Negative Externalities of Financial Reporting Frequency: PeerReporting Choice and the Loss of Investor Attention∗

Emmanuel T. De George†

University of Miami

Minh Phan‡

Columbia Business School

Robert C. Stoumbos§

Columbia Business School

December 2020 (Preliminary Draft)

Abstract

This study examines whether one firm’s choice of reporting frequency generates negative

externalities for other firms. Using various settings and specifications, we find that firms lose

investor attention when more of their peers report quarterly instead of semi-annually, and that

the loss of attention is associated with a decrease in market value and a reduction in liquidity.

These findings suggest that firms experience negative externalities when their peers report

quarterly. We also test for, but fail to find, evidence of positive externalities in the form of

information spillovers. Overall, our evidence suggests that firms are negatively impacted when

their peers choose to adopt a higher reporting frequency.

Key Words: Financial Reporting Frequency, Investor Attention, SpilloversJEL Classification: G10, M41, M48

∗We thank seminar participants at Northwestern University and the Columbia Business School Burton Conference,as well as Thomas Bourveau, Matthias Breuer, Juergen Ernstberger, Fabrizio Ferri, Martin Nienhaus, Nemit Shroff,Jacob Thomas, Rodrigo Verdi, and Florin Vasvari for their helpful comments. All errors and omissions are our own.This paper was previously titled “Financial Reporting Frequency and the Allocation of Investor Attention.”†Contact E-mail: edegeorge@miami.edu.‡Contact E-mail: mphan21@gsb.columbia.edu.§Contact E-mail: rcs2188@gsb.columbia.edu.

1 Introduction

The frequency at which public firms must report financial information to investors has

been the subject of intense debate in Europe (e.g., Kay, 2012; Wagenhofer, 2014), Asia (e.g.,

Kajuter et al., 2019; Shyan, 2016), and now the United States (e.g., Benoit, 2015; Michael

et al., 2018). Previous studies have examined the benefits and costs of mandatory increases

in financial reporting frequency. Specifically, this literature has found that firms which

are forced to increase their reporting frequencies benefit from a reduction in information

asymmetry and cost of capital (e.g., Fu et al., 2012), but tend to exhibit greater myopia in

their financial reporting and corporate investment decisions (e.g., Ernstberger et al., 2017;

Gigler et al., 2014; Kraft et al., 2017; Kajuter et al., 2019). However, largely overlooked in

the extant literature is whether and how one firm’s choice of reporting frequency generates

negative externalities for other firms. In this paper, we examine one potential negative

externality in the form of reallocation of investor attention. We provide novel evidence

that a firm loses investor attention when more of its peers report more frequently (namely,

quarterly rather than semi-annually).

Why might firms lose investor attention when their peers adopt a higher reporting

frequency? Prior literature provides evidence that investor attention is limited. Kahneman

(1973) argues that when individuals allocate their cognitive resources across tasks, allocating

attention to one task will reduce the attention available for other tasks. While limited

attention is a natural consequence of cognitive constraints, rational and behavioral models

of investment and trading behaviors posit different mechanisms by which investors’ limited

attention translates into capital market outcomes (e.g., risk averse investors inability to

attend to non-salient information given costly information acquisition and processing;

heuristic traders anchoring on attention grabbing-stocks; investor overconfidence, limited

learning capacity). Furthermore, empirical studies examining the relationship between

attention and relevant capital market outcomes have found evidence consistent with both

rational decision making and behavioral biases. For example, studies have shown that

1

investors tend to focus on a sub-set of stocks that grab their attention (e.g., Odean, 1999;

Barber and Odean, 2008; Lehavy and Sloan, 2008), and they fail to properly impound

earnings news when their attention is spread across multiple stocks or multiple news

announcements (e.g., DellaVigna and Pollet, 2009; Hirshleifer et al., 2009).

Based on the evidence discussed above, we posit that more frequent reporting

increases the demands on investors’ limited attention. This is also consistent with

assumptions in prominent theoretical models that it is too costly for investors to study

the disclosures of every firm (e.g., Fishman and Hagerty, 1989). If some of a firm’s peers

shifted from semi-annual to quarterly reporting, investors would need to process twice as

many financial reports if they wanted to stay informed about those peers. More specifically,

it is likely that acquisition and integration costs increase for peer disclosures in terms of

extracting, quantifying, analyzing and integrating twice as many disclosure signals (e.g.,

Blankespoor et al., 2019). Anecdotal evidence consistent with these arguments suggest

that these quarterly reports would require a significant amount of time to process; a

Bloomberg article asserts that U.S. sell-side analysts devote 30 consecutive days each quarter

to processing earnings announcements (Massa and Levingston, 2018). Moreover, as discussed

in (Hirshleifer and Teoh, 2003) if an individual focuses on understanding the implications

of the financial report of one firm, she may be unable to study another firm carefully at

the same time. In our context, if investors choose to allocate some of their attention to

the quarterly reports of the firm’s peers, they would likely have to divert some attention

away from the firm. Consequently, prior studies have shown that lower investor attention

translates into lower market value, reduction in market liquidity, and a higher cost of capital

(e.g., Merton, 1987; Lehavy and Sloan, 2008; Ding and Hou, 2015). We predict that firms

lose investor attention—and as a result, experience adverse market consequences—when their

peers increase their reporting frequencies.

While the prediction that the firms will lose investor attention is plausible, it is

not obvious. There are several reasons why the reporting frequency choices of peer firms

may have no effect, or may even increase investor attention. First, investors might prefer

2

to devote attention to semi-annual reporters rather than quarterly reporters because they

are more likely to discover private information, given that more time elapses between

earnings announcements for the semi-annual reporters (Stoumbos, 2019). This behavior

is consistent with models of rational inattention in which investors face an informational

capacity constraint and must optimize in order to allocate attention to signals that yield

the highest marginal return per unit of attention (e.g. Kacperczyk et al., 2016; Sims,

2003). Thus, since the return from information acquisition is likely higher for semi-annual

firms, it is plausible that investors may shift more attention to these firms and away from

their quarterly-reporting peers. Second, quarterly reporting in peer firms may actually

free up investor attention rather than consuming it, to the extent that less concentrated

dissemination decreases overall processing costs. For example, empirical evidence finds

that spreading disclosures out over time helps improve information processing by investors

(e.g. Atiase et al., 2005; Chapman et al., 2019), suggesting that more frequent reporting

(i.e., quarterly reporting) may reduce the costs of processing each report. Therefore, more

efficient information-processing may free up investor attention and allow them to devote more

attention to the disclosures of semi-annual firms.1 Finally, quarterly reporting by peer firms

may also reduce the private information acquisition and processing costs for semi-annual

reporters via information spillovers and improvement in the overall information environment

for the industry (e.g. Wang, 2014; Shroff et al., 2017). Under this scenario it is unclear

how investors may shift their attention between semi-annual and quarterly reporters. Given

the above arguments, it is therefore an open empirical question as to whether the reporting

choices of peer firms will impact investor attention.

To answer this question, we examine a broad sample of over 8,000 listed firms across

22 regulated markets within the European Union. While the EU mandates semi-annual

reporting, there is effectively a two-tiered system with many firms voluntarily reporting

quarterly. This allows us to exploit the variation in reporting frequency across, and within,

countries and industries to estimate whether firms experience a loss in investor attention1On the other hand, in support of our prediction, it is possible that the improved information processing for

quarterly reporters may shift investor attention towards these firms and away from semi-annual reporters.

3

when their peers report quarterly. In our tests, we regress various measures of firm-

level investor attention on the concentration of quarterly reporting among a firm’s peers,

measured as the fraction of the firm’s peers in the same country-industry that report earnings

on a quarterly basis. A key feature of this research design is that changes in quarterly

concentration are imposed on the firm by the actions of its peers, rather than by actions of

the firm itself. Importantly, we control for whether the firm itself is a quarterly reporter and

for whether the firm and its peers report under IFRS (Wang, 2014).2 Finally, we include firm

fixed-effects (to control for time-invariant differences between firms and for characteristics of

the firm’s country or stock exchange, which might be correlated with reporting choices) and

include industry-time fixed-effects (to control for temporary changes in industry conditions

as well as temporary spikes in investor enthusiasm for any particular industry).3

We proxy for investor attention with several measures related to analyst coverage

and trading volume. Given the difficulty in measuring investor attention from an empirical

standpoint, we use multiple proxies as way to triangulate our results. Analyst coverage

proxies for investor attention because analysts are more likely to actively cover stocks that

are of the greatest interest to their institutional clients (O’Brien and Bhushan, 1990). To

this end, we examine the number of analysts covering a firm and the number of forecasts

issued. We also proxy for investor attention using trading volume, since prior studies show

that significant increases in trading volume are likely to be driven by investor attention

(Barber and Odean, 2008; Gervais et al., 2001). Moreover, several theoretical models

imply that information processing costs reduce trading volume (e.g. Fishman and Hagerty,

1992; Kim and Verrecchia, 1994) and that changes in processing costs imply changes in

investor attention (e.g. Hirshleifer et al., 2004). Therefore, trading volume should also

capture changes in investor attention via changes in information processing costs brought

2Wang (2014)finds stronger information transfers between firms and peers when they report using IFRS due tocomparability benefits.

3Our paper focuses on quarterly reporting of earnings, which was voluntary in the EU during our sample period.We note that Interim Management Statements (IMS’s), which were required in the EU between 2007 and 2013, onlyrequired narrative information to be released during interim reporting periods and varied significantly in terms ofcontent and format. Quantitative disclosures (e.g., revenues or earnings) are not required and therefore, even underthe IMS regime, firms still had the choice of whether or not to report earnings on a quarterly basis. We seek tomitigate the impact of the mandatory IMS’s via the inclusion of industry-time fixed-effects.

4

about by more frequent reporting by peer firms. For all proxies, we find that firms lose

attention when a greater concentration of their peers voluntarily adopt quarterly reporting.

Moreover, we find that firms experience an increase in attention when they themselves

adopt quarterly reporting, which further supports our inference that quarterly reporters

are diverting attention away from other firms.

Next, we separately estimate the effect of peer quarterly reporting for semi-annual

reporters versus quarterly reporters. We find that only the semi-annual reporters lose

attention when their peers adopt quarterly reporting. This suggests that investors shift

their attention away from the existing semi-annual reporters towards the quarterly-reporting

peers, and supports the notion that the investors prefer to pay attention to the firms with

more information. More generally, this result indicates that firms lose attention when they

report less frequently than their peers. However, because of mixed findings from previous

work, we are careful in interpreting our results as being driven by a particular rational or a

behavioral mechanism.

We then provide several robustness tests and additional analyses to corroborate our

main findings. First, we provide evidence that the main result is driven by quarterly

reporting among the peers, and not by other actions taken by the peers to attract investor

attention. To this end, we show that our main results hold in a changes specification, and

are robust to the inclusion of additional controls that capture other voluntary disclosures

provided by the peers—including whether peers issue Interim Management Statements or

issue management forecasts.4 In addition, we ensure our main results are robust to a more

granular industry classification, and to restricting our analysis to a sample of semi-annual

reporters only. Finally, we provide evidence suggesting that that trading volume around

earnings announcements of semi-annual reporters does not significantly change when more

peers adopt quarterly reporting, indicating that earnings announcements are not sufficient

attention-grabbing events for these firms to recover the investor attention lost during the

4We use management forecasts as a proxy for broader voluntary disclosure policies by peer firms, since priorresearch finds that management guidance–at least in part–reflects a firm’s overall disclosure regime (e.g. Bourveauand Schoenfeld, 2017; Hirst et al., 2008; Shroff et al., 2013).

5

semester.

Given the nature of reporting frequency in our setting, self-selection concerns may cast

doubt on our inferences. To mitigate this concern we provide additional empirical evidence

by exploiting the 2003 introduction of mandatory quarterly reporting on the Singapore

Exchange (SGX). The reporting mandate required firms with a market capitalization greater

than S$75 million to report earnings a quarterly basis, while firms below this size threshold

could continue reporting semi-annually. As a result, industries with a higher concentration

of large firms saw a greater increase in the concentration of quarterly reporters following

this mandate. Our difference-in-differences research design exploits the variation across

industries to show that firms in industries with a larger increase in mandatory quarterly

reporting experienced a greater reduction in investor attention. In particular, following the

reporting mandate, we find a sustained decrease in analyst attention for firms with a higher

concentration of peers that were required to adopt quarterly reporting.

We then examine whether the observed loss in investor attention is associated with

observable capital market costs in order to provide more direct evidence of a negative

externality. Merton (1987) predicts that a firm losing investor attention will also see a

drop in its market value. His model suggests that when fewer investors are paying attention

to a given firm they are exposed to more of the firm’s idiosyncratic risk. As a result, they

offer a lower price for the stock. We test this prediction and find that firms experience a drop

in market value when a greater concentration of their peers adopt quarterly reporting. We

also examine whether firms experience a reduction in liquidity, as measured by average daily

price impact (Amihud, 2002). We anticipate that firms may suffer a reduction in liquidity if

the loss of investor attention is attributable to uninformed investors, since fewer uninformed

trades would lead to an increase in the price impact of trades (Kyle, 1985). Furthermore,

prior empirical evidence indicates that lower investor attention overall reduces stock trading

and is associated with a deterioration in market liquidity (e.g., Corwin and Coughenour, 2008;

Ding and Hou, 2015). Consistent with these arguments, we find that a firm’s market liquidity

deteriorates when a greater concentration of its peers report quarterly. Taken together,

6

our results suggest a negative externality is imposed on firms when their peers choose to

report more frequently. In additional analysis we find institutional ownership in semi-annual

reporters reduces when more peer firms report quarterly, however this reduction is primarily

attributable to foreign institutional owners, rather than domestic. This result is consistent

with arguments in prior studies that foreign institutions are at an informational disadvantage

relative to domestic investors (i.e., relatively uninformed) and thus may reallocate their

limited attention from semi-annual reporters as more peer firms report quarterly.

Finally, while we provide evidence of a negative externality in the form of a loss of

investor attention, whether or not this represents a net cost to the firm is unclear given

the potential for positive externalities, e.g., information spillovers. Several prior studies

document that investors use information contained in the financial reports of one firm to

update their valuations of other peer firms (e.g., Foster, 1981; Han and Wild, 1990; Shroff

et al., 2017). If reporting on a quarterly basis improves the industry-wide or market-wide

information environment, then one may expect to observe firm-specific benefits stemming

from information spillovers, such as: (1) improvement in the accuracy of analyst forecasts

and (2) more timely price discovery. To examine these predictions, we replace the investor

attention measures in our main analysis with common proxies for information spillovers,

including changes in forecast accuracy and the preemption of earnings announcement

information, and fail to find any association with changes in quarterly concentration. These

findings cast doubt on the suggestion that quarterly reporting by a firm’s peers may improve

their information environment. Consistent with our main results, it is possible that the loss

of attention may partially explain the lack of observed information spillovers, since a drop

in attention is predicted to reduce price discovery (Fishman and Hagerty, 1989; Hirshleifer

et al., 2009).

This paper makes several contributions. First, our paper contributes to the literature

on the externalities of disclosure regulation, in the spirit of Leuz and Wysocki (2016). While

prior studies indicate that investors rely on information contained in peer-firm disclosures,

leading to positive information spillovers in a variety of different contexts (e.g., Foster, 1981;

7

Wang, 2014; Shroff et al., 2017; ?; Han et al., 1989), we offer a contrasting view by providing

evidence of negative spillovers in the form of reduced investor attention stemming from peers’

higher reporting frequency.

