expected reporting speeds: information in firms’ relative ......⇤we thank wall street horizon...

Expected Reporting Speeds: Information in

Firms’ Relative and Aggregate Behavior

Ed deHaan⇤

Stanford UniversityGraduate School of Business

Eric C. SoMassachusetts Institute of Technology

Sloan School of Management

Prepared for Berkeley-Stanford WorkshopNovember 2015

Extremely early draft. Please do not cite or distribute.

Abstract

Existing studies show that intra-firm variation in earnings announcement speedscorrelates with earnings news, such that firms are more likely to decrease (increase)the speed of announcing bad (good) earnings news relative to their fiscal period end.We examine firms’ “expected abnormal reporting speeds” not just in relation to theirown past behaviors, but also in relation to other firms announcing in the same month.Relative expected abnormal reporting speeds predict future earnings realizations andstock returns not only for firms that deviate from their own historic reporting patterns,but also for the supermajority of firms that elect not to alter their reporting speeds.These results suggest that a firm’s choice not to change its reporting speeds is infor-mative when viewed relative to the behavior of other firms. Further, aggregating firms’month-end expected abnormal reporting speeds indicates that relative changes do notoffset one another, but rather give rise to predictable changes in aggregate earningsnews, treasury spreads, and market uncertainty.

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

⇤We thank Wall Street Horizon for generously providing data on expected earnings announcement dates.Contact emails: Ed deHaan, [email protected] and Eric So, [email protected]

Expected Reporting Speeds 1

1. Introduction

At least as far back as Kross (1981) and Givoly and Palmon (1982), prior research docu-

ments a positive link between firms’ reporting speeds and the nature of their earnings news.

A persistent finding is that firms with bad (good) news tend to increase their reporting speeds

relative to past behavior, where reporting speeds are measured as the time between firms’

fiscal period ends and their earnings announcements. These findings suggest that managers

both consider, and convey, earnings news when scheduling their earnings announcements.

However, recent studies find that stock prices fail to fully respond to the predictable earn-

ings information contained in firms’ announcements of expected reporting dates (deHaan,

Shevlin, and Thornock (2015), So and Weber (2015)).

Prior research also shows that firms’ earnings realizations are correlated (Foster (1981);

Freeman and Tse (1992)), and that managers consider peer firms’ releases (Dye and Sridhar

(1995)) and other information events (Acharya, DeMarzo, and Kremer (2011)) in timing their

disclosures, which suggests that managers strategically time news relative to other sources of

information. Building upon this prior research, we posit that there is information not only

in firms’ choices to change their reporting speeds relative to their past behavior, which has

been the focus of prior studies, but also in their choices to maintain their reporting speeds

in times when other firms are delaying or accelerating news, which is a key innovation and

insight of this study. Further, if firms’ collective expected delays or accelerations are due

to a shift in the distribution of forthcoming earnings as opposed to a reordering within a

fixed distribution, then we expect aggregate reporting speeds to convey information about

aggregate earnings news and market trends.

We provide empirical evidence consistent with the aforementioned predictions and, in

doing so, offer new evidence on the information content of firms’ expected earnings an-

nouncement dates. Specifically, we show that trends in firms accelerating or delaying their

reporting speeds are not only informative about those firms’ forthcoming earnings, but also


informative about earnings for the two-thirds of firms that do not alter their expected re-

porting reporting speeds from the prior year. We also show that firms’ reporting speeds

in aggregate signal predictable changes in aggregate earnings news, treasury spreads, and

market uncertainty. Finally, in conducting these tests, we design, implement, and validate

a novel methodology for studying firms’ expected reporting speeds using monthly earnings

calendar data.

Our empirics are based on a new measure of “expected abnormal reporting speed,” or

EARS, that can be calculated for a broad cross-section of firms on a synchronized, month-

end basis. The central input to calculate EARS is earnings calendar data, which provides

expected earnings announcement dates for the majority of U.S. public companies. We use the

earnings calendar to identify all firms at the end of each month that are expected to announce

earnings in the subsequent month. We then use the month-end expected announcement

dates to calculate within-firm changes in each firm’s reporting speed relative to the same

fiscal quarter in the prior year, where higher (lower) values correspond to cases when a

firm is expected to report abnormally fast (slow). Importantly, EARS captures changes in

reporting speed both for the minority of firms that materially delay or accelerate the earnings

announcement relative to past behaviors (and have been the subject of previous studies), as

well as for the majority of firms that do not significantly deviate from their past reporting

speeds.

In our firm-level tests we divide expected announcers into three EARS portfolios at each

month-end, whereby the largest (smallest) EARS values are designated as being in the “fast”

(“slow”) reporting group. Importantly, these portfolio assignments measure a firm’s expected

abnormal reporting speed relative to other firms, so the firm can be assigned to the fast or slow

reporting group even if its nominal speed is unchanged from the prior year. The minimal data

requirements for calculating EARS allows us to achieve a coverage ratio of approximately

80% of the earnings announcements at the intersection of the CRSP, Compustat, and IBES

databases from 2006 - 2013, which supports the likelihood that aggregate EARS predicts


market-level conditions.

Our firm-level tests show that EARS has strong predictive power for firms’ earnings

news as well as future returns. Specifically, the average analyst-based earnings surprise

and profitability growth in the fast EARS portfolio are significantly more positive than in

the slow EARS portfolio, and a long-short trading strategy formed at the end of month

m produces an average return of 140 basis points in month m + 1. Further, since the

combined long-short portfolios are formed synchronously and average 430 firms per month,

these results suggest the capacity for economically significant trading strategy returns. Our

findings continue to hold even in two-thirds of firms with no change in expected reporting

speed from the previous year, and the pricing results are robust to value-weighting and

standard risk adjustments. Combined, these results indicate that not only are within-firm

changes in expected reporting speeds informative about a firm’s forthcoming earnings, but

that across-firm relative changes are also indicative of earnings realizations.

We next turn our attention to whether aggregate EARS is predictive of aggregate news

as measured by one-month-ahead aggregate earnings surprises, aggregate returns, treasury

spread changes, and market uncertainty as proxied by changes in the VIX. Our motivation

for examining aggregate earnings surprise and returns is straight-forward: given our evidence

that EARS predicts firm-level earnings and returns, it is plausible that the aggregation of

earnings calendar information provides information about the broader economy. Our mo-

tivation for examining treasury spreads and VIX is based on prior findings that earnings

surprises are associated with uncertainty (Barth and So (2014)) and discount rates (Hirsh-

leifer et al 2009; Kothari et al 2006). Since EARS predicts firm-level earnings news not

yet reflected in stock prices, if EARS predicts aggregate earnings then it is plausible that

EARS also predicts changes in market-level uncertainty and risk.

There are also several reasons to expect no relations between aggregate EARS and

market-level outcomes. First, it is plausible that observed changes in EARS are a result

of firms shuffling their earnings announcement dates according to idiosyncratic positive or


negative earnings realizations, such that the overall earnings distribution is unchanged from

one month to the next. If so, EARS will aggregate to roughly zero and have no association

with market-level conditions. Second, because prior literature finds that earnings news is

informative about both future cash flows and risk, it is plausible that these effects counteract

each other such that we find no relation between aggregate EARS and returns (Kothari et al

2006). Finally, prior papers find evidence that markets are better able to anticipate earnings

in aggregate rather than at the firm level (Sadka and Sadka 2009), so it is plausible that

EARS is impounded at the aggregate level even if not at the firm-level.

We first show that both average analyst-based earnings surprises and abnormal expected

reporting speeds monotonically decrease across consecutive months of a calendar quarter.

More specifically, aggregate earnings surprises and abnormal reporting speeds are reliably

more positive in “earnings season” months (i.e., January, April, July, and October) and

become increasingly negative toward the end of the calendar quarter (i.e., March, June,

September, and December). This predictable seasonality in aggregated earnings news and

abnormal reporting speeds follows a striking jigsaw pattern over time, where good news

tends to arrive early and bad news tends to arrive late.

For our tests of earnings calendars and market outcomes, we develop two aggregate

EARS measures that reflect changes in aggregate reporting behavior relative to the trailing

12-month average: (1) equal-weighted EARS, referred to as “speed average” (or SA); and (2)

the month-end imbalance between positive and negative EARS firms, referred to as “speed

imbalance” (or SI). Univariate and regression tests using a sample of 95 calendar-month

observations find consistent evidence that both aggregate earnings innovations and analyst-

based earnings surprises are significantly greater in high relative to low SA and SI months,

which is consistent with aggregate EARS predicting aggregate earnings news. We also find

that both SA and SI are predictive of changes in the risk-free rate and VIX, consistent

with aggregate EARS predicting changes in discount rates and uncertainty. However, we

find evidence that SA and SI does not predict market-index returns, potentially due to the


offsetting effects of cash flow and risk changes.

