Time Will Tell

Time Will Tell: Information inthe Timing of Scheduled Earnings News

Eric C. So∗Massachusetts Institute of Technology

Sloan School of Management

July 2014

Abstract

This study examines information in the schedule of future earnings announcements.Using a novel dataset of daily earnings calendar data, I show firm-initiated revisions inexpected earnings announcement dates predict firms’ future earnings news and returns.Firms that significantly advance their announcement subsequently report better earn-ings news relative to firms that delay, consistent with firms strategically timing news.Advancers also outperform delayers by 260 basis points in the month after calendarrevisions, where returns are concentrated during earnings announcements and mirrorthe nature of earnings news. Together, the results indicate investors fail to unravelinformation embedded in the timing of scheduled news releases.

JEL Classifications: G10, G11, G12, G14, M40, M41

∗I thank Ed deHaan, Chris Noe, and seminar participants at the 2014 Citigroup Quant Research Confer-ence and Nasdaq Economic Research for helpful comments and suggestions. I also thank Wall Street Horizonfor generously providing data on expected earnings announcement dates. Corresponding author: Eric So,[email protected], E62-677 100 Main Street, Cambridge MA 02142.

Time Will Tell: Information in the Timing of Scheduled Earnings News 1

1. Introduction

News releases are often scheduled in advance. Discretion over this schedule allows for

the possibility that the timing and content of news releases are systematically related. This

systematic relation can reflect strategic reasons, such as attempts to hide negative news,

and/or non-strategic reasons, such as processing costs that vary with the underlying news.1

To the extent the scheduled time of a new release is a reliable ex ante indicator of its content,

rational agents should update their expectations of the news to reflect this information (e.g.,

Milgrom (1981), Dye and Sridhar (1995), and Acharya, DeMarzo, and Kremer (2011)).

This study focuses on the schedule of firms’ earnings announcements, which often convey

value-relevant information to investors and whose timing is subject to managerial discretion.

Using information extracted from the schedule of future earnings announcements, this study

address two central questions. First, does the schedule of earnings announcements provide

an ex ante signal regarding subsequently reported performance? Second, do agents unravel

information embedded in the timing of scheduled news releases in a timely fashion?

To address these questions, this study uses a novel dataset containing daily snapshots

of earnings calendar data, which provides a rolling list of expected earnings announcement

dates for a broad cross-section of firms. A key feature of this data is that it identifies firm-

initiated revisions in expected announcement dates and thus helps capture managers’ efforts

to alter the schedule of news releases. Using this forward-looking calendar data, I develop

and implement a new methodology for characterizing the relative timeliness of earnings

announcements before the actual earnings news is explicitly announced (or not announced),

which permits testing whether the schedule of firms’ earnings announcements is informative

about their subsequently reported performance. Additionally, the daily snapshots provide

a proxy for investors’ information set in real time, which facilitates testing whether market

prices adjust to signals in the schedule of earnings announcements in a timely fashion.1For example, auditors may apply higher verification thresholds for good versus bad earnings news, leading

to systematic variation in when a firm announces earnings (Bagnoli, Kross, and Watts (2002)).


Throughout the paper, I refer to calendar revisions as being ‘firm-initiated’ if they are

triggered by a firm’s disclosure that explicitly states when they intend to announce earnings

(see Section 2 for more details and Appendix A for an example of a firm-initiated revision).

The main sample for my analysis consists of 18,959 firm-initiated calendar revisions between

2006 and 2013 observable at least two weeks prior to a firm’s expected announcement date.

I categorize each observation based on the extent to which the calendar revision advances

(i.e., moves forward) or delays (i.e., moves back) the firm’s expected announcement date. To

illustrate the ease of extracting signals from calendar revisions, I create a simple summary

metric, referred to as ‘R-Score’, that is highest for firms advancing their announcement by

more than one week and lowest for firms delaying by more than one week.

My first tests show high R-Score firms (i.e., ‘advancers’) subsequently report better

earnings news than low R-Score firms (i.e., ‘delayers’) during their earnings announcements.

Specifically, advancers report greater return-on-assets (ROA), same-quarter growth in ROA,

and analyst-based earnings surprises, compared to delayers. These differences are both

statistically and economically significant. For example, average earnings innovations and

analyst-based earnings surprises increase monotonically across R-Score portfolios, where

the average ROA is positive for advancers and negative for delayers. These results are

consistent with prior evidence that managers delay announcing bad news (Kothari, Shu, and

Wysocki (2009)) and indicate that earnings calendar revisions have strong predictive power

for subsequently reported earnings news.

Given the predictive power of calendar revisions for earnings news, I next examine

whether investors impound signals from earnings calendars into prices in a timely fashion.

To conduct these tests, I examine differences in returns across high and low R-Score firms.

These tests show that although there is no significant difference in returns across advancers

and delayers at the time of their calendar revisions, there is a striking difference in returns

following the revisions. Specifically, advancers subsequently outperform delayers by more

than 260 basis points (i.e., 2.6%) in the month after calendar revisions.


The spread in returns across advancers and delayers is also symmetric. Advancers, on

average, outperform the market by approximately 138 basis points and delayers underperform

by 130 basis points over the month following calendar revisions, mitigating concerns that

returns are only predictable on the short-side of the portfolio. This symmetry highlights a

benefit of using calendar revisions to identify both advancers and delayers ex ante, rather

than realized announcement dates which can only identify advancers ex post. These findings

appear robust to a variety of standard risk-adjustments as well as value-weighted portfolios,

indicating that calendar revisions have strong predictive power for future returns.

My next analyses examine the return spread across advancers and delayers in event-

time within the month (21 trading days) surrounding firms’ earnings announcement dates.

These tests show that over 60% of the monthly event-time return is earned in the three-day

announcement window, suggesting that prices adjust to the information content of calendar

revisions at the time earnings are announced, rather than at the time of the revisions.

Perhaps most importantly, the predictable spread in returns across advancers versus delayers

aligns with the sign and magnitude of firms’ reported earnings news. The combination

of these results poses a significant challenge to the assumption in theoretical models that

investors fully unravel information embedded in the timing of news releases.

Subsequent tests show that the predictive power of calendar revisions for future returns is

intuitively pronounced among subsets of firms, for example, those with greater sensitivities

to earnings news. The results are also pronounced in small firms with low analyst coverage,

indicating prices are less likely to reflect the content of calendar revisions among firms with

weaker information environments. Finally, I show the results are stronger in firms where

managers face greater career concerns, suggesting the timing of news is more informative

when the news is likely to affect the manager’s wealth and/or human capital.

Additional analyses show the paper’s findings are robust to alternative implementations.

First, I show firm-initiated changes in expected announcement dates continue to predict

earnings news and returns when defining revisions relative to the date a firm reported same-


quarter earnings in the prior year. These results mitigate concerns that the results hinge upon

the expected announcement date in the earnings calendar prior to the firm-initiated revision.

Second, I find similar results when ranking firms into portfolios using the distribution of

calendar revisions from the prior calendar quarter, which mitigates concerns the results are

sensitive to the use of R-Score or intertemporal shifts in the distribution of revisions. Finally,

I show the paper’s results hold when using a larger sample that includes both firm-initiated

revisions and non-initiated revisions (e.g., those triggered by a peer firm’s behavior), but

that non-initiated revisions have significantly less predictive power for future returns. These

findings indicate calendar revisions are more likely to convey information about subsequently

reported earnings when the revision originates from within the firm.

This paper’s findings are consistent with Chambers and Penman (1984) which shows that

announcement delays accompany bad news and Penman (1984) and Bagnoli, Kross, and

Watts (2002) which show that prices drift downward when firms do not announce on time.

My findings compliment and extend these studies in at least four ways. First, this paper

introduces a new forward-looking methodology for quantifying the timeliness of earnings

announcements, which expands the scope of ex ante tests to include early announcements.

Second, using this new methodology, this study is the first to show that advancements

predict positive earnings news and returns ahead of the actual announcements. Third, by

identifying the timeliness of announcements multiple weeks in advance, the return results

in this study are not only notably larger than in prior studies but are also less sensitive to

the concern applicable to strategies based on announcement delays that transaction costs

increase dramatically prior to announcements (e.g., So and Wang (2014)). Finally, my tests

focus on how investors respond specifically to firms’ disclosures that state the timing of a

future announcement without explicit mention of its content, and thus provides a more direct

test of whether investors unravel signals embedded in the timing of scheduled news releases.