Second, we contribute to the academic literature on financial reporting frequency and

the ongoing debate among policymakers. Several prominent figures in the U.S., including

President Trump and BlackRock CEO Lawrence Fink, have called on the SEC to re-examine

the appropriateness of mandatory quarterly reporting. As such, the SEC is examining the

pros and cons of giving U.S. firms the flexibility to report on a semi-annual basis (SEC,

2018).5 By highlighting the potential consequences of a regime where firms are allowed

to choose different reporting frequencies, our evidence informs the policy debate and has

implications for firms subject to this type of regime, i.e., firms may want to report more

frequently than the required minimum to capture a greater share of investor attention.

However we acknowledge that a firm’s decision to voluntarily increase reporting frequency

will also come with costs (e.g., compliance and reporting costs, and the potential for investor

and managerial myopia)6

Finally, our paper speaks to the large and growing literature on limited attention and

price efficiency. Several prior studies argue that investors have limited capacity to collect,

interpret, and trade on value-relevant information, which may harm market efficiency (e.g.,

DellaVigna and Pollet, 2009; Hirshleifer et al., 2009, 2011; Lehavy and Sloan, 2008; Merton,

1987). We identify a novel factor—financial reporting frequency—that can influence the

attention investors allocate to stocks, which can in turn affect market value and liquidity.

The rest of the paper proceeds as follows. Section 2 discusses the prior academic

literature on financial reporting frequency and investor attention, and provides a description

5While the SEC requested comment from the public on measures to cut down the “burdens on reporting companiesassociated with quarterly reporting while maintaining, and in some cases enhancing, disclosure effectiveness andinvestor protections” SEC Chairman, Jay Clayton, has struck a more neutral tone, stating that, “Our markets thirstfor high-quality, timely information regarding company performance and material corporate events. We recognize theimportance of this information to well-functioning and fair capital markets. We also recognize the need for companiesand investors to plan for the long term. Our rules should reflect these realities.” (Clayton, 2018)

6While the current U.S. reporting regime allows firms to report earnings more often than quarterly, almost nofirms choose to do this. Thus, the U.S. would likely only experience variation in reporting frequencies if it relaxedthe quarterly reporting requirement.

8

of the EU reporting setting. Section 3 describes our data, provides descriptive statistics, and

discusses our empirical research design and measures. Section 4 presents our main empirical

findings, in addition to several additional analyses and robustness. Section 5 discusses and

presents results from our tests of adverse market consequences. Section 6 presents results

from our examination of positive externalities in the form of information spillovers, and

section 7 concludes.

2 Related Literature and Setting

2.1 Related Literature

In this sub-section we review the related literature on the consequences of financial

reporting frequency, spillovers, and investor attention. Within the US context, prior studies

have found that higher reporting frequency is associated with capital market benefits such

as improved analyst forecast accuracy (Brown and Rozeff, 1979), smaller surprises in annual

earnings announcements (McNichols and Manegold, 1983), lower cost of capital (Fu et al.,

2012), and improved earnings timeliness (Butler et al., 2007). More recent empirical evidence

is obtained from settings outside of the US.7 For example, Cuijpers and Peek (2010)

examine data from three European countries over the period 2002 – 2007 and find that

higher reporting frequency reduces information asymmetry (as measured by bid-ask spreads).

Similarly, Stoumbos (2019) utilizes a broad sample of EU firms and finds that semi-annual

announcers who switch to quarterly reporting reduce information asymmetry–and increase

liquidity–within their reporting periods by as much as five percent. However, we note that

these studies examine the impact of firm’s reporting choice on the firm itself; they do not

provide insight into the potential externalities and spillovers that may impact a firm from

the reporting choices made by their peers.

In contrast to these benefits, several studies also document significant costs.

7Given the SEC mandate for semi-annual reporting was enacted back in 1955, and quarterly reporting followingfrom 1970, studies using US data tend to use historical data from this sample period which may not be generalizableto the current capital market environment.

9

Theoretical papers in this space show that higher reporting frequency can create adverse

incentives for managers, and induce myopic investment decisions (e.g., Gigler et al., 2014;

Edmans et al., 2016).8 Consistent with these theoretical arguments, Kraft et al. (2017)

provide empirical evidence within the US that increased reporting frequency is associated

with a decline in overall investment and greater myopia in investment decisions. Ernstberger

et al. (2017) study a large sample of firms within the European Union and find that

they exhibit greater real earnings management after the introduction of narrative interim

disclosures in the form of Interim Management Statements (“IMS”), consistent with higher

reporting frequency inducing greater myopia in financial reporting decisions. In contrast,

Nallareddy et al. (2017) conduct an in-depth examination of financial reporting within the

UK context and find no evidence that the introduction of IMS disclosures affects investment

patterns or financial reporting choices.

More closely related to our paper are studies by Kajuter et al. (2019) and Arif and

De George (2020) that examine information spillovers. Kajuter et al. (2019) examine the

capital market impacts of mandatory quarterly reporting on small firms in Singapore. The

authors exploit a listing rule enacted in 2003 that requires firms with market values in excess

of S$75 million to publish quarterly statements but allows firms below the size threshold to

report semi-annually. While the focus of their paper is on documenting changes in firm value

around mandated reporting thresholds, the authors provide some evidence of information

spillovers in the form of short-window return reactions to news from quarterly-reporting

bellwethers. They also provide some evidence that quarterly reporting by peers may improve

overall liquidity in semi-annual reporting firms. However, as noted by the authors, the results

may not generalize to firms outside Singapore due to the unique institutional environment,

such as high family ownership, relationship-based lending, and personal networks. Relatedly,

Arif and De George (2020) find that investors in low reporting frequency stocks tend to

overreact to the earnings news of quarterly reporting US bellwether peer firms in interim

8For example, Gigler et al. (2014) develop a model where price pressure created by higher reporting frequencyinduces managers to adopt a short-term perspective in project selection. This behavior is driven by the fact that anyeffort expended on long-term value creation is not necessarily reflected in short-term earnings.

10

periods, when own-firm earnings news is absent. In addition, they find an increase in

volatility and abnormal trading volume around these US bellwether announcements.

These findings echo results in the more general spillover literature that find disclosures

from industry-peers contain value-relevant information and affect peer firms’ stock prices

(e.g., Foster, 1981; Han andWild, 1990; Shroff et al., 2017), analyst forecast errors (Ramnath,

2002), real investment decisions (Badertscher et al., 2013), and cost of capital (e.g., Shroff

et al., 2017). In particular, Shroff et al. (2017) provide empirical evidence that peer

information affects a firm’s cost of capital when firm information is scarce, but these positive

externalities decline as the amount of firm information increases. While Breuer et al. (2018)

provides evidence consistent with a negative externality of disclosure regulation. They find

that the mandated disclosures of regulated firms crowd out the voluntary disclosures of

unregulated firms, and subsequently mute the positive effect of disclosure regulation on the

market-wide information environment.

Finally, our paper also draws on the literature related to investor attention. When

individuals allocate their cognitive resources across tasks, allocating attention to one task

necessarily reduces the attention available for other tasks (e.g., Kahneman, 1973; Hirshleifer

et al., 2009). This notion of “limited attention” has been explored in several theoretical and

empirical studies that examine the associated effects on investor behavior (e.g., Hirshleifer

et al., 2011; Merton, 1987; Peng and Xiong, 2006). For example, Hirshleifer et al. (2011)

propose a model of limited investor attention that explains several mispricing anomalies in

the accounting and finance literature (e.g., Post-Earnings Announcement Drift, the accrual

anomaly, the cash flow anomaly and the profit anomaly). Consistent with analytical results,

experimental evidence also suggests limited attention affects how investors and other capital

market participants interpret accounting data (see review by Libby et al., 2002). Particularly

relevant for the arguments in this paper, prior studies document that investors tend to focus

on a sub-set of stocks that grab their attention (e.g., Barber and Odean, 2008; Lehavy and

Sloan, 2008), and fail to properly impound earnings news when their attention is spread

across multiple stocks or multiple news announcements (e.g., DellaVigna and Pollet, 2009;

11

Hirshleifer et al., 2009). These studies suggest that investor attention is decreasing in

information processing costs (see Blankespoor et al. (2019)for a review of the disclosure

processing cost literature).

While these studies suggest that investors exhibit limited attention in their ability to

follow and process earnings announcements, it is unclear whether a firm will lose attention

if its peers adopt a higher reporting frequency. Investors might prefer to devote their

limited attention to the firms with lower reporting frequencies, because they have more

time to discover private information between the earnings announcements of these firms

(e.g., Stoumbos, 2019). Thus, a firm with a low reporting frequency might receive more

attention, rather than less, when its peers increase their reporting frequencies. Of course,

it may be that the opposite occurs, if investors prefer to follow firms that provide more

information. Furthermore, it is not even clear that more-frequent reporting will require

more investor attention. Empirical evidence finds that spreading disclosures out over time

helps improve information processing by investors (e.g., Atiase et al., 2005; Chapman et al.,

2019), suggesting that more frequent reporting might actually reduce the attention required

to process all of a firm’s earnings announcements. Thus, it is an empirical question whether

firms lose attention when their peers report more frequently.

2.2 Setting: Financial Reporting Frequency within the EU

While the quarterly reporting regime that has been in place in the US since the early

1970s, the European Union mandates financial reporting at the semi-annual frequency. Initial

proposals within the EU to mandate quarterly reporting were rejected in 2004 as European

business leaders voiced concern to the European Commission that quarterly reporting would

encourage short-termism among managers and market participants, and impose significant

preparation costs. Moreover, several professional bodies and organizations, including the

UK Treasury, argued that a string of large corporate collapses in the US (e.g., Enron and

Worldcom) arose from myopic decisions taken by management due to pressure to meet

quarterly earnings expectations, and that greater financial reporting frequency is damaging

12

to the goal of better stewardship and does not serve the long-term interests of investors (e.g.,

Kay, 2012).9 Consequently, the Transparency Directive (TD 2004/109/EC) that ultimately

passed in 2004 mandated only semi-annual financial reporting within EU regulated markets.

However, the Directive did include a requirement for limited interim disclosures in the form

of interim management statements (“IMS”) to be published during semi-annual reporting

periods. These IMS’s were required to contain narrative information for first and third

quarter performance, and explanations of any material events and transactions. However,

given the lack of guidance on specific content and format, IMS disclosures varied significantly.

Examining a large sample of IMS disclosures by UK firms, Nallareddy et al. (2017) document

that only 7-10% of firms include quantitative sales and/or earnings figures as part of these

interim disclosures.10 Thus, many IMS’s lack earnings numbers, and many firms with IMS’s

still only report earnings on a semi-annual basis.

As opposition to quarterly reporting grew stronger, the Transparency Directive was

amended in 2013 to abolish the requirement to publish IMS’s altogether. EU regulated

markets are now prohibited from requiring any interim reporting for Q1 and Q3 unless

they can demonstrate that such requirements do not constitute an undue financial burden

for companies and that the additional information is needed for investment decisions (see

Wagenhofer (2014) for broader discussion). Therefore, the reporting landscape that has

evolved within the EU is effectively a two-tiered system; the minimum mandated reporting

frequency is semi-annual, however several firms still voluntarily provide earnings information

at the quarterly frequency.

9Outside of the EU, similar criticisms were leveled at regulators in Singapore when they introduced a dualreporting regime in 2003, whereby firms with greater than S$75m in market capitalization are required to report ona quarterly basis while firms below this threshold report only semi-annually. A recent poll at the Singapore Instituteof Directors (SID) Directors’ Conference in September 2014 found that 78% of directors and corporate executiveswere in favor abolishing mandatory quarterly reporting (Shyan, 2016). Interestingly, the Singapore regporting regimeswitched from a size-threshold to a risk-based system in early 2020.

10In addition, while semi-annual reports are, at a minimum, reviewed by auditors, no such requirement exists forIMS’s, and these disclosures tend to be disseminated as simple press releases as opposed to a formal financial report.

13

3 Data and Empirical design

In this section we provide a detailed discussion of our sample selection, data and

describe our empirical research design.

3.1 Data

Our sample selection begins with all firms subject to reporting regulation prescribed

by the EU and implemented by members of the European Economic Area (i.e., all EU

member states, plus Switzerland and Norway) during the period from 1998 to 2016. Focusing

on a sample of EU firms has three main advantages. First, we observe significant variation in

the frequency of financial reporting both within countries and across countries, even though

most firms fall under a mandatory semi-annual reporting regime. Second, these countries are

economically integrated, so our industry-time fixed-effects should more effectively capture

transient industry-level shocks over time. Third, these countries have relatively well-

developed and functioning capital markets, which allows for our findings to be generalized

to the U.S. and other developed countries.

Our final sample includes 8,175 firms from 22 European countries, over the period

1998 through 2016, with sufficient data for our analyses. We collect data from the following

sources: Worldscope, Datastream, Compustat Global, and IBES. We use Worldscope to

determine firm-level financial reporting frequency, the dates of earnings announcements and

fiscal period-ends.11 To minimize data errors, we drop firm-year observations in instances

where the reporting frequency reported by Worldscope does not agree with the number of

earnings announcements reported in Worldscope for a given firm-year.12 In cases where the

reporting frequency data is missing, but both the previous year and subsequent year report

11In particular, we use data items (#WC05901-#WC05905) to ascertain the dates of earnings announcements,data item (#WC05350) for period-ends, and data item (#WC05200) for frequency of earnings announcements withina given year. This measure of financial reporting frequency reflects the number of times a firm reports earnings in agiven fiscal year and therefore should capture those instances in which an earnings number was disclosed in an IMS.To the extent that classification errors exist however, this would simply introduce noise into our empirical analysis,as it is more likely that earnings disclosed in IMS are not captured as opposed to misclassification of a semi-annualreporter to a quarterly reporter.

12We note that this amounts to only 8% of all firm-year observations in Worldscope.

14

the same reporting frequency, we keep the firm-year and assign the same reporting frequency

designation as the two adjacent years. We exclude firm-years that either have no designation

or are designated as reporting at a frequency other than semi-annual or quarterly, and those

who change their fiscal period end during the year, resulting in shortened years. We impose

these data screens to ensure that we accurately measure firms’ reporting frequencies and to

minimize measurement error.

We collect volume, daily returns, market capitalization, and price data from

Datastream and employ several data screens to remove potential data errors and omissions,

consistent with prior literature (e.g., Arif and De George, 2020; Karolyi et al., 2012; Griffin

et al., 2009). First, we restrict our sample to those stocks listed on major EU exchanges.

Second, given companies may have securities listed on multiple exchanges within the EU,

we assign country of origin based on the country of main operating activities if the security

also trades on the local exchange. If the stock does not trade on a local exchange, we assign

the firm to the country of their primary listing, defined as the exchange in which the stock

trades the most actively. This ensures we keep one active stock listing per firm in our final

sample. Third, we apply the following screens to our data when computing our firm-level

market-based measures (e.g., liquidity and trading volume). We require: (1) non-missing

daily stock return data for at least 90% of the days in the firm-semester, and (2) a minimum

of 10-trading days of data for the relevant metric, or else these mean daily market-based

measures are set to missing for the firm-semester. These screens reduce mis-measurement

in our computed firm-semester means. Finally, we follow Karolyi et al. (2012) and Griffin

et al. (2009) and apply a series of additional data screens given the inherent issues with

Datastream market data, as described in Appendix B.

We obtain data regarding analyst following and EPS forecasts from IBES.13 For our

tests utilizing analyst-level forecast data, we obtain data from the IBES Detail History file,

omitting forecast observations occurring after the earnings announcement date.

13More specifically, we obtain analyst forecasts of annual earnings-per-share from the IBES Summary Historyfile, and actual annual earnings-per-share from the Detail Actuals file. We obtain analyst coverage from the IBESSummary History file, and we obtain the number of forecasts made by each analyst from the Detail History file.

