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AnalystInformationProductionandtheTimingofAnnualEarningsForecasts
Sami Keskek Department of Accounting
Sam Walton College of Business University of Arkansas (479) 575-6229 (office)
Senyo Tse Department of Accounting
Mays Business School Texas A&M University (979) 845-3784 (office)
Jennifer Wu Tucker Fisher School of Accounting
University of Florida (352) 273-0214 (office)
August 2013
Forthcoming in Review of Accounting Studies
We thank Anwer Ahmed, Shuping Chen, Xia Chen, Michael Clement, Gus De Franco, Matt Hart, Joost Impink, Marcus Kirk, Paul Madsen, Tom Omer, David Reppenhagen, Kathy Rupar, Jim Vincent, Greg Waymire, David Weber, two anonymous referees, and the participants of the 2011 AAA Annual Conference and the accounting workshops at the University of Connecticut, University of Florida, Peking University, University of Toronto, and Zhongshan University.
Analyst Information Production and the Timing of Annual Earnings Forecasts
ABSTRACT We investigate whether the reputation-herding theory or the tradeoff theory explains variation in the timing of individual analysts’ forecasts. Using forecast accuracy improvements, forecast boldness, and the price impact of forecasts as measures of forecast quality, we find that in the information discovery phase that precedes an earnings announcement event, earlier forecasts have higher quality than later forecasts and find a similar pattern in the information analysis phase that begins with the earnings announcement date. Our findings suggest that consistent with the herding theory, more-capable analysts participate early in discovering and analyzing information and, therefore, earlier forecasts in the information discovery and analysis phases are of higher quality than later forecasts in that phase.
Keywords: financial analysts, timing, earnings announcements, information discovery.
(JEL G14; G20; D82; D83)
1
1. Introduction
Sell-side security analysts perform two distinct tasks in predicting earnings: information
discovery and information analysis. Chen, Cheng, and Lo (2010) conclude that analysts focus
on discovering private information before a corporate earnings announcement event and switch
to analyzing information immediately after the event. Prior research compares the contribution
of analysts as a group in the information discovery and information analysis phases (Ivkovic
and Jegadeesh 2004; Chen et al. 2010; Livnat and Zhang 2012). We extend this research by
examining the timing of individual analysts’ forecasts within the information discovery and
information analysis phases. Our interest is in better understanding how the timing of an
analyst’s forecast may be used to gauge its quality. In our empirical analysis, we infer forecast
quality from forecast accuracy improvements, forecast boldness, and the price impact of
forecasts.
Two theories link analysts’ forecast quality with the timing of their forecasts. The
reputation-herding theory argues that more-capable agents act earlier and base their estimates on
their private information, whereas less-capable agents subsequently herd as they seek to hide
their low ability (Scharfstein and Stein 1990; Trueman 1994).1 Therefore, the theory predicts
that earlier forecasts in the information discovery and information analysis phases are issued by
more capable analysts and are therefore of higher quality than later forecasts in the same phase.
In contrast, the tradeoff theory predicts that analysts with more precise private information
forecast earlier and those with higher learning ability forecast later (Guttman 2010). So earlier
1 Several studies explore this theory’s predictions about analyst herding behavior. Hong, Kubik, and Solomon (2000), Clement and Tse (2005), and Clarke and Subramanian (2006) all examine analyst characteristics associated with herding and the career consequences of herding behavior. They find that experience, prior forecast accuracy, and brokerage size are negatively associated with an analyst’s tendency to herd. In contrast, we use the herding theory to predict timing-related differences in forecast quality within the information discovery and analysis phases.
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and later forecasts could both be informative, but for different reasons, and thus there should be
no clear relation between timeliness and forecast quality. Our findings are consistent with the
predictions of the herding theory.
We focus on a uniform task performed by analysts—predicting earnings for the fiscal
year. Analysts compete to issue high-quality forecasts.2 This task requires an analyst to discover
private information and analyze public information. In principle, information discovery never
ceases—analysts discover new information about a firm and its transactions throughout the
year. Routine information discovery is disrupted, however, by corporate disclosure events such
as earnings announcements for the previous fiscal year and the fiscal quarters of the current
year. Analysts then switch from discovering information to analyzing the corporate disclosure.
We refer to this phase as information analysis. After analyzing the disclosure and refining their
predictions of annual earnings, analysts resume information discovery. We expect information
discovery immediately after the analysis period to be less intensive than at other times,
however, because there are relatively few transactions and hence little new private information
to be discovered about the new quarter (prior-period earnings are typically announced 20 to 30
calendar days into the new period). We label the phase after information analysis as post-
analysis and, for completeness, consider it the third phase of analyst information production.
Analysts go through the three phases of information production in sequence. Their activities in a
year constitute cycles demarcated by prior-year and interim earnings announcements.
