How Does More Frequent Reporting Reduce Information

Asymmetry?∗

Robert Stoumbos†

Columbia Business School

July 25, 2017

Abstract

In countries around the world, policymakers are debating the costs and benefits of switching

from semiannual to quarterly reporting. This study contributes to that debate by examining the

mechanism that drives quarterly reporting’s reduction of information asymmetry. Using U.S.

data, I show that Amihud (2002) illiquidity, a common information asymmetry proxy, grows by

10.7% over the intervening period between two quarterly earnings announcements. This suggests

that cutting a semiannual period into two quarters cuts the growth time in half, leading to lower

information asymmetry in the period’s second half. I confirm this in international settings, where

I find switching from semiannual to quarterly reporting reduces Amihud (2002) illiquidity by

up to 5.3% in the second half of each semiannual period.

∗This paper formed part of my dissertation at Yale School of Management. I am grateful to the members ofmy dissertation committee, Jake Thomas (chair), Frank Zhang, Shyam Sunder, and Marina Niessner, for theirguidance, support, and encouragement. I also thank participants at the Carnegie Mellon University Emerging ScholarsSymposium and at workshops at Columbia University, Emory University, London Business School, Carnegie MellonUniversity, University of Toronto, University of British Columbia, George Washington University, and Yale University.I also thank Benedikt Downar, Juergen Ernstberger, Aytekin Ertan, Zeqiong Huang, Steve Karolyi, Peter Kelly, AlinaLerman, Thomas Steffen, Heather Tookes, Huai Zhang, and my fellow PhD students for their helpful comments. Igratefully acknowledge financial support from the Yale School of Management. All errors and omissions are my own.†Contact E-mail: rcs2188@gsb.columbia.edu.

1 Introduction

How does information asymmetry between informed and uninformed traders evolve from

one earnings announcement to the next? There is no clear prediction based on existing theory.

The classic Kyle model predicts a constant level of information asymmetry between earnings

announcements, because the informed trader reveals private information at a constant rate (Kyle,

1985). In the accounting literature, we seem to accept this prediction, except we expect leaks

to cause a brief jump in asymmetry right around the earnings announcement itself (e.g., Kim

and Verrecchia, 1994).1 But more recent theory shows that information asymmetry is unlikely

to remain constant between announcements. By extending the Kyle model to include multiple

informed traders and the arrival of new private information over time, Bernhardt and Miao (2004)

demonstrate that information asymmetry could rise or fall between earnings announcements in

different circumstances. Whether it tends to rise or fall in the real world is an empirical question.

In this paper, I show that information asymmetry increases monotonically during the entire period

from one earnings announcement to the next. This finding is important because it has real

implications for the reporting frequency debate, which is taking place around the world.

While the United States has required quarterly reporting since 1970, there is no global

consensus over the optimal reporting frequency. Many countries, including most of Europe, only

require semiannual reports. There is even disagreement within reporting jurisdictions. In 2004, the

European Union rejected proposed legislation to require quarterly financial reports (Euromoney

Institutional Investor PLC, 2004),2 and the Singapore Exchange recently commissioned a study to

reconsider local quarterly reporting requirements (Yahya, 2016). Even some in the United States

have questioned the wisdom of quarterly reporting (Benoit, 2015), prompting the SEC to consider

its pros and cons at length during a 2015 meeting (Higgins, 2016). In the face of this uncertainty,

we as academics should help real-world decision-makers—both regulators and firms—determine

appropriate reporting frequency policies.

1Kim and Verrecchia (1994) say the following in their conclusion (page 59): “[L]ogically market-makers are likelyto increase spreads in anticipation of an earnings announcement, to guard against investors acting on the informationbefore it is disclosed publicly (e.g., ‘leaks’). This suggests that spreads temporarily widen around announcements.As this advantage dissipates, spreads fall back to the level that prevailed before the announcement was anticipated.”

2The EU instead settled on a compromise, requiring narrative quarterly statements in addition to firms’ regularsemiannual reports. But in 2013 the EU decided that quarterly reporting was too costly and both revoked thisrequirement and prohibited EU member states from requiring quarterly reports for their own firms (Wagenhofer,2014).

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While many factors enter into the reporting frequency decision, this paper focuses on

the relationship between reporting frequency and information asymmetry. This relationship is

important, since lower information asymmetry is one of the academic literature’s few proposed

benefits of more frequent reporting.3 Practitioners also consider lower information asymmetry an

important benefit. Keith Higgins, a former Director of the SEC Division of Corporate Finance,

cites increased insider trading as a major cost of maintaining lower reporting frequencies (Higgins,

2016), and other commentators worry that these low frequencies aggravate information asymmetry

between informed and uninformed investors (Malmqvist, 2014; Szopo, 2014). These commentators

find this information asymmetry to be unfair, because they believe the firm already has more-

frequent internal reports that it could easily publish. Aside from potential unfairness, greater

information asymmetry is costly because it likely increases the firm’s cost of capital (Amihud and

Mendelson, 1986; Diamond and Verrecchia, 1991).

I am not the first to explore the relationship between reporting frequency and information

asymmetry. Prior literature shows that more frequent reporting reduced average information

asymmetry between informed and uninformed investors during the nineteen-fifties, sixties, and

early seventies (Fu et al., 2012), but this literature has not explored the mechanism that drove

this reduction. Managers and regulators need the mechanism to predict how the magnitude of

the reduction has changed since the early seventies, and how it might change for different firms

across different settings. This will help them accurately weigh the information asymmetry benefit

against any costs when deciding future reporting frequency changes.4 The mechanism will also

reveal whether the information asymmetry reduction comes with its own hidden costs, or whether

there is some easier way to achieve the reduction without increasing reporting frequency at all.

In this paper, I determine the mechanism. Higher reporting frequencies reduce the

average level of information asymmetry because information asymmetry grows over time between

earnings announcements, and a higher reporting frequency reduces the time that is available for

this interannouncement growth to occur. (In the rest of the paper, I refer to the information

asymmetry growth between earnings announcements as “interannouncement growth”.) Figure

3The other proposed benefits are improved price discovery (Butler et al., 2007; Arif and De George, 2015) andlower cost of capital (Fu et al., 2012).

4The costs of more frequent reporting include greater managerial myopia (Gigler et al., 2014; Kraft et al., 2015;Ernstberger et al., 2015) and compliance costs (Verdi, 2012).

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Figure 1: Illustration of Interannouncement Growth and Reporting Frequency

1 provides an illustration. It depicts a hypothetical firm’s information asymmetry path over

time when it reports quarterly versus when it reports semiannually. Under interannouncement

growth, information asymmetry grows between earnings announcements. These announcements

then cause information asymmetry to fall again, because they publicly reveal some of the informed

traders’ private information. The growth between announcements combined with the drop after

each new announcement creates a sawtooth pattern over time. Notice that there is less time for

interannouncement growth when the firm reports quarterly than when it reports semiannually. As

a result, a switch from semiannual to quarterly reporting reduces average information asymmetry.5

In this paper, I show graphical and statistical evidence that interannouncement growth occurs. I

also use a difference-in-differences design to show that interannouncement growth is the mechanism

at work when quarterly reporting reduces information asymmetry.

I first demonstrate that interannouncement growth occurs in the United States. Plots

visually demonstrate that the Amihud (2002) illiquidity measure, my proxy for information

asymmetry, follows interannouncement growth’s predicted sawtooth pattern from one earnings

5The purpose of this figure is to illustrate that interannouncement growth is sufficient for quarterly reporting toreduce average information asymmetry. To make this as clear as possible, it implicitly makes simplifying assumptionsthat my actual tests do not require. My tests do not require information asymmetry to be the same for semiannualand quarterly reporters absent any interannouncement growth. Nor do they require the information asymmetry levelto be the same each quarter. Nor do they require interannouncement growth to be linear.

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announcement to the next.6,7 Regressions confirm that this growth between announcements is

statistically significant. I estimate that Amihud (2002) illiquidity increases by 0.161% (t=39.24) for

each additional day without a new earnings announcement. Assuming two quarterly announcements

are 63 trading days apart, this estimate translates to an increase of around 10.7% from right

after the first earnings announcement to right before the second. Additional regressions show

that this increase in information asymmetry occurs throughout the entire period between earnings

announcements, and not only for a brief period right before each announcement.

I next turn to international settings, where firms report both semiannually and quarterly,

to estimate the effect on information asymmetry when a firm cuts interannouncement growth time

in half by switching from semiannual to quarterly reporting. My test is a difference-in-differences

made possible because, as shown in Figure 1, the decision to report quarterly does not affect

interannouncement growth in the first and third quarters. Thus, I can use these quarters to control

for cross-sectional differences in Amihud (2002) illiquidity from other factors. The difference-in-

differences compares the semiannual and quarterly reporters’ change in information asymmetry from

the first to the second quarter and from the third to the fourth quarter. This test estimates the

average second- and fourth-quarter Amihud (2002) illiquidity reduction that comes from quarterly

reporting’s reduction of interannouncement growth.

I estimate this difference-in-differences in three international settings. The first consists of

five large European countries where semiannual reporting is the norm, but many firms voluntarily

report quarterly. My regression includes firm-year fixed effects, which allow it to be well-identified

even for voluntary adopters.8 The other two settings, Singapore and Japan, both began to require

quarterly reporting in the first half of the 2000s. I estimate that the decision to report quarterly

reduces the average level of Amihud (2002) illiquidity by 5.3% (t=10.04) for European firms, 2.9%

(t=2.11) for Singaporean firms, and 1.1% (t=1.51) for Japanese firms. Robustness tests show that

these results are not driven by a violation of the parallel trends assumption.

In additional analyses, I first demonstrate that these results hold when I use the bid-

6Amihud (2002) illiquidity is my main information asymmetry proxy because it measures the price impact oftrades, which is the construct examined by Kyle (1985) and Bernhardt and Miao (2004).

7The Amihud (2002) illiquidity plots in Figure 2 look remarkably similar to the hypothetical pattern in Figure1, with Amihud (2002) illiquidity increasing until the earnings announcement and falling right after.

8This is true as long as the unobservables associated with adoption have the same effect on Amihud (2002)illiquidity in quarters one and three as in quarters two and four.

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ask spread as my information asymmetry proxy. I then explore how other disclosures affect

interannouncement growth, showing that the growth is less pronounced for firms with more analyst

forecasts and 8-K filings between earnings announcements. Finally, I show that interannouncement

growth occurred both before and after decimalization, and before and after the financial crisis.

Finding evidence of interannouncement growth is an important contribution. Not only does

it provide us with a better understanding of how accounting affects capital markets, it also has

implications for the reporting frequency debate, which is an important policy issue. Specifically, it

gives us three main insights.

First, interannouncement growth implies that more frequent reporting’s reduction of

information asymmetry provides a good reason to increase reporting frequencies. This was not

clear beforehand, because the two other likely possible mechanisms behind the reduction would

not justify more frequent reporting. These alternative mechanisms either generate additional costs

that may outweigh the benefits from reducing information asymmetry, or they suggest an easier

way to get the same reduction without increasing reporting frequency at all.9 In contrast, reducing

interannouncement growth does not generate any obvious additional costs, and the easiest way to

reduce it is by increasing reporting frequencies. So the drop in information asymmetry that comes

from reducing interannouncement growth provides a good reason to report more frequently.

Second, knowledge of interannouncement growth can help regulators and managers make

decisions about hypothetical reporting frequency changes. Based on this study, practitioners

can now expect higher reporting frequencies to provide a greater benefit to firms with steeper

interannouncement growth slopes. Because of interannouncement growth, practitioners can also

expect the marginal benefit from reduced information asymmetry to get smaller and smaller with

each reporting frequency increase.10 The decreasing marginal benefit suggests that information

asymmetry alone may not justify moving all the way to continuous reporting. If future research

finds that more frequent reporting’s marginal costs hold constant or increase as reporting frequency

increases, then these marginal costs would eventually outweigh the marginal benefits from lower

asymmetry.

Finally, existing theory shows that information asymmetry could rise or fall over time,

9In Section 5.1, I describe these alternative mechanisms and their implications in more detail.10In Section 5.2, I describe how the marginal information asymmetry reduction gets smaller with each reporting

frequency increase.

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depending on the circumstances. This paper increases our understanding of capital markets by

showing that it in fact rises over time. Future models should produce rising information asymmetry

between earnings announcements in order to better reflect the real world.

The rest of this paper proceeds as follows: Section 2 reviews related literature; Section

3 describes the three settings I use for the international tests, as well as my data sources;

Section 4 contains research designs and results for my main tests; Section 5 discusses some of

interannouncement growth’s policy implications; Section 6 contains additional analyses; and Section

7 concludes.

2 Literature Review

2.1 Literature on Information Asymmetry Patterns

Based on existing theory, how might we expect information asymmetry to evolve between

earnings announcements? Ex ante, it is unclear.

Beginning with the classic Kyle (1985) model, we have an informed trader who must decide

how to trade on her private information before a terminal date. With each trade, the informed

trader moves the stock price closer to its fundamental value, but she does not fully reveal her

information because uninformed trading camouflages her trades. In equilibrium, the informed

trader takes advantage of this camouflage and spreads her trades over time such that she gradually

reveals her private information at a constant rate. This results in a constant level of information

asymmetry. Therefore, if the Kyle model’s assumptions held in the real world, we could expect a

constant level of information asymmetry between earnings announcements.11

But what if there are multiple informed traders, and what if they receive new private

information over time? Bernhardt and Miao (2004) extend Kyle’s model to allow for these

possibilities. This extension adds two new considerations: (1) the arrival rate of new information

and (2) competition between informed investors, where more competition induces these investors to

trade more aggressively and in turn impound their information into prices more quickly. Depending

on whether the arrival of new information or the aggressiveness of informed trading dominates, this

11Back and Pedersen (1998) extend Kyle (1985) so that the single risk-neutral insider receives a continuous flow ofnew private information over time. They find that the intuition in Kyle (1985) extends to this setting: informationasymmetry does not change deterministically over time.

