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The timing of and incentives for annual earnings forecasts near interim earnings announcements
Journal
of Accounting
and Economics
11 (1989) 275-292.
North-Holland
THE TIMING OF AND INCENTIVES FOR ANNUAL EARNINGS FORECASTS NEAR INTERIM EARNINGS ANNOUNCEMENTS Scott E. STICKEL* Universit.v of Penns~vlvania, Philudeiphicr,
Received August
PA 19104. USA
1988, final version received February
1989
Security analysts anticipate and respond to interim earnings announcements when they revise their annual earnings forecasts. Analysts avoid revising for two weeks before an interim announcement and more frequently revise immediately after an announcement. As expected, revision activity after an interim announcement is greater if absolute unexpected interim earnings are larger, if there are more competing analysts, and if unexpected interim earnings are negative. Unexpectedly, analysts are reluctant to revise their forecasts early in the fiscal year. These findings carry implications for research designs that use analyst forecasts.
1. Introduction This paper provides evidence on the demand for and supply of analyst forecasts of annual earnings per share (EPS). Evidence is provided on the timing of forecasts around interim earnings announcements and on the effect of incentives on revision activity. Such evidence is important to investors who rely on analyst forecasts for timely information.’ It is also relevant to studies that examine abnormal returns surrounding forecast revisions and earnings announcements, that compare the relative accuracy of analyst forecasts, and that use analyst forecasts as market expectations proxies. I find that annual earnings forecasts are relatively stale or out-of-date, on average, in the two weeks prior to interim announcements. In the two weeks
*I am grateful for the cooperation of Zacks Investment Research. I received particularly helpful comments from Don Lewin and two anonymous referees. I also received helpful comments from Jeff Abarbanell. Linda Bamber, Larry Brown, Dave Larcker, Katherine Schipper, Rex Thompson, Brett Trueman. Ro Verrecchia, and Ross Watts. I am grateful for financial support from the Milken Family Foundation, the Peat Marwick Foundation, Deloitte Haskins & Sells. the Institute for Quantitative Research in Finance, and the Junior Faculty Research Fund at the Wharton School. ‘As an example of the size of the security analyst industry, for the 1985 tiscal year the Zacks Investment Research database has annual earnings forecasts for approximately 3.400 companies by approximately 1,500 analysts working for approximately 100 brokerage houses. The number of analysts forecasting earnings for a single company ranges from 1 to 39. Otlicials at Zacks believe their database includes every firm for which analyst forecasts are available and that there is no systematic exclusion of firms.
0165-4101/89/$3.5001989,
Elsevier Science Publishers
B.V. (North-Holland)
276
S. E. Stickel, Forecast revisions und earnings announcements
after interim announcements, revision activity increases. This increase is greater if absolute unexpected interim earnings are larger, if there are more competing analysts, if unexpected interim earnings are negative, and if it is late in the fiscal year. Despite these significant findings, I explain less than 5% of the cross-sectional variation in the timing of revisions. Finding that earnings announcements trigger forecast revisions has implications for studies documenting abnormal returns after earnings announcements [e.g., Watts (1978) Rendleman, Jones, and Latane (1982) Foster, Olsen, and Shevlin (1984)] and for studies documenting abnormal returns after forecast revisions [e.g., Givoly and Lakonishok (1979) Brown, Foster, and Noreen (1985)]. The strong association between announcements and revisions increases the probability that abnormal returns after earnings announcements and abnormal returns after forecast revisions are the same phenomenon. Evidence of stale forecasts affects the interpretation of studies that compare the accuracy of managerial and analyst forecasts. For example, Hassell and Jennings (1986) find managerial forecasts are more accurate than consensus analyst forecasts for up to four weeks after the managerial forecasts are released. My findings are consistent with stale individual analyst forecasts, embedded in the consensus forecast, explaining this curious result. The systematic timing of forecasts is also relevant to the literature that compares the accuracy of analyst forecasts with that of forecasts derived from the time series of earnings. For example, I find analysts are more likely to revise forecasts after third-quarter announcements than after first-quarter announcements, ceteris paribus. Their behavior suggests that their forecasts are more up-to-date after third-quarter than after first-quarter announcements. Thus, comparisons of forecast accuracy are less biased against analysts when the forecast horizon begins after third-quarter than after first-quarter announcements. Documenting the timing of forecasts provides evidence on when analyst forecasts are likely to be better proxies for market expectations. Stale forecasts are likely to be less accurate proxies for market expectations than recently revised forecasts, because earnings-related information revealed since the date of a stale forecast can be used by investors to update their expectations.3 Although I provide no direct evidence on the accuracy of market expectation proxies, analyst forecasts are likely to be more accurate proxies for market
20’Brien (1988) compares accuracy for horizons of 240, 180, 120, and 60 days prior to the annual earnings announcement date, which approximately corresponds to horizons after the prior year’s annual announcement and the current year’s first-, second-, and third-quarter announcements, respectively. However, O’Brien finds analyst forecast accuracy dominates time series forecast accuracy more at the longer time horizons, which is the opposite of expectations given the results of this study. 3ELxamples of earnings-related information newsletters, and M’crll Street Journal articles.
are
subsequent
stock
price
changes,
industry
S. E. Stickel,
Forecast revisions und eurnings announcements
277
expectations just after interim announcements than just before, especially if it is late in the fiscal year, if there are more competing analysts, and if unexpected quarterly earnings are negative. My research continues in a vein similar to Brown, Foster, and Noreen (1985, sections 2.2, 2.3). They compute the percentage of individual analysts who revise their annual forecasts in the months surrounding interim and annual announcements. Revision activity increases slightly in the month after an announcement (month + 1, not month 0) according to the I/B/E/S database and in the month of announcement (month 0) for forecasts by Wells Fargo Investment Advisors. Their evidence is limited to monthly data and is intentionally brief. Statistical tests of significance and cross-sectional tests using analyst incentives to revise are outside of the scope of their study. There is related evidence that Value Line Investment Survey analysts respond to interim announcements by revising their forecasts [Abdel-khalik and Espejo (1978) Brown and Rozeff (1979) and Brown, Hughes, Rozeff, and VanderWeide (1980)]. Although the research designs differ, the basic approach is typified by Brown, Hughes, Rozeff, and VanderWeide. They regress quarterly changes in forecasted annual EPS on unexpected quarterly EPS and find a significantly positive coefficient for unexpected quarterly EPS. While concluding that analysts respond to announcements, these studies provide no evidence on the precise timing of the response or other incentives to revise. Section 2 develops the hypotheses. Section 3 describes the data. Section 4 describes the method used to detect a change in forecast revision activity and reports mean revision activity. Section 5 describes the method used to detect cross-sectional differences in abnormal revision activity and reports the crosssectional results. Conclusions are contained in section 6.
2. Hypotheses 2.1. Mean abnormal revision activity near interim announcements
Analysts have an incentive to refrain from revising just prior to an interim announcement. Interim announcements render prior forecasts out-of-date. Such inaccurate forecasts adversely affect an analyst’s reputation.4 An annual earnings forecast will be more accurate if it accounts for interim earnings. Hence, if the cost of issuing forecasts forces a choice between revising an annual earnings forecast immediately before and immediately after an interim
4Eamings Institutional
forecasts Investor
are one of four criteria by which security annual ‘All-American Research Team’.
analysts
arc ranked
in the
278
S.E. Stickel, Forecast revisions and eumings unnouncements
earnings announcement, analysts will refrain from revising their forecasts immediately after the interim announcement.
before
and revise
H.l:
Ceteris paribus, the abnormal percentage of analysts forecasts before interim announcements is negative.’
revising
annual
H.2:
Ceteris paribus, the abnormal percentage of analysts forecasts after interim announcements is positive.6
revising
annual
2.2. Cross-sectional
abnormal revision activity
I expect six variables interim announcements: 1. 2. 3. 4. 5. 6.
to be associated 7
with analyst
incentives
to revise after
absolute unexpected interim EPS, firm size, level of competition among analysts, ex ante uncertainty of annual EPS, number of the interim quarter (1, 2, or 3) within the fiscal year, sign of unexpected earnings (positive or negative).
2.2.1. Absolute
unexpected
interim EPS
I assume the larger is the absolute unexpected interim EPS, the greater is the marginal benefit of a revision to investors. Consequently, analysts have more incentive to revise an annual earnings forecast if absolute unexpected interim EPS is larger.
‘Price changes that occur at interim announcements provide an incentive for analysts to revise before interim announcements. If an analyst can provide information about the price effects of an upcoming interim announcement through an annual forecast revision, customers could capture any price change caused by the announcement. A countervailing factor is that analysts could provide this information through interim forecasts or buy/sell recommendations. ‘Investors expect analysts will do more than annual earnings. See Brown, Griffin, Hagerman, Noreen (1985, section 4.4) for evidence consistent after interim announcements provide information series of earnings or price changes.
mechanically extrapolate interim earnings into and Zmijewski (1987) and Brown, Foster, and with the assumption that forecasts immediately that cannot be simply extrapolated from a time
‘I expected a cross-sectional relation between the uncertainty in interim earnings and revision activity before the interim announcement. If quarterly earnings are more difficult to predict, analysts may be more likely to refrain from revising annual forecasts just prior to interim announcements. Tests for a relation between the uncertainty in interim earnings and revision activity prior to the interim date found the relation to be negative. but insignificantly different from zero at conventional levels.
S. E. Stickel,
H.3:
2.2.2.
Forecast revisions und earnings unnouncements
279
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with absolute unexpected interim EPS.
Firm size
The revelation of earnings-related information through more timely media than earnings announcements increases with firm size. For example, larger firms have more entries in the Wall Street Journal [e.g., Thompson, Olsen, and Dietrich (1987)]. Thus, the relative informativeness of interim earnings announcements, vis-a-vis other firm-specific earnings-related information revealed at other times, should be less for larger firms. H.4:
2.2.3.
Ceteris paribus, the abnormal percentage of analysts revising forecasts after interim announcements decreases with firm size.
Level of competition
annual
among analysts
Competition among analysts can produce incentives to revise annual forecasts after interim announcements. In Trueman’s (1987) model, a single analyst is reluctant to revise because revision signals an inaccurate prior forecast. With many analysts, it would be more difficult for any one analyst to obscure an inaccurate forecast by not revising, since the revisions by competitors would reveal the strategy. H.5:
2.2.4.
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with the number of competing analysts.
Ex ante uncertainty
of annual EPS
Bayesian theory predicts revision activity after interim announcements is an increasing function of the ex ante uncertainty in annual earnings. If there is greater uncertainty, analyst prior distributions for earnings are more diffuse. With diffuse priors, the marginal impact of new information on the posterior distribution is greater. H.6:
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with the ex ante uncertainty in annual earnings.