Our final set of tests link together our firm- and market-level tests by examining whether

the predictive power of aggregate EARS for market-level earnings news gives rise to added

predictability in firm-level returns. First, we examine whether aggregate SA and SI are

predictive of firm-level returns, and in particular whether the predictive powers of SA and

SI are strongest among firms with greater aggregate earnings sensitivity. Second, we exam-

ine whether the documented association between firm-level EARS and firm-level returns is

stronger for firms that are more sensitive to aggregate earnings news.1 We estimate firms’

aggregate earnings sensitivities as the sensitivity of a firm’s monthly return in month t to

the average analyst-based earnings surprise of all firms announcing in month t, measured

over the 60 calendar months ending in month m � 1. Univariate and regression tests find

consistent evidence that both SA and SI are positively associated with future firm-level

returns among firms with greater aggregate earnings sensitivity, and that the EARS-returns

relation is stronger in the cross-section among high-sensitivity firms.

This study makes several contributions. First, we contribute to the literature examining

managers’ strategic choices in disclosing private information, and in particular studies exam-

ining firms’ choices to accelerate (delay) announcements of positive (negative) earnings news.

We find that the absence of abnormal reporting speeds is indicative of forthcoming earnings

when viewed relative to other firms’ behavior. Specifically, in periods where the majority of

firms are expected to announce abnormally slow, observing that a firm has not delayed its

expected reporting speed is a positive predictor of earnings and returns. While prior stud-

ies have found evidence that managers consider their own earnings news in timing earnings

releases, our findings indicate that timing decisions involve dynamic, across-firm consider-

ations. Examining managers’ incentives and benefits of timing earnings announcements in1For example, consider firms with zero change in expected speed but that are assigned to a low EARS

portfolio due to their position in relation to other firms at month-end (i.e., because most other firms acceleratetheir timing). Our firm-level tests indicate that these firms experience relatively negative returns in monthm + 1, which is in part likely driven by news contained in peer firms’ earnings announcements. Thus, weexpect that these firms’ experience incrementally smaller (larger) negative returns in m+ 1 when the firmsare less (more) sensitive to peer firms’ earnings news.


relation to peer firms is a potentially interesting avenue for future research.

Second, we contribute to the academic and practitioner literatures on earnings-based

trading strategies. Our approach of using end-of-month earnings calendar data is compu-

tationally simple and facilitates pricing tests using balanced long-short positions based on

synchronized signals. By contrast, prior studies tend to focus on the returns to positions

formed in event-time (e.g., selling after a firm misses its expected date), which increases

the difficulty of hedging risks and scaling position sizes in response to fluctuations in the

existence and nature of events within a given time period.2 Additionally, our methodology

permitting a larger sample also allows for layering multiple signals and screening firms along

multiple dimensions when forming positions, which more closely mimics how investment

strategies are implemented in practice.

Finally, we contribute to the literatures studying macroeconomic forecasting and aggre-

gate associations between earnings news and market trends (e.g., Konchitchki and Patatoukas

(2013); Konchitchki and Patatoukas (2014)). Our study shows that aggregate reporting

speeds have strong predictive power for aggregate earnings surprises, treasury spreads, and

changes in the VIX. Together, these findings show that information from earnings calendars

may be useful in both generating and improving macroeconomic forecasts.

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

Section 3 discusses our firm-level tests and Section 4 discusses our market-level tests. Section

5 concludes.2For example, our sample used in long-short positions increases ten-fold compared to So and Weber

(2015) despite using the same underlying calendar data over the same time period. Specifically, the revision-based pricing tests in So and Weber (2015) involve approximately 125 observations per quarter comparedto over 1,250 per quarter in this study. Further, our methodology permits forming synchronized long andshort positions at the end of each month. By contrast, because calendar revisions are non-synchronizedacross time, the main tests in So and Weber (2015) involve forming unbalanced long and short positionsin event-time and at uneven intervals. However, So and Weber (2015)’s sample documents greater returnpredictability, suggesting that firm-initiated calendar revisions may provide a more precise signal of firms’earnings news than expected reporting speeds. This tradeoff suggests that the appropriate methodologymay vary across contexts and depend on the goals of the researcher. The data limitations imposed by priormethodologies are even more pronounced in studies such as Penman (1984) and Bagnoli, Kross, and Watts(2002) that measure reporting speeds only ex post, after a firm misses an expected date or unexpectedlyannounces early.


2. Data and Methodology

The main analyses of this paper examine the information content of firms’ expected

reporting speeds, using variation measured both within-firm and across-firm. Empirically, we

test whether the month-end landscape of an earnings announcement calendar predicts future

firm-specific news and macroeconomic conditions. Forward-looking earnings announcement

calendar data are sourced from Wall Street Horizon, which has been used in related studies

including So and Weber (2015) and deHaan, Shevlin, and Thornock (2015).3

We begin by calculating firm-specific EARS at the end of each month for firms that are

expected, per the earnings calendar, to report earnings in the subsequent month. Notation-

ally, EARS is calculated as follows:

EARSi,m,q,y =�1 ⇤ (ExpectedSpeedi,m,q,y �RealizedSpeedi,q,y�1)

RealizedSpeedi,q,y�1(1)

where ExpectedSpeedi,m,q,y is the expected number of days between firm i’s announcement

date and its fiscal quarter end as of the end of month m for quarter q of year y, and

RealizedSpeedi,q,y�1 is defined as the difference between the realized announcement date and

fiscal quarter-end in the previous year.4 The difference between ExpectedSpeedi,m,q,y and

RealizedSpeedi,q,y�1 is multiplied by -1 so that higher (lower) values of EARS corresponds

to firms reporting abnormally fast (slow) relative to the prior year.

To mitigate the influence of data errors and outliers, we require firms to have an expected

reporting speed between 15 and 75 trading days relative to their fiscal period end, although

the main results do not appear sensitive to this requirement. To study the predictive power3As discussed in So and Weber (2015) and deHaan, Shevlin, and Thornock (2015), Wall Street Horizon

began disseminating daily snapshots of the earnings calendar in 2006, where each snapshot lists expectedannouncement dates for a broad cross-section of firms. The earnings calendar is updated daily in responseto public information including, but not limited to, firms’ investor relations webpages, press releases, anddirect correspondence with firms. The daily snapshots reflect information available to investors by 4am ETof each trading day. Wall Street Horizon data is commercially distributed via the Toronto Stock Exchange’sTMX Datalinx service, as well as other sources.

4RealizedSpeed is not indexed to m as it is a known date and, therefore, do not vary by month.


of EARS for firms earnings news and returns, we also require firms to have at least six

months of historical return data in CRSP, fundamental information and prior year earnings

announcement dates in Compustat, and at least one analyst-based earnings forecast in IBES.

The resulting sample consists of 83,411 observations spanning 2006-2013.

Our firm-level tests examine relative comparisons of firms’ EARS in calendar time based

on synchronized, month-end signals. For our firm-level tests, we assign each firm into one

of three portfolios on the last day of each month, where the 30% of firms with the highest

(lowest) EARS are assigned to the abnormally fast (slow) portfolio relative to the remaining

40% of firms at the same month-end. The timeline below illustrates the calculation of EARS

at the end of January for three hypothetical firms, referred to as ‘A’, ‘B’, and ‘C’, which all

have fourth quarter ending date of December 31st and are expected to announce earnings in

the month of February.

2/1/Y-1

A’s RA2/5

B’s RA2/10

C’s RA2/15

2/28/Y-1 2/1/Y

A’s EA2/5

B’s EA2/8

C’s EA2/10

2/28/Y+

A, B, and C on 1/31/YEARS calculated for firms

Calendar observed:Historical Speed:Calculate reporting speedsfrom prior calendar year

using firms’ realized dates| {z }

The timeline above provides an example of how EARS would be calculated on Jan. 31

of year y, where RA refers to a firm’s realized announcement date in (y�1) and EA refers to

a firm’s expected announcement date in year y. Assuming there are 31 days in January and

(for the sake of this example) ignoring weekends and holidays, RealizedSpeedi,q,y�1 for firms

A, B, an C would be 36, 41, and 46, respectively. Similarly, based on the earnings calendar

data observed on Jan. 31, ExpectedSpeedi,m,q,y for firms A, B, an C would be 36, 39, and 41,

respectively. Thus, EARS would be 0 (=-1*(36-36)/36) for firm A, 0.05 (=-1*(39-41)/41)

for firm B, and 0.11 (=-1*(41-46)/46) for firm C. In portfolio assignments, firm C would be

characterized as being abnormally fast, firm B as being in the middle group, and firm A as

being abnormally slow.


The above example demonstrates two important characteristics of our EARS measure.

First, our use of within-firm changes helps screen out variation in earnings announcement

speed that may be driven by firms’ innate reporting processes. Second, our use of month-

end EARS portfolio assignments allows us to characterize firms as being relatively early or

late even for firms that do not change their expected timing from the previous year. For

example, firm A is characterized as being in the slow portfolio group despite being the first

to announce earnings as well as having no change in timing from the previous year.

The above timeline also helps emphasize two important differences between our EARS-

based analyses as compared to previous studies of early versus late earnings announcements.

