Investor Competition and the Pricing of Information Asymmetry

Investor Competition and the Pricing of

Information Asymmetry

Brian Akins [email protected]

Jeffrey Ng

[email protected]

Rodrigo Verdi [email protected]

Abstract

Whether the information environment affects the cost of capital is a fundamental question in accounting and finance research. In this paper, we study the role of competition among informed investors on the pricing of information asymmetry and its implications for the pricing of information quality. Relying on theories on competition among informed investors and on the pricing of information asymmetry, we hypothesize and find that the pricing of information asymmetry and the pricing information quality decrease with more competition among informed investors. Our results suggest that competition among informed investors has an important effect on how the information environment affects the cost of capital.

First draft: September 2008 Current version: November 2009

JEL classification: G12, G14 Key Words: Information Risk, Information quality, Asset Pricing.

Data Availability: All data is publicly available on WRDS. ___________________________________ All authors are at MIT Sloan School of Management, 50 Memorial Drive E52-325G, Cambridge, MA 02142. We appreciate comments from David Aboody, Paul Asquith, Judson Caskey, Gus DeFranco, Jack Hughes, S.P. Kothari, Christian Leuz, Joe Weber, Paul Zarowin (the editor), two anonymous referees, and seminar participants at the MIT Sloan School of Management (Accounting and Finance workshops) and at the University of California at Los Angeles. We would like to thank Brian Bushee for providing us access to his institutional investor database. We would also like to thank Jefferson Duarte and Lance Young for providing us estimates of the components of PIN.

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I. Introduction Whether information asymmetry among investors affects the cost of capital is an

important issue in the theoretical (e.g., Diamond and Verrecchia 1991; Easley and O’Hara 2004;

Hughes et al. 2007) and empirical literature (e.g., Brennan and Subrahmanyam 1996; Easley et

al. 2002; Duarte and Young 2009; Mohanram and Rajgopal 2009). In this paper, we study the

role of competition among informed investors on the pricing of information asymmetry. Our key

finding is that the pricing of information asymmetry decreases when there is more competition

among informed investors. This finding is important because it suggests that in the presence of

information asymmetry, more competition among informed investors can lower the cost of

capital. Further, as we describe below, it has implications for a large literature that investigates

the pricing of information quality (e.g., Botosan 1997; Leuz and Verrecchia 2000; Francis et al.

2004, 2005).

The intuition for our hypothesis that the pricing of information asymmetry decreases in

competition among informed investors is briefly described as follows (we describe related

theories and formally develop our hypothesis in Section 2). Theories on investor competition

show that more competition among informed investors results in private information being

incorporated into prices more rapidly, i.e., more competition makes prices more efficient (Foster

and Viswanathan 1993, 1996; Holden and Subrahmanyam 1992, 1994). We argue that

competition among informed investors can then affect the pricing of information asymmetry due

to two reasons: (i) it could reduce trading costs arising from price protection by market makers

against information asymmetry (Kyle 1985; Admati and Pfleiderer 1988; Diamond and

Verrecchia 1991), and (ii) it could reduce the information risk uninformed investors bear when

trading against informed investors (Easley and O’Hara 2004).

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To measure competition among informed investors, we follow prior literature and

consider institutional investors as informed investors (Arbel and Strebel 1983; Sias and Starks

1997; Bartov et al. 2000; Jiambalvo et al. 2002). Using data on total institutional investor

ownership for each firm, we construct measures of competition based on (i) the number of total

institutional investors, (ii) the percentage of outstanding shares held by total institutional

investors, and (iii) a Herfindahl index of competition that captures both the level and the

distribution of total institutional ownership. Further, recognizing that transient institutional

investors (compared to quasi-indexers and dedicated institutional investors) are the ones most

likely to trade on information (Bushee 1998; Ke and Petroni 2004; Ke and Ramalingegowda

2005), we also construct analogous measures using data on transient institutional investor

ownership.

To measure information asymmetry, we use the information asymmetry component of

bid-ask spreads and the probability of informed trading (PIN) based on decomposition models

developed by Glosten and Harris (1988) and Duarte and Young (2009), respectively. We include

the non-information asymmetry components of spreads and PIN as control variables in our

empirical analyses to increase the confidence that our findings are driven by information

asymmetry. In untabulated analyses, we find that our inferences are unchanged if we use total

spreads and PIN.

To examine the role of competition among informed investors in the pricing of

information asymmetry, we use standard asset pricing regressions. We find significant evidence

that the pricing of information asymmetry decreases in the extent of competition among

informed investors. For instance, the difference in the pricing of the information asymmetry

component of spread between the most competitive quintile of firms and the least competitive

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quintile ranges from 0.70% to 1.23% per month, depending on the competition measure used.

The results with the information asymmetry component of PIN are in the same direction,

although the economic significance is smaller. Specifically, the differential pricing of

information asymmetry across competition among informed investors ranges from 0.22% to

0.25% per month.

In further analyses, we find that the evidence for the differential pricing of information

asymmetry conditional on competition is the strongest for competition among transient investors

and the weakest (and often insignificant) for competition among dedicated investors. This

evidence is consistent with our conjecture that competition among investors who trade actively

on information (compared to competition among investors who do not trade actively on

information) is likely to have a greater effect in mitigating the pricing of information asymmetry.

To address concerns that our measures of competition might be capturing broader aspects of the

trading environment that are not related to information asymmetry, we also run tests that control

for the cross-sectional variation in the broader trading environment. The results from these tests

indicate that the differential pricing of information asymmetry conditional on competition is

robust to controlling for share turnover, trading volume, and return volatility.

Finally, we examine whether investor competition influences the pricing of information

quality. This literature argues that information quality is priced because poor information quality

is associated with higher information asymmetry and information asymmetry is priced (e.g.,

Botosan 1997; Francis et al. 2004, 2005). To measure information quality, we use accruals

quality and earnings smoothness because these measures have been recently used to examine the

pricing of information quality (Francis et al., 2004, 2005; Core et al. 2008; McInnis 2009). We

also use annual report readability (FOG) developed by Li (2008) as another measure of

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information quality. We find consistent evidence that the pricing of information quality

decreases when there is more competition among informed investors. This result adds support to

the argument that one reason that information quality is priced is because of information

asymmetry

This study contributes to the literature in a number of ways. First, it draws upon the

theoretical literature to make predictions about the effect of competition among informed

investors on the pricing of information asymmetry. We show that the pricing of information

asymmetry decreases with competition among informed investors, and that the effect is

economically important. While the idea of competition among informed investors has been

discussed in the theoretical literature, to the best of our knowledge, no prior empirical paper has

investigated the outcomes of such competition.

Second, we extend the prior literature that has empirically investigated the pricing of

information asymmetry and information quality. Recent studies provide mixed evidence on

whether information asymmetry/quality is priced (e.g., Easley et al. 2002; Francis et al. 2005;

Core et al. 2008; Duarte and Young 2009; Mohanram and Rajgopal 2009). We show that the

extent of the competition among informed investors has an important role in determining

whether information asymmetry is priced. Stated differently, information asymmetry/quality is

more likely to be priced in trading environments with less competition among informed

investors. Finally, our consistent findings with proxies for information asymmetry and

information quality reinforce the idea that information asymmetry is one mechanism linking

information quality to the cost of capital. By doing so we address one claim in Leuz and

Wysocki (2008, p. 30) that “At present, however, the literature has primarily focused on

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establishing the link between disclosure and the cost of capital and has provided relatively little

evidence on the mechanism.”

Our paper is related to a concurrent working paper by Armstrong et al. (2009). While

both papers study the effect of competition on the pricing of information asymmetry, there are

some important differences. At the conceptual level, Armstrong et al. study the role of

competition among all investors. In contrast, as our paper relies on theories on competition

among informed investors, we study the role of competition among informed investors in

mitigating the pricing of information asymmetry. Consequently, these papers also differ at the

empirical level by using a non-overlapping set of proxies for competition. Another unique

feature of our paper is that we compare the relation between the pricing of information

asymmetry and competition among different types of informed investors. An interesting result is

that more competition among investors who actively trade base on information has a greater

effect in mitigating the pricing of information asymmetry.

