An empirical investigation of the effect of quarterly earnings announcement timing on stock returns

Accounting Research Center, Booth School of Business, University of Chicago

An Empirical Investigation of the Effect of Quarterly Earnings Announcement Timing on
Stock Returns
Author(s): William Kross and Douglas A. Schroeder
Reviewed work(s):
Source: Journal of Accounting Research, Vol. 22, No. 1 (Spring, 1984), pp. 153-176
Published by: Blackwell Publishing on behalf of Accounting Research Center, Booth School of Business,
University of Chicago
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Journal of Accounting Research
Vol. 22 No. 1 Spring 1984
Printed in U.S.A.

An Empirical Investigation of the
Effect of Quarterly Earnings
Announcement Timing on Stock
Returns

WILLIAM KROSS AND DOUGLAS A. SCHROEDER*

1. Introduction
This research examines both the association between quarterly an-
nouncement timing (early or late) and the type of news (good or bad)
reported,and the relationship between stock returns and timing around
the earnings announcement date. Recent research on announcement
timing (Givoly and Palmon [1982], Patell and Wolfson [1982], Kross
[1981], and Whittred [1980]) has provided evidence that delayed an-
nouncements of annual earnings more often convey bad news (i.e., lower
than expected earnings) than do early announcements. However, we
know of no study which has reportedevidence of the same phenomenon
for quarterlyearnings.Furthermore, there is a limited amount of evidence
regardingthe reaction of market participants to announcement timing.
While three studies (Givoly and Palmon [1982], Kross [1982], and
Chambersand Penman [1984]) find that early (late) announcementsare
associated with higher (lower) abnormal returns or high (low) stock
return variability, relative to late (early) announcments, only Kross
[1982] controlled for the sign of the earnings forecast error and none
controlled for the magnitudeof the earnings forecast error.
It is well accepted that stock returns are associated with the sign of
the earnings forecast error (EFE). Since announcement timing is also
* Associate Professor and Assistant Professor, Purdue University. We wish to thank the
members of the Purdue University and the University of Chicago accounting seminars for
their comments. [Accepted for publication June 1983.]
153
Copyright ?, Institute of Professional Accounting 1984

154 JOURNAL OF ACCOUNTING RESEARCH, SPRING 1984

associated with the EFE this variable must be controlled if one is to
examine market reaction to announcement timing. Similarly, Beaver,
Clarke, and Wright [1979] reported that stock returns are also associated
with the magnitude of the earnings forecast error, so it is necessary to
control for forecast error magnitudes as well. This is because early (late)
announcers could be releasing extremely good (bad) news. Finally, in the
light of recent research which shows that stock returns around the
announcement date are inversely related to the size (market value) of
the firm (Atiase [1980] and Ro [1983]), like Chambers and Penman
[1984], we decided to control for the potentially confounding size effect.1
Our objective, then, was to determine whether the association between
announcement timing and stock returns persists after controlling for the
sign and the magnitude of the earnings forecast error and firm size.
Briefly, our results show that early quarterly earnings announcements
(1) contain better news and (2) were associated with larger abnormal
returns relative to late announcements. These findings hold both for
large and small firms, for positive and negative EFEs, and for small
absolute values of the EFE.
In section 2 we describe the procedures and the data used in the tests.
The lag and earnings expectations models used to classify firms into
reporting and earnings categories are discussed in section 3. The results
appear in section 4, followed by a summary and conclusions (section 5).

2. Procedure and Data
2.1 PROCEDURE
First, we computed a time lag forecast error for each of 12 quarters on
the basis of a comparison of the actual quarterly announcement date
with a forecasted date. Second, we computed an earnings forecast error
for each firm and each quarter on the basis of a comparison of actual to
forecasted quarterly earnings. We then examined the earnings forecast
errors for the earliest and latest quarterly announcements for each firm
with the expectation that the earliest announcements (relative to expec-
tations) would have a higher (larger positive or smaller negative) median
EFE than observed for the latest announcements. Next, we categorized
each firm on the basis of both its lag forecast error (early, on time, late)
and its earnings forecast error (good news, bad news). This process
resulted in six distinct groups of firms: early-good, early-bad, on time-
good, on time-bad, late-good, late-bad. Finally we examined the abnormal
stock return behavior on the days surrounding the quarterly announce-
ments for all six groups of firms in order to determine whether the
announcement timing conveyed or was associated with information other
than that contained in the earnings number.
'We want to thank the reviewer for making this suggestion.