We examine the timing of a forecast within an information production phase—
information discovery, information analysis, and post-analysis—with a focus on the first two
phases. We consider forecasts issued earlier in an information production phase to be more
2 Researchers cannot directly observe this competition. We infer the effects of competition from analysts’ timing patterns and ex post forecast quality.
3
timely than those issued later in the same phase. In other words, our concept of timeliness is
based on calendar time, where timeliness declines as each day passes. This differs from the
leader-follower relation examined by Cooper, Day, and Lewis (2001) and Shroff,
Venkataraman, and Xin (2013), who classify analysts as leaders if their forecasts prompt a
string of forecasts by other analysts (followers). Cooper et al. find that leaders have a larger
price impact than followers. Shroff et al. find that followers’ forecasts also affect stock prices,
because they convey private information and reaffirm leaders’ information, and conclude that
both leaders and followers contribute to price discovery. The leader-follower relation is based
on the idea that followers quickly issue their forecasts after the release of a forecast by a leader,
but not after other followers release forecasts; it does not, however, predict whether a leader or a
follower issues an early forecast in calendar time. Analysts identified as followers may issue
early forecasts, but would prompt few forecasts by other analysts if they do so; analysts
identified as leaders may forecast late in the period, but their forecasts would prompt forecasts
by other analysts.3 Thus calendar timing in our study is distinct from the leader-follower
concept in their studies. Moreover, their leader-follower classification does not distinguish
among analyst activities in the three information production phases. We extend Cooper et al.
and Shroff et al. by separating forecast quality differences attributable to the leader/follower
status from those attributable to analyst herding in calendar time within an information
production phase.
We test the predictions of the herding and tradeoff theories using forecast-property-
based and returns-based forecast quality measures. In a given information production phase, we
examine the relation between forecast timing and the likelihood that the forecast is more
accurate than peers’ outstanding forecasts (a forecast property which we refer to as “forecast 3 We observe such leader and follower forecast patterns in our sample.
4
accuracy improvement”). We also examine the relation between timing and the likelihood that
the forecast is bold and thus innovative. For the return-based forecast quality measures, we
examine whether the intra-day absolute stock returns immediately after a forecast vary
systematically with the timing of forecast within an information production phase. We conduct
similar analysis for daily forecast response coefficients to forecast revisions.
We collect analyst forecasts of annual earnings issued during a fiscal year, identify the
earnings announcement that is closest to each forecast, and count the number of trading days
between the forecast and the announcement (which is designated as Day 0). We follow Chen et
al. (2010) in defining the starting and ending dates of the information discovery, information
analysis, and post-analysis phases (see details in Section 3.2). The information discovery phase
is the 30 trading days before each earnings announcement. The beginning of this period roughly
coincides with the end of the fiscal quarter whose results are announced on Day 0. The
information analysis phase is the five trading days starting with Day 0. The post-analysis phase
is Trading Days 5 to 29. A fiscal year typically has 252 trading days and includes four such 60-
trading-day windows. We indeed observe four similar cycles of analyst activities around each
earnings announcement during the fiscal year and therefore pool observations from the four
windows for most of our analysis. We measure the timing of each forecast relative to the closest
earnings announcement and thus forecast timing ranges from -30 to +29 trading days.
Using forecast accuracy improvement and forecast boldness as measures of forecast
quality, we find declines in forecast quality over time in the information discovery and analysis
phases, with steeper declines in the information analysis phase than in the longer information
discovery phase. These results suggest that well-informed analysts issue their forecasts early
and then leave the field to less-informed analysts. For our return-based measures of forecast
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quality, we find that earlier forecasts have a greater price impact than later forecasts in the
second half of the information discovery phase and in the information analysis phase. In
particular, the price impact of forecasts declines as the earnings announcement date approaches,
sharply increases at the announcement date (reflecting a large dose of news in the corporate
disclosure), rapidly declines over the next few days, and then gradually recovers over the next
few weeks as the next analyst activity cycle begins. Overall, these findings support the
reputation-herding theory as an explanation for individual analysts’ timing in information
production and are inconsistent with the tradeoff theory.
Our study makes three contributions. First, we contribute to the understanding of
individual analysts’ behavior by establishing that the timing of individual analysts’ forecasts
within an information production phase is strongly related to forecast quality. A large
proportion of research on individual analysts’ behavior examines the determinants of cross-
sectional variation in forecast quality and finds that several analyst characteristics such as
brokerage size, experience, All-Star status, and the number of firms or industries followed are
associated with forecast quality (Stickel 1992; Mikhail, Walther, and Willis 1997; Clement
1999; Jacob, Lys, and Neale 1999; Clement and Tse 2003; Bonner, Hugon, and Walther 2007).
We show that the timing of forecast within the information discovery and information analysis
phases is another important determinant of forecast quality.