The remainder of the paper is organized as follows. Section II develops our hypothesis.

Section III describes our research design. Sections IV and V present our results on the pricing of

information asymmetry and information quality, respectively. Section VI concludes.

II. Hypothesis development

In this section we develop our hypothesis of the role of competition among privately

informed investors on cost of capital. A seminal theoretical result obtained by Kyle (1985) is that

private information is incorporated into price over time due to the informed trades in the case of

an information monopolist. Several theory papers extend Kyle by introducing more than one

informed investor so that there is competition among informed investors. A key finding is that

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that competition among informed investors typically results in private information being

incorporated into prices more rapidly and, thereby, increases price informativeness. This finding

has been proven in the case of multiple risk-neutral homogeneously informed traders (Admati

and Pfleiderer 1988; Holden and Subrahmanyam 1992; Foster and Viswanathan 1993), of risk-

averse homogeneously informed traders (Holden and Subrahmanyam 1994), of heterogeneously

informed traders (Admati and Pfleiderer 1988; Foster and Viswanathan 1996), among others. We

build on this literature to hypothesize that the pricing of information asymmetry will decrease

with competition among informed investors.

To set the stage for linking competition with the pricing of information asymmetry, first

consider a Kyle (1985) model in which there are a single asset, a single risk neutral privately

informed trader, competitive market makers, and noise traders trading for liquidity reasons. The

asset would be priced in a manner that imposes trading costs, implying an effect on expected

return, i.e., the cost of capital. In this type of model, the revelation of information into the public

domain affects the cost of capital through a reduction in information asymmetry (Diamond and

Verrecchia 1991). Most importantly, an increase in the number of informed traders results in

competition among those traders that would reduce trading costs and thus lower the cost of

capital (Admati and Pfleiderer 1988). Thus, the implication from this theory is that the

information asymmetry component of trading costs would decrease with competition among

informed investors.

Next, consider a competitive market composed of privately informed investors,

uninformed investors, and a finite number of assets as in Easley and O’Hara (2004). In this

model, informed investors use their information advantage to trade with uninformed investors

and hold portfolios more heavily weighted to stocks with positive private information and

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weighted against stocks with negative private information. The information asymmetry increases

the risk to the uninformed investors, who cannot adjust their portfolios to account for private

information. In equilibrium, information asymmetry is priced to reflect the risk to the

uninformed investors. Once again, to the extent that more competition among informed investors

helps to make prices more informative and mitigate the information disadvantage of the

uninformed investors (Holden and Subrahmanyam 1992, 1993; Foster and Viswanathan 1993,

1996), the pricing of information asymmetry is expected to decrease with more competition.

In light of the above theories, our conjecture is that, ceteris paribus, more competition

among informed investors reduces the impact of information asymmetries on the cost of capital

due to (potentially) two reasons: (i) it could reduce trading costs arising from price protection by

market makers due to information asymmetry (Kyle 1985; Admati and Pfleiderer 1988; Diamond

and Verrecchia 1991), and (ii) it could reduce the information risk uninformed investors bear

when trading against informed investors (Easley and O’Hara 2004).

Hence, our hypothesis, stated in alternative form, is:

H1: The pricing of information asymmetry decreases in competition among informed investors.

III. Research Design

To test our hypothesis, we use cross-sectional asset pricing regressions similar to those

employed in prior research to investigate the effect of information asymmetry on the cross-

section of returns (e.g., Easley et al. 2002; Duarte and Young 2009). In particular, the typical

cross-sectional regression specification used to test for the pricing of information asymmetry is

as follows:

Ri,t+1 = α + Σβj Controlj,t + λ Information Asymmetryi,t + εi,t +1 (1)

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where Rt+1 is monthly excess return during the 12-month period in year t+1 (in the event of a

delisting, a firm’s delisting return, when available from CRSP, is used as the monthly return),

Control is a set of control variables that are expected to be associated with expected returns and

Information Asymmetry is a proxy for information asymmetry.

To examine whether the pricing of information asymmetry is conditional on the extent of

competition among informed investors, we modify Eq. (1) as follows:

Ri,t+1 = α + Σβj Controlj,t + λ1 Competitioni,t + λ2 Information Asymmetryi,t

+ λ3 Competitioni,t x Information Asymmetryi,t + εi,t+1 (2)

where Competition is a proxy for competition among informed investors. To reduce the effect of

outliers and to ease exposition, we rank Competition into quintiles using the distribution of its

value within the year and then scale the quintile rank so that it ranges from zero to one.

In the above regression, λ2 indicates the pricing of information asymmetry of firms in the

least competitive quintile and λ3 indicates the incremental pricing of information asymmetry as

one moves from the bottom to the top quintile of competition. Based on our hypothesis that the

pricing of information asymmetry is decreasing in the competition among informed investors, we

expect λ3 to be negative. To mitigate cross-sectional dependence in the regression residuals, we

follow the prior literature and estimate Fama-MacBeth (1973) regressions (e.g., Fama and

French 1992; Easley et al. 2002). Specifically, we first run cross-sectional regressions for each

month in the sample. Each reported coefficient is the average of the monthly coefficients. The t-

statistic for each reported coefficient is obtained by dividing the coefficient by the standard error

of the monthly coefficients.

Measures of competition among informed investors

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In order to test our hypothesis, we need a measure of informed competition. While the

prior theoretical literature has examined issues related to informed competition, we are unaware

of prior attempts in the empirical literature to measure informed competition. Before we describe

our measures, we note that our measures rely on the common assumption in the literature that

institutional investors, as opposed to individual retail investors, are more likely to be the class of

informed investors (e.g., Arbel and Strebel 1983; Sias and Starks 1997; Bartov et al. 2000;

Jiambalvo et al. 2002). For example, Sias and Starks (1997) provide evidence that the returns on

portfolios dominated by institutional investors lead the returns on portfolios dominated by

individual investors, consistent with the hypothesis that institutional trading increases the speed

with which prices reflect market-wide information.

Our first measure of informed competition is the number of institutional investors holding

the firm’s stock (#Inst). This measure is motivated by the theories that examine the effect of

competition among informed investors (e.g., Admati and Pfleiderer 1988; Holden and

Subrahmanyam 1992; Foster and Viswanathan 1993). These theories use the number of informed

investors to represent the extent of the informed competition, where a greater number of

informed investors indicates more informed competition. The second measure of informed

competition is the proportion of institutional stock ownership, i.e., percentage of outstanding

shares held by institutional investors (%Inst). More informed competition might be expected

when there is a higher percentage of institutional investor ownership in a stock because a higher

percentage of institutional investor ownership indicates a higher level of sophistication in the

investor base (Bartov et al. 2000; Jiambalvo et al. 2002).1

1 The use of the number of informed investors (as opposed to the fraction of investors) in theory models is for mathematical tractability. A similar prediction, however, can be developed with the fraction of informed investors. For instance, Edmans and Manso (2009) show that the percentage of informed ownership is negatively associated

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Our third measure of competition among informed investors captures both the level and

the distribution of total institutional ownership into a Herfindahl index. This index has been used

to measure competition in a variety of settings such as product market competition within the

industry (e.g., Comment and Jarrell 1995; Gande et al. 1999). In the context of competition in the

trading environment of a stock, we propose a measure of the amount of competition in stock i,

HerfInst, as follows:

2,

1

1N

i ji

j i

InvestorHerfInst x

Investors=

⎛ ⎞= − ⎜ ⎟

⎝ ⎠∑

(3)

where Investori,j is the number of shares held by institutional investor j in stock i, Investorsi is the

total shares held by all institutional investors of stock i, and N is the total number of institutional

investors in stock i. Following the standard interpretation of the Herfindahl index, highly

concentrated holdings indicates less competition in the trading environment. Thus, to ease

exposition, we multiply the Herfindahl index by minus one so that a higher value of HerfInst

signifies that there is more competition in the trades of stock i among institutional investors. By

construction, HerfInst could range from -1 to 0, with a number closer to 0 indicating more

competition. When there are no institutional investors for a firm, we assume that there is no

competition among informed investors and assign a value of -1 to the Herfindahl index.