EFFECT OF EARNINGS ANNOUNCEMENT TIMING 155
TABLE 1
Median Autocorrelations at Lags One-Four

Autocorrelation Raw Residuals Residuals
at Lag Time Lag RandomWalk Model Model
Autoregressive

1 -.1712 +.1909 -.1015
2 -.0939 +.0517 -.0069
3 -.1887 -.0086 -.0302
4 +.5809 -.2832 -.1310

2.2 THE SAMPLE
Our total sample consisted of 297 NYSE and American Stock Exchange
firms that met the following conditions: (1) daily stock price data were
available for the years 1977-80 on the daily ISL (Investment Statistics
Laboratory) tapes produced by Standard and Poor's and Chase Econo-
metrics; (2) quarterly earnings from the third quarter of 1968 through
the third quarter of 1980 (the latest available at the time this study was
conducted) were available on the quarterly COMPUSTAT tapes; (3)
quarterly earnings announcements dates were available from the second
quarter of 1971 through the third quarter of 1980 on the quarterly
COMPUSTAT tapes; and (4) the fiscal year ended in December.
These filters resulted in 3,564 observations-12 quarters for each of
297 firms. All observations were used when we examined the relationship
between announcement timing and the earnings forecast error. However,
missing stock return data caused us to eliminate one firm, yielding 3,552
observations for the examination of stock price behavior.

3. Models
3.1 ANNOUNCEMENT LAG FORECAST MODELS

A firm was classified as reporting early or late based on a comparison
of the actual time of announcement with the expected time of announce-
ment. The expected time of announcement was formulated via a time-
series analysis of each firm's reporting history. We examined the auto-
correlation functions for the 26 quarterly report time lags from the
second quarter of 1971 through the third quarter of 1977 for the 297
firms in our sample.2 The median autocorrelations for lags one through
four are presented in table 1.
As one would expect, there is clearly a spike in the autocorrelation
function at lag four. The autocorrelation function of the fourth differ-
ences (a random walk model) still produced small spikes at lags one and
four. Since we expected fourth-quarter (annual) earnings to be reported
at longer lags, on average, than interim reports, we estimated an auto-
2
A lag was measured by the number of days elapsing from the end of the reporting
period to the earnings announcement.

156 W. KROSS AND D. A. SCHROEDER

regressive model with an indicator variable associated with fourth-
quarter announcements for predicting report time lags for each firm.
Model (1) predicts the report lag as follows:
Lagiq = ai + fj(Lagiq-4) + Yi(Q4) + Fi(Lagiq-) (1)

where:
Lagiq= forecast of the number of days spanning the end of the quarter
and the earnings announcement for firm i in quarter q;
Lagi-4 = actual number of days spanning the end of the quarter and
the earnings announcement for the same quarter of the preceding year;
Lagi,_q = actual number of days spanning the end of the quarter and
the earnings announcement for the quarter immediately preceding
quarter q;
Q4= 1 if quarter q is the fourth fiscal quarter, 0 otherwise;
a, y,y, r = firm-specific parameters.
The medians of the autocorrelation function of the residuals for model
(1) (column 4 in table 1) indicate that they are nearly white noise. This
model was used to forecast the report time lag for each of the next 12
quarters with a reestimation of the parameters after each quarter's
forecast.
A second lag forecast model, model (2), was chosen as a benchmark
for comparison to model (1). This model is a random walk in which
report lags are predicted as:
La~gq= Lagiq-4 (2)
where the terms are defined as before. As reported in column 3, table 1,
small spikes appear in the autocorrelation function at lags one and four
for this model. Nevertheless model (2) seemed a reasonable and appro-
priate benchmark for purposes of comparison with model (1). Each of
these models was used to forecast the announcement lag for each of 12
consecutive quarters beginning with the fourth quarter of 1977.
3.2 EARNINGS FORECAST MODEL
The type of earnings news reported was classified vis-A-vis an extrap-
olation of each firm's quarterly earnings. We utilized the premier quar-
terly forecasting model proposed by Griffin [1977] and Watts [1975], the
parameters of which were estimated for each firm:
Zq = Zq-4 + Zq-i - Zq-5 - Oaq1 - yaq4 + Oyaq-5 (3)