280
2.2.5. Number
S. E. Stickel,
Forecusr revisions and eumings announcements
of the interim
quarter within a jiscal year
Unexpected first-quarter earnings affect the forecasts of a greater number of remaining quarters of the fiscal year than unexpected second- or third-quarter earnings. Thus, for a given amount of unexpected interim EPS, the change in current-year forecasted annual earnings should be greater for first-quarter announcements than second- or third-quarter announcements.’ H.7: Ceteris paribus, the abnormal percentage of analysts revising forecasted annual EPS after interim announcements decreases with the number of the interim quarter (1, 2, or 3) within the fiscal year. 2.2.6. Sign of unexpected earnings If investors are risk-averse, analyst revision activity should be greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude. Risk aversion implies that the utility lost by a given dollar amount of negative unexpected earnings is greater than the utility gained by the same dollar amount of positive unexpected earnings. H.8: Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements is greater for negative than positive unexpected earnings of the same magnitude. 3. Data bases and sample selection process Annual and quarterly EPS forecasts are obtained from Zacks Investment Research (Zacks). The database contains individual analyst forecasts of primary EPS before extraordinary items and discontinued operations for more than 3,500 companies. Sample selection begins with the set of firms for which there are annual earnings forecasts on the database for any fiscal year within the 1982 to 1985 period. For this set of firms and fiscal years, interim announcements are selected if the announcement meets the following requirements: (1) The 1985 Compustat Quarterly Industrial File must have figures for quarterly EPS before extraordinary items and discontinued operations, for volume of shares traded during the quarter, for price per share and number of outstanding shares at quarter-end, and the interim announcement date. Earnings figures are adjusted for any changes in shares outRThis proposition is supported by Brown and RozetT (1979) who regress changes in forecasted quarterly earnings on unexpected quarterly earnings. Holding unexpected interim earnings constant, their results suggest the change in forecasted annual earnings is greater for first-quarter than third-quarter announcements. For my sample, the mean absolute values of unexpected EPS for quarters 1, 2, and 3 are $0.20, $0.22, and $0.22, respectively.
S. E. Stickel,
2x1
Forecast reuisions und eumings unnouncements
Table 1 Sample
selection
process for analyst
forecasts
Interim NYSEh Total number of firms with fiscal quarters and years ending within the 1982-1985 period on the Zacks database of annual earnings forecasts Deletions Insufficient data on the 1985 Compustat Quarterly Industrial File Remaining announcements and firms No outstanding forecast of interim EPS on the Zacks Investment Research File at the interim earnings announcement date Remaining announcements and firms Insufficient data for forecasted urttrucrl EPS on Zacks around the interim earnings announcement date Remaining announcements and firms Quarterly EPS figure on the Compustat file has been restated Announcements and firms included in final analysis
and interim earnings
Number
announcements
ASE’
OTC?
announcements
Total
NYSE
ASE
of firms3 OTC
Total
3.544
(1,993) 1.079
14.615
11.349
2.1X7
(4,044)
(1,599)
(635)
(6.278)
7,305
588
444
8,337
(101)
(21)
(498)
6,929
487
423
7,839
(280)
(30)
a
(313)
457
420
(376)
6,649
7,526
1,221
(885) 1,069
(282) 1.038
(152) 1,033
284
124
1,629
(274)
(106)
(1,265)
154
91
1.314
(76)
(19)
133
88
1,259
(21)
m
(175)
130
88
1,251
“Column totals do not add because a single firm generally has many interim each of which may or may not meet the various sample selection criteria. bNYSE: New York Stock Exchange. ‘ASE: American Stock Exchange. ‘OTC: Over-the-counter.
(377)
announcements,
standing and must not be restated. Restated figures include the effect of subsequent mergers, acquisitions, and accounting changes. Such restatements are not made on the Zacks files.9 ‘Compustat does not explicitly note restated EPS figures, but does note restated net income figures. The final sample excludes any observation where Compustat noted that net income is restated (data item 36 not equal to 0) or EPS is a Standard L Poor’s calculation (data item 3X not equal to 0 or 1).
282
S. E. Stickel, Forecust revisions and earnings announcemenfs Table 2 Time profile of sample of interim earnings
1982 1983 1984 1985
1982 1983 1984 1985
Jan.
Feb.
31 32 47 51
26 39 48 46
Jan.
Feb.
0 85 106 94
19 33 49 49
Time profile of month March April May
announcements.
ofintenmecrrmngsjiscul
Total
32 42 41
91 116 95
1,834 2,273 2,560 -859 7.526
Timeprojiie ofmonthof interimearnings unnouncement dute March April May June July Aug. Sept. Oct. Nov.
Dec.
Total
* 37 43 42
1.734 2,245 2,582 965 7,526
39 43 55 45
50 65 69 24
438 569 599 653
26 38 53 14
July
Aug.
497 597 711 2
41 54 76
32 35 35
quurter end Sept. Oct. Nov.
Dec.
469 635 690 722
June
76 121 158 103
31 54 50 21
451 528 637
84 114 151
488 563 639
36 50 52
45 57 56
448 491 577
Time profile of weekdu_v ofinterimearnings announcement Sun. Mon. Tue. Wed. Thu. Number of observations Percentage of total
15 0.2
972 12.9
1,543 20.5
1.783 23.7
1,699 22.6
Time profile of number of interim quarter First Second Number of observations Percentage of total
2.919 38.8
2,446 32.5
15 114 106
dute Fri.
Sat.
Total
1.503 20.0
11 0.1
7,526 100.0
Third
Total
2,161 2X.7
7.526 100.0
(2) The Zacks database must contain at least one analyst with an outstanding forecast of interim EPS the day before the interim announcement and at least one analyst with an outstanding forecast of annual EPS during an estimation period that surrounds the interim announcement date. Table 1 summarizes the sample selection process. Market value of equity is used, on a sample basis, to assess any selection bias from systematically excluding firms that have insufficient Compustat or Zacks data. Firms with deleted announcements are, on average, smaller than firms subjected to empirical tests. Thus, the inferences made may not be applicable to very small firms that are followed by analysts. Table 2 summarizes the time profile of the dates used in the empirical tests. 4. Tests for mean abnormal revision activity 4.1. Method for detecting abnormal forecast revision activity The method used to test the effect of interim announcements on revision activity borrows from ‘event studies’ that examine the effect of new informa-
S. E. Srickel, Forecust reuisrons and earnings unnouncements
283
tion on security prices. Define the percentage revisions for firm i on day t, PRil, as the number of analysts revising their annual earnings forecast divided by the number of analysts with an outstanding forecast, multiplied by 100. The abnormal percentage revisions for firm i on day t is defined as APR,,
= PR,, - PR,,
-. where PR, 1s the mean percentage revisions over event days - 40 to - 21 and +21 to +40.” These days provide an adequate number of observations (a maximum of 40 with a minimum requirement of 30) to obtain a reliable benchmark and minimize the interference of other interim announcement dates, which occur approximately every 60 trading days. Mean percentage revisions (MPR) and mean abnormal percentage revisions ( MAPR ) on day t are calculated as
MPR,
= 2 PR,,/N, I=1
and
MAPR,
= z
APRJN,,
1=1
where N, is the number of firms followed by analysts on day t.” Finally, and mean abnormal percentage revisions are cumulated over time as
CMPR,,=
f MPR, t=a
and
CMAPR,,
mean
= f: MAPR,, f=U
where CMPR,, is the cumulative mean percentage revisions and CMAPR., is the cumulative mean abnormal percentage revisions from event day a to event day b. To test the null hypothesis that daily mean abnormal percentage revisions equal zero, t-statistics are calculated as t, =
MAf’R,/a^,,,,,
where gMAPR is the estimated standard deviation of MAPR, over days -40 to -21 and +21 to +40. The APRi, are not independent because of calendar clustering of interim announcements. Nonindependence could be caused by, among other things, one analyst simultaneously revising his or her forecasts of more than one “The sensitivity of the reported results to the particular benchmark period is examined by using days + 21 to + 40. This alternative is chosen because days - 40 to - 21 for quarter 1 may be affected by the annual earnings announcement of the prior year [see Chambers and Penman (1984)]. However, the results using this alternative benchmark did not change any conclusions. “Abnormal revision activity is also calculated with equally-weighted analysts, as opposed to equally-weighted firms. The results are nearly identical at the mean portfolio level and are not reported.
284
S. E. Stickel, Forecust revisions and eumings announcements
company because of industry-wide factors. To assess the effect of nonindependence, observations are grouped by calendar day and averaged before computing overall portfolio means [see Jaffe (1974) and Mandelker (1974)]. The mean abnormal percentage revisions and t-statistics using this calendar time method did not change any conclusions and are not reported.
4.2. Mean revision activity Table 3 reports mean revision results. There is a pattern of negative abnormal revision activity for about two weeks before an interim announcement and significantly positive abnormal revision activity for about two weeks after. The MAPRs for single event days prior to interim announcements are not different from zero at conventional significance levels. However, the mean defined as APR,, cumulated from event day - 10 to - 1 for CAPRiq(-lO,~*)~ quarter q, is - 3.41%. Using the cross-sectional standard deviation of CAPR rq(- 10, - 1) and the number of observations, the t-statistic testing the hypothesis that CAPRiq( _ 1o,_ 1j equals zero has a value of - 14.93, which rejects the null hypothesis at less than the 0.0001 level.12 CMPR proxies for the cumulative percentage of analysts who have revised their forecasts since the announcement date.13
4.3. Dating of forecast revisions Brokerage houses notify Zacks of analyst forecast revisions via reports that may not have the actual dates on which analysts make less newsworthy revisions. If a brokerage house does not supply the actual revision date, Zacks uses the date of the brokerage house report. Brokerage house report dates are always coincidental with, or later than, the actual revision dates. Zacks does not identify forecasts as having an actual date or a brokerage house report date. Thus, the possibility arises that the significant abnormal revision activity documented on and after event day 0 could actually have occurred prior to event day 0. This is extremely unlikely given the abruptness of the change in revision activity on event day 0. It would be an extraordinary coincidence for the revisions with brokerage house report dates to align on
‘*Using the same grouping procedures CAPR,,(_ 10, t) is - 3.31% with a t-statistic
that are of - 7.96.
described
in
section
5.1,
the
mean
t3CMPR slightly overstates the cumulative percentage because a single analyst is included as many times as he or she revised their forecast between days 0 and + 20. Of the analysts revising between day 0 and +20, less than 1.4% revised twice or more between day 0 and +20. Additionally, CMPR equally-weights firms rather than analysts. The CMPR for equally-weighted analysts from day 0 to + 20 is 56.55% versus 57.01% for equally-weighted firms.