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model can produce any information asymmetry path between earnings announcements: an increase,

a decrease, or no change. Since any path is possible, whether information asymmetry increases or

decreases between earnings announcements is an empirical question.12

Stepping away from the finance literature, the accounting literature seems to assume that

information asymmetry holds constant at some normal level for most of the quarter, and then

spikes for a brief period before the earnings announcement. For example, Kim and Verrecchia

(1994) express this intuition, reasoning that pre-announcement asymmetry is likely higher because

of leaks.13 Their hypothesis is reasonable given previous findings in Lee et al. (1993) and Skinner

(1993) (both cited by Kim and Verrecchia (1994)) that information asymmetry is higher in a short

window before the earnings announcement than after.14 Later studies of the bid-ask spread and

price impact of trades within the ten days before and after the earnings announcement generally

corroborate this finding (Amiram et al., 2016; Chae, 2005; Affleck-Graves et al., 2002; Yohn, 1998;

Krinsky and Lee, 1996).15,16 However, my results show that this intuition is incorrect. I find

that information asymmetry increases steadily throughout the entire period between earnings

announcements, not just for a brief period right before each earnings announcement. This

distinction is important in the reporting frequency context. If there were no interannouncement

growth and information asymmetry only increased briefly before each announcement, we would

not expect the time between announcements to directly affect average information asymmetry.

Doubling the reporting frequency would cut the information content of each earnings announcement

in half, so while it would double the number of pre-announcement jumps in information asymmetry,

it would likely cut the size of each jump in half. The net effect would cancel out.

A prominent result that may seem similar to mine at first blush comes from Patell and

12Aside from Bernhardt and Miao (2004), most other extensions of the Kyle model produce decreasing informationasymmetry over time (e.g., Holden and Subrahmanyam, 1992, 1994; Foster and Viswanathan, 1996; Back et al., 2000;Caldentey and Stacchetti, 2010), though most of these models do not include new private information arrivals overtime.

13See footnote 1.14An even earlier paper, Morse and Ushman (1983) (also cited by Kim and Verrecchia (1994)), found no significant

difference in spreads before and after the earnings announcement for a small sample of 375 announcements.15One paper, Acker et al. (2002), examines bid-ask spreads over a much longer window: 125 days before and after

the earnings announcement. Unlike my results, they find that bid-ask spreads decrease in the period leading up tothe earnings announcement, hit a trough on the day of the announcement, and then recover slowly back to “normallevels.” They find these results in a sample of 195 London Stock Exchange firms selected from the lowest 40% of firmsby size within the FT-All Share Index between 1986 and 1994.

16In a demonstration of their dynamic microstructure model on a sample of 16 stocks, Easley et al. (2008) showadditional consistent results for the probability of informed trading (PIN) before and after the earnings announcement.But due to their small sample, they do not test for significance.

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Wolfson (1979, 1981), who find that average implied volatilities to option expiration increase

in the period leading up to the earnings announcement.17 But their result does not actually

anticipate interannouncement growth, because it relates to average volatility to option expiration

rather than daily volatility. In fact, both papers hypothesize that daily volatility holds constant

apart from a brief spike around the earnings announcement. They expect that this spike causes

the average implied volatility to increase in the days leading up to the announcement, because the

announcement period itself becomes a larger fraction of the remaining time until option expiration.

In contrast, interannouncement growth does not predict a brief information asymmetry spike around

the announcement, but rather predicts that asymmetry will grow over time between announcements.

2.2 Reporting Frequency Literature

The literature has generally established that more-frequent reporting reduces the average

level of information asymmetry. In a difference-in-differences, Fu et al. (2012) show that the

U.S.’s reporting frequency increases during the nineteen-fifties, sixties, and early seventies caused

information asymmetry to fall.18 They suggest this asymmetry reduction might come from an

increase in total public information, but do not search further for the underlying mechanism.

They do not consider interannouncement growth—not even implicitly, since they test reporting

frequency’s effect on the mean of information asymmetry over the entire year. Had they been

testing for interannouncement growth, they would have focused on the second and fourth quarters,

the only periods when it predicts a decrease.

Apart from the decrease in information asymmetry, the literature has identified a few other

benefits from more frequent reporting. Fu et al. (2012) find that more frequent reporting reduces

a firm’s cost of capital. The reduction in information asymmetry likely contributes to this result

(Amihud and Mendelson, 1986; Diamond and Verrecchia, 1991). Butler et al. (2007) find that stock

prices reflect accounting information more quickly when firms voluntarily increase their reporting

17Rogers et al. (2009) find the same implied volatility pattern as Patell and Wolfson (1979, 1981) around earningsforecasts bundled with earnings announcements.

18Cuijpers and Peek (2010) find consistent evidence for European firms that voluntarily increase their reportingfrequencies. There are two papers that do not find this result. The first is Kubota and Takehara (2016), whichexamines quarterly reporting adoption on the Tokyo Stock Exchange. Consistent with my own insignificant results inthe Japanese setting, they conclude that the reporting frequency increase likely did not cause information asymmetryto fall. The second paper, Kajüter et al. (2015), finds no evidence that increased reporting frequency in Singaporecaused a decrease in information asymmetry. This may be due to the sample restrictions they adopt for their tests.

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frequencies. But they find no effect when the frequency increase is involuntary, suggesting that the

relationship may not be causal. On the other hand, Arif and De George (2015) find evidence that

more frequent reporting reduces mispricing.

The literature has also identified costs associated with more frequent reporting. Theory

suggests that higher reporting frequencies can cause managers to myopically prefer short-term

performance gains at the expense of overall firm value (Gigler et al., 2014). Recent empirical

evidence supports this prediction, showing that more-frequent reporting leads to lower investment

and higher real earnings management (Kraft et al., 2015; Ernstberger et al., 2015). In addition

to increased myopia, Verdi (2012) suggests that more-frequent reporting may increase compliance

costs, agency costs, monitoring costs, and proprietary costs. Gigler and Hemmer (1998) suggest

that it may also reduce managers’ incentives to make voluntary disclosures.

3 Research Settings and Data

3.1 International Research Settings

Because my difference-in-differences design (described in detail in Section 4.2) does not

require an exogenous change in reporting frequency, I can perform it with both voluntary and

involuntary adopters of quarterly reporting. The important thing is to find settings that have both

semiannual and quarterly reporters. In my international tests, I examine three such settings. The

first is a set of European countries where quarterly reporting is voluntary, and many firms have

chosen to voluntarily adopt it. The second is Singapore, which began to require quarterly reporting

for a subset of its firms beginning in 2003. The third is Japan, where the Tokyo Stock Exchange

began requiring quarterly reporting in 2004.

The European Union (EU) contains a number of countries that do not require quarterly

reporting. According to Link (2012), the five of these countries with the largest number of firms are

the United Kingdom, France, Germany, the Netherlands, and Denmark.19 These are the countries

from which I draw my sample, because I expect them to have the most even mix of semiannual

and quarterly reporters. Each contains firms that switched from semiannual to quarterly reporting

19While German national law does not require firms to report quarterly, the prime listing segment of the DeutscheBorse requires quarterly reporting for firms that register on it. This does not affect the interpretation of my results.

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during the sample period, which runs from 1993 to 2015.

This sample has one confounding factor that might work against my results. In 2004, the

EU adopted the Transparency Directive, which required all semiannual reporters on EU-Regulated

Markets to produce Interim Management Statements (IMS’s) halfway through each semiannual

period. These IMS’s did not need to include financial statements, such as balance sheets or income

statements. Rather, they were only required to include an explanation of material events and

transactions, as well as a “general description” of the financial performance and position of the

company since the last semiannual report (European Commission, 2004). My data, which comes

from Worldscope, only identifies firms as quarterly reporters if they report earnings-per-share each

quarter. So even though every firm provides IMS’s for part of the sample period, I still classify firms

as semiannual reporters if their IMS’s do not report the quarter’s earnings. If any of the remaining

disclosures in these IMS’s were useful, treating them as if they were not would bias against finding

the predicted results in my tests. But since this bias works against my predictions, it does not

affect my overall inferences.

My second setting, Singapore, introduced a quarterly reporting requirement in 2003 for

some firms. According to Listing Rule 705(2) of the Singapore Exchange, all firms that had a

market capitalization greater than S$75 million on March 31, 2003 had to file quarterly reports.

Starting on December 31, 2006, this requirement has been extended to all firms whose market

capitalizations have grown since 2003 to exceed the S$75 million threshold. This extension gave

these firms a year to comply, so they did not have to begin reporting quarterly until their 2008

fiscal years. From then on, the requirement kicked in for any firm whose market capitalization grew

to exceed S$75 million. As a result, there are many firms that switch from semiannual to quarterly

reporting throughout the sample period.

On April 1, 2004, Japan’s Tokyo Stock Exchange began requiring firms listed on its First

and Second Sections to disclose quarterly reports (Kubota and Takehara, 2015). Even though there

initially was no punishment for failing to comply with this requirement, most firms began reporting

quarterly immediately after the rule went into effect.

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3.2 Data

For my U.S. sample, earnings announcement dates come from Compustat, and daily security

data comes from CRSP. The U.S. sample runs from 1993 to 2015. I only include years after 1992

because CRSP’s NYSE bid and ask data are not continuously available until December 28, 1992. I

drop earnings announcements when the next quarter’s announcement occurred before it or on the

same day. I also drop earnings announcements where the earnings announcement date is before the

balance sheet date, because I assume this represents a data error. Finally, I drop firm-days when

two SEC filing deadlines have passed without a new earnings announcement from Compustat.20

For continuous variables, I either take the logarithm of the variable or I Winsorize at the 1% and

99% levels to reduce the influence of outliers.

Panel A of Table 1 shows yearly averages of Size, measured as the logarithm of market

value of equity at the firm-day level. It also shows yearly averages of log(Price Impact), calculated

for each firm-day as the logarithm of Amihud (2002) illiquidity (i.e, as log(

|return|$ volume of trades

), where

dollar volume is in terms of millions of dollars). Panel A shows that log(Price Impact) has dropped

steadily over time.

The main U.S. test relies on cross-sectional variation on each calendar-day in the time

since the firm’s most recent earnings announcement. Panels B and C show the extent of this

variation. Panel B shows that eighty-eight percent of the firm-years have a fiscal year that ends

in either March, June, September, or December, so differences in the most recent quarter-end only

contribute a little of the needed variation. Panel C shows that there is a reasonable amount of

variation at the firm-quarter level in the number of trading days between quarter-end and the

earnings announcement. This is the source of most of the variation in the number of days since the

most recent earnings announcement.

International data comes from Datastream and Worldscope, both provided by Thomson

Reuters. The European sample goes from 1992 to 2015. It starts in 1992 because that is when the

European firms’ earnings announcement dates first become available in the data. The Singaporean

sample goes from October 27, 2000—the date when bid and ask data is first available for Singapore—

20In untabulated robustness checks, I further restrict both the U.S. sample and the international samples (describedbelow) by dropping the bottom 5% of observations by stock price within each sample. The results with this samplerestriction are the same as those presented in the paper.

11

to the end of 2015. For the Japanese sample, a preliminary check showed that bid and ask data was

not available before February, 2001, so my Japanese sample begins then. My full Japanese sample

runs from February, 2001 to September, 2008, a period that contains the Tokyo Stock Exchange’s

implementation of its quarterly reporting requirement in 2004. In the international samples, I only

include firm-years that I could identify as either reporting quarterly or semiannually. I also drop

any firms that are listed on exchanges located outside of the country (or, in the case of Europe,

countries). Finally, I drop earnings announcements if they occur on the balance sheet date or if

they occur before the earnings announcement of an earlier quarter. For continuous variables, I

again either take the logarithm of the variable, or I Winsorize at the 1% and 99% levels to reduce

the influence of outliers.

Panel A of Table 2 shows the number of semiannual reporters and quarterly reporters in

each year of the European sample. The number of quarterly reporters generally increases over time,

though there are fluctuations from year to year as firms list and delist. The table also shows average

log(Price Impact) each year. It is much larger than in the U.S. sample, indicating that European

firms have lower liquidity. Interestingly, the quarterly reporters in later years have higher values

of log(Price Impact) than the semiannual reporters do. As previously noted, firms voluntarily

adopt quarterly reporting in this setting. Because quarterly reporting reduces average information

asymmetry (Fu et al., 2012), this might indicate that firms are more likely to choose quarterly

reporting when asymmetry is higher. The table also shows that the quarterly reporters tend to be

larger than the semiannual reporters.

Panel B shows the same table for the Singaporean sample. The number of quarterly

reporters jumps in 2003, which is consistent with the adoption of mandatory quarterly reporting

for large firms that year. It jumps again in 2008, when the quarterly reporting requirement was

extended to more firms. Unlike in Europe, the quarterly reporters have lower log(Price Impact) than

the semiannual reporters do. Because adoption in this sample was mandatory, this is consistent

with the finding that quarterly reporting reduces information asymmetry.

Panel C shows this table for Japan. Few firms reported quarterly before the mandated

change, which took place on April 1, 2004. The table shows that almost all of the firms switched

soon after the time of the mandate.