where:
Zq = one-quarter-ahead forecast of EPS in quarter q,
Zq = actual EPS in quarter q,
0= a regular first-order moving average parameter,
y= a seasonal moving average parameter,
a= a serially uncorrelated error term.

The choice of a "common model" structure follows from Jenkins [1979]
as well as empirical studies of earnings. Jenkins suggests that for rela-
tively short time series a common model may be appropriate where the
environments generating the data have common features. In addition,
empirical studies such as Foster [1977], Lorek [1979], and Schroeder
[1980] provide evidence that time-series models generate quarterly fore-
casts that are superior to naive martingalelike models.
For each firm, the 37 quarterly EPS figures (adjusted for stock divi-
dends and stock splits) from the third quarter of 1968 through the third
quarter of 1977 were used to estimate the parameters of this model. All
forecasts of interim and annual (fourth-quarter) EPS were one-quarter-
ahead forecasts generated by updating the model for each of the next 12
quarters.

3.3 STOCK RETURN MODEL
In the evaluation of stock return response to earnings announcements,
we used the traditional market model posited by Sharpe [1963]:
Rik = ai + O2iRmk Like
+ (4)
The parameters of this model were estimated using one year of daily
returns (approximately 252 observations) prior to the quarter of the
earnings announcement. The parameter estimates were used to compute
the abnormal return for days -2 through +2.

4. Results
4.1 RESULTS-TIMING VERSUS TYPE OF NEWS

In this section we report on the nature of the report timing for both
interim and annual earnings and the association of report timing with
the EFE.
Figures 1 and 2 depict the distributions of the actual reporting lags
(the number of days between the end of the fiscal period and the earnings
announcement), while figures 3-6 depict the unexpected (actual-ex-
pected) lag over four-(calendar) -day reporting intervals. The distribution
of the raw reporting lag is quite similar to that reported by Chambers
and Penman [1984]. Interim reports, reported in figure 1, cluster between
22 to 30 days after the quarter and are characterized by a distribution
that is skewed to the right. However, the distribution of annual an-
nouncements, reported in figure 2, seems more symmetric, with some
clustering of announcements between 28 and 40 days after the end of the
year. The distributions of the lag forecast errors, reported in figures 3-6,
are almost symmetrical for both annual and interim announcements. As
expected, all four distributions are characterized by a sharp spike at zero
with a similar number of observations above (late) and below (early) the
mean.


bays -6 10 14 18 22 26 30 34 38 42 46 50 54 58 -62

FIG. 1.-Frequency distribution of reporting time lags: interim announcements.

Days .56 104 14_ 18
- 224 264 30 1-38 42 -46 50 54 58 62

FIG. 2.-Frequency distribution of reporting time lags: annual announcements.

Tests of the relationship between the unexpected lag and EFEs are
reported in table 2 for both interim and annual announcements. The raw
forecast error was first deflated by its standard error (se). Thus:
EFE = (Zq Zq)/se.
When EFE is positive (negative), earnings are greater (less) than ex-
pected.
Panel A of table 2 shows the average earnings forecast error across all
firms from the earliest to the latest announcement (relative to expecta-
tions). The Wilcoxon statistic on the difference between the averages for


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the earliest and the latest announcing quarters is significant at a = .01
for both the autoregressive and the random walk models. The results of
the Page L test (which tests the null against the ordered alternative a,1>
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significant at a = .01.
Panel B of table 2 shows the results when the three annual announce-
ments are omitted for each firm. Again, the results are significant and
consistent with the results based on all quarters. We conclude, therefore,
that earlier (later) quarterly announcements are characterized by higher
(lower) unexpected earnings. We next investigate whether this relation-
ship had any effect on stock returns.