S. E. Stickel,
Forecast revisions and eurnings announcements
285
Table 3 Mean percentage of analysts who revised their forecast of annual earnings, The sample consists of 7,526 interim earnings announcements for 1,251 different firms with fiscal quarter ends and year ends within the 1982-1985 period. Event day 0 is the day of the interim earnings announcement. Event day - 20 -19 -18 -17 -16 -15 - 14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1
MPR=
CMPRb
1.65% 1.84 1.87 1.84 1.91 1.84 1.93 2.05 1.75 1.60 1.56 1.63 1.53 1.17 1.16 1.18 1.21 1.20 1.25 1.60
0
2.63
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
3.62 3.86 3.77 3.97 3.85 3.15 3.27 3.12 2.78 2.72 2.39 2.24 2.15 1.95 1.91 1.85 1.76 1.75 1.74 1.92
2.63% 6.26 10.12 13.90 17.86 21.71 25.45 28.72 31.85 34.62 37.35 39.74 41.98 44.14 46.09 47.99 49.84 51.60 53.36 55.09 57.01
MAPRC
I
- 0.04% 0.15 0.19 0.15 0.22 0.15 0.25 0.37 0.06 - 0.09 -0.13 - 0.06 -0.16 - 0.52 - 0.53 - 0.50 - 0.48 - 0.49 -0.44 - 0.09
-0.11 0.48 0.59 0.47 0.71 0.48 0.78 1.16 0.18 - 0.28 - 0.41 -0.19 - 0.51 - 1.64 - 1.67 - 1.60 - 1.53 - 1.57 - 1.39 -0.29
0.94
3.00
1.94 2.17 2.09 2.28 2.16 2.06 1.58 1.43 1.09 1.03 0.71 0.55 0.46 0.26 0.22 0.16 0.08 0.07 0.05 0.23
6.15 6.91 6.63 7.24 6.85 6.53 5.03 4.56 3.45 3.29 2.24 1.76 1.48 0.82 0.69 0.50 0.24 0.21 0.15 0.73
CMAPRd
w
- 0.04% 0.11 0.30 0.45 0.67 0.82 1.07 1.44 1.49 1.41 1.28 1.21 1.05 0.54 0.01 - 0.49 - 0.98 - 1.47 - 1.91 - 2.00
7,525 7,525 7,525 7,525 7,525 7,525 7,525 7,525 7,525 7.525 7,525 7,526 7,526 7,526 7,526 7,526 7,526 7,526 7,526 7,526
-1.05
7.526
0.88 3.06 5.14 7.42 9.58 11.63 13.21 14.65 15.73 16.77 17.47 18.03 18.49 18.75 18.97 19.13 19.20 19.27 19.32 19.55
7,526 7,526 7.526 7,526 7,525 1,525 7,525 7,525 7,526 7,526 7,526 7.526 7,526 7,526 1,525 7,526 7,526 7,526 7,526 7,526
aMean percentage of analysts who revised their forecast of annual EPS on event day t. using equally-weighted firm interim announcements and equally-weighted analysts within each equallyweighted firm interim announcement. bCumulative MPR. ‘Mean abnormal percentage who revised their forecast on event day t. dCumulative MAPR. ‘Number of interim announcement dates.
286
S. E. Sttckel,
Forecast reo,stom and eurnings unnouncements
event day 0 and not on event day - 1, because the corresponding actual revision dates would be occurring an unknown and random number of days before the brokerage house report date. Since the revisions with actual dates cause the abrupt change on day 0 and do not cause positive abnormal revision activity prior to day 0, the logical conclusion is that analysts refrain from revising before interim announcements and increase revision activity on and after interim announcements.r4
5. Tests for cross-sectional
differences in abnormal revision activity
Positive abnormal revision activity after interim announcements. To test tives to revise and abnormal revision are cumulated over these ten days and
continues for about ten working days for associations between analyst incenactivity, abnormal percentage revisions denoted CAPRjqcO, +9j.
5.1. Method and results of cross-sectional tests A number of problems arise when performing multiple regressions. The residuals from the regressions are nonnormal (leptokurtic) and heteroscedastic, their variance being a decreasing function of analyst following. They are also cross-sectionally dependent, due to calendar clustering. The tests in this section mitigate these problems in three ways. First, to judge the effect of outliers on the estimated coefficients, two sets of regressions are performed using (1) all observations and (2) a trimmed sample that excludes any observation where the absolute value of CAPR,,(,, +9j exceeds 100 percent. Second, to mitigate the effect of heteroscedasticity and nonindependence on the significance of the estimated coefficients, mean results are calculated from ten independent cross-sectional regressions.15 Third, I examine the sensitivity of my results to alternative proxies for analyst incentives and for abnormal revision activity. Observations are grouped by interim announcement date into ten subsamples, which subsumes any cross-sectional temporal dependence within subsamples and minimizes the cross-sectional temporal dependence between subsamples. A cross-sectional regression is performed with each of the ten
14This conclusion does not preclude the possibility that a relatively small portion of the documented abnormal revision activity on and after day 0 relates to forecasts made prior to day 0. However, this possibility would be offset by any documented revisions immediately prior to day 0 that actually are forecasts made long before day 0. t5The test is based Sefcik and Thompson
on a design (1986).
used by Fama
and MacBeth
(1973) and elaborated
upon
by
S. E. Stickel,
subsamples,
using the following
CAPR rq(O,+9)
287
Forecast revisions and eumings announcements
model:
=Po+Plln(l~Qj,l/lQ,,l>
+/%lnMARKETVALW,
+~,lnNUMANALYSTS,,+~~ln(~~,,/IQ,,l> + &QTRZDUM,, + ,B,UQSIGND
+ &QTRSDUM,, UM,, + E,~.
The proxy for unexpected interim earnings, I UQrql, is defined as the absolute deviation of actual quarterly EPS from the mean consensus forecast on the day before the interim announcement.‘” The variable lQ,4l is used to scale unexpected earnings for comparability across firms.” The proxy for firm size, MARKETVALUE,,, is the market value of common stock at the end of quarter q. The proxy for the level of competition, NUMANALYSTS,,, is the number of analysts with an outstanding annual earnings forecast on the day of the interim announcement. The proxy for the precision of prior information, ,. u~,~, is defined as the estimated cross-sectional standard deviation of annual forecasts on the day before the interim announcement and is scaled for comparability across firms. ‘* The variables QTRZDUM,, and QTRSDUM,, are binary dummies used to determine if analyst revision activity varies by the interim quarter of the fiscal year (1, 2, or 3). The variable UQSIGNDUM,, is a dummy variable set equal to 0 if UQ,, 2 0.0 and set equal to 1 if UQ,4 < 0.0. Because abnormal revision activity is cumulated over ten days, errors from adjacent subsamples can be cross-sectionally dependent. Consequently, interim earnings dates are excluded if they are within ten days before any of the nine cutoff dates for the ten subsamples. This reduces the sample size from 7,526 to 5,164 interim dates. The ten estimates of each p, obtained from these regressions are unbiased, but their standard errors are affected by any heteroscedasticity or dependence. A test of whether each p, differs from zero, that incorporates any het“For the sample subjected to empirical tests. the mean 1L’QI is $0.221. The aensitivitv of the results to alternative measures of 1c/Q1 is examined by using three other measures of LIQ: actual quarterly EPS minus the last analyst forecast issued prior to the announcement date, which has a mean / L’QI of $0.222: actual quarterly EPS minus last year’s quarterly EPS. which has a mean 1L’QI of $0.339; and actual quarterly EPS minus the estimate from Foster’s (1977) time series model, which has a mean ) CiQI of $0.347. Results using these measures of I L:Qj did not change any conclusions. “Values of I r/Q I/I Q I greater than 2.0 are set equal to 2.0 to avoid distortions values of ( Q I The same procedure is used for the variable o^,/ I Q / ‘XUnfortunately, tAAlyincludes ment among analysts.
divergence
caused
by out-of-date
forecasts
caused by small
as well as disagree-
S. E. Stickel, Forecust revisionsand eurnings unnouncements
288
Table 4 Mean regression results for the relation between the abnormal percentage of analysts who revised their forecast of annual EPS immediately after interim earnings announcements and proxies for analyst incentives to revise forecasts. The main model is CAPR ,q(~. + g)a= PO + PI ln(l
UQ,, I / I Qiq I ) + BZ In MA RKETVALW, +& In NUMANALYSTS,, + /?.,ln( i&IQi,l) +&QTR2DUM,,
+ &QTR3DUM,,
+ &fJQSIGNDUM,,
+ qq
Mean regressionresultsfrom ten independentregressions Main model Independent
variable
Predicted sign
Entire sample
Mean coefficient INTERCEPT
(1)
Trimmedb sample
Alternative specifications of model using entire sample Model 2
Model 3
Model 4’
( t-statistic)d
11.59 (1.76)
1.49 (0.24)
11.91 (3.79)
6.65 (4.64)
4.88 (2.71)
6.64 (4.60)
15.46 (2.48)
14.52 (2.96)
~~(I~Q,,I/lQ,,IY
(+I
IN I UQ,,I/f’RfC4q)’
(+)
In MARKETVALUE,,a
(-)
In VOL.UME,,h
(-)
In NUMANALYSTS,,’
(+)
3.07 (2.70)
6.05 (6.19)
3.77 (4.74)
2.97 (2.54)
5.22 (5.99)
In( sAA,JI Q,q I P
(+I
- 3.97 (- 2.27)
- 1.16 (- 0.65)
- 3.35 (-- 1.94)
- 1.34 (- 1.08)
- 0.55 ( - 0.44)
QTR2DUM,<,k
(-)
5.60 (3.34)
5.26 (5.18)
5.16 (3.61)
5.77 (3.52)
5.71 (5.52)
QTR_IDUM,,’
c--j
12.30 (4.98)
10.57 (4.71)
12.64 (5.38)
12.79 (5.27)
12.24 (8.11)
UQSIGNDUM,qm
(+)
3.62 (4.27)
3.21 (3.18)
3.70 (4.33)
3.83 (4.89)
0.08 (0.11)
Adjusted
R’
Total number of interim earnings announcements
2.15 (2.26) - 7.06 ( - 0.28)
-0.53 ( - 0.82)
-0.76 (- 1.18)
-0.37 (- 0.65)
- 0.98 (-2.18)
- 1.15 (- 2.83)
3.38% 5,164
4.75% 5,079
3.34% 5,164
3.43% 5,164
2.12% 9,313
“Cumulative abnormal percentage of analysts who revised their estimate of annual EPS for firm i over the ten-day period beginning with the date of the interim earnings announcement for qutrter 4. Trimmed sample excludes any CAPR,,o, +9j with an absolute value greater than 100.0. ‘Model 4 uses the time series of quarterly earnings in the estimate of 1UQ 1. All other models use the mean consensus analyst forecast of interim earnings. d The t-statistics are calculated by using the estimated standard deviation of the ten independent estimated coefficients: r-statistics of 2.26 and 3.25 indicate the mean estimated coefficient is significantly different from zero at the 0.05 and 0.01 levels, respectively. ‘Absolute value of (actual EPS - consensus analyst EPS) for firm i in quarter y, divided by the absolute value of actual EPS for quarter 4. For this variable and those below, ln( ) refers to the natural logarithm of (1.0 + variable value).