As discussed above, we use institutional investors as a proxy for informed competition.

Prior research, however, has shown that certain types of institutional investors are more likely to

trade on information (e.g., Grinblatt and Keloharju, 2000; Ke and Petroni, 2004; Ke and

Ramalingegowda, 2005). For example, a commonly used institutional investor classification is

the division of institutional investors into transient investors, dedicated investors, and quasi-

with the Kyle’s lambda if one allows the percentage holdings to be correlated with the precision of the information impounded in prices when informed investors trade.

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indexers that was developed by Bushee (1998). Ke and Petroni (2004) and Ke and

Ramalingegowda (2005) find that transient institutional investors (i.e., institutional investors

who hold small stakes in numerous firms and trade frequently in and out of stocks) trade on

information to make profitable trades. Ke and Petroni (2004) find that transient institutional

investors have information that allows them to predict a break in a string of consecutive quarterly

earnings increases and thereby avoid the economically significant negative stock price response

associated with the break announcement. Ke and Ramalingegowda (2005) provide evidence that

transient institutional investors trade on knowledge about the post-earnings announcement drift.

Hence, in the same vein as our earlier measures based on total institutional investor ownership,

we measure the number of transient institutional investors, #Tran, and percentage of transient

institutional investor ownership, %Tran, and the Herfindahl index for competition among

transient investor, HerfTran.

We construct the above measures of informed competition by using the institutional

investor database provided the author of Bushee (1998), who, in turn, creates his variables using

data on shares held by institutional investors from the CDA/Spectrum Institutional (13f)

Holdings (s34) database available from Thomson Reuters. Specifically, his dataset provides

information on the institutional investors of each firm, as well as the classification of the investor

as transient, dedicated, or quasi-indexing.

Measures of information asymmetry

The extensive empirical literature has used various measures of information asymmetry,

with the bid-ask spread and probability of informed trading (PIN) being the more common

measures (e.g., Copeland and Galai 1983; Glosten and Milgrom 1985; Lee et al. 1993; Easley et

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al. 2002). One issue with these measures is that they also capture other components, specifically

order processing and inventory holding costs for spread and liquidity for PIN, in addition to

information asymmetry. We thus follow prior literature and use the information asymmetry

component of spread and PIN to better capture our construct of interest – information asymmetry

(e.g., Glosten and Harris 1988; Duarte and Young 2009).

We measure the information asymmetry component of the bid-ask spread for NYSE,

AMEX, and NASDAQ firms for the period from 1983 to 2004 using the model of price

formation developed in Glosten and Harris (1988). The empirical implementation of this model

is often used in the literature to estimate the information asymmetry component of the bid-ask

spread (e.g., Brennan and Subrahmanyam 1995, 1996; Verrecchia and Weber 2008).

Specifically, the bid-ask spread is decomposed into an information asymmetry component

(IASpread) and a non-information asymmetry component (NIASpread). We use IASpread as our

first proxy for information asymmetry. In addition, in all tests utilizing this measure we also

include the NIASpread as a control variable to ensure that our results are not driven by the non-

information asymmetry component of the bid-ask spread. More details of the computation of

these measures are presented in Appendix A.

Our second measure of information asymmetry is the information asymmetry component

of PIN developed by Duarte and Young (2009). Duarte and Young (2009) decompose PIN into

an information asymmetry component, adjusted PIN (AdjPIN), and a non-information

asymmetry component, the probability of symmetric order flow shocks (PSOS). They motivate

this decomposition by noting that the sequential trade model on which PIN is based attributes

abnormal trading to private information, but this trading could also result from liquidity shocks.

To address this concern, Duarte and Young (2009) extend the model to account for non-

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information-based liquidity shocks. We use AdjPIN as our second proxy for information

asymmetry. In addition, as with spreads, when using AdjPIN we control from the non-

information asymmetry component of PIN, the probability of symmetric order-flow shocks

(PSOS). These measures, available for NYSE and AMEX firms from 1983 to 2004, were

provided to us by Duarte and Young. We refer interested readers to Duarte and Young (2009) for

more information underlying the construction of AdjPIN and PSOS.

Other control variables

When discussing our key measures, we have indicated that we add non-information-

asymmetry component of spread and PIN as control variables. In addition, we follow asset

pricing literature and include beta (Beta), market capitalization (Size), and the book-to-market

ratio of equity (BTM) as additional control variables (e.g., Fama and French 1992; Easley et al.

2002). To calculate Beta, we follow Easley et al. (2002) and begin by estimating rolling five-year

Dimson (1979) betas for each firm. For estimation, we require a minimum of 24 monthly return

observations. We then regress these returns on lagged and contemporaneous market returns to

correct for potential bias resulting from non-synchronous trading. Firms are then sorted by these

pre-ranking betas into 40 portfolios at the beginning of each year and rolling five-year Dimson

betas are calculated for each portfolio. Each firm is assigned the beta from its portfolio for that

year. Size is the natural log of the market value of equity measured at the end of the calendar

year. To measure book-to-market, BTM, we obtain the book value of equity for the fiscal year

ending at least three months prior to the calendar end (this ensures that book value is publicly

available at the end of the calendar year). We then divide the book value of equity by the market

value of equity at the end of the calendar year and take the natural logarithm of this ratio.

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IV. Pricing of information asymmetry

Sample description

Table 1 presents, for each calendar year from 1983 to 2004, the number of firms for

which we are able to compute our measures of information asymmetry. The sample consists of

59,723 (36,071) firm-year observations with available data to compute IASpread (AdjPIN). The

sample with IASpread is larger because spread data is available to compute IASpread for

NASDAQ firms starting from 1987, while the AdjPIN file is comprised of firms from NYSE and

AMEX.

[Insert Table 1 here]

Summary statistics and the correlations for the variables used in our study are provided in

Table 2. Panel A presents the descriptive statistics for our sample. First, we present statistics for

our measures of the information asymmetry and non-information asymmetry components of

spread and PIN. For the spread-based measures, the sample consists of NYSE, AMEX, and

NASDAQ firms for the period from 1983 to 2004 and there are a total of 59,723 firm-year

observations. For the PIN-based measures, the sample consists of NYSE and AMEX, and firms

for the period from 1983 to 2004 and there are a total of 36,071 firm-year observations. The

average information asymmetry component of spread is 0.44%, whereas the average fixed

component is 1.75%. The average probability of information-based trades is 0.18, whereas the

average probability of trades originating because of symmetric order flow shocks is 0.29.

Moving on to our investor competition measures, we find that on average, there are 77.61

(22.93) institutional investors (transient institutional investors) per firm and these investors hold

35% (10%) of the outstanding shares of the firms. The mean investor competition index for

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institutional investors (transient institutional investors) is -0.19 (-0.44). The distributions for

some of our competition proxies are highly skewed. For example, the number of institutional

investors per firm has a median of 32.25, compared to a mean of 77.61. Given that we convert

the continuous competition measures into ranked measures in our regressions, skewness is

unlikely to be a significant concern. The average market beta is 1.26, indicating that the

systematic market risk of the sample is slightly higher than the market. The average market

capitalization and book-to-market of equity are $19.12 billion and -0.71, respectively.