15

Following Bhojraj et al. (2003) and Arif and De George (2020), we use GICS to

determine industry membership. Bhojraj et al. (2003) note that while the correlation between

the GICS classification and the widely used SIC classification is only about 56%, GICS

classifications explain significantly more return co-movement among securities, as well as

cross-sectional variation in valuation multiples and growth rates. Thus, GICS better captures

homogeneity across firms. We obtain historical GICS industry classifications from Standard

& Poor’s. In cases where historical GICS industry classification data is missing, we use the

current GICS industry classification from Compustat Global. Our sample contains firms

from 11 GICS sectors.

3.2 Descriptive Statistics

Table 1 presents descriptive statistics for the main sample that forms the basis of

our empirical analysis. In Panel A, we report the average percentage of quarterly reporters

across countries, for the sample period 1998 to 2016. We note that the United Kingdom

provides the largest number of observations (43,998), followed by France (13,333), Germany

(12,905), and Sweden (8,815). Notably, while 44% of our sample firms are quarterly reporters,

less than four percent of observations from the United Kingdom are classified as quarterly

reporters. Ireland also has a relatively low number of quarterly reporters, with just over 5%.

In contrast, sample firms from Norway, Poland, Bulgaria, Croatia, and Lithuania are almost

exclusively all quarterly reporters (>99%).

In Panel B we report descriptive statistics (e.g., mean and medians) for quarterly and

semi-annual reporters during the years in our sample. On average, semi-annual reporters are

smaller firms that are less likely to have peers that report quarterly, are less likely to adopt

international reporting standards, experience lower investor attention, and exhibit lower

market liquidity relative to quarterly reporters. These differences are statistically significant

for most of the variables examined.

16

3.3 Empirical Research Design

In order to test for externalities associated with higher reporting frequencies, we

employ a research design that allows us to examine how firms are affected by the

concentration of quarterly reporters within the same country and industry. For our main

tests, the regression equation takes the following form:

Yijs = αi + αjs + β0Qtrly−Concentrationijs (1)

+β1Quarterlyijs + γControlsijs + εijs

The observations are at the firm-semester level, with each semester (i.e., half-year) bounded

by earnings announcements rather than period-end dates.14 For the indices, i indexes firms,

j indexes industries, and s indexes semesters.

In Equation (1), Y represents various dependent variables that capture investor

attention, market outcomes, and information spillovers, depending on the particular analysis.

Quarterly is an indicator that turns on if the firm itself reports quarterly, where quarterly

reporting is defined as reporting earnings four times in a given fiscal year. We note this

measure is not intended to capture IMS disclosures which often lack quantitative data

(Nallareddy et al., 2017), unless these disclosures contain specific earnings information. The

Quarterly indicator controls for any additional trading volume or analyst forecasts that are

caused by the firm’s own quarterly report in the middle of the firm-semester.

Qtrly_Concentration is our variable of interest and is measured as the fraction of the

firm’s country-industry peers that report quarterly during the firm-semester. These peers

consist of the firm-semester observations that are in the same country-industry-semester

14For example, for semi-annual reporters, each semester is bounded by the two semi-annual earningsannouncements on either side. For the quarterly reporters, each semester is bounded by the second-quarter earningsannouncement on one side and the fourth-quarter earnings announcement on the other. We drop any firm-semestersthat are missing either of the earnings announcements that bound the firm-semester period. We also drop firm-semesters where the earnings announcement at the beginning of the semester is recorded as occurring after theearnings announcement at the end of the semester—this indicates that some of the firm’s earnings announcementshave occurred out of order. Finally, we also drop observations if the earnings announcement at the end of the semesteroccurred more than 365 days after the earnings announcement at the beginning of the semester.

17

group as each other.15 When determining the country-industry-semester group of a particular

firm-semester observation, we assign the firm to the country of its primary stock quote, we

assign it to the industry-group that shares its two-digit GICS code, and we assign it to the

calendar-semester that contains the end-date of its fiscal semester. We restrict the sample

to firms that have at least 10 peers within the same country-industry-semester peer group.16

The regression includes both firm fixed-effects and industry-semester fixed-effects,

and we cluster standard errors by firm and industry-semester to account for correlation in

the errors arising from within-firm auto-correlation and common industry-level shocks over

time. The industry-semester fixed-effects capture all firms across Europe that belong to

the same industry and semester, where industry is defined by the two-digit GICS code and

fiscal semesters are grouped together if they end within the same calendar semester.These

fixed-effects control for temporary changes over time in the volatility of industry conditions,

as well as temporary spikes in investor enthusiasm for any particular industry. The firm

fixed-effects ensure that the results are not driven by cross-sectional differences that are

fixed over time, including differences based on the firm’s country or stock exchange. Thus,

the coefficient on Qtrly_Concentration is driven by within-firm variation over time. While

we note that the average within-firm value of Qtrly_Concentration is fairly close to zero,

the variation in within-firm values is reasonably broad. For example, in the last year of

our sample-period, we observe ten percent of firms have values of Qtrly_Concentration that

are almost seven percentage-points higher than their within-firm means, while another ten

percent of firms have values that are just under five percentage points lower than their within-

firm means. The early years of our sample period see even more variation in within-firm

Qtrly_Concentration.

We employ a similar vector of control variables across the various regressions that

15We create peer groups within country, consistent with prior studies that have shown investors tend to exhibita home bias in their equity holdings, even within EU member states (e.g., Balta and Delgado, 2008; Rouwenhorst,1999; Yu and Wahid, 2014).

16We use two digit GICS codes in order to maximize the power our tests by ensuring we have a significant numberof firms in our peer groups. Moreover, using this coarser classification allows for the maximum flexibility in howinvestors form their peer groups and investment opportunity set. In robustness tests discussed in later sections ofthe paper we re-run our specifications using four digit GICS to ensure our results are not sensitive to the choice ofGICS classifications.

18

form our empirical analyses. Included in all specifications, we control for the market value

of the firm (Beginning Market Cap) measured as the logarithm of the firm’s market value of

equity at the beginning of the fiscal year, and the average market value of firms in the peer

group (Mean Peer Beg. Market Cap) measured as the logarithm of the average beginning-

of-the-year market value of equity among the firm’s peers in its country-industry-semester.

We obtain market value of equity from Worldscope. These controls are included to capture

changes in the firm’s value over time relative to its peers, since more-valuable firms might

capture more investor attention.

In addition, we include an indicator variable, IFRS, which captures whether the firm

reports its financial results in accordance with International Financial Reporting Standards

(IFRS), along with IFRS Concentration, which is the fraction of the firm’s country-industry-

semester peers that report in accordance with IFRS. To determine which firms report

financial information in accordance with IFRS, we utilize Worldscope data and follow the

classifications in Tan et al. (2011).17 We include these control variables because of the

established relation between IFRS adoption and many of our dependent variables. For

example, Tan et al. (2011) show that IFRS adoption increases coverage and forecast accuracy

among foreign analysts, while Daske et al. (2008) and Christensen et al. (2013) show that

IFRS adoption and the accompanying changes in enforcement increase market liquidity.

Moreover, Wang (2014) shows larger information spillovers between peer firms when firms

adopt IFRS, due to enhanced comparability.

Finally, we control for Second Semester, an indicator that turns on for the second

semester of the firm-year, and we control for Semester Length, which measures the number of

days in the firm-semester. Firm-semesters—which are bounded by earnings announcements

in our tests—have varying lengths, typically because the earnings announcements for annual

reports come out later than those for interim reports. We describe all variables in further

17Tan et al. (2011) identify firms as IFRS reporters if they have the following values in the Worldscope variable#WC07536: 02 (International standards), 06 (International standards and some EU guidelines), 08 (Local standardswith EU and IASC guidelines), 12 (International standards—inconsistency problems), 16 (International standards andsome EU guidelines—inconsistency problems), 18 (Local standards with some IASC guidelines), 19 (Local standardswith OECD and IASC guidelines), and 23 (IFRS).

19

detail in Appendix A.

4 Main Results

In the following sub-sections, we examine whether firms with more quarterly-reporting

peers have lower analyst coverage and trading volume, both of which proxy for investor

attention.

4.1 Quarterly Reporting and Externalities Related to Investor Attention

Our main analysis gauges whether a firm receives negative externalities, in the form

of lost investor attention, from its peers when those peers report quarterly. We estimate

our main specification, equation (1), with dependent variables related to investor attention.

We capture investor attention via two dimensions commonly used in the literature: analyst

coverage and market trading volume (e.g., Koester et al., 2016). Analyst coverage is an

appropriate proxy for institutional investor attention because analysts are more likely to

actively cover stocks that are of the greatest interest to their institutional clients (O’Brien and

Bhushan, 1990). We believe trading volume is also an appropriate proxy for investor attention

since significant investor attention is likely to drive trading volume (Barber and Odean, 2008;

Gervais et al., 2001). That said, we acknowledge that an increase in investor attention may

not necessarily result in an observable increase in trading volume if the investors decide,

upon examining the firm, that the current stock price reflects all current expectations about

discounted cash flows, i.e. a no-trade equilibrium (e.g. Milgrom and Stokey, 1982). However,

if we observe an abnormal increase in trading volume of a given stock, then it is likely that

more investors are paying attention to the stock (Barber and Odean, 2008).18

18For example, Barber and Odean (2008) argue that trading volume in the firm’s stock is likely to be greater thanusual when news about a firm reaches many investors. While investors may recognize this news to be irrelevant, orinterpret the news similarly, and not trade, if the news is significant it will often affect investors’ beliefs and portfoliogoals heterogeneously, resulting in more investors trading than is usual. If an unusual number of investors trade astock, then it is highly likely that an unusual number are paying attention to that stock. But we acknowledge thathigh abnormal trading volume could also be driven by the liquidity or information-based trades of few large investors.However, in untabulated results we confirm our abnormal trading volume results hold in a sub-sample of larger stockswhich alleviates this concern.

20

The first three columns in Table 2 report results examining how analyst coverage

changes when a firm’s peers adopt quarterly reporting. In column (1) the dependent variable

is the total number of forecasts made by analysts (Num_Forecasts), which is the natural

logarithm of one plus the total number of analyst forecasts in the IBES Detail file for the

firm’s annual earnings-per-share (EPS) during the semester. We include all forecasts reported

in IBES for a given firm-semester (i.e., initial forecasts, revisions, and confirmations of earlier

forecasts). Given firm-semesters are bounded by earnings announcements, Num_Forecasts

counts the total number of analyst forecasts made between the beginning- and end-of-

semester earnings announcements. We document a negative and significant coefficient on

Qtrly_Concentration, suggesting that firms see a drop in the number of analyst forecasts

when more of their peers switch from semi-annual to quarterly reporting. In particular, we

estimate that the number of analyst forecasts received by a firm decreases by approximately

3% (t=-4.43) when quarterly concentration increases by 10 percentage-points.19

Column (2) of Table 2 tells a similar story where the dependent variable is

Analyst_Cover, measured as the natural logarithm of one plus the number of sell-side

analysts who are covering the firm in a given firm-semester. To measure the number

of sell-side analysts who are covering the firm, we count the number of unique analysts

included in the most recent consensus EPS forecast provided by IBES for one year ahead,

one quarter ahead, and one semester ahead, and take the largest number of forecasting

analysts from among these three types of forecasts. We designate a firm as having zero

analyst coverage if IBES does not record any consensus forecast statistics within a 45-day

window just prior to the firm’s end-of-semester earnings announcement. This is the case for

approximately 30% of the observations. We document a negative and significant coefficient

on Qtrly_Concentration, suggesting that some of the drop in analyst forecasts observed in

column (1) stems from a drop in the number of analysts covering the firm when the firm’s

peers adopt quarterly reporting. In particular, we find that the number of analysts covering

a firm decreases by approximately 2% (t=-4.10) when quarterly concentration among the19For the specification in Column (1) of Table 2, we control for Semester Length, since a longer semester will

mechanically enable the analysts to make more forecasts.

21

firm’s peers increases by 10 percentage-points.

In column (3) of Table 2 we estimate the effect on the number of forecasts reported

by each analyst. Our dependent variable, Num_Forecasts_Ana, is measured as the natural

logarithm of the number of annual EPS forecasts reported during the semester divided by

the number of contributing analysts. In this specification we only include firm-semesters

that have at least one analyst covering the firm. Consistent with our previous results, we

document a negative and significant coefficient on Qtrly_Concentration. Specifically, our

results suggest a reduction in the average number of forecasts per analyst by 1% (t=-2.47)

when quarterly concentration increases by 10 percentage-points.

Collectively, the results from our analyst coverage specifications in Table 2 show that

firms lose analyst attention when their peers switch from semi-annual to quarterly reporting,

and this loss comes from a drop in both the number of analysts covering the firm and

the attention received from each analyst. Notably, these results also show that quarterly

reporters are gaining analyst attention, since the coefficient on the Quarterly indicator is

significantly positive in all three specifications. Our findings suggest that firms lose attention

when their peers adopt quarterly reporting, and firms gain attention when they themselves

adopt quarterly reporting, which is consistent with quarterly reporters generating a negative

spillover by diverting attention away from peers.

In untabulated analysis, we examine whether the total supply of analyst coverage

is fixed, or if it grows to meet the new demands of increased quarterly reporting. If the

supply were fixed, then the diversion of analyst attention would represent a long-term loss

to the firm when its peers adopt quarterly reporting. However, if new analysts are hired in

response to an increase in the number of quarterly reporters, then the firm might eventually

recover some of the lost attention. We fail to detect a significant increase in the number of

analysts covering a country-industry group when more firms in that group adopt quarterly

reporting. This suggests that the supply of analysts might be fixed, and the reduction in

investor attention is likely to persist. That said, we do find some evidence (significant at

the 10% level) that the existing analysts increase their forecasting activity when more firms

22

in the group adopt quarterly reporting. We estimate that a 10 percentage-point increase in

the fraction of firms that report quarterly corresponds to a 3% increase in the total number

of analyst forecasts made for firms in the group.20

We next examine market trading volume as a proxy for investor attention in column

(4) of Table 2. Our dependent variable, Avg_Trading_Volume, is the natural logarithm of

mean daily share turnover during the firm-semester, where share turnover is measured as the

daily volume of trades divided by the number of shares outstanding that day. We measure

trading volume in terms of turnover because it is natural to measure trading activity as a

percentage of shares outstanding (Lo and Wang, 2000), and turnover is the most common

measure of trading volume used in the literature (Bamber et al., 2011). We document a

negative and significant coefficient on Qtrly_Concentration, indicating that investors trade

less in firms when a greater proportion of the firm’s peers adopt quarterly reporting. In

particular, we estimate that a firm’s average trading volume decreases by 2% (t=-2.55) when

quarterly concentration increases by 10 percentage-points. Consistent with our previous

results relating to analyst coverage, this finding suggests that firms are losing the attention of

investors when their peers choose to report quarterly. Moreover, the coefficient on Quarterly

is significantly positive, which shows that firms gain investor attention when they themselves

adopt quarterly reporting. As discussed above, this is consistent with quarterly reporters

generating a negative spillover for their peers by diverting attention away from those peers.

4.2 Impact on Semi-annual versus Quarterly Reporters

We now examine whether the average reduction in investor attention documented in

section 4.1 occurs for semi-annual reporters versus quarterly reporters. If investors prefer

20In order to examine the change in total analyst coverage for a country-industry group, we regress measures ofaggregate analyst coverage on the percentage of firms that report quarterly. We measure aggregate analyst coveragein two ways: (1) Total Number of Forecast, measured as the logarithm of the total number of analyst forecasts issuedfor firms in a country-industry group at calendar semester s; and (2) Total Number of Analysts, measured as thelogarithm of the within country-industry sum of the percentage of forecasts issued by an analyst for a given firm in acountry-industry at calendar semester s. Our variable of interest, Quarterly Share, is measured as the percentage offirms that report quarterly in the country-industry group at calendar semester s. We treat firms as quarterly reportersif they report quarterly at the beginning of the calendar semester. The observations are at the country-industry level.All specifications include both country-industry fixed-effects and industry-semester fixed-effects, and standard errorsare clustered at both the country-industry level and the industry-semester level.

