HOW DOES THE INFORMATION ENVIRONMENT AFFECT INFORMATION
ARCHIVES
ASYMMETRY AROUND EARNINGS ANNOUNCEMENTS?
IN'JTITI ITE
MASSACHUSEOFTECHNJL'.n)y
by
Patricia L. Naranjo
MAR 112015
B. Eng. in Industrial and Bioprocess Engineering
Pontificia Universidad Cat6lica de Chile, 2003
LIBRAR ES
SUBMITTED TO THE SLOAN SCHOOL OF MANAGEMENT IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY IN MANAGEMENT
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
( 2014 Massachusetts Institute of Technology. All rights reserved.
Signature redacted
Signature of Author:
Sloan School of Management
May 2, 2014
Certified by:
Signature redacted
Nanyang Te nol
John Core
Accounting
of
Professor
ical University
Thesis Co-Supervisor
Signature redacted
Certified by:
Rodrigo Verdi
Sarofim Family Associate Professor of Accounting
Thesis Co-Supervisor
Accepted by:
Signature redacted
Ezra Zuckerman
Nanyang Technological University Professor
Chair, MIT Sloan PhD Program
How does the information environment affect
information asymmetry around earnings announcements?
by
Patricia L. Naranjo
Submitted to the Sloan School of Management
on May 2, 2014 in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy in Management
ABSTRACT
This paper shows that the information environment is an important determinant of private
information acquisition and changes in information asymmetry around earnings announcements.
I create a sample of earnings announcements from 39 countries and investigate whether firmand country-level variation in the information environment affects private information
acquisition. To do this, I examine the level of information asymmetry before and during earnings
announcements. A stronger firm-level information environment is (1) negatively related to
changes in pre-event information asymmetry and (2) positively related to changes in eventperiod information asymmetry. Similarly, a stronger country-level information environment is
associated with the firm-level information environment having a stronger effect on information
asymmetry before the announcement and a weaker effect during the announcement.
Thesis Co-Supervisor John Core
Title: Nanyang Technological University Professor of Accounting
Thesis Co-Supervisor: Rodrigo Verdi
Title: Sarofim Family Associate Professor of Accounting
3
Acknowledgments
I am grateful for the invaluable guidance of my dissertation committee: John Core (Co-Chair),
Eric So, and Rodrigo Verdi (Co-Chair). I deeply appreciate the amount of time that they spent
guiding me through the PhD program, the thesis, and the job market.
I appreciate helpful comments from Asher Curtis, Richard Frankel, Joao Granja, Michelle
Hanlon, Ole-Kristian Hope, Rafael La Porta, Katharina Lewellen, Dawn Matsumoto, Hai Lu,
Karen Nelson, K. Ramesh, Richard Sansing, Shiva Sivaramakrishnan, Nemit Shroff, Joseph
Weber, Stephen Zeff, and workshop participants at Dartmouth College, the MIT Sloan School of
Management, Rice University, University of Toronto, University of Washington, and
Washington University in St. Louis.
I am also grateful to the other faculty members of the Accounting Group at the Sloan School of
Management: Anna Costello, Joao Granja, Michelle Hanlon, Scott Keating, S.P. Kothari,
Christopher Noe, Reining Petacchi, Nemit Shroff, Ross Watts, and Joseph Weber.
I would like to thank the current and former PhD students for their friendship and input on this
project, including Brian Akins, Josh Anderson, Nick Guest, Derek Johnson, Zawadi Lemayian,
Rebecca Lester, Lynn Lei Li, Mihir Mehta, Heidi Packard, Daniel Saavedra, Benjamin Yost,
Kexin Zheng, and Luo Zuo.
Finally, I would like to thank my husband, Marcelo Fuenzalida, and my family for their
unconditional support.
I gratefully acknowledge financial support from the MIT Sloan School of Management and the
Deloitte Foundation. I thank RavenPack for generously sharing the press coverage data.
4
Table of Contents
1.
2.
Introduction.............................................................................................................................7
Literature Review ..................................................................................................................
2.1 U .S. Studies..............................................................................................................
2.2 International Studies...................................................................................................
3. Hypothesis Developm ent ...................................................................................................
4. Research Design and Variable M easurem ent...................................................................
4.1
Pre-Event Period - Firm Level.....................................................................................
4.2 Event Period - Firm Level.............................................................................................
4.3 Country-Level Tests...................................................................................................
4.4 V ariable M easurement ................................................................................................
4.4.1
Change in spreads................................................................................................
4.4.2
Firm-level proxies for the information environment ...........................................
4.4.3
Country-level proxies for information environment...........................................
4.4.4
Controls...................................................................................................................
5. Results...................................................................................................................................
5.1
Sample and Descriptive Statistics...............................................................................
5.2 Univariate Analysis.....................................................................................................
5.3 Pre-Event Change in Inform ation Asym m etry..........................................................
5.4 Event Change in Inform ation Asym metry ................................................................
5.5 Country-Level Inform ation Environm ent ...................................................................
5.6 A dditional Analyses...................................................................................................
5.6.1
U nanticipated events...........................................................................................
5.6.2
A dditional country-level measures .....................................................................
5.6.3
A lternative inform ation environm ent controls.....................................................
5.6.4
Country number of observations........................................................................
5.6.5
Alternative abnorm al spread m easures..............................................................
6. Conclusion ............................................................................................................................
References.....................................................................................................................................
Appendix A ...................................................................................................................................
5
12
12
13
14
17
17
18
19
20
20
20
21
21
23
23
26
28
28
29
30
30
31
32
33
34
35
37
40
List of Tables
Table A l: Unanticipated events.................................................................................................
Panel A : Types of unanticipated events................................................................................
40
40
Panel B: Unanticipated events by country ...........................................................................
41
Table 1: Descriptive statistics ...................................................................................................
43
Panel A : Descriptive statistics by country ...........................................................................
43
Panel B: Descriptive statistics..............................................................................................
45
Panel C: Pearson / Spearm an correlations .............................................................................
46
Table 2: Univariate analysis......................................................................................................
47
Table 3: Pre-event period change in spreads (% )....................................................................
48
Table 4: Event period change in spreads (% ) ...........................................................................
49
Table 5: Partitions based on country-level information environment - Pre-event period........ 50
Table 6: Partitions based on country-level information environment - Event period.............. 51
Table 7: Unanticipated Events ...................................................................................................
52
Table 8: Partitions based on country-level institutional factors................................................
53
Panel A : Pre-Event....................................................................................................................
53
Panel B: Event Period ..........................................................................................................
54
Table 9: Robustness analyses - Earnings surprises .................................................................
55
Table 10: Robustness analyses -U .S. and Japan.......................................................................
56
Panel A : Pre-Event....................................................................................................................
56
Panel B: Event Period..........................................................................................................
57
List of Figures
Figure 1: Tim eline.........................................................................................................................
6
42
1.
Introduction
Information asymmetry plays a central role in determining market liquidity, price
discovery, and the information content of market prices. An extensive literature in accounting
and finance examines changes in information asymmetry around earnings announcements and
finds that information asymmetry increases during earnings announcements (e.g., Lee et al.,
1993; Krinsky and Lee, 1996; Affleck-Graves et al., 2002). In contrast, there is little evidence of
increases in information asymmetry before earnings announcements. The few exceptions are
small increases in bid-ask spreads in the short window (one to four days) immediately prior to
the announcement (e.g., Krinsky and Lee, 1996; Yohn, 1998). Collectively, this literature
suggests that pre-announcement trading on private information is limited. Rather, investors trade
based on their differential interpretation of the disclosure, resulting in a sharp increase in
information asymmetry during the announcement.
I hypothesize that private information acquisition, and consequently information
asymmetry, before earnings announcements are a function of the information environment. A
strong information environment limits the net benefits of gathering and trading on private
information. Specifically, if private and public information are substitutes, a stronger information
environment decreases the net benefits of private information acquisition, which in turn reduces
incentives for investors to acquire private information before earnings announcements
(Verrecchia, 1982; Diamond, 1985). Thus, I hypothesize that in weaker information
environments there is more private information acquisition prior to the announcement, leading to
a greater increase in information asymmetry before the announcement.
To test this hypothesis, I use a sample of 88,902 quarterly earnings announcements from
39 countries between 2000 and 2010. The international setting allows for substantial within-
7
country variation in information environments, which is difficult to obtain in the U.S. setting
used in prior studies. Further, an international setting provides cross-country variation in
mandatory disclosure requirements, and is therefore a setting where firm-level features of the
information environment may have differential effects on private information acquisition around
earnings announcements. Since private information acquisition is unobservable, I use a firm's
bid-ask spread as a measure of information asymmetry and study the change in bid-ask spreads
before and during earnings announcements.
Testing my hypothesis also requires measuring the information environment's strength. I
use firm- and country-level measures of the information environment. The firm-level information
environment includes public information generated by the firm through voluntary disclosures or
by information intermediaries such as financial analysts. As firm-level measures of the
information environment, I use the number of firm press releases to measure voluntary
disclosures (Shroff et al., 2013), analyst coverage (Lang and Lundholm, 1996; Bushman et al.,
2004), and the market value of the firm's equity as an aggregate measure of public information
about the firm (Yohn, 1998). The country-level information environment captures country-level
features that affect finms' information environments, such as information mandated by countrylevel reporting requirements. To this end, I use the disclosure index proposed by La Porta et al.
(2006) and the CIFAR index used in prior research (e.g., Hope, 2003, Bushman et al., 2004).
My first set of tests focuses on the firm-level information environment, while keeping the
country-level attributes constant. Consistent with my hypothesis, I find that in the pre-event
period (measured as the nine-day window ending one day prior to the earnings announcement)
the change in bid-ask spreads before earnings announcements decreases with the strength of the
information environment. In economic terms, firms in weaker information environments
8
experience an increase in spreads that is three times as large as it is for firms in stronger
information environments. This result suggests that private information acquisition decreases
with the strength of the information environment, resulting in a lower increase in information
asymmetry.
To provide further evidence, I examine the announcement period (measured as the threeday window around the announcement). Here I predict that when a stronger information
environment leads to a lower increase in information asymmetry before the announcement, there
is a greater increase in information asymmetry during it. This occurs because weaker
information environments provide greater benefits of acquiring private information; therefore,
investors are more likely to take advantage of such information by trading in anticipation of the
announcement before prices partially capture this information. That is, investors face an
opportunity cost when trading during the announcement as opposed to before the announcement,
when they can obtain greater benefits. Further, in weaker information environments, informed
investors are less likely to revise their beliefs and engage in processing activities at the time of
the announcement because they obtain more precise private information before the
announcement. Therefore, in weaker information environments there are fewer investors that
trade during the announcement based on informed judgments, resulting in a lower increase in
information asymmetry during the announcement. Consistent with this prediction, I find that the
increase in bid-ask spreads during the announcement increases with the strength of the
information environment. Firms in strong information environments experience an increase in
spreads in the event period that is 1.3 times that of firms in weaker information environments.
My second set of tests examines country-level forces by studying how the fin-level
information environment interacts with the country-level information environment. The
9
motivation for these tests is to contrast changes in information asymmetry in countries with
strong information environments, which is the setting used by previous studies, with that of
weaker information environments, such as countries with less stringent reporting and disclosure
requirements. Consistent with more private information acquisition in countries with weaker
information environments, I find that, the finn-level information environment is negatively
related to changes in information asymmetry before the announcement but unrelated to changes
in information asymmetry during it. In countries with stronger information environments (e.g.,
the U.S.), the opposite picture emerges. Specifically, private information acquisition is
sufficiently costly that there is limited cross-sectional variation in information asymmetry due to
the firm-level information environment ahead of the announcement. Rather, most of the crosssectional variation exists during the announcement, as investors differentially process the
disclosed public information. Taken together, these results suggest that the country-level
information environment further inhibits the incentives for private information gathering ahead
of the announcement, leading to a weaker effect of the firm-level information environment on
information asymmetry before the announcement and a stronger effect during the announcement.
The paper makes two contributions. First, it shows that the information environment is a
key determinant of changes in information asymmetry around earnings announcements. The
empirical literature, which consists primarily of U.S. studies, provides little evidence that
information asymmetry increases prior to earnings announcements, but substantial evidence that
it increases during the announcement. I provide a rationale for these findings. Specifically, in
stronger information environments, private information gathering is sufficiently costly for
investors to be more likely to wait and trade during the announcement by differentially
processing the public information. In contrast, in weaker information environments, investors are
10
more likely to trade before the announcement based on pre-event private information because
gathering such information is cost efficient. This evidence extends our knowledge of the
mechanism through which the information environment affects investors' trading behaviors and
changes in information asymmetry around earnings announcements.
Second, the paper adds to the international literature that investigates differences in
market reactions to public information around the world (Ball et al., 2000; Morck et al., 2000;
Ball et al., 2003, DeFond et al., 2007, Griffin et al., 2011). Previous studies that look at price and
volume reactions around earnings announcements only capture changes in the total level of
information (both public and private). By looking at returns and volume, these studies do not
differentiate between differences in market reactions due to private information and differences
due to information disclosed before the announcement, such as management and analysts'
forecasts. This could explain why DeFond et al. (2007) find an insignificant relation between the
extent of public information and the market reaction to earnings announcements. By looking at
changes in information asymmetry, I am better able to identify changes in private information
acquisition and provide evidence on the mechanisms through which the information environment
shapes price discovery and liquidity around earnings announcements.
