disclosure quality, information asymmetry, and the

COC.

While our IR results appear to be consistent with the results in Lang and Lundholm

[2000], Marquardt and Wiedman [1998], and Schrand and Verrecchia [2002], they appear to be

less consistent with the results in Botosan and Plumlee's [2002] analysis of the association

between disclosure quality (as proxied by the AIMR scores) and their ex ante measure of the cost

of capital based on Value Line forecasts. Botosan and Plumlee find that the Total and IR

17 Consistent with this conclusion, Coller and Yohn [1997] find that information asymmetry, as proxied for by the bid-ask spread, decreases in the nine days after a firm issues a management forecast.

27

coefficients are insignificant in their COC regressions, while the Annual coefficient is

significantly negative and the Quarterly coefficient is unexpectedly positive and significant.18

They speculate that the positive Quarterly coefficient arises because higher quality quarterly

reports lead to increased stock volatility (through an increase in the proportion of transient

institutional investors) and that the higher volatility is priced. Our finding of a negative

Quarterly coefficient in the information asymmetry regression appears to be inconsistent with

the positive Quarterly coefficient in Botosan and Plumlee's [2002] COC regression.

Economic theory suggests that disclosure quality can be related to the cost of capital

through two channels: information asymmetry and estimation risk.19 A possible reconciliation

between the two results occurs if 1) estimation risk and information asymmetry are negatively

correlated, and 2) disclosure quality is negatively associated with information asymmetry but is

positively associated with information risk. In this case, the relation between disclosure quality

and the cost of capital is ambiguous. However, it seems implausible that higher disclosure

quality leads to more estimation risk – a view that is supported by the discussions of the

estimation risk literature in Botosan [1997] and Schrand and Verrecchia [2002]. Another

possible explanation is that differences between the two samples’ compositions are responsible,

but considering that our sample is a subset of the Botosan and Plumlee [2002] sample, this

explanation seems unlikely. A further possible explanation is that their positive Quarterly

coefficient is due to bias caused by endogeneity between disclosure quality and the cost of

capital. Such an endogenous relation results in less negative coefficients on the disclosure

18 In their article, the coefficients in Table 4 for the component scores are from a combined model where all three scores are included in the same regression. When each score is regressed separately, Annual is insignificant. 19 Estimation risk occurs because an asset’s risk and return distribution parameters are unknown and this risk is priced to the degree that it is non-diversifiable (Coles, Loewenstein and Suay [1995]).

28

quality variables, and if the effect is severe enough, it will cause the disclosure coefficient to be

positive.

7. Summary and Conclusions

This paper examines the relation between the quality of a firm’s disclosures and the level of

information asymmetry among investors. We use the AIMR analysts’ evaluations of disclosure

quality as our measure of a firm’s overall disclosure quality. We employ a methodology from

the market microstructure literature (Easley, Kiefer and O'Hara [1997]) to estimate the firm-

specific level of information asymmetry among investors.

Our primary result is that the Total disclosure quality score is negatively associated with

the level of information asymmetry. We also examine the relation between asymmetry and the

three components of the Total score: Annual, Quarterly, and IR. We find that all three scores

have negative coefficients that are significant at the 1% level or better (one-sided tests), and the

IR score has the highest level of significance. Further analyses examine the underlying sources

of the negative relation between disclosure quality and information asymmetry. We find that

disclosure quality is associated with less trading by privately-informed investors compared to the

level of trading by uninformed investors. These findings are consistent with the theoretical

results in Fishman and Hagerty [1989] and Merton [1987] that more informative disclosures by

firms increase the amount of uninformed trading. We find no evidence that disclosure quality is

negatively related to the frequency with which investors obtain private information. These

findings are important because the results in Easley, Hvidkjaer and O'Hara [2002] indicate that

lower information asymmetry is associated with a lower cost of capital.