Panel B presents Pearson correlations above the main diagonal. All the correlations are

significant at the 1% level. IASpread has a positive and significant correlation of 0.32 with

AdjPIN. We also find that the non-information asymmetry components of spread and PIN,

NIASpread and PSOS, have a positive and significant correlation of 0.31. Next, we examine the

properties of our competition measures, which are all positively and significantly correlated with

each other. For example, #Inst has a correlation of 0.51, 0.41, 0.86, 0.33, and 0.55 with %Inst,

HerfInst, #Trans, %Trans, and HerfTrans, respectively.


Cross-sectional asset pricing tests

The next two tables present the regression results of the cross-sectional asset pricing tests

of information asymmetry. Table 3 (Table 4) uses IASpread (AdjPIN) as the measure of

information asymmetry. For comparability across different measures, when we make inferences

about the economic significance from the coefficients of interest, we assume a one standard

deviation difference in the information asymmetry (standard deviations are obtained from Table

2) and examine how this difference translates into expected returns.

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In the first column of Table 3, we examine whether information asymmetry, as measured

by IASpread, is priced on average. The results indicate that investors expect higher returns when

the information asymmetry component of spread is higher. Specifically, the statistically

significant coefficient on IASpread of 0.25 indicates that a one standard deviation difference in

the information asymmetry component of spread between two stocks translates into a difference

in required return of 0.34% (=1.35 x 0.25) per month (or 4.06% per year).

In the remaining columns, we examine whether the pricing of information asymmetry

decreases in the extent of competition among informed investors. As noted earlier, the

coefficient of interest is λ3, the coefficient on the interaction term between IASpread and a proxy

for competition among informed investors. This coefficient is statistically significant in all the

columns, although only marginally so when using HerfInst as a competition proxy. Specifically,

the coefficients range from -0.52 to -0.91, depending on the measure of competition. This

indicates that the expected return for a one standard deviation difference in information

asymmetry in the most competitive quintile ranges from -0.70% (1.35 x -0.52) to -1.23% (1.35 x

-0.91) per month less than that in the least competitive quintile. This result is consistent with our

hypothesis that the pricing of information asymmetry decreases in the extent of the competition

among informed investors.

We note that the pricing of the non-information-asymmetry component of spread,

NIASpread, also decreases when there is more competition among informed investors.

Conceptually, this component captures the fixed costs per share, specifically inventory holding

cost and order processing cost per share, of market-making. A possible explanation for lower

fixed costs per share is that the trading activities of institutional investors, which is presumably

17

higher when there is more competition among them, helps to lower spread the fixed costs over

more traded shares and thus, lowers fixed costs per share.


In Table 4, we repeat the analyses with AdjPIN as the measure of information asymmetry.

In the first column, the results indicate that AdjPIN is not priced whereas the non-information

asymmetry component of PIN, PSOS is priced. These results are consistent with those in Duarte

and Young (2009). The remaining columns present the regression results conditional on the

extent of competition among informed investors. All of the coefficients on the interaction term

between AdjPIN and Competition are negative, although the results are only statistically

significant when percentage ownership of institutional and transient institutional investors (%Inst

and %Trans) are used to proxy for competition among informed investors. Hence, the evidence

that the pricing of information asymmetry (as proxied by AdjPIN) decreases when there is more

competition among informed investors is not as strong as the results with IASpread. We note that

there is almost no evidence that the pricing of PSOS, which is the liquidity component of PIN,

varies with competition.

In terms of economic significance, the -2.73 coefficient on the interaction term between

AdjPIN and %Inst indicates a monthly differential in the required rate of return of 0.25% per

month (2.95% per year) between stocks in the least and most competitive quintiles for a one

standard deviation difference in information asymmetry. The -2.39 coefficient on the interaction

term between AdjPIN and %Trans indicates a monthly differential in the required rate of return

of 0.22% per month (2.58% per year) between stocks in the least and most competitive quintiles

for a one standard deviation difference in information asymmetry.


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Overall, the results from the cross-sectional asset pricing results in Tables 3 and 4 are

consistent with our hypothesis that the pricing of information asymmetry decreases with

competition among informed investors.

Analysis across Different Types of Institutional Investors

As discussed in Section 3, we consider transient institutional investors as type of

institution investors more likely to actively trade on information and construct measures of

competition among informed investors based on their stock ownership. In this section we discuss

the results for measures of competition constructed based on other types of institutional

investors.

Bushee (1998) uses the ownership concentration and trading activity to classify

institutional investors into three categories following Porter (1992): transient institutional

investors, dedicated institutional investors, quasi-indexers. As noted early, transient institutional

investors have small holdings in many firms and trade frequently. Dedicated institutional

investors have larger, more concentrated holdings. Finally, quasi-indexers have characteristics

that place them in the middle of the two other categories. They generally have less concentrated

holdings like transient investors, but also have longer holding periods, like the dedicated

investors. Because they frequently trade in and out of a variety of stocks, transient institutional

investors to be those who most active in making information-based trades (Ke and Petroni 2004;

Ke and Ramalingegowda 2005). In contrast, dedicated investors are long-term investors who are

less likely to actively trade on information. The activeness of quasi-indexers in making

information-based trades is likely to be somewhere between those of transient institutional

investors and dedicated institutional investors. Thus, we conjecture that competition among

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quasi-indexers and dedicated institutional investors will have a smaller effect in decreasing the

pricing of information asymmetry, compared to the competition among transient institutional

investors.

To test this conjecture, we reconstruct our measures of competition - number of informed

investors, proportion of informed investors, and Herfindahl index of competition - based on

quasi-indexers institutional investor ownership and dedicated institutional investor ownership.

Specifically, #Ded (#QInd) is the number of shares held by dedicated (quasi-indexing)

institutional investors. %Ded (%QInd) is the percentage of shares outstanding held by dedicated

(quasi-indexing) institutional investors. HerfDed (HerfQInd) is the Herfindahl measure for

dedicated (quasi-indexing) institutional holdings multiplied by -1 so that it is increasing in

market competition. Just like before, the measures are ranked into quintiles using the distribution

of its value within the year and then scale the quintile rank so that it ranges from zero to one.

Table 5 presents the results using these measures. The regression specification in Panel A

(Panel B) is similar to that in Table 3 (Table 4), except that different measures of competition are

used. For brevity, we present only the coefficients on information asymmetry, as well as those on

the interaction terms between competition and information asymmetry.

Similar to Table 3, Panel A uses IASpread as the measure of information asymmetry. The

first three columns of Panel A focuses on the role of competition among dedicated investors in

the pricing of information asymmetry. We observe that there is generally no significant evidence

that the pricing of information asymmetry decreases with competition among dedicated

institutional investors. In contrast, the next three columns, which focus on quasi-indexers,

provide significant evidence that the pricing of information asymmetry decreases with

competition among quasi-indexers, though the results are marginally significant with HerfQInd.

20

Similar to Table 4, Panel B uses AdjPIN as the measure of information asymmetry. Once

again, the first three columns of Panel B indicate that there is no significant evidence that the

pricing of information asymmetry decreases with competition among dedicated institutional

investors. Next, we examine how the pricing of information asymmetry decreases with

competition among quasi-indexers. The results with %QInd indicate that the pricing of

information asymmetry decreases with competition, but not the results with #QInd or HerfQInd.

Overall, the results in Table 5 support our conjecture that the nature of the competition

among investors is important in the effect of competition on the pricing of information

asymmetry. Specifically, competition among investors who are more likely to actively make

information-based trades has a greater effect in mitigating the pricing of information asymmetry.


Controlling for the Broader Trading Environment

As noted earlier, our use of institutional ownership characteristics to develop measures of

informed competition is guided by prior theoretical and empirical literature. However, there is

the concern that our results could be simply capturing cross-sectional variation in the broader

trading environment that is unrelated to competition among informed investors in the trading

environment. The challenge with controlling for the trading environment is that some of the

aspects of the trading environment are likely to be outcomes of the competition among informed

investors. For example, higher stock turnover might result from more competition, and including

these trading characteristics as control variables might not be “over-control” for the effect of

competition. Nevertheless, we examine if our results are robust to attempts to control for the

cross-sectional variation in the general trading environment.