The remainder of the paper is organized as follows. Section 2 discusses related prior
research. Section 3 presents the hypothesis development. Section 4 describes the research design
and variable measurement. Section 5 describes the sample and presents the results. Section 6
concludes.
11
2.
2.1
Literature Review
U.S. Studies
A substantial body of research finds that information asymmetry increases during
earnings announcements and other information events (e.g., Lee et al., 1993; Skinner, 1993;
Krinsky and Lee, 1996). The increase in information asymmetry is stronger for firms that
experience stronger market reactions. In particular, information asymmetry increases more
during announcements that have more extreme earnings surprises (Lee et al., 1993; Skinner,
1993; Ng, 2007; Affleck-Graves et al., 2002), greater analyst forecast dispersion (Affleck-Graves
et al., 2002), lower earnings quality (Bhattacharya et al., 2008), and higher earnings precision
(Gow et al., 2012). This literature also studies changes in information asymmetry before the
announcement. Few papers, however, find evidence of changes in information asymmetry prior
to earnings announcements (Krinsky and Lee, 1996; Yohn, 1998). Those that do, find that the
change is small and is concentrated in the short window (one to four days) before the
announcement. 1 Overall, the literature provides scant evidence that investors acquire private
information before earnings announcements and indicates that information asymmetry increases
during earnings announcements as investors trade based on informed judgments about the
disclosure.
In this paper, I argue that the information environment explains the observed pattern in
changes in information asymmetry in U.S. settings and that this pattern is different in weaker
information environments. In particular, in stronger information environments, private
1 Some papers show that information asymmetry decreases in the post-event period compared to the pre-event
period. In particular, Daley et al. (1995) find that permanent price changes of block trades are lower after an
announcement than before it. Tetlock (2010) proposes and tests a model in which public news resolves information
asymmetry. However, these studies do not investigate whether the observed decrease in information asymmetry
after an announcement is related to changes in information asymmetry in the pre-announcement period.
12
information acquisition is sufficiently costly that investors are more likely to wait and trade
during the announcement by processing and interpreting public information, leading to a greater
increase in information asymmetry during the announcement. In contrast, in weaker information
environments, private information acquisition is cost efficient and investors are more likely to
trade before the announcement based on pre-event private information, leading to a greater
increase in information asymmetry before the announcement.
2.2
InternationalStudies
The international literature provides evidence that country characteristics explain price
and volume reactions to earnings announcements. For example, DeFond et al. (2007) and Griffin
et al. (2011) show that earnings announcements are more informative in countries with higher
earnings quality and less insider trading. However, DeFond et al. (2007) do not find a relation
between the extent of public information and the information content of earnings
announcements. Landsman et al. (2012) find that the information content of earnings
announcements increased after the adoption of LFRS.
However, the literature scarcely
investigates the determinants of changes in information asymmetry around earnings
announcements.
In contrast to previous studies that consider price and volume reactions around earnings
announcements, which only capture the total levels of information (both public and private), I
investigate whether the information environment is a key determinant of changes in information
asymmetry around earnings announcements. By looking at information asymmetry instead of
returns and/or volume, I am better able to identify changes in private information and as opposed
to changes in the total level of information. In that regard, my paper is related to the findings of
Maffett (2012), who shows that the information environment is associated with the profitability
13
of informed trading. I extend his findings by showing that the information environment affects
changes in information asymmetry around earnings announcements, providing further evidence
on the mechanisms through which the information environment affects trading behaviors and
private information acquisition around earnings announcements.
3.
Hypothesis Development
I predict that holding the country-level attributes of the information environment
constant, a firm's information environment affects the benefits of private information acquisition
before the disclosure and consequently changes in information asymmetry before the
announcement. I define the information environment as the extent of public information
available about the firm that limits the incentives to acquire and trade on private information. I
define private information as information generated by investors through proprietary research
activities and/or information received from an insider. As Verrecchia (1982) and Diamond
(1985) show, if private and public information are substitutes, investors choose to acquire less
private information when more public information is available. This occurs because a stronger
information environment decreases the net benefits of private information. Thus, in stronger
information environments there is less private information acquisition before the announcement,
resulting in a lower increase in information asymmetry during that time (Kim and Verrecchia,
1991).
The above analysis leads to my first hypotheses (in alternate form):
Hla: A strongerfirm-level information environment is associated with a lower increase
in information asymmetry before the announcement.
As a second test of my hypothesis, I predict that the opposite relation holds between the
information
environment
and
changes
in
information
asymmetry
during
earnings
announcements. That is, I predict that in weaker information environments, a greaterincrease in
14
information asymmetry before the announcement is associated with a lower increase in
information asymmetry during it. This occurs because the higher net benefit of trading before the
announcement makes trading during the announcement less attractive, reducing the number of
investors that engage in processing activities.
Since a weaker information environment increases the benefits of acquiring private
information before the announcement, investors are more likely to take advantage of such
information by trading in anticipation of the announcement before prices capture this
2
information. That is, investors face an opportunity cost when trading during the announcement
as opposed to before the announcement, when they can obtain greater benefits. Further, in
weaker information environments investors obtain more precise private information before the
announcement because the precision of the private information is increasing in the anticipated
benefit of the disclosure (Kim and Verrecchia, 1991; McNichols and Trueman, 1994).
Therefore, in weaker information environments, investors are less likely to revise their beliefs at
the time of the announcement.3 This, in turn, reduces the number of investors that trade during an
announcement based on informed judgments of the disclosure, which leads to a lower increase in
information asymmetry during the announcement (Kim and Verrecchia, 1994).
The above analysis leads to my second hypothesis (in alternate form).
Hib: A strongerfirm-level information environment is associatedwith a greaterincrease
in information asymmetry during the announcement.
Finally, to explore the role of country-level forces, I study the interaction between the
firm-level and the country-level information environments. Specifically, I hypothesize that the
As investors trade on private information prior to the announcement, stock prices partially reflect this information.
For example, prices respectively increase or decrease in anticipation of good or bad news.
3 In the extreme case when the private information and the public announcement are perfect substitutes, the
announcement is uninformative to the informed investors.
2
15
information environment of a country affects the extent to which the firm-level information
environment is associated with changes in information asymmetry around the announcement.
The country-level information environment includes mandatory reporting and disclosure
requirements and therefore is associated with the amount of public information available to
investors in a given country.4
Finns in countries with weaker disclosure requirements have, on average, less public
information available, which provides more opportunities to trade ahead of earnings
announcements based on private information acquisition. Therefore, the firm-level information
environment plays a more important role in limiting private information acquisition before
earnings announcements in countries with weak disclosure and reporting requirements compared
to countries with stronger disclosure and reporting requirements. In the latter, in contrast,
investors face a higher cost of private information acquisition and have fewer opportunities to
trade before earnings announcements, shifting trading to the announcement period. Therefore,
for these countries, the effect of the firm-level information environment is more important in
explaining changes in information asymmetry during the announcement.
The above analysis leads to my last hypothesis (in alternate form).
H2a: The relation between the strength of the firm-level information environment and
changes in information asymmetry before the announcement decreases with the strength
of the country-level information environment.
H2b: The relation between the strength of the firm-level information environment and
changes in information asymmetry during the announcement increases with the strength
of the country-level information environment.
In Section 0, I investigate the effect of country-level features that directly private information acquisition, such as
the level of enforcement.
4
16
4. Research Design and Variable Measurement
In this section, I describe the research design and variable measurement. I investigate how
information asymmetry, as proxied by bid-ask spreads, changes before and during quarterly
earnings announcements for different firm- and country-level proxies for the information
environment.
Figure 1 illustrates the timeline and the time period definitions. I follow previous research
(e.g., DeFond et al., 2007; Ng, 2007; Bushee et al., 2010; Landsman et al., 2012) and define the
earnings announcement window (Event) as the three trading days period (days t-1, t, and, t+1)
surrounding the earnings announcement date (t=O). Following Yohn (1998), I define the preevent window (Pre-Event) as the nine trading days t-10 to t-2 relative to the earnings
announcement date (t=0). The Non-Event period is constructed to capture normal levels of
spreads before the announcement and to avoid capturing changes in information asymmetry from
the previous quarter. Following Griffin et al. (2011), I define the Non-Event period to be trading
days t-55 to t-11 relative to the earnings announcement.5
4.1
Pre-Event Period- Firm Level
To test my first hypothesis (hypothesis Hla) regarding changes in information asymmetry
in the pre-event period, I estimate the following model:
AInfo
Asymmetry PRE-EVENT it
=
act + a + f1 Rank Info Environmenti +
yj Controjt + Eit,
(1)
5 All countries in my sample provide interim earnings announcements. The proportion of end of fiscal year earnings
announcements is 32%. Nine countries, including Australia, France, Hong Kong, and the United Kingdom provide
semi-annual earnings announcements. In untabulated analysis, I extend the Non-Event period for these countries to
t=-115 to t=-11; I obtain similar results.
17
where AInfo Asymmetry
PRE-EVENT
is the change in information asymmetry in the Pre-Event
period. Rank Info Environment is the tercile rank by country of Info Environment scaled to range
between zero and one.6 Info Environment is a firm-level proxy for the information environment.
Control1 is the set control variables. act amd a are country-year and industry fixed effects,
respectively. The inclusion of the country-year fixed effect is important because it controls for
time-varying country-level determinants of the information environment and institutional
features (e.g., the adoption of IFRS) that may affect private information acquisition, allowing me
to identify the effect of the firm-level information environment. I cluster observations at the firm
and year levels.
I predict that information asymmetry increases in the pre-event period and that this
increase is negatively related to firm-level proxies for the information environment. Hence, I
predict that fl1 < 0.
4.2
Event Period- Firm Level
To test the effect of the firm-level information environment on changes in information
asymmetry during the announcement (hypothesis Hlb), I estimate the changes in information
asymmetry during the Event period for different firm-level proxies for the information
environment. Specifically, I estimate the following model:
AInfo Asymmetry EVENTi
=
at + aj + fl1 Rank Info Environmenti +
yj Controli + eit.
(2)
Hypothesis Hlb predicts that the change in information asymmetry increases with the
firm-level information environment. Thus, I predict that fl, > 0.
I choose terciles instead of a continuous measure to facilitate the interpretation of the results. The results are robust
to using continuous measures, quintiles, and deciles.
6
18
4.3
Country-Level Tests
To test hypothesis H2, which is related to how the country-level information environment
affects the relation between the firm-level information environment and changes in information
asymmetry, I follow Lang et al. (2012) and partition the sample based on country-level measures
of the information environment. I estimate equations (1) and (2) separately for each partition and
test the difference between the coefficients. To assess the significance of the difference of the
coefficients on Rank Info Environment across partitions, I cluster standard errors at the country
level.
Hypothesis H2a predicts that the relation between the strength of the firm-level
information environment and changes in information asymmetry before the announcement
decreases with the strength of the country-level information environment. Thus, I predict that
fl is more negative for countries in the low country-level information environment partition
during the pre-event period. Hypothesis H2b predicts that the relation between the strength of the
firm-level information environment
and changes in information
asymmetry during the
announcement increases with the strength of the country-level information environment. Thus, I
predict that fl, is more positive for countries in the high country-level information environment
partition during the event period.
19
4.4
4.4.1
VariableMeasurement
Change in spreads
I use changes in bid-ask spreads as a measure of changes in information asymmetry.7 I
compute the change in information asymmetry, ASpreadre-Event (ASpreadEent), as the percentage
change in the spreads in the Pre-Event(Event), as follows:
ASpreadt
-
Spreadt - SpreadNon-Event
SpreadNon-Event
(3)
where Spread is the difference between the ask and bid prices deflated by the midpoint of the ask
and bid prices. SpreadNon-Event is the mean spread during the Non-Event period. I trim the
minimum and maximum Spread values during the Non-Event period to avoid the incidence of
outliers.
4.4.2
Firm-levelproxiesfor the information environment
I use four firm-level proxies for the information environment. First, following Shroff et
al. (2013), I use the number of press releases initiated by the firm during the calendar year before
the end of the fiscal quarter as a measure of voluntary disclosure (Disclosure). The selection of
the measurement window mitigates possible endogeneity concerns that disclosures made before
the announcement influence the reaction around the announcement. The second measure is the
number of analysts providing a forecast for the quarterly earnings (Analysts). Prior research
7 Measures of information asymmetry that are more precise by using intraday data are difficult to obtain
internationally (e.g., the probability of informed trading). Other measures readily available for international firms
are not suitable for event studies where volume and price experience abnormal levels. In particular, measures based
on returns, such as the percentage of zero returns, provide limited cross-sectional variation at the time of a public
disclosure. In addition, measures based on price and volume, such as the Amihud's price impact measure, may be
biased because at the time of an announcement, volume is positively associated with information asymmetry (Kim
and Verrecchia, 1994; Kim and Verrecchia, 1997). Therefore, firms that experience a higher increase in information
asymmetry during the announcement also experience greater volume, resulting in a lower price impact. Finally, I do
not use returns run-ups as a measure of informed trading before the announcement because public information
disclosed during this period, such as analysts' or management forecasts, also affects returns.