The firms rated by AIMR tend to be larger, industry-leading firms and are generally

thought to have higher and more uniform levels of disclosure quality and lower and more

29

uniform levels of information asymmetry compared to other firms. These characteristics should

reduce the variation in our sample in addition to reducing the size and significance of the

observed associations. Thus, while our results might not be generalizable to other firms, we

conjecture that the negative association between disclosure quality and information asymmetry is

actually stronger for firms outside of our sample.

Finally, our results should be interpreted in light of the limitations of our three-stage

least-squares estimation procedure. Our analysis only treats disclosure quality and information

asymmetry as endogenous variables. Other firm-specific variables are assumed to be exogenous

or pre-determined variables. We acknowledge that one or more of the instrumental variables

(e.g., institutional ownership) may be viewed as endogenous, and we would need to specify a

separate equation to explain the determinants of such endogenous variables. Doing so, however,

would necessitate the difficult task of finding an identifying variable for each such equation.

Hence, the possibility that one or more of the exogenous variables in our setting are themselves

endogenous could influence our results, and they should be interpreted accordingly.

30

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Kim, O. and R. E. Verrecchia, 1991, Market Reaction to Anticipated Announcements, Journal of Financial Economics, 30, 2, 37 Kim, O. and R. E. Verrecchia, 1994, Market Liquidity and Volume around Earnings Announcements, Journal of Accounting & Economics, 17, 1,2, 41-67 Kim, O. and R. E. Verrecchia, 2001, The Relation among Disclosure, Returns, and Trading Volume Information, Accounting Review, 76, 4, 633-654 Kyle, A., 1985, Continuous Auctions and Insider Trading, Econometrica, 53, 1315-1335 Lang, M. and R. Lundholm, 1993, Cross-Sectional Determinants of Analyst Ratings of Corporate Disclosures, Journal of Accounting Research, 31, 2, 246-271 Lang, M. and R. Lundholm, 1996, Corporate Disclosure Policy and Analyst Behavior, Accounting Review, 71, 467-492 Lang, M. and R. Lundholm, 2000, Voluntary Disclosure and Equity Offerings: Reducing Information Asymmetry or Hyping the Stock?, Contemporary Accounting Research, 17, 4, 623-662 Lee, C. M. C. and M. J. Ready, 1991, Inferring Trade Direction from Intraday Data, Journal of Finance, 46, 2, 733-747 Lee, C. M. C., B. Mucklow and M. Ready, 1994, Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis, Review of Financial Studies, 6, 345-374 Leuz, C. and R. Verrecchia, 2000, The Economic Consequences of Increased Disclosure, Journal of Accounting Research, 38, Supplement, 91-124 Levitt, A., 1998, The Importance of High Quality Accounting Standards, Accounting Horizons, 12, 1, 79-82 Lundholm, R. and L. Myers, 2002, Bringing the Future Forward: The Effect of Disclosure on the Returns-Earnings Relation, Journal of Accounting Research, 40, 3, 809-839 Maddala, G., 1983, Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press, New York, NY Mahoney, W., 1991, Investor Relations: The Professional's Guide to Financial Marketing and Communications, New York Institute of Finance, New York, NY Marcus, B. and S. Wallace, 1991, Competing in the New Capital Markets: Investor Relations Strategies for the 1990s, HarperBusiness, New York, NY