23

to pay attention to quarterly reporters rather than semi-annual reporters, we should find

that semi-annual reporters experience a greater loss of investor attention when their peers

adopt quarterly reporting. We might expect investors to prefer paying attention to quarterly

reporters if they prefer firms with stronger information environments.

To test this conjecture, we perform regressions similar to those reported in Table 2,

except we interact Qtrly_Concentration with indicator variables that capture whether the

firm is a quarterly reporter (Qtrly_Concentration * Quarterly) or a semi-annual reporter

(Qtrly_Concentration * Semiannual). We report results in Table 3.

Consistent with investors preferring to pay attention to quarterly reporters more than

semi-annual reporters, we find that the negative impacts on our attention-based outcomes

are significantly stronger for semi-annual reporters. In fact, our results suggest that only

semi-annual reporters lose investor attention when their peers adopt quarterly reporting.

The coefficient on Qtrly_Concentration * Semiannual is significantly negative in all four

regressions. In contrast, the negative coefficient on Qtrly_Concentration * Quarterly is

significant at only the 10% level in column (1), and is insignificant in the remaining three

columns. This suggests that quarterly reporters do not appear to lose investor attention

as more of their peers adopt quarterly reporting. Unlike in Table 2, the coefficient on

Quarterly in Table 3 cannot be easily interpreted, because it is now being interacted with

Qtrly_Concentration, a continuous variable.

4.3 Robustness and Sensitivity Tests

In this section, we perform robustness and sensitivity tests. We begin with two tests

that corroborate our main findings and provide further comfort that the observed drop in

attention is likely caused by peer quarterly reporting, as opposed to broader changes in the

disclosure environment that may be correlated with peer quarterly reporting. Our first set

of specifications shows that the drop in attention occurs at the same time as more peers

adopt quarterly reporting. In our second set of tests we include additional variables that

specifically control for other disclosures provided by the peer firms.

24

In Panel A of Table 4, we report the OLS results for a changes specification. We

modify our original regression specifications presented in Table 2 by omitting firm fixed

effects and converting all dependent and independent variables to changes. We measure

first differences with respect to changes from the previous firm-semester to the current firm-

semester. We denote “changes” with the symbol ∆. This test shows that the change in

quarterly concentration is reliably negatively associated with the change in the number of

forecasts (column (1)), the number of analysts covering the firm (column (2)), and the average

trading volume during the firm-semester (column (4)). We fail to find significance for changes

in the number of forecasts per analyst (column (3)), perhaps due to analysts not immediately

adjusting the number of forecasts they give per firm when quarterly concentration changes.

However, the results for all other specifications suggest that an increase in the concentration

of quarterly reporting among peers leads to an immediate drop in trading activity and analyst

coverage for the firm. This result provides comfort that the effect is driven by the peers’

adoption of quarterly reporting, rather than by some other action taken by the peers to

draw attention away from the firm. For the result to be driven by some event other than the

adoption of quarterly reporting, that other event would have to occur at the same time. We

also note that the coefficient on ∆Quarterly is positive in all four columns, and significant in

the first two of them. This indicates that the firms themselves see an increase in attention

at the same time that they adopt quarterly reporting. This further supports the hypothesis

that attention is being diverted to the quarterly reporters and away from their peers.

Next, we provide evidence that the negative association between attention and

peer quarterly reporting cannot be explained by a positive correlation between peer

quarterly reporting and other peer disclosures. In Panel B of Table 4, we take the main

regressions in Table 2 and add controls for the fraction of peers that issue management

forecasts (Management Forecast Concentration) and the fraction of peers that issue Interim

Management Statements (IMS Concentration).21 Adding the control Management Forecast21We obtain data on IMS announcements and management earnings forecasts from Standard & Poor’s Capital IQ

database for our sample years. In particular, Capital IQ Key Developments collects data on over 800,000 firms across165 countries using various public sources, including press releases and articles from more than 20,000 news wiresand publications, regulatory files, company websites, web agents, conference call transcripts, and investor conference

25

Concentration demonstrates that the main result is driven by the peers’ decisions to report

quarterly, and not by other disclosures the peers might make to improve their information

environments. Adding the control IMS Concentration demonstrates that our research design

is not confounded in some way by the introduction of narrative Interim Management

Statements partway through the sample period. For all four dependent variables, the

coefficient on Qtrly_Concentration remains significantly negative after adding these controls.

Furthermore, for all four variables, the coefficient has almost the same magnitude as when

the controls were missing.

In panel C of Table 4 we report results from re-estimating our main regressions

in Table 2 on a restricted sample of semi-annual reporters only. Given the restricted

sample, Quarterly always equals zero, and thus is omitted from the regressions. We find

that Qtrly_Concentration remains significantly negative and results are stronger that those

reported with the full sample. By performing this sample restriction, we demonstrate that

the results in Table 2 are not driven by the inclusion of the quarterly reporters in the tests,

i.e., the documented reduction in attention is not simply a relative result–where investor

attention as a whole may be growing–but rather that semi-annual reporters are experiencing

a real reduction in investor attention proxies as their peers report at a higher frequency.

In Panel D of Table 4 we report robustness analysis to assess the impact of non-

linearities. We replace our continuous variable Qtrly_Concentration with three indicator

variables that capture whether Qtrly_Concentration is either low, medium or high (based

on within-sample tercile rank). We find significant reductions across all four of our attention

proxies when Qtrly_Concentration is in the top two terciles and no significant change in

attention for the bottom tercile of Qtrly_Concentration. This provides some comfort that

our results are not driven by a small number of outliers.

organizer websites. Capital IQ translates all information into English. Capital IQ also provides information oncompany identifiers, forecast headlines, news sources, and forecasting dates in a machine-readable format, whichallows for easy merge with other databases. To ascertain which firms issue management forecasts we extract KeyDevelopment Type “Corporate Guidance” (event type ID: “26”, “27” and “29”) and merge to our sample of firms. Weperform a similar procedure for IMS announcements, by extracting Key Development Type “Announcement of InterimManagement Statement” (event type ID: “218”). We then compute the IMS and Management forecast concentrationvariables in the same manner as our quarterly concentration variable, i.e., the fraction of industry-semester peersthat report management forecasts or IMSs, respectively.

26

Finally, in untabulated analysis we redefine industry groups using 4-digit GICS,

rather than 2-digit GICS, and measure all variables and fixed effects using this new industry

grouping. This more granular industry peer definition does not change our overall inferences

or conclusions. The coefficient on Qtrly_Concentration is negative in all four specifications,

and significant for both Num_Forecasts and Analyst_Cover at the 1% and 5% levels,

respectively.

4.4 Investor Attention Around the Earnings Announcement

The results thus far provide evidence that firms lose investor attention during the

semester as more of their peers adopt a higher reporting frequency. We now examine whether

firms recover this lost attention during their subsequent earnings announcements.

In column (1) of Table 5, we find that trading volume around the earnings

announcement does not change when more peers adopt quarterly reporting. We measure

trading volume immediately surrounding the earnings announcement (EA_Volume), as the

natural logarithm of average share turnover during the two-day earnings announcement

window (i.e., the day of the earnings announcement and the next trading day after the

earnings announcement). Share turnover is measured each day as the daily trading volume

divided by the number of shares outstanding. For this specification, each observation

represents an earnings announcement that occurs at the end of a firm-semester period.

We find the coefficient on Qtrly_Concentration to be insignificant, meaning there is no

detectable increase in investor attention at the earnings announcement to offset the loss of

investor attention between the earnings announcements which we previously documented.

In column (2) of Table 5, we connect the result in column (1) with our previous

findings of a reduction in average trading volume during the semester. Specifically, we

show that Quarterly_Concentration increases the proportion of attention that occurs around

the earnings announcement versus the rest of the firm-semester. Our dependent variable

is Relative_EA_volume, which is the natural logarithm of the average share turnover

during the two-day earnings announcement window divided by the share turnover during

27

a benchmark period, where share turnover is again measured as the daily trading volume

divided by the number of shares outstanding. The benchmark period runs from 25 trading

days before the earnings announcement to 6 trading days before. We document a positive and

significant coefficient on Qtrly_Concentration, suggesting a larger relative spike in trading

volume around the earnings announcement when more of a firm’s peers report quarterly.

This finding is consistent with our earlier results given Qtrly_Concentration has a negative

association with trading volume throughout the semester (as documented in Section 4.1)

but no detectable association during the earnings announcement window (as reported in

column (1) of Table 5). Therefore, while Qtrly_Concentration seems to have no discernible

impact on the amount of investor attention around the earnings announcement, effectively

we observe an increase in the proportion of investor attention that occurs around the earnings

announcement relative to during the semester.

4.5 Alternative Setting: Mandatory Changes in Quarterly Reporting at the

Singapore Exchange

In order to further corroborate our main findings, we examine a setting where the

change in reporting frequency is mandatory, rather than voluntary. The benefit of this test

is the variation does not come from choices made by firms to adopt quarterly reporting; such

choices could be correlated with other firm choices or industry conditions that change the

allocation of investor attention. The drawback of this test is that it uses a setting, Singapore,

where the results may not generalize to other countries due to the unique institutional

environment, which includes high family ownership, relationship-based lending, and personal

networks (Kajuter et al., 2019). Hence, we view this test as a corroboration of our main

findings.

In 2003, the Singapore Exchange (SGX) introduced a quarterly reporting mandate

for listed stocks above a size threshold (Kajuter et al., 2019). Specifically, a firm was required

to report quarterly if its market capitalization was above $75 million Singapore Dollars (S$),

whereas firms below this threshold were able to continue reporting semi-annually. While the

28

initial mandate called for quarterly reporting for all listed firms with a market capitalization

greater than S$20 million for periods from 1 January, 2003, this size threshold was amended

to S$75 million in April 2003 with immediate effect. A firm’s market capitalization as of

March 31, 2003 determined whether it had to report quarterly going forward (Singapore

Exchange Listing Rule 705(2)(a)). For example, firms with a market capitalization above

S$75 million as of March 31, 2003 were required to continue reporting quarterly, even if their

market capitalization subsequently fell below S$75 million (Singapore Exchange Listing Rule

705(3)(a)). The measurement date for reporting thresholds remained in place until 2008

when the classification for firms was revisited.22 Therefore, for the years between 2003 and

2008, the reporting frequency requirements were stable.

We use this setting to construct a continuous difference-in-differences analysis around

the threshold requirement and employ a panel of firm-semester observations from 1998 to

2007. We end our sample period in 2007 because the SGX revisited reporting thresholds in

2008. Inclusion in this sample requires firms to be listed on the Singapore stock exchange as

of March 31, 2003. We exclude observations from 2003—the year of the rule change—because

many firms below the S$75 million threshold were initially required to start reporting

quarterly in the first few months of 2003, and were only exempted from quarterly reporting

when the S$75 million threshold was introduced (in April) of that year. Our data screens

are similar to those described for our main sample (see section 3.1), with the exception that

we do not omit firms in the bottom 10% of market capitalization; this is due to the relatively

low market capitalization threshold of S$75 million. Table 6, Panel A, shows descriptive

statistics for this sample, comparing firms above and below the S$75M threshold.

The continuous difference-in-differences takes the following form:

Yijs = αi + αs + β0Fraction of Industry Above $75Mj × Posts (2)

+γControlsijs + εijs

22The classification of firms into quarterly or semi-annual reporters was revisited in 2008. Firms were required toreport quarterly from 2008 onwards if their market capitalization was above the S$75M SGD size threshold as of thelast day of the reporting period ending two years prior. For example, for 2008, they had to look back to December31, 2006 (Singapore Exchange Listing Rule 705(2)(c)).

29

In this equation, i indexes firms, j indexes industries, and s indexes semesters. As in our

main specification in the voluntary setting, the observations are at the firm-semester level.

The regression includes both firm fixed-effects and calendar-semester fixed-effects, where a

firm-semester observation is assigned to the calendar semester that contains the end-date of

its fiscal semester. The regression includes the same controls that we use in the voluntary

setting. The standard errors are clustered by firm and by calendar-semester to account for

correlation in the errors arising from within-firm auto-correlation and common shocks over

time.

In our empirical specification, Post is an indicator that turns on for all firm-semesters

after the quarterly reporting requirement is introduced in 2003. The treatment variable is

represented by Fraction of Industry Above $75M, which captures the fraction of firms within

a given firm’s industry that were affected by the mandatory adoption of quarterly reporting.

Specifically, this variable is measured as the fraction of firms in a given firm’s two-digit GICS

industry that had market values above S$75 million as of March 31, 2003. By construction,

this variable only varies across industries, meaning it is the same for all firms in the same

industry, and it is constant over time.23 By fixing our continuous treatment measure in the

period immediately before the 2003 rule change, we are capturing an observational study

analog of an intent-to-treat effect that is common in the experimental literature (e.g., Costello

et al. 2019; Gassen and Muhn 2018). Intent-to-treat estimates are unbiased and mitigate

endogeneity concerns of selecting into the treatment group that may arise if we were to allow

our measure to be time varying.24 Only the interaction of Post and Fraction of Industry

Above $75M appears in the regression, since Post is subsumed by the calendar-semester

23A handful of firms in the sample switch industries during the sample period. The continuous treatment variableis still constant for these firms, and it is based on the industry they belonged to as of 2002, right before the rulechange.

24There are also institutional reasons to fix Fraction of Industry Above $75M at levels as of March 31, 2003. First,in the years after 2003, changes to the fraction of peers above the threshold did not directly change the fraction ofpeers that were forced to adopt quarterly reporting, since firms were not forced to adopt quarterly reporting if theirmarket values started below S$75 million on March 31, 2003, and grew above S$75 million at a later date (this is truefor the years up to 2007, when our sample period ends). Second, while new listings on the stock exchange will changethe fraction of peers that are affected, changing the measure to incorporate these listings would introduce variationthat might not be driven by the regulatory shock. Instead, some of the variation would be driven directly by changesin growth patterns across different industries. This would defeat the purpose of using this setting, since we are usingit to show that the drop in attention is not explained by changes in underlying conditions across industries.

30

fixed-effects and Fraction of Industry Above $75M is subsumed by the firm fixed-effects.

The results for the difference-in-differences appear in Panel B of Table 6. We estimate

our difference-in-differences specification across all four of our attention proxies. For all three

of our analyst-based proxies of attention, we find a negative coefficient on the interaction of

the treatment and post variables, indicating that Singaporean firms lost analyst attention

due to the mandatory increase in quarterly reporting among their peers. In economic terms,

our coefficient estimates suggest that firms belonging to an industry that experiences a 10%

increase in the number of quarterly reporters following the reporting mandate, exhibit a

7.17% larger drop in the number of analyst forecasts (t=-2.472) and a 5.53% larger drop in the

number of analysts covering the firm (t=-2.356), relative to control firms in other industries.

The coefficient on Num_Forecasts_Ana is also negative, but insignificant (t=-1.513). This

indicates that the drop in analyst forecasts (column (1)) predominantly comes from a drop

in the number of analysts covering the firm (column (2)) rather than a drop in the number

of forecasts per analyst (column (3)). The fourth attention variable, Avg_Trading_Volume,

also has an insignificant negative coefficient on the interaction (t=-0.0542).