Second, our findings complement Cooper et al. (2001) and Shroff et al. (2013), who
show returns-based evidence that an analyst’s leader/follower status provides incremental
information about the quality of the analyst’s forecast. We find that both leaders and followers
appear to recognize information discovery and information analysis as distinct information
production phases and engage in both activities with roughly similar patterns. That is, leaders
6
and followers exhibit the same downward trend in the relation between timing and forecast
quality within the information discovery and information analysis phases as we observe for all
analysts. Although on average leaders’ forecasts generate a stronger price impact than
followers’ concurrent forecasts, the price impact of followers’ forecasts in most of the
information discovery phase is higher than that of leaders’ in the second half of the information
analysis phase. These results indicate that it is important to separate the information production
phases and that forecast timing within a phase is incrementally informative about forecast
quality beyond an analyst’s leader/follower status.
Finally, our study sheds light on the importance of analysts’ information discovery and
information analysis roles in the capital market. The literature has advanced from determining
whether analysts’ primary role is information discovery (Brennan, Jegadeesh, and Swaminathan
1993; Brennan and Subrahmanyam 1995; Frankel and Li 2004) or information analysis (Lang
and Lundholm 1996; Healy, Hutton, and Palepu 1999; Francis, Schipper, and Vincent 2002;
Zhang 2008) to determining when analysts perform these roles (Chen et al. 2010). Recent
studies examine the relative importance of analysts’ roles using returns-based tests. Ivkovic and
Jegadeesh (2004) conclude that analysts’ information discovery is more useful to investors than
their information analysis, whereas Livnat and Zhang (2012) conclude the opposite. Our study
shows that information discovery and information analysis both decline in importance with time
in the respective information production phases and thus provides researchers with a new metric
(i.e., timing) for evaluating forecast quality.
The rest of the paper is organized as follows. Section 2 discusses the theoretical
background and hypotheses. Section 3 describes sample selection, identifies analyst information
production phases, and discusses analyst forecast timing patterns. Section 4 discusses the
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research design and Section 5 presents the test results. Section 6 examines the relation of timing
within an information production phase and the analyst’s leader/follower status and provides
further analysis regarding when the information analysis phase ends. Section 7 concludes.
2. Theoretical background and hypotheses
Earnings forecasting is a highly visible and widely followed financial analyst activity.
Analyst earnings forecasts help investors predict a firm’s future cash flows and are most useful
if they are accurate and timely.4 All else being equal, forecast accuracy increases with the
amount of information that analysts use in forecasting earnings. Assuming a steady flow of
information to the market, the longer analysts wait to issue forecasts, the more information they
would have for predicting earnings. By waiting, analysts can also glean information from their
peers’ forecasts to improve their own forecast accuracy. Therefore, analysts who are solely
concerned about forecast accuracy would prefer to delay their forecasts. On the other hand,
investors value timely information because it facilitates trading in real time. Analysts who delay
their forecasts to improve accuracy would risk having their information preempted by other
sources and deprive their clients of opportunities to generate trading gains.
Analysts face the tradeoff between accuracy and timeliness in two key information
production phases: information discovery and information analysis. Guttman (2010) models the
tradeoff between forecast accuracy and timeliness for analysts endowed with ability on two
important dimensions: the precision of analysts’ private information and their learning ability.
He shows that in equilibrium analysts with more precise private information forecast earlier and
4 Bias and accuracy both contribute to forecast quality, but we focus on accuracy in this study. Forecast bias may reflect analyst incentives (e.g., investment banking relationship and favored access to management). For example, Chen and Jiang (2006) examine analyst incentives to issue forecasts that overweight favorable private information and underweight unfavorable information. We examine forecast timing patterns in the general population, so such incentives are beyond the scope of this paper.
8
those with higher learning ability forecast later. Intuitively speaking, analysts with precise
private information have little to gain from waiting and analysts with high learning ability can
benefit from the additional public information that is yet to arrive as well as the information that
they can extract from their peers’ forecasts. Therefore, both early and late forecasts could be
informative, but for different reasons. Under this theory, there should be no clear relation
between forecast timeliness and forecast quality.
The reputation-herding theory offers different predictions, however. This theory posits
that more-capable agents act early and base their estimates on their private information, whereas
less-capable agents herd because they seek to hide their low ability (Scharfstein and Stein 1990;
Trueman 1994). Under this theory, earlier forecasts in an information production phase are
expected to be issued by more capable financial analysts and thus to be of higher quality than
later forecasts.
It is unclear which theory better describes individual analysts’ behavior in forecasting
earnings. The herding theory is well established and has been tested in a variety of other
contexts. For example, Graham (1999) finds that investment advisers herd to protect their
reputation. Hong, Kubik, and Solomon (2000) find that young and inexperienced financial
analysts are more likely to herd and issue less-timely earnings forecasts. However, the herding
theory assumes that an analyst’s information set is fixed. In contrast, Guttman’s (2010) tradeoff
theory additionally considers the dimension of active learning—a benefit of waiting. The
herding and tradeoff theories provide conflicting predictions about the relation between forecast
timing and forecast quality, so we do not offer directional predictions.