First, our EARS measure can be constructed without look-ahead bias and even before the

firm surpasses its realized earnings speed from the prior year, while prior studies could

often only identify early and late announcements on an ex post basis (e.g., Penman (1984);

Bagnoli, Kross, and Watts (2002)). Second, the sample examined in So and Weber (2015)

relies on explicit, firm-initiated announcements of changes in earnings timing, while our

approach includes firms that do not confirm or revise their expected earnings announcement

date. These two EARS characteristics allow us to construct a much broader sample of firms

representing over 80 percent of the merged Compustat, CRSP, and IBES universe.5

3. Main Findings

We begin this section by providing descriptive statistics. Section 3.2 examines the asso-

ciation between EARS and future earnings news, both for firms that do and do not change

their reporting speed from the prior year. Section 3.3 discusses the predictive ability of

EARS for firms’ returns.5Of course, these EARS characteristics are not without tradeoffs. For example, firm-initiated announce-

ments of changes in previously-scheduled earnings times, as examined in So and Weber (2015), likely providea much cleaner signal about the content of future earnings news than do our tests of month-end earningscalendar snapshots. The usefulness of the approach advocated in this paper relative to those in prior researchinvolves tradeoffs and thus depends on the goals of the researcher.


3.1. Descriptive Statistics

Panel A of Table 1 presents annual sample averages of the data contributing to our EARS

measure. OBS equals the number of firm-month observations and FIRMS indicates the

number of unique firms, which show that the sample consists of approximately ten thousand

observations per year and roughly three thousand unique firms. %SAMP measures the

number of observations in the sample relative to the full CRSP/Compustat/IBES universe,

which shows that the sample in this study achieves a coverage ratio of approximately 80% of

the earnings announcements. �RS is the level change in a firm’s expected reporting speed

relative to the realized reporting speed in the prior year, which shows that most firms are

expected to report within one trading day of their past reporting speed.

%SAMEDATE equals the proportion of firms that are expected to report earnings on

roughly the same date as the prior year, defined as firms with an absolute �RS of one or

zero. Roughly two-thirds (66%) of firms are scheduled to announce earnings within one

trading day of their past reporting speed, which highlights the magnitude of the sample that

are omitted when only studying firms that materially change their expected announcement

date. DEV equals the number of days between a firm’s expected announcement date and

the future realized announcement date, showing that most firms eventually announce within

one day of their expected date. LRS is firms’ lagged reporting speed, where the average firm

reported earnings 25 trading days after their fiscal period end. Finally, the pooled average

EARS indicates that the average firm is expected to announce one-half of one percent faster

than in the prior year, although there is variation across years. Figure 1 shows that nearly

50% of EARS observations fall within ±2.5 of zero, and over 20% of observations fall within

the range of +2.5% to 7.5%.

Panel B of Table 1 presents descriptive statistics across tercile portfolios of EARS, where

the top (bottom) 30 percent are assigned to the ‘High’ (‘Low’) portfolio and the remaining

are assigned to the ‘Mid’ portfolio at the end of each calendar month. Panel B also reports

average differences across the high and low portfolios, where the reported t-statistics corre-


spond to the time-series average monthly difference. There are approximately 228 firms per

month in the high portfolio and 201 firms per month in the low portfolio, indicating that the

long-short strategy proposed in this paper consists of approximately 428 firms per month.6

�RS row shows that abnormally fast firms decrease their reporting speed by an average of

3.9 days and abnormally slow firms increase by their reporting speed by an average of 3.9

days. DEV indicates that abnormally slow firms are more likely to report late relative to

their expected date than abnormally fast firms, but the average difference is less than one

trading day.

Panel B of Table 1 also reports firms’ market capitalization, MCAP , reported in millions,

and return momentum, MOMEN , defined as the cumulative market-adjusted return over

the prior 12-months. Abnormally fast firms tend to be roughly the same size as abnormally

slow firms, although both groups are larger than the middle-tercile firms. Abnormally fast

firms have higher return momentum compared to abnormally slow firms.

3.2. EARS and Future Earnings News

Table 2 examines the association between EARS and firms’ subsequently reported earn-

ings news. Panel A presents sample averages of earnings news proxies across EARS portfolios.

SURP equals the actual EPS number reported in IBES minus the last consensus forecast

available immediately prior to the announcement, and scaled by beginning-of-quarter assets.7

SUE is the standardized unexplained earnings, defined as the realized EPS minus EPS from

four quarters prior, divided by the standard deviation of this difference over the prior eight

quarters.

The first two rows of Panel A of Table 2 present results for the pooled sample. Analysts-

based earnings surprises are significantly higher among abnormally fast firms compared to6By comparison, the average number of positions in So and Weber (2015) is less than one-tenth the size

at approximately 42 per month.7We scale EPS forecast errors by assets as opposed to price to avoid a the mechanical relation between

prices and earnings expectations noted in Cheong and Thomas (2011) and Ball (2011). However, results arequalitatively unchanged if EPS surprise is scaled by market value. Throughout this paper, we use the term"qualitatively unchanged" to mean that the tests of interest are of the same sign as the reported results andare significant at 10% or higher.


abnormally slow firms, where the average high-low spread accounting for 0.11% of firms’

assets (t-statistic = 4.91). Average SUE’s monotonically increase across EARS portfolios,

consistent with expected abnormal reporting speeds predicting subsequently reported earn-

ings news. Panel B (C) presents similar analysis for only those firms without (with) a change

in their expected reporting speed relative to the prior year (i.e., with SAMEDATE = 1 and

0, respectively). We again find significantly more positive SURP and SUE for firms in the

highest EARS tercile relative to the lowest tercile, which indicates that EARS is predictive

of earnings news even for firms that do not alter their earnings timing from the previous

year.

Panel D contains results from monthly Fama-MacBeth regressions of earnings news prox-

ies on EARS (in its continuous form) and firm controls. LBM and SIZE are the log of one

plus the book-to-market ratio and log of market capitalization, and MOMEN is the cumu-

lative market-adjusted return over the prior 12-months ending in month m. The parentheses

contain t-statistics from the Fama-MacBeth regressions after Newey-West adjustments for

autocorrelation up to three lags. The results show that the positive link between EARS and

firms’ earnings news is incremental to standard controls.

3.3. EARS and Future Returns

Table 3 provides evidence on the predictive power of EARS for firms’ returns in month

m+ 1. Specifically, Table 3 contains both equal- and value-weighted average returns across

EARS portfolios, using five measures of firm-level returns. RET(1) is the the firm’s raw

return in the month of its expected earnings announcement month. The Five-Factor Alpha

is the intercept from a regression of raw returns minus the risk-free rate, regressed on the

excess market return (MKTRF); two Fama-French factors (SMB and HML); the Pastor-

Stambaugh liquidity factor (LIQ), and the momentum factor (UMD). Similarly, the One-

Factor Alpha results from returns regressed MKTRF; the Three-Factor Alpha results from

returns regressed on MKTRF, SMB, and HML; and the Four-Factor Alpha results from

returns regressed on MKTRF, SMB, HML, and UMD. The reported t-statistics correspond


to time-series average monthly difference across High and Low EARS portfolios.

Univariate tests in Panel A of Table 3 shows that EARS has strong predictive power for

future returns. The average spread in equal-weighted returns across high and low EARS

portfolios is relatively constant across the five return metrics at approximately 1.4 percent

per month with a corresponding t-statistic of approximately six. Panel B of Table 3 shows

that the results weaken somewhat using value-weighted returns, but that the return spreads

remain economically and statistically significant.

Figure 2 shows that a long-short strategy based on EARS portfolios generates positive

equal-weighted returns in 76 of 95 months in our sample period. Figure 3 plots daily long-

short strategy returns within month m+1 relative to firms’ expected earnings announcement

dates t. The majority of the strategy return appears to occur within days [0, 2] relative the

earnings announcement, indicating that the predictive power of EARS is indeed a result of

EARS forecasting earnings news that is not yet reflected in market prices.

Panel C of Table 3 contains results from monthly Fama-MacBeth regressions of raw

returns on EARS, firm-level risk proxies, and alternative measures of firms’ expected an-

nouncement speeds. Column (1) shows that EARS predicts returns incremental to standard

risk proxies including firm size, book-to-market, return momentum, and volatility. Columns

(2) and (3) of Panel C are intended to further distinguish this paper from prior research on

the informativeness of changes in earnings announcement timing. REV is the cumulative

change in firms’ expected earnings announcement date over the prior two months. Consis-

tent with So and Weber (2015), column (2) shows that REV is predictive of future returns.

Finally, column (3) shows that EARS and REV both retain statistically significant predic-

tive power for future returns when both variables are included in the regression, indicating

that EARS and calendar revisions capture distinct aspects of firms’ earnings news.

To mitigate concerns that the announcement-month returns are driven by return rever-

sals, column (4) of Panel C of Table 3 repeats the model from column (3) but includes

a control for the prior month’s return. Results are qualitatively unchanged. Column (5)


shows that the level change in expected reporting speed, �RS, the numerator of Equation

(1), also has predictive power for announcement-month returns. However, column (6) shows

that when both EARS and �RS are included together, only EARS retains incremental

explanatory power, indicating that the informativeness of abnormal announcement speeds

for firms returns depends on the magnitude of the change relative to the level of the firms’

reporting speed.

Panel A (B) of Table 4 presents average portfolio returns across EARS portfolios for the

subsample of firms that are expected to report earnings at the same (different) reporting

speed as in the prior year. Panel A shows that EARS portfolio assignments predict equal-

weighted returns even among firms that do not alter their reporting speed from the prior year.

Among non-change firms, the high EARS portfolio outperforms the low EARS portfolio by

115 basis points per month (t-statistic = 2.44) using firms’ raw returns. These results speak

to the value of identifying cross-sectional variation in reporting speeds using all announcing

firms rather than only those that change their behavior from the prior year.