20
suggests that greater analyst coverage indicates the quality of information available about a firm
(e.g., Lang and Lundholm, 1996; Bushman et al., 2004; Shroff et al., 2013). As a third measure, I
use the market value of equity (Market Value), defined as the log of the firm's market value of
equity in USD at the end of the fiscal quarter before the earnings announcement (Atiase, 1985;
Collins et al., 1997; Yohn, 1998). The final measure is Environment, which is the average of the
standardized values of Disclosure, Analysts, and Market Value. I rank each measure in terciles
by country to capture within-country variation in the firm-level information environment. The
Rank variables are scaled to range between zero and one.
4.4.3
Country-levelproxiesfor information environment
The country-level proxies for information environment that I use capture the disclosure
requirements that shape a country's information environment. First, following Lang et al. (2012)
and Maffett (2012), I use the disclosure index proposed by La Porta et al. (2006). This measure
captures the quantity of financial disclosure requirements. Second, following Bushman et al.,
(2004), I use the disclosure index developed by the Center for Financial Analysis Research
(CIFAR).
This measure captures a country's overall level of mandatory and voluntary
disclosures (Hope et al., 2006).
4.4.4
Controls
Following previous research, I include a number of controls associated with measures of
information asymmetry and the information content of the announcement. Since this study
focuses on the information asymmetry component of bid-ask spreads, I follow previous research
and include a number of controls for the components of bid-ask spreads that are related to order
21
processing and inventory holding costs.8 Following Bushee et al. (2010), I include the log price
in dollars at the end of the previous quarter as a control for order processing costs (Log Price).
To control for inventory holding costs, I follow Ng et al. (2007) and Bushee et al. (2010) and
include the prior quarter average daily turnover, defined as volume scaled by outstanding shares
(Tumovert.1), and the prior quarter average daily volatility (Volatilityt.1) during the period of
interest (Pre-Event or Event periods). These measures control for changes in order processing
and inventory holding costs before and during earnings announcements.
To test my predictions, it is important to control for the announcement's quality and
information content. For example, a less precise announcement could lead to lower private
information acquisition prior to it due to lower anticipated profits (Kim and Verrecchia, 1991)
and a higher increase in information asymmetry during it (Kim and Verrecchia, 1994).9 To
address these issues, I directly control for the information content and impact of the disclosure by
including the event period absolute abnormal return in my regressions.' 0
I include a number of additional controls associated with the information content of
earnings announcements. Following Landsman and Maydew (2002), I include an indicator
variable to control for the differential information content of earnings announcements in the
fourth quarter versus interim quarters (Fiscal Year-End). Kothari, Shu, and, Wysocki (2009)
show that managers withhold bad news, leading to a stronger market reaction to negative news as
8 Controlling for inventory holding costs is important because previous research finds that inventory holding costs
increase before earnings announcements (So and Wang, 2014).
9 As a second example, the higher level of private information in less transparent information environments before
the disclosure makes the announcement less informative because prices partially capture this private information
before the announcement. Conversely, a richer information environment is related to a higher amount of public
information, which may also pre-empt the anticipated disclosure.
1 In section 5.6.3 I assess the robustness of my results to the inclusion of abnormal returns, which is an endogenous
variable, and find that the results are robust to using earnings surprise as a control. These results mitigate the
concern that the relation between the information environment and the market reaction during the announcement
may affect the documented relation between the firm-level information environment and changes in information
asymmetry during the announcement.
22
compared to positive news. Therefore, I also include a control for whether the announcement
was related to negative abnormal returns. Neg ret is an indicator variable that equals one if the
firm's three day cumulative abnormal return is negative and zero otherwise. In robustness
analyses, I include earnings surprise (SUE), defined as the absolute earnings surprise scaled by
price, as a control for the information content. Expected earnings are based on analysts'
forecasts, if available, and a seasonal random walk model otherwise.
I include a control for the percentage of closely held shares (% Closely Held Shares)
because larger institutional investors may possess information advantages due to greater access
to management and private firm-specific information (Yohn, 1998; Piotroski and Roulstone,
2004). In addition, I control for whether the firm is cross-listed in an U.S. stock exchange
because these firms have different disclosure and regulatory requirements.
Finally, for the Event period test, I control for the dissemination of the earnings
announcement news. Bushee et al. (2010) show that information asymmetry during earnings
announcements is lower when the news is more widely disseminated. Therefore, I include the
log of the number of business press sources covering the earnings announcement plus one
(Dissemination).
5.
5.1
Results
Sample andDescriptive Statistics
The sample consists of a set of quarterly earnings announcements from 2000 to 2010."
Following Griffin et al. (2011) and Barber et al. (2013), I obtain earnings announcement dates
from Bloomberg. Because an incorrect identification of earnings announcement dates may lead
" The beginning of sample period is limited by data availability of press releases information from Ravenpack.
12 Griffin et al. (2011) find that Bloomberg earnings announcement dates are more than twice
as accurate as I/B/E/S
dates.
23
to incorrect inferences, 13 I follow Griffin et al. (2011) and check the accuracy of the earnings
announcement dates by the first press release related to an earnings announcement. I exclude
earnings announcement dates for which the article is released at a different earnings
announcement date. Following Barber et al. (2013), I delete as errors announcements that are
made more than 150 calendar days after the fiscal period-end date. Finally, following Griffin et
al. (2011), I only include countries with at least 20 earnings announcement dates.
I obtain earnings announcement articles and press release information from RavenPack
News Analytics. This dataset has several important features. First, the RavenPack News
Analytics database provides comprehensive international business press coverage. 14 Second, the
database includes categories for each article, which permits the identification of articles related
to earnings announcements, press releases, and unanticipated events. Finally, the database
provides the number of sources covering a particular story, which allows me to control for the
dissemination of the earnings announcement news.
The total sample consists of a set of 88,902 quarterly earnings announcements from 39
countries. To be included, each firm is required to have financial accounting and security price
data. I obtain financial accounting data from Worldscope; daily price and bid-ask spread data
from Datastream for non-U.S. firms, and from CRSP for U.S. firms; and analyst coverage
information from I/B/F/S. To mitigate the influence of outliers, I winsorize all continuous
variables at the 1% and 99% levels.
Specifically, if earnings announcements occur before the reported date in Bloomberg, I would capture the
announcement date in the pre-announcement period. Therefore, I would obtain an artificial increase in the bid-ask
period and no increase in bid-ask spreads in the announcement period.
sreads
i Griffininettheal.pre-announcement
(2011) use Factiva as their source of media articles. Ravenpack and Factiva use similar underlying
sources for news events, but Ravenpack has a comprehensive machine-readable database. Ravenpack receives news
directly from Dow Jones including Dow Jones Newswires, all editions of the Wall Street Journal, and Barron's.
Shroff et al. (2013) validate the press releases obtained from Ravenpack for a subsample of 50 firms and find that
the correlation of press release frequency between both data sources is 94.7%.
13
24
Table 1 presents descriptive statistics for the sample. Panel A presents the list of the 39
countries included in my sample. The sample includes developed economics (e.g., the United
States, the United Kingdom, and Canada) and emerging economies (e.g., Brazil, Thailand, and
South Africa). The U.S. and Japan present the highest number of observations in the sample.
Most countries show an increase in information asymmetry both before (28 countries) and during
the announcement (26 countries). In terms of proxies for the information environment, the
Netherlands, China, and France have the highest number of analysts (7.83, 7.27, and 7.08
respectively), while Argentina, South Korea, Taiwan, and Japan have, on average, less than one
analyst following the firm. In terms of press releases, the Netherlands, the United States, and
Germany have the highest average number of press releases per year (43.22, 31.04, and 26.55
respectively), while fourteen countries, including Japan, have fewer than 10 press releases per
year.
Panel B presents descriptive statistics for the sample. The mean spread in the non-event
period is 1.06%. On average, firms experience a 3.78% increase in spreads in the pre-event
period and an 11.50% increase in spreads in the three days surrounding the earnings
announcement. However, the median value for both changes in information asymmetry are
negative, suggesting that less than half of the firms in the sample experience an increase in
information asymmetry in the pre-event and event periods.' 5 The sample provides substantial
cross-sectional variation in terms of the firm-level information environment. An important
portion of the sample has a low level of analysts and press releases. Analyst coverage has a
median value of one and the median number of yearly press releases is 12.
15
The mean and median values for the event period are consistent with those of Bushee et al. (2010).
25
Panel C of Table 1 presents Pearson and Spearman correlations among the variables. The
three firm-level proxies for the information environment are highly correlated (Pearson
correlations between 0.47 and 0.56), suggesting that these measures capture similar variations in
the information environment. Environment has Pearson correlations of about 0.8 with each of the
individual firm-level measures. Further, the fin-level measures of the information environment
are positively correlated with the Disclosure Index and the CIFAR index.
correlations
between all four firm-level measures of the information
The Pearson
environment and
ASpreadre-Even, are negative, which is consistent with a lower increase in information asymmetry
before the announcement in stronger information environments. In contrast, all four Pearson
correlations between the measures of information environment and ASpreadEvent are positive,
which is consistent with a greater increase in information asymmetry during the announcement
in stronger information environments.
5.2
UnivariateAnalysis
Table 2 presents the mean values of Pre-Event and Event period changes in spreads for the
different firm- and country-level information environment partitions. The mean values for the
entire sample (All) provide preliminary support for my first two hypotheses predicting the effect
of the firm-level information environment on changes in information asymmetry in the PreEvent (Hla) and Event (Hlb) periods. For the Pre-Event period, firms experience an average
increase in spreads of 3.78%. Given that the information asymmetry component of the bid-ask
spread is approximately 47% (Krinsky and Lee, 1996), this increase in spreads represents an
increase of 8% in the bid-ask spreads' information asymmetry component. The Low firm-level
information environment partition has, on average, an increase in spreads which is 3.61% higher
26
than the High partition. These statistics suggest a negative relation between the firm-level
information environment and changes in information asymmetry in the Pre-Eventperiod.
For the Event period, fmns experience an average increase in spreads of 11.50%, which
represents an approximate increase in the information asymmetry component of bid-ask spreads
of 25%. The High Environment partition shows, on average, an increase in spreads of 13.87%.,
which is 5.28% higher than the average increase in spreads for the Low Environment partition.
Overall, these statistics suggest that during the announcement, information asymmetry increases
more in stronger information environments than in weaker ones.
The partitions based on country-level information environment provide preliminary
support for my last two hypotheses, which predict a greater effect of the firm-level information
environment on Pre-Event (Event) information asymmetry when the country-level information
environment is weaker (stronger). For the Pre-Event period, the difference in spreads of 4.76%
between the High and Low firm-level partition is greater in the Low country-level partition than
in the High country-level partition. The greatest increase in spreads, 6.75%, occurs when both
the country-level and the fimn-level information environments are low. For the Event period,
only the High country-level partitions show a statically different change in spreads between the
Low and High firn-level
partitions, 9.65%.
Further, the greater increase
in spreads,
approximately 17%, occurs for firms that have both high finn- and country-level information
environments. Overall, these statistics suggest that the country-level information environment
also limits the incentives to gather private information, leading to a lower effect of the finn-level
information environment on information asymmetry before the announcement and a greater
effect during it.
27
5.3
Pre-Event Change in InformationAsymmetry
Table 3 presents the regression results for my first hypothesis, which predicts that stronger
information environments experience a lower increase in information asymmetry before earnings
announcements. The dependent variable is pre-event period changes in spreads (ASpreadre-Event).
Table 3 shows that firm-level proxies for the information environment are negatively related to
the pre-event changes in spreads. The coefficients on Rank for all four proxies are negative and
statistically significant. When considering the overall measure of Environment, the results show
that firms in the highest tercile of Environment experience a 3.81% lower increase in information
asymmetry. Compared to the mean change in spread for the lowest tercile of 5.72% (Table 2),
the results suggest that firms in the lowest tercile of environment experience approximately a
three times greater increase in information asymmetry in the Pre-Event period than do firms in
the highest tercile.
5.4
Event Change in InformationAsymmetry
Table 4 presents regression results for hypothesis H2, which predicts that information
asymmetry
during
earnings
announcements
increases
with
the
firm-level
information
environment. The dependent variable is the Event period change in spreads (ASpreadEvent).
Consistent with my prediction, the coefficients on Rank for three of the four proxies for
information environment are positive and significant. When considering the overall measure of
Environment, the results show that firms in the highest Environment tercile experience a 2.73%
greater increase in information asymmetry. Compared to the mean change in spread for the
lowest tercile, 8.59% (Table 2), the results suggest that finms in the highest tercile of
environment experience approximately a 1.3 times greater increase in information asymmetry in
the Event period.
28
5.5
Country-Level Information Environment
Table 5 presents the regression results for hypothesis H2a, which predicts that the effect of
the firm-level information environment on pre-event changes in information asymmetry is
stronger in countries with weaker information environments. For parsimony, I only present the
6
results corresponding to the combined measure, Environment.1 Consistent with my prediction, I
find that the coefficient on Rank Environment is more negative in the low country-level
information environment partition. For the Low Disclosure Index partition, the coefficient on
Rank Environment is -5.46 (t-stat=-2.88), while the coefficient for the High Disclosure Index
partition is -1.89 (t-stat=-1.41). Further, the difference in coefficient between the Low and High
partitions of -3.57 is statistically significant at the 1% level. The results are similar when using
the CIFAR Index as the country-level measure.