As a result, this paper provides new evidence of a sizable anomaly that appears to be driven

by investors underweighting information related to the timing of future announcements.


This paper’s findings also align with evidence in Graham, Harvey, and Rajgopal (2005)

that some managers delay releasing bad news in hopes of receiving offsetting good news

(e.g., pairing poor current earnings with promising signs of future earnings), as well as

media accusations that managers operate within a “corporate culture reluctant to pass along

bad news” (Fletcher and Mufson (2014)). These findings compliment prior evidence that

managers strategically schedule news releases (e.g., Damodaran (1989) and Gennotte and

Trueman (1996)) and extends these studies by showing that managers may postpone releasing

negative news without prices fully reflecting this information in a timely fashion.

Additionally, this paper relates to growing evidence that prices underreact to low saliency

signals (e.g., Hirshleifer, Lim, and Teoh (2009), Da, Engelberg, and Gao (2011), Cohen,

Diether, and Malloy (2013), and Giglio and Shue (2014)) and suggest that investors may

underweight firms’ disclosures regarding expected announcement dates due to low saliency

but that calendar revisions should be treated as significant sources of information ahead of

the actual announcements.

More broadly, the findings of this paper relate to a substantial literature spanning eco-

nomics, finance, and accounting studying informed agents who possess discretion over their

communication with outsiders. A common assumption in these studies is that outsiders

rationally infer insiders’ motives and/or private information using signals embedded in the

insiders’ actions. For example, the evidence in this paper relates to the “unraveling result”,

which predicts agents favor disclosing information because outsiders rationally interpret the

absence of disclosure as a negative signal (Grossman and Hart (1980), Grossman (1981)).

This study provides evidence that investors do not always rationally uncover signals con-

veyed by the timing of news releases and thus highlights a need for further research into

investors’ ability to infer information and motives embedded in managerial actions.

The rest of the paper is organized as follows. Section 2 details the data and main results.

Section 3 contains additional analyses and robustness checks. Section 4 concludes.


2. Empirical Tests

This section provides details on the earnings calendar data used throughout the paper,

discusses the sample selection process, and details the main tests and empirical results.

2.1. Earnings Calendar Data and Sample Selection

The main analyses of this paper examine information in firms’ earnings calendar revisions.

To calculate calendar revisions, I use daily snapshots of earnings calendar data provided by

Wall Street Horizon from 2006 through 2013. Wall Street Horizon began disseminating

earnings calendar data in 2006, where each snapshot lists expected announcement dates for

a broad cross-section of firms. The calendar data reflects information available to investors

by 4am ET of each trading day. I use the data to proxy for investors’ daily information

set regarding expected announcement dates, which is likely conservative because Wall Street

Horizon provides this data to clients at much higher frequencies through streaming feeds.2

Some clients license the calendar data and post it online as a service to their customers.

The earnings calendar provides a rolling view of expected announcement dates by con-

tinually updating the calendar in response to new information. A key feature of the data is

that it indicates whether an expected announcement date stems from a firm explicitly stating

when they intend to announce earnings (See Appendix A for an example). In these cases, I

refer to the resulting expected date as being ‘confirmed’ (or, equivalently, ‘firm-initiated’).

All confirmed expected dates are primary sourced based on public information including,

but not limited to, firms’ investor relations webpage, press releases, and direct correspon-

dence. Prior to an announcement becoming confirmed, the calendar contains ‘unconfirmed’

expected dates that are forecasts based on information from several sources including a firm’s

past reporting behavior. Throughout the text, I use the term ‘firm-initiated revisions’ to

refer to changes in the earnings calendar that accompany an expected announcement date

changing status from unconfirmed to confirmed.2The calendar revisions used in this study are summarized via Wall Street Horizon’s DateBreaks Moni-

toring Service. See WallStreetHorizon.com for more details regarding the company’s data products.


My primary tests focus on firm-initiated revisions in the earnings calendar.3 I calculate

earnings calendar revisions as the change in a firm’s expected announcement date from one

trading day to the next. To capture economically meaningful calendar revisions, I focus

the analysis on revisions that alter the expected announcement date by at least two days.

Because calendar revisions can occur at any time prior to earnings announcements, I use the

timeline below to detail the sample requirements and structure of my main tests:

t-31

Calendar revision (REV ):Confirmed date, EXDTr, on day r ∈ (t-31, t-11)compared to prior expected date, EXDTr−1,

⇒REV=(EXDTr-EXDTr−1)︸︷︷︸r t-11

︷︸︸︷Calendar-time returns:R− Score portfolio returns

starting 1-day after revision, r+1

︷︸︸︷Event-time returns:

R− Score portfolio returnsrelative to announcement date t

t-10 t t+10⇓

observed on announcement date t

Profitability and earnings surpriseOutcomes observed:

The above timeline helps emphasize that the empirical tests are constructed to avoid look

ahead bias. I calculate a sample of revisions where the revision date, r, occurs in the month

(21 trading days) ending two weeks prior to firms’ confirmed expected announcement date, t.

Notationally, I require that r falls between t-31 and t-11. The requirement that the revision

occurs no earlier than t-31 helps identify revisions occurring after a firm’s fiscal period and

thus those which are potentially informed by managers’ knowledge of the firm’s performance.

Similarly, the requirement that the revision occurs no later than t-11 helps mitigate the risk

that investors learn about earnings through other sources such as pre-announcement media

coverage and, more importantly, as depicted in the timeline, facilitates examining event-time

returns surrounding firms’ announcements without subjecting the results to look ahead bias.

The results reported below regarding the predictive power of calendar revisions for earnings

news and returns do not appear sensitive to this sample requirement.3My sample consists of unique firm-quarter observations. If there are more than one confirmed earnings

announcement date that meets the sample requirements, I only include calendar revisions based on the firstconfirmation to avoid look ahead bias. In untabulated results, I find that firms rarely confirm alternativeannouncement dates for the same quarter (less than 2% of all firm-quarters).


To characterize the extent of a given revision, I create a variable, REV , which equals

the number of trading days the firm’s expected announcement date shifts when the date

changes status from unconfirmed to confirmed. Notationally, REV=(EXDTr-EXDTr−1),

where EXDT is the expected date and the subscripts indicate that REV is measured on the

date of firm-initiated revisions (r) relative to the immediately preceding trading day (r-1).

Higher values of REV indicate the firm moved forward their expected date and vice versa

for lower values of REV . I merge the calendar revision sample with return data from CRSP,

financial information from Compustat, and analyst-based earnings surprise data from IBES.

The final sample consists of 18,959 observations spanning 2006 through 2013.

I categorize each observation based on the extent to which the calendar revision advances

(i.e., moves forward) or delays (i.e., moves back) a firm’s expected announcement date.

To illustrate the ease of extracting signals from calendar revisions, I implement a simple

summary metric, referred to as ‘R-Score’, that is highest for firms advancing their expected

announcement date by more than one week and lowest for firms delaying by more than one

week. Specifically, for each value of REV , I define R-Score as follows:

R-Score =

0 (“Delay”) for REV < −5

0.25 for REV ∈ [−3,−5]

0.5 for REV ∈ [−2,+2]

0.75 for REV ∈ [+3,+5]

1 (“Advance”) for REV > +5

(1)

where the cutoff points are selected to provide a simple classification rule that creates sym-

metry in the average magnitude of REV across R-Score portfolios (see Table 1 for details).

Using static cutoff points also ensures that subsequent tests can be implemented without

referencing the full sample of calendar revisions within a given period, however a potential

concern is that the classification rule is ad hoc and can lead to unequal sample partitions.

To mitigate this concern, additional analyses show the paper’s inferences are not specific to

this classification rule and hold when using the cross-sectional distribution of REV .


As depicted in the above timeline, my main analyses examine the predictive power of

firms’ R-Score for earnings news and returns that become observable after calendar revisions.