Panel B of Table 4 confirms that there is strong predictive power for EARS portfolios

among firms that alter their reporting speeds. Among these firms, the high EARS portfolio

outperforms the low EARS portfolio by 164 basis points per month (t-statistic = 4.70) using

firms’ raw returns, which is slightly larger than the excess performance among firms in Panel

A that did not change their reporting speed.

By showing that EARS predicts firms’ returns even for firms that did not change their

expected reporting speeds, the results in Panels A and B of of Table 4 demonstrate that

their is information contained in the relative speed of announcing earnings and not simply

in the within-firm variation in reporting speeds. Panels C and D of Table 4 extend these

results by examining the predictive content of SAMEDATE in states where most firms are

reporting faster versus slower in a given reporting period. We predict that a firm choosing

to keep its reporting speed unchanged is a strong signal of positive news when the average

reporting firm is reducing their reporting speeds.


To test this prediction, we calculate two aggregate EARS measures for each month m.

Our first measure is referred to as “speed average,” or SA, and is intended to capture the

overall average EARS observed at the end of the month. Specifically, SA is defined as the

equal-weighted firm-level EARS at the end of month m, minus the twelve-month historical

average. We subtract the twelve-month historical average to adjust for the overall trend

towards faster reporting speeds observed in Panel A of Table 1. Our second measure is in-

tended to capture the imbalance between abnormally fast and slow announcers. Specifically,

our measure of “speed imbalance” (SI) is calculated the difference in number of firms that

are expected to report faster relative to slower than in the prior year, scaled by the total of

firms expected to report faster and firms expected to report slower, minus its twelve-month

historical average. The "High" ("Low") SA and SI subsamples include those months where

SA and SI is greater than or equal to (less than) zero.

The results in Panels C and D show that the positive relation between SAMEDATE

and announcement returns is driven by firms that do not change their reporting speed in

periods when the majority of announcing firms have opted to reduce their reporting speed.

These findings underscore the importance of considering the behavior of firms relative other

firms in the economy, suggesting that a firm’s choice to report at the same speed as in prior

years is a positive signal of firm performance if other firms are opting to report slower.

To summarize the results so far, we develop a simple measure of expected abnormal

reporting speed, EARS, that can be calculated for a broad cross-section of firms and used

to measure relative changes in speed at month-end. We find strong associations between

relative EARS and future earnings, both for firms that do and do not change their speed

from the prior year, suggesting that there is information in both within-firm and across firm-

variation in expected reporting speeds. We also find that the market does not appear to

price this information about future earnings, as indicated by predictable future returns. In

the next section, we extend the firm-level results to show that earnings calendars also have

strong predictive power for market-level information.


4. Market-Level Tests

Section 4.1 discusses our analysis of aggregate EARS for predicting macro-economic

conditions. Section 4.2 discusses cross-sectional tests of the whether the predictive ability of

firm-level EARS for firms’ earnings and returns varies depending on the firms’ sensitivity

to macroeconomic conditions.

4.1. Market-Level Outcomes

The objective of the tests in this section is to investigate whether there is information

about the macroeconomy in aggregate variation in firms’ reporting speeds. We conduct

these tests by examining whether the overall landscape of the earnings calendar in month

m is predictive of market-level results and macroeconomic conditions in month m + 1. To

begin, Figures 4 and 5 provide descriptive information on average earnings announcement

frequency, SA, SI, and analyst-based earnings surprise (SURP ) by month.

Panel A of Figure 4 shows that the first and second months of each calendar quarter

contain the most earnings announcements, which is expected given that most firms’ fiscal

quarters align with calendar quarters. Panels B and C of Figure 4 plot the average SA and

SI in each month m, as calculated at the end of m�1. Both SA and SI tend to decrease by

month within each calendar quarter, which is consistent with firms with positive (negative)

news accelerating (delaying) their earnings announcements.

Figure 5 provides striking evidence regarding the arrival of earnings news across months of

an earnings season. The downward jigsaw pattern within each reporting cycle (i.e., calendar

quarter) is consistent with this intuition in showing that average SURP decreases by month

within each calendar quarter. Since SURP is based on analyst-based earnings surprises, the

pattern in Figure 5 is also consistent with analysts (and plausibly other market participants)

not incorporating EARS information into their expectations of forthcoming earnings.

We also supplement the findings in Figure 4 by presenting descriptive statistics regarding

how aggregate measures of news relate to aggregated calendar information. The first row of


Panel A of Table 5 tabulates average equal- and value-weighted SURP for the first month

of each calendar quarter (i.e., the “base” period) relative to the second and third months

(i.e., the “comparison” period). Consistent with the visual evidence in Figure 5, we find that

equal- and value-weighted SURP are significantly higher in the base period relative to the

comparison period. Tests also show that average SURP is higher in the high SA months

relative to low SA months, as well as high SI versus low SI months.

The regressions in Panels B and C of Table 5 investigate whether the aggregate signals

are predictive of future earnings news after including year-quarter fixed effects to control for

seasonal patterns. Column (1) of Panel B regresses equal-weighted SURP on an indicator

for the first month of each reporting period (variable FMD for "first month dummy").

Consistent with the results in Panel A, SURP is significantly more positive in the first

month of each quarter. Columns (2) and (3) find similar results when regressing SURP on

indicators for high SA and SI, respectively. Importantly, columns (4) and (5) include FMD

as well as SA and SI, respectively, and find that SA and SE both have incrementally stronger

associations with SURP than does FMD. Panel C of Table 5 finds similar results for value-

weighted SURP , except that SA is not statistically significant in column (4) when controlling

for the first month indicator. In sum, the results in Table 5 find that SA and SI are strongly

associated with future analyst-based earnings surprises and that the aggregation of earnings

calendar data provides information incremental to the sequence of months within a given

reporting period. In the tests below, we extend these results by examining the usefulness of

aggregate calendar information in forecasting alternative market-level outcomes.

In Panel A of Table 6, we examine how SA and SI at the end of month m predict

the change in SP500 earnings in month m + 1, SP500 returns, and change in the risk-

free rate.8 Column (1) finds that high SA months are associated with positive changes in

SP500 earnings, which is consistent with the results found in Table 5. Column (2) finds an

insignificant result for SI, and column (3) finds a significantly positive result for months8Our thanks to Robert Shiller for providing this data on his website


that have both SA and SI equal to one. In columns (4) through (6) we find insignificant

associations with SP500 returns. Thus, while EARS predicts future firm-level returns, no

such association is observed at the market level. Columns (7) through (9) find positive

associations with changes in the future risk-free rate, consistent with prior evidence that

market-level earnings news results in increases in discount rate that investors apply to firms’

earnings (e.g., Cready and Gurun (2010)).

4.2. Cross-sectional analysis: firm-level tests based on macroeconomic sensitivities

In this section, we provide evidence that the predictive power of aggregate expected

reporting speeds for market-level information yields added return predictability among stocks

sensitive to aggregate news. Specifically, we predict aggregated market-level calendar data is

informative for firm-level returns among firms that are more sensitive to aggregate earnings

news.

Akin to an ‘earnings-surprise-beta’, we calculate firms’ market earnings sensitivities as

the sensitivity of a firm’s monthly return in month t to the average analyst-based earnings

surprise of all firms announcing in month t, measured over the 60 calendar months ending

in month m� 1. A firm is classified as “High” (“Low”) aggregate earnings sensitivity if it is

above the median of all firms expected to announce in month m+1, and is identified as such

using the binary variable MACRO.

Panel A of Table 7 tabulates average equal-weighted returns in the high and low MACRO

firms, by high versus low SI. High MACRO firms earn an average of 2.261% in the high

SA months, while low MACRO firms earn 1.550%. The difference of 0.711% is statistically

significant, consistent with EARS having a stronger association when firms are more sensitive

to market earnings news. No significant returns are observed in the low SA months. The

difference-in-differences is 0.831 and significantly positive. Results in Panels B for SI are

qualitatively similar. These results show that combining the market-level information in

aggregate expected reporting speeds with firm-level tests further improves the predictive

power of expected reporting speeds for firm-level earnings and returns realizations.


Table 8 examines the interaction effect between SA and MACRO in predicting future

returns. All regressions include untabulated controls for firm size, book-to-market, momen-

tum, and return volatility. Results in column (1) of Panel A finds that EARS is positively

associated with one-month-ahead returns, and the interaction between SA ⇤MACRO indi-

cates that this association is stronger in high-SA months. Column (2) includes an additional

control for stock market beta (BETA) and results are qualitatively unchanged.

Columns (3) and (4) find that EARS is insignificantly associated with two-months-ahead

returns, and that the SA ⇤ MACRO interaction is significantly negative. Together the

results in columns (1) through (4) show that firms with high sensitivities to aggregate news

initially earning higher returns, consistent with their prices predictably rising in response

to initially positive aggregate news, but also subsequently earn lower returns, consistent

with their returns predictably falling in response subsequently announced negative aggregate

news. Columns (5) and (6) find that EARS is positively associated with three-months-ahead

returns, but that the interaction between SA ⇤ MACRO is insignificant. Panel B provide

similar results for SI.