Table 6 presents regression results for hypothesis H2b, which predicts that the effect of the
firm-level information environment on Event period changes in information asymmetry is
stronger in countries with stronger country-level information environments. Consistent with my
prediction, I find that the coefficient on Rank Environment is more positive in the High countrylevel information environment partition. For the Low DisclosureIndex partition, the coefficient
on Rank Environment is -1.89 (t-stat=-0.48), while the coefficient for the High Disclosure Index
partition is 6.25 (t-stat=1.86). Further, the difference in coefficients between the Low and High
partitions is -8.14 and statistically significant at the 1% level. The results are similar when using
the CIFAR Index as the country-level measure. Overall, the results in Tables 5 and 6 show that
16 The results are similar when
using Disclosure, Analysts, and Market Value as proxies for the firm-level
information environment. For the pre-event level tests, the differences in coefficients on the firm-level information
environment proxies between the High and Low partitions are statistically different at the 10% level in all
specifications for DisclosureIndex and are weaker for Disclosure and Analysts for the CIFAR Index. For the event
period tests, the differences in coefficients on the firm-level information environment variables between the High
and Low partitions are statistically different at the 10% level in all specifications.
29
countries
with
stronger disclosure
requirements
successfully
limit private
information
acquisition, leading to a weaker relation between the firn-level information environment and
changes in information asymmetry before the announcement and a stronger relation during it.
5.6
Additional Analyses
Overall, the results in my earlier analyses are consistent with my hypothesis that the
information environment affects private information acquisition before earnings announcements,
which in turn, affects changes in information asymmetry around the announcement. In this
section, I conduct a number of sensitivity analyses to assess the robustness of my results.
5.6.1
Unanticipatedevents
A potential concern with my empirical tests is that the observed relation between changes
in information asymmetry and the information environment is due to illegal insider trading. To
mitigate this concern, I investigate the relation between the firm-level information environment
and changes in information asymmetry in the pre-event period when an information event is
unanticipated. Insiders have information about unanticipated events and may trade on this
information. In contrast, outside investors do not have incentives to gather private information
before unanticipated
events. Therefore, the relation between the firm-level information
environment and changes in information asymmetry before earnings announcements will be
similar before unanticipated events if illegal insider trading explains this relation.
I select a sample of press releases that are likely to be unanticipated and relevant
information events. These events include merger and acquisitions, announcements related to
facilities, product releases, business contracts, layoffs, and reorganizations.
To mitigate the
concern that the information has been leaked prior to the announcement,
30
I exclude
announcements for which there is a related article in the same category group in the previous 90
calendar days. The final sample consists of 49,088 events.
Appendix A presents the number of observations and absolute abnormal returns per type of
event and country. On average, unanticipated events have a mean absolute abnormal return of
4.88%, which is similar to the earnings announcements absolute abnormal returns of 5.04%. The
similar mean absolute returns suggest that these unanticipated events provide important
information to investors. Further, the unanticipated events sample has a similar distribution
across countries as the sample of earnings announcements.
I repeat my analysis for the pre-event period and estimate equation (1) using my sample of
unanticipated events. Table 7 presents the results for this test. I find that for the Pre-Event period,
the coefficient on Rank Environment for unanticipated announcements is -0.08 (t-stat=-0.09).
Therefore, there is little evidence that the firm-level information environment explain changes in
information asymmetry before unanticipated events.
These results mitigate the concern that
illegal insider trading might explain the documented relation between the firm-level information
environment and changes in information asymmetry before earnings announcements.
5.6.2
Additional country-level measures
My country-level analysis investigates how country-level features that promote public
information interact with the firm-level information environment. As an additional test, I
investigate how a country institutional environment, which limits private information acquisition,
influences the extent to which the firm-level information environment affects private information
acquisition before the announcement. Similar to hypotheses H2a and H2b, I predict that the
effect of the firm-level information environment on Pre-Event (Event) information asymmetry is
weaker (stronger) in countries that have stronger institutions.
31
Countries with stronger
institutions may have stricter laws that limit the access of outsiders to management (e.g.,
regulation FD in the case of the United States), which makes private information acquisition
more costly. The higher cost of acquiring private information, in turn, provides a disincentive for
investors to obtain private information before earnings announcements, reducing the role of the
finn-level information environment in limiting private information acquisition.
I use two country-level measures that capture the extent to which investors are protected
from insider expropriation. First, I follow Bushman et al. (2004) and use the country's legal
origin. Second, I follow Lang et al. (2012) and use the Self-Dealing index developed by Djankov
et al. (2008).
Table 8 presents the results for this test. Consistent with my prediction, I find that for the
Pre-Event period, the coefficient on Rank Environment is statistically negative only for firms
from countries in the low Self-Dealing Index partition or from Code Law countries. In contrast, I
find that for the Event period the coefficient on Rank Environment is statistically positive only
for firms from countries in the high Self-DealingIndex partition or from Common Law countries.
These results suggest that strong country-level institutions limit private information acquisition,
leading to a lower effect of the firm-level information environment on information asymmetry in
the pre-event period and a greater effect in the event period.
5.6.3
Alternative information environment controls
I assess the robustness of my results to the inclusion of abnormal returns as a control for
the information content of the announcement. I repeat my analyses by using earnings surprises as
a control for the information content of the announcement instead of, and in addition to,
abnormal returns. Table 9 presents the results for the subsample of earnings announcements
when the earnings surprise SUE is included as a control. My sample decreases due to the lower
32
number of firms having either IIB/IS forecasts or quarterly earnings information. The results for
the firm-level information environment tests are robust to the inclusion of SUE as a control. For
the pre-event period test (columns 1 and 2), the coefficients on Rank Environment are negative
and significant consistent with hypothesis Hla. The coefficient on SUE is positive and
insignificant when not including absolute abnormal returns as an additional control, consistent
with higher information content earnings announcements providing greater anticipated benefits
of gathering private information prior to the announcement. For the event period test (columns 3
and 4), the coefficient on Rank Environment is positive and significant, consistent with
hypothesis Hib. The coefficient on SUE is positive but insignificant, consistent with higher
information content earnings announcements spurring more beliefs revisions during the
announcement. Furthermore, in the pre-event and event periods, the results remain practically
unchanged after the inclusion of abnormal returns as an additional control for the information
content. These results mitigate the concern that a potential relation of the information
environment and the market reaction at the time of the announcement affects the observed
relation between the firm-level information environment and changes in information asymmetry
around earnings announcements.
5.6.4
Country number of observations
Since the U.S. and Japan represent a considerable proportion of my sample, I follow a
research design similar to Daske et al. (2008) and repeat the country-level analyses by selecting
2,986 random observations from the U.S. and Japan. This number is selected because it
represents the number of observations of the third largest country (the United Kingdom). Table
10 presents the results for this specification. Panel A shows the results for the pre-event period
tests. I find that the coefficients on Rank Environment for the Low Disclosure Index and Low
33
CIFAR Index are more negative than they are for the High partitions. However, the difference
between the coefficients is statistically significant only when using Disclosure Index as the
country-level measure of information environment. Panel B shows the results for the event
period tests. I find that the coefficient on Rank Environment is positive and significant for both
country-level measures. For both indices, the differences in coefficients are statistically different.
These results are consistent with hypotheses H2a and H2b.
Overall, the results in Table 10 show that neither the U.S. nor Japan drive the results
related to the firm-level information environment and changes in information asymmetry in the
pre-event and event periods. However, the results related to the effect of country-level
information environment on the pre-event period are weaker for the CIFAR index, indicating the
results are somewhat sensitive to the exclusion of U.S. observations. These findings are
consistent with the U.S. having a strong information environment that inhibits pre-event private
information acquisition.
5.6.5
Alternative abnormalspreadmeasures
My measure of changes in bid-ask spreads may be affected by the incidence of outliers and
by the volatility of spreads in the non-event period. To address this concern, in untabulated
analyses I repeat my tests using two alternative measures of changes in bid-ask spreads. The first
measure is the difference between the average of the logarithm of spreads in the Pre-Event
(Event) period and the Non-Event period. The second measure corresponds to standardized
abnormal spreads that is the difference between the mean spreads in the Pre-Event (Event) and
Non-Event periods, scaled by the standard deviation in the Non-Event period. The results are
robust to using either of these measures.
34
6.
Conclusion
Despite the extensive research investigating changes in information asymmetry around
earnings announcements, there is little evidence of private information acquisition and increases
in information asymmetry before earnings announcements. In this paper, I revisit this evidence
by investigating the role of the information environment on changes in information asymmetry
around earnings announcements.
Using a global sample of earnings announcements from 39 countries, I find that the
strength of the firm-level information environment is negatively associated with pre-event
changes in information asymmetry and positively associated with event-period changes in
information asymmetry. Furthermore, I show that the strength of the country-level information
environment further limits private information acquisition prior to the announcement, reducing
the effect of the firm-level information environment on information asymmetry before the
announcement and strengthening the effect during the announcement. Overall, my results
suggest that in stronger information environments, investors trade during the announcement
based on informed judgments about public information. In contrast, in weaker information
environments, investors trade before the announcement based on private information.
My findings contribute to the literature by showing that the information environment
shapes
trading
behaviors
and
changes
in
information
asymmetry
around
earnings
announcements. In particular, the results provide an explanation for the empirical evidence on
changes in information asymmetry using U.S. data and illustrate that a different pattern is present
in weaker information environments. In addition, the paper contributes to the international
literature by providing further evidence on the mechanisms through which the information
35
environment affects trading behaviors and private information acquisition around information
events.
36
References
Affleck-Graves, J., C. M. Callahan, and N. Chipalkatti. 2002. Earnings predictability,
information asymmetry, and market liquidity. Journalof Accounting Research 40 (3): 561583.
Atiase, R. K. 1985. Predisclosure information, firm capitalization, and security price behavior
around earnings announcements. Journalof Accounting Research 23 (1): 21-36.
Ball, R., S. P. Kothari, and A. Robin. 2000. The effect of international institutional factors on
properties of accounting earnings. Journal ofAccounting and Economics 29 (1): 1-52.
Ball, R., A. Robin, and J. S. Wu. 2003. Incentives versus standards: Properties of accounting
income in four East Asian countries. Journalof Accounting and Economics 36 (1-3): 235-
270.
Barber, B. M., E. T. De George, R. Lehavy, and B. Trueman. 2013. The earnings announcement
premium around the globe. Journalof FinancialEconomics 108 (1): 118-138.
Bhattacharya, N., H. Desai, and K. Venkataraman. 2008. Earnings quality and information
asymmetry: Evidence from trading costs. Working Paper, Southern Methodist University.
Bushee, B. J., J. E. Core, W. Guay, and S. J. W. Hamm. 2010. The role of the business press as
an information intermediary. JournalofAccounting Research 48 (1): 1-19.
Bushman, R., Q. Chen, E. Engel, and A. Smith. 2004. Financial accounting information,
organizational complexity and corporate governance systems. Journal of Accounting and
Economics 37 (2): 167-201.
Collins, D. W., S. P. Kothari, and J. D. Rayburn. 1987. Firm size and the information content of
prices with respect to earnings. JournalofAccounting and Economics 9 (2): 111-138.
Daley, L. A., J. S. Hughes, and J. D. Rayburn. 1995. The impact of earnings announcements on
the permanent price effects of block trades. Journal of Accounting Research 33 (2): 317334.
Daske, H., L. Hail, C. Leuz, and R. Verdi. 2008. Mandatory IFRS reporting around the world:
Early evidence on the economic consequences. Journal of Accounting Research 46 (5):
1085-1142.
DeFond, M., M. Hung, and R. Trezevant. 2007. Investor protection and the information content
of annual earnings announcements: International evidence. Journal of Accounting and
Economics 43 (1): 37-67.
Demski, J. S., and G. A. Feltham. 1994. Market response to financial reports. Journal of
Accounting andEconomics 17 (1-2): 3-40.
37
Diamond, D. W. 1985. Optimal release of information by firms. The Journal of Finance 40 (4):
1071-1094.
Djankov, S., R. La Porta, F. Lopez-de-Silanes, and A. Shleifer. 2008. The law and economics of
self-dealing. Journalof FinancialEconomics 88 (3): 430-465.
Gow, I. D., D. Taylor, and R. Verrecchia. 2012. Persistence of the complementary relation
between earnings and private information. Working Paper, University of Pennsylvania.
Griffin, J. M., N. H. Hirschey, and P. J. Kelly. 2011. How important is the financial media in
global markets? Review of FinancialStudies.
Hope, O.-K. 2003. Firm-level disclosures and the relative roles of culture and legal origin.
Journalof InternationalFinancialManagement & Accounting 14 (3): 218-248.
Hope, O.-K., J. Jin, and T. Kang. 2006. Empirical evidence on jurisdictions that adopt IFRS.
Journalof InternationalAccounting Research 5 (2): 1-20.
Kim, 0., and R. E. Verrecchia. 1991. Market reaction to anticipated announcements. Journalof
FinancialEconomics 30 (2): 273-309.
Kim, 0., and R. E. Verrecchia.
1994. Market liquidity and volume around earnings
announcements. JournalofAccounting and Economics 17 (1-2): 41-67.
Kim, 0., and R. E. Verrecchia. 1997. Pre-announcement and event-period private information.
JournalofAccounting and Economics 24 (3): 395-419.