My main tests focus on differences between high R-Score firms (hereafter referred to as

‘advancers’) and low R-Score firms (hereafter referred to as ‘delayers’). Additionally, I refer

to tests implemented relative to the calendar revision date (r) as calendar-time analyses and

tests implemented relative to the expected announcement date (t) as event-time analyses.

2.2. Descriptive Statistics

Table 1 contains descriptive statistics of the main sample used throughout the paper.

Panel A presents annual descriptive statistics, where the first two columns indicate the

number of unique firm-quarters and firms, respectively. The sample consists of approximately

2,300 firms-quarters per year and an average of 1,524 unique firms. HORIZON equals the

number of trading days between the revision date r and expected announcement date t. Panel

A shows that the average revision date in my sample occurs approximately 16 trading days

prior to their expected announcement date, which is closer to the announcement than the

midpoint of the sample requirement that r falls between t-31 and t-11. The REV column

of Panel A shows the average revision shifts the expected date back by one to two days,

suggesting firms are more likely to delay than advance their expected announcement date.

The |REV | column contains absolute values of REV and indicates the average revision shifts

the expected announcement date by approximately 4.5 days on an absolute basis.

The final two columns of Panel A contain descriptive statistics on firms’ deviations from

their expected announcement dates. Specifically, DEV equals the number of days between

the confirmed expected announcement date and the actual announcement date, and |DEV |,

equals the absolute value of DEV . The averages of DEV and |DEV | demonstrate that

firm-initiated expected announcement dates are highly accurate. Specifically, the average

value of DEV is insignificantly different than zero and the average value of |DEV | indicates

that less than one in four firms announce earnings on a day that differs from their expected

announcement date.


Panel B of Table 1 contains descriptive statistics across R-Score portfolios. The REV

column shows revisions are nearly symmetric across R-Score portfolios, where the average

delayer (advancer) moves back (forward) expected announcement dates by 8.8 days. The

OBS column indicates the average number of firm-quarters within each portfolio, and shows

there are approximately twice as many low R-Score firms than high R-Score firms, which is

consistent with the evidence in Panel A that firms are more likely to delay than advance their

announcements. The HORIZON column in Panel B shows that delayers revise the earnings

calendar approximately one day closer to their expected date compared to advancers.

The next two columns of Panel B contain descriptive statistics for each R-Score portfolio.

MCAP equals firms’ market capitalization reported in millions and MOMEN equals a

firm’s cumulative market-adjusted return over the twelve months ending on r-11. The market

capitalization statistics indicate the average firm in my sample is a mid-cap firm, though

firm size does not vary significantly with R-Score. By contrast, the MOMEN results show

delayers significantly underperform advancers over the year prior to the calendar revision.

The average value of DEV indicates that delayers are significantly more likely to deviate

from their expected announcement date by further delaying the announcement, though the

average difference in DEV across advancers and delayers is less than one-third of a day.

The final column of Panel B contains descriptive statistics of firms’ market-adjusted

return in the three-day window surrounding their calendar revision, RET (r-1,r+1). The

results show that there is no significant difference in returns across advancers and delayers

at the time of the calendar revision.4 In the analysis below, I examine differences in reported

earnings and future returns that occur after the calendar revision date.

Panel A of Table 2 contains average earnings metrics across R-Score portfolios based on

(1) the reported level of profitability, ROA; (2) changes in ROA; and (3) reported earnings

relative to consensus analyst forecasts. Specifically, ROA equals the firm’s return on assets4These results contrast with the findings in Duarte-Silva et al. (2013) that prices significantly decline at

the time of press releases explicitly mention ‘delaying’, ‘postposing’, or ‘deferring’ an earnings announcement,suggesting that the type and nature of announcement may make delays more salient to investors.


defined as net income scaled by beginning-of-quarter total assets, ∆ROA equals the firm’s

annual change in ROA for the same fiscal quarter, and SURP equals the firm’s EPS reported

in IBES minus the consensus EPS forecast available immediately prior to the announcement,

and scaled by beginning-of-quarter assets. Additionally, ROA<0, ∆ROA<0, and SURP<0

are indicator variables that equal one when the correspond variable is negative.

Panel A captures the first main result of the paper. Specifically, advancers subsequently

announce greater ROA, changes in ROA, and earnings surprises, compared to delayers.

These differences are both statistically and economically significant. For example, earnings

declines and negative analyst-based surprises are concentrated among delayers, where average

ROA is positive for advancers and negative for delayers. Additionally, average earnings

innovations and earnings surprises increase monotonically across R-Score portfolios. This

evidence relates to Bagnoli, Kross, and Watts (2002), which finds earnings news arrives late

in proportion to the severity of bad news but that there is no significant relation between

reporting early and the extent of good news. In contrast, by using calendar revisions instead

of the timing of news arrival as in Bagnoli, Kross, and Watts (2002), I find the link between

announcement timeliness and earnings news is symmetric; the extent to which firms advance

announcement dates is also proportional to the magnitude of good news.

Panel B of Table 2 presents results from regressing each earnings metric on R-Score

and controls for firms’ log market capitalization (SIZE), log book-to-market ratio (LBM),

return momentum (MOMEN), and historical return volatility (V LTY ). Panel B shows that

R-Score has significant predictive power for all three earnings metrics (t-statistics ranging

from 3.46 to 5.90) that is not subsumed by standard controls.

Taken together, these results show calendar revisions predict subsequently reported earn-

ings, which is inconsistent with Skinner (1994) which argues that managers advance bad news

to avoid litigation but consistent with survey evidence in Graham, Harvey, and Rajgopal

(2005), empirical evidence in Kothari, Shu, and Wysocki (2009), and experimental evidence

in Legg and Sweeny (2014) that, on average, individuals delay announcing bad news.


2.3. Return Prediction

Given the evidence that calendar revisions predict subsequently reported earnings news,

my next set of tests examines whether investors impound signals from earnings calendars

into prices in a timely fashion. To conduct these tests, I examine differences in returns across

advancers and delayers following earnings calendar revisions.

Table 3 contains average equal- and value-weighted returns to each R-Score portfolio

following the revision date r, using four return metrics measured over the month following

the calendar revision from r+1 to r+21. Specifically, the first two columns contain raw

and market-adjusted returns denoted as RR(r+1,r+21) and RET (r+1,r+21), respectively.

SAR(r+1,r+21) refers to size-adjusted returns which equals the firm’s raw return minus the

contemporaneous size-matched portfolio return. Finally, FAR(r+1,r+21) refers to factor-

adjusted returns defined as the firm’s raw return minus the return calculated by estimating a

firm’s daily sensitivity to the market (MKTRF ), small-minus-big (SMB), high-minus-low

(HML), and up-minus-down momentum (UMD) factors over the year prior to the revision

and applying those sensitivities to the contemporaneous factors.

Panel A of Table 3 captures the second main result of the paper. Specifically, the results

demonstrate a robust positive relation between calendar revisions and future returns. For

each return metric, the average return spread across advancers and delayers exceeds 260

basis points in the month after calendar revisions, with corresponding t-statistics ranging

from 3.95 to 5.17. The spread in market-adjusted returns is also fairly symmetric across

advancers and delayers, mitigating concerns that returns are limited to the short side of the

portfolio. Advancers, on average, outperform the market by approximately 138 basis points

and delayers underperform by 130 basis points over the month following calendar revisions,

which align with the nature of firms’ subsequently reported earnings news. The magnitude

of these returns compares favorably to other well-known anomalies, however, the amount of

capital deployable to exploit this evidence is limited by the fact that only a subset of firms

revise earnings dates within a given trading period.


To address the possibility that return prediction is limited to small firms where trans-

action costs are highest, Panel B of Table 3 presents value-weighted future returns across

R-Score portfolios. Value-weighting lowers the portfolio’s performance but the resulting

returns remain economically significant. The average difference in value-weighted returns

across advancers and delayers ranges from 153 to 216 basis points in the month after calen-

dar revisions, with corresponding t-statistics ranging from 1.95 to 2.13. This evidence of a

robust post-revision return spread contrasts sharply with the insignificant price reaction at

the time of calendar revisions shown in Table 1, suggesting that investors do not unravel the

implications of calendar revisions for earnings news in a timely fashion.