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4 Main Empirical Tests

4.1 Main U.S. Tests

Amihud (2002) illiquidity is my main proxy for information asymmetry because it measures

the price impact of trades, which is the construct examined by the models in Kyle (1985) and

Bernhardt and Miao (2004).21 Conceptually, the price impact is the amount the stock price moves

in response to trading volume, and it increases when the information advantage of informed traders

increases (Kyle, 1985). Goyenko et al. (2009) compare Amihud (2002) illiquidity to the price impact

measured using intraday data, and they find that Amihud (2002) illiquidity measures the intraday

price impact well. In additional analyses, I show that the results below also hold when I use the

bid-ask spread as my information asymmetry proxy.

Interannouncement growth predicts that Amihud (2002) illiquidity will increase between

earnings announcements and fall after each new announcement in a sawtooth pattern. This

sawtooth is easy to see in visual representations of the data. Panel A of Figure 2 plots average

Amihud (2002) illiquidity in the 45 trading days before and after the earnings announcement for

the entire U.S. sample. To create the figure, I measure log(Price Impact) for each firm-day as

the logarithm of the Amihud (2002) illiquidity measure, |return|$ volume of trades (where dollar volume is in

terms of millions of dollars). The Amihud (2002) illiquidity measure is highly skewed; taking the

logarithm reduces the influence of outliers. I then subtract the mean value of log(Price Impact) for

the market on each observation’s calendar day to get Abnormal log(Price Impact). Finally, I plot the

average Abnormal log(Price Impact) in event time for each of the 45 trading days before and after

all earnings announcements in the sample.22 Consistent with interannouncement growth, Abnormal

log(Price Impact) increases gradually over the period leading up to the earnings announcement, and

falls after the announcement’s release.

In Panel B of Figure 2, I produce the same plot in a wider window that includes the 80

trading days before and after each earnings announcement. This plot clearly shows the sawtooth

21Consistent with the literature, I measure Amihud (2002) illiquidity throughout the paper as |return|$ volume of trades

.I recognize that the numerator and denominator have different scales, so in untabulated robustness tests I measureAmihud (2002) illiquidity as |∆ market capitalization|

$ volume of trades(i.e., the change in firm market value for every dollar of trading).

All of my results hold with this alternate measure.22For each plot, I only include an earnings announcement’s observations if the data needed to make the plot is

available on both the first and last day of the window. This ensures that the composition of firms in the plot doesnot change over the window.

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pattern predicted by interannouncement growth. In the data, information asymmetry grows over

time between the earnings announcements—which occur on event days -63 (approximately), 0, and

+63 (approximately)—and then falls after each new announcement’s release.

In order to better line up the three consecutive earnings announcements in event time,

Panel B only plots Abnormal log(Price Impact) in the 80 trading days before and after the second

quarter announcement, rather than the announcements of all four quarters.23 Even so, the adjacent

announcements do not always occur on days -63 and +63, which is why the information asymmetry

drop around these event days does not look as deep as it does around day 0.

I next show that the growth in information asymmetry between earnings announcements is

statistically significant after controlling for other factors. The regression takes the following form:

log(Price Impact)iyt = αiy + αt + βDays Since EAiyt + γControlsiyt + εiyt.

Each observation is a firm-day, with i indexing firms, y indexing calendar years, and t indexing

calendar days. I calculate log(Price Impact), my information asymmetry proxy, for each firm-day

as log(

|return|$ volume of trades

)(i.e., the logarithm of the daily Amihud (2002) illiquidity measure), where

dollar volume is in terms of millions of dollars. Taking the logarithm allows me to interpret my

coefficients as percentage changes. Days Since EA is the number of trading days since the firm’s

most recent earnings announcement (it resets to zero on each new earnings announcement day).

Interannouncement growth predicts that information asymmetry increases between announcements,

so the coefficient on Days Since EA, β, should be positive.

The regression includes firm-year fixed effects, where a year is a calendar year, which fully

control for fixed differences across firm-years. This means that firm-specific trends from year to year

are accounted for. It also includes calendar day fixed effects to control for aggregate fluctuations

that affect all of the firms over time. I cluster standard errors at both the firm and day levels to

account for correlation in the errors arising from within-firm autocorrelation and common daily

shocks.

In the Controls vector, I control for Size, the logarithm of the firm-day’s market value of

23While the number of days between the fourth quarter announcement and the first and third quarterannouncements varies across firm-years, the lag between the second quarter announcement and the first and thirdquarter announcements is generally about a quarter, or 63 trading days.

14

equity. Because turnover and return volatility are associated with liquidity (Stoll, 1978), I control

for Turnover, measured as the logarithm of one plus the fraction of the firm’s shares outstanding

traded that day, and Volatility, measured as the logarithm of one plus the standard deviation

of returns over the twenty trading days before the firm-day. Turnover also acts as a proxy for

investor attention—if attention wanes after the earnings announcement, then noise trading may

fall, causing liquidity to decrease. My measurements of Size, Turnover, and Volatility closely follow

prior literature (Leuz and Verrecchia, 2000; Fu et al., 2012). In particular, Turnover is measured

in accordance with the most common specification used in the literature (Bamber et al., 2011):

measuring trading activity as a percentage of shares outstanding is natural (Lo and Wang, 2000),

and taking the logarithm counteracts high skewness to the right (Ajinkya and Jain, 1989).

I also include Midpoint, measured as the logarithm of the midpoint between the bid and

the ask, because I expect trading to mechanically move prices more when prices are lower, given

that bid and ask quotes must be in one-penny increments. I include an indicator for the three-day

earnings announcement window (Earnings Window). I also include an indicator for the period after

the fourth-quarter earnings announcement (Annual Report), since it tends to have lower values of

Days Since EA, and the annual report may cause a larger reduction in information asymmetry than

the other quarterly reports. I include an indicator (Late Filing) that turns on when more trading

days than expected have passed since the quarter-end without an announcement.24 Amihud (2002)

illiquidity may be higher when the earnings announcement is late, since a late announcement is

usually an indication of bad news (Chambers and Penman, 1984). Finally, I include an indicator for

days that occur within five trading days before the earnings announcement (One Week Before) and

another for days that occur within five trading days after (One Week After). These two controls

make sure that the period around the earnings announcement, which may have greater information

leaks and noise trading, is not driving my results.25

Panel A of Table 3 contains the results for this test. Consistent with interannouncement

growth, the coefficient on Days Since EA is significantly positive. On average, each additional

day without a new earnings announcement comes with with a 0.161% increase (t=39.24) in the

24I estimate the expected filing date to be the median seasonal earnings announcement delay from the previousthree years (Cohen et al., 2007).

25In untabulated robustness checks, I re-perform this paper’s tests (both U.S. and international) while excludingfrom the sample the 11-day window around the earnings announcement. Results throughout the paper are robust tothis exclusion. It causes one substantive change, which I discuss in Section 6.1.

15

firm’s Amihud (2002) illiquidity. If a quarter (approximately 63 trading days) passes between two

earnings announcements, then this regression predicts a 10.7% increase in Amihud (2002) illiquidity

from just after the first announcement to just before the second one.

The controls tend to have sensible coefficients with the notable exception of Volatility,

which ought to be positively related to Amihud (2002) illiquidity. The negative coefficient on

Volatility flips to a positive sign in untabulated robustness tests when firm and year fixed effects

are used rather than firm-year fixed effects. Additional untabulated robustness checks show that

removing the Volatility control from the paper’s Amihud (2002) illiquidity tests does not change

any of the results. The coefficients on Size and Turnover are both consistent with prior literature.

The coefficient on Midpoint is negative, as expected. The coefficient on Earnings Window is

negative, consistent with the sharp drop around the earnings announcement shown in Figure 2.

The coefficient on Annual Report is insignificant. Contrary to my expectations, the coefficient

on Late Filing is negative. The coefficients on One Week Before and One Week After are both

significantly negative.

Panel B of Table 3 demonstrates that interannouncement growth occurs throughout the

entire interval between earnings announcements. Column (1) shows results from a regression like

the one in Panel A, except I include an indicator that turns on for days between quarter-end and

the earnings announcement date (Post Quarter-end), and I interact this indicator with Days Since

EA. The uninteracted Days Since EA coefficient remains significantly positive, demonstrating that

a brief information asymmetry spike right before the earnings announcement does not drive the

result. Instead, interannouncement growth occurs before the books close at quarter-end. In fact,

it is greater before quarter-end than after, since the coefficient on the interaction between Days

Since EA and Post Quarter-end is significantly negative. This seems to indicate that most of the

informed traders’ private information is not coming from insider leaks. If it were, I would expect

interannouncement growth to become more pronounced after the books close at quarter-end, when

the managers become more certain about recent performance.

Column (2) of Panel B uses a piecewise regression to show that interannouncement growth

occurs throughout the quarter. I create three indicators: One that turns on for the first 20 trading

days after the earnings announcement (1st Month), one that turns on for the days between the 21st

trading day and the 40th trading day after the earnings announcement (2nd Month), and one that

16

turns on for all days from the 41st trading day on (3rd Month). I then interact these indicators with

Days Since EA to see how the slope varies in each month. Column (2) reveals that the relationship

between Amihud (2002) illiquidity and Days Since EA is significantly positive in all three months

after the earnings announcement, consistent with interannouncement growth occurring throughout

the period between announcements.

Column (3) of Panel B corroborates that information asymmetry monotonically increases

between earnings announcements, without assuming linearity. Instead of regressing Amihud (2002)

illiquidity on Days Since EA, I regress it on an indicator that turns on for all days greater than 20

trading days after the previous earnings announcement (Greater Than 1 Month) and another that

turns on for all days greater than 40 trading days after the previous earnings announcement (Greater

Than 2 Months). The coefficients on both of these indicators are significantly positive, showing

a monotonic increase in information asymmetry. This is again consistent with interannouncement

growth occurring throughout the period between earnings announcements.

4.2 Main International Difference-in-Differences Test

I now estimate the amount that information asymmetry falls in response to the interan-

nouncement growth reduction brought about by a switch from semiannual to quarterly reporting.

I use a difference-in-differences design to obtain this estimate. The U.S. has required quarterly

reporting since 1970, so I use international settings with both semiannual and quarterly reporters

to perform this test.26

Figure 3 illustrates the logic behind the difference-in-differences. The figure shows a

hypothetical firm’s interannouncement growth when the firm reports semiannually versus when

it reports quarterly. Notice that interannouncement growth only causes higher information

asymmetry for the semiannual reporter in the second and fourth quarters, but not in the first

and third quarters.27 Therefore, in order to estimate the effect of a switch from semiannual to

quarterly reporting, I must estimate the drop in average information asymmetry during the second

and fourth quarters. A naïve test would cross-sectionally compare the second- and fourth-quarter

Amihud (2002) illiquidity of semiannual and quarterly reporters. But this cross-sectional difference26I describe these settings in Section 3.1.27For the difference-in-differences, a “quarter” is bounded by the previous earnings announcement and the next

earnings announcement, as depicted in Figure 3.

17

would also contain the effects of any other factors that differ between these two groups of firms. I

perform a difference-in-differences to remove the effects of these other factors from the estimate.

The difference-in-differences compares the semiannual and quarterly reporters’ change in

Amihud (2002) illiquidity from the first to the second quarter and from the third to the fourth

quarter.28 As depicted in Figure 3, I expect this change to be lower for quarterly reporters than

for semiannual reporters because of interannouncement growth (i.e., I expect B – A < D – C). The

difference-in-differences controls for any effects from other factors that differ between semiannual

and quarterly reporters. As depicted in the figure, a firm’s decision to report semiannually or

quarterly does not affect interannouncement growth in the first and third quarters, so any cross-

sectional difference in Amihud (2002) illiquidity during these quarters comes from factors other

than interannouncement growth. As long as these other factors have the same effect in quarters

one and three as in quarters two and four, a difference-in-differences subtracts out their impact,

leaving an estimate of the effect from reducing interannouncement growth.

The regression equation for the difference-in-differences is as follows:

log(Price Impact)iyt = αiy + αt + β0Q2 or Q4iyt + β1Quarterlyiy ×Q2 or Q4iyt

+γControlsiyt + εiyt. (1)

Each observation is a firm-day, with i indexing firms, y indexing years, and t indexing calendar

days. For this regression, I define a year to run from the earnings announcement of the previous

fiscal year’s last period to the earnings announcement of the current fiscal year’s last period.29

In other words, I treat the four quarters shown in Figure 3 as a year. This ensures that all four

quarters get the same designation as either a semiannual or a quarterly reporter.

Q2 or Q4 is an indicator that turns on for days that occur between the first and second

quarter earnings announcements or the third and fourth quarter earnings announcements. In other

words, Q2 or Q4 turns on during the second and fourth quarters in Figure 3. Of course, semiannual

reporters do not have first or third quarter announcements, so I estimate the day that these interim

28This research design is similar to the one used in Hu (2016).29More precisely, I define firm-years to run from two days after the earnings announcement of the previous fiscal

year’s last period to the day after the earnings announcement of the current fiscal year’s last period. I include theday after the earnings announcement in the same period as the day of the earnings announcement in keeping withthe convention of treating the day after the earnings announcement as part of the earnings announcement window.

18

announcements would have occurred if the firm were to report quarterly, and I turn on the Q2 or

Q4 indicator for days after this estimated announcement date.

To estimate when an interim earnings announcement would have occurred, I first count the

number of trading days for each quarterly reporter from the beginning of the fiscal semester to the

date of the interim quarter’s earnings announcement.30 For example, for the first semester of a

calendar-year firm, I would count the number of trading days from January 1st to the date of the

first quarter earnings announcement. Once I have calculated this interval for each fiscal semester of

each quarterly reporter, I take the median for each firm across the years when it reports quarterly.