Next, for the two attention proxies that have significant results, we investigate

whether the firms would have experienced parallel trends if there were no mandatory switch

to quarterly reporting among their peers. To check this, we run regressions similar to those

in Panel B. However, instead of interacting Fraction of Industry Above $75M with Post, we

interact it with year indicators for every year other than a hold-out year, which we set to 2002

(the year before the rule change). The coefficients from these interactions are plotted in Panel

C of Table 6. In the years before the rule change, we detect no difference in trends based

on Fraction of Industry Above $75M for the two attention variables Num_Forecasts and

Analyst_Cover. In the years after the rule change, we find that firms with higher values of

Fraction of Industry Above $75M experience a significant drop in the two attention variables.

The lack of a difference in trends before the rule change provides suggestive evidence that

the trends would have continued to be parallel without the rule change.

We cautiously interpret these results as support for the notion that firms lost

31

attention, at least from analysts, because their peers were required to adopt quarterly

reporting. For analyst attention, we detect a sustained relative drop in attention after

the rule change, and we fail to detect differences in trends before the rule-change, when a

higher fraction of a firm’s peers were required to adopt quarterly reporting.

Our results complement other studies that have used Singapore as a setting to examine

consequences of financial reporting frequency. For example, Kajuter et al. (2019) also use

the mandatory reporting change on the SGX and find that firms forced to adopt quarterly

reporting experienced a drop in their market values, relative to exempted firms who were

able to remain as semi-annual reporters. Another paper, Rahman et al. (2007), examines

only one year of data—2001, just prior to the quarterly reporting mandate—and compares

66 voluntary quarterly reporters to over 450 semi-annual reporters. They find that voluntary

quarterly reporters have higher stock return volatility and greater analyst following compared

to semi-annual reporters. While these results provide some initial evidence that voluntary

quarterly reporters may receive more investor attention, the small sample and cross-sectional

nature of their analysis make it difficult to draw strong inferences and generalize. In contrast,

we are able to provide broader evidence that when firms were forced to adopt quarterly

reporting, attention was diverted away from their peers.

5 Capital Market Consequences

Having documented that firms experience a reduction in investor attention when more

of their peers report on a quarterly basis, we next examine whether this drop in attention is

associated with a drop in market value. Our motivation for this analysis is based on Merton

(1987), who models investors that are only able to pay attention, and trade, in a subset of

firms in the market. Because each investor only holds a subset of firms in the market, they

are not able to fully diversify away the idiosyncratic risk of the firms in their portfolios, and

they demand a premium for bearing this risk. When fewer investors are paying attention to

a given firm, the remaining investors have to take on more of the firm’s idiosyncratic risk,

32

and therefore they apply a larger discount to the price of the firm’s securities. Therefore,

when a firm loses investor attention, Merton (1987) predicts a drop in market value. In line

with this, we predict that firms will experience a drop in market value when more of their

peers adopt quarterly reporting because, as we have already shown, the firms lose attention

when this occurs.

To empirically test this prediction, we use equation (1) to set up a regression of firm

value on Qtrly_Concentration. Importantly, equation (1) includes firm fixed-effects (as it

did in all previous specifications), so it estimates how firm value and quarterly concentration

are related within firms over time, as opposed to a pure cross-sectional comparison of firm

value. We measure firm value with Firm_Capitalization, which is the natural logarithm

of the firm’s market value, measured as of the last day of the firm-semester.25 In addition

to our previous controls, we also control for Market Total Firm Value, which is the natural

logarithm of the total value of the equity market in the firm’s country, measured on the same

day as Firm_Capitalization.26 This variable controls for market-level fluctuations caused by

sentiment or news.

We report results in column (1) of Table 7. The coefficient on Qtrly_Concentration

is significantly negative, indicating that a firm’s value is lower when more of the firm’s

peers adopt quarterly reporting. Specifically, we estimate that a firm’s market capitalization

drops by 1% (t=-3.188) when quarterly concentration among the firm’s peers increases by

10 percentage-points. This result is consistent with the loss in investor attention leading to

a reduction in market value (Merton, 1987).27

We next examine whether firms experience worse liquidity when more of their peers

adopt quarterly reporting. Liquidity might get worse if the quarterly reports draw away

25We measure a firm’s market value as the total market value of its primary security in terms of local currency. Asnoted before, our tests bound each firm-semester with earnings announcement dates rather than period-end dates,so the last day of the semester is an earnings announcement.

26For a small percentage of the firms within Datastream, the firm’s market value is recorded in a currency that isdifferent from the most common currency in its country. We exclude these firms from this test so that Market TotalFirm Value only sums market values that are recorded in the same currency. This effectively drops only about 0.5%of our sample for the test.

27This test differs from the test in Kajuter et al. (2019), which examines whether a firm loses market value whenit is forced report quarterly. In contrast, we examine whether a firm causes its peers to lose market value when itreports quarterly.

33

the attention of uninformed investors more than informed investors (Kyle, 1985). It is

reasonable to expect that more of the attention drawn away comes from relatively uninformed

investors, since earnings announcements draw increased Google search activity (Drake et al.,

2012), and Google searches indicate attention from uninformed/retail investors (Da et al.,

2011). Furthermore, individual investors pay more attention to firms receiving news coverage

(Barber and Odean, 2008), and earnings announcements often get mentioned by the financial

press. In column (2) of Table 7, the dependent variable is Avg_Prc_Impact, which is the

natural logarithm of the mean daily Amihud (2002) Illiquidity measure during the firm-

semester.28 The coefficient on Qtrly_Concentration is significantly positive, indicating a

higher price impact of trades—and thus a reduction in liquidity—when more of a firm’s

peers adopt quarterly reporting. In particular, our results suggest that firms experience

a 3% (t=1.99) increase in the price impact of trades when quarterly concentration among

their peers increases by 10 percentage-points. We note that these liquidity results stand in

contrast to those presented in Kajuter et al. (2019) who examine the Singapore setting. They

find evidence that quarterly reporting by peers tends to improve liquidity. The difference in

results between Europe and Singapore may be driven by institutional differences between the

two settings. In particular, information asymmetry is likely to be higher in Singapore than in

Europe, given Singapore’s high family ownership, relationship-based lending, and personal

networks (Kajuter et al., 2019). In this sense, the high level of information asymmetry in

Singapore may mean that any improvements in the information environment outweigh any

associated loss of attention.

Next, in additional analysis, we examine the impact of quarterly concentration on

institutional ownership. Specifically, we re-estimate our main specifications with three new

outcome variables: (1) Inst_Invst, is the percentage of the firm’s market cap owned by

institutional investors, (2) Inst_Invst_Domestic, is the percentage of the firm’s market cap

owned by institutional investors located within the firm’s country of incorporation, and

(3) Inst_Invst_Foreign, is the percentage of the firm’s market cap owned by institutional28We measure Amihud (2002) Illiquidity each day as the daily absolute return divided by the volume of trades in

terms of the local currency.

34

investors located outside the firm’s country of incorporation. We use Factset Ownership

data, and follow the approach in Ferreira and Matos (2010) to identify domestic and foreign

institutional investors. Our analysis is predicated on the notion that foreign investors are

at an informational disadvantage relative to domestic investors (e.g., , and therefore are

relatively uninformed. Table 8 reports our results. We find institutional ownership reduces

when more peer firms report quarterly, however this reduction is primarily attributable to

foreign institutional owners, with no discernible impact on domestic investors (see Panel

A of Table 8). Moreover, in Panel B of Table 8) we find that the reduction in foreign

institutional ownership is isolated to semi-annual reporting firms. These results are consistent

with relatively less informed investors reallocating their (limited) attention away from semi-

annual reporters as more peer firms report quarterly.

Finally, in untabulated analysis, we re-estimate the tests reported in Table 7 as

changes specifications. We omit firm fixed-effects and measure all variables, other than

indicators, as changes from the previous firm-semester to the current one. We note that

the coefficients on the change in quarterly concentration lose their significance for the

changes specification. This indicates that any resulting change in market capitalization

and liquidity does not occur immediately for a firm when more of the firm’s peers adopt

quarterly reporting.

6 Positive Externalities: Information Spillovers

In this section, we examine whether a high reporting frequency brings about benefits

for other firms in the form of information spillovers. It is well documented that investors use

information contained in the financial reports of one firm to update their valuations of other

peer firms (e.g., Foster, 1981; Freeman and Tse, 1992; Shroff et al., 2017; Wang, 2014). If

reporting on a quarterly basis improved the market-wide information environment, we would

expect to observe the following firm-specific benefits stemming from information spillovers:

(1) improvement in the accuracy of analyst forecasts and (2) more timely price discovery.

35

We fail to find evidence of either of these benefits in tests using the same European sample

that we use in the main tests of the paper.

The first column of Table 9, Panel A, tests whether peer quarterly reporting improves

the accuracy of the firm’s analyst forecasts. The dependent variable is the average analyst

forecast error over the course of the firm-semester, and it is defined as follows:

Avg−FEis = log(

1N

N∑n=1

|Forecastisn − Actualis||Actualis|

),

where i indexes firms, s indexes semesters, and n indexes the summary forecast reported by

IBES each month during the firm-semester. Actual is the actual annual earnings-per-share

for the firm-year, as reported by IBES. Forecast is the mean analyst forecast of the annual

earnings-per-share recorded by IBES in the middle of each month.29 Thus, Avg_FE is

the logarithm of the average absolute difference between the actual EPS and the consensus

forecast during the entire firm-semester, scaled by the magnitude of the actual EPS. If a

firm’s analysts benefited from information spillovers when more of the firm’s peers adopted

quarterly reporting, then we should see a reduction in Avg_FE when Qtrly_Concentration

increases. In column (1) of Table 9, Panel A, the test estimates an insignificantly positive

coefficient on Qtrly_Concentration, instead of the negative coefficient we would expect from

information spillovers. Thus, we fail to detect information spillovers to analyst forecasts. In

an untabulated test, we find that the positive coefficient on Qtrly_Concentration becomes

significant at the 5% level when we estimate it separately for semi-annual reporters. Thus,

semi-annual reporters appear to experience higher average forecast errors when more of

29We restrict the sample to forecasts of earnings-per-share for the current annual fiscal period, since we includeboth semi-annual and quarterly reporters in our sample, and both types of firms receive forecasts for their annualperformance. The annual earnings-per-share is the value for the current year, and is reported during the earningsannouncement that occurs at the end of the second semester of the year (since we bound semesters by earningsannouncements, and the second semester ends with the fourth-quarter/second-semester earnings announcement). Weexclude any summary forecasts that IBES recorded after the actual values were already announced, and restrict oursample to observations where the currency of the forecast matches the currency of the actual earnings-per-share.While an IBES manual says that IBES adjusts the numbers reported in the dataset so that the currency matchesfor all forecasts and actual values, an examination of the data revealed that the forecast and actual values wereoften of different orders of magnitude when the currency of the forecast was different from the currency of the actualvalue. Thus, we suspect that IBES does not always adjust the currencies to make the forecasts and actual valuescomparable. To minimize data errors, we only keep observations where the currency of the forecast is the same asthe currency of the actual value.

36

their peers adopt quarterly reporting, which is the opposite of what we would expect from

information spillovers.

The second column of Table 9, Panel A, tests whether price discovery improves

for firms when more of their peers report on a quarterly basis. We use a firm’s absolute

earnings announcement return to assess price discovery. If the firm benefits from information

spillovers, then it should have more price discovery before the earnings announcement, which

would in turn reduce the price discovery—and the absolute return—during the earnings

announcement. The dependent variable in this test is EA_Abret, measured as the natural

log of the absolute return over the two-day earnings announcement window for the firm’s

semi-annual earnings announcement (or in the case of a quarterly reporter, the 2nd or 4th

quarter earnings announcement).30 The results in column (2) show an insignificant coefficient

on Qtrly_Concentration, meaning that this test fails to detect an improvement in price

discovery when more of a firm’s peers report quarterly.

Finally, we search for information spillovers in a sub-sample where they are more likely

to occur. Accordingly, we repeat the tests from Panel A using a sub-sample of firms whose

returns are highly correlated with those of their industry peers. This test is predicated on

the notion that a high correlation between the firm and its industry peers likely means that

industry news is more value-relevant for these firms, suggesting that information spillovers

should have a larger effect. We report the results in Panel B of Table 9. Even with this

sub-sample, we still fail to find a significant negative coefficient on Qtrly_Concentration for

our proxies of forecast accuracy and price discovery. Thus, we find it unlikely that firms

receive positive externalities through information spillovers when more of their peers report

on a quarterly basis.

30We include an additional control in this specification, Absolute Market Return, measured as the logarithm ofthe absolute value-weighted return for the market (where the market is defined to be the firm’s country) during thefirm’s two-day earnings announcement window. For between one and two percent of the firms within Datastream,the currency of the firm’s market value is different from the most-common currency for that country. We drop thoseobservations when creating the value-weighted market return, so that the value-weights are all in the same currency.

37

7 Conclusion

In this paper, we address a largely-overlooked aspect of the financial reporting

frequency debate: the negative externalities that can arise when a subset of firms choose

to adopt more-frequent financial reporting. The externality we focus on is the re-allocation

of investor attention, which we examine in a large European sample, where firms report both

semi-annually and quarterly. Our empirical tests provide evidence that firms lose investor

attention to their peers when more of those peers choose to report on a quarterly basis,

rather than a semi-annual basis. We corroborate this finding by examining a setting where

quarterly reporting was mandated for a subset of firms. In this setting, we find that firms

lost more analyst attention when a greater fraction of their industry was required to adopt

quarterly reporting. In further tests, we provide further evidence that the firms are hurt

when their peers adopt quarterly reporting. Namely, they lose lose equity market value and

liquidity. We also fail to find evidence of positive externalities, in the form of information

spillovers.

38

Appendix A: Description of Variables

Variable Description and Calculation MethodologyAnalyst_Cover Defined as analyst coverage, measured as the natural log of one plus the total number

of sell-side analysts covering firm i in semester t;Avg_Prc_Impact Defined as average price impact of trades during the semester, measured as the natural

logarithm of the average daily Amihud (2002) Illiquidity measure during the firm-semester. We measure Amihud (2002) Illiquidity on as the absolute return on day tdivided by the volume of trades, denominated in local currency;

Avg_Trading_Vol Defined as the average trading volume, measured as the natural log of mean dailyshare turnover for firm i, during semester t, where share turnover is defined as thedaily volume of share trades divided by the total outstanding number of shares;

Beginning MarketCap

Defined as the logarithm of the firm’s market value of equity at the beginningof the fiscal year;

EA_Volume Defined as the logarithm of the average share turnover during the two-day earningsannouncement window, where share turnover is measured as the trading volume dividedby the number of shares outstanding;

Firm Capitalization Measured as the natural logarithm of the firm’s market value;IFRS An indicator for whether the firm reports its financial results in accordance with

International Financial Reporting Standards (IFRS). We follow Tan et al. (2011) anduse the Worldscope variable #WC07536 to determine which firms report financialresults in accordance with IFRS;

IFRS Concentration Measured as the fraction of the firm’s country-industry-semester peers that report inaccordance with IFRS;

IMS An indicator for whether the firm reported an Interim Management Statement duringthe semester;

IMS Concentration Measured as the fraction of the firm’s country-industry-semester peers that reportedInterim Management Statements during the semester;

Inst_Invst Institutional ownership holdings, defined as total shares held by institutions dividedby total shares outstanding at the end of semester t. (Source: Factset Ownership).

Inst_Invst_Domestic Institutional ownership holdings by domestic institutions, defined as total shares heldby domestic institutions divided by total shares outstanding at the end of semester t.Domestic institutions are those identifed as located in the same country of the relevantfirm. (Source: Factset Ownership; see also Ferreira and Matos, 2010).