S. E. Stickel,
Forecasr revisions and earnings announcemenls
Table 4
289
(continued)
‘Absolute value of (actual EPS - consensus analyst EPS) for firm i in quarter 4, divided by price per share at the end of quarter q. gNumber of common shares outstanding for firm I at the end of quarter 4, multiplied by price per share at the end of quarter 4. hNumber of common shares traded of firm i during quarter 4. ‘Number of analysts with an outstanding forecast of annual earnings for firm I at the date of the interim earnings announcement for quarter 4. ‘Estimated cross-sectional standard deviation of forecasts of annual EPS for firm i the day before the announcement of interim earnings for quarter 4, divided by the absolute value of actual EPS for quarter 4. kDummy variable for quarter 2; QTRZDUM,, = 1 if 4 = 2, QTRTDUM,, = 0 if 4 = 1 or 3. ‘Dummy variable for quarter 3: QTR3DUM,, = 1 if CJ= 3, Q7’R3DUM,, = 0 if (I = 1 or 2. = 0 if UQ,, 2 0.0, UQSIGNDUM,, = 1 “Dummy variable for the sign of Up,,; UQSIGNDUM,, if UQ,, < 0.0.
eroscedasticity t, =
or dependence,
is based on the t-statistic
[cm/[ c;,m] 2
i=O,...,7,
where SPi is the estimated standard deviation of each p, from the ten estimates. Table 4 reports the results of the regressions. As hypothesized, abnormal revision activity increases with absolute unexpected interim earnings (as proxied by 1UQ,,I/ 1Qi,l) and the number of competing analysts (as proxied by NUMANALYSTS,,) and is greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude (as determined by the sign of the coefficient on UQSIGNDUM,,). The mean coefficient on the uncertainty in annual earnings (as proxied by &&IQ,,l) is negative, but its significance is not maintained across the different econometric models.” Unexpectedly, abnormal revision activity increases with the number of the interim quarter within the fiscal year, as represented by QTRZDUM,, and QTR~DUIW,,.~~
5.2. Sensitivity
analysis of cross-sectional
results
The trimmed sample results, based on 85 observations less than the full sample of 5,164, indicate that the coefficients are not heavily influenced by outliers. As reported for model 2, substituting VOLUME,,, the number of
“With only one analyst there is no dispersion, but there could be uncertainty. Excluding observations with one analyst resulted in virtually no change to the coefficient on c?*,J / Q,, 1. “The benchmark mean percentage revisions for quarters 1, 2, and 3 have an overall average of 1.42%. 1.43%, and 2.33’76, respectively. Thus, the positive abnormal percentage revisions for quarter 3 are not caused by a relatively low benchmark. The higher benchmark in quarter 3 makes the third-quarter percentage revisions even more noteworthy.
290
SE. Stickel, Forecust revisions and earnings unnouncements
common shares traded during quarter q, for MARKETVALUE,, results in a negative sign for firm size. This supports the hypothesis that earnings announcements are relatively less timely information sources for larger firms. As reported for model 3, fit is not significant when PRICE,, is used scale ) UQjql, which is unexpected and unexplained. Using a time series-based forecast of interim earnings [see Foster (1977)] increases the sample size from 5,164 to 9,313 observations but, as noted earlier, results in less accurate estimates. Results using this measure of UQ are presented as model 4. The coefficients and t-statistics are generally robust to the use of a time series forecast of interim earnings. The coefficients are insensitive to performing one regression with the 5,164 observations, although the significance levels of the t-statistics are generally higher. The results are consistent with the contention that, while the single regression coefficients are unbiased, the technique of grouping observations into subsamples is valuable in reducing any problems caused by leptokurtic, heteroscedastic, or cross-sectionally dependent error terms. Using a single the main model coefficients (t-statistics) for INTERCEPT, regression, ln(]UQ]/IQ]), In MARKETVALUE, In NUMANALYSTS, ln(&A/(Q]), QTRZDUM, QTR3DUM, and UQSIGNDUM are 15.70 (3.72) 8.97 (6.58) - 1.07 (- 2.86) 4.00 (5.61) - 3.37 (- 2.79), 4.99 (6.58) 13.85 (17.46) and 2.28 (3.42) respectively. The analysis is also insensitive to the use of an expected percentage revisions model that conditions expectations on the day of the month. Because Zacks sometimes uses brokerage house report dates, the percentages of total revisions that are dated on the 1st 29th, 30th, and 31st of the month are 7.2%, 4.7%, 8.5%, and 8.9%, respectively. For the remaining 27 days of the month, the minimum is 1.9% and the maximum is 3.8%, with a mean of 2.6%. The use of an expectations model that calculates the benchmark mean percentage revisions separately for days 1, 29, 30, and 31 and for days 2 through 28, does not change any conclusions.
6. Conclusions Analysts anticipate and respond to interim earnings announcements by timing their annual earnings forecast revisions. The abnormally low number of revisions for approximately two weeks prior to interim announcements suggests that analysts anticipate an announcement and delay their revisions. After an interim announcement, revision activity is abnormally high for approximately two weeks. As expected, this heightened activity increases with the absolute unexpected interim EPS and with the level of competition among analysts and is also greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude. Thus, I find evidence that analysts respond to incentives to revise forecasts.
S. E. Stickel,
Forecust revisions und eumings announcements
291
Unexpectedly, heightened revision activity after an interim announcement also increases as the fiscal year progresses. It seems that analysts are less likely to revise their forecasts early in the fiscal year. Officials at Zacks believe their reluctance is due to the possibility that offsetting factors in later quarters could vindicate outstanding forecasts. The flurry of revision activity after a thirdquarter announcement is consistent with this hypothesis. Nevertheless, the results suggest that the outstanding forecasts are, on average, not vindicated and that analyst forecasts are relatively more out-of-date earlier than they are later in the fiscal year. While revision activity increases following an interim announcement, many analysts still do not change their forecasts. One avenue for future research would be to compare the accuracy of analysts who respond to interim announcements with that of analysts who do not. Better experimental benchmarks may be obtained by excluding or updating the forecasts of analysts who do not respond. Another avenue would be to compare the accuracy of managerial forecasts with indiuidual analyst forecasts issued (revised) after the managerial forecasts are disclosed. A plausible explanation for finding management forecasts are more accurate than consensus analyst forecasts is that consensus forecasts embody stale forecasts. Finally, it would be interesting to examine abnormal returns following forecast revisions according to whether or not they are made soon after an earnings announcement. It is possible that studies which report abnormal returns after earnings announcements and studies that find abnormal returns after forecast revisions are documenting the same phenomenon.
References Abdel-khalik. A.R. and J. Espejo, 1978, Expectations data and the predictive value of interim reporting, Journal of Accounting Research, Spring, 1-13. Brown, L.D., P.A. Griffin, R.L. Hagerman, and M.E. Zmijewski, 1987, Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings, Journal of Accounting and Economics, April, 61-87. Brown, L.D., J.S. Hughes, M.S. Rozeff, and J.H. VanderWeide. 1980. Expectations data and the predictive value of interim reporting: A comment, Journal of Accounting Research, Spring. 278-288. Brown, L.D. and MS. Rozeff, 1979, Adaptive expectations, time-series models, and analyst forecast revision, Journal of Accounting Research, Autumn, 341-351. Brown, P., G. Foster, and E. Noreen, 1985. Security analyst multi-year earnings forecasts and the capital market, Studies in accounting research no. 21 (American Accounting Association, Sarasota, FL). Chambers, A.E. and S.H. Penman. 1984, Timeliness of reporting and the stock price reaction to earnings announcements, Journal of Accounting Research, Spring, 21-47. Fama, E.F. and J.D. MacBeth, 1973, Risk, return, and equilibrium: Empirical tests, Journal of Political Economy, May-June, 607-636. Foster, G., 1977, Quarterly accounting data: Time series properties and predictive ability results, The Accounting Review, Jan., l-21.
292
S. E. Srickel, Forecusi revisions und earnings announcements
Foster, G., C. Olsen, and T. Shelvin, 1984, Earnings releases, anomalies, and the behavior of security returns, The Accounting Review, Oct., 574-603. Givoly, D. and J. Lakonishok, 1979, The information content of financial analysts’ forecasts of earnings, Journal of Accounting and Economics, March, 165-185. Hassell, J.M. and R.H. Jennings, 1986, Relative forecast accuracy and the timing of earnings forecast announcements, The Accounting Review, Jan., 58-75. Jaffe, J., 1974, The effect of regulation changes on insider trading, Bell Journal of Economics and Management Science, 92-121. Mandelker, G., 1974, Risk and return: The case of merging firms, Journal of Financial Economics, Dec., 303-336. O’Brien, P.C., 1988, Analysts’ forecasts as earnings expectations, Journal of Accounting and Economics, Jan., 53-83. Rendleman, R.J., C.P. Jones, and H.A. Latane, 1982, Empirical anomalies based on unexpected earnings and the importance of risk adjustments. Journal of Financial Economics, Nov., 269-287. Sefcik, SE. and R. Thompson, 1986, An approach to statistical inference in cross-sectional models with security abnormal returns a dependent variable, Journal of Accounting Research, Autumn, 316-334. Thompson, R.B., C. Olsen, and J.R. Dietrich, 1987, Attributes of news about firms: An analysis of firm-specific news reported in the Wall Street Journal Index, Journal of Accounting Research, Autumn, 245-274. Trueman, B.. 1987, A theoretical investigation into the relative accuracy of management and analyst earnings forecasts, Working paper (University of California at Los Angeles. CA). Watts, R.L., 1978, Systematic ‘abnormal’ returns after quarterly earnings announcements, Journal of Financial Economics, June-Sept., 127-150.
of Accounting
and Economics
11 (1989) 275-292.