Kothari, S. P., S. Shu, and P. D. Wysocki. 2009. Do managers withhold bad news? Journal of
Accounting Research 47 (1): 241-276.
Krinsky, I., and J. Lee. 1996. Earnings announcements and the components of the bid-ask
spread. The Journalof Finance51 (4): 1523-1535.
Landsman, W. R., and E. L. Maydew. 2002. Has the information content of quarterly earnings
announcements declined in the past three decades? Journalof Accounting Research 40 (3):
797-808.
Landsman, W. R., E. L. Maydew, and J. R. Thornock. 2012. The information content of annual
earnings announcements and mandatory adoption of IFRS. Journal of Accounting and
Economics 53 (1-2): 34-54.
Lang, M. H., and R. J. Lundholm. 1996. Corporate disclosure policy and analyst behavior.
Accounting Review 71 (4): 467-492.
Lang, M., K. V. Lins, and M. Maffett. 2012. Transparency, liquidity, and valuation: International
evidence on when transparency matters most. Journalof Accounting Research 50 (3): 729774.
38
La Porta, R., F. Lopez-De-Silanes, and A. Shleifer. 2006. What works in securities laws? The
Journalof Finance61 (1): 1-32.
Lee, C. M., B. Mucklow, and M. J. Ready. 1993. Spreads, depths, and the impact of earnings
information: An intraday analysis. Review of FinancialStudies 6 (2): 345-374.
Maffett, M. 2012. Financial reporting opacity and informed trading by international institutional
investors. JournalofAccounting and Economics 54 (2-3): 201-220.
McNichols, M., and B. Trueman. 1994. Public disclosure, private information collection, and
short-term trading. JournalofAccounting and Economics 17 (1-2): 69-94.
Morck, R., B. Yeung, and W. Yu. 2000. The information content of stock markets: Why do
emerging markets have synchronous stock price movements? Journal of Financial
Economics 58 (1-2): 215-260.
Ng, J. 2007. Earnings surprises and changes in liquidity. Working Paper, Singapore
Management University.
Piotroski, J. D., and D. T. Roulstone. 2004. The influence of analysts, institutional investors, and
insiders on the incorporation of market, industry, and firm-specific information into stock
prices. The Accounting Review 79 (4): 1119-1151.
Shroff, N., A. X. Sun, H. D. White, and W. Zhang. 2013. Voluntary disclosure and information
asymmetry: Evidence from the 2005 securities offering reform. Journal of Accounting
Research 51 (5): 1299-1345.
Shroff, N., R. S. Verdi, and G. Yu. 2013. Information Environment and the Investment Decisions
of Multinational Corporations. The Accounting Review 89 (2): 759-790.
Skinner, D. J. 1993. Bid-ask spreads around earnings announcements: Evidence from the Nasdaq
National Market System. Working Paper, University of Chicago.
So, E. C. and S. Wang. 2014. News-driven return reversals: Liquidity provision ahead of
earnings announcements. Journalof FinancialEconomics Forthcoming
Tetlock, P. C. 2010. Does public financial news resolve asymmetric information? Review of
FinancialStudies 23 (9): 3520 -3557.
Verrecchia, R. E. 1982. The use of mathematical models in financial accounting. Journal of
Accounting Research 20: 1-42.
Yohn, T. L. 1998. Information asymmetry around earnings
QuantitativeFinance and Accounting 11 (2): 165-182.
39
announcements.
Review of
Appendix A
Table Al
Unanticipatedevents
Panel A: Types of unanticipated events
Event
# Observations
Mergers and Acquisitions
Facilities
Abs Ret (%)
17,415
4.36
6,888
4.73
5,338
4.22
12,775
4.95
Layoffs
3,164
6.99
Reorganizations
3,508
6.67
Product releases
Business-contracts
40
Panel B: Unanticipated events by country
Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Hong Kong
India
Indonesia
Ireland
Israel
Italy
Japan
Luxembourg
Malaysia
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Singapore
EarningsAnnouncement
N
Abs Ret (%)
46
3.54
368
4.70
108
3.27
307
3.91
316
3.80
5.58
2,152
2.00
24
4.01
104
191
4.67
445
5.11
3.93
783
4.46
856
4.83
1,152
53
4.17
127
3.34
5.78
191
5.58
167
3.30
304
4.01
33,240
7.22
45
2.74
1,997
98
2.74
270
4.53
5.19
29
4.91
345
116
3.11
3.34
180
2.35
31
852
3.23
UnanticipatedEvents
N
Abs Ret (%)
13
3.28
1,549
5.44
112
3.79
3.52
305
3.13
322
6.81
2,875
19
5.41
97
4.31
4.73
122
3.42
272
2.95
624
1,029
4.21
724
4.04
2.44
105
3.43
39
4.70
152
4.10
230
2.92
245
3.02
1,461
26
4.70
297
3.28
2.71
104
4.83
276
130
2.91
437
4.74
60
2.71
75
2.57
24
2.51
355
2.87
3.49
195
177
3.81
South Africa
3.58
763
3.87
1,754
South Korea
2.73
224
2.70
361
Spain
613
3.85
5.48
753
Sweden
3.50
600
4.73
765
Switzerland
2.31
249
2.92
686
Taiwan
2.86
112
2.84
530
Thailand
5.42
27
2.81
55
Turkey
4.32
2,845
5.37
2,986
United Kingdom
5.18
31,381
6.39
35,938
United States
4.88
49,173
5.04
88,902
Total
The table presents descriptive statistics for the earnings announcements sample and the unanticipated events sample.
Panel A presents descriptive statistics by the type of event. Panel B presents descriptive statistics by country. I use
events for which no other article related to the press release has been issue in the previous 90 calendar days.
41
Figure 1
Timeline
Event period (t- 1 to t+1)
Non-Event period (t-5 5 to t- 11)
-------------------
I
I--
II
-
I------------------
Hib and H2b
I
'-v-'
t=0
Pre-Event period (t- 10 to t-2)
Hia and H2a
The figure illustrates the definitions of the Non-Event, Pre-Event, and Event periods. The Non-Event period is
trading days t-55 to t-11 relative to the earnings announcement date (t=O).The Pre-Event period is defined as trading
days t-10 to t-2. The Event period is defined as trading days t-1 to t+1.
42
Table 1
Descriptivestatistics
Panel A: Descriptive statistics by country
Country level
Firm level
Spreads
Country
N
NonEvent (%)
APre-Event
(%)
AEvent
(%)
Disclosure
Analysts
Market
Value
Disclosure
Index
CIFAR
Index
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Hong Kong
India
Indonesia
Ireland
Israel
Italy
Japan
Luxembourg
Malaysia
Mexico
46
1.15
1.55
0.66
0.76
0.89
1.68
1.04
0.41
0.45
0.77
0.39
1.01
1.38
0.70
1.12
2.31
0.44
0.48
1.40
0.48
1.06
1.05
0.35
7.70
4.50
0.16
4.92
2.32
5.55
-14.73
3.04
15.47
12.09
2.31
11.68
-10.58
-5.52
-2.26
3.17
1.47
0.26
-2.73
7.17
1.60
-2.48
0.95
15.31
10.97
30.87
-1.44
3.95
6.96
13.26
17.74
16.61
14.24
20.82
10.42
15.77
14.11
12.02
21.03
26.55
9.72
17.26
6.35
11.37
23.68
16.19
8.45
12.71
5.80
7.10
43.22
0.43
3.79
2.81
3.06
4.28
13.63
13.78
14.20
13.87
15.27
13.51
15.13
14.69
14.57
13.66
15.41
14.60
13.52
13.79
14.57
13.28
13.92
15.47
12.47
15.05
13.24
15.07
0.50
0.75
0.25
0.42
0.25
0.92
0.58
0.58
0.50
0.50
0.75
0.42
0.92
0.92
0.50
0.67
0.67
0.67
0.75
68
80
62
68
56
75
78
0.92
0.58
15.95
0.50
79
74
74
Netherlands
368
108
307
316
2,152
24
104
191
445
783
856
1,152
53
127
191
167
304
33,240
45
1,997
98
270
1.95
-3.19
4.96
3.94
-0.19
0.11
0.33
3.71
-1.83
-0.43
2.23
1.91
4.60
20.17
1.30
-7.25
-1.66
-0.76
43
4.81
1.21
7.27
5.03
5.22
7.08
4.74
4.50
3.70
4.13
2.24
1.67
5.67
0.79
4.22
2.34
3.31
7.83
75
83
78
67
73
61
81
74
66
71
Table 1 (continued)
Firm level
Spreads
New Zealand
Norway
Philippines
Poland
Portugal
Singapore
South Africa
South Korea
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
United Kingdom
United States
N
29
345
116
180
31
852
177
1,754
361
753
765
686
530
55
2,986
35,938
NonEvent (%)
APre-Event
(%)
AEvent
(%)
Disclosure
Analysts
Market
Value
Disclosure
Index
CIFAR
Index
1.22
0.65
1.46
0.63
0.71
0.97
0.61
0.41
0.31
0.67
0.77
0.54
0.76
0.70
1.73
0.75
3.49
2.94
0.55
5.43
-0.62
-0.18
-0.78
2.05
-0.30
2.61
3.37
21.11
-0.87
4.71
1.37
3.71
5.22
1.13
-3.29
2.91
-1.73
-2.46
-4.92
1.08
4.74
1.79
0.50
13.41
-0.80
1.15
10.25
16.18
8.97
18.50
8.04
9.28
4.00
8.44
14.98
9.19
17.15
18.31
14.82
15.58
9.51
4.67
12.47
31.04
2.14
6.14
1.56
2.18
1.94
3.18
3.60
0.51
6.30
4.66
4.32
0.73
3.82
4.20
3.27
6.35
13.27
14.54
13.82
14.75
12.55
14.05
15.58
13.91
15.41
14.03
14.15
12.50
14.12
15.25
13.68
13.40
0.67
0.58
0.83
0.42
1.00
0.83
0.75
0.50
0.58
0.67
0.75
0.92
0.50
0.83
1.00
80
75
64
44
-
Country
Country level
56
79
79
68
72
83
80
58
66
58
85
76
Table 1 (Continued)
Panel B: Descriptive statistics
Variable
Spreads Non-Event (%)
ASpreads Pre-Event (%)
A Spreads Event (%)
Disclosure
Analysts
Market Value
% Closely Held
Log Price
ADR
Volatility, 1 P-E,,e,,,
Volatility,.I Event
Turnovert-I Pre-Event
Turnovert-I Eent
Fiscal Year-End
Abs Ret (%)
Neg Ret
Dissemination
SUE
Neg SUE
N
88,902
88,902
88,902
Mean
1.06
3.78
11.50
Stdev
P25
Median
1.64
0.22
0.49
39.94
63.66
-21.03
-27.48
-2.99
-2.20
P75
1.13
19.73
32.83
88,902
88,902
18.85
3.68
18.05
12.00
1.00
26.00
5.00
88,902
88,902
88,902
13.18
25.33
2.16
5.77
1.96
22.71
1.45
7.00
0.00
11.75
6.00
1.24
13.02
20.00
2.25
14.54
41.00
3.16
88,902
88,902
0.06
0.09
0.24
0.17
3.03
6.06
88,902
0.19
0.38
88,902
5.29
11.25
88,902
0.32
0.47
0.00
0.03
0.01
0.06
0.01
0.00
0.00
0.08
88,902
0.00
0.02
0.00
0.02
0.00
0.00
88,902
5.04
6.24
1.41
3.23
6.44
88,902
0.49
1.14
66,421
66,421
0.03
0.42
0.50
0.76
0.12
0.49
0.00
0.69
0.00
0.00
0.00
0.69
0.00
0.00
1.00
88,902
45
3.62
0.17
5.43
1.00
1.61
0.01
1.00
Table 1 (Continued)
Panel C: Pearson / Spearman correlations
(13)
(14)
(15)
(16)
(17)
(8)
(9)
(10)
(11)
(12)
(2)
(3)
(4)
(5)
(6)
(7)
Variable
(1)
0.00 -0.02
0.00 0.00 -0.02 0.05
0.34 -0.01 -0.03 -0.04 -0.03 0.00 -0.02 0.01 -0.01 0.00
ASpreads Pre-Event
(1)
0.01 -0.03
0.00 0.06 -0.02 0.07
0.02 -0.01 0.06
0.05 0.01
0.06 0.03 0.02 0.05
0.35
(2) ASpreads Event
0.00 0.09
0.08
-0.04
0.43
0.07
0.42 0.25 -0.02 0.01 0.37
0.54 0.47 0.77
-0.03 0.03
(3) Disclosure
0.18 0.02 -0.01 0.15
0.34 0.01 0.39
0.56 0.80 0.30 0.25 -0.03 0.08
-0.03 0.02 0.57
(4) Analysts
0.53 -0.16 0.16 -0.03 -0.13 -0.01 0.35
0.80 0.04 0.13 -0.04 0.30
-0.03 0.01 0.39 0.53
(5) Market Value
0.01 -0.01 0.23
0.05
0.52 -0.01 0.43
0.38 0.29 -0.05 0.16
-0.04 0.03 0.79 0.83 0.79
(6) Environment
0.47 -0.04 0.15 0.00 -0.17
0.50 -0.09 -0.21 0.11 0.17
0.46
(7) DisclosureIndex -0.02 0.01 0.58 0.49 0.09
0.11 -0.02 0.06
0.23 0.01
-0.05 -0.03 0.09 0.11
0.41 0.66
-0.03 -0.02 0.41 0.43 0.18
(8) CIFAR Index
0.00 0.02
-0.02
0.03
-0.04
-0.03
0.05
-0.01
-0.10
-0.12
-0.07
-0.06
-0.06
-0.03
0.01
0.01
(9) % Closely Held
0.00 0.30
-0.05
0.02
-0.12
-0.05
0.03
-0.02
-0.04
-0.19
0.17
0.05 0.29
0.00 0.00 0.03
(10) ADR
0.07
-0.01
-0.05
-0.04
0.23
-0.08
0.04
0.04
0.16
-0.02 0.03 0.42 0.44 0.58 0.58 0.22
(11) Log Price
-0.06
0.01
0.24
0.00
0.32
0.13 -0.01 -0.05 -0.06
-0.01 0.00 0.12 0.08 -0.15 0.02 0.17
(12) Volatility.1 Event
-0.04 0.19 0.01 -0.09
0.54 -0.13 -0.10 0.41 0.32
0.62 0.77
0.67 0.59 0.29
-0.01 0.04
(13) Turnover,-1 Event
0.01 -0.02 0.06
(14) Fiscal Year-End -0.01 -0.01 -0.06 0.23 -0.03 0.05 -0.05 -0.01 0.04 0.02 -0.04 -0.01 -0.06
-0.02 -0.06
0.20 0.01
0.05 0.11 0.09 -0.08 0.05 0.14 0.11 -0.01 -0.04 -0.02 0.25