4.2 RESULTS-ANNOUNCEMENT TIMING AND STOCK
RETURNS

Using daily data, the possibility of cross-sectional correlation in the
residuals existed due to earnings announcement clustering. For example,
as many as 39 firms announced their earnings on the same calendar date.
In order to mitigate the problem of cross-sectional correlation we gener-
ated controlled residuals by subtracting the residuals of a randomly
selected nonannouncing sample firm from that of each announcing firm
during the earnings announcement time period (day -2 through day +2).
The controlled residual was computed as follows:

Vik = fik - fjk

where:
Vik = controlled residual for treatment firm i, day k;
cik = market model residual for treatment firm i, day k;
cjk = market model residual for control firm j, day k.
The only restriction applied in the selection of the control firm was that
the quarterly earnings announcement of the treatment firm did not occur
within seven calendar days of the announcement of its control firm. This
process was repeated for each firm for each quarter tested. The results
of all succeeding tests are reported using these controlled residuals
(hereafter residuals).3
In order to assess the relationship between announcement timing and
stock returns, we had to control for the announcements' news effects on
those returns. We did so using a two-factor analysis of variance. One
factor classified firms by the type of news (good or bad) reported, while
the other represented the timing (early, on time, or late) of the announce-
ment. This approach allowed us to examine the timing effect indepen-
dently of the type of news reported. For test purposes we classified firms
as reporting on time if the actual announcement date was within ? one

3We also conductedtests on the raw residualsof the treatment firms. Our conclusions
were not altered.

day of the expected announcement date. Announcements arriving earlier
(later) were categorized as early (late).
The results for all observations are presented in figure 7 and table 3
for the autoregressive lag expectation model. The residuals are reported
as percentages. A glance at figure 7 reveals immediately that announce-
ment timing as well as the type of news had a distinct association with
the stock return residuals.
Since the interaction between the two factors was never significant we
do not report it. Consistent with expectations, good news firms, with a
five-day cumulative average residual (CAR) of .83%, outperformed bad
news firms whose five-day CAR was -.97%. A stratification on timing
alone yielded a CAR on early firms of .43%, while late or on time
announcements had CARs of -.16% and -.27% respectively. Since earlier
announcements typically contain good news the better performance for
early firms is not surprising. However, when the sample is stratified into
news/timing categories the effect of announcement timing still persists.

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FIG. 7.-Cumulative averageresiduals (in percentages)aroundthe announcementdate
(day 0): good/early (GE), good/late (GL), good/on time (GOT), bad/early (BE), bad/late
(BL), bad/on time (BOT).


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The CAR for good news announced early was 1.39%, but only .54% for
good news announced late. Early bad news produced a five-day CAR of
-.79%, compared to a -1.02% CAR when announced late. Thus, even
after controlling for the sign of the earnings forecast error, the timing
effect was still significant at day -2 through day 0 and for the five-day
CAR.
The results of separate tests on annual and interim announcements
are reported in panels A and B of table 4. Both timing and news effects
were significant for the five-day CAR for both annual and interim
announcements, at day -2 for annual announcements and at day -2
through day 0 for interim announcements. These results lend strong
support to the notion that the timing effect is independent of the sign of
the EFE.
Although the ANOVA results indicate that the timing effect was
distinct from the sign of the EFE, it is still plausible that it was a function
of the magnitude of the EFE. That is, "good news" firms might have
reported very good news when they announced early, but only moderately
good news when they announced late. Similarly, late "bad news" might
have been very bad compared to early bad news announcements. This is
a reasonable alternative hypothesis, particularly in the light of the study
by Beaver, Clarke, and Wright [1979] which reported a positive relation-
ship between stock returns and the magnitude of EFE.
In order to address this issue, we subclassified each main group into a
moderate and an extreme subgroup-for example, moderately bad (neg-
ative EFE less than one standard error) and very bad (negative EFE
greater than one standard error in magnitude), with a similar dichotomy
for "good news" firms. If the report timing was a surrogate for magnitude
of EFE, we would not expect a relationship between announcement
timing and stock returns for moderate news classifications. As before, an
ANOVA was used, but only on the firms that reported moderately good
or bad news in early and late announcements.
The test results in table 5 are not consistent with the proposition that
the timing effect was a proxy for the magnitude of the EFE. The timing
effect persisted at day -1 and day 0 and for the five-day CAR even
though, by construction, the magnitude of the EFE was small. Separate
examinations on annual and interim announcements, reported in panels
A and B of table 6, yielded similar results. Again the timing effect was
significant for the five-day CAR and for one or more of the days around
the announcement date. The news effect was very weak, as would be
expected since all extreme EFEs were omitted for these tests. These
results provide strong evidence that report timing was not a surrogate
for the magnitude of the earnings forecast error, but rather conveyed or
was correlated with information distinct from the sign and the magnitude
of EFE.