North-Holland
THE TIMING OF AND INCENTIVES FOR ANNUAL EARNINGS FORECASTS NEAR INTERIM EARNINGS ANNOUNCEMENTS Scott E. STICKEL* Universit.v of Penns~vlvania, Philudeiphicr,
Received August
PA 19104. USA
1988, final version received February
1989
Security analysts anticipate and respond to interim earnings announcements when they revise their annual earnings forecasts. Analysts avoid revising for two weeks before an interim announcement and more frequently revise immediately after an announcement. As expected, revision activity after an interim announcement is greater if absolute unexpected interim earnings are larger, if there are more competing analysts, and if unexpected interim earnings are negative. Unexpectedly, analysts are reluctant to revise their forecasts early in the fiscal year. These findings carry implications for research designs that use analyst forecasts.
1. Introduction This paper provides evidence on the demand for and supply of analyst forecasts of annual earnings per share (EPS). Evidence is provided on the timing of forecasts around interim earnings announcements and on the effect of incentives on revision activity. Such evidence is important to investors who rely on analyst forecasts for timely information.’ It is also relevant to studies that examine abnormal returns surrounding forecast revisions and earnings announcements, that compare the relative accuracy of analyst forecasts, and that use analyst forecasts as market expectations proxies. I find that annual earnings forecasts are relatively stale or out-of-date, on average, in the two weeks prior to interim announcements. In the two weeks
*I am grateful for the cooperation of Zacks Investment Research. I received particularly helpful comments from Don Lewin and two anonymous referees. I also received helpful comments from Jeff Abarbanell. Linda Bamber, Larry Brown, Dave Larcker, Katherine Schipper, Rex Thompson, Brett Trueman. Ro Verrecchia, and Ross Watts. I am grateful for financial support from the Milken Family Foundation, the Peat Marwick Foundation, Deloitte Haskins & Sells. the Institute for Quantitative Research in Finance, and the Junior Faculty Research Fund at the Wharton School. ‘As an example of the size of the security analyst industry, for the 1985 tiscal year the Zacks Investment Research database has annual earnings forecasts for approximately 3.400 companies by approximately 1,500 analysts working for approximately 100 brokerage houses. The number of analysts forecasting earnings for a single company ranges from 1 to 39. Otlicials at Zacks believe their database includes every firm for which analyst forecasts are available and that there is no systematic exclusion of firms.
0165-4101/89/$3.5001989,
Elsevier Science Publishers
B.V. (North-Holland)
276
S. E. Stickel, Forecast revisions und earnings announcements
after interim announcements, revision activity increases. This increase is greater if absolute unexpected interim earnings are larger, if there are more competing analysts, if unexpected interim earnings are negative, and if it is late in the fiscal year. Despite these significant findings, I explain less than 5% of the cross-sectional variation in the timing of revisions. Finding that earnings announcements trigger forecast revisions has implications for studies documenting abnormal returns after earnings announcements [e.g., Watts (1978) Rendleman, Jones, and Latane (1982) Foster, Olsen, and Shevlin (1984)] and for studies documenting abnormal returns after forecast revisions [e.g., Givoly and Lakonishok (1979) Brown, Foster, and Noreen (1985)]. The strong association between announcements and revisions increases the probability that abnormal returns after earnings announcements and abnormal returns after forecast revisions are the same phenomenon. Evidence of stale forecasts affects the interpretation of studies that compare the accuracy of managerial and analyst forecasts. For example, Hassell and Jennings (1986) find managerial forecasts are more accurate than consensus analyst forecasts for up to four weeks after the managerial forecasts are released. My findings are consistent with stale individual analyst forecasts, embedded in the consensus forecast, explaining this curious result. The systematic timing of forecasts is also relevant to the literature that compares the accuracy of analyst forecasts with that of forecasts derived from the time series of earnings. For example, I find analysts are more likely to revise forecasts after third-quarter announcements than after first-quarter announcements, ceteris paribus. Their behavior suggests that their forecasts are more up-to-date after third-quarter than after first-quarter announcements. Thus, comparisons of forecast accuracy are less biased against analysts when the forecast horizon begins after third-quarter than after first-quarter announcements. Documenting the timing of forecasts provides evidence on when analyst forecasts are likely to be better proxies for market expectations. Stale forecasts are likely to be less accurate proxies for market expectations than recently revised forecasts, because earnings-related information revealed since the date of a stale forecast can be used by investors to update their expectations.3 Although I provide no direct evidence on the accuracy of market expectation proxies, analyst forecasts are likely to be more accurate proxies for market
20’Brien (1988) compares accuracy for horizons of 240, 180, 120, and 60 days prior to the annual earnings announcement date, which approximately corresponds to horizons after the prior year’s annual announcement and the current year’s first-, second-, and third-quarter announcements, respectively. However, O’Brien finds analyst forecast accuracy dominates time series forecast accuracy more at the longer time horizons, which is the opposite of expectations given the results of this study. 3ELxamples of earnings-related information newsletters, and M’crll Street Journal articles.
are
subsequent
stock
price
changes,
industry
S. E. Stickel,
Forecast revisions und eurnings announcements
277
expectations just after interim announcements than just before, especially if it is late in the fiscal year, if there are more competing analysts, and if unexpected quarterly earnings are negative. My research continues in a vein similar to Brown, Foster, and Noreen (1985, sections 2.2, 2.3). They compute the percentage of individual analysts who revise their annual forecasts in the months surrounding interim and annual announcements. Revision activity increases slightly in the month after an announcement (month + 1, not month 0) according to the I/B/E/S database and in the month of announcement (month 0) for forecasts by Wells Fargo Investment Advisors. Their evidence is limited to monthly data and is intentionally brief. Statistical tests of significance and cross-sectional tests using analyst incentives to revise are outside of the scope of their study. There is related evidence that Value Line Investment Survey analysts respond to interim announcements by revising their forecasts [Abdel-khalik and Espejo (1978) Brown and Rozeff (1979) and Brown, Hughes, Rozeff, and VanderWeide (1980)]. Although the research designs differ, the basic approach is typified by Brown, Hughes, Rozeff, and VanderWeide. They regress quarterly changes in forecasted annual EPS on unexpected quarterly EPS and find a significantly positive coefficient for unexpected quarterly EPS. While concluding that analysts respond to announcements, these studies provide no evidence on the precise timing of the response or other incentives to revise. Section 2 develops the hypotheses. Section 3 describes the data. Section 4 describes the method used to detect a change in forecast revision activity and reports mean revision activity. Section 5 describes the method used to detect cross-sectional differences in abnormal revision activity and reports the crosssectional results. Conclusions are contained in section 6.
2. Hypotheses 2.1. Mean abnormal revision activity near interim announcements
Analysts have an incentive to refrain from revising just prior to an interim announcement. Interim announcements render prior forecasts out-of-date. Such inaccurate forecasts adversely affect an analyst’s reputation.4 An annual earnings forecast will be more accurate if it accounts for interim earnings. Hence, if the cost of issuing forecasts forces a choice between revising an annual earnings forecast immediately before and immediately after an interim
4Eamings Institutional
forecasts Investor
are one of four criteria by which security annual ‘All-American Research Team’.
analysts
arc ranked
in the
278
S.E. Stickel, Forecast revisions and eumings unnouncements
earnings announcement, analysts will refrain from revising their forecasts immediately after the interim announcement.
before
and revise
H.l:
Ceteris paribus, the abnormal percentage of analysts forecasts before interim announcements is negative.’
revising
annual
H.2:
Ceteris paribus, the abnormal percentage of analysts forecasts after interim announcements is positive.6
revising
annual
2.2. Cross-sectional
abnormal revision activity
I expect six variables interim announcements: 1. 2. 3. 4. 5. 6.
to be associated 7
with analyst
incentives
to revise after
absolute unexpected interim EPS, firm size, level of competition among analysts, ex ante uncertainty of annual EPS, number of the interim quarter (1, 2, or 3) within the fiscal year, sign of unexpected earnings (positive or negative).
2.2.1. Absolute
unexpected
interim EPS
I assume the larger is the absolute unexpected interim EPS, the greater is the marginal benefit of a revision to investors. Consequently, analysts have more incentive to revise an annual earnings forecast if absolute unexpected interim EPS is larger.
‘Price changes that occur at interim announcements provide an incentive for analysts to revise before interim announcements. If an analyst can provide information about the price effects of an upcoming interim announcement through an annual forecast revision, customers could capture any price change caused by the announcement. A countervailing factor is that analysts could provide this information through interim forecasts or buy/sell recommendations. ‘Investors expect analysts will do more than annual earnings. See Brown, Griffin, Hagerman, Noreen (1985, section 4.4) for evidence consistent after interim announcements provide information series of earnings or price changes.
mechanically extrapolate interim earnings into and Zmijewski (1987) and Brown, Foster, and with the assumption that forecasts immediately that cannot be simply extrapolated from a time
‘I expected a cross-sectional relation between the uncertainty in interim earnings and revision activity before the interim announcement. If quarterly earnings are more difficult to predict, analysts may be more likely to refrain from revising annual forecasts just prior to interim announcements. Tests for a relation between the uncertainty in interim earnings and revision activity prior to the interim date found the relation to be negative. but insignificantly different from zero at conventional levels.
S. E. Stickel,
H.3:
2.2.2.
Forecast revisions und earnings unnouncements
279
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with absolute unexpected interim EPS.
Firm size
The revelation of earnings-related information through more timely media than earnings announcements increases with firm size. For example, larger firms have more entries in the Wall Street Journal [e.g., Thompson, Olsen, and Dietrich (1987)]. Thus, the relative informativeness of interim earnings announcements, vis-a-vis other firm-specific earnings-related information revealed at other times, should be less for larger firms. H.4:
2.2.3.
Ceteris paribus, the abnormal percentage of analysts revising forecasts after interim announcements decreases with firm size.
Level of competition
annual
among analysts
Competition among analysts can produce incentives to revise annual forecasts after interim announcements. In Trueman’s (1987) model, a single analyst is reluctant to revise because revision signals an inaccurate prior forecast. With many analysts, it would be more difficult for any one analyst to obscure an inaccurate forecast by not revising, since the revisions by competitors would reveal the strategy. H.5:
2.2.4.
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with the number of competing analysts.
Ex ante uncertainty
of annual EPS
Bayesian theory predicts revision activity after interim announcements is an increasing function of the ex ante uncertainty in annual earnings. If there is greater uncertainty, analyst prior distributions for earnings are more diffuse. With diffuse priors, the marginal impact of new information on the posterior distribution is greater. H.6:
Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements increases with the ex ante uncertainty in annual earnings.