34

Marquardt, C. and C. Wiedman, 1998, Voluntary Disclosure, Information Asymmetry, and Insider Selling through Secondary Equity Offerings, Contemporary Accounting Research, 15, 4, 505-537 McNichols, M. and B. Trueman, 1994, Public Disclosure, Private Information Collection, and Short-Term Trading, Journal of Accounting and Economics, 17, 1,2, 69-94 Merton, R. C., 1987, A Simple Model of Capital Market Equilibrium with Incomplete Information, Journal of Finance, 42, 3, 483-510 Neal, R. and S. Wheatley, 1998, Adverse Selection and Bid-Ask Spreads: Evidence from Closed-End Funds, Journal of Financial Markets, 1, 1, 121-149 O'Hara, M., 1995, Market Microstructure Theory, Blackwell Publishers, Ltd, Malden, MA O'Hara, M., 2003, Liquidity and Price Discovery, Journal of Finance, 58, 4, 1335-1364 Schrand, C. and R. Verrecchia, 2002, Disclosure Choice and the Cost of Capital: Evidence from Underpricing in Initial Public Offerings, Wharton School Working Paper, Sengupta, P., 1998, Corporate Disclosure Quality and the Cost of Debt, Accounting Review, 73, 4, 459-474 Verrecchia, R. E., 1982, The Use of Mathematical Models in Financial Accounting/Discussions, Journal of Accounting Research, 20, 55 Verrecchia, R. E., 2001, Essays on Disclosure, Journal of Accounting & Economics, 32, 1-3, 97-180 Welker, M., 1995, Disclosure Policy, Information Asymmetry, and Liquidity in Equity Markets, Contemporary Accounting Research, 11, 2, 801-827 Zhang, G., 2001, Private Information Production, Public Disclosure, and the Cost of Capital: Theory and Implications, Contemporary Accounting Research, 18, 2, 363-384

35

Mean Std Dev Median 5% 95%

PIN 17.6 4.8 17.4 10.1 25.9α 47.5 9.0 48.3 31.7 60.7ln( µ / ε ) -0.8 0.4 -0.8 -1.5 -0.1Total 73.1 13.3 74.8 48.2 92.0Annual 74.9 13.0 77.0 51.0 93.3Quarterly 72.3 15.1 74.9 44.4 93.3IR 74.7 16.4 76.7 44.2 97.6Size 7.7 1.3 7.7 5.5 10.0InstOwn 53 16 55 24 76Analysts 19.5 9.2 19.0 6.0 36.0Dispersion -4.9 0.9 -5.0 -6.2 -3.5Leverage 0.25 0.15 0.24 0.03 0.49EarnVol -3.8 1.0 -3.6 -5.7 -2.4Return 0.00 0.25 -0.01 -0.37 0.40Surprise 0.0 0.1 0.0 -0.1 0.0Correlation 0.1 0.3 0.2 -0.4 0.7Capital 0.6 0.5 1.0 0.0 1.0Owners 2.9 1.3 2.9 0.8 5.2

Descriptive Statistics for Regression Variables Used in Tests of the Association between Disclosure Quality and Information Asymmetry

TABLE 1

Sample is based on 2,432 firm-year observations that have disclosure scores based on desclosure quality evaluations by the AIMR between 1986 and 1996. PIN is the Probabilty of Informed Trade based on the methodology in Easley, Kiefer and O'Hara [1997] measured over the annual period beginning 8 months before the firm's fiscal year end and expressed as a percentage; α is the percentage of days on which private information events occur; ln( µ/ε ) is the natural logarithm of the ratio of the number of private information based trades occuring on private information event days to the daily number of uninformed trades; Total is the score of the firm's total disclosure quality; Annual (Quarterly ) [IR ] is the score of the quality of the firm's annual report (quarterly report) [investor relations activities], where each disclosure quality score is expressed as a percentage of the maximum score in each disclosure category; Size is the natural log of the firm’s market value of equity measured at the end of the firm’s fiscal period; InstOwn is the percentage of shares owned by institutional shareholders; Analysts is the number of analysts covering the firm; Dispersion is the ln((standard deviation of forecast earnings per share in the 4th month of fiscal year/stock price) +.001); Leverage is the firm's debt-to-assets ratio; EarnVol is the natural log of the standard deviation of earnings (scaled by assets) measured over the previous ten years; Return is the market-adjusted stock return of the firm’s equity measured over the fiscal period; Surprise is the difference between the firm’s actual per share earnings and the consensus forecast scaled by price measured eight months prior to fiscal year end and winsorized at the 1% level; Correlation is the correlation between annual stock returns and annual earnings measured over the ten years prior to the current fiscal period; Capital is an indicator variable equal to 1 if the firm issues public debt or equity during the current and following two-year period, and 0 otherwise; Owners is the natural log of the number of shareholders.