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4.3 RESULTS-ANNOUNCEMENT TIMING AND FIRM SIZE

Previous studies (Chambers and Penman [1984] and Ro [1983]) found
an inverse relationship between firm size and the absolute value of stock
returns around the earnings announcement date. A question naturally
arises about whether the timing effect phenomenon would be observable
for both large and small firms. In order to provide evidence on this issue
we took our "moderate news" subsample and split it into small and large
firms and conducted tests on each size substratum. All firms whose
market value was below (above) the medium market value at the end of
the second quarter of 1978 were defined as small (large) for the entire
sample period. The test results on large (small) firms are reported in
panel A (panel B) of table 7.
As shown there, the timing effect persisted across both size categories.
For large firms (panel A), announcement timing was significantly related
to stock returns at day -2, day -1, (weakly) at day 0, and for the five-
day CAR. Late announcers of moderate news saw their share prices drop
by .89%, on average, as opposed to an increase of .30% for their early-
announcing counterparts. Announcement timing also affected moderate
good news firms, which had residual returns of .72% if they announced
early, but only .24% if they announced late.
The test results on the small firms, reported in panel B of table 7, tell
a similar story. The announcement timing effect was significant at day
-1, day 0, and (weakly) for the five-day CAR. The share prices of firms
that announced moderately bad news late fell by .90%, on average, over
the five-day announcement period, whereas the early-announcing coun-
terparts fell by only .03%. Moderately good news announced early was
rewarded by a .31% abnormal return, whereas later-announced good news
was greeted with a .32% negative return. Thus, it appears that the "timing
effect" persisted for both large and small firms.

5. Summary and Conclusions
Generally, we found that earnings announcement timing was associ-
ated with abnormal stock returns around the earnings announcement
date. Abnormal returns of firms that announced early (late) were sig-
nificantly higher (lower) than the returns of firms that announced late
(early). This general result is consistent with previous research by Givoly
and Palmon [1982], Kross [1982], and Chambers and Penman [1984];
however, these other studies did not completely control for potentially
confounding factors regarding the "timing effect." After controlling for
these, our results are remarkably consistent. The "timing effect" persisted
whether the earnings announcement (1) contained good news or bad
news, (2) was an annual or interim announcement, (3) was made by a
large or small firm, or (4) contained moderately good or moderately bad
news.

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should affect stock returns. Indeed, we believe that the announcement
timing itself should have no effect, but rather is probably associated with
some other event that either is typically associated with a reporting delay
or is usually viewed as "bad" news. Loss contingency disclosures or CPA
switches could be two such types of events. Of course, additional research
is needed to explore this possibility.
Because "timing" (or other events associated with it) can greatly
influence stock returns, we suggest that future studies on announcements
incorporate adjustments which control for announcement timing when
examining stock return responses to the announcements. Failure to do
this could bias, or induce noise into, the test results.
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CHAMBERS, A. E., AND S. H. PENMAN. "Timeliness of Reporting and the Stock Price
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FOSTER, G. "Quarterly Accounting Data: Time-Series Properties and Predictive Ability
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SCHROEDER, D. A. "Expectations Data and the Stochastic Properties of Accounting
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