My next analyses examine the spread in event-time returns within the month (21 trading

days) surrounding firms’ expected earnings announcement dates.5 As shown in Table 3, I

find no evidence that the results significantly vary across return metrics and, in the remaining

tests, focus on predicting market-adjusted returns. Table 4 contains market-adjusted returns

around earnings announcements, where RET (t+X,t+Y) denotes the cumulative market-

adjusted return from day X to Y relative to the expected earnings announcement date t.

The RET (t-10,t+10) column of Table 4 shows advancers outperform delayers by 253 basis

points in the month centered on firms’ expected announcement date (i.e., from t-10 to t+10),

consistent with the magnitude of the calendar-time return spread documented in Table 3.

The RET (t-10,t-2) column of Table 4 shows advancers only weakly outperform delayers

by 54 basis points (t-statistic = 1.77) prior to announcements from t-10 to t-2. In contrast,

the RET (t-1,t+1) column of Table 4 shows that event-time returns are heavily concentrated

in the three-day window surrounding firms’ announcements dates. Specifically, over 60%

(=158/253 basis points) of monthly event-time returns are earned during the announcement.

This evidence suggests that prices adjust to the information content of calendar revisions at

the time earnings are announced, rather than at the time of the revisions.5The analysis focuses on expected, rather than actual, announcement dates, to avoid look ahead bias.

Use of actual announcement dates yields qualitatively identical results, which is not surprising given theevidence in Table 1 that firms generally announce earnings on their confirmed expected date.


The RET (t+2,t+10) column of Table 4 also shows that advancers outperform delayers

by approximately 50 basis points following earnings announcements, which is consistent with

prior evidence of post-earnings announcement drift (PEAD). However, Table 4 also shows

that 80% (=211/253 basis points) of monthly event-time returns are earned up through the

announcement window, indicating the results are likely distinct from PEAD.

Related evidence in Figure 1 depicts the spread in returns across advancers and delayers

in event-time. The top graph shows that the cumulative spread in returns reaches 50 basis

points two days prior to the announcement (i.e., t-2), but nearly doubles on the announce-

ment date t, and jumps over four-fold by t+1 to over 200 basis points. The bottom panel of

Figure 1 decomposes the event-time return spread into the average cumulative returns for

advancers and delayers and shows the price reaction at announcements is fairly symmetric

across advancers and delayers.6

Figure 2 shows the average spread in returns across advancers versus delayers for each

calendar quarter, where RET (r+1,r+21) is shown in black bars and RET (t-1,t+1) is shown

in grey bars. The results show that the average return spread is positively skewed and gen-

erally positive over time, yielding positive average monthly (announcement-window) returns

in 23 (29) of the 32 calendar quarters in my sample window. This evidence helps mitigate

concerns that the return-based results are isolated within a specific period.

Finally, Table 5 contains regression results of monthly and announcement-window returns

on R-Score and standard risk controls. The dependent variable in the first three columns is

RET (t-1,t+1) and RET (r+1,r+21) in the latter three columns. In both sets of regressions, I

find that R-Score predicts returns incremental to the control variables, where the coefficient

magnitudes align with the return spreads shown in earlier tables. These findings help mitigate

concerns that the relation between R-Score and future returns is driven by a firm’s exposure

to standard risk proxies.6The graph also depicts rising pre-announcement prices for both advancers and delayers, which is consis-

tent with the evidence in Johnson and So (2014) that prices predictably rise ahead of earnings announcementsdue to asymmetrically high costs of acting on negative news.


Taken together, the results of this section indicate that calendar revisions have strong

predictive power for future returns, which relate to evidence in Penman (1984) and Bagnoli,

Kross, and Watts (2002) that prices react negatively when firms do not announce on time but

continue to drift downward through the announcement. The findings in those studies suggest

that investors recognize the failure to announce on time as a negative signal but are subject

to the concern that the initial price reaction is incomplete due to transaction costs which

increase dramatically prior to announcements (So and Wang (2014)) and particularly so for

demands to sell (Johnson and So (2014)). These same concerns regarding pre-announcement

transaction costs are unlikely to apply to this paper because R-Score portfolios are formed

multiple weeks in advance of announcements based on forward-looking calendar revisions.

The novel data used this study allows me to examine how investors interpret disclosures

that reference only the timing of an announcement, with no explicit indication of its content.

As a result, this study provides a more direct test of, and challenge to, the assumption that

investors fully unravel signals embedded in the timing of scheduled news releases.7 Moreover,

the methodological innovation in this paper yields returns that are balanced across long and

short positions and significantly larger than in prior research. At 260 basis points per month,

the return results suggest a massive underreaction to earnings calendar information.

The most likely explanation for the findings in this study are that investors underreact to

the schedule of future earnings announcements because it is primarily conveyed through low

salience disclosures, in contrast to firms failing to announce on time, which are likely more

attention-grabbing events. As the example in Appendix A shows, it is not immediately ob-

vious that these seemingly ‘boilerplate’ disclosures contain information regarding the nature

of earnings news; only when the news is to be announced. Thus, identifying value-relevant

signals embedded in these disclosures requires viewing them in the context of past reporting

behavior and with a skeptical lens that managers strategically time news announcements.7The idea that investors underweight the information content of calendar revisions is directly related to

prior studies showing that prices underreact to firms’ financial ratios (e.g., Lakonishok, Shleifer, and Vishny(1994), Ou and Penman (1989), and So (2013)) and fundamental signals (e.g., Sloan (1996), Piotroski (2000),and Piotroski and So (2012)) that have strong predictive power for subsequently reported earnings news.


3. Additional Analyses

This section provides details on extensions of the main results as well as tests designed

to gauge the robustness of the paper’s findings to alternative implementations.

3.1. Contextual Analysis

The preceding analysis establishes a robust link between firm-initiated calendar revisions

and future returns. A natural extension of these tests is to examine whether the return

results are predictably concentrated among subsets of firms in which calendar revisions are

more likely to be relevant for prices. To address this possibility, Table 6 contains results

from regressing RET (r+1,r+21) on R-Score interacted with four conditioning variables.

The first two conditioning variables in Table 6 capture the extent of a firm’s information

environment and trade frictions. Specifically, 1(Small Firm) is an indicator variable that

equals one if the firm is in the lowest tercile of market capitalization and 1(Low Coverage) is

defined analogously for analyst coverage, where terciles are measured each calendar quarter.

I predict that the predictive power of R-Score for returns is concentrated among smaller

firms where investors are less likely to learn about the earnings information embedded in

calendar revisions through other sources such as media coverage.

Abarbanell and Lehavy (2003) and So (2013) provide evidence that analysts’ investment

recommendations signal a firm’s incentives to meet or beat analysts’ forecasts, such that

higher recommendations signal a greater sensitivity of a firm’s share price to earnings news.

Thus, I define 1(Buy Recommendation) as an indicator variable that equals one if the firm

has a consensus ‘BUY’ recommendation in IBES and predict that it has a positive interaction

effect with R-Score in predicting future returns because prices should react more strongly

to subsequently announced earnings news for this subset of firms.

Finally, Gilson (1989) shows that managers’ career concerns increase when a firm ap-

proaches distress and Kothari, Shu, and Wysocki (2009) argues that managers of distressed

firms have a heightened incentive to suppress bad news. I define 1(High Distress) as an


indicator variable that equals one if the firm is in the lowest tercile of the Zmijewski (1984)

Z-Score financial distress measure and predict that it has a positive interaction effect with

R-Score in predicting returns. This prediction is based on the idea that managers are more

likely to use discretion over the timing of earnings news when it has a greater impact on

their human capital and/or personal wealth.

Consistent with these predictions, the interaction terms in Table 6 show that return

prediction increases among firms with greater sensitivities to earnings news, small firms

with low analyst coverage, and in firms whose management faces greater career concerns.

These results show the predictive power of calendar revisions for future returns is intuitively

correlated with firm characteristics through contextual analysis.