36

PIN

α ln(µ/ε)

Total

Annual

Quarterly

IR Size

InstOwn

Analysts

Dispersion

Leverage

EarnVol

Return

Surprise

Correlation

Capital

α 0.00ln( µ / ε ) 0.84 -0.48Total -0.12 0.09 -0.16Annual -0.12 0.10 -0.16 0.85Quarterly -0.07 0.07 -0.10 0.81 0.62IR -0.14 0.06 -0.15 0.77 0.48 0.46Size -0.66 0.33 -0.77 0.22 0.22 0.11 0.25InstOwn -0.10 0.03 -0.10 0.11 0.08 0.10 0.09 0.09Analysts -0.56 0.29 -0.66 0.26 0.24 0.20 0.23 0.67 0.20Dispersion 0.14 -0.06 0.17 -0.17 -0.15 -0.10 -0.18 -0.34 -0.09 -0.20Leverage -0.04 0.05 -0.06 -0.01 -0.03 0.01 0.00 -0.07 0.05 0.07 0.26EarnVol 0.15 -0.06 0.19 -0.14 -0.14 -0.09 -0.13 -0.27 0.05 -0.20 0.28 0.02Return -0.04 -0.01 -0.05 0.10 0.07 0.05 0.12 0.20 0.07 0.03 -0.02 -0.04 -0.07Surprise -0.04 0.02 -0.05 0.11 0.09 0.06 0.12 0.20 0.05 0.03 -0.23 -0.17 -0.10 0.45Correlation 0.08 0.04 0.05 -0.10 -0.06 -0.07 -0.12 -0.06 -0.04 -0.12 0.11 0.06 0.14 -0.09 -0.06Capital -0.20 0.07 -0.21 0.15 0.12 0.09 0.16 0.20 0.07 0.21 0.03 0.22 -0.11 0.03 0.01 0.01Owners -0.53 0.36 -0.66 0.15 0.14 0.09 0.17 0.67 -0.12 0.55 -0.04 0.06 -0.15 0.01 -0.01 -0.03 0.21The sample consists of 1,951 firm-year observations with available data for all variables between 1986 and 1996. Variable definitions are in Table 1 where all variables are industry-year adjusted by subtracting the mean value for the corresponding industry-year. Coefficients with absolute value greater than 0.05 are significant at the 0.01 level.

TABLE 2

Correlation Structure for Regression Variables: Spearman Rank Correlation Coefficients

37

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat

PIN Equation:Score (-) -0.12 -2.8 ** -0.25 -2.3 * -0.15 -2.7 ** -0.12 -4.0 **

Size (-) -2.22 -23.0 ** -2.07 -13.5 ** -2.43 -19.2 ** -2.05 -16.7 **

InstOwn (+) 0.93 1.8 * -0.16 -0.2 -0.33 -0.5 -0.47 -0.8Analysts (-) -0.08 -4.0 ** -0.08 -3.1 ** -0.08 -3.1 ** -0.11 -6.9 **

Dispersion (+) -0.60 -5.1 ** -0.77 -4.2 ** -0.72 -4.7 ** -0.88 -6.0 **

Leverage (+) -1.58 -3.3 ** -1.72 -2.5 ** -1.21 -2.1 * -0.53 -1.1EarnVol (+) -0.15 -1.3 -0.25 -1.3 -0.12 -0.8 -0.05 -0.3

PIN (+) 3.79 2.5 ** 3.21 2.4 ** 3.51 2.1 * 5.38 2.3 *

Size (+) 6.83 2.5 ** 6.75 2.7 ** 4.84 1.6 10.30 2.4 **

Return (+) 2.58 1.2 -1.31 -0.7 1.15 0.5 2.35 0.8Surprise (-) 9.93 2.0 * 1.67 0.2 9.14 1.1 16.60 1.5Correlation (-) -2.91 -2.1 * -2.23 -1.8 * -3.54 -2.4 ** -5.32 -2.7 **