Collectively, the results in Table 8 show that predictable variation in aggregate news leads

to predictable variation in firm-level returns. Recall that high SA months corresponds to

periods when the average announcing firm is more likely to report positive news. As a result,

firms with sensitivities to aggregate news tend to experience an uptick in prices. However,

in months following the realization of high SA, the average firm is less likely to report

positive news. In fact, empirically we show that non-high (i.e., low) SA months tend to

correspond to more negative earnings news. As a result, firms with sensitivities to aggregate

news tend to also experience a subsequent downtick in prices as negative aggregate earnings

are reported. The symmetry and significance of these findings show that earnings calendar

information is useful in understanding not only aggregate patterns in earnings information

but also predictable return patterns among firms sensitive to aggregate news.


4.3. Market-Level Uncertainty

A central insight from this paper is that there is information in inter-temporal variation

in aggregate reporting behavior, rather than simply in within-firm variation that has been

the focus on prior research. The analyses so far have primarily focused on predicting the

sign and magnitude of earnings news and returns. In our final analyses, we examine whether

earnings calendar information also provides information about the second moment of earn-

ings news and returns as captured by innovations in the CBOE volatility index, commonly

referred to as the VIX. To the extent that pervasive changes in reporting speeds corresponds

to greater dispersion in firms’ subsequently announced earnings performance, we expect

earnings calendar information to help predict changes in market-level uncertainty.

Table 9 contains time-series regressions of changes in the CBOE volatility index, VIX,

on the average absolute value of EARS denoted last ABS(AGGEARS). The dependent

variable in these regressions is Log(V IXm+1/V IXm) which equals the log change in the VIX

in month M+1, where M is the month of the earnings calendar. All independent variables

are measured in month M and V IXm denotes the level of the VIX in month M .

The results in Table 9 show that ABS(AGGEARS) has strong univariate and incremen-

tal explanatory power for innovations in the VIX, even after controlling for past innovations

and levels. These findings add strong support to the idea that aggregated information re-

garding firms’ reporting speeds, and earnings calendars more broadly, contain information

useful in forecasting market-level outcomes.

5. Conclusion

Prior research shows that firms’ earnings realizations are correlated (Foster (1981); Free-

man and Tse (1992)), and that managers consider peer firms’ releases (Dye and Sridhar

(1995)) and other information events (Acharya, DeMarzo, and Kremer (2011)) in timing

their disclosures, which suggests that managers strategically time news relative to other

sources of information. Building upon this prior research, we provide evidence that there is


information not only in firms’ choices to change their reporting speeds relative to their past

behavior, which has been the focus of prior studies, but also in their choices to maintain

their reporting speeds in times when other firms are delaying or accelerating news, which is

a key innovation and insight of this study.

We show that trends in firms accelerating or delaying their reporting speeds are not only

informative about those firms’ forthcoming earnings, but also informative about earnings for

the two-thirds of firms that do not alter their expected reporting reporting speeds from the

prior year. We also show that firms’ reporting speeds in aggregate signal predictable changes

in aggregate earnings news, treasury spreads, and market uncertainty.

Collectively, this paper makes three contributions. First, conceptually, while prior stud-

ies have found evidence that managers consider their own earnings news in timing earnings

releases, our findings indicate that timing decisions involve dynamic, across-firm consider-

ations. Examining managers’ incentives and benefits of timing earnings announcements in

relation to peer firms is a potentially interesting avenue for future research. Second, method-

ologically, our approach of using end-of-month earnings calendar data is computationally

simple and facilitates pricing tests using balanced long-short positions based on synchro-

nized signals. Our methodology permitting a larger sample also allows for layering multiple

signals and screening firms along multiple dimensions when forming positions, which more

closely mimics how investment strategies are implemented in practice. Finally, we contribute

to the literatures studying macroeconomic forecasting by showing that aggregate reporting

speeds have strong predictive power for aggregate earnings surprises, treasury spreads, and

changes in the VIX, suggesting that information from earnings calendars may be useful in

both generating and improving macroeconomic forecasts.


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Figure 1. Histogram of Expected Abnormal Reporting Speed

The figure contains a histogram of values of expected abnormal reporting speed, EARS. EARS is the percentage change ina firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed isdefined as the number of days between its expected earnings announcement date and fiscal period end date and the laggedreporting speed is defined as the difference between a firm’s realized earnings announcement date from the prior year and itscorresponding fiscal period end date. Expected earnings announcement dates of firms expected to announce in month M + 1are measured in the earnings calendar data on the final trading date of calendar month M . The histogram plots the percentageof sample correspond to each bucket falling within ±2.5 of the X-axis value. The sample for this analysis consists of 83,411firm-month observations spanning 2006-2013.


Figure 2. Monthly Strategy Returns

The figure contains the monthly spread in returns across high and low EARS portfolios. EARS is the percentage change ina firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed isdefined as the number of days between its expected earnings announcement date and fiscal period end date and the laggedreporting speed is defined as the difference between a firm’s realized earnings announcement date from the prior year and itscorresponding fiscal period end date. Expected earnings announcement dates of firms expected to announce in month M+1 aremeasured in the earnings calendar data on the final trading date of calendar month M . Returns are measured in month M+1.Observations are assigned to portfolios at the end of each calendar month, where the top (bottom) 30 percent are assigned tothe ‘High’ (‘Low’) portfolio and the remaining are assigned to the ‘Mid’ portfolio. The return spread corresponds to the returnin month M+1 from a long (short) position in firms within the high (low) EARS portfolios. The sample for this analysisconsists of 83,411 firm-month observations spanning 2006-2013.


Figure 3. Cumulative Returns in Event-Time

The figure contains cumulative difference in returns across high and low EARS portfolios in the 21 trading days relative tofirms’ expected announcement date, t. EARS is the percentage change in a firm’s expected reporting speed relative to thesame fiscal quarter in the prior year. A firm’s expected reporting speed is defined as the number of days between its expectedearnings announcement date and fiscal period end date and the lagged reporting speed is defined as the difference betweena firm’s realized earnings announcement date from the prior year and its corresponding fiscal period end date. Expectedearnings announcement dates of firms expected to announce in month M+1 are measured in the earnings calendar data on thefinal trading date of calendar month M . Returns are measured relative to the expected announcement date in month M+1.Observations are assigned to portfolios at the end of each calendar month, where the top (bottom) 30 percent are assigned tothe ‘High’ (‘Low’) portfolio. The sample for this analysis consists of 83,411 firm-month observations spanning 2006-2013.


Figure 4. Aggregate Earnings Announcements Statistics

The top panel presents the average number of expected earnings announcements for each month within a given year of our2006-2013 sample. Expected earnings announcement dates of firms expected to announce in month M + 1 are measured in theearnings calendar data on the final trading date of calendar month M . The second panel plots the average expected abnormalreporting speed, EARS, of all observations where the firm is expected to announce in a given calendar month. EARS is thepercentage change in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expectedreporting speed is defined as the number of days between its expected earnings announcement date and fiscal period end dateand the lagged reporting speed is defined as the difference between a firm’s realized earnings announcement date from the prioryear and its corresponding fiscal period end date. Expected earnings announcement dates of firms expected to announce inmonth M+1 are measured in the earnings calendar data on the final trading date of calendar month M . The bottom panel plotsthe aggregate speed imbalance, SI, in a given calendar month. SI equals the difference in number of firms that are expected toreport faster relative to slower than in the prior year, scaled by the total of firms expected to report faster and firms expectedto report slower in a given calendar month. Each earnings season is divided into the first, second, and third months, where thefirst month denotes January, April, July, and October (shown in black bars); the second month denotes February, May, August,and November (shown in grey bars); and the third month denotes, March, June, September, and December (shown in whitebars). The sample for this analysis consists of 83,411 firm-month observations spanning 2006-2013.


Figure 4: [Continued] Aggregate Earnings Announcements Statistics


Figure 5. Average Earnings Surprises by Month

The figure presents the average analyst-based earnings surprise, SURP , for each month within a given year of our 2006-2013sample. Expected earnings announcement dates of firms expected to announce in month M + 1 are measured in the earningscalendar data on the final trading date of calendar month M . Each earnings season is divided into the first, second, and thirdmonths, where the first month denotes January, April, July, and October (shown in black bars); the second month denotesFebruary, May, August, and November (shown in grey bars); and the third month denotes, March, June, September, andDecember (shown in white bars). The sample for this analysis consists of 83,411 firm-month observations spanning 2006-2013.


Table 1. Descriptive statistics

Panel A presents annual sample averages of the main variables used throughout the paper. OBS equals the number of firm-month observations and FIRMS indicates the number of unique firms. EARS is the percentage change in a firm’s expectedreporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed is defined as the numberof days between its expected earnings announcement date and fiscal period end date and the lagged reporting speed is definedas the difference between a firm’s realized earnings announcement date from the prior year and its corresponding fiscal periodend date. Expected earnings announcement dates of firms expected to announce in month M +1 are measured in the earningscalendar data on the final trading date of calendar month M . �RS is the level change in a firm’s expected versus laggedreporting speeds and LRS indicates the firm’s lagged reporting speed. DEV equals the number of days between a firm’sexpected announcement date and the actual announcement date. %SAMP measures the number of observations in the samplerelative to the full CRSP/Compustat/IBES universe. SAMEDATE is a binary variable that equals one if the firm is expectedto announce earnings at the same speed as in the prior calendar year. Panel B presents descriptive statistics across portfoliosof EARS, where observations are assigned to portfolios at the end of each calendar month, where the top (bottom) 30 percentare assigned to the ‘High’ (‘Low’) portfolio and the remaining are assigned to the ‘Mid’ portfolio. MCAP equals firms’ marketcapitalization reported in millions and MOMEN is the cumulative market-adjusted return over the prior 12-months ending inmonth M . The reported t-statistics correspond to time-series average monthly difference across High and Low EARS portfolios.The sample for this analysis consists of 83,411 firm-month observations spanning 2006-2013.