280
2.2.5. Number
S. E. Stickel,
Forecusr revisions and eumings announcements
of the interim
quarter within a jiscal year
Unexpected first-quarter earnings affect the forecasts of a greater number of remaining quarters of the fiscal year than unexpected second- or third-quarter earnings. Thus, for a given amount of unexpected interim EPS, the change in current-year forecasted annual earnings should be greater for first-quarter announcements than second- or third-quarter announcements.’ H.7: Ceteris paribus, the abnormal percentage of analysts revising forecasted annual EPS after interim announcements decreases with the number of the interim quarter (1, 2, or 3) within the fiscal year. 2.2.6. Sign of unexpected earnings If investors are risk-averse, analyst revision activity should be greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude. Risk aversion implies that the utility lost by a given dollar amount of negative unexpected earnings is greater than the utility gained by the same dollar amount of positive unexpected earnings. H.8: Ceteris paribus, the abnormal percentage of analysts revising annual forecasts after interim announcements is greater for negative than positive unexpected earnings of the same magnitude. 3. Data bases and sample selection process Annual and quarterly EPS forecasts are obtained from Zacks Investment Research (Zacks). The database contains individual analyst forecasts of primary EPS before extraordinary items and discontinued operations for more than 3,500 companies. Sample selection begins with the set of firms for which there are annual earnings forecasts on the database for any fiscal year within the 1982 to 1985 period. For this set of firms and fiscal years, interim announcements are selected if the announcement meets the following requirements: (1) The 1985 Compustat Quarterly Industrial File must have figures for quarterly EPS before extraordinary items and discontinued operations, for volume of shares traded during the quarter, for price per share and number of outstanding shares at quarter-end, and the interim announcement date. Earnings figures are adjusted for any changes in shares outRThis proposition is supported by Brown and RozetT (1979) who regress changes in forecasted quarterly earnings on unexpected quarterly earnings. Holding unexpected interim earnings constant, their results suggest the change in forecasted annual earnings is greater for first-quarter than third-quarter announcements. For my sample, the mean absolute values of unexpected EPS for quarters 1, 2, and 3 are $0.20, $0.22, and $0.22, respectively.
S. E. Stickel,
2x1
Forecast reuisions und eumings unnouncements
Table 1 Sample
selection
process for analyst
forecasts
Interim NYSEh Total number of firms with fiscal quarters and years ending within the 1982-1985 period on the Zacks database of annual earnings forecasts Deletions Insufficient data on the 1985 Compustat Quarterly Industrial File Remaining announcements and firms No outstanding forecast of interim EPS on the Zacks Investment Research File at the interim earnings announcement date Remaining announcements and firms Insufficient data for forecasted urttrucrl EPS on Zacks around the interim earnings announcement date Remaining announcements and firms Quarterly EPS figure on the Compustat file has been restated Announcements and firms included in final analysis
and interim earnings
Number
announcements
ASE’
OTC?
announcements
Total
NYSE
ASE
of firms3 OTC
Total
3.544
(1,993) 1.079
14.615
11.349
2.1X7
(4,044)
(1,599)
(635)
(6.278)
7,305
588
444
8,337
(101)
(21)
(498)
6,929
487
423
7,839
(280)
(30)
a
(313)
457
420
(376)
6,649
7,526
1,221
(885) 1,069
(282) 1.038
(152) 1,033
284
124
1,629
(274)
(106)
(1,265)
154
91
1.314
(76)
(19)
133
88
1,259
(21)
m
(175)
130
88
1,251
“Column totals do not add because a single firm generally has many interim each of which may or may not meet the various sample selection criteria. bNYSE: New York Stock Exchange. ‘ASE: American Stock Exchange. ‘OTC: Over-the-counter.
(377)
announcements,
standing and must not be restated. Restated figures include the effect of subsequent mergers, acquisitions, and accounting changes. Such restatements are not made on the Zacks files.9 ‘Compustat does not explicitly note restated EPS figures, but does note restated net income figures. The final sample excludes any observation where Compustat noted that net income is restated (data item 36 not equal to 0) or EPS is a Standard L Poor’s calculation (data item 3X not equal to 0 or 1).
282
S. E. Stickel, Forecust revisions and earnings announcemenfs Table 2 Time profile of sample of interim earnings
1982 1983 1984 1985
1982 1983 1984 1985
Jan.
Feb.
31 32 47 51
26 39 48 46
Jan.
Feb.
0 85 106 94
19 33 49 49
Time profile of month March April May
announcements.
ofintenmecrrmngsjiscul
Total
32 42 41
91 116 95
1,834 2,273 2,560 -859 7.526
Timeprojiie ofmonthof interimearnings unnouncement dute March April May June July Aug. Sept. Oct. Nov.
Dec.
Total
* 37 43 42
1.734 2,245 2,582 965 7,526
39 43 55 45
50 65 69 24
438 569 599 653
26 38 53 14
July
Aug.
497 597 711 2
41 54 76
32 35 35
quurter end Sept. Oct. Nov.
Dec.
469 635 690 722
June
76 121 158 103
31 54 50 21
451 528 637
84 114 151
488 563 639
36 50 52
45 57 56
448 491 577
Time profile of weekdu_v ofinterimearnings announcement Sun. Mon. Tue. Wed. Thu. Number of observations Percentage of total
15 0.2
972 12.9
1,543 20.5
1.783 23.7
1,699 22.6
Time profile of number of interim quarter First Second Number of observations Percentage of total
2.919 38.8
2,446 32.5
15 114 106
dute Fri.
Sat.
Total
1.503 20.0
11 0.1
7,526 100.0
Third
Total
2,161 2X.7
7.526 100.0
(2) The Zacks database must contain at least one analyst with an outstanding forecast of interim EPS the day before the interim announcement and at least one analyst with an outstanding forecast of annual EPS during an estimation period that surrounds the interim announcement date. Table 1 summarizes the sample selection process. Market value of equity is used, on a sample basis, to assess any selection bias from systematically excluding firms that have insufficient Compustat or Zacks data. Firms with deleted announcements are, on average, smaller than firms subjected to empirical tests. Thus, the inferences made may not be applicable to very small firms that are followed by analysts. Table 2 summarizes the time profile of the dates used in the empirical tests. 4. Tests for mean abnormal revision activity 4.1. Method for detecting abnormal forecast revision activity The method used to test the effect of interim announcements on revision activity borrows from ‘event studies’ that examine the effect of new informa-
S. E. Srickel, Forecust reuisrons and earnings unnouncements
283
tion on security prices. Define the percentage revisions for firm i on day t, PRil, as the number of analysts revising their annual earnings forecast divided by the number of analysts with an outstanding forecast, multiplied by 100. The abnormal percentage revisions for firm i on day t is defined as APR,,
= PR,, - PR,,
-. where PR, 1s the mean percentage revisions over event days - 40 to - 21 and +21 to +40.” These days provide an adequate number of observations (a maximum of 40 with a minimum requirement of 30) to obtain a reliable benchmark and minimize the interference of other interim announcement dates, which occur approximately every 60 trading days. Mean percentage revisions (MPR) and mean abnormal percentage revisions ( MAPR ) on day t are calculated as
MPR,
= 2 PR,,/N, I=1
and
MAPR,
= z
APRJN,,
1=1
where N, is the number of firms followed by analysts on day t.” Finally, and mean abnormal percentage revisions are cumulated over time as
CMPR,,=
f MPR, t=a
and
CMAPR,,
mean
= f: MAPR,, f=U
where CMPR,, is the cumulative mean percentage revisions and CMAPR., is the cumulative mean abnormal percentage revisions from event day a to event day b. To test the null hypothesis that daily mean abnormal percentage revisions equal zero, t-statistics are calculated as t, =
MAf’R,/a^,,,,,
where gMAPR is the estimated standard deviation of MAPR, over days -40 to -21 and +21 to +40. The APRi, are not independent because of calendar clustering of interim announcements. Nonindependence could be caused by, among other things, one analyst simultaneously revising his or her forecasts of more than one “The sensitivity of the reported results to the particular benchmark period is examined by using days + 21 to + 40. This alternative is chosen because days - 40 to - 21 for quarter 1 may be affected by the annual earnings announcement of the prior year [see Chambers and Penman (1984)]. However, the results using this alternative benchmark did not change any conclusions. “Abnormal revision activity is also calculated with equally-weighted analysts, as opposed to equally-weighted firms. The results are nearly identical at the mean portfolio level and are not reported.
284
S. E. Stickel, Forecust revisions and eumings announcements
company because of industry-wide factors. To assess the effect of nonindependence, observations are grouped by calendar day and averaged before computing overall portfolio means [see Jaffe (1974) and Mandelker (1974)]. The mean abnormal percentage revisions and t-statistics using this calendar time method did not change any conclusions and are not reported.
4.2. Mean revision activity Table 3 reports mean revision results. There is a pattern of negative abnormal revision activity for about two weeks before an interim announcement and significantly positive abnormal revision activity for about two weeks after. The MAPRs for single event days prior to interim announcements are not different from zero at conventional significance levels. However, the mean defined as APR,, cumulated from event day - 10 to - 1 for CAPRiq(-lO,~*)~ quarter q, is - 3.41%. Using the cross-sectional standard deviation of CAPR rq(- 10, - 1) and the number of observations, the t-statistic testing the hypothesis that CAPRiq( _ 1o,_ 1j equals zero has a value of - 14.93, which rejects the null hypothesis at less than the 0.0001 level.12 CMPR proxies for the cumulative percentage of analysts who have revised their forecasts since the announcement date.13
4.3. Dating of forecast revisions Brokerage houses notify Zacks of analyst forecast revisions via reports that may not have the actual dates on which analysts make less newsworthy revisions. If a brokerage house does not supply the actual revision date, Zacks uses the date of the brokerage house report. Brokerage house report dates are always coincidental with, or later than, the actual revision dates. Zacks does not identify forecasts as having an actual date or a brokerage house report date. Thus, the possibility arises that the significant abnormal revision activity documented on and after event day 0 could actually have occurred prior to event day 0. This is extremely unlikely given the abruptness of the change in revision activity on event day 0. It would be an extraordinary coincidence for the revisions with brokerage house report dates to align on
‘*Using the same grouping procedures CAPR,,(_ 10, t) is - 3.31% with a t-statistic
that are of - 7.96.
described
in
section
5.1,
the
mean
t3CMPR slightly overstates the cumulative percentage because a single analyst is included as many times as he or she revised their forecast between days 0 and + 20. Of the analysts revising between day 0 and +20, less than 1.4% revised twice or more between day 0 and +20. Additionally, CMPR equally-weights firms rather than analysts. The CMPR for equally-weighted analysts from day 0 to + 20 is 56.55% versus 57.01% for equally-weighted firms.