We operationalize these predictions by inferring forecast quality from two forecast
properties—forecast accuracy improvements and forecast boldness—and the price impact of
9
forecasts. First, we examine whether earlier forecasts are as likely as later forecasts to improve
on the accuracy of peers’ outstanding forecasts in the same information production phase.
Researchers have traditionally viewed forecast accuracy as an essential property of analyst
forecasts. We expect high-quality forecasts to be more accurate than peers’ outstanding
forecasts and explore how this tendency changes with forecast timing. Second, we examine the
relation between forecast timing and boldness. Prior studies classify a forecast as bold if it
differs markedly from peers’ outstanding forecasts (Hong et al. 2000) or from both peers’
expectations and the analyst’s previous forecast (Gleason and Lee 2003; Clement and Tse
2005). Bold forecasts indicate that the analysts provide new information to the market,
reflecting either their superior private information or unique insights and data analysis skills. In
contrast, the other forecasts mostly reflect information already revealed by other analysts’
forecasts (Gleason and Lee 2003). Consistent with this view, Gleason and Lee (2003) and
Clement and Tse (2005) find that bold forecast revisions are more accurate and generate a
stronger price impact than other forecast revisions. Accuracy and boldness are complementary
properties that jointly capture forecast quality better than either of them alone. We state the first
set of hypotheses with the suffix “a” for the information discovery phase and “b” for the
information analysis phase:5
H1a: The timeliness of a forecast in the information discovery phase is not associated with whether it is more accurate than peers’ outstanding forecasts and whether it is bold.
H1b: The timeliness of a forecast in the information analysis phase is not associated with whether it is more accurate than peers’ outstanding forecasts and whether it is bold.
5 In a different setting, Gul and Lundholm (1995) demonstrate that analysts with extreme news (i.e., innovative estimates) are likely to forecast early. Their prediction is consistent with the prediction of the herding theory.
10
Last, we examine the association of forecast timing with the price impact of forecasts. If,
consistent with the herding theory, more-capable analysts participate early and investors
rationally anticipate this timing pattern, investors would respond more strongly to earlier
forecasts than to later forecasts.6 If investors are unaware of analysts’ behavior or do not
perceive timing-related differences in forecast quality, the price impact would be unrelated to
forecast timing. If the tradeoff theory explains individual analysts’ behavior, earlier and later
forecasts could have similar impact on stock prices because investors value information from all
sources, including private information revealed in earlier forecasts and synthesized public
information revealed in later forecasts. We state the second set of hypotheses:
H2a: The timeliness of forecasts in the information discovery phase is not associated with the price impact of forecasts.
H2b: The timeliness of forecasts in the information analysis phase is not associated with the price impact of forecasts.
3. Sample selection, analyst information production phases, and timing patterns
3.1 Sample selection
Our sample is comprised of firms whose fiscal years end between 1999 and 2008. We
begin the sample period in 1999 because the I/B/E/S time-of-day stamps for quarterly earnings
announcement dates that we require for the returns tests are incomplete before 1999. We include
a firm-year in our sample if (1) it has the earnings announcement dates for the preceding fiscal
year (t-1) and interim quarters of the current year (t) in I/B/E/S, (2) its fiscal year-end month as
reported by Compustat is the same in years t-1 and t, (3) it announces earnings for years t and t-
1 within 90 days after the respective fiscal year ends, and (4) its realized earnings per share
6 Trueman (1994, p.109) argues that ability cannot be the sole determinant of forecast timing. If it were then investors would be able to infer analyst ability from timing, removing analysts’ ability to hide their low ability and hence their incentive for delaying their forecasts. Thus analysts must have other (exogenous) reasons to release forecasts at certain dates for the reputation-related timing incentives to function.
11
number for year t is available in I/B/E/S. We collect individual analysts’ forecasts of year t’s
earnings issued during the fiscal year from I/B/E/S and exclude forecasts with an analyst code
of “0,” which I/B/E/S uses for unidentifiable individual analysts. We require a firm to have at
least five forecasts for year t. Finally, we identify the earnings announcement event that is
closest to each forecast and retain forecasts issued within 30 trading days before and 29 trading
days after the announcement.7 In the rest of this paper, we use “day” to mean “trading day” and
refer to the earnings announcement day as Day 0. Thus, forecast timing ranges from -30 to +29.
These procedures give us 712,946 individual analyst forecasts provided by 9,369 unique
analysts for 6,330 unique firms and 28,010 firm-years around 97,005 earnings announcement
events. The number of observations for specific tests varies from the full sample when we
impose further data requirements, such as the existence of an outstanding forecast for testing
forecast accuracy improvements and boldness and the availability of intra-day returns for testing
the price impact of forecasts.