Capital (+) 2.99 4.3 ** 2.43 3.1 ** 2.34 2.5 ** 4.92 3.9 **

InstOwn (+) 4.19 1.5 8.44 2.4 ** 10.31 2.4 ** 12.60 2.2 *

Analysts (+) 0.78 4.8 ** 0.51 3.4 ** 0.70 3.9 ** 0.72 2.8 **

Owners (+) 2.00 1.7 * 1.80 1.8 * 2.55 2.1 * 4.16 2.5 **

EarnVol (+) 0.17 0.3 -0.93 -1.9 * -0.75 -1.2 -0.27 -0.3

TABLE 3

Three Stage Least Squares Coefficient Estimates and t-statistics for Tests of the Endogenous Association between Disclosure Quality and Information Asymmetry

PIN = γ 0 + γ 1 Score + γ 2 Size + γ 3 InstOwn + γ 4 Analysts + γ 5 Dispersion + γ 6 Leverage + γ 7 EarnVol + ψ

Score = β 0 + β 1 PIN + β 2 Size + β 3 Return + β 4 Surprise + β 5 Correlation + β 6 Capital + β 7 InstOwn + β 8 Analysts + β 9 Owners + β 10 EarnVol + ε

Disclosure Quality Measure

Variable definitions are in Table 1 where all variables are industry-year adjusted by subtracting the mean value for the corresponding industry-year. * (**) signifies significance at the 5% (1%) level (one-sided test). Score refers to one of the four disclosure quality scores, Total , Annual , Quarterly , and IR , depending on the specification. The expected signs of the coefficients are in parentheses after the variable name.

Annual (N = 1,951)

Quarterly (N = 1,951)

IR (N = 1,951)

Total (N = 2,432)

Disclosure Quality Equation:

38

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-statα Equation:Score (-) 0.03 0.3 -0.02 -0.1 0.11 0.9 0.14 2.0 *

Size (?) 2.34 10.6 ** 2.12 7.1 ** 2.21 8.1 ** 1.87 6.8 **

InstOwn (?) -0.71 -0.6 0.34 0.2 -0.18 -0.1 -0.10 -0.1Analysts (?) 0.24 5.2 ** 0.22 4.3 ** 0.17 3.3 ** 0.19 5.4 **

Dispersion (?) 1.53 5.4 ** 0.82 2.4 ** 0.97 3.0 ** 1.18 3.4 **

Leverage (?) 1.20 1.5 0.47 1.3 0.49 0.9 -0.56 -0.8

EarnVol (?) 0.21 0.8 0.31 0.9 0.45 1.4 0.44 1.5

α (?) 0.25 0.3 8.91 1.3 9.04 1.0 4.50 0.4

Size (+) 0.92 0.8 -2.15 -0.3 -4.47 -0.5 2.15 0.2Return (+) 3.05 2.4 ** 11.52 2.2 * 16.55 2.4 * 15.22 2.1 *

Surprise (-) 15.64 2.8 ** -16.12 -1.4 -5.10 -0.3 12.88 0.6

Correlation (-) -6.99 -3.0 ** -24.32 -2.1 * -28.96 -1.9 * -28.25 -1.6Capital (+) 3.03 4.4 ** 8.77 2.3 * 10.49 2.2 * 13.97 2.3 *

InstOwn (+) -3.35 -0.9 -40.21 -1.8 * -41.94 -1.4 -33.53 -0.9

Analysts (+) 0.42 2.0 * -1.10 -0.9 -0.94 -0.6 -0.34 -0.2

Owners (+) -3.06 -1.6 -23.32 -2.0 * -23.85 -1.6 -17.25 -0.9

EarnVol (+) -0.05 -0.1 -2.99 -1.1 -2.51 -0.7 -0.24 -0.1

Annual (N = 1,951)

Quarterly (N = 1,951)

Disclosure Quality Equation:

Variable definitions are in Table 1 where all variables are industry-year adjusted by subtracting the mean value for the corresponding industry-year. * (**) signifies significance at the 5% (1%) level (one-sided test). Score refers to one of the four disclosure quality scores, Total , Annual , Quarterly , and IR , depending on the specification. The expected signs of the coefficients are in parentheses after the variable name.