Inst_Invst_Foreign Institutional ownership holdings by foreign institutions, defined as total shares heldby foreign institutions divided by total shares outstanding at the end of semester t.Foreign institutions are those identifed as located in a different country than that ofthe relevant firm. (Source: Factset Ownership; see also Ferreira and Matos, 2010).

ManagementForecast

An indicator for whether the firm issued at least one management forecast during thesemester;

ManagementForecastConcentration

Measured as the fraction of the firm’s country-industry-semester peers that issued atleast one management forecast during the semester;

Mean Peer Beg.Market Cap

Defined as the logarithm of the average beginning-of-the-year market value of equityamong the firm’s peers in its country-industry-semester group;

39

Num_Forecasts Defined as the total number of forecasts, measured as the total number of analystforecasts issued for firm i in semester t;

Num_Forecasts_Ana Defined as the number of forecasts per analyst, measured as the average number offorecasts issued by individual analysts covering firm i during semester t;

Qtrly_Concentration Measures the fraction of the firm’s country-industry peers that report quarterly duringthe firm’s semester;

Quarterly An indicator that equals 1 if the firm is a quarterly reporter in during the semester;Relative_EA_Volume Defined as the logarithm of the average share turnover during the two-day earnings

announcement window divided by share turnover measured during a benchmark period,where share turnover is measured as the trading volume divided by the number ofshares outstanding. We employ a benchmark period of 20 trading days prior to theEA, running from t-25 through t-6;

Semester Length Defined as the number of days in the firm-semester;Second_Semester An indicator variable set to 1 for the second semester in the fiscal year, and 0 otherwise;

40

Appendix B: Standard Screens Applied to Data from DatastreamFollowing procedures in prior literature (e.g., Arif and De George, 2020; Karolyi et al.,

2012; Griffin et al., 2009), we exclude the following non-common equity securities: depositoryreceipt listing (“DRs”); real-estate investment trusts (“REITs”); preferred and non-voting stocks;investment funds; and other stocks with special features. To exclude these stocks we examinethe names of individual stocks and remove those with names including: “REIT,” “REALEST,” “GDR,” “PF,” “PREF,” or “PRF” as these terms may represent REITs, Global DRs,or preferred stocks. We drop stocks with names including “ADS,” “RESPT,” “UNIT,” “TST,”“TRUST,” “INCOME FD,” “INCOME FUND,” “UTS,” “RST,” “CAP.SHS,” “INV,” “HDG,”“SBVTG,” “VTG.SAS,” “GW.FD,” “RTN.INC,” “VCT,” “ORTF,” “HI.YIELD,” “PARTNER,””HIGH INCOME,” “INC.&GROWTH,” and “INC.&GW” due to various special features. We thenemploy the following country-specific screens. In Belgium, “AFV” and “VVPR” shares are droppedsince they have preferential dividend or tax incentives. In France, shares of the types “ADP” and“CIP” are dropped as they have cash flow rights only, but no voting. In Germany, “GSH” sharesare excluded since they offer fixed dividends and carry no voting rights, while for Italian firms wedrop “RSP” shares as they are non-voting. See Appendix B in Griffin et al. (2009) for a detailedlist of other specific country exclusions.

We then employ the following screens–common in prior literature–to ensure integrity of ourreturn, market value and volume data: (1) to exclude non-trading days, we remove days on which90% or more of the stocks listed on a given exchange have a return equal to zero; (2) we excludeindividual stocks if the number of zero-return days is more than 80% in a given month; (3) we setreturns to missing if they are in the top or bottom 0.1% of the distribution for their exchange on agiven day; (4) we set daily returns to missing if the value of the total return index (RI) for eitherthe previous or the current day is below 0.01 (see Ince and Porter (2006) for a discussion on dealingwith Datastream errors); (5) we also remove daily observations if (1 + Rit) ∗ (1 + Rit−1)− 1 < 0.20,where Rit and Rit−1 are the returns of firm i on day t and day t-1, respectively, and at least one isgreater than or equal to 100% (e.g. Griffin et al., 2009). Finally, we remove the bottom 10 percentof stocks, by daily market capitalization, within each country to mitigate the impact of errors andhighly illiquid stocks. Note that these data screens only apply to the tests that use Datastreamdata, i.e. analyses with market-based outcome variables.

41

ReferencesAmihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of financial

markets 5 (1), 31–56.

Arif, S. and E. T. De George (2020). The dark side of low financial reporting frequency: Investors reliance onalternative sources of earnings news and excessive information spillovers. The Accounting Review 95 (6).

Atiase, R. K., H. Li, S. Supattarakul, and S. Tse (2005). Market reaction to multiple contemporaneousearnings signals: Earnings announcements and future earnings guidance. Review of AccountingStudies 10 (4), 497–525.

Badertscher, B., N. Shroff, and H. D. White (2013). Externalities of public firm presence: Evidence fromprivate firms’ investment decisions. Journal of Financial Economics 109 (3), 682–706.

Balta, N. and J. Delgado (2008). Home bias and market integration in the eu. CESifo EconomicStudies 55 (1), 110–144.

Bamber, L. S., O. E. Barron, and D. E. Stevens (2011). Trading volume around earnings announcements andother financial reports: Theory, research design, empirical evidence, and directions for future research.Contemporary Accounting Research 28 (2), 431–471.

Barber, B. M. and T. Odean (2008). All that glitters: The effect of attention and news on the buyingbehavior of individual and institutional investors. Review of Financial Studies 21 (2), 785–818.

Benoit, D. (2015). Time to end quarterly reports, law firm says. Wall Street Journal, August 19, 2015 .

Bhojraj, S., C. M. Lee, and D. K. Oler (2003). What’s my line? a comparison of industry classificationschemes for capital market research. Journal of Accounting Research 41 (5), 745–774.

Blankespoor, E., E. deHaan, and I. Marinovic (2019). Disclosure processing costs and investors’ informationchoice: A literature review. Working Paper, Available at: https://ssrn.com/abstract=3449751 .

Bourveau, T. and J. Schoenfeld (2017). Shareholder activism and voluntary disclosure. Review of AccountingStudies 22 (3), 1307–1339.

Breuer, M., K. Hombach, and M. A. Muller (2018). When you talk, i remain silent: Spillovereffects of peers’ mandatory disclosures on firms’ voluntary disclosures. Working Paper, Available at:https://ssrn.com/abstract=2820209 .

Brown, L. D. and M. S. Rozeff (1979). The predictive value of interim reports for improving forecasts offuture quarterly earnings. The Accounting Review, 585–591.

Butler, M., A. Kraft, and I. S. Weiss (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 (2-3),181–217.

Chapman, K., N. Reiter, H. D. White, and C. D. Williams (2019). Information overload and disclosuresmoothing. Review of Accounting Studies.

Christensen, H., L. Hail, and C. Leuz (2013). Mandatory ifrs reporting and changes in enforcement. Journalof Accounting and Economics 56, 147–177.

Clayton, J. (2018). Sec solicits public comment on earnings releases and quarterly reports. Press Release2018-287 .

Corwin, S. A. and J. F. Coughenour (2008). Limited attention and the allocation of effort in securitiestrading. The Journal of Finance 63 (6), 3031–3067.

42

Costello, A. M., A. K. Down, and M. N. Mehta (2019). Machine+ man: A field experimenton the role of discretion in augmenting ai-based lending models. Working Paper, Available at:https://ssrn.com/abstract=3490370 .

Cuijpers, R. and E. Peek (2010). Reporting frequency, information precision and private informationacquisition. Journal of Business Finance & Accounting 37 (1-2), 27–59.

Da, Z., J. Engelberg, and P. Gao (2011). In search of attention. The Journal of Finance 66 (5), 1461–1499.

Daske, H., L. Hail, C. Leuz, and R. Verdi (2008). Mandatory ifrs reporting around the world: Early evidenceon the economic consequences. Journal of Accounting Research 46 (5), 1085–1142.

DellaVigna, S. and J. M. Pollet (2009). Investor inattention and friday earnings announcements. The Journalof Finance 64 (2), 709–749.

Ding, R. and W. Hou (2015). Retail investor attention and stock liquidity. Journal of international financialmarkets, institutions and money 37, 12–26.

Drake, M. S., D. T. Roulstone, and J. R. Thornock (2012). Investor information demand: Evidence fromgoogle searches around earnings announcements. Journal of Accounting Research 50 (4), 1001–1040.

Edmans, A., M. S. Heinle, and C. Huang (2016). The real costs of financial efficiency when some informationis soft. Review of Finance 20 (6), 2151–2182.

Ernstberger, J., B. Link, M. Stich, and O. Vogler (2017). The real effects of mandatory quarterly reporting.The Accounting Review 92 (5), 33–60.

Fishman, M. J. and K. M. Hagerty (1989). Disclosure decisions by firms and the competition for priceefficiency. The Journal of Finance 44 (3), 633–646.

Fishman, M. J. and K. M. Hagerty (1992). Insider trading and the efficiency of stock prices. The RANDJournal of Economics, 106–122.

Foster, G. (1981). Intra-industry information transfers associated with earnings releases. Journal ofaccounting and economics 3 (3), 201–232.

Freeman, R. and S. Tse (1992). An earnings prediction approach to examining intercompany informationtransfers. Journal of Accounting and Economics 15 (4), 509–523.

Fu, R., A. Kraft, and H. Zhang (2012). Financial reporting frequency, information asymmetry, and the costof equity. Journal of Accounting and Economics 54 (2-3), 132–149.

Gassen, J. and M. Muhn (2018). Financial transparency of private firms: Evidence from a randomized fieldexperiment. Working Paper, Available at: https://ssrn.com/abstract=3290710 .

Gervais, S., R. Kaniel, and D. H. Mingelgrin (2001). The high-volume return premium. The Journal ofFinance 56 (3), 877–919.

Gigler, F., C. Kanodia, H. Sapra, and R. Venugopalan (2014). How frequent financial reporting can causemanagerial short-termism: An analysis of the costs and benefits of increasing reporting frequency. Journalof Accounting Research 52 (2), 357–387.

Griffin, J. M., P. J. Kelly, and F. Nardari (2009). Are emerging markets more profitable? implications forcomparing weak and semi-strong form efficiency. Univerity of Texas, Working Paper .

Han, J. C. and J. J. Wild (1990). Unexpected earnings and intraindustry information transfers: Furtherevidence. Journal of Accounting Research, 211–219.

Han, J. C., J. J. Wild, and K. Ramesh (1989). Managers’ earnings forecasts and intra-industry informationtransfers. Journal of Accounting and Economics 11 (1), 3–33.

43

Hirshleifer, D., S. S. Lim, and S. H. Teoh (2009). Driven to distraction: Extraneous events and underreactionto earnings news. The Journal of Finance 64 (5), 2289–2325.

Hirshleifer, D., S. S. Lim, and S. H. Teoh (2011). Limited investor attention and stock market misreactionsto accounting information. The Review of Asset Pricing Studies 1 (1), 35–73.

Hirshleifer, D. and S. H. Teoh (2003). Limited attention, information disclosure, and financial reporting.Journal of accounting and economics 36 (1-3), 337–386.

Hirshleifer, D. A., S. S. Lim, and S. H. Teoh (2004). Disclosure to an audience with limited attention.Available at SSRN 604142 .

Hirst, D. E., L. Koonce, and S. Venkataraman (2008). Management earnings forecasts: A review andframework. Accounting horizons 22 (3), 315–338.

Ince, O. S. and R. B. Porter (2006). Individual equity return data from thomson datastream: Handle withcare! Journal of Financial Research 29 (4), 463–479.

Kacperczyk, M., S. Van Nieuwerburgh, and L. Veldkamp (2016). A rational theory of mutual funds’ attentionallocation. Econometrica 84 (2), 571–626.

Kahneman, D. (1973). Attention and effort, Volume 1063. Citeseer.

Kajuter, P., F. Klassmann, and M. Nienhaus (2019). The effect of mandatory quarterly reporting on firmvalue. The Accounting Review.

Karolyi, G. A., K.-H. Lee, and M. A. Van Dijk (2012). Understanding commonality in liquidity around theworld. Journal of Financial Economics 105 (1), 82–112.

Kay, J. (2012). The kay review of uk equity markets and long-term decision making. Final Report 112.

Kim, O. and R. E. Verrecchia (1994). Market liquidity and volume around earnings announcements. Journalof accounting and economics 17 (1-2), 41–67.

Koester, A., R. Lundholm, and M. Soliman (2016). Attracting attention in a limited attention world:exploring the causes and consequences of extreme positive earnings surprises. Management Science 62 (10),2871–2896.

Kraft, A. G., R. Vashishtha, and M. Venkatachalam (2017). Frequent financial reporting and managerialmyopia. The Accounting Review 93 (2), 249–275.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the EconometricSociety, 1315–1335.

Lehavy, R. and R. G. Sloan (2008). Investor recognition and stock returns. Review of AccountingStudies 13 (2-3), 327–361.

Leuz, C. and P. D. Wysocki (2016). The economics of disclosure and financial reporting regulation: Evidenceand suggestions for future research. Journal of Accounting Research 54 (2), 525–622.

Libby, R., R. Bloomfield, and M. W. Nelson (2002). Experimental research in financial accounting.Accounting, Organizations and Society 27 (8), 775–810.

Lo, A. W. and J. Wang (2000). Trading volume: definitions, data analysis, and implications of portfoliotheory. The Review of Financial Studies 13 (2), 257–300.

Massa, A. and I. Levingston (2018). Wall street’s 30 days of hell: No sleep, no meals, no family. BloombergNews, September 4, 2018 .

44

McNichols, M. and J. G. Manegold (1983). The effect of the information environment on the relationshipbetween financial disclosure and security price variability. Journal of Accounting and Economics 5, 49–74.

Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. Journalof Finance 42 (3), 483–510.

Michael, D., M. Rapoport, and J. Maloney (2018). Trump asks sec to study six-month reporting for publiccompanies. Wall Street Journal, August 17, 2018 .

Milgrom, P. and N. Stokey (1982). Information, trade and common knowledge. Journal of economictheory 26 (1), 17–27.

Nallareddy, S., R. Pozen, and S. Rajgopal (2017). Consequences of mandatory quarterly reporting: The ukexperience. Columbia Business School Research Working Paper No.17-33 .

O’Brien, P. C. and R. Bhushan (1990). Analyst following and institutional ownership. Journal of AccountingResearch, 55–76.

Odean, T. (1999). Do investors trade too much? American economic review 89 (5), 1279–1298.

Peng, L. and W. Xiong (2006). Investor attention, overconfidence and category learning. Journal of FinancialEconomics 80 (3), 563–602.

Rahman, A. R., T. M. Tay, B. T. Ong, and S. Cai (2007). Quarterly reporting in a voluntary disclosureenvironment: Its benefits, drawbacks and determinants. The International Journal of Accounting 42 (4),416–442.

Ramnath, S. (2002). Investor and analyst reactions to earnings announcements of related firms: An empiricalanalysis. Journal of Accounting Research 40 (5), 1351–1376.

Rouwenhorst, K. G. (1999). European equity markets and the emu. Financial Analysts Journal 55 (3),57–64.

SEC (2018). Request for comment on earnings releases and quarterly reports. Release No. 33-10588,December 18, 2018 .

Shroff, N., A. X. Sun, H. D. White, and W. Zhang (2013). Voluntary disclosure and information asymmetry:Evidence from the 2005 securities offering reform. Journal of Accounting Research 51 (5), 1299–1345.

Shroff, N., R. S. Verdi, and B. P. Yost (2017). When does the peer information environment matter? Journalof Accounting and Economics 64 (2-3), 183–214.

Shyan, L. S. (2016). Quarterly reporting losing it’s shine. The Straights Times, Feb 17, 2016 .

Sims, C. A. (2003). Implications of rational inattention. Journal of monetary Economics 50 (3), 665–690.