For the semiannual reporting years of each of these firms, I then use this median to create estimates

of what the first and third quarter earnings announcement dates would have been if the firm had

reported quarterly that year.31 For example, say that a particular firm has a median of 90 trading

days between the beginning of the fiscal semester and the interim earnings announcement for years

when it reports quarterly. For the years when the firm reports semiannually, I estimate that a first

or third quarter earnings announcement would have happened on the 90th trading day of the fiscal

semester. So for the first semester of these semiannual reporting years, the Q2 or Q4 indicator will

be zero from the day after the second semester earnings announcement to the 89th day of the fiscal

semester, and will be one from the 90th day of the fiscal semester to the day of the first semester

earnings announcement.

The Quarterly variable in the regression is an indicator that turns on when the firm-year is

a quarterly reporter. Reporting frequency is recorded by Worldscope at the annual level under the

variable WC05200. If WC05200 indicates that a firm reports quarterly, but the firm-year has no

first-quarter or third-quarter observations, then I exclude the observations for that firm-year from

the sample. If the firm only reports annually, I also exclude the observations for that firm-year.

The regression also includes both firm-year and calendar day fixed effects. The firm-year

fixed effects control for the difference between the semiannual and quarterly reporters’ Amihud

(2002) illiquidity during quarters one and three. This controls for differences arising from factors

30Here and elsewhere in the paper, I define the first fiscal semester as the first two fiscal quarters of the year, andthe second fiscal semester as the last two fiscal quarters.

31For firms that never switch to quarterly reporting, I use the median interval for the entire sample to make thisdetermination. This is 92 trading days in the European sample, 93 trading days in the Singaporean sample, and 91trading days in the Japanese sample. In all three samples, there is very little variation in the median from year toyear.

19

other than interannouncement growth. Note that Quarterly does not enter the regression equation

on its own, since a firm’s decision to report quarterly is fixed for a given firm-year. This means

that the test is well-identified for firms that voluntarily adopt quarterly reporting, as long as the

unobservables that caused the firm to adopt have the same effect on Amihud (2002) illiquidity in

quarters one and three as in quarters two and four. I cluster standard errors at both the firm

and day levels to account for correlation in the errors arising from within-firm autocorrelation and

common daily shocks.32

The Q2 or Q4 indicator captures the average change in Amihud (2002) illiquidity from

the first to the second quarter and from the third to the fourth quarter for semiannual reporters.

The interaction between the Q2 or Q4 indicator and the Quarterly indicator is my variable of

interest, and it captures the difference-in-differences. In other words, the interaction term coefficient

estimates the difference between the quarterly and semiannual reporters’ change in Amihud (2002)

illiquidity from the first to the second quarter and from the third to the fourth quarter. I expect

this coefficient to be negative, because I expect a lower change for the quarterly reporters than the

semiannual reporters.

In the regression, I include controls for Size, Turnover, Volatility, Midpoint, Earnings

Window, One Week Before, and One Week After, which have the same definitions as in Section

4.1.33 I also control for First Semester, an indicator that turns on between the year-end earnings

announcement of the previous year and the second quarter/first semester earnings announcement

of the current year.

Results are in Table 4. The Quarterly by Q2 or Q4 interaction term’s coefficient estimates

that quarterly reporting reduces Amihud (2002) illiquidity in the second and fourth quarters by

5.3% (t=-10.04) for European firms and 2.9% (t=-2.11) for Singaporean firms. This is consistent

with quarterly reporting reducing information asymmetry because it reduces interannouncement

growth. While the coefficient is negative for the Japanese sample, it is insignificantly different

from zero. It could be that the true coefficient is actually negative, or it could be that there is

no interannouncement growth in Japan.34 If the latter is true, this should not be taken as strong

32Clustering at the firm-quarter level, rather than the firm level, does not change the significance of the results.33The results are the same, and generally more significant, when I omit these controls.34In support of the former explanation, when I repeat the U.S. test in Panel A of Table 3 using the Japanese sample

(untabulated), I find a significant positive relationship between Days Since EA and log(Price Impact). So it may bethat the difference-in-differences just lacks power. I also find a significant postive relationship between Days Since

20

evidence against interannouncement growth elsewhere, since common asset pricing patterns—such

as those based on size and momentum—do not exist in Japan (Fama and French, 2012; Chui

et al., 2010), and accounting measures explain significantly less of firm market value in Japan

than in the U.S. (Ely and Pownall, 2002). Also, Harris and Darrough (1989) note that the vast

majority of Japanese firms provide management forecasts of earnings, and they find evidence that

these forecasts are value-relevant. If these management forecasts preempt much of the earnings

announcement’s information, then the switch to quarterly reporting may not have much of an

effect on interannouncement growth, since most of the growth would occur between management

forecasts rather than between earnings announcements.

For both Europe and Singapore, the coefficient on Q2 or Q4 is significantly positive,

consistent with semiannual reporters’ information asymmetry increasing from quarters one and

three to quarters two and four. The illustration in Figure 3 predicts that quarterly reporting will

undo this increase, which means the negative coefficient on the interaction term should have the

same magnitude as the positive coefficient on Q2 or Q4. This appears to be true for the European

sample. Both the positive coefficient on Q2 or Q4 and the negative coefficient on the interaction

term have a magnitude of about 0.05, and their sum is insignificantly different from zero with a

t-statistic of 0.18. But the Singaporean sample’s Q2 or Q4 coefficient has double the magnitude

of its interaction term coefficient, and the sum of the two coefficients is significantly positive. This

suggests that the Singaporean firms have some trend other than interannouncement growth that

causes Amihud (2002) illiquidity to increase over the fiscal year. As long as this other trend is

parallel for the semiannual and quarterly reporters, then the difference-in-differences controls for

it, and the coefficient on the interaction term identifies the effect from interannouncement growth.

I test for parallel trends in the next subsection.

4.3 Testing Parallel Trends Assumption in International Test

To gain comfort that interannouncement growth drives the result in the previous subsec-

tion’s difference-in-differences, I need to show that the result is not driven by other information

asymmetry trends over the course of the fiscal year.

Such other trends might exist. For instance, there could be a trend based on seasonality.

EA and log(Price Impact) when I repeat this test for the European and Singaporean samples (also untabulated).

21

If most of a firm’s business is concentrated at the end of its fiscal year, there may not be much

information asymmetry early in the year because there is little new information to begin with.

When business activity picks up at the end of the year, there would be new information to gather,

leading to higher information asymmetry. Thus information asymmetry would increase over the

course of the fiscal year. In the previous subsection, I found evidence that Singaporean firms have

some trend other than interannouncement growth that causes information asymmetry to increase

from the first and third quarters to the second and fourth quarters. If such a trend is not parallel

between the semiannual and quarterly reporters, it may drive the difference-in-differences results.

But if it is parallel, then the difference-in-differences is well-identified.

Figure 4 illustrates how non-parallel trends could drive Table 4’s result. Panel A shows the

schematic from Figure 3. It depicts interannouncement growth for a semiannual and a quarterly

reporter, with the implicit assumption that the firms have parallel underlying Amihud (2002)

illiquidity trends over time.35As before, the change in Amihud (2002) illiquidity from the first to

the second quarter and from the third to the fourth quarter is larger for the semiannual reporter

than the quarterly reporter (i.e., Bi – Ai < Di – Ci for i ∈ {1, 2}), and this inequality is caused by

interannouncement growth. But Panel B shows that, even without interannouncement growth, the

same inequality can come from an Amihud (2002) illiquidity trend that increases more over time for

semiannual reporters than for quarterly reporters (i.e., Panel B still predicts that Bi – Ai < Di –

Ci for i ∈ {1, 2}, even without interannouncement growth). So Table 4’s results could come from

this difference in trends rather than from interannouncement growth. Fortunately, a comparison of

Panels A and B suggests another difference-in-differences that can test for this difference in trends.

If the underlying trend for semiannual reporters increases faster than for quarterly reporters, as in

Panel B, then a difference-in-differences should find that Amihud (2002) illiquidity grows more from

the first half of the year to the second half for semiannual reporters than for quarterly reporters

(i.e., A2 – A1 < C2 – C1 and B2 – B1 < D2 – D1). In contrast, if the underlying trends

are parallel over time and interannouncement growth drives the result in Table 4, as in Panel A,

then I should find that the change from the first half of the year to the second half is the same for

semiannual and quarterly reporters (i.e., A2 – A1 = C2 – C1 and B2 – B1 = D2 – D1).

35More precisely, Panel A implicitly assumes that there are no underlying trends affecting Amihud (2002) illiquidity,other than interannouncement growth. But the analysis would be the same if the semiannual and quarterly reportershad a trend that increased Amihud (2002) illiquidity by the same amount for both each quarter.

22

Based on this logic, I perform another difference-in-differences to test the parallel trends

assumption. The regression takes the following form:

log(Price Impact)iyt = αiy + αt + β0Second Half-yeariyt

+β1Quarterlyiy × Second Half-yeariyt + γControlsiyt + εiyt.

Second Half-year is an indicator that equals 1 for days in quarters three and four (the firm-year’s

second half-year), and 0 for days in quarters one and two (the firm-year’s first half-year). Quarterly

is defined the same as in Section 4.2, and the regression includes the same controls and fixed-

effects as that section’s regression other than First Semester, which I omit because it has perfect

collinearity with Second Half-year. I continue to cluster standard errors at both the firm and day

levels.

If semiannual and quarterly reporters have parallel underlying Amihud (2002) illiquidity

trends over time, then β1 would equal zero. This would indicate that interannouncement growth

drives Table 4’s difference-in-differences result. On the other hand, if semiannual and quarterly

reporters have different underlying trends, then β1 will be different from zero. If β1 is negative,

then this difference in trends may be responsible for the results in Table 4. A negative β1 would

indicate that quarterly reporters have a lower Amihud (2002) illiquidity trend than semiannual

reporters, which would give quarterly reporters a lower change from the first to the second quarter

and from the third to the fourth quarter, even without interannouncement growth. On the other

hand, a positive β1 would indicate a difference in trends that works against interannouncement

growth.

In Table 5, the coefficient on the interaction between Quarterly and Second Half-year

is insignificantly different from zero for the European, Singaporean, and Japanese samples, so

the test does not reject the parallel trends assumption. This increases my confidence that

interannouncement growth drives the result in Table 4’s difference-in-differences.

23

5 Policy Implications

5.1 Reducing Information Asymmetry is a Good Reason to Report More

Frequently

This paper provides evidence that interannouncement growth is the mechanism through

which more frequent reporting reduces information asymmetry. Establishing this mechanism is

important for decision-makers. If managers and regulators do not know how a higher reporting

frequency reduces information asymmetry, they cannot know whether the reduction provides a

good reason to report more frequently. This is especially true, given that neither of the two other

likely possible mechanisms justifies a higher reporting frequency.

The first of these other possible mechanisms is as follows: more frequent reporting may

reduce information asymmetry by simply increasing total public information, as suggested by Fu

et al. (2012). Unlike interannouncement growth, which focuses on the timing of the disclosures,

this explanation imagines an increase in the overall level of public information.36 More frequent

reports may increase this level by providing a finer breakout of firm performance into shorter time

intervals, which would make it easier to spot trends. If informed traders were already aware of

these trends, then the finer breakout would decrease information asymmetry. But if more-frequent

reporting only reduced information asymmetry through this channel, then firms could achieve the

reduction without increasing reporting frequency at all. Rather than adopt quarterly reporting, a

semiannual reporter could just continue reporting semiannually and break firm performance out

by quarter within each semiannual report. This would reveal the trends and reduce information

asymmetry without incurring the costs of more frequent reporting.

The second alternative is that quarterly reporting may reduce information asymmetry by

reducing private research.37 When there is less time to discover private information before it

becomes public, there may be a lower benefit to searching for it. This could in turn reduce

the amount of private research conducted. But while reducing private research would reduce

asymmetry, it would also likely reduce price discovery.38 The costs from less efficient prices could

36This is clear because Fu et al. (2012) examine reporting frequency’s effect on information asymmetry’s mean overthe entire year, and not just the second and fourth quarters. Interannouncement growth only predicts a reductionduring the second and fourth quarters.

37This mechanism is also suggested in Cuijpers and Peek (2010).38This would happen if private research yields private information about the future as well as about the current

24

outweigh the benefits from this channel’s information asymmetry reduction.

In contrast to these two mechanisms, reducing interannouncement growth does not generate

any obvious additional costs, and the easiest way to reduce it is by increasing reporting frequency.

So the drop in information asymmetry—and any resulting cost of capital reduction—caused

by lower interannouncement growth provides a good reason to report more frequently. Unlike

interannouncement growth, neither of the other two mechanisms predict my results, because neither

say anything about how information asymmetry evolves over time. A change in the total amount

of public information or private research would change the overall level of information asymmetry,

even if there were no interannouncement growth.

Finding evidence of interannouncement growth helps decision-makers, both managers and

regulators, as they weigh trade-offs to determine an optimal reporting frequency. The costs of more

frequent reporting include increased managerial short-termism and disclosure costs (Gigler et al.,

2014; Kraft et al., 2015; Ernstberger et al., 2015; Verdi, 2012). Evidence of interannouncement

growth demonstrates that the information asymmetry reduction is a countervailing benefit to

weigh against these costs. This finding also informs decision-makers that firms with steeper

interannouncement growth slopes benefit more from increasing reporting frequencies. Finally,

interannouncement growth implies that the marginal benefit from reducing information asymmetry

gets smaller with each reporting frequency increase. I discuss this decreasing marginal benefit in

the next subsection.