3.2. Robustness Tests

This subsection presents three sets of tests designed to extend and gauge the robustness

of the paper’s main findings using alternative empirical implementations. First, although the

confirmed announcement dates in this study are primary sourced to public information, one

potential concern is that unconfirmed expected announcement dates are based on proprietary

forecasting techniques and/or are specific to the earnings calendar data used in this study.

To mitigate this concern, Table 7 re-examines the main analyses when defining calendar

revisions as the difference between a confirmed date and the date a firm reported same-

quarter earnings in the prior year.

Panel A of Table 7 contains earnings metrics and future returns across five RW -Score

portfolios (the RW is short for ‘random-walk’), where the portfolio assignments are based

on revisions in expected announcement dates relative to firms’ prior year announcement

date (i.e., assuming announcement dates follow a random-walk). Using these random-walk

expected announcement dates as the benchmark, I assign firms to RW -Score portfolios using

the same cutoff points as in Equation (1) when calculating R-Score. Table 7 shows that

firm-initiated revisions in expected announcement dates continue to significantly predict both

earnings news and returns when benchmarking to random-walk dates, though the predictive


power of revisions for RET (r+1,r+21) declines to 220 basis points (t-statistic = 3.79). These

results mitigate concerns that the results hinge upon the expected announcement date in

the earnings calendar prior to the firm-initiated revision.

To supplement the tests based on random-walk dates, Panel B contains descriptive statis-

tics of RET (r+1,r+21) when partitioning the sample across firms with above and below

median announcement date consistency. I define announcement date consistency using the

historical standard deviation of the difference between random-walk forecasts and firms’

actual earnings announcement dates over the prior ten quarters, where lower standard de-

viations indicate greater consistency (i.e., the firm’s announcement pattern conforms more

closely to a random-walk). Panel B captures the intuitive result that the predictive power of

RW -Score for returns increases among firms that consistently reported earnings on the same

day in prior years. Specifically, the return spread more than doubles among high consistency

firms (360 bps, t-statistic = 2.95) compared to low consistency firms (159 bps, t-statistic =

1.88). These results indicate that historical reporting patterns are also a useful conditioning

variable for strategies based on calendar revisions.

An additional potential concern with the paper’s main results is that firms are categorized

as advancers and delayers using static cutoff points for calendar revisions rather than allowing

the distribution to shift over time. To mitigate this concern, Table 8 contains average

earnings metrics and future returns across portfolios where firms are sorted into quintiles

(Panel A) and terciles (Panel B) of calendar revisions each calendar quarter, where higher

(lower) values are assigned to quintile 1 (0).

Both Panels A and B of Table 8 show the paper’s main results hold when ranking firms

into portfolios using the distribution of calendar revisions from the prior calendar quarter.

Specifically, using both quintile and tercile portfolios, I find firms with relatively large positive

calendar revisions tend to subsequently announce better earnings performance and have

higher returns, compared to firms with relatively large negative calendar revisions. For

example, use of terciles in Panel B produces a spread in RET (r+1,r+21) of 144 basis points


(t-statistic = 3.20). These findings mitigate concerns the results are sensitive to intertemporal

shifts in the distribution of calendar revisions.

In my final analyses, I relax the sample requirement that calendar-revisions be firm-

initiated. In the introduction, I argued that a key feature of the calendar data is that it

identifies firm-initiated revisions in expected announcement dates and thus helps capture

managers’ efforts to alter the schedule of news releases. In Table 9, I examine whether this

feature of the data is important by re-examining my main tests using a sample that aggregates

both firm-initiated and non-initiated calendar revisions, where non-initiated revisions reflect

changes in the earnings calendar that result for reasons other than a firm explicitly stating

when they intend to announce earnings (e.g., a peer firm delays their announcement).

Table 9 reports the spread in RET (r+1,r+21) and RET (t-1,t+1) across R-Score port-

folios for 30,265 observations that includes both firm-initiated and non-initiated revisions.

The first two columns contain returns for this combined sample. Although the return spread

across advancers and delayers remains economically and statistically significant, the magni-

tude and significance decline. Specifically, compared to the results in Table 3, returns in the

month following revisions fall from 269 basis points (t-statistic = 4.15) in the firm-initiated

sample to 226 basis points (t-statistic = 3.47) in the aggregate sample, which represents a

15% decline. Similarly, announcement-window returns fall from 158 basis points (t-statistic

= 4.85) to 115 basis points (t-statistic = 3.46), which represents a 27% decline.

The latter two columns of Table 9 show that the average monthly return falls to 160 basis

points (t-statistic = 1.54) and the announcement-window return falls to 82 basis points (t-

statistic = 1.95) among the subsample of revisions that are not firm-initiated. Together, the

results in Table 9 show that calendar revisions continue to predict returns when aggregating

firm-initiated and non-initiated revisions, though non-initiated revisions have significantly

lower predictive power. These results highlight the benefit of identifying the subset of cal-

endar revisions that are firm-initiated and suggests calendar revisions are more likely to be

informative when they originate from within the firm.


4. Conclusion

This study examines information in the schedule of firms’ earnings announcements using

a novel dataset containing daily snapshots of expected announcement dates. A key feature of

this data is that it identifies firm-initiated revisions in expected earnings announcement dates

and thus helps capture managers’ efforts to alter the schedule of news releases. Firms that

significantly advance their expected announcement date subsequently report better earnings

news than those that delay, consistent with firms strategically timing their announcements.

Despite calendar revisions predicting earnings news, I find no evidence prices react at the time

of the revisions but find a striking difference in post-revision returns. Specifically, advancers

outperform delayers by more than 260 basis points in the month after revisions, which aligns

with the sign and magnitude of subsequently reported earnings news. Additionally, the

return spread is concentrated during firms’ earnings announcements, suggesting that prices

adjust to the information content of calendar revisions at the time earnings are announced,

rather than at the time of the revisions. The central implication of these findings is that

investors fail to fully unravel information embedded in the schedule of news releases.


Appendix A. Example of Firm-Initiated Calendar Revisions

This appendix uses the change in the expected earnings announcement date of OracleCorporation (ORCL) in March of 2010 as an example of a firm-initiated calendar revision.On March 3, 2010, Oracle issued a press release with the following information:

In the prior year, Oracle announced its 2009 third quarter earnings on March 18th, 2009(the third Thursday of the month) and, prior to the above press release, the earnings calendarforecasted Oracle’s 2010 third quarter expected earnings announcement date as March 17,2010 (also the third Thursday of the month).

In response to Oracle’s press release, Wall Street Horizon revised the expected announce-ment date to March 25, 2010. Because the calendar revision resulted from the firm explicitlyindicating when they intend to announce earnings, I categorize the revision as being ‘firm-initiated’ (or, equivalently, ‘confirmed’). The calendar data appears as follows:

Date Ticker Fyear FQtr NextDt etype

3/1/2010 ORCL 2010 3 3/17/2010 T3/2/2010 ORCL 2010 3 3/17/2010 T3/3/2010 ORCL 2010 3 3/17/2010 T3/4/2010 ORCL 2010 3 3/25/2010 V3/5/2010 ORCL 2010 3 3/25/2010 V3/8/2010 ORCL 2010 3 3/25/2010 V

Note that the press release is issued on March 3, 2010 but is recorded one day laterin the March 4, 2010 earnings calendar data, which Wall Street Horizon dissiminated by4am ET on March 4. Accompanying this date change, the ‘etype’ column of the calendardata changes from ‘T’, indicating it was unconfirmed, to ‘V’, indicating it was based oninformation directly conveyed by the firm regarding when they intend to announce earnings.

In this example, my measure of the calendar revision, REV , equals negative six becauseOracle’s press release caused the expected announcement date in the earnings calendar toshift back by six trading days. Because the value of REV is greater than one week, Oracle’srevision is assigned an R-Score of zero and Oracle would be treated as a ‘delayer’.