Panel A: Sample Averages by Year

OBS Firms %SAMP �RS %SAMEDATE DEV LRS EARS

2006 8,399 2,678 0.654 0.216 0.690 0.399 24.501 -1.1762007 10,064 3,056 0.762 0.326 0.698 0.275 25.772 -1.0492008 10,399 3,028 0.816 0.728 0.569 0.468 25.606 0.9242009 10,812 3,090 0.881 0.388 0.687 0.351 25.336 0.0842010 11,028 3,120 0.848 0.677 0.742 0.049 25.434 1.1532011 10,794 3,100 0.832 0.756 0.738 0.145 25.155 1.2852012 11,253 3,157 0.850 0.858 0.471 0.429 25.721 2.1242013 10,662 3,097 0.798 0.520 0.694 0.301 26.635 0.679

Avg 10,426 3,041 0.805 0.559 0.661 0.302 25.520 0.503

Panel B: Monthly Sample Averages by EARS Portfolios

EARS Portfolios High-Low

High (Faster) Mid Low (Slower) Mean t-statistic

OBS 227.6 449.3 201.1 26.56 (3.52)EARS 10.90 2.33 -17.44 28.33 (45.77)�RS 3.91 0.62 -3.90 7.81 (26.52)LRS 24.06 28.53 28.27 -4.21 -(13.11)DEV 0.70 0.23 -0.08 0.78 (8.48)

MCAP 41.09 27.97 44.25 -3.16 -(1.26)MOMEN 0.81 -0.75 -2.47 3.28 (5.90)


Table 2. Predicting Earnings News

Panel A presents sample averages of earnings news proxies across EARS portfolios. EARS is the percentage change in a firm’sexpected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed is defined as thenumber of days between its expected earnings announcement date and fiscal period end date and the lagged reporting speed isdefined as the difference between a firm’s realized earnings announcement date from the prior year and its corresponding fiscalperiod end date. Expected earnings announcement dates of firms expected to announce in month M + 1 are measured in theearnings calendar data on the final trading date of calendar month M . Observations are assigned to portfolios at the end of eachcalendar month, where the top (bottom) 30 percent are assigned to the ‘High’ (‘Low’) portfolio and the remaining are assignedto the ‘Mid’ portfolio. 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 and SUE is the standardized unexplainedearnings, defined as the realized EPS minus EPS from four quarters prior, divided by the standard deviation of this differenceover the prior eight quarters. The reported t-statistics correspond to time-series average monthly difference across High andLow EARS portfolios. Panel B (C) presents sample averages across EARS portfolios for the subsample of firms that areexpected to report earnings at the same (different) reporting speed as in the prior year. A firm is categorized as announcingat the same speed (the Panel B sample) if it the difference between its expected earnings announcement date and fiscal periodend date is within one day of the difference between a firm’s realized earnings announcement date from the prior year and itscorresponding fiscal period end date. Panel D contains results from monthly Fama-MacBeth regressions of earnings news proxieson EARS and additional firm controls. LBM and SIZE are the log of one plus the book-to-market ratio and log of marketcapitalization, respectively. MOMEN is the cumulative market-adjusted return over the prior 12-months ending in month M .The parentheses contain t-statistics from the Fama-MacBeth regressions after Newey-West adjustments for autocorrelation upto 3 lags. The notations ***, **, and * indicate the coefficient is significant at the 1%, 5%, and 10% level, respectively. Thesample for this analysis consists of 83,411 firm-month observations spanning 2006-2013.

Panel A: Earnings Metrics Across EARS Portfolios

EARS Portfolios

High (Faster) Mid Low (Slower) Low-High

SURP 0.071 -0.056 -0.034 0.105(4.33) -(2.02) -(1.64) (4.91)

SUE 0.043 -0.069 -0.263 0.305(0.87) -(1.28) -(3.52) (6.67)

Panel B: Same Reporting Speed Subsample

EARS Portfolios

High (Faster) Mid Low (Slower) High-Low

SURP 0.117 -0.060 -0.069 0.186(9.28) -(1.95) -(1.11) (3.16)

SUE 0.064 -0.092 -0.115 0.179(1.22) -(1.46) -(1.38) (2.24)

Panel C: Changed Reporting Speed Subsample

EARS Portfolios


SURP 0.056 -0.247 -0.029 0.085(2.61) -(1.70) -(1.34) (3.39)

SUE 0.049 -0.323 -0.293 0.342(0.87) -(1.21) -(3.63) (5.94)


Table 2: [Continued] Predicting Earnings News

Panel D: Fama-MacBeth Regressions of Earnings Metrics

SURP SUE

(1) (2) (3) (4)

EARS 0.002*** 0.002*** 0.009*** 0.007***(4.31) (3.62) (5.59) (5.28)

SIZE – 0.050*** – 0.063***– (4.70) – (4.15)

LBM – -0.093*** – -0.275***– (-2.81) – (-4.70)

MOMEN – 0.002*** – 0.011***– (3.66) – (6.54)

INT -0.013 -0.616*** -0.081 -0.747***(-0.61) (-3.77) (-0.84) (-3.05)

R2(%) 0.431 3.692 0.762 5.842


Table 3. Predicting Future Returns

Panel A presents results from monthly Fama-MacBeth regressions of raw returns on EARS and additional firm controls, wherereturns are measured in month M + 1 and all explanatory variables are measured in M . EARS is the percentage change ina firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed isdefined as the number of days between its expected earnings announcement date and fiscal period end date and the laggedreporting speed is defined as the difference between a firm’s realized earnings announcement date from the prior year and itscorresponding fiscal period end date. Expected earnings announcement dates of firms expected to announce in month M+1 aremeasured in the earnings calendar data on the final trading date of calendar month M . Observations are assigned to portfoliosat the end of each calendar month, where the top (bottom) 30 percent are assigned to the ‘High’ (‘Low’) portfolio and theremaining are assigned to the ‘Mid’ portfolio. 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 monthly returns over the prior 12-months ending in month M . RET (0) is the firm’s raw return in month M . The parenthesescontain t-statistics from the Fama-MacBeth regressions after Newey-West adjustments for autocorrelation up to 3 lags. Thenotations ***, **, and * indicate the coefficient is significant at the 1%, 5%, and 10% level, respectively. Panel B (C) presentsequal-weighted (value-weighted) sample averages of return metrics across EARS portfolios. RET(1) is the the firm’s raw returnin the month of its expected earnings announcement month, M + 1. The Five-Factor Alpha is the intercept from a regressionof raw returns minus the risk-free rate, regressed on the excess market return (MKTRF); two Fama-French factors (SMB,and HML); the Pastor-Stambaugh liquidity factor (LIQ), and the momentum factor (UMD). The One-Factor Alpha resultsfrom returns regressed MKTRF; the Three-Factor Alpha results from returns regressed on MKTRF, SMB, and HML; and theFour-Factor Alpha results from returns regressed on MKTRF, SMB, HML, and UMD. The reported t-statistics correspond totime-series average monthly difference across High and Low EARS portfolios. The sample for this analysis consists of 83,411firm-month observations spanning 2006-2013. The sample for this analysis consists of 83,411 firm-month observations spanning2006-2013.

Panel A: Equal-Weighted Returns Across EARS Portfolios

EARS Portfolios


RET(1) 1.636 1.127 0.196 1.440(2.40) (1.64) (0.30) (6.23)

One-Factor Alpha 0.847 0.333 -0.551 1.399(3.29) (1.27) -(2.10) (6.04)

Three-Factor Alpha 0.832 0.314 -0.561 1.392(4.15) (1.49) -(2.75) (5.94)

Four-Factor Alpha 0.836 0.320 -0.555 1.391(4.35) (1.70) -(2.91) (5.91)

Five-Factor Alpha 0.843 0.321 -0.554 1.397(4.38) (1.70) -(2.89) (5.91)

Panel B: Value-Weighted Returns Across EARS Portfolios

EARS Portfolios


RET(1) 1.174 1.034 0.287 0.887(2.23) (1.89) (0.52) (2.62)

One-Factor Alpha 0.559 0.397 -0.313 0.871(3.03) (1.98) -(1.16) (2.54)





Table 3: [Continued] Predicting Future Returns

Panel C: Fama-MacBeth Regressions

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

EARS 0.033*** – 0.024** 0.022** – 0.030***(5.04) – (2.41) (2.35) – (2.72)

REV – 0.131*** 0.073* 0.078* – –– (4.46) (1.70) (1.84) – –

�RS – – – – 0.077*** 0.029– – – – (3.71) (0.73)