The academic literature identifies two other possible benefits from more frequent reporting:

improved price discovery (Butler et al., 2007; Arif and De George, 2015) and lower cost of capital

(Fu et al., 2012). Among the possible benefits, the information asymmetry reduction is particularly

important. This is because the evidence on improved price discovery is mixed—Butler et al. (2007)

find that it only improves for firms that voluntarily increase their reporting frequencies—and some

of the cost of capital reduction likely comes from the reduction in information asymmetry.

quarter’s performance. If quarterly reporting reduces private research, then it may reduce the speed with whichnews that will affect future earnings gets incorporated into the price, even as it increases the speed with which pastperformance gets incorporated into the price through more timely public reports.

25

5.2 Interannouncement Growth Implies a Decreasing Marginal Benefit

One of interannouncement growth’s implications is that the marginal information asymme-

try reduction diminishes with each increase in reporting frequency. With this diminishing marginal

benefit, if there is even a small constant cost to prepare each financial report, then at some point this

cost will outweigh the marginal benefit from lower information asymmetry. So while the presence

of interannouncement growth implies that the information asymmetry reduction is beneficial, it

also implies that the benefit decreases at higher reporting frequencies.

To see this, suppose that information asymmetry, x, is simply equal to the amount of time

since the earnings announcement, t (i.e., x(t) = t). If there is an interval of length T between

earnings announcements, then average information asymmetry is 1T

´ T0 x(t)dt = 1

2T . Doubling the

reporting frequency cuts T in half, which in turn cuts average information asymmetry in half. At

the same time, if there is a fixed cost, c > 0, to produce each financial report, then doubling the

reporting frequency doubles report preparation costs. Suppose that t is in terms of trading days,

with 250 days in a year. Also suppose that the preparation cost per report is c = 1, and that the

cost of information asymmetry is equal to average information asymmetry during the year. Then

the annual costs across different reporting frequencies are as follows:

Reporting Interval Information Report Total

Frequency Between Asymmetry Preparation Annual

Per Year Reports (T ) Cost (12T ) Cost Cost

Once 250 125 1 126

Twice 125 62.5 2 64.5

Four Times 62.5 31.25 4 35.25

Eight Times 31.25 15.625 8 23.625

Sixteen Times 15.625 7.8125 16 23.8125

Notice the decreasing marginal benefit. Moving from one report per year to two decreases the

information asymmetry cost by 62.5, moving from two to four decreases it by 31.25, and so on. At

the same time, the marginal increase in report preparation costs is doubling. So even though the

preparation cost is less than 1% of the information asymmetry cost when the firm reports annually,

doubling the number of reports quickly moves the two costs towards each other. Among the options

26

presented, costs are lowest when the firm reports eight times per year.

As reporting frequency grows, the marginal information asymmetry reduction will even-

tually become vanishingly small. If the marginal costs from increasing reporting frequencies are

constant or growing, then they will eventually outweigh the marginal benefit from lower asymmetry.

As shown in the example, it is not unreasonable to expect the marginal cost to increase as reporting

frequency doubles, since there are twice as many new reports to prepare after each doubling. So

while reducing information asymmetry through the interannouncement growth channel could justify

switching from semiannual to quarterly reporting, it probably cannot justify moving all the way to

continuous reporting.

6 Additional Analysis

6.1 Repeating U.S. Tests with Bid-Ask Spreads

I next re-perform the previous tests using the bid-ask spread as my measure of information

asymmetry. Both the U.S. and international results are robust when I switch to this measure.

Panel A of Figure 5 plots the bid-ask spread in the forty-five trading days before and after

the earnings announcement for the entire U.S. sample. In creating this plot, I follow the same steps

as I did for the plot in Panel A of Figure 2. Consistent with interannouncement growth, the plot

shows that bid-ask spreads increase over time and then drop right after a brief spike during the

earnings announcement. Panel B of Figure 5 extends the window to include eighty trading days

before and after the earnings announcement, which includes the previous and subsequent earnings

announcements in the window.39 The pattern shown in Panel A repeats over time, with information

asymmetry again rising between each earnings announcement.

I next repeat the U.S. regressions described in Section 4.1 with bid-ask spreads on the

left-hand side rather than Amihud (2002) illiquidity.40 These results are in Table 6. In Panel

A, I regress the bid-ask spread on Days Since EA, defined as in Section 4.1, and controls. The

coefficient on Days Since EA is significantly positive, which is consistent with interannouncement

growth and with the results when Amihud (2002) illiquidity is on the left-hand side (see Panel A of39As in Panel B of Figure 2, the window is centered on the second quarter earnings announcement.40Specifically, the left-hand side variable in these regressions is log(Spread), which is calculated for each firm-day

as log(

100 ∗ ask−bid(ask+bid)/2

).

27

Table 3). On average, each additional day without a new earnings announcement is associated with

a 0.03% increase (t=16.61) in the firm’s bid-ask spread. If a quarter (approximately 63 trading

days) passes between earnings announcements, then this regression predicts a 1.9% increase in the

bid-ask spread from right after the first earnings announcement to right before the second.

The controls tend to have sensible coefficients. The coefficients on Size, Turnover, and

Volatility are all consistent with prior literature. The coefficient on Midpoint is negative, which is

to be expected since it enters into the denominator in Bid-Ask Spread. The coefficient on Earnings

Window is positive, consistent with the spike around the earnings announcement shown in Figure 5.

The coefficient on Annual Report is significantly positive. The coefficient on Late Filing is negative,

but insignificantly different from zero. The coefficients on One Week Before and One Week After

are both positive.

Panel B of Table 6 repeats the tests in Panel B of Table 3 with bid-ask spreads on the left-

hand side instead of Amihud (2002) illiquidity. Again, the results reveal that interannouncement

growth occurs throughout the entire period between earnings announcements. Column (1)

shows results from the regression that interacts Post Quarter-end with Days Since EA. The

significant positive coefficient on the uninteracted Days Since EA variable demonstrates that

interannouncement growth occurs before the books close at quarter-end. This indicates that leaks

right before the earnings announcement do not drive the positive coefficient on Days Since EA in

the main specification.

Column (2) shows results from the piecewise regression, which examines how the slope

on Days Since EA varies from month to month. While the relationship between bid-ask spreads

and Days Since EA is significantly positive during the second and third months after the earnings

announcement, it is negative during the first month. The first month’s negative coefficient likely

comes from the large drop in bid-ask spreads right after the earnings announcement, which can

be seen in Figure 5. In support of this hypothesis, untabulated results show that this coefficient

becomes significantly positive when I re-perform this test with the 11-day window around the

earnings announcement excluded from the sample.

Column (3) regresses bid-ask spreads on the indicators Greater Than 1 Month and Greater

Than 2 Months. The coefficients on both indicators are significantly positive, again indicating that

information asymmetry grows monotonically between earnings announcements.

28

6.2 Repeating International Results with Bid-Ask Spreads

In this section, I repeat my international tests with the bid-ask spread as my information

asymmetry proxy. Table 7 presents results for the same difference-in-differences test as in Section

4.2, except that bid-ask spreads are on the left-hand side. As before, the variable of interest is the

interaction between Quarterly and Q2 or Q4. The test estimates that bid-ask spreads in quarters

two and four are 1.3% lower (t=-5.12) when European firms report quarterly and 1.5% lower (t=-

3.48) when Singaporean firms report quarterly. While the coefficient is negative for the Japanese

sample, it is again insignificantly different from zero.

For both Europe and Singapore, the coefficient on Q2 or Q4 is significantly positive, which

is consistent with interannouncement growth. But as with the Singaporean sample in the Amihud

(2002) illiquidity tests, this coefficient’s magnitude is different from that of the interaction term’s

coefficient, suggesting that there are trends other than interannouncement growth during the year.

As before, these trends do not affect the difference-in-differences estimate as long as they are parallel

for semiannual and quarterly reporters.

Table 8 presents results for the parallel trends test, which is the same as in Section 4.3,

except that bid-ask spreads are on the left-hand side. The coefficient on the interaction between

Quarterly and Second Half-year is insignificantly different from zero for the Singaporean and

Japanese samples. This indicates that there is no significant difference between the underlying

bid-ask spread trends of semiannual and quarterly reporters in these countries.

The positive coefficient on the European sample’s interaction term is significant at the

10% level, indicating a more-positive underlying trend for quarterly reporters than semiannual

reporters. This difference in trends works against interannouncement growth, meaning that the

European sample’s interaction term coefficient in Table 7 is likely biased towards zero. So while

this difference in trends affects the accuracy of Table 7’s estimate, it also provides evidence that

interannouncement growth drives the result.

6.3 Other Public Information and Interannouncement Growth

If the information released by the earnings announcement causes information asymmetry

to fall, then other public information should cause it to fall too. Indeed, Amiram et al. (2016)

29

show that analyst forecasts cause a drop in information asymmetry. If the analyst forecast—or

any other disclosure, for that matter—contains information about earnings, then some of this drop

should reduce the accumulation of earnings-related information asymmetry. When more of these

disclosures occur between earnings announcements, the accompanying drops should reduce the total

amount of interannouncement growth during the quarter. Therefore, I should find that firms with

more interannouncement disclosures have lower interannouncement growth slopes when I regress

information asymmetry on the number of days since the earnings announcement.

The prediction is less clear when a disclosure comes out during the earnings announcement

itself. If all of the disclosure’s information relates to next quarter’s earnings, then it likely reduces

information asymmetry throughout the entire period between the current earnings announcement

and the next one. A reduction that lasts for the entire period might not affect the rate of

interannouncement growth—it might just reduce the intercept of information asymmetry and leave

the slope unchanged. On the other hand, if some of the disclosure’s information is unrelated

to next quarter’s results, it could create a drop in information asymmetry during the earnings

announcement that does not last until the next announcement. Because such a drop would reverse

between the two announcements, it would increase the total growth in information asymmetry over

the period. In other words, the initial drop during the announcement would reduce the intercept,

and the reversal during the quarter would increase the slope. Therefore, I expect disclosures

that occur during the earnings announcement to increase the interannouncement growth slope, if

anything.

I test these two hypotheses by examining the effects of analyst forecasts and 8-K filings on

interannouncement growth. The regression takes the following form:

log(Price Impact)iyt = αiy + αt + β0Days Since EAiyt + β1EA Disclosure Countiq

+β2Non-EA Disclosure Countiq

+β3Days Since EAiyt × EA Disclosure Countiq

+β4Days Since EAiyt ×Non-EA Disclosure Countiq

+γControlsiyt + εiyt.

30

Each observation is a firm-day, with i indexing firms, y indexing calendar years, t indexing calendar

days, and q indexing fiscal quarters as bounded by earnings announcements. The regression again

puts my information asymmetry proxies on the left-hand side. On the right-hand side, it interacts

Days Since EA, defined as in the main specification (see Section 4.1), with EA Disclosure Count,

which counts the number of disclosures on the day of and the day after the most recent earnings

announcement, as well as with Non-EA Disclosure Count, which counts the number of disclosures

during the current interval between two earnings announcements (not including disclosures during

the earnings announcement windows themselves).41 I run two seperate sets of specifications,

where the disclosures are either analyst forecasts42 or 8-K filings.43 Since I expect disclosures

between earnings announcements to reduce the interannouncement growth slope, I expect β4 to be

negative. And since I expect disclosures during the earnings announcement to either increase the

interannouncement growth slope or leave it unchanged, I expect β3 to be non-negative. As in the

main specification in Section 4.1, the regression includes firm-year fixed effects and calendar day

fixed effects, and I cluster standard errors at both the firm and day levels. The Controls vector is

also the same as the one in the main specification.

Table 9 contains the results. As predicted, β4 is significantly negative in all specifications.

In other words, the interannouncement growth slope is less steep when there are more analyst

forecasts (columns (1) and (2)) and 8-K filings (columns (3) and (4)) during the period between

two earnings announcements. Also as predicted, β3 is significantly positive in the 8-K specification

(columns (3) and (4)), meaning that 8-K filings during the earnings announcement increase the

steepness of the interannouncement growth slope. On the other hand, β3 is insignificant in columns

1 and 2, indicating that more analyst forecasts during the earnings announcement do not increase

the slope. Perhaps this occurs because analyst forecasts only provide information about the next

earnings announcement. As discussed above, this could lead to no change in the interannouncement

growth slope when the number of forecasts during the earnings announcement increases.

41By “current” interval, I mean the interval that contains the firm-day observation. By “earnings announcementwindow,” I mean the day of and the day after an earnings announcement.

42The analyst forecasts come from I/B/E/S. If an analyst makes multiple forecasts on the same day, then I countit as a single forecast.

438-K filing data comes from EDGAR. All public companies were required to post their filings on EDGAR beginningon May 6, 1996, so the sample period for the 8-K specification of this test begins on that date.

31

6.4 U.S. Results Hold in Different Periods

The main U.S. results run from 1993 to 2015, a period that includes the decimalization of

bid and ask quotes and the rise of high-frequency trading. In Table 10, I re-run the main U.S.

regression separately for the years 1993 to 2001, 2002 to 2007, and 2007 to 2015.44 The NYSE,

AMEX, and NASDAQ switched to decimal prices in 2001, so the first period shows the results

before decimalization. The second period is between the implementation of decimalization and the

financial crisis. The last period is post-crisis, and is also a period characterized by the prominence

of high-frequency trading. With either log(Price Impact) or log(Spread) on the left-hand side, the

coefficient on Days Since EA is significantly positive in all of these periods.

7 Conclusion

In this paper, I propose and find evidence for a mechanism that explains the negative

relationship between information asymmetry and reporting frequency. Information asymmetry

between informed and uninformed investors grows steadily until a new earnings announcement

causes it to fall again. This creates a sawtooth pattern that I call interannouncement growth.