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Figure 1. Cumulative Returns Around Earnings Announcements

Panel A contains the average spread in cumulative returns across high and low R-Score portfolios in event-time in the monthsurrounding expected earnings announcement dates. Firms are assigned to R-Score portfolios on date r using confirmedrevisions in their expected expected announcement dates, REV , defined as the number of days between a confirmed expectedannouncement date and the immediately preceding expected announcement date. R-Score equals 0 for firms with REV<-5and 1 for firms with REV>5. Firms in the highest R-Score portfolio are deemed ‘advancers’ and firms in the lowest R-Scoreportfolio are deemed ’delayers’. In Panel A, the value on day d equals the average cumulative return spread from day t-10 today d. Panel B contains the average cumulative, market-adjusted returns to high and low R-Score firms. The sample consistsof 18,959 firm-quarters spanning 2006 through 2013 in which firms issued a confirmed earnings announcement date at least twoweeks prior to their expected announcement date.


Figure 2. Quarterly Strategy Returns

The figure contains the average spread in returns across high and low R-Score firms (i.e., advancers vs. delayers) each calendarquarter. Firms are assigned to R-Score portfolios on date r using confirmed revisions in their expected expected announcementdates, REV , defined as the number of days between a confirmed expected announcement date and the immediately precedingexpected announcement date. R-Score equals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with-2≤REV≤2; 0.75 for firms with 3≤REV≤5; and 1 for firms with REV>5. Firms in the highest R-Score portfolio are deemed‘advancers’ and firms in the lowest R-Score portfolio are deemed ’delayers’. RET (r+1,r+21), shown in black bars, equals themarket-adjusted return over the month following the calendar revision. RET (t-1,t+1), shown in grey bars, equals the three-day return surrounding the expected earnings announcement date t. The sample consists of 18,959 firm-quarters spanning2006 through 2013 in which firms issued a confirmed earnings announcement date at least two weeks prior to their expectedannouncement date.


Table 1. Descriptive statistics

Panel A presents annual descriptive statistics of the main variables used throughout the paper. N equals the number of firm-quarters and Firms indicates the number of unique firms. HORIZON equals the number of days between the date on whicha firm issues a confirmed date and the expected announcement date. REV equals the number of days between a confirmedexpected announcement date and the immediately preceding expected announcement date. |REV | equals the absolute value ofREV . DEV equals the number of days between the confirmed expected announcement date and the actual announcement date.|DEV | equals the absolute value of DEV . Panel B contains descriptive statistics across R-Score portfolios. Firms are assignedto R-Score portfolios on date r using confirmed revisions in their expected expected announcement dates, REV , defined asthe number of days between a confirmed expected announcement date and the immediately preceding expected announcementdate. R-Score equals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with -2≤REV≤2; 0.75 forfirms with 3≤REV≤5; and 1 for firms with REV>5. OBS denotes the average number of firm-quarters within each portfolio.MCAP equals firms’ market capitalization reported in millions. MOMEN is the cumulative market-adjusted return over theprior 12-months ending on r-11. RET (r-1,r+1) is the three-day return surrounding the calendar revision. Reported t-statisticsare based on the difference in high and low R-Score portfolios over the time-series of calendar quarters. The sample consists of18,959 firm-quarters spanning 2006 through 2013 in which firms issued a confirmed earnings announcement date at least twoweeks prior to their expected announcement date.

Panel A: Summary Statistics by Year

N Firms HORIZON REV |REV | DEV |DEV |2006 1,926 1,357 15.069 -2.965 5.372 0.080 0.2872007 2,536 1,652 16.123 -1.647 4.744 0.062 0.2652008 3,031 1,843 16.747 -1.638 4.551 0.021 0.2742009 2,402 1,524 16.622 -1.993 4.635 -0.001 0.2482010 2,146 1,418 16.224 -1.137 4.754 -0.017 0.1502011 2,076 1,378 16.205 -1.665 4.564 0.013 0.2112012 2,395 1,495 16.250 -1.857 4.466 0.030 0.1942013 2,447 1,528 16.145 -1.884 4.620 -0.094 0.182

Panel B: Descriptive Statistics by R-Score Portfolios

R-Score REV OBS HORIZON MCAP MOMEN DEV RET (r-1,r+1)

0 (Delay) -8.849 84.6 16.495 3,579 -8.525 -0.040 -0.2300.25 -4.158 248.8 16.434 5,272 -2.871 -0.058 -0.1800.5 -0.019 134.1 15.894 5,275 0.579 -0.052 -0.0520.75 4.118 83.8 15.442 4,106 1.404 0.095 -0.081

1 (Advance) 8.836 41.2 15.487 3,025 6.311 0.247 -0.047

Advance-Delay 17.685 -43.5 -1.009 -554 14.835 0.289 0.183

t-statistic -(9.16) -(4.17) -(1.40) (8.62) (4.45) (1.52)


Table 2. Profitability and Earnings Surprises

Panel A contains average earnings metrics (shown as percentages) across R-Score portfolios. Firms are assigned to R-Scoreportfolios on date r using confirmed revisions in their expected expected announcement dates, REV , defined as the number ofdays between a confirmed expected announcement date and the immediately preceding expected announcement date. R-Scoreequals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with -2≤REV≤2; 0.75 for firms with 3≤REV≤5;and 1 for firms with REV>5. ROA is the firm’s return on assets defined as net income scaled by beginning-of-quarter totalassets and ROA<0 equals one for firms with negative ROA. ∆ROA equals same-quarter annual change in ROA and ∆ROA<0equals one for firms with annual decreases in ROA. SURP equals the actual EPS number reported in IBES minus the lastconsensus forecast available immediately prior to the announcement, and scaled by beginning-of-quarter assets, and SURP<0equals one for firms with negative SURP . Reported t-statistics are based on the difference in high and low R-Score portfoliosover the time-series of calendar quarters. Panel B contains regression results of earnings metrics on R-Score and additionalfirm controls. LBM and SIZE are the log of one plus the book-to-market ratio and log of market capitalization, respectively.MOMEN is the cumulative market-adjusted return and V LTY is the standard deviation of returns over the prior 12-monthsending on r-11. The reported t-statistics are based on two-way cluster robust standard errors, clustered by firm and quarter.***, **, and * indicate significance at the 1, 5, and 10% level, respectively. Industry fixed effects are based on two-digit SICcodes. The sample consists of 18,959 firm-quarters spanning 2006 through 2013 in which firms issued a confirmed earningsannouncement date at least two weeks prior to their expected announcement date.

Panel A: Earnings Metrics by R-Score Portfolios

R-Score ROA ROA<0 ∆ROA ∆ROA<0 SURP SURP<0

0 (Delay) -0.316 0.418 -0.824 0.600 -0.028 0.3740.25 0.507 0.338 -0.254 0.549 0.055 0.3190.5 0.636 0.312 -0.059 0.501 0.058 0.3100.75 0.394 0.353 0.285 0.479 0.116 0.287

1 (Advance) 0.525 0.377 0.854 0.423 0.124 0.283

Advance-Delay 0.841 -0.041 1.678 -0.177 0.152 -0.091

t-statistic (4.80) -(2.79) (6.08) -(9.70) (4.95) -(6.58)

Panel B: Regression Results of Earnings Metrics

ROA ∆ROA SURP

(1) (2) (3) (4) (5) (6)

R-Score 0.481*** 0.439*** 1.326*** 1.162*** 0.128*** 0.118***(3.79) (3.62) (5.58) (5.07) (5.59) (5.10)

SIZE 0.628*** 0.420*** 0.065 0.112*** 0.016 0.024***(12.46) (10.00) (1.04) (2.97) (1.61) (2.82)

LBM -0.588** -0.226 -0.997*** -0.649*** -0.304*** -0.205***(-2.72) (-1.04) (-6.31) (-3.20) (-8.35) (-5.73)

MOMEN – 0.013*** – 0.016*** – 0.001***– (6.51) – (10.58) – (4.34)

V LTY – -0.581*** – 0.197* – -0.001– (-8.85) – (1.78) – (-0.05)

R2 0.074 0.110 0.015 0.040 0.015 0.016

Year FE? Y Y Y Y Y YIndustry FE? N Y N Y N Y


Table 3. Equal- and Value-Weighted Future Returns

This table contains average equal- and value-weighted returns to each R-Score portfolio. All returns are shown as percentages.Firms are assigned to R-Score portfolios on date r using confirmed revisions in their expected expected announcement dates,REV , defined as the number of days between a confirmed expected announcement date and the immediately preceding expectedannouncement date. R-Score equals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with -2≤REV≤2;0.75 for firms with 3≤REV≤5; and 1 for firms with REV>5. The reported returns are calculated over the month following thecalendar revision from r+1 to r+21. The first two columns contain raw and market-adjusted returns denoted as RR(r+1,r+21)and RET (r+1,r+21), respectively. SAR(r+1,r+21) refers to size-adjusted returns defined as the firm’s raw return minus thefirm’s decile-matched return as the benchmark return. FAR(r+1,r+21) refers to factor-adjusted returns defined as the firm’s rawreturn minus the return calculated by estimating a firm’s daily sensitivity to the market (MKTRF ), small-minus-big (SMB),high-minus-low (HML), and up-minus-down momentum (UMD) factors over the year prior to the earnings announcement andapplying those sensitivities to the contemporaneous factors, following Fama and French (1993). Reported t-statistics are basedon the difference in high and low R-Score portfolios over the time-series of calendar quarters. The sample consists of 18,959firm-quarters spanning 2006 through 2013 in which firms issued a confirmed earnings announcement date at least two weeksprior to their expected announcement date.