0.03
(15) Abs Ret
0.00
0.02 -0.02 -0.02
0.02 0.00 -0.01 -0.01 -0.01 0.00 -0.01 0.00 0.00 -0.01 0.01
0.00
(16) Neg Ret
0.07 -0.09 -0.06 0.07 -0.06 0.00
0.11 0.30 0.19 -0.13 0.03 -0.01 0.26
0.00 -0.01 0.03
(17) Dissemination
Panel A presents descriptive statistics by country. Panel B presents descriptive statistics for the entire sample. Panel C presents Pearson (above the diagonal) and
Spearman (below the diagonal) correlations. The Pre-Event period is defined as trading days t-10 to t-2 relative to the earnings announcement date (t=0). The
Event period is defined as trading days t-1 to t+1. The Non-Event period is trading days t-55 to t-1 1. ASpreads is the percentage change in spreads between the
Pre-Event period average Spreads and the Non-Event period average Spreads. Spreads is the difference between the ask and bid prices, deflated by the midpoint
of the ask and bid prices Disclosureis the number of press releases during the calendar year period prior to the fiscal quarter period-end. Analysts is the number
of analysts providing a forecast for the fiscal quarter prior to the earnings announcement. Market Value is the log of the market value of equity at the end of the
fiscal quarter prior to the announcement measured in U.S. dollars. Environment is the average of the standardized values of Analysts, Disclosure, and Market
Value. DisclosureIndex is the disclosure index developed by La Porta (2006). CIFAR Index is the disclosure index developed by CIFAR. ADR is an indicator
variable equal to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Price is the log of the price at the end of the fiscal quarter prior
to the announcement measured in U.S. dollars. Turnover,.1 is the prior quarter average daily turnover during the Pre-Eventperiod. Volatility,.1 is the prior quarter
average daily volatility during the Pre-Event period. % Closely Held is the percentage of the strategic number of shares held by institutional investors or other
institutions and not available to ordinary investors. FiscalYear-End is an indicator variable equal to one for earnings announcements corresponding to the fourth
fiscal quarter and zero otherwise. Abs Ret is the three days absolute cumulative abnormal return. Neg Ret is an indicator variable that equals one if Abs Ret is
negative and zero otherwise. Disseminationis the log of one plus the number of sources disseminating the earnings announcement. SUE is the absolute earnings
surprise based on analysts' earnings forecasts, if available, or a seasonal random walk model otherwise scaled by price. Neg SUE is an indicator variable that
equals one if the earnings surprise is negative and zero otherwise. Each firm is required to have earnings announcement dates from Bloomberg and RavenPack,
financial data from Worldscope, and price data from Datastream or CRSP. All continuous firm-level variables are winsorized at the 1% and 99% levels.
46
Table 2
Univariateanalysis
ASpreads Event Period (%)
Environment
ASpreads Pre-EventPeriod(%)
Environment
All
All
3.78*
(1.89)
Low DisclosureIndex
4.30**
(2.25)
High DisclosureIndex
3.31
(1.21)
0.98
(0.39)
DifLow-High
Low CIFAR Index
High CIFAR Index
Dif Low-High
4.35**
(2.19)
3.25
(1.26)
1.10
(0.49)
Low
Mid
High
Hig
Hgh-Low
Low
Mid
High
High-Low
11.50***
(3.97)
8.59***
(3.48)
12.03***
(3.99)
13.87***
(4.23)
5.28***
(3.66)
3.51*
2.11
(2.60)
(1.89)
(1.00)
(-3.87)
6.75***
(3.97)
4.77
1.99
(0.86)
-4.76***
(-2.60)
-2.55*
(-1.86)
-2.21
(-0.83)
9.22***
(4.57)
9.46***
(4.53)
8.22***
(3.58)
9.99***
(3.53)
0.53
(0.17)
13.59***
(3.31)
-4.37
(-1.40)
7.80**
(2.48)
1.66
(0.78)
15.54***
(4.08)
17.45***
(3.12)
-7.46
(-1.22)
9.65***
(2.72)
-9.14
(-1.47)
-4.57**
(2.50)
-2.70*
(-1.94)
-1.87
(-0.70)
9.49***
(4.36)
13.39***
(3.38)
3.90
(1.36)
9.64***
(4.23)
7.60***
(2.54)
2.04
(0.99)
10.22***
(3.62)
17.31***
(3.20)
-7.09
(-1.23)
0.58
(0.19)
9.71***
(2.79)
-9.13
(-1.53)
5.72***
1.97
4.15*
(1.98)
2.87
(1.22)
1.23
(0.947
(0.56)
2.18
(0.70)
-0.24
(0.07)
6.72***
(3.66)
4.78*
(1.70)
1.94
(1.07)
4.18*
(1.93)
2.89
(1.38)
1.29
(0.68)
2.15
(0.92)
2.08
(0.68)
0.06
(0.02)
(1.62)
-3.61***
All
-7.32***
(-3.06)
8.61***
(3.52)
15.25***
(4.13)
-6.64***
(-3.11)
The table reports mean values of changes in the bid-ask spreads in the Pre-Event and Event periods for different partitions based on the firm- and country-level
information environment. The Pre-Event period is defined as trading days t-10 to t-2 relative to the earnings announcement date (t=0). The Event period is
defined as trading days t-1 to t+1 relative to the earnings announcement date (t=0).The Non-Event period is trading days t-55 to t-11. The sample consists of
quarterly earnings announcements from 39 countries between 2000 and 2010. ASpreads is the percentage change in spreads between the Pre-Event or Event
period average Spreads and the Non-Event period average Spreads. Spreads is the difference between the ask and bid prices, deflated by the midpoint of the ask
and bid prices. All includes all observations. The columns present partitions based on tercile values of Environment by country. Environment is the average of the
standardized values of Analysts, Disclosure, and Market Value. Disclosure is the number of press releases during the calendar year period prior to the earnings
announcement. Analysts is the number of analysts providing a forecast for the fiscal quarter prior to the earnings announcement. Market Value is the log of the
market value of equity at the end of the fiscal quarter prior to the announcement measured in U.S. dollars. Low (High) Disclosure Index includes firms with a
DisclosureIndex below (above) the median. DisclosureIndex is the disclosure index developed by La Porta (2006). Low (High) CIFAR Index includes firms with
a CIFAR Index below (above) the median. CIFAR Index is the disclosure index developed by CIFAR. Each firm is required to have earnings announcement dates
from Bloomberg and RavenPack, financial data from Worldscope, and price data from Datastream or CRSP. All continuous firm-level variables are winsorized
at the 1% and 99% levels. t-statistics are presented in parentheses below the mean values and are estimated using a two way cluster at the firm and year levels.
***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
47
Table 3
Pre-event period change in spreads (%)
Variables
Rank Disclosure
(1)
(2)
(3)
(4)
-1.17***
(-3.55)
Rank Analysts
-2.06**
(-2.29)
Rank Market Value
-3.35***
(-4.00)
Rank Environment
% Closely Held
-3.81***
(-4.58)
0.03***
0.03***
(2.92)
(3.02)
Log Price
ADR
Volatility,
Turnover,.1
FiscalYear-End
Abs Ret
Neg Ret
Observations
R-squared
Cluster
FE
0.02***
(2.59)
0.02**
(2.35)
-0.25
-0.13
0.22
0.19
(-0.78)
0.57
(0.42)
-3.99
(-1.31)
(-0.49)
0.64
(0.45)
-4.33
(-1.43)
(0.96)
1.17
(0.90)
-4.71*
(-1.65)
(0.76)
1.32
(1.05)
-4.90*
(-1.76)
0.00
0.03
0.04
0.05
(0.02)
-1.19
(-0.65)
(0.43)
-0.54
(-0.32)
(0.56)
-1.15
(-0.63)
(0.82)
-0.86
(-0.47)
0.30***
0.30***
(3.96)
-0.26
(-0.64)
(3.97)
-0.27
(-0.66)
0.30***
0.30***
(3.89)
-0.28
(-0.69)
(3.90)
-0.27
(-0.67)
88,902
88,902
88,902
88,902
0.0357
0.0359
0.0363
0.0366
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) in the Pre-Event period. ASpreads is
the percentage change in spreads between the Pre-Event period average Spreads and the Non-Event period average
Spreads. The Pre-Event period is defined as trading days t-10 to t-2 relative to the earnings announcement date
(t=0). The Non-Event period is trading days t-55 to t-11. Rank Disclosure is the tercile rank of the number of press
releases during the calendar year period prior to the fiscal quarter period-end scaled to range between zero and one.
Rank Analysts is the tercile rank of the number of analysts providing a forecast for the fiscal quarter prior to the
earnings announcement scaled to range between zero and one. Rank Market Value is the tercile rank of the market
value of equity at the end of the fiscal quarter prior to the announcement measured in U.S. dollars scaled to range
between zero and one. Rank Environment is the tercile rank of the average of the standardized values of Disclosure,
Analysts, and Market Value scaled to range between zero and one. % Closely Held is the percentage of the strategic
number of shares held by institutional investors or other institutions and not available to ordinary investors. ADR is
an indicator variable equal to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Priceis
the log of the average price at the end of the fiscal quarter prior to the announcement measured in U.S. Dollars.
Turnover,- is the prior quarter average daily turnover during the Pre-Event period. Volatility, is the prior quarter
average daily volatility during the Pre-Event period. Fiscal Year-End is an indicator variable equal to one for
earnings announcements corresponding to the fourth fiscal quarter and zero otherwise. Abs Ret is the three days
absolute cumulative abnormal return. Neg Ret is an indicator variable that equals one if the three-day cumulative
return is negative and zero otherwise. All continuous firm-level variables are winsorized at the 1% and 99% levels.
The specification includes country-year (CY) and industry (I) fixed effects. t-statistics are presented in parentheses
below the coefficients and are estimated using a two way cluster at the firm and year levels. *
**, and * denote
significance at the 1%, 5%, and 10% levels, respectively.
48
Table 4
Event period change in spreads (%)
Variables
Rank Disclosure
(1)
(2)
(3)
(4)
2.50*
(1.88)
Rank Analysts
2.61**
(2.01)
Rank Market Value
1.89
(1.23)
Rank Environment
% Closely Held
Log Price
ADR
Volatilityt,1
Turnover,.,
FiscalYear-End
Abs Ret
2.73**
(2.01)
0.03
(1.46)
1.52***
(7.43)
3.02
(1.25)
-2.34***
(-3.07)
0.09**
(2.42)
-1.60
(-0.90)
0.03
(1.44)
1.40***
(6.40)
3.14
(1.39)
-2.25***
(-2.98)
0.08**
(2.08)
-2.47
(-1.63)
0.03
(1.47)
1.33***
(9.51)
3.12
(1.48)
-2.31***
(-2.73)
0.09**
(2.09)
-1.70
(-0.97)
0.04
(1.58)
1.27***
(7.81)
2.93
(1.34)
-2.24***
(-2.86)
0.08**
(2.01)
-1.90
(-1.09)
0.56***
0.56***
0.56***
0.56***
(3.51)
(3.54)
1.72***
(2.68)
(3.55)
(2.68)
(3.54)
1.72***
(2.72)
Dissemination
0.40
(1.01)
0.52
(1.16)
0.51
(1.10)
0.41
(0.95)
Observations
88,902
88,902
88,902
88,902
R-squared
0.0367
0.0367
0.0366
0.0367
Neg Ret
1.71***
1.72***
(2.70)
Cluster
Firm-Year
Firm-Year
Firm-Year
Firm-Year
FE
CY, I
CY, I
CY, I
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) in the Event period. ASpreads is the
percentage change in spreads between the Event period average Spreads and the Non-Event period average Spreads.