More frequent reporting reduces a firm’s average information asymmetry by reducing the time

available for interannouncement growth to occur. In univariate plots and multivariate regressions,

I provide evidence for interannouncement growth in the United States. I estimate that Amihud

(2002) illiquidity, my main proxy for information asymmetry, grows by 10.7% over the period

between two quarterly earnings announcements. I then move to international settings to estimate

the effect when firms switch from semiannual to quarterly reporting. I estimate that quarterly

reporting’s reduction of interannouncement growth causes Amihud (2002) illiquidity to fall by up

to 5.3% on average in the second half of each semiannual period.

Interannouncement growth has direct implications for the reporting frequency debate. First

of all, it implies that more frequent reporting’s information asymmetry reduction provides a

good reason to increase reporting frequency. This is because reducing interannouncement growth

does not generate any obvious additional costs, and the easiest way to reduce it is to report

44More specifically, I include firm-days based on the year in which the previous earnings announcement occurred.So if the previous earnings announcement for a firm-day observation in January of 2002 was in December of 2001,the observation would be included in the 1993 to 2001 sample.

32

more frequently. Interannouncement growth also implies that not all (and perhaps none) of the

information asymmetry reduction comes from breaking firm performance out into shorter time

intervals in the financial reports. This means firms cannot achieve the entire reduction by simply

reporting more about current performance trends—they need to actually increase their reporting

frequencies. In addition, by explaining the drop in information asymmetry, interannouncement

growth makes it less likely that more-frequent reporting reduces private research, which would be

costly since less private research means slower price discovery.

In order to determine optimal reporting frequencies, regulators and managers need to

know the mechanism behind the information asymmetry reduction. Without knowing this

mechanism, they cannot know how the reduction might vary across firms, or how it might change

with further increases in reporting frequency (e.g., from quarterly to monthly reporting). This

study tells practitioners that higher reporting frequencies more-greatly benefit firms with steeper

interannouncement growth slopes. Interannouncement growth also implies that the marginal benefit

from reducing information asymmetry gets smaller and smaller with each reporting frequency

increase, and vanishes as the reporting frequency becomes large. As a result, information

asymmetry alone likely cannot justify continuous reporting.

More generally, interannouncement growth increases our understanding of how accounting

affects capital markets. Theory shows that information asymmetry could rise or fall over time,

depending on the circumstances. By showing empirical evidence of interannouncement growth,

this paper shows that information asymmetry in fact rises, and this rise is interrupted by each

earnings announcement. Future theoretical models should reproduce this pattern in order to better

reflect the real world.

33

References

Acker, D., Stalker, M., and Tonks, I. (2002). Daily closing inside spreads and trading volumes

around earnings announcements. Journal of Business Finance & Accounting, 29(9-10):1149–

1179.

Affleck-Graves, J., Callahan, C. M., and Chipalkatti, N. (2002). Earnings predictability, information

asymmetry, and market liquidity. Journal of Accounting Research, 40(3):561–583.

Ajinkya, B. B. and Jain, P. C. (1989). The behavior of daily stock market trading volume. Journal

of Accounting and Economics, 11(4):331–359.

Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of

Financial Markets, 5(1):31–56.

Amihud, Y. and Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial

Economics, 17(2):223–249.

Amiram, D., Owens, E. L., and Rozenbaum, O. (2016). Do information releases increase or

decrease information asymmetry? New evidence from analyst forecast announcements. Journal

of Accounting and Economics, 62:121–138.

Arif, S. and De George, E. T. (2015). Does financial reporting frequency affect how earnings news

travels around the world? Evidence from transnational information transfers.

Back, K., Cao, C. H., and Willard, G. A. (2000). Imperfect competition among informed traders.

The Journal of Finance, 55(5):2117–2155.

Back, K. and Pedersen, H. (1998). Long-lived information and intraday patterns. Journal of

financial markets, 1(3):385–402.

Bamber, L. S., Barron, O. E., and Stevens, D. E. (2011). Trading volume around earnings

announcements and other financial reports: Theory, research design, empirical evidence, and

directions for future research. Contemporary Accounting Research, 28(2):431–471.

Benoit, D. (August 19, 2015). Time to end quarterly reports, law firm says; Wachtell Lipton argues

the ritual distracts companies from long-term results. The Wall Street Journal.

34

Bernhardt, D. and Miao, J. (2004). Informed trading when information becomes stale. The Journal

of Finance, 59(1):339–390.

Butler, M., Kraft, A., and Weiss, I. S. (2007). The effect of reporting frequency on the timeliness

of earnings: The cases of voluntary and mandatory interim reports. Journal of Accounting and

Economics, 43(2):181–217.

Caldentey, R. and Stacchetti, E. (2010). Insider trading with a random deadline. Econometrica,

78(1):245–283.

Chae, J. (2005). Trading volume, information asymmetry, and timing information. The Journal of

Finance, 60(1):413–442.

Chambers, A. E. and Penman, S. H. (1984). Timeliness of reporting and the stock price reaction

to earnings announcements. Journal of accounting research, pages 21–47.

Chui, A. C., Titman, S., and Wei, K. J. (2010). Individualism and momentum around the world.

The Journal of Finance, 65(1):361–392.

Cohen, D. A., Dey, A., Lys, T. Z., and Sunder, S. V. (2007). Earnings announcement premia and

the limits to arbitrage. Journal of Accounting and Economics, 43:153–180.

Cuijpers, R. and Peek, E. (2010). Reporting frequency, information precision and private

information acquisition. Journal of Business Finance & Accounting, 37(1-2):27–59.

Diamond, D. W. and Verrecchia, R. E. (1991). Disclosure, liquidity, and the cost of capital. The

Journal of Finance, 46(4):1325–1359.

Easley, D., Engle, R. F., O’Hara, M., and Wu, L. (2008). Time-varying arrival rates of informed

and uninformed trades. Journal of Financial Econometrics, 6(2):171–207.

Ely, K. M. and Pownall, G. (2002). Shareholder-versus stakeholder-focused Japanese companies:

Firm characteristics and accounting valuation. Contemporary Accounting Research, 19(4):615–

636.

Ernstberger, J., Link, B., Stich, M., and Vogler, O. (2015). The real effects of mandatory quarterly

reporting. Available at SSRN 2604030.

35

Euromoney Institutional Investor PLC (2004). Europe drops quarterly reporting. International

Financial Law Review.

European Commission (2004). Directive 2004/109/EC of the European parliament and of the

council. Official Journal of the European Union, L 390:38–57.

Fama, E. F. and French, K. R. (2012). Size, value, and momentum in international stock returns.

Journal of financial economics, 105(3):457–472.

Foster, F. D. and Viswanathan, S. (1996). Strategic trading when agents forecast the forecasts of

others. The Journal of Finance, 51(4):1437–1478.

Fu, R., Kraft, A., and Zhang, H. (2012). Financial reporting frequency, information asymmetry,

and the cost of equity. Journal of Accounting and Economics, 54(2–3):132–149.

Gigler, F. and Hemmer, T. (1998). On the frequency, quality, and informational role of mandatory

financial reports. Journal of Accounting Research, 36:117–147.

Gigler, F., Kanodia, C., Sapra, H., and Venugopalan, R. (2014). How frequent financial reporting

can cause managerial short-termism: An analysis of the costs and benefits of increasing reporting

frequency. Journal of Accounting Research, 52(2):357–387.

Goyenko, R. Y., Holden, C. W., and Trzcinka, C. A. (2009). Do liquidity measures measure

liquidity? Journal of financial Economics, 92(2):153–181.

Harris, T. and Darrough, M. N. (1989). Do management forecasts of earnings affect stock prices in

japan?

Higgins, K. F. (2016). International developments - past, present and future. PLI - Fifteenth

Annual Institute on Securities Regulation in Europe.

Holden, C. W. and Subrahmanyam, A. (1992). Long-lived private information and imperfect

competition. The Journal of Finance, 47(1):247–270.

Holden, C. W. and Subrahmanyam, A. (1994). Risk aversion, imperfect competition, and long-lived

information. Economics Letters, 44(1-2):181–190.

36

Hu, D. (2016). Does the public availability of market participants’ trading data affect firm

disclosure? evidence from short sellers. Kellog School of Management Working Paper.

Kajüter, P., Klassmann, F., and Nienhaus, M. (2015). Causal effects of quarterly reporting–an

analysis of benefits and costs. unpublished but available at SSRN.

Kim, O. and Verrecchia, R. E. (1994). Market liquidity and volume around earnings announcements.

Journal of accounting and economics, 17(1-2):41–67.

Kraft, A. G., Vashishtha, R., and Venkatachalam, M. (2015). Real effects of frequent financial

reporting. Available at SSRN 2456765.

Krinsky, I. and Lee, J. (1996). Earnings announcements and the components of the bid-ask spread.

The Journal of Finance, 51(4):1523–1535.

Kubota, K. and Takehara, H. (2015). Reform and Price Discovery at the Tokyo Stock Exchange:

From 1990 to 2012. Palgrave Macmillan US.

Kubota, K. and Takehara, H. (2016). Information asymmetry and quarterly disclosure decisions by

firms: Evidence from the tokyo stock exchange. International Review of Finance, 16(1):127–159.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the

Econometric Society, pages 1315–1335.

Lee, C. M., Mucklow, B., and Ready, M. J. (1993). Spreads, depths, and the impact of earnings

information: An intraday analysis. Review of Financial Studies, 6(2):345–374.

Leuz, C. and Verrecchia, R. (2000). The economic consequences of increased disclosure. Journal of

Accounting Research, 38:91–124.

Link, B. (2012). The struggle for a common interim reporting frequency regime in Europe.

Accounting in Europe, 9:191–226.

Lo, A. W. and Wang, J. (2000). Trading volume: definitions, data analysis, and implications of

portfolio theory. Review of Financial Studies, 13(2):257–300.

Malmqvist, P. (October 21, 2014). The quarterly report is a great equaliser. The Financial Times.

37

Morse, D. and Ushman, N. (1983). The effect of information announcements on the market

microstructure. Accounting Review, pages 247–258.

Patell, J. M. and Wolfson, M. A. (1979). Anticipated information releases reflected in call option

prices. Journal of Accounting and Economics, 1(2):117–140.

Patell, J. M. and Wolfson, M. A. (1981). The ex ante and ex post price effects of quarterly earnings

announcements reflected in option and stock prices. Journal of Accounting Research, pages 434–

458.

Rogers, J. L., Skinner, D. J., and Van Buskirk, A. (2009). Earnings guidance and market

uncertainty. Journal of Accounting and Economics, 48(1):90–109.

Skinner, D. J. (1993). Bid-ask spreads around earnings announcements: Evidence from the Nasdaq

national market system. Manuscript (University of Michigan).

Stoll, H. (1978). The pricing of security dealer services: An empirical study of Nasdaq stocks. The

Journal of Finance, 33(4):1153–1172.

Szopo, P. (October 26, 2014). The eminent good sense of quarterly reporting. The Financial Times.

Verdi, R. S. (2012). Discussion of financial reporting frequency, information asymmetry, and the

cost of equity. Journal of Accounting and Economics, 54(2):150–153.

Wagenhofer, A. (2014). Trading off costs and benefits of frequent financial reporting. Journal of

Accounting Research, 52(2):389–401.

Yahya, Y. (February 2, 2016). Much to debate over quarterly reporting. The Straits Times.

Yohn, T. L. (1998). Information asymmetry around earnings announcements. Review of

Quantitative Finance and Accounting, 11(2):165–182.

38

Figure 2: Average Price Impact of Trades Around the Earnings Announcement

This graph plots my information asymmetry proxy around the earnings announcement. My proxy is log(Price Impact),which I measure as the logarithm of the Amihud (2002) illiquidity measure, |return|

$ volume of trades(where dollar volume

is in terms of millions of dollars). For the plots, I remove market effects by subtracting the mean log(Price Impact)for the market that day; I call the difference the Abnormal log(Price Impact). For Panel A, I then plot the meanAbnormal log(Price Impact) in event time during the 45 trading days before and after the earnings announcement.Panel B widens the window to 80 trading days before and after the earnings announcement in order to show thatthe pattern repeats from quarter to quarter. For the Panel B plot, I restrict the sample to windows centered on thesecond quarter earnings announcement. This reduces cross-sectional variation in the lag between adjacent quarters’announcements, which makes the announcements line up better in event time. (While the number of days between the4th-quarter announcement and the 1st- and 3rd-quarter announcements varies across firm-years, the lag between the2nd-quarter announcement and the 1st- and 3rd-quarter announcements is generally about a quarter, or 63 tradingdays.) For both graphs, I only include a firm-quarter’s observations if both the first and last day of the window arenot missing.

Panel A: 91 Trading Days Around Earnings Announcement

−.1

5−

.1−

.05

0.0

5M

ean

Abn

orm

al lo

g(P

rice

Impa

ct)

−40 −20 0 20 40Days Relative to Earnings Announcement

Panel B: 161 Trading Days Around 2nd-Quarter Earnings Announcement

−.1

5−

.1−

.05

0.0

5M

ean

Abn

orm

al lo

g(P

rice

Impa

ct)

−100 −50 0 50 100Days Relative to Earnings Announcement

39

Figure 3: Illustration of Logic Behind Difference-in-Differences Test

This visual aid illustrates the logic behind the difference-in-differences test in Table 4. It showsa hypothetical firm’s interannouncement growth when the firm reports semiannually versus whenit reports quarterly. Switching from semiannual to quarterly reporting reduces the time availablefor interannouncement growth, reducing information asymmetry in the second and fourth quarters,but not in the first and third quarters. I estimate the second- and fourth-quarter informationasymmetry reduction with a difference-in-differences that compares the semiannual and quarterlyreporter’s change in Amihud (2002) illiquidity from the first (third) quarter to the second (fourth)quarter. The semiannual reporter’s change is D – C, and the quarterly reporter’s change is B –A. Interannouncement growth predicts that B – A < D – C.