TABLE 4 - PANEL A

Three Stage Least Squares Coefficient Estimates and t-statistics for Tests of the Endogenous Association between Disclosure Quality and the Frequency of Private Information Events

α = γ 0 + γ 1 Score + γ 2 Size + γ 3 InstOwn + γ 4 Analysts + γ 5 Dispersion + γ 6 Leverage + γ 7 EarnVol + ψ

Score = β 0 + β 1 α + β 2 Size + β 3 Return + β 4 Surprise + β 5 Correlation + β 6 Capital + β 7 InstOwn + β 8 Analysts + β 9 Owners + β 10 EarnVol + ε

IR (N = 1,951)

Disclosure Quality Measure

Total (N = 2,432)

39

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-statLn( µ / ε ) Equation:Score (-) -0.01 -2.8 ** -0.02 -2.2 * -0.01 -3.0 ** -0.01 -4.8 **

Size (?) -0.22 -33.3 ** -0.21 -20.5 ** -0.23 -26.0 ** -0.20 -21.9 **

InstOwn (?) 0.10 2.8 ** 0.00 -0.1 0.00 0.0 -0.01 -0.3Analysts (?) -0.01 -8.4 ** -0.01 -6.3 ** -0.01 -5.4 ** -0.01 -10.2 **

Dispersion (?) -0.08 -9.1 ** -0.07 -5.6 ** -0.07 -6.2 ** -0.08 -7.5 **

Leverage (?) -0.14 -3.8 ** -0.15 -3.3 ** -0.12 -2.9 ** -0.06 -1.6EarnVol (?) -0.01 -1.4 -0.02 -1.5 -0.01 -1.3 -0.01 -0.9

Ln( µ/ε ) (+) 31.36 3.0 ** 37.70 2.6 ** 44.26 2.4 ** 63.95 2.7 **

Size (+) 5.14 3.0 ** 7.14 3.0 ** 5.68 1.9 * 11.29 3.0 **

Return (+) 2.12 1.5 -1.52 -0.9 -0.37 -0.2 0.24 0.1Surprise (-) 6.94 1.5 1.55 0.2 7.56 1.0 12.41 1.3Correlation (-) -1.79 -2.3 * -0.74 -0.9 -2.00 -1.8 * -3.68 -2.7 **

Capital (+) 2.38 4.6 ** 1.82 3.1 ** 1.78 2.4 ** 4.08 4.5 **

InstOwn (+) 4.70 2.2 * 9.13 2.6 ** 12.35 2.9 ** 14.54 2.7 **

Analysts (+) 0.75 5.8 ** 0.59 3.5 ** 0.81 3.9 ** 0.85 3.2 **

Owners (+) 2.35 2.2 * 2.75 2.1 * 4.14 2.6 ** 5.90 2.9 **

EarnVol (+) -0.02 0.0 -0.89 -2.0 * -0.70 -1.2 -0.17 -0.2

Annual (N = 1,951)

Quarterly (N = 1,951)

Disclosure Quality Equation:

Variable definitions are in Table 1 where all variables are industry-year adjusted by subtracting the mean value for the corresponding industry-year. * (**) signifies significance at the 5% (1%) level (one-sided test). Score refers to one of the four disclosure quality scores, Total , Annual , Quarterly , and IR , depending on the specification. The expected signs of the coefficients are in parentheses after the variable name.