21

To control for cross-sectional variation in the broader trading environment, we use share

turnover (Turnover). The literature has considered share turnover to be a measure of stock

liquidity (Datar et al. 1998), disagreement among investors (D’Avolio 2002), and investor

sentiment (Baker and Wurgler 2006). Specifically, in all our regressions we include share

turnover and its interactions with measures of information asymmetry and non-information

asymmetry. In untabulated analyses we find that our results are robust to the use of alternative

proxies for the broader trading environment such as total trading volume and idiosyncratic

volatility.

Table 6 shows our results after including Turnover and its interactions with measures of

information asymmetry and non-information asymmetry as additional control variables into

Equation (2). For the sake of brevity, we report only the coefficients on the measures of

information asymmetry, share turnover, competition, as well as the interaction terms between

share turnover and information asymmetry and between competition and information

asymmetry. Panel A, which repeats the analyses in Table 3 with the additional control variables,

uses IASpread to measure information asymmetry. The inclusion of Turnover does not

significantly affect our earlier results. Specifically, the pricing of IASpread decreases in the

cross-section with more competition among informed investors, after controlling for the cross-

sectional variation in share turnover. Interestingly, the pricing of information asymmetry does

not vary cross-sectionally with share turnover. This suggests that competition among informed

investors, and not the broader trading environment, drives the cross-sectional variation in the

pricing of information asymmetry.

The results in Panel B, which uses AdjPIN as the measure for information asymmetry,

show that our earlier results in Table 4 are robust to controlling for the cross-sectional variation

22

in the broader trading environment using share turnover. Similar to the results in Table 4, we find

that when competition among informed investors is measured using %Inst and %Trans, the

pricing of AdjPIN decreases in the cross-section with more competition. In fact, the statistical

significance of the interaction terms between %Inst and AdjPIN and between %Trans and

AdjPIN are now at a 1% level, whereas they are significant at a 5% level in Table 4. Moreover,

the coefficient on the interaction term between AdjPIN and competition is now marginally

significant at a 10% level when HerfInst is used to measure competition. Similar to Panel A, we

find that the pricing of information asymmetry does not vary cross-sectionally with share

turnover.


V. Informed competition, information asymmetry, and information quality

Our hypothesis predicts that the pricing of information asymmetry decreases with

competition among informed investors. We test this hypothesis using the information asymmetry

component of spread and PIN. Our goal is to provide evidence with empirical proxies that best

approximate the economic construct information asymmetry.

In this section, however, we examine the implications of our earlier results for a

fundamental issue in the accounting literature that has attracted extensive theoretical and

empirical research: the pricing of information quality. The general prediction in this literature is

that cost of capital is higher when information quality is poorer (e.g., Botosan 1997; Francis et al.

2004, 2005). To the extent that poorer information quality captures higher information

asymmetry, as argued in this literature, the pricing of information quality could also decrease

with more informed competition if the pricing of information asymmetry decreases with more

23

informed competition. Hence, in this section, we investigate whether the pricing of information

quality decreases with competition, under the assumption that information quality proxies for

information asymmetry.

To proxy for information quality (IQ), we use accruals quality (AQ) and earnings

smoothness (Smoothness). These measures have been recently used in the literature with mixed

findings on their association with the cost of capital (Francis et al., 2004, 2005; Core et al. 2008;

McInnis 2009). In addition, we also use a measure of annual report readability (FOG) developed

by Li (2008) as another proxy for information quality. The intuition behind this measure is that,

everything else equal, more syllables per word or more words per sentence make a document

harder to read.

We follow Francis et al. (2005) and estimate AQ as the standard deviation of the firm-

level residuals from the Dechow and Dichev (2002) model as modified by McNichols (2002)

during the years t-5 to t-1. The model is a cross-sectional regression of working capital accruals

on lagged, current, and future cash flows, plus the change in revenue and PPE. All variables are

scaled by average total assets.2 Following Francis et al. (2004), Smoothness is the ratio of firm’s

standard deviation of net income before extraordinary items to its standard deviation of cash

flows from operations (as with AQ all variables are scaled by average total assets). FOG is the

readability index developed by Li (2008). This measure is developed using computational

linguistics based on syntactical textual features (such as words per sentence and syllables per

word) in the 10-K filing. Following this literature and for consistency with our measures of

information asymmetry, we code all information quality proxies such that higher values mean

lower information quality and correspondingly, higher information asymmetry.

2 Wysocki (2008) proposes a modified version of accruals quality to address some potential construct validity issues with AQ. In untabulated analyses we have re-estimated the regressions in Table 7 using Wysopcki’s accrual quality measure and have found that the pricing of this measure decreases with all our measures of competition.

24

Table 7 presents the results of the cross-sectional asset pricing tests examining whether

the pricing of information quality varies cross-sectionally with competition. The regression

specification follows Eq. (2), except that we now replace the information asymmetry measures

with measures of information quality. Unlike the earlier regressions, we do not include a non-

information asymmetry component in the regression because unlike spread and PIN, prior

literature does not provide guidance on how to decompose information quality measures into

information asymmetry and non-information-asymmetry components. For brevity, we only

tabulate the results with measures of competition based on transient institutional investor

ownership. The results are essentially the same with measures of competition based on total

institutional investor ownership.

The first three columns documents how the pricing of AQ varies with competition among

informed investors. The next three columns and the final three columns report the results with

Smoothness and FOG, respectively. Similar to our earlier regressions, the coefficient on the

interaction term of IQ and Competition is the coefficient of interest in each of the columns. A

statistically significant negative coefficient on this interaction term indicates that more

competition is associated with less pricing of information quality.

To the extent that poorer information quality proxies for higher information asymmetry,

the results support our hypothesis that the pricing of information asymmetry decreases in

competition among informed investors. From the first three columns, we observe that the

coefficient on the interaction term between AQ and #Trans (%Trans, HerfTrans) is -13.87 (-7.56,

-13.59). This indicates that for a one standard deviation (which equals 0.03) difference in AQ, the

monthly difference in the expected return in the most competitive quintile is 0.42% (0.23%,

25

0.41%) less than in the least competitive quintile.3 In the next three columns, the coefficient on

the interaction term between Smoothness and #Trans (%Trans, HerfTrans) is -0.34 (-0.28, -0.45).

This indicates that for a one standard deviation (which equals 0.52) difference in Smoothness, the

monthly difference in the expected return in the most competitive quintile is 0.18% (0.15%,

0.23%) less than in the least competitive quintile. Finally, in the last three columns, the

coefficient on the interaction term between FOG and #Trans (%Trans, HerfTrans) is -0.17 (-

0.21, -0.16). This indicates that for a one standard deviation (which equals 1.41) difference in

FOG, the monthly difference in the expected return in the most competitive quintile is 0.24%

(0.30%, 0.23%) less than in the least competitive quintile. Overall, the results in Table 7 are

present support for the joint hypothesis that the pricing of information quality decreases with

competition and that information quality proxies for information asymmetry.


VI. Conclusion

The issue of whether information asymmetry is priced has been of significant academic

interest. In this paper, we re-examine this question by emphasizing an important aspect of capital

markets with information asymmetry - competition among informed investors. While prior

empirical literature has investigated whether information asymmetry is priced on average, it has

not studied whether there is cross-sectional variation in the pricing conditional on the extent of

the competition among informed investors. Relying on theories that highlight that more

competition among informed investors leads to more informative prices, our study examines the

3 To estimate the economic significance of the interaction term, we multiply the coefficient on the interaction term by the standard deviation of the respective IQ variable. For example, the economic significance of the interaction term on AQ and #Trans equals 0.42% (=0.03 x -13.87).

26

variation in the pricing of information asymmetry conditional on the extent of competition

among informed investors.