SIZE -0.038 -0.066 -0.042 -0.025 -0.029 -0.020(-0.48) (-0.78) (-0.53) (-0.32) (-0.38) (-0.26)

LBM 0.663 0.638 0.618 0.504 0.538 0.569(1.46) (1.47) (1.40) (1.30) (1.43) (1.44)

MOMEN -0.004 -0.003 -0.004 -0.004 -0.004 -0.004(-0.44) (-0.37) (-0.49) (-0.47) (-0.41) (-0.43)

VLTY -0.363* -0.361* -0.364* -0.418* -0.416* -0.400*(-1.69) (-1.67) (-1.70) (-1.75) (-1.73) (-1.66)

RET(0) – – – -0.048*** -0.048*** -0.049***– – – (-3.07) (-2.97) (-3.10)

INT 1.995 2.419* 2.106* 1.991 1.975 1.810(1.64) (1.91) (1.71) (1.57) (1.59) (1.45)

R2 (%) 4.435 4.345 4.722 5.807 5.575 5.908


Table 4. Sample Partitions Based on Changes in Reporting Speed

Panel A (B) presents sample averages across EARS portfolios for the subsample of firms that are expected to report earningsat the same (different) reporting speed as in the prior year. A firm is categorized as announcing at the same speed (the PanelA sample) if it the difference between its expected earnings announcement date and fiscal period end date is within one day ofthe difference between a firm’s realized earnings announcement date from the prior year and its corresponding fiscal period enddate. EARS is the percentage change in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year.A firm’s expected reporting speed is defined as the number of days between its expected earnings announcement date and fiscalperiod end date and the lagged reporting speed is defined as the difference between a firm’s realized earnings announcementdate from the prior year and its corresponding fiscal period end date. Expected earnings announcement dates of firms expectedto announce in month M + 1 are measured in the earnings calendar data on the final trading date of calendar month M .Observations are assigned to portfolios at the end of each calendar month, where the top (bottom) 30 percent are assigned tothe ‘High’ (‘Low’) portfolio and the remaining are assigned to the ‘Mid’ portfolio. RET(1) is the the firm’s raw return in themonth of its expected earnings announcement month, M + 1. SURP equals the actual EPS number reported in IBES minusthe last consensus forecast available immediately prior to the announcement, and scaled by beginning-of-quarter assets andSUE is the standardized unexplained earnings, defined as the realized EPS minus EPS from four quarters prior, divided by thestandard deviation of this difference over the prior eight quarters. OBS equals the number of firm-month observations. Thereported t-statistics correspond to time-series average monthly difference across High and Low EARS portfolios. Panels C andD present results from monthly Fama-MacBeth regressions of raw returns on SAMEDATE and additional firm controls whenthe sample is partitioned based on aggregate changes in firms’ reporting speeds. SA is the monthly average value of EARSminus its twelve-month historical average. SI equals the difference in number of firms that are expected to report faster relativeto slower than in the prior year, scaled by the total of firms expected to report faster and firms expected to report slowerwithin in a given calendar month, minus its twelve-month historical average. The ‘High’ (‘Low’) subsamples for SA and SIcorresponds to months where the aggregate measures are greater than or equal to (less than) zero. SAMEDATE is a binaryvariable that equals one if the firm is expected to announce earnings at the same speed as in the prior calendar year. LBMand SIZE are the log of one plus the book-to-market ratio and log of market capitalization, respectively. MOMEN is thecumulative market-adjusted return and V LTY is the standard deviation of monthly returns over the prior 12-months endingin month M . RET (0) is the firm’s raw return in month M . The parentheses contain t-statistics from the Fama-MacBethregressions after Newey-West adjustments for autocorrelation up to 3 lags. The sample for this analysis consists of 83,411firm-month observations spanning 2006-2013.

Panel A: Returns for Same Reporting Speed Subsample

EARS Portfolios


RET(1) 1.706 1.084 0.739 1.154(2.32) (1.59) (0.99) (2.44)

One-Factor Alpha 0.776 0.296 -0.006 1.090(2.49) (1.15) -(0.01) (2.27)




Panel B: Returns for Changed Reporting Speed Subsample

EARS Portfolios


RET(1) 1.751 2.502 0.107 1.644(2.40) (1.14) (0.16) (4.70)

One-Factor Alpha 0.946 1.912 -0.636 1.582(2.77) (1.09) -(2.32) (4.50)





Table 4: [Continued] Sample Partitions Based on Changes in Reporting Speed

Panel C: Regression of Returns on SAMEDATE

Full Sample Low SA High SA

(1) (2) (3)

SAMEDATE 0.315*** 0.357* 0.265(3.30) (1.82) (1.18)

SIZE -0.069 -0.236*** 0.125**(-0.97) (-2.68) (2.24)

LBM 0.604 0.757 0.427*(1.43) (0.89) (1.86)

MOMEN -0.003 0.012*** -0.020(-0.32) (2.92) (-1.44)

VLTY -0.378** -0.729*** 0.028(-2.01) (-3.26) (0.09)

Intercept 2.217* 4.850*** -0.836(1.91) (3.28) (-0.72)

R2 (%) 4.130 4.215 4.031

OBS 83,411 47,682 35,729

Panel D: Regression of Returns on SAMEDATE

Full Sample Low SI High SI

(1) (2) (3)

SAMEDATE 0.315*** 0.317* 0.312(3.30) (1.73) (1.35)

SIZE -0.069 -0.188 0.075(-0.97) (-1.60) (0.70)

LBM 0.604 0.702 0.485(1.43) (0.77) (1.56)

MOMEN -0.003 0.009*** -0.018(-0.32) (2.73) (-1.22)

VLTY -0.378** -0.689*** -0.002(-2.01) (-3.45) (-0.01)

Intercept 2.217* 4.226** -0.213(1.91) (2.03) (-0.14)

R2 (%) 4.130 3.894 4.416

OBS 83,411 44,661 38,750


Table 5. Aggregate Earnings Surprises

This table provides equal- and value-weighted average analyst-based earnings surprises, SURP , across monthly subsamples.SURP equals the actual EPS number reported in IBES minus the last consensus forecast available immediately prior to theannouncement, and scaled by beginning-of-quarter assets. The first two rows compares SURP across the first versus secondand third months of an earnings season. The first month denotes January, April, July, and October; the second month denotesFebruary, May, August, and November; and the third month denotes, March, June, September, and December. A firm isincluded in a given month if it is expected to announce in the month according to the earnings calendar measured at the end ofprior calendar month. The second two rows compares SURP across subsamples partitioned by SA, where SA is the monthlyaverage value of EARS minus its twelve-month historical average. The ‘High’ (‘Low’) subsample corresponds to months wherethe value of SA is greater than or equal to (less than) zero. EARS is the percentage change in a firm’s expected reportingspeed relative to the same fiscal quarter in the prior year. A firm’s expected reporting speed is defined as the number of daysbetween its expected earnings announcement date and fiscal period end date and the lagged reporting speed is defined as thedifference between a firm’s realized earnings announcement date from the prior year and its corresponding fiscal period enddate. The bottom two rows compare SURP across subsample partitioned by SI, where SI equals the difference in number offirms that are expected to report faster relative to slower than in the prior year, scaled by the total of firms expected to reportfaster and firms expected to report slower within in a given calendar month, minus its twelve-month historical average. The‘High’ (‘Low’) subsample corresponds to months where the average value of SI is greater than or equal to (less than) zero.Expected earnings announcement dates of firms expected to announce in month M + 1 are measured in the earnings calendardata on the final trading date of calendar month M . Reported t-statistics are shown in parentheses underneath the sampleaverages. Panel B (C) present results from regressing monthly equal-weighted (value-weighted) average earnings surprises onindicator variables for aggregate earnings news, where calendar quarter fixed-effects are included throughout. The sample forthis analysis consists of 95 calendar month observations spanning 2006-2013.

Panel A: Average Earnings Surprises

Equal-Weighted Averages Value-Weighted Averages

Comparison Base Comparison Difference Base Comparison Difference

FMD vs. Non-FMD 0.071 -0.011 0.084 0.109 0.060 0.049(7.24) -(0.50) (4.38) (11.06) (7.70) (5.02)

High vs. Low SA 0.057 -0.055 0.116 0.102 0.050 0.051(4.11) -(1.48) (4.06) (11.09) (5.99) (4.64)

High vs. Low SI 0.061 -0.060 0.124 0.104 0.048 0.056(4.47) -(1.58) (4.10) (10.79) (5.14) (4.97)

Panel B: Regressions of Equal-Weighted Surprise

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

FMD vs. Non-FMD 0.128*** – – 0.050 0.034(5.29) – – (1.05) (0.75)

High vs. Low SA – 0.138*** – 0.097* –– (5.43) – (1.96) –

High vs. Low SI – – 0.141*** – 0.114**– – (5.50) – (2.39)

R2 (%) 20.139 22.738 25.227 23.719 25.711

Panel C: Regressions of Value-Weighted Surprise

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

FMD vs. Non-FMD 0.040*** – – 0.015 0.004(3.49) – – (0.63) (0.19)

High vs. Low SA – 0.044*** – 0.031 –– (3.86) – (1.31) –

High vs. Low SI – – 0.047*** – 0.044**– – (4.35) – (1.99)

R2 (%) 10.530 12.005 14.801 12.484 14.842


Table 6. Market-Level Regressions

This table provides regression results of monthly S&P500 earnings growth, S&P500 index returns, and changes in the 10-yeartreasury yield. SA is the monthly average value of EARS minus its twelve-month historical average. The ‘High’ (‘Low’)subsample corresponds to months where the value of SA is greater than or equal to (less than) zero. EARS is the percentagechange in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reportingspeed is defined as the number of days between its expected earnings announcement date and fiscal period end date and thelagged reporting speed is defined as the difference between a firm’s realized earnings announcement date from the prior year andits corresponding fiscal period end date. SI equals the difference in number of firms that are expected to report faster relative toslower than in the prior year, scaled by the total of firms expected to report faster and firms expected to report slower within ina given calendar month, minus its twelve-month historical average. The ‘High’ (‘Low’) subsample corresponds to months wherethe average value of SI is greater than or equal to (less than) zero. Expected earnings announcement dates of firms expectedto announce in month M + 1 are measured in the earnings calendar data on the final trading date of calendar month M . HighSA & High SI is a dummy variable that equals one for months where SA and SI are greater than zero. Reported t-statisticsare shown in parentheses underneath the sample averages, where calendar quarter fixed-effects are included throughout. Thesample for this analysis consists of 95 calendar month observations spanning 2006-2013.