3.2 Analyst information production phases
We identify analyst information production phases based on Chen et al.’s (2010)
findings and our conjecture about analyst activity cycles. Chen et al. examine the association
between a firm’s absolute stock return at the earnings announcement date and the absolute stock
returns on days with analyst forecasts in the surrounding weeks. They interpret a negative
association as evidence of information discovery and a positive association as information
analysis.8 They find a significantly negative association in the six calendar weeks (equivalent to
30 trading days) before the earning announcement, suggesting that analysts engage in
7 There are 252 trading days in a typical year and an average of 62 trading days between two quarterly earnings announcements. Almost all forecasts fall in one and only one 60-trading-day window. 8 Chen et al. (2010) refer to “information analysis” as “information interpretation.”
12
information discovery during this period. Thus, we label Days -30 to -1 as the “information
discovery” phase and set the indicator variable Before30to01 to 1 for days in this interval and 0
for other days. Chen et al. find a significantly positive association in the first calendar week
(equivalent to five trading days) immediately after the earnings announcement suggesting that
analysts focus on analyzing public disclosure in this period. They find only a marginally
significantly positive association in the second week and mark this week along with the
following two weeks with a “zero” relation in their summary figure in the introduction, leaving
ambiguity regarding whether Week 2 resembles the preceding week or the subsequent week. In
our primary analysis, we label the first calendar week, Days 0 to 4, as the “information
analysis” phase and set the indicator variable Aft00to04 to 1 for days in this interval and 0 for
other days. We group Week 2, Days 5 to 9, with the subsequent weeks and refer to Days 5 to 29
in the 60-trading-day window as the “post-analysis” phase with the indicator variable Aft05to29
being 1 for these days and 0 for other days.9 In supplementary analysis, we separate Week 2
from the information analysis and post-analysis phases; our results suggest that Week 2 is best
characterized as a transition from information analysis and thus its inclusion in the post-analysis
phase seems appropriate.
Table 1 shows the percentage of analyst forecasts from each phase by the four earnings
announcement events in a year. Across all events, 26.5% of the forecasts come from the
information discovery phase, 57.8% from the information analysis phase, and 15.7% from the
post-analysis phase. Analysts are more active in the second half of the information discovery
phase than in the first half. Within the information analysis phase, more than half of the
forecasts are issued on the first day after the earnings announcement.
9 Chen et al. (2010) find no statistically significant association in Weeks 3 and 4 (i.e., Trading days 10 to 14) and a significantly negative association in Weeks 5 and 6 (i.e., Trading days 15 to 29), suggesting that analysts resume information discovery by Day 29.
13
3.3 Analyst forecast timing patterns
We observe variation in analyst forecasting activity during the year. In Figure 1 we plot
the distribution of analyst forecasts of fiscal year t’s earnings in the 60-trading-day windows
(about three calendar months) around earnings announcements for year t-1 and the first three
quarters of year t. The graph shows that analyst forecasting activity increases slightly over the
information discovery phase, declines modestly in the ten or so days before the earnings
announcement, spikes at the earnings announcement, and then drops drastically in the next few
days. The decline continues at a more gradual pace, reaching the lowest point about 30 days
after the announcement for year t-1’s earnings and 20 to 25 days after the announcements of
interim earnings. The next analyst cycle then begins. The pattern suggests that analyst
information production runs in cycles anchored at earnings announcement events and that
analyst forecasts follow clear timing patterns.
4. Research design
We use our measures of forecast quality—forecast accuracy improvements, boldness,
and the price impact of forecasts—as dependent variables in separate models. The explanatory
variable in each model is the timing of a forecast measured by the number of days between the
forecast and the closest earnings announcement; we label this variable as Day. We use separate
intercepts and slope coefficients for the information discovery, information analysis, and post-
analysis phases to allow the relation to vary for each phase. We use the information discovery
phase as the base estimation period. The effect of forecast timing in this phase is captured by the
slope coefficient on Day, with a negative coefficient indicating a declining effect over time. The
indicator variables for the information analysis and post-analysis phases are Aft00to04 and
Aft05to29, respectively. We interact Day with the indicator variables so that the coefficients on
14
the interactions represent incremental effects in the information analysis and post-analysis
phases over that of the information discovery phase. Our interest is in the slope coefficients for
the information discovery and analysis phases; we include the post-analysis phase for
completeness of an analyst activity cycle.
Although we define the information discovery phase for each quarter as beginning 30
days before that quarter’s earnings announcement date, it is unclear when analysts start
competing to discover private information about the quarter. This may not occur immediately
on Day -30, which is typically 20 calendar days before the fiscal quarter ends: analysts may not
have a confident view of the quarter’s performance because some transactions would not yet
have occurred. To reflect this uncertainty, we assume that competition for information
discovery starts on Day -30 or alternatively on Day -15, which is about three days after the end
of the fiscal quarter. We report results for the “Day -30” assumption when the “Day -15”
assumption yields similar inferences and discuss both sets of results when the two assumptions
yield different inferences.