In our study, we use the information asymmetry components of bid-ask spread and PIN

as proxies for information asymmetry. To measure the degree of competition among informed

investors, we use a variety of proxies based on institutional investor ownership, with the

underlying assumption that institutional investors are more likely to be the informed investors.

While the theories on competition among informed investors typically depict competition in

terms of the number of informed investors, we also develop alternative proxies that take into

account the size of the ownership by informed investors and the distribution of shares among

them.

Consistent with our hypothesis, we show that the pricing of information asymmetry

decreases with competition among informed investors, and that the effect is economically

important. We then explore its implications on the accounting literature that examines whether

information quality, as a proxy for information asymmetry, is priced. We repeat our analyses by

replacing our measures of information asymmetry proxies with measures of information quality,

and we find similar results. That is, our results indicate that the pricing of information quality

also decreases in competition among informed investors.

Our results suggest that future research investigating the effects of the information

environment should consider the level of competition among informed investors in the trading

environment. A direct implication of our findings is that in face of information asymmetry, firms

could potentially reduce their cost of capital by encouraging more competition among informed

investors through higher institutional ownership and/or more even distribution of shares among

institutional investors. An indirect implication of our findings is that efforts to mitigate

27

information asymmetry such as increased corporate disclosure and transparent financial reporting

might have greater cost of capital effects in markets (either within a single country or across

different countries) characterized by less competition among informed investors.

28

Appendix A. Decomposition of spread

The following regression specification to obtain the parameters required to decompose

spread:

i,s 0 i,s 1 i,s i,s 0 i,s 1 i,s i,sΔPrice =C ΔTrade +C ΔTrade TradeSize +Z Trade +Z Trade TrdSize +ε (A1)

where for the trade at time s for firm i, ΔPrice is the change in trade price scaled by the previous

trade price, TradeSize is the number of shares traded, and Trade is an indicator that is equal to +1

if the trade is classified as buyer-initiated and -1 if the trade is seller-initiated.

A brief description of the intuition underlying Eq. (A1) is as follows: Glosten and Harris

(1988) indicate that for a round-trip transaction, the non-information asymmetry component is

given by 2(C0 + C1TradeSize) and the information asymmetry component of the bid-ask spread

is given by 2(Z0 + Z1TradeSize), with the estimated spread being the sum of the two components.

The first component allows market makers to generate revenue from a seemingly random order

flow to cover inventory holding and order processing costs, as well as provide monopoly profits.

It is a transitory component because it is unrelated to the underlying value of the securities. The

second component assumes that order flows will be correlated with future price changes. It arises

because rational market makers in a competitive environment will widen the spread in response

to information asymmetry. Scaling of price changes by the previous price, i.e., the use of

intraday return, facilitates cross-sectional comparability in the extent of information asymmetry

across firms (Armstrong et al. 2009).

Econometrically, it can be observed from Eq. (A1), the key distinction between the

information asymmetry component and the non-information asymmetry component is that the

coefficients for the non-information asymmetry component is based on ΔTrade, while the

information asymmetry component is based on Trade. The intuition for the difference is as

29

follows. The non-information asymmetry component assumes that market makers generate

revenue using random switching between buyer- and seller-initiated trades to “buy low and sell

high” on average. ΔTrade captures the idea that when a buy (sell) order is filled, market makers

raise bid and/or ask prices to increase the probability that the next order will be a sell (buy) to

maintain inventory. Price changes, which reflect the compensation to the market makers, reverse

on average (i.e., the effect of trades on prices is transitory). The information asymmetry

component captures the idea that buy orders (i.e., Trade = 1) cause “true” prices to rise by (Z0 +

Z1TradeSize) while sell orders (i.e., Trade = -1) cause them to fall by -(Z0 + Z1TradeSize). Buy

and sell orders cause a permanent effect on prices since they are due to a change in expectations

of firm value. Eq. (A1) provides the regression coefficients, Z0, Z1, C0, and C1. For trade size, we

compute the average trade size (AvgTradeSize).

We compute IASpread and NIASpread using the intraday data from the Institute for the

Study of Security Markets database (ISSM) and NYSE Trade and Quotes database (TAQ). ISSM

provides the data for NYSE and AMEX firms from 1983 to 1992 and NASDAQ firms from 1987

to 1992. TAQ provides the data for NYSE, AMEX, and NASDAQ firms from 1993 to 2004.

Prior to using the data, the intraday data is cleaned following the procedure discussed in Ng et al.

(2008). All the intraday data in each year is then used to compute annual measures of IASpread

and NIASpread.

30

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Table 1: Observations by Year

The number of firm-year observations for each information asymmetry proxy (IASpread and AdjPIN) is listed. IASpread is the scaled information asymmetry component of spread calculated according to the modified Glosten and Harris (1988) methodology. AdjPIN is Duarte and Young’s (2008) adjusted PIN measure representing the information asymmetry component of PIN (Easley et al. 2002).

Year IASpread AdjPIN

1983 1,498 1,725 1984 1,512 1,640 1985 1,396 1,580 1986 1,335 1,525 1987 2,437 1,461 1988 2,399 1,492 1989 2,412 1,492 1990 2,374 1,520 1991 2,502 1,540 1992 2,866 1,574 1993 2,085 1,792 1994 2,281 1,813 1995 2,821 1,863 1996 3,015 1,829 1997 3,620 1,790 1998 3,841 1,844 1999 3,849 1,725 2000 3,603 1,635 2001 3,541 1,589 2002 3,486 1,579 2003 3,462 1,549 2004 3,388 1,514

Total 59,723 36,071

35

Table 2: Summary Statistics Panel A presents summary statistics on the variables of interest used in this study. Panel B shows the Pearson correlations for these variables along with the respective p-values. All variables are calculated over the calendar year. IASpread is the scaled information asymmetry component of spread calculated according to the modified Glosten and Harris (1988) methodology. NIASpread is the non-information asymmetry proportion of spread. AdjPIN is Duarte and Young’s (2008) adjusted PIN measure representing the information asymmetry component of PIN (Easley et al. 2002). PSOS is the probability of symmetric order flow shock based on Duarte and Young (2008). #Inst (#Trans) is the number of shares held by institutional (transitory) investors. %Inst (%Trans) is the percentage of shares outstanding held by institutional (transitory) investors. HerfInst (HerfTrans) is the Herfindahl measure for institutional (transitory) holdings multiplied by -1 so that it is increasing in competition. Beta is the post-ranking Dimson (1979) beta calculated using 40 portfolios formed on five-year rolling pre-ranking individual firm betas. Size is the natural log of the year end market value of equity. BTM is the book to market ratio, which is the natural log of year end market value of equity divided by the book value of equity known three months prior to the calendar year end. Variables are winsorized at the 1st and 99th percentiles. Panel A: Descriptive statistics Variable Mean STD P25 Median P75 Measures of information and non-information asymmetry IASpread (%) 0.44 1.35 0.09 0.21 0.44 NIASpread (%) 1.75 2.67 0.30 0.82 2.13 AdjPIN 0.18 0.09 0.12 0.16 0.22 PSOS 0.29 0.17 0.17 0.23 0.36 Measures of competition #Inst 77.61 118.41 11.50 32.25 94.50 %Inst 0.35 0.25 0.14 0.32 0.55 HerfInst -0.19 0.21 -0.25 -0.11 -0.05 #Trans 22.93 38.87 2.50 8.25 27.00 %Trans 0.10 0.11 0.02 0.06 0.15 HerfTrans -0.44 0.33 -0.74 -0.33 -0.14 Control variables Beta 1.26 0.49 0.92 1.18 1.49 Size 19.12 2.06 17.61 18.96 20.51 BTM -0.71 0.95 -1.23 -0.64 -0.13