Panel A: Equal-Weighted Returns in Month Following Revision

R-Score RR(r+1,r+21) RET (r+1,r+21) SAR(r+1,r+21) FAR(r+1,r+21)

0 (Delay) -0.773 -1.304 -0.743 -1.1650.25 0.173 -0.677 -0.267 -0.3000.5 0.750 -0.094 0.335 0.3190.75 1.365 0.189 0.656 0.607

1 (Advance) 2.263 1.384 1.896 1.627

Advance-Delay 3.036 2.688 2.639 2.792

t-statistic (4.59) (4.15) (3.95) (5.17)

Panel B: Value-Weighted Returns in Month Following Revision

R-Score RR(r+1,r+21) RET (r+1,r+21) SAR(r+1,r+21) FAR(r+1,r+21)

0 (Delay) 0.584 -0.496 -0.247 0.0980.25 0.618 -0.639 -0.185 0.0860.5 0.695 -0.657 -0.260 0.2460.75 1.148 -0.490 0.065 0.219

1 (Advance) 2.745 1.261 1.716 1.624

Advance-Delay 2.162 1.757 1.962 1.526

t-statistic (2.13) (1.95) (2.07) (2.01)


Table 4. Returns Around Earnings Announcements

This table contains market-adjusted returns around earnings announcements across R-Score portfolios. All returns are shownas percentages. Firms are assigned to R-Score portfolios on date r using confirmed revisions in their expected expected an-nouncement dates, REV , defined as the number of days between a confirmed expected announcement date and the immediatelypreceding expected announcement date. R-Score equals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 forfirms with -2≤REV≤2; 0.75 for firms with 3≤REV≤5; and 1 for firms with REV>5. RET (t+X,t+Y) equals the cumulativemarket-adjusted return from day X to Y relative to the expected earnings announcement date t. Reported t-statistics are basedon the difference in high and low R-Score portfolios over the time-series of calendar quarters. The sample consists of 18,959firm-quarters spanning 2006 through 2013 in which firms issued a confirmed earnings announcement date at least two weeksprior to their expected announcement date.

R-Score RET (t-10,t+10) RET (t-10,t-2) RET (t-1,t+1) RET (t+2,t+10) RET (t-10,t+1)

0 (Delay) -0.911 0.078 -0.707 -0.294 -0.6750.25 -0.323 -0.175 -0.083 -0.081 -0.2880.5 -0.016 -0.105 0.149 -0.046 0.0210.75 0.412 0.044 0.627 -0.253 0.656

1 (Advance) 1.623 0.616 0.870 0.198 1.403

Advance-Delay 2.534 0.538 1.578 0.493 2.077

t-statistic (4.36) (1.77) (4.85) (2.13) (4.17)


Table 5. Cross-Sectional Return Regressions

This table contains results from regressing future returns on R-Score and additional controls. R-Score is measured on dater using confirmed revisions in firms’ expected expected announcement dates, REV , defined as the number of days betweena confirmed expected announcement date and the immediately preceding expected announcement date. R-Score equals 0 forfirms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with -2≤REV≤2; 0.75 for firms with 3≤REV≤5; and1 for firms with REV>5. RET (r+X,r+Y) equals the cumulative market-adjusted return from day X to Y relative to thecalendar revision date. Similarly, RET (t+X,t+Y) equals the cumulative market-adjusted return from day X to Y relative tothe expected earnings announcement date t. LBM and SIZE are the log of one plus the book-to-market ratio and log ofmarket capitalization, respectively. MOMEN is the cumulative market-adjusted return and V LTY is the standard deviationof returns over the prior 12-months ending on r-11. The reported t-statistics are based on two-way cluster robust standarderrors, clustered by firm and quarter. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. Industry fixedeffects are based on two-digit SIC codes. The sample consists of 18,959 firm-quarters spanning 2006 through 2013 in whichfirms issued a confirmed earnings announcement date at least two weeks prior to their expected announcement date.

RET (t-1,t+1) RET (r+1,r+21)

(1) (2) (3) (4) (5) (6)

R-Score 1.520*** 1.499*** 1.522*** 2.404*** 2.336*** 2.351***(6.66) (6.95) (6.82) (4.77) (4.80) (4.89)

SIZE -0.067 0.007 -0.038 0.017 -0.009 0.019(-1.13) (0.12) (-0.55) (0.11) (-0.06) (0.12)

LBM -0.353* -0.182 -0.207 -0.280 -0.228 -0.120(-1.79) (-0.92) (-1.04) (-0.34) (-0.30) (-0.16)

MOMEN 0.004* 0.003 0.004* 0.001 0.000 0.001(1.95) (1.55) (1.87) (0.26) (0.09) (0.09)

V LTY -0.281** -0.140 -0.272** -0.140 -0.136 -0.069(-2.40) (-1.33) (-2.18) (-0.30) (-0.35) (-0.14)

R2 0.004 0.003 0.004 0.003 0.002 0.002

Year FE? Y N Y Y N YIndustry FE? N Y Y N Y Y


Table 6. Interaction Effects

This table contains results from regressing RET (r+1,r+21) on R-Score and additional controls. Firms are assigned to R-Scoreportfolios on date r using confirmed revisions in their expected expected announcement dates, REV , defined as the number ofdays between a confirmed expected announcement date and the immediately preceding expected announcement date. R-Scoreequals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with -2≤REV≤2; 0.75 for firms with 3≤REV≤5;and 1 for firms with REV>5. RET (r+1,r+21) equals the cumulative market-adjusted return in the month (21 trading days)following the calendar revision. 1(Small Firm) is an indicator variable that equals one if the firm is in the lowest tercile ofmarket capitalization within a given calendar quarter, 1(Low Coverage) is an indicator variable that equals one if the firm is inthe lowest tercile of analyst coverage within a given calendar quarter, and 1(High Distress) is an indicator variable that equalsone if the firm is in the lowest tercile of the Zmijewski (1984) Z-Score financial distress measure within a given calendar quarter.1(Buy Recommendation) is an indicator variable that equals one if the firm has an outstanding ‘BUY’ recommendation in theIBES consensus database. The reported t-statistics are based on two-way cluster robust standard errors, clustered by firm andquarter. ***, **, and * indicate significance at the 1, 5, and 10% level, respectively. Year and industry fixed effects are includedthroughout. Industry fixed effects are based on two-digit SIC codes. The sample consists of 18,959 firm-quarters spanning2006 through 2013 in which firms issued a confirmed earnings announcement date at least two weeks prior to their expectedannouncement date.