TABLE 4 - PANEL B

Three Stage Least Squares Coefficient Estimates and t-statistics for Tests of the Endogenous Association between Disclosure Quality and the Relative Amount of Informed Trading

Ln( µ / ε ) = γ 0 + γ 1 Score + γ 2 Size + γ 3 InstOwn + γ 4 Analysts + γ 5 Dispersion + γ 6 Leverage + γ 7 EarnVol + ψ

Score = β 0 + β 1 Ln( µ/ε ) + β 2 Size + β 3 Return + β 4 Surprise + β 5 Correlation + β 6 Capital + β 7 InstOwn + β 8 Analysts + β 9 Owners + β 10 EarnVol + ε

IR (N = 1,951)

Disclosure Quality Measure

Total (N = 2,432)

40

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-statPIN Equation:Score (-) -0.14 -2.8 ** -0.24 -2.4 ** -0.11 -1.9 * -0.10 -3.2 **

Size (-) -2.19 -22.0 ** -2.03 -13.0 ** -2.36 -20.0 ** -2.09 -17.9 **

InstOwn (+) 1.53 2.5 ** 0.39 0.5 -0.01 0.0 0.17 0.3Analysts (-) -0.08 -4.0 ** -0.08 -3.6 ** -0.09 -3.8 ** -0.11 -7.1 **

Dispersion (+) -0.53 -4.5 ** -0.68 -3.8 ** -0.61 -4.2 ** -0.70 -5.2 **

Leverage (+) -1.85 -3.4 ** -1.91 -2.7 ** -1.33 -2.3 * -1.18 -2.3 *

EarnVol (+) -0.26 -2.0 * -0.38 -1.7 * -0.13 -0.9 -0.09 -0.7

PIN (+) 3.68 3.1 ** 3.01 2.5 ** 3.37 2.2 * 5.84 2.6 **

Size (+) 6.87 3.0 ** 6.51 2.8 ** 4.69 1.6 10.99 2.6 **

Return (+) 0.01 0.0 -3.53 -1.8 * 0.10 0.0 1.81 0.6Surprise (-) 9.43 2.1 * 5.26 0.8 11.72 1.5 10.30 1.0Correlation (-) -2.52 -2.3 * -2.23 -2.2 * -2.66 -2.1 * -3.48 -2.1 *

Capital (+) 2.38 3.8 ** 2.11 3.1 ** 1.49 1.8 * 4.34 3.9 **

InstOwn (+) 7.62 3.2 ** 8.68 2.9 ** 10.56 2.8 ** 14.76 2.7 **

Analysts (+) 0.63 5.2 ** 0.42 3.4 ** 0.65 4.2 ** 0.75 3.2 **

Owners (+) 2.53 2.8 ** 1.86 2.2 * 2.51 2.4 ** 4.35 2.8 **

EarnVol (+) -0.32 -0.6 -1.37 -2.8 ** -1.12 -1.8 * -0.66 -0.7

Annual (N = 1,951)

Quarterly (N = 1,951)

Disclosure Quality Equation:

Variable definitions are as in Table 1. IND is a vector of indicator variables for industries (41) and years (11). * (**) signifies significance at the 5% (1%) level (one-sided test). Score refers to one of the four disclosure quality scores, Total , Annual , Quarterly , and IR , depending on the specification. The expected signs of the coefficients are in parentheses after the variable name.

TABLE 5

Three Stage Least Squares Coefficient Estimates and t-statistics for Tests of the Endogenous Association between Disclosure Quality and Information Asymmetry without Industry Adjusting the Variables

PIN = γ 0 + γ 1 Score + γ 2 Size + γ 3 InstOwn + γ 4 Analysts + γ 5 Dispersion + γ 6 Leverage + γ 7 EarnVol + γ IND + ψ

Score = β 0 + β 1 PIN + β 2 Size + β 3 Return + β 4 Surprise + β 5 Correlation + β 6 Capital + β 7 InstOwn + β 8 Analysts + β 9 Owners + β 10 EarnVol + γ IND + ε

IR (N = 1,951)

Disclosure Quality Measure

Total (N = 2,432)

41