36

Table 2: continued Panel B: Correlation matrix IASpread NIASpread AdjPIN PSOS #Inst %Inst HerfInst #Trans %Trans HerfTrans IASpread 1 0.33 0.32 0.33 -0.14 -0.19 -0.22 -0.13 -0.14 -0.24 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 NIASpread 1 0.20 0.31 -0.31 -0.43 -0.45 -0.29 -0.33 -0.52 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 AdjPIN 1 0.37 -0.48 -0.44 -0.47 -0.45 -0.36 -0.57 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 PSOS 1 -0.35 -0.43 -0.54 -0.29 -0.28 -0.61 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 #Inst 1 0.51 0.41 0.86 0.33 0.55 <.0001 <.0001 <.0001 <.0001 <.0001 %Inst 1 0.61 0.51 0.76 0.72 <.0001 <.0001 <.0001 <.0001 HerfInst 1 0.37 0.42 0.74 <.0001 <.0001 <.0001 #Trans 1 0.47 0.54 <.0001 <.0001 %Trans 1 0.59 <.0001 HerfTrans 1

37

Table 3: Pricing of Information Asymmetry – IASpread

The following Fama-MacBeth regression is estimated with monthly returns from 1984 through 2005 (717,145 firm-months). Rt+1 = α + Σ βj Controls j,t + λ1t Competitioni,t + λ2t IASpread i,t + λ3t Competitioni,t x IASpread i,t + εi,t+1 where the dependent variable, Rt+1, is a monthly realized return in year t+1. All independent variables are from year t. IASpread is the scaled information asymmetry component of spread calculated according to the modified Glosten and Harris (1988) methodology. NIASpread is the non-information asymmetry proportion of spread. Competition is either #Inst, %Inst, HerfInst, #Trans, %Trans, or HerfTrans. #Inst (#Trans) is the number of shares held by institutional (transitory) investors. %Inst (%Trans) is the percentage of shares outstanding held by institutional (transitory) investors. HerfInst (HerfTrans) is the Herfindahl measure for institutional (transitory) holdings multiplied by -1 so that it is increasing in competition. Competition measures are ranked into quintiles and scaled so that their quintile rankings range from zero to one. Beta is the post-ranking Dimson (1979) beta calculated using 40 portfolios formed on five-year rolling pre-ranking individual firm betas. Size is the natural log of the year end market value of equity. BTM is the book to market ratio, which is the natural log of year end market value of equity divided by the book value of equity known three months prior to the calendar year end. The Fama-MacBeth (1973) t-statistics are below the coefficient estimates in parentheses. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

Competition Proxy #Inst %Inst HerfInst #Trans %Trans HerfTrans

Intercept -1.09 2.15* 0.11 0.56 1.29 -0.71 0.57 (-1.18) (1.92) (0.12) (0.62) (1.13) (-0.75) (0.58)

Beta 0.02 0.00 0.00 0.02 -0.01 0.00 0.02 (0.06) (0.00) (0.01) (0.04) (-0.03) (0.01) (0.07)

Size 0.10* -0.10 0.02 -0.01 -0.05 0.07 -0.01 (1.89) (-1.54) (0.40) (-0.13) (-0.72) (1.33) (-0.09)

BTM 0.32*** 0.28*** 0.32*** 0.32*** 0.30*** 0.32*** 0.32*** (3.22) (2.81) (3.22) (3.22) (3.05) (3.27) (3.28)

Competition 1.38*** 0.73*** 0.84*** 1.05*** 0.37* 0.83*** (4.38) (3.54) (4.31) (3.45) (1.80) (3.78)

NIASpread 0.20*** 0.24*** 0.24*** 0.24*** 0.22*** 0.22*** 0.24*** (3.13) (3.74) (3.46) (3.73) (3.50) (3.28) (3.63)

IASpread 0.25** 0.33*** 0.37*** 0.32*** 0.36*** 0.40*** 0.33*** (2.28) (2.72) (2.76) (2.59) (2.90) (3.08) (2.61)

Competition x NIASpread -0.45*** -0.30*** -0.42*** -0.35*** -0.11 -0.42*** (-3.27) (-2.97) (-3.55) (-2.67) (-1.02) (-3.54)

Competition x IASpread -0.75** -0.75*** -0.52* -0.91*** -0.68*** -0.83*** (-2.22) (-2.87) (-1.69) (-2.66) (-2.58) (-2.66)

Adj-R2 (%) 4.03 4.36 4.35 4.30 4.39 4.40 4.33

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Table 4: Pricing of Information Asymmetry – AdjPIN

The following Fama-MacBeth regression is estimated with monthly returns from 1984 through 2005 (433,621 firm months). Rt+1 = α + Σ βj Controls j,t + λ1t Competitioni,t + λ2t AdjPIN i,t + λ3t Competitioni,t x AdjPIN i,t + εi,t+1 where the dependent variable, Rt+1, is a monthly realized return in year t+1. All independent variables are from year t. AdjPIN is Duarte and Young’s (2008) adjusted PIN measure representing the information asymmetry component of PIN. PSOS is the probability of symmetric order flow shock based on Duarte and Young (2008). Competition is either #Inst, %Inst, HerfInst, #Trans, %Trans, or HerfTrans. #Inst (#Trans) is the number of shares held by institutional (transitory) investors. %Inst (%Trans) is the percentage of shares outstanding held by institutional (transitory) investors. HerfInst (HerfTrans) is the Herfindahl measure for institutional (transitory) holdings multiplied by -1 so that it is increasing in competition. Competition measures ranked into quintiles and are scaled so that their quintile rankings range from zero to one. Beta is the post-ranking Dimson (1979) beta calculated using 40 portfolios formed on five-year rolling pre-ranking individual firm betas. Size is the natural log of the year end market value of equity. BTM is the book to market ratio, which is the natural log of year end market value of equity divided by the book value of equity known three months prior to the calendar year end. The Fama-MacBeth (1973) t-statistics are below the coefficient estimates in parentheses. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.


Intercept 0.29 2.35 0.36 0.79 2.07 0.31 1.09 (0.24) (1.64) (0.30) (0.65) (1.40) (0.25) (0.84)

Beta -0.19 -0.22 -0.22 -0.21 -0.23 -0.20 -0.21 (-0.62) (-0.74) (-0.72) (-0.69) (-0.79) (-0.66) (-0.70)

Size 0.03 -0.11 0.02 -0.01 -0.09 0.02 -0.03 (0.53) (-1.46) (0.27) (-0.20) (-1.18) (0.38) (-0.48)

BTM 0.23** 0.19** 0.23** 0.22** 0.20** 0.23** 0.22** (2.48) (2.05) (2.42) (2.40) (2.18) (2.47) (2.40)

Competition 1.41*** 0.70*** 0.88*** 1.31*** 0.48** 1.00*** (3.45) (2.74) (3.16) (3.39) (2.08) (3.33)

PSOS 0.62*** 0.91*** 0.89*** 0.95*** 0.93*** 0.79*** 0.96*** (2.65) (2.79) (2.84) (2.81) (2.84) (2.61) (2.89)

AdjPIN 0.26 0.92 0.83 0.68 0.85 0.71 0.74 (0.42) (1.43) (1.28) (1.05) (1.37) (1.13) (1.16)

Competition x PSOS -0.59 -0.72 -1.11* -0.87 -0.45 -1.05 (-0.95) (-1.27) (-1.76) (-1.36) (-0.78) (-1.59)

Competition x AdjPIN -2.19 -2.73** -2.28 -1.99 -2.39** -2.10 (-1.25) (-2.35) (-1.45) (-1.14) (-2.00) (-1.31)