Stoumbos, R. (2019). How does more frequent reporting reduce information asymmetry? Columbia BusinessSchool Research Working Paper No.17-75 .

Tan, H., S. Wang, and M. Welker (2011). Analyst following and forecast accuracy after mandated ifrsadoptions. Journal of Accounting Research 49 (5), 1307–1357.

Wagenhofer, A. (2014). Trading off costs and benefits of frequent financial reporting. Journal of AccountingResearch 52 (2), 389–401.

Wang, C. (2014). Accounting standards harmonization and financial statement comparability: Evidencefrom transnational information transfer. Journal of Accounting Research 52 (4), 955–992.

Yu, G. and A. S. Wahid (2014). Accounting standards and international portfolio holdings. The AccountingReview 89 (5), 1895–1930.

45

Table 1: Descriptive Statistics

This table reports descriptive statistics for our sample. Panel A reports the number of observations per country, and theproportion of quarterly reporters (i.e., Quarterly) for our sample firms, during 1998-2016. Panel B segments the sample intofirm-semester observations where the firm reports quarterly versus firm-semester observations where the firm reports semi-annually. We report the observation count, median, and mean for each sub-sample and indicate whether the t-test for thedifference in means across firm characteristics is statistically significant. Variable definitions are provided in Appendix A. Wereport the largest available N for each variable within our sample.

Panel A: Frequency of observations and quarterly reporters, across country.

Country N Fraction of Quarterly ReportersAustria 893 95.86%Belgium 1,934 26.47%Bulgaria 79 100.00%Switzerland 5,645 18.90%Germany 12,905 76.76%Denmark 3,781 63.45%Estonia 2,315 93.82%Finland 3,190 95.80%France 13,333 6.98%United Kingdom 43,998 3.30%Greece 4,395 96.66%Croatia 265 100.00%Ireland 72 5.56%Italy 6,222 88.01%Lithuania 20 100.00%Netherlands 3,139 32.05%Norway 4,677 99.25%Poland 6,967 99.67%Portugal 491 98.37%Romania 89 87.64%Sweden 8,815 98.31%Slovenia 41 73.17%

46

Panel B: Comparison of firm characteristics across semi-annual and quarterly reporters for thepooled sample. Analysis is conducted at the firm-semester time level. The time period is 1998-2016. *** p<0.01, ** p<0.05, * p<0.1.

Variable Count Median Mean Count Median Mean Test ofSemi-annual Semi-annual Semi-annual Quarterly Quarterly Quarterly Difference in Means

Qtrly_Concentration 68,246 0.0370 0.1104 53,957 0.9714 0.8524 -0.741986***IFRS_Concentration 68,246 0.7931 0.5911 53,957 0.8904 0.7833 -0.192145***

IFRS 68,246 1.0000 0.5795 53,957 1.0000 0.8079 -0.228392***Management Forecast Concentration 68,246 0.0154 0.0308 53,957 0.0000 0.0250 0.005796***

Management Forecast 68,246 0.0000 0.0255 53,957 0.0000 0.0214 0.004077***IMS Concentration 68,246 0.0233 0.0445 53,957 0.0000 0.0387 0.005825***

IMS 68,246 0.0000 0.0377 53,957 0.0000 0.0322 0.005467***Semester Length 68,246 182.0000 182.7792 53,957 182.0000 182.3060 0.473219**Num_Forecasts 68,246 1.0000 8.0773 53,957 2.0000 13.4967 -5.419477***Analyst_Cover 68,246 1.0000 3.5534 53,957 2.0000 5.1774 -1.624055***

Num_Forecasts_Ana 39,592 1.6667 1.7900 32,998 2.0000 2.1175 -0.327527***Mean Peer Market Cap 68,246 1.5258 2.2414 53,957 1.0453 1.7803 0.461134***Firm Capitalization 68,246 0.0905 1.4866 53,957 0.1566 2.8948 -1.408185***Avg_Prc_Impact 39,959 0.0000 0.0000 40,049 0.0000 0.0000 -0.000022***Avg_Trading_Vol 40,660 0.0019 0.0027 40,333 0.0016 0.0028 -0.000077**

47

Table 2: Attention and Quarterly Reporting Among a Firm’s Peers

This table provides evidence that firms lose attention from analysts and investors when their peers choose to report quarterly.We report coefficient estimates from OLS estimation of equation (1) with dependent variables as described in Section 4.1. Weexamine four attention-based outcome variables: (1) Total number of forecasts (Num_Forecasts) measured as the logarithm ofone plus the total number of analyst forecasts issued for firm i in semester s; (2) Analyst Coverage (Analyst_Cover), measuredas the logarithm of one plus the total number of sell-side analysts covering firm i in semester s; (3) Number of forecasts peranalyst (Num_Forecasts_Ana), measured as the logarithm of the average number of forecasts issued by individual analystscovering firm i during semester s; and (4) Average trading volume (Avg_Trading_Vol) measured as the logarithm of meandaily share turnover for firm i in semester s, where share turnover is defined as the daily volume of share trades divided bythe total outstanding number of shares. Our variable of interest, Qtrly Concentration, is measured as the fraction of firm i’scountry-industry peers that report quarterly during semester s. All variables are described in Appendix A. The observationsare at the firm-semester level, with each semester bounded by earnings announcements rather than period-end dates. Allspecifications include both firm fixed-effects and industry-semester fixed-effects. Standard errors are clustered at both the firmlevel and the industry-semester level. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Qtrly_Concentration -0.316*** -0.241*** -0.102** -0.244**(-4.43) (-4.10) (-2.47) (-2.55)

Quarterly 0.187*** 0.105*** 0.0631*** 0.163***(6.76) (4.49) (4.16) (4.13)

Mean Peer Beg. Market Cap -0.001 0.012 0.017** 0.034(-0.07) (1.01) (2.23) (1.48)

Beginning Market Cap 0.264*** 0.199*** 0.064*** 0.011(35.20) (35.81) (19.49) (0.95)

IFRS Concentration 0.102*** 0.208*** -0.014 -0.268***(2.86) (5.77) (-0.80) (-4.97)

IFRS 0.021 0.043*** -0.029** 0.014(1.19) (3.26) (-2.26) (0.51)

Second Semester -0.016* 0.005 -0.039*** 0.038***(-1.86) (0.86) (-5.17) (5.52)

Semester Length 0.002*** 0.002***(20.01) (27.12)

Observations 122,203 122,203 72,590 80,993R-squared 87.2% 88.6% 46.8% 73.4%Firm FE YES YES YES YESInd-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

48

Table 3: Impact of Quarterly Concentration on Attention: Semi-annual vs. Quarterly Reporters

This table examines the impacts on semi-annual reporters and quarterly reporters separately, providing evidence that semi-annual reporters (as opposed to quarterly reporters) lose attention when a greater fraction of their peers choose to reportquarterly. We report coefficient estimates from OLS estimation of equation (1), including two additional interaction terms toobtain separate estimates for semi-annual and quarterly reporters. These interaction terms interact Qtrly_Concentration withindicator variables that capture whether a firm is a semi-annual reporter (Semiannual) or a quarterly reporter (Quarterly).We examine the same four attention-based outcome variables as in Table 2: (1) Total number of forecasts (Num_Forecasts);(2) Analyst Coverage (Analyst_Cover); (3) Number of forecasts per analyst (Num_Forecasts_Ana); and (4) Average tradingvolume (Avg_Trading_Vol). All other variables are described in Appendix A. The observations are at the firm-semester level.The regressions include both firm fixed-effects and industry-semester fixed-effects. Standard errors are clustered at both thefirm level and the industry-semester level. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Qtrly_Concentration * Semiannual -0.538*** -0.431*** -0.225*** -0.562***(-6.14) (-6.30) (-3.73) (-4.64)

Qtrly_Concentration * Quarterly -0.130* -0.083 -0.053 -0.085(-1.77) (-1.28) (-1.32) (-0.84)

Quarterly -0.008 -0.061 0.008 -0.006(-0.17) (-1.62) (0.37) (-0.10)

Mean Peer Beg. Market Cap 0.001 0.014 0.019** 0.037(0.041) (1.11) (2.40) (1.65)

Beginning Market Cap 0.264*** 0.199*** 0.064*** 0.011(35.27) (35.94) (19.46) (0.95)

IFRS Concentration 0.096*** 0.202*** -0.017 -0.274***(2.69) (5.69) (-0.93) (-5.09)

IFRS 0.021 0.043*** -0.029** 0.015(1.20) (3.27) (-2.29) (0.54)

Second Semester -0.016* 0.005 -0.039*** 0.039***(-1.88) (0.86) (-5.18) (5.50)

Semester Length 0.002*** 0.002***(20.06) (27.09)

Observations 122,203 122,203 72,590 80,993R-squared 87.3% 88.6% 46.8% 73.4%Firm FE YES YES YES YESInd-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

49

Table 4: Robustness

This table provides additional empirical evidence to corroborate our main findings that the drop in investor attention isattributable to the frequency of peers’ financial reporting, rather than other events and broader disclosure policies. Panel Areports the OLS results for a changes specification, where the regressions in Table 2 are adjusted to remove the firm fixed-effectsand convert the variables from levels to changes. Changes are denoted with the symbol ∆ in the variable name. Panel Bre-estimates the regressions in Table 2 with the inclusion of additional measures that capture whether a firm’s peers makeother disclosures, such as management forecasts and interim management statements (IMS’s). Specifically, we augment ourmain models with the following four control variables: (1) Management Forecast Concentration, measured as the fraction ofthe firm’s country-industry-semester peers that issued at least one management forecast during the semester; (2) ManagementForecast, an indicator for whether the firm issued at least one management forecast during the semester; (3) IMS Concentration,measured as the fraction of the firm’s country-industry-semester peers that reported Interim Management Statements duringthe semester; and (4) IMS, an indicator for whether the firm reported an Interim Management Statement during the semester.The regressions include both firm fixed-effects and industry-semester fixed-effects. Panel C restricts the sample to semi-annualreporters only, and reports results for this sub-sample from running the regressions in Table 2. These regressions include bothfirm fixed-effects and industry-semester fixed-effects. Panel D reports results for our main regressions in Table 2 after wereplace our continuous variable Qtrly_Concentration with three indicator variables that identify whether Qtrly_Concentrationis low, medium or high (i.e. tercile rank). In all panels, the observations are at the firm-semester level, and standard errorsare clustered at both the firm level and the industry-semester level. All variables are described in Appendix A. T-statistics areincluded in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Panel A: Effect of Changes in Quarterly Concentration on Changes in Investor Attention

(1) (2) (3) (4)VARIABLES ∆Num_Forecasts ∆Analyst_Cover ∆Num_Forecasts_Ana ∆Avg_Trading_Volume

∆Qtrly_Concentration -0.217*** -0.181*** 0.0407 -0.397***(-3.347) (-3.330) (0.741) (-3.219)

∆Quarterly 0.0939*** 0.0551*** 0.0349 0.0298(4.207) (3.573) (1.279) (0.922)

∆Mean Peer Beg. Market Cap -0.00700 0.0188** -0.000381 -0.0103(-0.560) (2.357) (-0.0270) (-0.541)

∆Beginning Market Cap 0.0907*** 0.0508*** 0.0392*** -0.0276***(15.66) (16.00) (4.789) (-2.827)

∆IFRS Concentration -0.108*** -0.164*** -0.123*** 0.0289(-2.770) (-4.221) (-2.831) (0.524)

∆IFRS -0.0262* 0.00390 -0.0699*** -0.0176(-1.706) (0.382) (-3.757) (-0.944)

∆Second Semester -0.0379*** 0.00792* -0.0814*** 0.0581***(-3.383) (1.857) (-6.321) (6.100)

∆Semester Length 0.00155*** 0.00191***(22.67) (30.33)

Observations 114,163 114,163 62,218 72,238R-squared 7.0% 5.0% 8.8% 4.4%Ind-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

50

Panel B: Controlling for Management Forecasts and Interim Management Statements

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Qtrly_Concentration -0.332*** -0.246*** -0.106** -0.243**(-4.643) (-4.142) (-2.562) (-2.551)

Quarterly 0.188*** 0.107*** 0.0638*** 0.163***(6.805) (4.571) (4.213) (4.119)

Mean Peer Beg. Market Cap 0.000703 0.0130 0.0170** 0.0334(0.0499) (1.058) (2.159) (1.480)

Beginning Market Cap 0.263*** 0.198*** 0.0640*** 0.0103(35.20) (35.79) (19.44) (0.910)

IFRS Concentration 0.0973*** 0.203*** -0.0186 -0.267***(2.703) (5.648) (-1.036) (-4.959)

IFRS 0.0198 0.0424*** -0.0286** 0.0154(1.134) (3.244) (-2.241) (0.569)

Management Forecast Concentration 0.561*** 0.453*** 0.318*** -0.0652(3.402) (3.199) (2.594) (-0.302)

Management Forecast -0.201*** -0.139*** -0.0488** -0.222***(-8.617) (-9.021) (-2.101) (-4.956)

IMS Concentration -0.416*** -0.109 -0.254*** -0.0453(-3.622) (-0.948) (-2.693) (-0.291)

IMS 0.0126 0.0164 0.0158 0.107***(0.719) (1.331) (0.737) (2.694)

Second Semester -0.0196** 0.00553 -0.0400*** 0.0359***(-2.266) (0.927) (-5.273) (5.321)

Semester Length 0.00154*** 0.00176***(19.86) (26.86)

Observations 122,203 122,203 72,590 80,993R-squared 87.3% 88.6% 46.8% 73.4%Firm FE YES YES YES YESInd-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

51

Panel C: Restricting the Sample to Semi-Annual Reporters

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Qtrly_Concentration -0.662*** -0.580*** -0.291*** -0.605***(-5.638) (-5.610) (-3.052) (-3.455)

Mean Peer Beg. Market Cap -0.0441* -0.0295 0.0237* -0.00177(-1.914) (-1.403) (1.675) (-0.0491)

Beginning Market Cap 0.231*** 0.175*** 0.0535*** 0.0291**(26.56) (26.57) (12.29) (2.341)

IFRS Concentration 0.196*** 0.383*** -0.0871*** -0.229***(4.149) (8.372) (-3.045) (-3.360)

IFRS -0.00508 0.0352** -0.0442*** 0.0397(-0.270) (2.474) (-2.718) (1.319)

Second Semester -0.0423*** 0.00194 -0.0530*** 0.0327***(-4.637) (0.436) (-6.251) (4.645)

Semester Length 0.00138*** 0.00161***(15.95) (19.39)

Observations 68,230 68,230 39,685 40,750R-squared 86.0% 87.9% 39.4% 78.0%Firm FE YES YES YES YESInd-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

52

Panel D: Non-Linearity in Quarterly Concentration and Investor Attention

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Qtrly_Conc. Tercile 1 0.463 0.836 0.0376 -2.396***(0.747) (1.592) (0.0910) (-3.201)

Qtrly_Conc. Tercile 2 -0.441*** -0.340*** -0.131** -0.432***(-5.038) (-4.999) (-2.444) (-3.910)

Qtrly_Conc. Tercile 3 -0.335*** -0.251*** -0.107** -0.300***(-4.510) (-4.155) (-2.435) (-3.119)

Quarterly 0.193*** 0.110*** 0.0653*** 0.170***(6.995) (4.725) (4.364) (4.331)

Mean Peer Beg. Market Cap 3.37e-05 0.0139 0.0173** 0.0346(0.00239) (1.130) (2.197) (1.522)

Beginning Market Cap 0.265*** 0.199*** 0.0640*** 0.0107(35.29) (35.85) (19.43) (0.942)

IFRS Concentration 0.0975*** 0.202*** -0.0155 -0.272***(2.719) (5.628) (-0.864) (-5.073)