4.1 Forecast timing and forecast properties
Our H1a examines the association between forecast timeliness and forecast quality in the
information discovery phase; our H1b examines the association in the information analysis
phase. We estimate the following logit models that use all annual forecasts (k) for a given firm
(i) in the 60-day window around an earnings announcement event (j):10
ijkijkijkijk
ijkijkijk
ijk
ijk
toAftDayatoAftDaya
DayatoAftatoAftaaF
Bold
Improve
29050400
29050400Prob
54
3210. (1)
10 We pool four earnings announcement events in a year because we find almost identical results when we analyze the prior-year announcement and the interim announcements separately.
15
Our measure of forecast accuracy improvements is Improve. We set this variable to 1 if
a forecast is more accurate than peers’ outstanding forecasts, calculated as the most recent
forecast by a peer analyst (we use the mean estimate if more than one analyst issues a forecast
for the firm on that day), and 0 otherwise.11 By definition, Improve is 0 if a forecast merely
mimics recent forecasts. Improve is a relative forecast accuracy measure and allows us to focus
on the forecast’s contribution to overall forecast accuracy. We do not use absolute forecast
accuracy (i.e., the absolute difference between a forecast and the realization) because it might
reflect analysts’ collective accuracy at the time of the forecast. Absolute forecast accuracy
increases during a year as information about the firm’s economic activities becomes available.
Late forecasts are typically more accurate than early forecasts because analysts who issue late
forecasts will have observed and therefore incorporated in their estimates the information
revealed in other analysts’ early forecasts.12
Our forecast boldness variable is Bold. Following Clement and Tse (2005), we set Bold
to 1 if a forecast is outside the interval defined by the analyst’s previous forecast and the most
recent forecast by a peer analyst, and 0 otherwise. Intuitively speaking, bold forecasts reflect
new information, whereas the other forecasts move towards peers’ forecasts, perhaps reflecting
a compromise between the analyst’s previous forecast and peers’ forecasts.
The herding theory predicts a positive association between timeliness and forecast
quality in the information discovery phase and therefore a negative 3a coefficient, whereas the
tradeoff theory predicts no association and thus an insignificant 3a . Similarly, the herding
11 Our proxy for peers’ outstanding forecasts is consistent with Brown and Caylor (2005, Footnote 8), who argue that this measure is superior to the often-used analyst consensus because long-window consensus forecasts may include stale forecasts. Moreover, this proxy better captures the daily change in information than does the analyst consensus in our setting. 12 This conjecture is confirmed by the upward trend in the absolute forecast accuracy chart in Figure 1.
16
theory predicts a negative coefficient of 43 aa for the information analysis phase, whereas the
tradeoff theory predicts no association and thus an insignificant coefficient.
4.2 Forecast timing and the price impact of forecasts
H2a examines the association between forecast timing and the price impact of forecasts
in the information discovery phase; H2b examines this association in the information analysis
phase. We measure the price impact of forecasts in two ways: (1) the absolute stock return right
after an analyst forecast and (2) the forecast revision coefficient (FRCs) estimated from daily
regressions of stock returns.
Our test using absolute stock returns is Equation (2):
ijkijkijkijkijkijk
ijkijkijk
toAftDaybtoAftDaybDayb
toAftbtoAftbbReturn
29050400
29050400||
543
210
. (2)
Return is the two-hour intra-day return after an analyst forecast or in the first two trading hours
on the next trading day if the forecast is issued after the stock market closes. The intra-day
returns data are from the TAQ database. We eliminate forecasts that are within two hours of the
earnings announcement to avoid confounding news. |Return| is the absolute value of Return.
The absolute returns test ignores the consistency between the forecast news and price
change. To address this issue, we estimate a forecast revision coefficient for each trading day, t,
by regressing returns on forecast news in Equation (3). The explanatory variable, Revision, is
the difference between the analyst’s current and prior forecasts, scaled by the stock price at the
beginning of the return window. We include the earnings announcement surprise, Surprise, to
control for potential leakage or lingering effects of the earnings announcement news. Surprise is
the difference between reported earnings for the announced quarter and the pre-announcement
17
consensus forecast, scaled by the stock price at the beginning of the return window. We
estimate Equation (3) for each of the 60 trading days.
SurpriseaRevisonaaReturn 210 . (3)
We then regress the FRC estimates on the information production phase indicators and forecast
timing variables in Equation (4) with each trading day, t, being one observation:
tttttt
ttt
toAftDayctoAftDaycDayc
toAftctoAftccFRC
29050400
29050400
543
210 . (4)
The herding theory predicts a positive association between timeliness and return
response in the information discovery phase and therefore negative coefficients of 3b in
Equation (2) and 3c in Equation (4), whereas the tradeoff theory predicts no association and
thus insignificant 3b and 3c . Similarly, the herding theory predicts negative coefficients of
43 bb in Equation (2) and 43 cc in Equation (4) for the information analysis phase, whereas
the tradeoff theory predicts no association and thus insignificant coefficients.