40

Figure 4: Illustration of Logic Behind Parallel Trends Test

This visual aid illustrates the logic behind my test of the parallel trends assumption, which is crucial to the difference-in-differences test described in Figure 3. (Table 5 contains the results of this parallel trends test.) Panel A containsthe schematic from Figure 3, which shows interannouncement growth for a semiannual and a quarterly reporter.The schematic also assumes that the semiannual and quarterly reporters have parallel underlying Amihud (2002)illiquidity trends over time. As in Figure 3, Bi – Ai < Di – Ci for i ∈ {1, 2}. Panel B shows that this inequality,Bi – Ai < Di – Ci , can also result from an underlying Amihud (2002) illiquidity trend that increases more overtime for semiannual reporters than for quarterly reporters, even in the absence of interannouncement growth. But ifPanel B is true, then I should also find that A2 – A1 < C2 – C1 and B2 – B1 < D2 – D1. In contrast, underPanel A’s assumptions—where the underlying trends are parallel over time and interannouncement growth drives theBi – Ai < Di – Ci inequality—I should find that A2 – A1 = C2 – C1 and B2 – B1 = D2 – D1.

Panel A: Amihud (2002) Illiquidity with Parallel Trends and Interannouncement Growth

Panel B: Amihud (2002) Illiquidity without Parallel Trends or Interannouncement Growth

41

Figure 5: Average Bid-Ask Spread Around the Earnings Announcement

Panels A and B in this figure are created the same way as the plots in Panels A and B of Figure 2, except they plotthe mean Abnormal log(Bid-Ask Spread). I measure the bid-ask spread for each firm-day as 100 ∗ ask−bid

(ask+bid)/2 .

Panel A: 91 Trading Days Around Earnings Announcement

−.0

20

.02

.04

Mea

n A

bnor

mal

log(

Bid

−A

sk S

prea

d)

−40 −20 0 20 40Days Relative to Earnings Announcement

Panel B: 161 Trading Days Around 2nd-Quarter Earnings Announcement

−.0

4−

.02

0.0

2.0

4M

ean

Abn

orm

al lo

g(B

id−

Ask

Spr

ead)

−100 −50 0 50 100Days Relative to Earnings Announcement

42

Table 1: U.S. Sample Descriptive Statistics

Panel A shows the average Size, which is the logarithm of a firm-day’s market value of equity, and the averagelog(Price Impact), which is calculated for each firm-day as log

( |return|$ volume of trades

)(where dollar volume is in terms

of millions of dollars), each year during the years covered by the sample. Panel B counts the number of firm-yearobservations with each year-end month. Panel C shows the percentage of firm-quarter observations by fiscal quarter(i.e., Q1 through Q4) with different ranges of trading days between the quarter end and the earnings announcement.

Panel A: Price Impact Over Timelog(Price

Year Size Impact)1993 18.62 -2.651994 18.61 -2.531995 18.67 -2.781996 18.85 -3.061997 18.97 -3.281998 18.96 -3.201999 18.96 -3.262000 18.99 -3.252001 18.85 -3.092002 18.88 -3.212003 19.15 -3.892004 19.58 -4.582005 19.73 -4.832006 19.88 -5.092007 19.98 -5.262008 19.64 -4.362009 19.43 -4.202010 19.83 -4.952011 19.98 -5.072012 20.01 -5.152013 20.25 -5.582014 20.42 -5.842015 20.35 -5.61

Panel B: Number of Firm-Year Observations by Year-EndMonth

Year-End Month N %1 4,592 3.152 1,526 1.053 7,518 5.164 1,881 1.295 1,853 1.276 9,800 6.727 1,848 1.278 1,808 1.249 8,629 5.9210 2,515 1.7311 1,371 0.9412 102,417 70.27

Panel C: % of Firm-Quarters by Number of Trading DaysBetween Quarter End and Earnings Announcement

Percentage of Firm-QuartersDays Before EA Q1 Q2 Q3 Q4

0-9 3.23 3.23 2.98 1.0010-19 41.42 41.33 39.96 18.5820-29 39.24 39.04 40.13 23.6630-39 14.59 14.51 15.72 23.7140-49 0.98 1.18 0.83 12.9350-59 0.22 0.33 0.17 8.52≥60 0.32 0.38 0.20 11.59Total 100 100 100 100

43

Table 2: International Sample Descriptive Statistics

This table shows the number of firm-year observations, the average Size (the logarithm of a firm-day’s market valueof equity), and the average log(Price Impact) (calculated for each firm-day as log

( |return|$ volume of trades

), with “dollar”

volume in terms of millions of euros, etc.) each year during the years covered by the sample. I calculate theseaverages separately for semiannual and quarterly reporters. Panel A shows the European sample, Panel B shows theSingaporean sample, and Panel C shows the Japanese sample.

Panel A: European SampleSemiannual Quarterly

log(Price log(PriceYear N Size Impact) N Size Impact)1992 943 4.53 1.55 34 8.10 -1.441993 1572 4.40 1.40 43 7.90 -1.271994 1771 4.59 1.46 49 7.79 -1.041995 1851 4.60 1.60 60 7.52 -0.661996 1812 4.83 1.48 64 7.61 -0.631997 1753 5.01 1.33 66 7.94 -1.091998 1973 4.79 1.85 107 7.11 0.041999 2194 4.65 2.70 245 6.29 2.892000 2209 4.66 2.72 383 6.21 3.172001 2187 4.16 2.61 502 5.68 3.902002 2217 3.77 3.05 671 4.89 5.502003 2309 3.72 3.30 651 5.03 5.222004 2291 4.00 2.80 638 5.47 4.412005 2486 4.02 2.58 630 5.73 3.802006 2709 4.06 2.63 647 5.92 3.922007 2860 4.14 2.76 694 5.97 4.182008 2807 3.71 3.99 707 5.54 5.552009 2676 3.45 4.27 706 5.21 6.012010 2548 3.75 3.69 698 5.46 5.452011 2395 3.88 3.51 723 5.54 5.392012 1929 3.61 3.14 719 5.52 5.322013 1768 3.64 2.77 669 5.74 4.942014 1794 3.83 2.67 591 6.14 4.542015 1893 3.95 2.86 582 6.34 4.45

44

Panel B: Singaporean SampleSemiannual Quarterly

log(Price log(PriceYear N Size Impact) N Size Impact)2000 82 5.09 6.16 6 6.79 3.182001 404 4.35 7.03 8 6.40 3.422002 409 4.26 6.76 31 5.58 5.032003 297 3.68 7.00 167 5.72 4.122004 302 3.60 7.85 225 5.80 4.152005 333 3.43 8.45 252 5.75 4.312006 361 3.51 7.85 280 5.90 3.932007 361 4.09 5.95 316 6.11 3.542008 285 3.46 8.75 436 5.35 5.782009 238 3.09 9.16 480 5.00 5.842010 230 3.28 8.61 499 5.30 5.322011 210 3.20 8.92 516 5.24 5.832012 195 3.14 8.78 529 5.18 5.652013 187 3.41 7.76 535 5.35 5.062014 181 3.48 8.03 545 5.36 5.352015 165 3.49 8.57 539 5.21 5.86

Panel C: Japanese SampleSemiannual Quarterly

log(Price log(PriceYear N Size Impact) N Size Impact)2001 3227 9.44 0.52 7 14.12 -5.602002 3505 9.28 0.47 13 13.53 -4.812003 3452 9.24 0.11 99 11.17 -2.112004 1423 9.24 -0.41 1916 9.82 -0.992005 355 9.34 -1.00 3357 10.07 -1.982006 201 9.42 -0.70 3696 10.07 -1.622007 62 10.10 -1.86 3926 9.81 -1.352008 40 10.79 -2.49 3825 9.50 -0.52

45

Table 3: Interannouncement Growth in the U.S., with Amihud (2002) Measure

Panel A: Interannouncement Growth of Information Asymmetry

Each observation is a firm-day. The left-hand-side variable is the log(Price Impact), which is calculated for eachfirm-day as log

( |return|$ volume of trades

)(i.e., the logarithm of the daily Amihud (2002) illiquidity measure), where dollar

volume is in terms of millions of dollars. Days Since EA, the variable of interest, is the number of trading dayssince the firm’s most recent earnings announcement. Size is the logarithm of the firm-day’s market value of equity.Turnover is the logarithm of one plus the fraction of the firm’s shares outstanding traded that day. Volatility is thelogarithm of one plus the standard deviation of returns over the twenty trading days before the firm-day. Midpointis the logarithm of the bid-ask spread’s midpoint. Earnings Window is an indicator for the three-day earningsannouncement window. Annual Report is an indicator for the period after the fourth-quarter earnings announcement.Late Filing is an indicator that turns on when more trading days than expected have passed since the quarter-endwithout an announcement. (I estimate the expected filing date as the median seasonal earnings announcement delayfrom the previous three years.) One Week Before is an indicator for days that occur within five trading days beforethe earnings announcement and One Week After is an indicator for days that occur within five trading days after.The regression includes firm-year fixed effects and calendar day fixed effects. Standard errors are clustered at boththe firm level and the calendar day level. T-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2)VARIABLES log(Price Impact) log(Price Impact)

Days Since EA 0.00234*** 0.00161***(59.75) (39.24)

Size -0.768***(-37.20)

Turnover -12.59***(-15.02)

Volatility -2.674***(-22.27)

Midpoint -0.752***(-32.56)

Earnings Window -0.0131***(-2.873)

Annual Report -0.00183(-1.026)

Late Filing -0.0309***(-10.89)

One Week Before -0.00800***(-4.458)

One Week After -0.0747***(-36.89)

Observations 27,926,439 25,418,852R-squared 0.864 0.880Firm-Year FE YES YESDay FE YES YESClusters by Firm & Day YES YES

46

Panel B: Interannouncement Growth Throughout the Quarter

Post Quarter-end is an indicator that turns on for days between the end of the fiscal quarter and that quarter’searnings announcement date. 1st Month, 2nd Month, and 3rd Month are indicators that turn on when Days SinceEA is, respectively, between 0 and 20 trading days, between 21 and 40 trading days, and greater than 40 tradingdays. Greater Than 1 Month is an indicator that turns on when Days Since EA is greater than 20 trading days,and Greater than 2 Months is an indicator that turns on when Days Since EA is greater than 40 trading days. Allother variables are defined the same as in Panel A, and the regressions include the same controls as in Column (2) ofPanel A. The regressions include firm-year fixed effects and calendar day fixed effects. Standard errors are clusteredat both the firm level and the calendar day level. T-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3)VARIABLES log(Price Impact) log(Price Impact) log(Price Impact)

Days Since EA 0.00222***(25.94)

Post Quarter-end 0.0488***(9.187)

Days Since EA x Post Quarter-end -0.00131***(-10.31)

Days Since EA x 1st Month 0.00155***(10.50)

Days Since EA x 2nd Month 0.00184***(24.90)

Days Since EA x 3rd Month 0.00160***(35.11)

Greater Than 1 Month 0.0395***(24.71)

Greater Than 2 Months 0.0304***(18.48)

Observations 25,418,852 25,418,852 25,418,852R-squared 0.880 0.880 0.880Controls YES YES YESFirm-Year FE YES YES YESDay FE YES YES YESClusters by Firm & Day YES YES YES

47

Table 4: Difference-in-Differences Using International Settings, with Amihud (2002) Measure

Each observation is a firm-day. The left-hand-side variable is log(Price Impact), which is calculated for each firm-dayas log

( |return|$ volume of trades

)(i.e., the logarithm of the daily Amihud (2002) illiquidity measure), where “dollar” volume

is in terms of millions of euros, etc. Q2 or Q4 is an indicator for days that either occur between the first and secondquarter earnings announcements or the third and fourth quarter earnings announcements. (The semiannual reportershave estimated hypothetical earnings announcement dates for quarters one and three; see Section 4.2.) Quarterly is anindicator that turns on for firm-years that report quarterly. Size, Turnover, Volatility, Midpoint, Earnings Window,One Week Before, and One Week After have the same definitions as in Table 3. First Semester is an indicator thatturns on between the year-end earnings announcement of the previous year and the second quarter/first semesterearnings announcement of the current year. The regression includes calendar day fixed effects and firm-year fixedeffects. I cluster standard errors by firm and by calendar day. T-statistics are in parentheses. *** p<0.01, ** p<0.05,* p<0.1.