The Pre-Event period is defined as trading days t-1 to t+1 relative to the earnings announcement date (t=0). The
Non-Event period is trading days t-55 to t-11. Rank Disclosure is the tercile rank of the number of press releases
during the calendar year period prior to the fiscal quarter period-end scaled to range between zero and one. Rank
Analysts is the tercile rank of the number of analysts providing a forecast for the fiscal quarter prior to the earnings
announcement scaled to range between zero and one. Rank Market is the tercile rank of the market value of equity at
the end of the fiscal quarter prior to the announcement measured in U.S. dollars scaled to range between zero and
one. Rank Environment is the tercile rank of the average of the standardized values of Disclosure, Analysts, and
Market Value scaled to range between zero and one. % Closely Held is the percentage of the strategic number of
shares held by institutional investors or other institutions and not available to ordinary investors. ADR is an indicator
variable equal to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Priceis the log of
the average price at the end of the fiscal quarter prior to the announcement measured in U.S. Dollars. Turnover,-, is
the prior quarter average daily turnover during the Event period. Volatility,-, is the prior quarter average daily
volatility during the Event period. FiscalYear-End is an indicator variable equal to one for earnings announcements
corresponding to the fourth fiscal quarter and zero otherwise. Abs Ret is the three days absolute cumulative
abnormal return. Neg Ret is an indicator variable that equals one if the three-day cumulative return is negative and
zero otherwise. All continuous firm-level variables are winsorized at the 1% and 99% levels. The specification
includes country-year (CY) and industry (I) fixed effects. t-statistics are presented in parentheses below the
coefficients and are estimated using a two way cluster at the firm and year levels. *
**, and * denote significance
at the 1%, 5%, and 10% levels, respectively.
49
Table 5
Partitionsbased on country-level information environment - Pre-eventperiod
CIFAR Index
DisclosureIndex
Variables
Rank Environment
% Closely Held
High
-1.89
(-1.41)
Low
-5.46***
(-2.88)
0.02**
0.02*
(2.43)
(1.76)
ADR
Log Price
Volatility,,
Turnovert,1
Fiscal Year-End
Abs Ret
(2.61)
1.94**
-1.03
1.35
0.60
(-0.84)
-0.35
(-0.99)
-0.43
(-0.27)
(1.18)
(0.48)
-0.28
(-0.65)
-0.54
(-0.32)
0.03
(0.58)
0.11
(0.10)
0.25**
0.43***
(2.45)
-0.82***
(-2.70)
0.28
(0.41)
Difference in Coef Rank
p-value
Observations
R-squared
Cluster
FE
0.02***
0.02**
(2.07)
(2.19)
0.53*
(1.82)
-19.69**
(-2.08)
-1.37***
(-2.81)
-1.89
(-0.46)
(2.72)
Neg Ret
High
-2.29*
(-1.81)
Low
-5.06***
(-2.67)
0.50*
(1.84)
-17.94**
(-2.16)
-0.39
(-1.44)
-2.06
(-0.49)
0.35***
(3.11)
-0.04
(-0.06)
0.03
(0.65)
0.27
(0.23)
0.27***
(2.80)
-0.52*
(-1.65)
-2.77***
-3.57***
0.009
0.000
45,764
42,620
46,282
43,138
0.0397
0.0372
0.0397
0.0365
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) in the Pre-Event period for partitions
based on the DisclosureIndex and the CIFAR Index. ASpreads is the percentage change in spreads between the PreEvent period and the Non-Event period average Spreads. Low (High) Disclosure Index includes firms with a
DisclosureIndex below (above) the median. DisclosureIndex is the disclosure index developed by La Porta (2006).
Low (High) CIFAR Index includes firms with a CIFAR Index below (above) the median. CIFAR Index is the
disclosure index developed by CIFAR. The Pre-Event period is trading days t-10 to t-2 relative to the earnings
announcement date (t=0). The Non-Event period is trading days t-55 to t-11. Rank Environment is the tercile rank of
the average of the standardized values of Disclosure, Analysts, and Market Value scaled to range between zero and
one. Disclosureis the number of press releases during the calendar year period prior to the fiscal quarter period-end.
Analysts is the number of analysts providing a forecast for the fiscal quarter prior to the earnings announcement
Market Value is the log of the market value of equity at the end of the fiscal quarter prior to the announcement
measured in U.S. dollars. % Closely Held is the percentage of the strategic number of shares held by institutional
investors or other institutions and not available to ordinary investors. ADR is an indicator variable equal to one if the
firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Price is the log of the average price at the end
of the fiscal quarter prior to the announcement measured in U.S. Dollars. Turnover,-, is the prior quarter average
daily turnover during the Pre-Event period. Volatility,.I is the prior quarter average daily volatility during the PreEvent period. Fiscal Year-End is an indicator variable equal to one for earnings announcements corresponding to the
fourth fiscal quarter and zero otherwise. Abs Ret is the three days absolute cumulative abnormal return. Neg Ret is an
indicator variable that equals one if the three-day cumulative return is negative and zero otherwise. All continuous
firm-level variables are winsorized at the 1% and 99% levels. The specification includes country-year (CY) and
industry (I) fixed effects. t-statistics are presented in parentheses below the coefficients and are estimated using a
two way cluster at the firm and year levels. Assessments of significance across partitions are made based on
standard errors clustered at the country level. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively.
50
Table 6
Partitionsbased on country-level informationenvironment - Event period
CIFAR Index
DisclosureIndex
Variables
Rank Environment
% Closely Held
ADR
Log Price
Volatility,j
Turnover,.,
Fiscal Year-End
Abs Ret
Neg Ret
Dissemination
Low
-1.89
(-0.48)
High
6.25*
(1.86)
-0.01
0.06*
(1.78)
-2.88*
(-1.73)
(-0.56)
5.54***
(3.34)
0.96***
1.81***
(2.70)
-4.10
(-1.51)
0.50
(0.48)
-2.21
(-0.49)
(3.91)
-1.00
(-1.42)
0.04
(1.45)
-1.41
(-0.90)
0.61***
0.57***
(5.84)
0.29
(0.39)
(2.67)
3.06***
(4.92)
-0.01
(-0.03)
0.98
(1.48)
Difference in Coef. Rank
p-value
Observations
R-squared
Cluster
FE
High
5.78*
(1.71)
Low
-1.28
(-0.34)
-0.01
0.07*
(1.91)
-0.33
(-0.18)
(-0.95)
5.32***
(2.85)
1.00***
(3.59)
-3.27
(-1.59)
0.11
(0.24)
-1.93
(-0.42)
1.88***
(4.31)
-1.23*
(-1.74)
0.04
(1.56)
-1.80
(-1.01)
0.57***
(2.63)
3.19***
(5.48)
0.60***
(7.19)
0.21
(0.25)
0.40
0.41
(0.57)
(0.83)
-8.14***
-7.06***
0.00
0.00
42,620
46,282
43,138
0.0281
0.0419
0.0282
0.0421
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
45,764
The table presents the results of a regression of changes in spreads (ASpreads) in the Event period for partitions
based on the DisclosureIndex and the CIFAR Index. ASpreads is the percentage change in spreads between the PreEvent period and the Non-Event period average Spreads. Low (High) Disclosure Index includes firms with a
DisclosureIndex below (above) the median. DisclosureIndex is the disclosure index developed by La Porta (2006).
Low (High) CIFAR Index includes firms with a CIFAR Index below (above) the median. CIFAR Index is the
disclosure index developed by CIFAR The Event period is trading days t-1 to t+1 relative to the earnings
announcement date (t=0). The Non-Event period is trading days t-55 to t-1 1. Rank Environment is the tercile rank of
the average of the standardized values of Disclosure,Analysts, and Market Value scaled to range between zero and
one. Disclosure is the number of press releases during the calendar year period prior to the fiscal quarter period-end.
Analysts is the number of analysts providing a forecast for the fiscal quarter prior to the earnings announcement
Market Value is the log of the market value of equity at the end of the fiscal quarter prior to the announcement
measured in U.S. dollars. % Closely Held is the percentage of the strategic number of shares held by institutional
investors or other institutions and not available to ordinary investors. ADR is an indicator variable equal to one if the
firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Priceis the log of the average price at the end
of the fiscal quarter prior to the announcement measured in U.S. Dollars. Turnover,.1 is the prior quarter average
daily turnover during the Pre-Event period. Volatility, is the prior quarter average daily volatility during the PreEvent period. Fiscal Year-End is an indicator variable equal to one for earnings announcements corresponding to the
fourth fiscal quarter and zero otherwise. Abs Ret is the three days absolute cumulative abnormal return. Neg Ret is an
indicator variable that equals one if the three-day cumulative return is negative and zero otherwise. All continuous
firm-level variables are winsorized at the 1% and 99% levels. The specification includes country-year (CY) and
industry (I) fixed effects. t-statistics are presented in parentheses below the coefficients and are estimated using a
two way cluster at the firm and year levels. Assessments of significance across partitions are made based on
standard errors clustered at the country level. *
**, and * denote significance at the 1%, 5%, and 10% levels,
respectively.
51
Table 7
UnanticipatedEvents
Variables
Rank Environment
% Closely Held
ADR
Log Price
UnanticipatedEvents
EarningsAnnouncements
-0.08
(-0.09)
-0.00
(-0.16)
1.25*
(1.74)
-3.81***
(-4.58)
0.02**
(2.35)
0.19
(0.76)
1.32
0.14
(0.40)
(1.05)
Volatility,.,
Turnover,.,
Fiscal Year-End
Abs Ret
Neg Ret
0.86
-4.90*
(-1.76)
0.05
(1.00)
-0.01
(-0.42)
(0.82)
-0.86
(-0.47)
0.28***
0.30***
(4.85)
(3.90)
-0.27
(-0.67)
1.14***
(3.25)
-3.73***
Difference in Coef. Rank
p-value
0.00
49,088
88,902
Observations
0.0388
0.0366
R-squared
Firm-Year
Firm-Year
Cluster
CY, I
CY, I
FE
The table presents the results of a regression of changes in spreads (ASpreads) in the Pre-Event for the sample of
earnings announcements (Column 1) and the sample of unanticipated events (Column 2). ASpreads is the percentage
change in spreads between the Pre-Event period and the Non-Event period average Spreads. The Pre-Eventperiod is
trading days t-10 to t-2 relative to the earnings announcement date (t=0). The Non-Event period is trading days t-55
to t-11. Rank Environment is the tercile rank of the average of the standardized values of Disclosure, Analysts, and
Market Value scaled to range between zero and one. Disclosure is the number of press releases during the calendar
year period prior to the fiscal quarter period-end. Analysts is the number of analysts providing a forecast for the
fiscal quarter prior to the earnings announcement Market Value is the log of the market value of equity at the end of
the fiscal quarter prior to the announcement measured in U.S. dollars. % Closely Held is the percentage of the
strategic number of shares held by institutional investors or other institutions and not available to ordinary investors.
ADR is an indicator variable equal to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log
Price is the log of the average price at the end of the fiscal quarter prior to the announcement measured in U.S.
Dollars. Turnover,1 is the prior quarter average daily turnover during the Pre-event (Event) period. Volatility, is the
prior quarter average daily volatility during the Pre-Event (Event) period. Fiscal Year-End is an indicator variable
equal to one for earnings announcements corresponding to the fourth fiscal quarter and zero otherwise. Abs Ret is
the three days absolute cumulative abnormal return. Neg Ret is an indicator variable that equals one if the three-day
cumulative return is negative and zero otherwise. All continuous firm-level variables are winsorized at the 1% and
99% levels. The specification includes country-year (CY) and industry (I) fixed effects. t-statistics are presented in
parentheses below the coefficients and are estimated using a two way cluster at the firm and year levels.