5. Test results
5.1 Descriptive statistics
Table 2 presents descriptive statistics of our key measures for the information discovery,
information analysis, and post-analysis phases. To investigate how forecast quality changes
over the information discovery and analysis phases, we compare values of each of the key
measures in the early and late periods of the information discovery and analysis phases. We
split the information discovery phase in the middle into the early period (Days -30 to -16) and
the late period (Days -15 to -1) and split the information analysis phase into the early period
(Days 0 to 2)—a typical three-day window for event studies—and the late period (Days 3 and
4).
18
The mean of Improve is 0.50 in the information discovery phase, 0.52 in the information
analysis phase, and 0.48 in the post-analysis phase. Improve is significantly higher in the early
period than in the late period for both the information discovery and information analysis
phases. The percentage of bold forecasts ranges from 57% to 59% across the three phases. Bold
is significantly higher in the early period than in the late period for both the information
discovery and analysis phases. These patterns indicate that analyst forecasts issued early in
these phases are more likely to improve on the accuracy of peers’ outstanding forecasts and are
more likely to be bold than those issued late in the phases. The mean absolute return, |Return|,
is approximately 1.7% in the information discovery and analysis phases and is 1.4% in the post-
analysis phase. Early returns in the information discovery phase are no different from late
returns in the phase, whereas early returns in the information analysis phase are much higher
than late returns in that phase.
5.2 Forecast Timing and forecast properties
To illustrate the effects of timing on forecast quality, we plot the daily mean of Improve
in Figure 2 after pooling all earnings announcement events. The daily mean of Improve
measures the percentage of forecasts from all analysts on a given trading day that are more
accurate than peers’ outstanding forecasts. Between announcements, the measure peaks at 53%
about 25 days before the upcoming announcement. A downward trend ensues until the earnings
announcement date. The measure jumps to a high of 60% at the announcement date and slumps
quickly to a low of 45% five days after the announcement. Figure 2 also plots the daily mean of
Bold, corresponding to the percentage of bold forecasts on a given day. This measure starts at
about 60% at the beginning of the information discovery phase and declines noticeably during
this phase. It then spikes to about 70% on the earnings announcement day and declines rapidly
19
to its lowest level of 50% in three or four days. After that, the measure climbs gradually to 60%
at the end of the post-analysis phase. We conclude from these patterns that analyst forecast
quality declines over both the information discovery and analysis phases, suggesting that
analysts with superior information tend to provide their forecasts earlier in each phase than the
other analysts. The discontinuity in earnings quality at the earnings announcement and the
increase over the post-analysis phase indicate that analyst conduct distinct activities in the
information discovery, information analysis, and post-analysis phases.
Table 3 reports the estimation results of the relation of forecast timing with forecast
accuracy improvements in the first two columns and with forecast boldness in the last two
columns. We cluster standard errors by analyst and year in all analyses unless otherwise noted.
For the “Improve” estimation, the Day coefficient is -0.002, statistically significant, indicating
that analysts are less likely to issue more accurate forecasts than peers’ outstanding forecasts as
time elapses in the information discovery phase. The sum of coefficients on Day and
Day×Aft00to04 is -0.192, indicating that forecast accuracy improvements decline rapidly in the
information analysis phase.13 For the “Bold” equation, the Day coefficient of -0.005 is
statistically significant, indicating that earlier forecasts are more likely to be bold than later
forecasts in the information discovery phase. The sum of coefficients on Day and
Day×Aft00to04 is -0.254, significantly negative, indicating that the likelihood of a forecast
being bold declines rapidly in the information analysis phase. These results suggest that analyst
forecasts issued earlier in the information discovery and analysis phases are more likely to
13 Although it is not our focus, the steeper negative slope for the information analysis period than for the information discovery period suggests that analysts compete much more intensely in information analysis than in information discovery (perhaps because information analysis is confined to a very short window). Such intense competition facilitates price discovery after corporate disclosure.
20
improve on the accuracy of peers’ outstanding forecasts and be bold than those issued later in
the same phase. These findings are consistent with the predictions of the herding theory.14
Managers favor prior-year and interim earnings announcement events as a venue to
provide annual earnings guidance (Aniloswki, Feng, and Skinner 2007; Lansford, Lev, and
Tucker 2013). Forecasts issued soon after the earnings announcement may improve on the
accuracy of peers’ outstanding forecasts or be bold because they incorporate managers’
guidance rather than analysts’ insights. Managers’ guidance issued outside an earnings
announcement window may also enhance the quality of analyst forecasts issued after the
guidance. We partition the sample into analyst forecasts issued in a 60-day earnings
announcement event window in which managers provided guidance and forecasts in windows
without such guidance. Results for the two subsamples are similar to those for the full sample,
suggesting that our results are robust to managers’ guidance (untabulated).
We also investigate the sensitivity of our results to alternative measures of Bold and
Improve. Instead of using the most recent forecast by a peer analyst to proxy for peers’
outstanding forecasts, we use a consensus calculated as the mean estimate in the preceding 60-
calendar-day window. We find similar results and conclude that our measures are robust.