(1) (2) (3)Europe Singapore Japan

VARIABLES log(Price Impact) log(Price Impact) log(Price Impact)

Q2 or Q4 0.0545*** 0.0652*** -0.0169***(11.37) (4.743) (-2.666)

Quarterly x Q2 or Q4 -0.0534*** -0.0287** -0.0107(-10.04) (-2.107) (-1.505)

Size -0.920*** -1.324*** -0.756***(-15.86) (-15.94) (-11.27)

Turnover -18.97*** -27.66*** -10.98***(-2.881) (-13.15) (-8.383)

Volatility -2.887*** -2.657*** -7.260***(-10.09) (-7.073) (-17.49)

Midpoint -0.349*** -1.186*** -0.710***(-6.209) (-13.20) (-10.27)

Earnings Window -0.140*** -0.139*** -0.0585***(-12.38) (-12.17) (-10.78)

First Semester 0.0173** -0.0218 0.0778***(2.177) (-1.069) (8.041)

One Week Before -0.0161*** 0.00906 0.00782*(-4.560) (1.055) (1.849)

One Week After -0.122*** -0.166*** -0.0405***(-24.63) (-17.19) (-8.789)

Observations 6,518,086 1,113,323 3,064,532R-squared 0.913 0.821 0.862Firm-Year FE YES YES YESDay FE YES YES YESClusters at Firm & Day YES YES YES

48

Table 5: Test of Parallel Trends Assumption, with Amihud (2002) Measure

Each observation is a firm-day. The left-hand-side variable is log(Price Impact), which is calculated for each firm-dayas log

( |return|$ volume of trades

)(i.e., the logarithm of the daily Amihud (2002) illiquidity measure), where “dollar” volume

is in terms of millions of euros, etc. Second Half-year is an indicator that equals one for days in quarters 3 and 4, andzero for days in quarters 1 and 2. Quarterly is an indicator that turns on for firm-years that report quarterly. Size,Turnover, Volatility, Midpoint, Earnings Window, One Week Before, and One Week After have the same definitionsas in Table 3. The regression includes calendar day fixed effects and firm-year fixed effects. I cluster standard errorsby firm and by calendar day. T-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3)Europe Singapore Japan

VARIABLES log(Price Impact) log(Price Impact) log(Price Impact)

Second Half-year -0.0611*** -0.0357* -0.0499***(-11.78) (-1.785) (-4.921)

Quarterly x Second Half-year 0.00426 -0.0145 0.00594(0.575) (-0.740) (0.508)

Size -0.922*** -1.325*** -0.755***(-15.91) (-15.92) (-11.24)

Turnover -18.99*** -27.67*** -10.98***(-2.881) (-13.14) (-8.384)

Volatility -2.896*** -2.655*** -7.260***(-10.10) (-7.063) (-17.49)

Midpoint -0.346*** -1.185*** -0.711***(-6.177) (-13.19) (-10.26)

Earnings Window -0.169*** -0.165*** -0.0471***(-13.36) (-15.06) (-9.258)

One Week Before -0.0214*** -0.00255 0.0146***(-6.392) (-0.307) (3.430)

One Week After -0.128*** -0.158*** -0.0461***(-25.36) (-16.97) (-10.28)

Observations 6,518,086 1,113,323 3,064,532R-squared 0.913 0.821 0.862Firm-Year FE YES YES YESDay FE YES YES YESClusters at Firm & Day YES YES YES

49

Table 6: Interannouncement Growth in the U.S., with Bid-Ask Spread

Panel A: Interannouncement Growth of Information Asymmetry

The tests in this table are the same as in Panel A of Table 3, except the left-hand-side variable is log(Spread), whichis calculated for each firm-day as log

(100 ∗ ask−bid

(ask+bid)/2

). T-statistics are in parentheses. *** p<0.01, ** p<0.05, *

p<0.1.

(1) (2)VARIABLES log(Spread) log(Spread)

Days Since EA 0.000180*** 0.000304***(11.08) (16.61)

Size -0.237***(-26.21)

Turnover -1.604***(-16.30)

Volatility 1.168***(31.67)

Midpoint -0.419***(-43.75)

Earnings Window 0.0576***(41.97)

Annual Report 0.00637***(6.036)

Late Filing -0.00169(-1.155)

One Week Before 0.00887***(10.44)

One Week After 0.00141*(1.721)

Observations 31,032,772 28,725,843R-squared 0.868 0.875Firm-Year FE YES YESDay FE YES YESClusters by Firm & Day YES YES

50

Panel B: Interannouncement Growth Throughout the Quarter

The tests in this table are the same as in Panel B of Table 3, except the left-hand-side variable is log(Spread), whichis calculated for each firm-day as log

(100 ∗ ask−bid

(ask+bid)/2

). T-statistics are in parentheses. *** p<0.01, ** p<0.05, *

p<0.1.

(1) (2) (3)VARIABLES log(Spread) log(Spread) log(Spread)

Days Since EA 0.000319***(8.657)

Post Quarter-end -0.00628***(-2.656)

Days Since EA x Post Quarter-end 8.04e-05(1.465)

Days Since EA x 1st Month -0.000149***(-2.697)

Days Since EA x 2nd Month 0.000156***(5.448)

Days Since EA x 3rd Month 0.000232***(12.58)

Greater Than 1 Month 0.00602***(9.022)

Greater Than 2 Months 0.00735***(10.04)

Observations 28,725,843 28,725,843 28,725,843R-squared 0.875 0.875 0.875Controls YES YES YESFirm-Year FE YES YES YESDay FE YES YES YESClusters by Firm & Day YES YES YES

51

Table 7: Difference-in-Differences Using International Settings, with Bid-Ask Spread

The tests in this table are the same as in Table 4, except the left-hand-side variable is log(Spread), which is calculatedfor each firm-day as log

(100 ∗ ask−bid

(ask+bid)/2

). T-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3)Europe Singapore Japan

VARIABLES log(Spread) log(Spread) log(Spread)

Q2 or Q4 0.00404** 0.0214*** -0.0118***(2.245) (5.062) (-3.419)

Quarterly x Q2 or Q4 -0.0128*** -0.0151*** -0.00384(-5.115) (-3.477) (-0.995)

Size -0.215*** -0.317*** 0.0658*(-9.057) (-8.827) (1.925)

Turnover -1.278*** -2.557*** -0.289***(-3.599) (-9.829) (-3.344)

Volatility 0.534*** 0.740*** 2.323***(10.95) (5.088) (18.32)

Midpoint -0.298*** -0.457*** -0.337***(-12.83) (-11.13) (-9.893)

Earnings Window 0.00387* -0.0156*** 0.0149***(1.765) (-4.959) (4.494)

First Semester 0.00905*** -0.00361 0.0326***(2.669) (-0.604) (6.139)

One Week Before 0.00143 2.81e-05 0.00855***(0.882) (0.0118) (3.560)

One Week After -0.00497*** -0.0190*** -0.00427*(-3.184) (-7.237) (-1.757)

Observations 8,984,194 1,502,046 3,423,133R-squared 0.842 0.848 0.626Firm-Year FE YES YES YESDay FE YES YES YESClusters at Firm & Day YES YES YES

52

Table 8: Test of Parallel Trends Assumption, with Bid-Ask Spread

The tests in this table are the same as in Table 5, except the left-hand-side variable is log(Spread), which is calculatedfor each firm-day as log

(100 ∗ ask−bid

(ask+bid)/2

). T-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3)Europe Singapore Japan

VARIABLES log(Spread) log(Spread) log(Spread)

Second Half-year -0.0111*** -0.00650 -0.0109**(-4.734) (-1.023) (-2.045)

Quarterly x Second Half-year 0.00674* -0.0100 -0.00460(1.880) (-1.470) (-0.760)

Size -0.215*** -0.318*** 0.0656*(-9.069) (-8.819) (1.918)

Turnover -1.279*** -2.558*** -0.289***(-3.596) (-9.834) (-3.339)

Volatility 0.532*** 0.741*** 2.323***(10.93) (5.091) (18.32)

Midpoint -0.298*** -0.456*** -0.337***(-12.88) (-11.10) (-9.883)

Earnings Window 0.00282 -0.0223*** 0.0219***(1.547) (-7.577) (7.196)

One Week Before 0.00207 -0.00233 0.0125***(1.353) (-1.001) (5.330)

One Week After -0.00619*** -0.0179*** -0.00739***(-4.001) (-6.903) (-3.156)

Observations 8,984,194 1,502,046 3,423,133R-squared 0.842 0.848 0.626Firm-Year FE YES YES YESDay FE YES YES YESClusters at Firm & Day YES YES YES

53

Table9:

How

Other

Disc

losuresCha

ngeInterann

ounc

ementGrowth

Thistableuses

theU.S.sam

ple.

EA

Dis

clos

ure

Cou

ntcoun

tsthenu

mbe

rof

disclosureson

theda

yof

andtheda

yafterthemostrecent

earnings

anno

uncement,

and

Non

-EA

Dis

clos

ure

Cou

ntcoun

tsthenu

mbe

rof

disclosuresdu

ringtheinterval

betw

eentw

oearnings

anno

uncements.The

disclosure

iseither

anan

alyst

forecast

(for

EA

/Non

-EA

Fore

cast

Cou

nt)or

an8-K

filing(for

EA

/Non

-EA

8-K

Cou

nt).

Allothe

rvaria

bles

arede

fined

thesameas

inTa

ble3an

dTa

ble6.

The

regression

includ

esfirm-yearfix

edeff

ects

andcalend

arda

yfix

edeff

ects.Stan

dard

errors

areclusteredat

both

thefirm

levelan

dthecalend

arda

ylevel.

T-statis

ticsarein

parenthe

ses.

***p<

0.01,*

*p<

0.05,*

p<0.1.

(1)

(2)

(3)

(4)

VARIA

BLE

Slog(Pr

iceIm

pact)

log(Sp

read

)log(Pr

iceIm

pact)

log(Sp

read

)

DaysSinceEA

0.00182***

0.000319***

0.00174***

0.000312***

(41.62)

(15.80)

(36.03)

(12.77)

EAFo

recast

Cou

nt-0.00138***

-0.00478***

(-2.838)

(-12.84)

Non

-EA

Forecast

Cou

nt-0.00383***

-0.000826***

(-16.34)

(-5.308)

DaysSinceEA

xEA

Forecast

Cou

nt-8.35e-07

6.22e-06

(-0.107)

(1.644)

DaysSinceEA

xNon

-EA

Forecast

Cou

nt-3.88e-05***

-5.52e-06***

(-10.60)

(-2.839)

EA8-K

Cou

nt-0.0325***

-0.0110***

(-11.23)

(-6.154)

Non

-EA

8-K

Cou

nt-0.0110***

-0.00204***

(-14.77)

(-4.892)

DaysSinceEA

xEA

8-K

Cou

nt0.000540***

0.000197***

(9.912)

(7.219)

DaysSinceEA

xNon

-EA

8-K

Cou

nt-0.000198***

-3.37e-05***

(-13.65)

(-4.288)

Observatio

ns25,327,036

28,598,837

22,374,647

24,588,220

R-squ

ared

0.880

0.875

0.879

0.860

Con

trols

YES

YES

YES

YES

Firm

-YearFE

YES

YES

YES

YES

Day

FEYES

YES

YES

YES

Clustersby

Firm

&Day

YES

YES

YES

YES

54

Table10

:Interann

ounc

ementGrowth

ofInform

ationAsymmetry

intheU.S.D

uringDifferentPe

riods

Thistableuses

theU.S.sam

ple.

The

regression

specificatio

nsin

thistablearethesameas

inPa

nelA

ofTa

ble3an

dTa

ble6,

except

that

thesamplepe

riods

are

restric

tedin

each

regression

—either

from

1993

to2001,2

002to

2007,o

r2008

to2015.T-statis

ticsarein

parenthe

ses.

***p<

0.01,*

*p<

0.05,*

p<0.1.

1993

to20

0120

02to

2007

2008

to20

15(1)

(2)

(3)

(4)

(5)

(6)

VARIA

BLE

Slog(Pr

iceIm

pact)

log(Sp

read

)log(Pr

iceIm

pact)

log(Sp

read

)log(Pr

iceIm

pact)

log(Sp

read

)

DaysSinc

eEA

0.00

149*

**0.00

0150

***

0.00

172*

**0.00

0506

***

0.00

171*

**0.00

0446

***

(30.69

)(6.832

)(23.19

)(12.38

)(20.09

)(13.51

)Size

-0.764

***

-0.188

***

-0.710

***

-0.206

***

-0.884

***

-0.353

***

(-27

.43)

(-19

.01)

(-20

.47)

(-13

.05)

(-21

.82)

(-16

.72)

Turnover

-22.82

***

-3.004

***

-9.822

***

-1.301

***

-7.907

***

-0.851

***

(-27

.32)

(-37

.05)

(-17

.51)

(-22

.77)

(-6.81

4)(-7.97

5)Vo

latility

-1.225

***

1.58

9***

-3.911

***

1.11

1***

-3.459

***

0.55

3***

(-14

.41)

(32.98

)(-26

.76)

(16.47

)(-16

.38)

(9.116

)Midpo

int

-0.740

***

-0.466

***

-0.885

***

-0.454

***

-0.546

***

-0.284

***

(-25

.82)

(-43

.58)

(-24

.23)

(-26

.99)

(-12

.78)

(-13

.18)

Earnings

Windo

w-0.070

6***

0.01

37**

*0.02

11**

*0.06

93**

*0.02

02**

0.10

7***

(-18

.02)

(14.01

)(4.228

)(27.23

)(2.435

)(49.17

)Ann

ualR

eport

0.00

166

0.00

133

-0.001

520.01

40**

*-0.006

77**

0.00

477*

**(0.678

)(1.030

)(-0.44

5)(5.570

)(-2.03

1)(2.877

)La

teFilin

g-0.022

6***

-0.007

43**

*-0.027

8***

0.00

0671

-0.045

5***

0.00

290

(-5.50

1)(-3.91

2)(-5.58

8)(0.233

)(-8.29

1)(1.001

)One

WeekBefore

-0.009

49**

*0.00

0967

-0.000

108

0.01

49**

*-0.013

9***

0.01

24**

*(-4.17

2)(0.990

)(-0.03

27)

(7.329

)(-3.82

9)(8.452

)One

WeekAfter

-0.069

6***

-0.000

698

-0.072

2***

-0.000

553

-0.081

9***

0.00

840*

**(-29

.33)

(-0.73

5)(-20

.15)

(-0.28

4)(-19

.69)

(6.071

)

Observatio

ns9,59

2,05

512

,085

,947

7,35

9,24

17,81

7,43

48,46

7,49

58,82

2,41

0R-squ

ared

0.86

60.77

80.86

90.74

40.88

20.87

5Firm

-YearFE

YES

YES

YES

YES

YES

YES

Day

FEYES

YES

YES

YES

YES

YES

Clustersby

Firm

&Day

YES

YES

YES

YES

YES

YES

55