�S&P500 Earnings S&P500 Returns �Risk-Free Rate

(1) (2) (3) (4) (5) (6) (7) (8) (9)

High vs. Low SA 2.435** – – 0.008 – – 6.659* – –(2.28) – – (0.98) – – (1.72) – –

High vs. Low SI – 1.387 – – -0.000 – – 8.739** –– (1.45) – – (-0.02) – – (2.44) –

High SA & High SI – – 2.247** – – 0.003 – – 7.425**– – (2.00) – – (0.41) – – (2.04)

R2 (%) 4.924 1.706 4.476 1.087 0.001 0.188 3.553 6.533 4.716


Table 7. Predicting Firm-Level Returns Using Aggregate Earnings News

This table presents the difference-in-differences of firms’ raw return in their expected announcement month partitioned by theirsensitivity to aggregate earnings news. Expected earnings announcement dates of firms expected to announce in month M + 1are measured in the earnings calendar data on the final trading date of calendar month M . Aggregate earnings sensitivity ismeasured as the sensitivity of a firm’s monthly return in month t to the average analyst-based earnings surprise of all firmsannouncing in month t, measured over the 60 calendar months ending in month M � 1. A firm is classified as ‘High’ (‘Low’)aggregate earnings sensitivity if it is above the median of all firms expected to announce in month M+1. Panel A comparesreturns across subsamples partitioned by SA, where SA is the monthly average value of EARS minus its twelve-month historicalaverage. The ‘High’ (‘Low’) subsample corresponds to months where the value of SA is greater than or equal to (less than)zero. EARS is the percentage change in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year.A firm’s expected reporting speed is defined as the number of days between its expected earnings announcement date and fiscalperiod end date and the lagged reporting speed is defined as the difference between a firm’s realized earnings announcementdate from the prior year and its corresponding fiscal period end date. Panel B compares returns across subsample partitionedby SI, where SI equals the difference in number of firms that are expected to report faster relative to slower than in the prioryear, scaled by the total of firms expected to report faster and firms expected to report slower within in a given calendar month,minus its twelve-month historical average. The ‘High’ (‘Low’) subsample corresponds to months where the average value of SIis greater than or equal to (less than) zero. Reported t-statistics based on the time-series of calendar quarters are shown inparentheses underneath the sample averages. The sample for this analysis consists of 83,411 firm-month observations spanning2006-2013.

Panel A: Returns Across High and Low SA Months

Aggregate Earnings Sensitivity

High Low High-Low

High SA 2.261 1.550 0.711(1.57) (1.22) (2.47)

Low SA 0.715 0.836 -0.121(0.64) (0.80) -(0.52)

Difference-in-Difference: 0.831[H0: High vs. Low SA] (2.33)

Panel B: Returns Across High and Low SI Months

Aggregate Earnings Sensitivity

High Low High-Low

High SI 1.998 1.357 0.641(1.43) (1.10) (2.31)

Low SI 0.476 0.717 -0.241(0.44) (0.69) -(1.10)

Difference-in-Difference: 0.882[H0: High vs. Low SI] (2.54)


Table 8. Regression of Returns on Aggregate Earnings News

This table presents results from regressing firms’ raw returns on calendar-based measures of aggregate earnings news. RET(X)is the firm’s return X months after month M , where earnings calendar data is measured on the final trading date of calendarmonth M for all firms expected to announce earnings in month M + 1. Panel A measures expected aggregate news using SAmeasured in month M , where SA is the monthly average value of EARS minus its twelve-month historical average. EARSis the percentage change in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’sexpected reporting speed is defined as the number of days between its expected earnings announcement date and fiscal periodend date and the lagged reporting speed is defined as the difference between a firm’s realized earnings announcement date fromthe prior year and its corresponding fiscal period end date. Panel B measures expected aggregate news using SI measured inmonth M , where SI equals the difference in number of firms that are expected to report faster relative to slower than in theprior year, scaled by the total of firms expected to report faster and firms expected to report slower within in a given calendarmonth, minus its twelve-month historical average. Aggregate earnings sensitivity, MACRO, is measured as the sensitivity of afirm’s monthly return in month t to the average analyst-based earnings surprise of all firms announcing in month t, measuredover the 60 calendar months ending in month M � 1. All regressions include controls for firm size, book-to-market, momentum,and return volatility. 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. The sample for this analysis consists of83,411 firm-month observations spanning 2006-2013.

Panel A: Regression Results on Earnings Season Month Indicators

RET(1) RET(2) RET(3)

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

EARS 0.021*** 0.021*** 0.000 0.000 0.005 0.005(4.35) (4.35) (0.09) (0.04) (1.22) (1.19)

SA 0.172 0.174 -0.350 -0.329 0.281 0.292(0.97) (0.98) (-1.26) (-1.20) (1.22) (1.28)

SA*MACRO 0.354** 0.355** -0.364** -0.357** 0.031 0.035(2.43) (2.43) (-2.52) (-2.47) (0.19) (0.21)

MACRO 0.396 0.396 0.070 0.058 -0.041 -0.049(1.45) (1.45) (0.30) (0.25) (-0.17) (-0.20)

SA*BETA – -0.002 – -0.020 – -0.011– (-0.15) – (-1.38) – (-0.56)

BETA – 0.001 – 0.044 – 0.026– (0.02) – (1.09) – (0.49)

R2 (%) 0.304 0.305 0.599 0.608 0.216 0.219

Panel B: Regression Results on Earnings Season Month Indicators

RET(1) RET(2) RET(3)

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

EARS 0.018*** 0.018*** 0.002 0.002 0.006 0.006(3.91) (3.92) (0.44) (0.41) (1.35) (1.35)

SI 2.776 2.702 -4.800* -4.667* 2.723 2.753(1.41) (1.38) (-1.73) (-1.71) (1.13) (1.15)

SI*MACRO 4.975*** 4.951*** -3.120* -3.071* -0.076 -0.065(2.73) (2.71) (-1.96) (-1.93) (-0.04) (-0.03)

MACRO 0.482* 0.488* -0.026 -0.033 -0.026 -0.028(1.82) (1.84) (-0.11) (-0.14) (-0.10) (-0.11)

SI*BETA – 0.067 – -0.127 – -0.028– (0.69) – (-1.17) – (-0.20)

BETA – -0.020 – 0.024 – 0.006– (-0.76) – (0.79) – (0.14)

R2 (%) 0.473 0.474 0.800 0.806 0.193 0.194


Table 9. Predicting Changes in the VIX

This table contains time-series regressions of changes in the CBOE volatility index, VIX, on the average absolute value of EARSand additional controls. The dependent variable is Log(V IXm+1/V IXm) which equals the log change in the VIX in monthM+1, where M is the month of the earnings calendar. All independent variables are measured in month M. V IXm is the levelof the VIX in month M . Expected earnings announcement dates of firms expected to announce in month M + 1 are measuredin the earnings calendar data on the final trading date of calendar month M . FMD is a dummy variable that equals one forthe first month of each calendar quarter. The first month denotes January, April, July, and October. EARS is the percentagechange in a firm’s expected reporting speed relative to the same fiscal quarter in the prior year. A firm’s expected reportingspeed is defined as the number of days between its expected earnings announcement date and fiscal period end date and thelagged reporting speed is defined as the difference between a firm’s realized earnings announcement date from the prior yearand its corresponding fiscal period end date. The sample for this analysis consists of 95 calendar month observations spanning2006-2013.

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

ABS(AGGEARS) 2.940*** 2.733*** 2.772*** 3.825*** 2.985***(3.48) (3.40) (3.82) (3.97) (3.17)

Log(VIXm/VIXm�1) – -0.284** – – -0.153– (-2.17) – – (-1.38)

VIXm – – -1.595*** – -1.481***– – (-4.23) – (-3.72)

FMD – – – 4.059 1.460– – – (1.11) (0.44)

R2 (%) 8.818 16.241 37.963 9.769 40.239

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