Adj-R2 (%) 3.42 3.74 3.80 3.67 3.78 3.80 3.72

39

Table 5: Competition among other Types of Institutional Investors This table presents the regressions that examine how the pricing of information asymmetry varies with measures of competition constructed using dedicated institutional investor ownership and quasi-indexer institutional investor ownership. The regression specification in Panel A (Panel B) is similar to that in Table 3 (Table 4), except that different measures of competition are used. Information asymmetry and non-information-asymmetry are proxied by IASpread and NIASpread (AdjPIN and PSOS), respectively, in Panel A (Panel B). Competition is either #Ded, %Ded, HerfDed, #Trans, %Trans, or HerfTrans. #Ded (#QInd) is the number of shares held by dedicated (quasi-indexing) institutional investors. %Ded (%QInd) is the percentage of shares outstanding held by dedicated (quasi-indexing) institutional investors. HerfDed (HerfQInd) is the Herfindahl measure for dedicated (quasi-indexing) institutional holdings multiplied by -1 so that it is increasing in market competition. Competition measures are ranked into quintiles and scaled so that their quintile rankings range from zero to one. For brevity, we do not tabulate the coefficients on Beta, Size, and BTM. We also do not tabulate the coefficients on NIASpread, PSOS, as well as on the interactions of these variables with Competition. All variables are defined in Table 2. The Fama-MacBeth (1973) t-statistics are below the coefficient estimates in parentheses. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively. Panel A – IASpread

Competition Proxy #Ded %Ded HerfDed #QInd %QInd HerfQInd

IASpread 0.31** 0.31** 0.32*** 0.36*** 0.37*** 0.34*** (2.27) (2.20) (2.59) (2.90) (2.62) (2.69)

Competition x IASpread -0.32 -0.32 -0.52* -0.91*** -0.64** -0.55* (-1.04) (-1.35) (-1.69) (-2.66) (-2.31) (-1.88)

Panel B – AdjPIN

Competition Proxy #Ded %Ded HerfDed #QInd %QInd HerfQInd

AdjPIN 0.93 0.78 0.68 0.85 0.90 0.67 (1.36) (1.12) (1.05) (1.37) (1.35) (1.01)

Competition x AdjPIN -2.27 -1.09 -2.28 -1.99 -2.96*** -2.35 (-1.38) (-1.10) (-1.45) (-1.14) (-2.44) (-1.42)

40

Table 6: Controlling for the Trading Environment

This table presents the regressions that examine how the pricing of information asymmetry varies with competition after controlling for the broader trading environment. The regression specification is similar to that in Table 3. We add additional controls for trading environment by including Turnover, and its interactions with proxies for information asymmetry and non-information-asymmetry. Turnover is ranked into quintiles based on the distribution within the year. The quintile ranks are then scaled to range from zero to one. Information asymmetry and non-information-asymmetry are proxied by IASpread and NIASpread (AdjPIN and PSOS), respectively, in Panel A (Panel B). Competition is measured as either #Inst, %Inst, HerfInst, #Trans, %Trans, or HerfTrans. For brevity, we do not tabulate the coefficients on Beta, Size, BTM. We also do not tabulate the coefficients on NIASpread, PSOS, as well as on the interactions of these variables with Competition and Turnover. Except for Turnover, all variables are defined in Table 2. The Fama-MacBeth (1973) t-statistics are below the coefficient estimates in parentheses. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

Panel A – IASpread


Turnover -0.66** -0.57* -0.56* -0.75*** -0.66** -0.65** (-2.33) (-1.90) (-1.91) (-2.65) (-2.19) (-2.22)

Competition 1.89*** 1.00*** 1.15*** 1.73*** 0.78*** 1.30*** (6.46) (4.40) (5.53) (6.17) (3.70) (5.98)

IASpread 0.29** 0.34*** 0.30** 0.29** 0.34*** 0.28** (2.30) (2.60) (2.33) (2.33) (2.67) (2.26)

Turnover x IASpread 0.15 0.14 0.10 0.23 0.27 0.13 (0.38) (0.38) (0.26) (0.58) (0.70) (0.33)

Competition x IASpread -1.06** -0.93*** -0.73** -1.22*** -0.87*** -1.01** (-2.55) (-2.82) (-2.08) (-2.82) (-2.77) (-2.49)

Panel B – AdjPIN


Turnover -0.37 -0.24 -0.23 -0.54 -0.26 -0.30 (-1.12) (-0.77) (-0.70) (-1.59) (-0.76) (-0.93)

Competition 1.60*** 0.79*** 0.92*** 1.69*** 0.69*** 1.15*** (3.41) (2.85) (2.88) (3.71) (2.58) (3.34)

AdjPIN 0.03 0.10 -0.02 -0.10 0.09 -0.04 (0.06) (0.17) (-0.03) (-0.17) (0.15) (-0.08)

Turnover x AdjPIN 1.63 1.70 1.40 1.97 1.92 1.26 (1.01) (0.99) (0.86) (1.17) (1.08) (0.77)

Competition x AdjPIN -2.96 -3.89*** -2.91* -3.02 -3.84*** -2.75 (-1.52) (-2.92) (-1.69) (-1.50) (-2.75) (-1.56)

41

Table 7: Pricing of Information Quality

This table presents the regressions that examine how the pricing of information quality (IQ) varies with competition. The regression specification is identical to that in Table 3. Information quality is proxied by AQ, Smoothness, and FOG. AQ is the standard deviation calculated over a five year period of a firm’s residuals from an annual estimation of the modified Dechow-Dichev (2002) model (Francis et al. 2005). Smoothness is the ratio of firm’s standard deviation of net income before extraordinary items divided by beginning total assets, to its standard deviation of cash flows from operations divided by beginning total assets. FOG is the measure of financial statement readability developed in Li (2008). The regressions for AQ, Smoothness, and FOG use 546,882, 546,882, and 220,721 firm-months. Competition is measured as either #Trans, %Trans, or HerfTrans, with the quintile rankings scaled to range from zero to one. All other variables are defined in Table 2. The Fama-MacBeth (1973) t-statistics are below the coefficient estimates in parentheses. Significance levels are based on two-tailed tests. ***, **, and * denotes significance at the 1%, 5%, and 10% levels, respectively.

IQ = AQ IQ = Smoothness IQ = FOG

#Trans %Trans HerfTrans #Trans %Trans HerfTrans #Trans %Trans HerfTrans Intercept 5.84*** 2.70*** 3.98*** 5.68*** 2.75*** 3.89*** 3.97** 0.32 1.89

(5.60) (3.14) (4.41) (5.10) (2.97) (4.04) (2.27) (0.21) (1.25)

Beta -0.06 0.02 -0.01 -0.11 0.03 -0.05 0.32 0.39 0.36 (-0.20) (0.06) (-0.04) (-0.31) (0.07) (-0.16) (0.58) (0.71) (0.64)

Size -0.29*** -0.09* -0.18*** -0.27*** -0.09* -0.16*** -0.32*** -0.13 -0.19** (-4.66) (-1.81) (-3.40) (-4.04) (-1.65) (-2.92) (-3.25) (-1.35) (-2.36)

BTM 0.26*** 0.32*** 0.30*** 0.27*** 0.32*** 0.31*** 0.26 0.31* 0.30* (2.87) (3.48) (3.30) (3.02) (3.51) (3.43) (1.62) (1.87) (1.88)

Competition 1.77*** 0.21 1.05*** 1.27*** 0.01 0.70*** 4.39*** 3.99*** 3.44** (5.88) (1.24) (4.90) (4.35) (0.09) (3.22) (2.59) (2.67) (2.06)

IQ 5.41** 3.68 5.37** 0.24** 0.23* 0.31** 0.15** 0.17** 0.15* (2.42) (1.64) (2.46) (2.04) (1.95) (2.57) (1.97) (2.55) (1.95)

IQ x Competition -13.87*** -7.56*** -13.59*** -0.34** -0.28* -0.45*** -0.17* -0.21*** -0.16* (-4.12) (-2.69) (-4.35) (-2.15) (-1.91) (-2.87) (-1.91) (-2.74) (-1.77)

Adj-R2 (%) 3.61 3.65 3.58 3.44 3.51 3.42 4.43 4.49 4.39

Investor Competition and the Pricing of Information Asymmetry

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