Assessments of significance across partitions are made based on standard errors clustered at the country level. ***,
**, and * denote significance at the 1%, 5%, and 10% levels, respectively.
52
Table 8
Partitionsbased on country-level institutionalfactors
Panel A: Pre-Event
Self-dealing Index
Legal Origin
Variables
Rank Environment
% Closely Held
ADR
Log Price
Volatility,,
Turnovers,
Fiscal Year-End
Abs Ret
Neg Ret
Common Law
Code Law
-2.13
-5.26***
(-1.55)
0.02**
(2.25)
-0.79
(-2.75)
0.02*
(1.80)
1.80*
(1.86)
0.49*
(1.80)
-19.72**
(-2.11)
-0.82***
(-3.00)
(-0.65)
-0.28
(-0.75)
-0.47
(-0.30)
0.03
(0.63)
0.07
(0.06)
0.25**
(2.52)
-0.78**
(-2.41)
-1.86
(-0.45)
0.42***
(2.74)
0.22
(0.34)
Difference in Coef Rank
-3.13***
p-value
Observations
0.00
R-squared
Cluster
FE
42,654
0.0395
Firm-Year
CY, I
High
Low
-2.12
-5.35***
(-1.59)
0.03***
(2.71)
-0.48
(-0.40)
-0.27
(-0.76)
-0.40
(-0.25)
0.03
(-2.85)
0.02*
(1.72)
1.81*
(1.81)
0.49*
(1.76)
-20.04**
(-2.12)
-1.02**
(-2.50)
(0.55)
0.08
(0.07)
0.25**
(2.52)
-0.78**
(-2.41)
-1.91
(-0.45)
0.43***
(2.74)
0.24
(0.37)
-3.23***
0.00
46,248
0.0371
Firm-Year
CY, I
53
42,132
0.0394
Firm-Year
CY, I
46,770
0.0373
Firm-Year
CY, I
Table 8 (Continued)
Panel B: Event Period
Variables
Rank Environment
% Closely Held
ADR
Legal Origin
Common Law
Code Law
6.21*
-1.94
(1.86)
(-0.49)
0.06*
-0.01
(1.84)
(-0.93)
-1.82
5.28***
(3.65)
Log Price
Volatility,.1
0.93***
(2.75)
-3.86
(-1.39)
Turnover,.,
Fiscal Year-End
Abs Ret
Neg Ret
0.16
(0.22)
Dissemination
1.09
(1.60)
0.06*
(1.79)
-1.84
5.43***
(3.28)
(-1.27)
0.92***
(2.81)
1.84***
(4.00)
-4.00
-1.05
(-1.53)
(-1.41)
(-1.52)
0.04
1.50**
(2.12)
-2.06
(1.51)
-1.61
(-0.45)
(-1.01)
0.61***
(5.64)
0.17
(0.23)
0.57***
(2.70)
3.13***
(4.87)
1.01
-0.03
(1.46)
(-0.07)
-7.74***
-8.15***
0.00
Difference in Coef. Rank
Observations
R-squared
Cluster
FE
-0.01
(-0.65)
(-1.01)
1.82***
(3.83)
-1.05
0.03
(1.32)
-1.59
(-1.00)
0.58***
(2.70)
3.18***
(4.78)
-0.09
(-0.25)
0.89**
(2.27)
-2.03
(-0.45)
0.61***
(5.80)
Self-Dealing Index
Low
High
-1.76
5.98*
(-0.45)
(1.81)
0.00
42,654
46,248
42,132
0.0287
0.0416
0.0279
46,770
0.0422
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) for partitions based on Legal Origin
and the Self-Dealing Index. Panel A presents results for the Pre-Event period and Panel B presents results for the
Event period. ASpreads is the percentage change in spreads between the Pre-Event period and the Non-Event period
average Spreads. The partitions are based on legal origin (Common versus Code law), and the median value of the
Self-Dealing Index (Djankov et al., 2008). The Pre-Event period is trading days t-10 to t-2 relative to the earnings
announcement date (t=0). The Event period is trading days t-1 to t+1 relative to the earnings announcement date
(t=0). The Non-Event period is trading days t-55 to t-11. Rank Environment is the tercile rank of the average of the
standardized values of Disclosure,Analysts, and Market Value scaled to range between zero and one. Disclosure is
the number of press releases during the calendar year period prior to the fiscal quarter period-end. Analysts is the
number of analysts providing a forecast for the fiscal quarter prior to the earnings announcement Market Value is
the log of the market value of equity at the end of the fiscal quarter prior to the announcement measured in U.S.
dollars. % Closely Held is the percentage of the strategic number of shares held by institutional investors or other
institutions and not available to ordinary investors. ADR is an indicator variable equal to one if the firm is crosslisted in a U.S. stock exchange and zero otherwise. Log Price is the log of the average price at the end of the fiscal
quarter prior to the announcement measured in U.S. Dollars. Turnover,.1 is the prior quarter average daily turnover
during the Pre-event period. Volatility, is the prior quarter average daily volatility during the Pre-event period.
Fiscal Year-End is an indicator variable equal to one for earnings announcements corresponding to the fourth fiscal
quarter and zero otherwise. Abs Ret is the three days absolute cumulative abnormal return. Neg Ret is an indicator
variable that equals one if the three-day cumulative return is negative and zero otherwise. All continuous firm-level
variables are winsorized at the 1% and 99% levels. The specification includes country-year (CY) and industry (I)
fixed effects. t-statistics are presented in parentheses below the coefficients and are estimated using a two way
cluster at the firm and year levels. Assessments of significance across partitions are made based on standard errors
clustered at the country level. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
54
Table 9
Robustness analyses - Earningssurprises
Event
Pre-Event
Variables
Rank Environment
SUE
Neg SUE
(1)
-2.89***
(-3.33)
-0.56
(-0.23)
1.01***
(2.60)
(2)
-2.80***
(-3.20)
-0.87
(-0.36)
0.99**
(2.57)
(3)
4.06**
(2.44)
2.39
(0.72)
2.09***
(4.00)
(4)
4.22**
(2.29)
0.77
(0.23)
1.09**
(2.07)
% Closely Held
0.02
0.01
0.04
0.04
ADR
(1.51)
0.39
(0.38)
(1.40)
0.28
(0.29)
(1.35)
3.96*
(1.92)
(1.33)
3.31
(1.46)
Log Price
Volatility,,
Turnover,.
Fiscal Year-End
-0.03
0.13
(-0.10)
(0.47)
1.40***
(6.32)
-2.82
-4.10
-0.75
-1.36*
(-0.81)
(-1.27)
(-0.76)
(-1.86)
0.07
0.05
0.11**
0.07*
(1.02)
-2.06
(-1.37)
(0.75)
-2.13
(-1.39)
(2.20)
-4.00***
(-3.15)
(1.75)
-3.73***
(-3.10)
0.60***
0.29***
Abs Ret
(3.36)
-0.41
(-0.99)
Neg Ret
Dissemination
Observations
R-squared
Cluster
FE
0.95***
(5.08)
(3.21)
1.89***
(2.67)
0.24
-0.40
(-0.74)
(0.67)
66,421
66,421
66,421
66,421
0.0377
0.0397
0.0253
0.0392
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) in the Pre-Event and Event periods.
ASpreads is the percentage change in spreads between the Pre-Event period average Spreads and the Non-Event
period average Spreads. The Pre-Event period is trading days t-10 to t-2 relative to the earnings announcement date
(t=0). The Event period is trading days t-1 to t+l relative to the earnings announcement date (t=0). The Non-Event
period is trading days t-55 to t-11. Rank Environment is the tercile rank of the average of the standardized values of
Disclosure, Analysts, and Market Value scaled to range between zero and one. Disclosure is the number of press
releases during the calendar year period prior to the fiscal quarter period-end. Analysts is the number of analysts
providing a forecast for the fiscal quarter prior to the earnings announcement Market Value is the log of the market
value of equity at the end of the fiscal quarter prior to the announcement measured in U.S. dollars. SUE is the
absolute earnings surprise scaled by the price, obtained from the most recent analysts' earnings forecasts, if
available, and from a seasonal random walk mode otherwise. Neg SUE is an indicator variable that equals one if the
earnings surprise is negative and zero otherwise. % Closely Held is the percentage of the strategic number of shares
held by institutional investors or other institutions and not available to ordinary investors. ADR is an indicator
variable equal to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Price is the log of
the average price at the end of the fiscal quarter prior to the announcement measured in U.S. Dollars. Turnover,- is
the prior quarter average daily turnover during the Pre-Event (Event) period. Volatility,, is the prior quarter average
daily volatility during the Pre-Event (Event) period. Fiscal Year-End is an indicator variable equal to one for
earnings announcements corresponding to the fourth fiscal quarter and zero otherwise. Abs Ret is the three days
absolute cumulative abnormal return. Neg Ret is an indicator variable that equals one if the three-day cumulative
return is negative and zero otherwise. All continuous firm-level variables are winsorized at the 1% and 99% levels.
he specification includes country-year (CY) and industry (I) fixed effects. t-statistics are presented in parentheses
below the coefficients and are estimated using a two way cluster at the firm and year levels. ***, **, and * denote
significance at the 1%, 5%, and 10% levels, respectively.
55
Table 10
Robustness analyses -U.S. and Japan
Panel A: Pre-Event
CIFAR Index
DisclosureIndex
Variables
Rank Environment
% Closely Held
ADR
Log Price
Volatility,.;
Turnover,.;
Fiscal Year-End
Abs Ret
Neg Ret
Low
High
Low
High
-4.36**
(-2.39)
-1.20
(-0.99)
-2.89*
(-1.69)
-2.60**
(-2.17)
0.01
0.01
0.01
0.01
(0.62)
1.19
(1.07)
0.67***
(3.29)
(0.35)
-1.43
(-1.10)
-0.02
(-0.06)
(1.00)
-0.26
(-0.18)
0.46*
(1.84)
(1.15)
0.60
(0.50)
0.26
(0.70)
-12.40**
-1.13
-8.39***
-1.93
(-2.11)
(-0.67)
(-2.75)
(-0.89)
-1.53***
-0.15
-0.49*
-0.13
(-3.13)
(-1.35)
(-1.81)
(-1.20)
-1.24
-0.92
-2.01
-0.24
(-0.66)
0.31***
(4.12)
1.21**
(2.18)
(-0.65)
0.05
(1.11)
-1.33**
(-2.46)
(-0.88)
0.15
(1.55)
0.13
(0.20)
(-0.19)
0.13***
(3.64)
-0.29
(-0.65)
Difference in Coef. Rank
p-value
Observations
R-squared
Cluster
FE
12,367
0.0614
Firm-Year
CY, I
-3.16*
-0.29
0.09
0.89
13,331
0.0402
Firm-Year
CY, I
56
12,885
0.0618
Firm-Year
CY, I
12,813
0.0353
Firm-Year
CY, I
Table 10 (Continued)
Panel B: Event Period
Variables
Rank Environment
DisclosureIndex
Low
High
-2.15
7.10***
(-0.86)
(4.06)
% Closely Held
0.00
ADR
(0.17)
4.16***
(3.69)
Log Price
0.00
(0.06)
-2.77*
(-1.74)
0.37
1.15**
(1.21)
Volatility,.,
Turnover,.,
FiscalYear-End
(2.03)
CIFAR Index
Low
(0.03)
-0.01
(-0.55)
2.77*
(1.79)
0.59*
(1.91)
-2.24
0.43
-0.57
-0.70
(0.63)
-0.03
(-0.35)
-2.09
(-1.01)
(-0.26)
(-0.53)
0.09
0.01
(0.18)
-1.82
(-1.12)
(0.06)
-3.05
(-1.09)
0.11
0.28***
0.23*
Neg Ret
(0.75)
0.99
(1.23)
(2.83)
3.77***
(3.91)
(1.78)
0.71
(0.73)
-0.77
(-1.30)
0.50
0.13
(0.76)
(0.17)
-9.25***
Difference in Coef Rank
0.21
(1.55)
4.27***
(5.91)
1.06
(1.12)
-5.47*
0.00
Observations
R-squared
Cluster
FE
0.03
(0.98)
-0.64
(-0.40)
1.13*
(1.95)
(-0.99)
0.54
(0.51)
-2.40
(-0.93)
Abs Ret
Dissemination
Hi zh
5.55**
(2.41)
0.08
0.09
12,367
13,330
12,884
12,813
0.0220
0.0397
0.0271
0.0343
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
Firm-Year
CY, I
The table presents the results of a regression of changes in spreads (ASpreads) for partitions based on the Disclosure
Index and the CIFAR Index. ASpreads is the percentage change in spreads between the Pre-Event period average
Spreads and the Non-Event period average Spreads. Panel A presents results for the Pre-Event period and Panel B
for the Event period. For the U.S. and Japan, I select 2,986 random observations from each country. Low (High)
Disclosure Index includes firms with a Disclosure Index below (above) the median. Disclosure Index is the
disclosure index developed by La Porta (2006). Low (High) CIFAR Index includes firms with a CIFAR Index below
(above) the median. CIFAR Index is the disclosure index developed by CIFAR. The Pre-Event period is trading
days t-10 to t-2 relative to the earnings announcement date (t=0). The Event period is trading days t-1 to t+1 relative
to the earnings announcement date (t=0). The Non-Event period is trading days t-55 to t-11. Rank Environment is the
tercile rank of the average of the standardized values of Disclosure, Analysts, and Market Value scaled to range
between zero and one. Disclosureis the number of press releases during the calendar year period prior to the fiscal
quarter period-end. Analysts is the number of analysts providing a forecast for the fiscal quarter prior to the earnings
announcement Market Value is the log of the market value of equity at the end of the fiscal quarter prior to the
announcement measured in U.S. dollars. % Closely Held is the percentage of the strategic number of shares held by
institutional investors or other institutions and not available to ordinary investors. ADR is an indicator variable equal
to one if the firm is cross-listed in a U.S. stock exchange and zero otherwise. Log Price is the log of the average
price at the end of the fiscal quarter prior to the announcement measured in U.S. Dollars. Turnover,.- is the prior
quarter average daily turnover during the Pre-Event (Event) period. Volatility,., is the prior quarter average daily
volatility during the Pre-Event (Event) period. Fiscal Year-End is an indicator variable equal to one for earnings
announcements corresponding to the fourth fiscal quarter and zero otherwise. Abs Ret is the three days absolute
cumulative abnormal return. Neg Ret is an indicator variable that equals one if the three-day cumulative return is
negative and zero otherwise. All continuous firm-level variables are winsorized at the 1% and 99% levels. The
specification includes country-year (CY) and industry (I) fixed effects. t-statistics are presented in parentheses below
the coefficients and are estimated using a two way cluster at the firm and year levels. Assessments of significance
across partitions are made based on standard errors clustered at the country level. ***, **, and * denote significance
at the 1%, 5%, and 10% levels, respectively.
57