S. E. Stickel,
Forecast revisions and eurnings announcements
285
Table 3 Mean percentage of analysts who revised their forecast of annual earnings, The sample consists of 7,526 interim earnings announcements for 1,251 different firms with fiscal quarter ends and year ends within the 1982-1985 period. Event day 0 is the day of the interim earnings announcement. Event day - 20 -19 -18 -17 -16 -15 - 14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1
MPR=
CMPRb
1.65% 1.84 1.87 1.84 1.91 1.84 1.93 2.05 1.75 1.60 1.56 1.63 1.53 1.17 1.16 1.18 1.21 1.20 1.25 1.60
0
2.63
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
3.62 3.86 3.77 3.97 3.85 3.15 3.27 3.12 2.78 2.72 2.39 2.24 2.15 1.95 1.91 1.85 1.76 1.75 1.74 1.92
2.63% 6.26 10.12 13.90 17.86 21.71 25.45 28.72 31.85 34.62 37.35 39.74 41.98 44.14 46.09 47.99 49.84 51.60 53.36 55.09 57.01
MAPRC
I
- 0.04% 0.15 0.19 0.15 0.22 0.15 0.25 0.37 0.06 - 0.09 -0.13 - 0.06 -0.16 - 0.52 - 0.53 - 0.50 - 0.48 - 0.49 -0.44 - 0.09
-0.11 0.48 0.59 0.47 0.71 0.48 0.78 1.16 0.18 - 0.28 - 0.41 -0.19 - 0.51 - 1.64 - 1.67 - 1.60 - 1.53 - 1.57 - 1.39 -0.29
0.94
3.00
1.94 2.17 2.09 2.28 2.16 2.06 1.58 1.43 1.09 1.03 0.71 0.55 0.46 0.26 0.22 0.16 0.08 0.07 0.05 0.23
6.15 6.91 6.63 7.24 6.85 6.53 5.03 4.56 3.45 3.29 2.24 1.76 1.48 0.82 0.69 0.50 0.24 0.21 0.15 0.73
CMAPRd
w
- 0.04% 0.11 0.30 0.45 0.67 0.82 1.07 1.44 1.49 1.41 1.28 1.21 1.05 0.54 0.01 - 0.49 - 0.98 - 1.47 - 1.91 - 2.00
7,525 7,525 7,525 7,525 7,525 7,525 7,525 7,525 7,525 7.525 7,525 7,526 7,526 7,526 7,526 7,526 7,526 7,526 7,526 7,526
-1.05
7.526
0.88 3.06 5.14 7.42 9.58 11.63 13.21 14.65 15.73 16.77 17.47 18.03 18.49 18.75 18.97 19.13 19.20 19.27 19.32 19.55
7,526 7,526 7.526 7,526 7,525 1,525 7,525 7,525 7,526 7,526 7,526 7.526 7,526 7,526 1,525 7,526 7,526 7,526 7,526 7,526
aMean percentage of analysts who revised their forecast of annual EPS on event day t. using equally-weighted firm interim announcements and equally-weighted analysts within each equallyweighted firm interim announcement. bCumulative MPR. ‘Mean abnormal percentage who revised their forecast on event day t. dCumulative MAPR. ‘Number of interim announcement dates.
286
S. E. Sttckel,
Forecast reo,stom and eurnings unnouncements
event day 0 and not on event day - 1, because the corresponding actual revision dates would be occurring an unknown and random number of days before the brokerage house report date. Since the revisions with actual dates cause the abrupt change on day 0 and do not cause positive abnormal revision activity prior to day 0, the logical conclusion is that analysts refrain from revising before interim announcements and increase revision activity on and after interim announcements.r4
5. Tests for cross-sectional
differences in abnormal revision activity
Positive abnormal revision activity after interim announcements. To test tives to revise and abnormal revision are cumulated over these ten days and
continues for about ten working days for associations between analyst incenactivity, abnormal percentage revisions denoted CAPRjqcO, +9j.
5.1. Method and results of cross-sectional tests A number of problems arise when performing multiple regressions. The residuals from the regressions are nonnormal (leptokurtic) and heteroscedastic, their variance being a decreasing function of analyst following. They are also cross-sectionally dependent, due to calendar clustering. The tests in this section mitigate these problems in three ways. First, to judge the effect of outliers on the estimated coefficients, two sets of regressions are performed using (1) all observations and (2) a trimmed sample that excludes any observation where the absolute value of CAPR,,(,, +9j exceeds 100 percent. Second, to mitigate the effect of heteroscedasticity and nonindependence on the significance of the estimated coefficients, mean results are calculated from ten independent cross-sectional regressions.15 Third, I examine the sensitivity of my results to alternative proxies for analyst incentives and for abnormal revision activity. Observations are grouped by interim announcement date into ten subsamples, which subsumes any cross-sectional temporal dependence within subsamples and minimizes the cross-sectional temporal dependence between subsamples. A cross-sectional regression is performed with each of the ten
14This conclusion does not preclude the possibility that a relatively small portion of the documented abnormal revision activity on and after day 0 relates to forecasts made prior to day 0. However, this possibility would be offset by any documented revisions immediately prior to day 0 that actually are forecasts made long before day 0. t5The test is based Sefcik and Thompson
on a design (1986).
used by Fama
and MacBeth
(1973) and elaborated
upon
by
S. E. Stickel,
subsamples,
using the following
CAPR rq(O,+9)
287
Forecast revisions and eumings announcements
model:
=Po+Plln(l~Qj,l/lQ,,l>
+/%lnMARKETVALW,
+~,lnNUMANALYSTS,,+~~ln(~~,,/IQ,,l> + &QTRZDUM,, + ,B,UQSIGND
+ &QTRSDUM,, UM,, + E,~.
The proxy for unexpected interim earnings, I UQrql, is defined as the absolute deviation of actual quarterly EPS from the mean consensus forecast on the day before the interim announcement.‘” The variable lQ,4l is used to scale unexpected earnings for comparability across firms.” The proxy for firm size, MARKETVALUE,,, is the market value of common stock at the end of quarter q. The proxy for the level of competition, NUMANALYSTS,,, is the number of analysts with an outstanding annual earnings forecast on the day of the interim announcement. The proxy for the precision of prior information, ,. u~,~, is defined as the estimated cross-sectional standard deviation of annual forecasts on the day before the interim announcement and is scaled for comparability across firms. ‘* The variables QTRZDUM,, and QTRSDUM,, are binary dummies used to determine if analyst revision activity varies by the interim quarter of the fiscal year (1, 2, or 3). The variable UQSIGNDUM,, is a dummy variable set equal to 0 if UQ,, 2 0.0 and set equal to 1 if UQ,4 < 0.0. Because abnormal revision activity is cumulated over ten days, errors from adjacent subsamples can be cross-sectionally dependent. Consequently, interim earnings dates are excluded if they are within ten days before any of the nine cutoff dates for the ten subsamples. This reduces the sample size from 7,526 to 5,164 interim dates. The ten estimates of each p, obtained from these regressions are unbiased, but their standard errors are affected by any heteroscedasticity or dependence. A test of whether each p, differs from zero, that incorporates any het“For the sample subjected to empirical tests. the mean 1L’QI is $0.221. The aensitivitv of the results to alternative measures of 1c/Q1 is examined by using three other measures of LIQ: actual quarterly EPS minus the last analyst forecast issued prior to the announcement date, which has a mean / L’QI of $0.222: actual quarterly EPS minus last year’s quarterly EPS. which has a mean 1L’QI of $0.339; and actual quarterly EPS minus the estimate from Foster’s (1977) time series model, which has a mean ) CiQI of $0.347. Results using these measures of I L:Qj did not change any conclusions. “Values of I r/Q I/I Q I greater than 2.0 are set equal to 2.0 to avoid distortions values of ( Q I The same procedure is used for the variable o^,/ I Q / ‘XUnfortunately, tAAlyincludes ment among analysts.
divergence
caused
by out-of-date
forecasts
caused by small
as well as disagree-
S. E. Stickel, Forecust revisionsand eurnings unnouncements
288
Table 4 Mean regression results for the relation between the abnormal percentage of analysts who revised their forecast of annual EPS immediately after interim earnings announcements and proxies for analyst incentives to revise forecasts. The main model is CAPR ,q(~. + g)a= PO + PI ln(l
UQ,, I / I Qiq I ) + BZ In MA RKETVALW, +& In NUMANALYSTS,, + /?.,ln( i&IQi,l) +&QTR2DUM,,
+ &QTR3DUM,,
+ &fJQSIGNDUM,,
Mean regressionresultsfrom ten independentregressions Main model Independent
variable
Predicted sign
Entire sample
Mean coefficient INTERCEPT
(1)
Trimmedb sample
Alternative specifications of model using entire sample Model 2
Model 3
Model 4’
( t-statistic)d
11.59 (1.76)
1.49 (0.24)
11.91 (3.79)
6.65 (4.64)
4.88 (2.71)
6.64 (4.60)
15.46 (2.48)
14.52 (2.96)
~~(I~Q,,I/lQ,,IY
(+I
IN I UQ,,I/f’RfC4q)’
(+)
In MARKETVALUE,,a
(-)
In VOL.UME,,h
(-)
In NUMANALYSTS,,’
(+)
3.07 (2.70)
6.05 (6.19)
3.77 (4.74)
2.97 (2.54)
5.22 (5.99)
In( sAA,JI Q,q I P
(+I
- 3.97 (- 2.27)
- 1.16 (- 0.65)
- 3.35 (-- 1.94)
- 1.34 (- 1.08)
- 0.55 ( - 0.44)
QTR2DUM,<,k
(-)
5.60 (3.34)
5.26 (5.18)
5.16 (3.61)
5.77 (3.52)
5.71 (5.52)
QTR_IDUM,,’
c--j
12.30 (4.98)
10.57 (4.71)
12.64 (5.38)
12.79 (5.27)
12.24 (8.11)
UQSIGNDUM,qm
(+)
3.62 (4.27)
3.21 (3.18)
3.70 (4.33)
3.83 (4.89)
0.08 (0.11)
Adjusted
R’
Total number of interim earnings announcements
2.15 (2.26) - 7.06 ( - 0.28)
-0.53 ( - 0.82)
-0.76 (- 1.18)
-0.37 (- 0.65)
- 0.98 (-2.18)
- 1.15 (- 2.83)
3.38% 5,164
4.75% 5,079
3.34% 5,164
3.43% 5,164
2.12% 9,313
“Cumulative abnormal percentage of analysts who revised their estimate of annual EPS for firm i over the ten-day period beginning with the date of the interim earnings announcement for qutrter 4. Trimmed sample excludes any CAPR,,o, +9j with an absolute value greater than 100.0. ‘Model 4 uses the time series of quarterly earnings in the estimate of 1UQ 1. All other models use the mean consensus analyst forecast of interim earnings. d The t-statistics are calculated by using the estimated standard deviation of the ten independent estimated coefficients: r-statistics of 2.26 and 3.25 indicate the mean estimated coefficient is significantly different from zero at the 0.05 and 0.01 levels, respectively. ‘Absolute value of (actual EPS - consensus analyst EPS) for firm i in quarter y, divided by the absolute value of actual EPS for quarter 4. For this variable and those below, ln( ) refers to the natural logarithm of (1.0 + variable value).