IFRS 0.0233 0.0449*** -0.0283** 0.0175(1.338) (3.449) (-2.219) (0.645)

Second Semester -0.0165* 0.00553 -0.0393*** 0.0382***(-1.893) (0.885) (-5.165) (5.493)

Semester Length 0.00155*** 0.00176***(19.92) (26.86)

Observations 122,082 122,082 72,537 80,909R-squared 87.2% 88.6% 46.8% 73.4%Firm FE YES YES YES YESInd-semester FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Ind-semester YES YES YES YES

53

Table 5: Earnings Announcement Trading and Quarterly Concentration

This table examines investor attention around subsequent earnings announcements. The table reports coefficient estimates fromOLS estimation of equation (1) with dependent variables as described in Section 4.4. We examine two outcome variables relatedto market reactions around earnings announcements: (1) EA_Volume, which is the logarithm of the average share turnoverduring the two-day earnings announcement window; and (2) Relative_EA_Volume, which is the logarithm of the average shareturnover during the two-day earnings announcement window, divided by share turnover during a 20-day benchmark periodbefore the earnings announcement. All other variables are described in Appendix A. The observations are at the firm-semesterlevel. The regressions include both firm fixed-effects and industry-semester fixed-effects. Standard errors are clustered at boththe firm level and the industry-semester level. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2)VARIABLES EA_Volume Relative_EA_Volume

Qtrly_Concentration -0.003 0.285***(-0.00) (3.84)

Quarterly 2.791*** 0.016(5.82) (0.57)

Mean Peer Beg. Market Cap 1.070*** 0.023(3.55) (1.44)

Beginning Market Cap 0.426*** 0.003(3.13) (0.37)

IFRS Concentration -2.044*** -0.001(-3.03) (-0.03)

IFRS 0.003 -0.033(0.01) (-1.59)

Second Semester 0.919*** 0.008(7.55) (0.64)

Observations 101,282 100,766R-squared 58.1% 20.2%Firm FE YES YESInd-semester FE YES YESSE Clustered at Firm YES YESSE Clustered at Ind-semester YES YES

54

Table 6: Alternate Setting: Mandatory Change in Reporting Requirements for the SingaporeExchange

This table provides the results from a difference-in-difference regression utilizing a mandated reporting frequency change in 2003which required firms listed on the Singapore exchange with market capitalization above S$75 million (SGD) to adopt quarterlyreporting. The sample includes firm-semesters between 1998 and 2007, omitting observations in calendar year 2003—the yearwhen Singapore adopted the quarterly reporting requirement. Panel A shows descriptive statistics for the Singapore sample,with the sample segmented based on whether the firm was above or below S$75 million (SGD) in market capitalization asof March 31, 2003. We report the observation count, median, and mean for each sub-sample and indicate whether the t-testfor the difference in means across firm characteristics is statistically significant. Panel B shows results for the difference-in-differences test. We measure the treatment variable as the fraction of Singapore-listed firms that have a market capitalizationabove $75M SGD on March 31, 2003 within a 2-digit GICS industry. By construction, our treatment variable only variesacross industries, meaning it is the same for all firms in same industry and is constant across time. The variable Post is anindicator variable that turns on for all semesters after 2003. The variable of interest is the interaction term, Fraction of IndustryAbove $75M x Post, which captures the difference in outcomes for firms in industries with different fractions of mandatoryquarterly reporters after the change in reporting frequency requirements. We examine attention-based outcome variables fromTable 2: (1) total number of forecasts (Num_Forecasts), (2) analyst coverage (Analyst_Cover), (3) number of forecasts peranalyst (Num_Forecasts_Ana), and (4) average trading volume (Avg_Trading_Volume). All specifications include both firmfixed-effects and semester-time fixed-effects. Standard errors are clustered at both the firm and the semester-time level. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Panel C provides graphical representation of ourresults and also provides evidence in support of the parallel trends assumption. We plot the coefficients from the interactionof Fraction of Industry Above $75M and indicators for every year between 1998 and 2007, other than 2002, which is the hold-out year. 2003—the year when Singapore adopted the quarterly reporting requirement—is also missing because it is omittedfrom the sample. This figure plots the coefficients for the two outcome variables where the interaction term is statisticallysignificant in the difference-in-differences: the total number of analyst forecasts (Num_Forecasts) and total analyst coverage(Analyst_Cover). In the plots, the points represent the coefficient estimates, and the bars show 95% confidence intervals.

Panel A: Comparison of firm characteristics across firms below and above S$75M SGD that arelisted on the Singapore Exchange

Variable Count Median Mean Count Median Mean Test of<75M SGD <75M SGD <75M SGD >75M SGD >75M SGD >75M SGD Difference in Means

Qtrly_Concentration 1,998 0.4194 0.3475 2,850 0.4591 0.3976 -0.050094***Quarterly 1,998 0.0000 0.0926 2,850 1.0000 0.5779 -0.485302***

IFRS_Concentration 1,998 0.0526 0.0675 2,850 0.0778 0.0849 -0.017463***IFRS 1,998 0.0000 0.0250 2,850 0.0000 0.1119 -0.086905***

Semester Length 1,998 182.0000 181.8624 2,850 182.0000 181.5940 0.268327Num_Forecasts 1,998 0.0000 0.5946 2,850 1.0000 7.2049 -6.610318***Analyst_Cover 1,998 0.0000 0.4980 2,850 1.0000 3.9133 -3.415335***

Num_Forecasts_Ana 446 1.0000 1.3638 1,581 1.5000 1.6405 -0.276667***Mean Peer Market Cap 1,998 0.2470 0.3277 2,850 0.2751 0.5344 -0.206759***Firm Capitalization 1,998 0.0296 0.0460 2,850 0.1328 0.7497 -0.703647***Avg_Trading_Vol 852 0.0026 0.0050 2,053 0.0017 0.0030 0.002016***

55

Panel B: Difference-in-Differences Analysis

(1) (2) (3) (4)VARIABLES Num_Forecasts Analyst_Cover Num_Forecasts_Ana Avg_Trading_Volume

Fraction of Industry Above $75M x Post -0.717** -0.553** -0.191 -0.0274(-2.472) (-2.356) (-1.513) (-0.0542)

Quarterly -0.117 -0.142** 0.133*** -0.126(-1.365) (-2.187) (2.988) (-1.023)

Mean Peer Beg. Market Cap 0.0720 0.107** 0.0288 0.241**(1.226) (2.237) (0.992) (2.270)

Beginning Market Cap 0.281*** 0.250*** 0.0925*** 0.0302(6.615) (7.902) (3.870) (0.495)

IFRS Concentration -0.603 -0.327 0.0596 -2.615**(-1.114) (-0.686) (0.174) (-2.540)

IFRS -0.388 -0.301 -0.237*** 0.285(-1.260) (-1.209) (-3.081) (1.393)

Second Semester -0.0227 0.00809 -0.0559** -0.0410(-1.129) (0.447) (-2.852) (-0.916)

Semester Length 0.00152*** 0.00230***(3.907) (6.672)

Observations 4,848 4,848 1,967 2,848R-squared 0.822 0.852 0.451 0.651Firm FE YES YES YES YESSemester-Time FE YES YES YES YESSE Clustered at Firm YES YES YES YESSE Clustered at Semester-Time YES YES YES YES

Panel C: Trend Analysis

56

Table 7: Quarterly Concentration, Firm Value, and Illiquidity

This table provides evidence that firms experience a reduction in market value and increased illiquidity (i.e., higher price impactof trades) when their peers choose to report quarterly. We report coefficient estimates from OLS estimation of equation (1) withdependent variables as described in Section 5. We examine two outcome variables: (1) Firm_Capitalization, which is measuredas the natural logarithm of the firm’s market value of equity as of the last day of the firm-semester, and (2) Avg_Prc_Impact(the average price impact of trades), which is measured as the natural logarithm of the average daily Amihud (2002) Illiquiditymeasure during the firm-semester. When Firm_Capitalization is the dependent variable, we control for Market Total FirmValue, measured as the natural logarithm of the total value of the equity market in the firm’s country, which controls for market-level fluctuations in value caused by sentiment or news. All other variables are described in Appendix A. The observationsare at the firm-semester level, with each semester bounded by earnings announcements rather than period-end dates. Theregressions include both firm fixed-effects and industry-semester fixed-effects. Standard errors are clustered at both the firmlevel and the industry-semester level. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

(1) (2)VARIABLES Firm_Capitalization Avg_Prc_Impact

Qtrly_Concentration -0.144*** 0.301**(-3.188) (1.996)

Quarterly 0.0219 -0.221***(1.095) (-3.764)

Mean Peer Beg. Market Cap 0.0101 -0.239***(0.982) (-5.829)

Beginning Market Cap 0.606*** -0.993***(52.39) (-45.16)

IFRS Concentration 0.139*** 0.229**(4.861) (2.310)

IFRS -0.0108 0.0390(-0.811) (0.839)

Second Semester 0.0180* -0.0428*(1.666) (-1.792)

Market Total Firm Value 0.376***(17.17)

Observations 110,202 80,008R-squared 96.5% 0.895Firm FE YES YESInd-semester FE YES YESSE Clustered at Firm YES YESSE Clustered at Ind-semester YES YES

57

Table 8: Quarterly Concentration and Institutional Ownership

This table provides evidence of the impact of quarterly concentration on institutional ownership. We report coefficient estimatesfrom OLS estimation of equation (1) with dependent variables as described in Section 5. Specifically, we examine three outcomevariables: (1) Inst_Invst, is the percentage of the firm’s market cap owned by institutional investors, (2) Inst_Invst_Domestic,is the percentage of the firm’s market cap owned by institutional investors located within the firm’s country of incorporation,and (3) Inst_Invst_Foreign, is the percentage of the firm’s market cap owned by institutional investors located outside thefirm’s country of incorporation. All other variables are described in Appendix A. The observations are at the firm-semesterlevel, with each semester bounded by earnings announcements rather than period-end dates. The regressions include both firmfixed-effects and industry-semester fixed-effects. Standard errors are clustered at both the firm level and the industry-semesterlevel. T-statistics are included in parentheses. *** p<0.01, ** p<0.05, * p<0.1.Panel A: Effect of Quarterly Concentration on Institutional Investor Share

(1) (2) (3)VARIABLES Inst_Invst Inst_Invst_Domestic Inst_Invst_Foreign

Qtrly_Concentration -0.428** -0.188 -0.364*(-2.427) (-0.856) (-1.885)

Quarterly 0.164** 0.0213 0.166*(2.274) (0.274) (1.962)

Mean Peer Beg. Market Cap -0.0410 -0.00805 -0.0175(-1.455) (-0.243) (-0.496)

Beginning Market Cap 0.319*** 0.263*** 0.408***(22.35) (17.78) (19.45)

IFRS Concentration 0.137** 0.308*** 0.128(2.052) (4.149) (1.583)

IFRS -0.0384 -0.0561 -0.0341(-1.099) (-1.430) (-0.705)

Second Semester -0.00147 0.00351 -0.00131(-0.158) (0.377) (-0.0806)

Semester Length -0.000128 -0.000148 1.33e-05(-1.330) (-1.296) (0.0967)

Observations 71,607 68,077 59,377R-squared 76.8% 76.7% 74.7%Firm FE YES YES YESInd-semester FE YES YES YESSE Clustered at Firm YES YES YESSE Clustered at Ind-semester YES YES YES

58

Panel B: Heterogeneous Effects for Quarterly versus Semi-Annual Reporters

(1) (2) (3)VARIABLES Inst_Invst Inst_Invst_Domestic Inst_Invst_Foreign

Qtrly_Concentration * Semiannual -0.748*** -0.389 -0.739***(-3.341) (-1.515) (-2.899)

Qtrly_Concentration * Quarterly -0.289 -0.106 -0.220(-1.587) (-0.456) (-1.125)

Quarterly -0.0380 -0.101 -0.0445(-0.412) (-1.010) (-0.391)

Mean Peer Beg. Market Cap -0.0392 -0.00704 -0.0144(-1.388) (-0.212) (-0.410)

Beginning Market Cap 0.320*** 0.264*** 0.409***(22.43) (17.81) (19.51)

IFRS Concentration 0.132** 0.305*** 0.125(1.980) (4.116) (1.547)

IFRS -0.0387 -0.0563 -0.0363(-1.112) (-1.436) (-0.753)

Second Semester -0.00128 0.00361 -0.000941(-0.139) (0.389) (-0.0585)

Semester Length -0.000128 -0.000147 1.01e-05(-1.329) (-1.291) (0.0743)

Observations 71,607 68,077 59,377R-squared 76.9% 76.7% 74.7%Firm FE YES YES YESInd-semester FE YES YES YESSE Clustered at Firm YES YES YESSE Clustered at Ind-semester YES YES YES

59

Table 9: Information Spillovers and Quarterly Reporting Among a Firm’s Peers

This table examines whether firms benefit from information spillovers when their peers report quarterly. This table reportscoefficients from our OLS estimation of equation (1) with dependent variables as described in Section 6. We examine twomarket-based outcome variables related to information spillovers: (1) Average Forecast Error (Avg_FE) which is measured asthe natural logarithm of the average forecast error reported during the semester, using the monthly consensus EPS forecast fromIBES. (2) Earnings Announcement Absolute Return (EA_Abret) which is measured as the natural logarithm of the absolutereturn during the two-day earnings announcement window. All other variables are described in Appendix A. Panel A reportsresults for the full sample, while Panel B reports results for a sub-sample of firms whose returns are highly correlated with thoseof their industry peers. The observations are at the firm-semester level, with each semester bounded by earnings announcementsrather than period-end dates. All specifications include both firm fixed-effects and industry-semester fixed-effects. Standarderrors are clustered at both the firm level and the industry-semester level. T-statistics are included in parentheses. *** p<0.01,** p<0.05, * p<0.1.

Panel A: Information Spillovers and Quarterly Reporting Among a Firm’s Peers within the fullsample

(1) (2)VARIABLES Avg_FE EA_Abret

Qtrly_Concentration 0.152 -0.029(1.14) (-0.15)

Quarterly -0.010 0.157(-0.14) (1.61)

Mean Peer Beg. Market Cap -0.005 -0.045(-0.15) (-1.01)

Beginning Market Cap -0.256*** -0.090***(-15.39) (-4.32)

IFRS Concentration -0.211*** -0.074(-2.73) (-0.76)

IFRS 0.024 -0.035(0.51) (-0.56)

Second Semester -0.473*** -0.009(-31.94) (-0.34)

Semester Length 0.001***(4.32)

Absolute Market Return 0.033***(3.85)

Observations 72,615 97,987R-squared 45.9% 10.6%Firm FE YES YESInd-semester FE YES YESSE Clustered at Firm YES YESSE Clustered at Ind-semester YES YES

60

Panel B: Information Spillovers and Quarterly Reporting Among a Firm’s Peers within highlycorrelated GICS industries

(1) (2)VARIABLES Avg_FE EA_Abret

Qtrly_Concentration 0.034 -0.169(0.21) (-0.65)

Quarterly 0.084 0.130(0.93) (0.97)

Mean Peer Beg. Market Cap -0.052 -0.049(-1.17) (-0.77)

Beginning Market Cap -0.306*** -0.064*(-12.44) (-1.92)

IFRS Concentration -0.086 0.023(-0.73) (0.16)

IFRS 0.005 -0.069(0.06) (-0.70)

Second Semester -0.487*** -0.005(-24.21) (-0.11)

Semester Length 0.001***(2.63)

Absolute Market Return 0.056***(4.36)

Observations 37,303 49,563R-squared 52.2% 14.2%Firm FE YES YESInd-semester FE YES YESSE Clustered at Firm YES YESSE Clustered at Ind-semester YES YES

61