(1) (2) (3) (4) (5) (6)

R-Score 2.412*** 1.308*** 1.754*** 2.298*** 1.926*** 0.575(4.81) (2.77) (2.92) (4.79) (4.38) (1.24)

R-Score *1(Small Firm) – 2.988*** – – – 2.307***– (3.58) – – – (3.08)

1(Small Firm) – -1.387** – – – -1.109***– (-2.27) – – – (-3.47)

R-Score *1(Low Coverage) – – 2.021** – – 1.497*– – (2.35) – – (1.80)

1(Low Coverage) – – -0.785* – – -0.526*– – (-1.75) – – (-1.96)

R-Score *1(Buy Recommendation) – – – 3.689* – 2.370**– – – (1.74) – (2.09)

1(Buy Recommendation) – – – -0.719 – -0.108– – – (-0.63) – (-0.16)

R-Score *1(High Distress) – – – – 3.273*** 2.834**– – – – (2.78) (2.28)

1(High Distress) – – – – -1.890*** -1.670***– – – – (-2.76) (-4.41)

R-square 0.002 0.003 0.003 0.003 0.003 0.004


Table 7. Benchmarking to Random-Walk Expected Announcement Dates

Panel A contains reported earnings metrics and returns across five RW -Score portfolios based on calendar revisions, whererevisions in expected announcement dates are defined relative to firms’ historical reporting patterns. For each firm-quarter, Icalculate the expected announcement date by using a ‘random-walk’ expected announcement date that equals the same datefrom the prior year. Using these random-walk expected announcement dates as the benchmark, firms are assigned to RW -Scoreportfolios on date r using confirmed revisions in their expected expected announcement dates, REVRW , defined as the numberof days between a confirmed expected announcement date and the historically estimated random-walk expected announcementdates. RW -Score equals 0 for firms with REVRW<-5; 0.25 for firms with -5≤REVRW≤-3; 0.5 for firms with -2≤REVRW≤2;0.75 for firms with 3≤REVRW≤5; and 1 for firms with REVRW>5. ROA is the firm’s return on assets defined as net incomescaled by beginning-of-quarter total assets. ∆ROA equals same-quarter annual change in ROA. SURP equals the actualEPS number reported in IBES minus the last consensus forecast available immediately prior to the announcement, and scaledby beginning-of-quarter assets. OBS denotes the average number of firm-quarters within each portfolio. RET (r+1,r+21)equals the market-adjusted return over the month following the calendar revision. RET (t-1,t+1) equals the three-day returnsurrounding the expected earnings announcement date t. All returns are shown as percentages. Reported t-statistics arebased on the difference in high and low R-Score portfolios over the time-series of calendar quarters. The sample consists of15,439 firm-quarters spanning 2006 through 2013 with a random-walk forecasts from the prior year and that issue confirmedearnings announcement dates at least two weeks prior to their expected announcement date. Panel B contains descriptivestatistics of RET (r+1,r+21) across RW -Score portfolios, when the sample is partitioned across firms with above and belowmedian announcement date consistency. I define announcement date consistency using the historical standard deviation of thedifference between random-walk forecasts and firms’ actual earnings announcement dates over the prior ten quarters, wherelower standard deviations indicate greater consistency. Reported t-statistics are based on the difference in high and low RW -Score portfolios over the time-series of calendar quarters. The sample for the analysis in Panel B consists of 15,024 firm-quartersspanning 2006 through 2013 with at least five prior quarters to calculate announcement date consistency. Reported t-statisticsare based on the difference in high and low RW -Score portfolios over the time-series of calendar quarters.

Panel A: Revisions Benchmarked to Random-Walk Dates

RW -Score ROA ∆ROA SURP RET (r+1,r+21) RET (t-1,t+1)

0 (Delay) -0.410 -0.887 -0.089 -1.169 -0.6380.25 0.525 -0.244 0.048 -0.635 -0.1310.5 0.674 -0.132 0.044 -0.489 -0.0430.75 0.398 0.189 0.102 0.305 0.476

1 (Advance) 0.177 0.418 0.094 1.039 0.791

High-Low 0.587 1.305 0.183 2.208 1.429

t-statistic (2.63) (6.61) (4.42) (3.79) (3.27)

Panel B: Conditioning on Historical Consistency

High Consistency Low Consistency

RW -Score N=7,590 N=7,434

0 (Delay) -0.209 -2.0070.25 -0.486 -0.9190.5 -0.354 -0.4710.75 0.852 0.009

1 (Advance) 3.397 -0.418

Advance-Delay 3.606 1.589

t-statistic (2.95) (1.88)


Table 8. Alternative Portfolio Assignments of Earnings Calendar Revisions

This contains average earnings metrics and future returns across earnings calendar revision (REV ) portfolios. REV is defined asthe number of days between a confirmed expected announcement date and the immediately preceding expected announcementdate. In Panel A, firms are assigned to REV quintiles using the distributional breakpoints from the prior quarter, where higher(lower) values are assigned to quintile 1 (0). In Panel B, firms are analogously assigned to terciles ranging from 1 to 0. ROAis the firm’s return on assets defined as net income scaled by beginning-of-quarter total assets. ∆ROA equals same-quarterannual change in ROA. SURP equals the actual EPS number reported in IBES minus the last consensus forecast availableimmediately prior to the announcement, and scaled by beginning-of-quarter assets. RET (r+1,r+21) equals the market-adjustedreturn over the month following the calendar revision. RET (t-1,t+1) equals the three-day return surrounding the expectedearnings announcement date t. All returns are shown as percentages. Reported t-statistics are based on the difference in highand low R-Score portfolios over the time-series of calendar quarters. The sample consists of 18,959 firm-quarters spanning2006 through 2013 in which firms issued a confirmed earnings announcement date at least two weeks prior to their expectedannouncement date.

Panel A: Earnings Metrics and Returns by REV Quintiles

ROA ∆ROA SURP RET (r+1,r+21) RET (t-1,t+1)

Q1 (Low REV) -0.316 -0.851 -0.033 -1.376 -0.704Q2 0.522 -0.261 0.073 -0.784 -0.168Q3 0.477 -0.266 0.042 -0.720 0.047Q4 0.590 -0.046 0.050 -0.124 0.100

Q5 (High REV) 0.441 0.415 0.092 0.430 0.569

High-Low 0.757 1.266 0.125 1.806 1.273

t-statistic (4.30) (5.26) (3.00) (4.07) (4.44)

Panel B: Earnings Metrics and Returns by REV Terciles

ROA ∆ROA SURP RET (r+1,r+21) RET (t-1,t+1)

T1 (Low REV) -0.079 -0.670 0.016 -1.117 -0.441T2 0.470 -0.250 0.039 -0.575 -0.091

T3 (High REV) 0.494 0.294 0.088 0.326 0.496

High-Low 0.573 0.964 0.072 1.443 0.937

t-statistic (3.51) (4.53) (2.00) (3.20) (4.45)


Table 9. Initiated Versus Non-Initiated Revisions

This table contains average equal-weighted returns to each R-Score portfolio for a sample that includes both firm-initiatedrevisions in announcement dates as well as non-initiated revisions. Specifically, the sample consists of 30,265 firm-quartersspanning 2006 through 2013 where the earnings announcement dates was revised at least two weeks prior to their expectedannouncement date. The non-initiated sample consists of those calendar revisions where the firm did not confirm the earningscalendar change. Firms are assigned to R-Score portfolios on date r using revisions in their expected expected announcementdates, REV , defined as the number of days between a revised expected announcement date and the immediately precedingexpected announcement date. R-Score equals 0 for firms with REV<-5; 0.25 for firms with -5≤REV≤-3; 0.5 for firms with-2≤REV≤2; 0.75 for firms with 3≤REV≤5; and 1 for firms with REV>5. The reported returns are calculated over the monthfollowing the calendar revision from r+1 to r+21. The first two columns contain raw and market-adjusted returns for the fullsample. The last two columns contain raw and market-adjusted returns for non-initiated revisions in the earnings calendar. Allreturns are shown as percentages. Reported t-statistics are based on the difference in high and low R-Score portfolios over thetime-series of calendar quarters.

All: N=30,265 Non-Confirmed: N=12,728

R-Score RET (r+1,r+21) RET (t-1,t+1) RET (r+1,r+21) RET (t-1,t+1)

0 (Delay) -1.225 -0.549 -0.919 -0.2970.25 -0.600 0.022 -0.499 0.1420.5 -0.389 0.043 -0.700 -0.1510.75 0.195 0.421 0.011 -0.093

1 (Advance) 1.036 0.605 0.682 0.527

Advance-Delay 2.261 1.154 1.601 0.824

t-statistic (3.47) (3.46) (1.54) (1.95)

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