S. E. Stickel,
Forecasr revisions and earnings announcemenls
Table 4
289
(continued)
‘Absolute value of (actual EPS - consensus analyst EPS) for firm i in quarter 4, divided by price per share at the end of quarter q. gNumber of common shares outstanding for firm I at the end of quarter 4, multiplied by price per share at the end of quarter 4. hNumber of common shares traded of firm i during quarter 4. ‘Number of analysts with an outstanding forecast of annual earnings for firm I at the date of the interim earnings announcement for quarter 4. ‘Estimated cross-sectional standard deviation of forecasts of annual EPS for firm i the day before the announcement of interim earnings for quarter 4, divided by the absolute value of actual EPS for quarter 4. kDummy variable for quarter 2; QTRZDUM,, = 1 if 4 = 2, QTRTDUM,, = 0 if 4 = 1 or 3. ‘Dummy variable for quarter 3: QTR3DUM,, = 1 if CJ= 3, Q7’R3DUM,, = 0 if (I = 1 or 2. = 0 if UQ,, 2 0.0, UQSIGNDUM,, = 1 “Dummy variable for the sign of Up,,; UQSIGNDUM,, if UQ,, < 0.0.
eroscedasticity t, =
or dependence,
is based on the t-statistic
[cm/[ c;,m] 2
i=O,...,7,
where SPi is the estimated standard deviation of each p, from the ten estimates. Table 4 reports the results of the regressions. As hypothesized, abnormal revision activity increases with absolute unexpected interim earnings (as proxied by 1UQ,,I/ 1Qi,l) and the number of competing analysts (as proxied by NUMANALYSTS,,) and is greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude (as determined by the sign of the coefficient on UQSIGNDUM,,). The mean coefficient on the uncertainty in annual earnings (as proxied by &&IQ,,l) is negative, but its significance is not maintained across the different econometric models.” Unexpectedly, abnormal revision activity increases with the number of the interim quarter within the fiscal year, as represented by QTRZDUM,, and QTR~DUIW,,.~~
5.2. Sensitivity
analysis of cross-sectional
results
The trimmed sample results, based on 85 observations less than the full sample of 5,164, indicate that the coefficients are not heavily influenced by outliers. As reported for model 2, substituting VOLUME,,, the number of
“With only one analyst there is no dispersion, but there could be uncertainty. Excluding observations with one analyst resulted in virtually no change to the coefficient on c?*,J / Q,, 1. “The benchmark mean percentage revisions for quarters 1, 2, and 3 have an overall average of 1.42%. 1.43%, and 2.33’76, respectively. Thus, the positive abnormal percentage revisions for quarter 3 are not caused by a relatively low benchmark. The higher benchmark in quarter 3 makes the third-quarter percentage revisions even more noteworthy.
290
SE. Stickel, Forecust revisions and earnings unnouncements
common shares traded during quarter q, for MARKETVALUE,, results in a negative sign for firm size. This supports the hypothesis that earnings announcements are relatively less timely information sources for larger firms. As reported for model 3, fit is not significant when PRICE,, is used scale ) UQjql, which is unexpected and unexplained. Using a time series-based forecast of interim earnings [see Foster (1977)] increases the sample size from 5,164 to 9,313 observations but, as noted earlier, results in less accurate estimates. Results using this measure of UQ are presented as model 4. The coefficients and t-statistics are generally robust to the use of a time series forecast of interim earnings. The coefficients are insensitive to performing one regression with the 5,164 observations, although the significance levels of the t-statistics are generally higher. The results are consistent with the contention that, while the single regression coefficients are unbiased, the technique of grouping observations into subsamples is valuable in reducing any problems caused by leptokurtic, heteroscedastic, or cross-sectionally dependent error terms. Using a single the main model coefficients (t-statistics) for INTERCEPT, regression, ln(]UQ]/IQ]), In MARKETVALUE, In NUMANALYSTS, ln(&A/(Q]), QTRZDUM, QTR3DUM, and UQSIGNDUM are 15.70 (3.72) 8.97 (6.58) - 1.07 (- 2.86) 4.00 (5.61) - 3.37 (- 2.79), 4.99 (6.58) 13.85 (17.46) and 2.28 (3.42) respectively. The analysis is also insensitive to the use of an expected percentage revisions model that conditions expectations on the day of the month. Because Zacks sometimes uses brokerage house report dates, the percentages of total revisions that are dated on the 1st 29th, 30th, and 31st of the month are 7.2%, 4.7%, 8.5%, and 8.9%, respectively. For the remaining 27 days of the month, the minimum is 1.9% and the maximum is 3.8%, with a mean of 2.6%. The use of an expectations model that calculates the benchmark mean percentage revisions separately for days 1, 29, 30, and 31 and for days 2 through 28, does not change any conclusions.
6. Conclusions Analysts anticipate and respond to interim earnings announcements by timing their annual earnings forecast revisions. The abnormally low number of revisions for approximately two weeks prior to interim announcements suggests that analysts anticipate an announcement and delay their revisions. After an interim announcement, revision activity is abnormally high for approximately two weeks. As expected, this heightened activity increases with the absolute unexpected interim EPS and with the level of competition among analysts and is also greater for negative unexpected earnings than for positive unexpected earnings of the same magnitude. Thus, I find evidence that analysts respond to incentives to revise forecasts.
S. E. Stickel,
Forecust revisions und eumings announcements
291
Unexpectedly, heightened revision activity after an interim announcement also increases as the fiscal year progresses. It seems that analysts are less likely to revise their forecasts early in the fiscal year. Officials at Zacks believe their reluctance is due to the possibility that offsetting factors in later quarters could vindicate outstanding forecasts. The flurry of revision activity after a thirdquarter announcement is consistent with this hypothesis. Nevertheless, the results suggest that the outstanding forecasts are, on average, not vindicated and that analyst forecasts are relatively more out-of-date earlier than they are later in the fiscal year. While revision activity increases following an interim announcement, many analysts still do not change their forecasts. One avenue for future research would be to compare the accuracy of analysts who respond to interim announcements with that of analysts who do not. Better experimental benchmarks may be obtained by excluding or updating the forecasts of analysts who do not respond. Another avenue would be to compare the accuracy of managerial forecasts with indiuidual analyst forecasts issued (revised) after the managerial forecasts are disclosed. A plausible explanation for finding management forecasts are more accurate than consensus analyst forecasts is that consensus forecasts embody stale forecasts. Finally, it would be interesting to examine abnormal returns following forecast revisions according to whether or not they are made soon after an earnings announcement. It is possible that studies which report abnormal returns after earnings announcements and studies that find abnormal returns after forecast revisions are documenting the same phenomenon.
References Abdel-khalik. A.R. and J. Espejo, 1978, Expectations data and the predictive value of interim reporting, Journal of Accounting Research, Spring, 1-13. Brown, L.D., P.A. Griffin, R.L. Hagerman, and M.E. Zmijewski, 1987, Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings, Journal of Accounting and Economics, April, 61-87. Brown, L.D., J.S. Hughes, M.S. Rozeff, and J.H. VanderWeide. 1980. Expectations data and the predictive value of interim reporting: A comment, Journal of Accounting Research, Spring. 278-288. Brown, L.D. and MS. Rozeff, 1979, Adaptive expectations, time-series models, and analyst forecast revision, Journal of Accounting Research, Autumn, 341-351. Brown, P., G. Foster, and E. Noreen, 1985. Security analyst multi-year earnings forecasts and the capital market, Studies in accounting research no. 21 (American Accounting Association, Sarasota, FL). Chambers, A.E. and S.H. Penman. 1984, Timeliness of reporting and the stock price reaction to earnings announcements, Journal of Accounting Research, Spring, 21-47. Fama, E.F. and J.D. MacBeth, 1973, Risk, return, and equilibrium: Empirical tests, Journal of Political Economy, May-June, 607-636. Foster, G., 1977, Quarterly accounting data: Time series properties and predictive ability results, The Accounting Review, Jan., l-21.
292
S. E. Srickel, Forecusi revisions und earnings announcements
Foster, G., C. Olsen, and T. Shelvin, 1984, Earnings releases, anomalies, and the behavior of security returns, The Accounting Review, Oct., 574-603. Givoly, D. and J. Lakonishok, 1979, The information content of financial analysts’ forecasts of earnings, Journal of Accounting and Economics, March, 165-185. Hassell, J.M. and R.H. Jennings, 1986, Relative forecast accuracy and the timing of earnings forecast announcements, The Accounting Review, Jan., 58-75. Jaffe, J., 1974, The effect of regulation changes on insider trading, Bell Journal of Economics and Management Science, 92-121. Mandelker, G., 1974, Risk and return: The case of merging firms, Journal of Financial Economics, Dec., 303-336. O’Brien, P.C., 1988, Analysts’ forecasts as earnings expectations, Journal of Accounting and Economics, Jan., 53-83. Rendleman, R.J., C.P. Jones, and H.A. Latane, 1982, Empirical anomalies based on unexpected earnings and the importance of risk adjustments. Journal of Financial Economics, Nov., 269-287. Sefcik, SE. and R. Thompson, 1986, An approach to statistical inference in cross-sectional models with security abnormal returns a dependent variable, Journal of Accounting Research, Autumn, 316-334. Thompson, R.B., C. Olsen, and J.R. Dietrich, 1987, Attributes of news about firms: An analysis of firm-specific news reported in the Wall Street Journal Index, Journal of Accounting Research, Autumn, 245-274. Trueman, B.. 1987, A theoretical investigation into the relative accuracy of management and analyst earnings forecasts, Working paper (University of California at Los Angeles. CA). Watts, R.L., 1978, Systematic ‘abnormal’ returns after quarterly earnings announcements, Journal of Financial Economics, June-Sept., 127-150.