5.3 Forecast timing and return responses
Table 4 reports the estimation results of the relation between forecast timing and
absolute stock returns. The coefficient on Day is not statistically significant from 0, indicating
no evidence of a downward slope in absolute stock returns over the information discovery
phase. The sum of coefficients on Day and Day×Aft00to04 is -0.269 with a t-statistic of -12.13,
14 A concern arising from our measurement of Improve and Bold is that the arrival of corporate news may bias these measures upward at the earnings announcement date. Our results are similar if we exclude forecasts issued on Days 0 and 1 (untabulated).
21
significantly negative, suggesting that investors respond more strongly to earlier forecasts than
to later forecasts in the information analysis phase. The positive coefficient on Aft00to04
indicates a jump in return responses soon after the earnings announcement due to the arrival of
corporate news.
To understand the absence of a downward slope in the information discovery phase, we
use the alternative assumption regarding when analyst competition starts in this phase. Instead
of assuming that it begins on Day -30 as in Equation (2), we investigate whether competition
differs in the two halves of the phase, centered on Day -15. We add an indicator, Bef30to16, for
the interval of Days -30 to -16, and its interaction with Day. The model is:
.290504001630
290504001630||
765
43210
ijkijkijkijkijkijkijk
ijkijkijkijkijk
toAftDaybtoAftDaybtoBefDayb
DaybtoAftbtoAftbtoBefbbReturn
(5)
We report the results in the third and fourth columns of Table 4. The Day coefficient
now represents the slope for the second half of the information discovery phase, Days -15 to -1,
and is significantly negative at -0.026. In contrast, the coefficient for the first half of the
information discovery phase (the sum of coefficients on Day and Day×Bef30to16) is statistically
insignificant at -0.002. These results indicate that analyst competition is absent in the first half
but occurs in the second half of the information discovery phase.
We plot absolute stock returns around analyst forecasts in Figure 3 and find a pattern
consistent with the regression results. Specifically, returns exhibit a downward slope in the
second half of the information discovery phase, spike at the earnings announcement date, fall
rapidly in the next four to five days, and then recover gradually from about Day 10 onward. We
conclude that investors respond to analyst forecasts as if they recognize differences in forecast
quality related to the timing within each analyst information production phase.
22
In Table 5 we report regression results for Equation (4) in the first two columns. The
slope on Day is -0.011, weakly significantly negative, indicating a downward slope in the
information discovery phase. The sum of coefficients on Day and Day×Aft00to04 is -0.281,
statistically significantly negative, indicating a substantial decline in return responses as each
day passes in the information analysis phase. As in Table 4, we present the estimation results in
the last two columns of Table 5 with the assumption that analyst competition begins midway in
the information discovery phase. The coefficient on Day is -0.047, significantly negative,
indicating a decline in return responses that is concentrated in the second half of the information
discovery phase. Our return-based test results in Tables 4 and 5 are largely consistent with the
predictions of the herding theory.
Figure 4 plots FRCs, estimated from Equation (3), in the 60-trading-day window and
shows that FRC decreases as time elapses in the second half or the last third of the information
discovery phase. Although FRC increases sharply at the earnings announcement date, it drops
quickly over the five days of the information analysis phase before climbing to just below the
level of the early information discovery phase by the end of the post-analysis phase.15 The
patterns in the figure are consistent with the regression results and support our conclusion that
investors recognize timing-related quality differences in individual analysts’ forecasts.
The return-based test results could be influenced by management earnings guidance.
Earnings guidance preceding analyst forecasts may inflate the reported information content of
analyst forecasts because investors might be responding to corporate news as well. For
robustness, we eliminate all forecasts that were issued on the same day as management earnings
15 Although it is not the focus of our study, we observe that FRC is higher on several days in the information discovery phase than in the information analysis phase, suggesting that investors sometimes value information discovery more highly than they do information analysis.
23
guidance or on the next two days. Our original sample of observations with available intra-day
returns is reduced to 467,590 observations, but our results remain unchanged (untabulated). We
therefore conclude that our findings are unaffected by management earnings guidance.
In our primary analyses we measure returns in the two-hour window and eliminate
forecasts that are within two hours of the earnings announcement to avoid the confounding
effect of earnings announcement. As a robustness check we repeat our analysis after eliminating
forecasts issued on the earnings announcement day and the next day.16 We still find strong
negative trends in return responses in the information analysis phase (untabulated). Thus, our
primary results are not driven by confounding earnings announcement news.
6. Further analyses
6.1 Leader vs. follower analysts
In this section we distinguish the herding phenomenon documented in our study from
the leader-follower phenomenon in Cooper et al. (2001) and Shroff et al. (2013). Following
Shroff et al., we identify leader/follower analysts in a series of steps. We require that at least
five analysts follow a firm during the fiscal year and eliminate forecasts issued on Days 0 and 1.
We calculate a leader-follower ratio (LFR) as the ratio of the cumulative number of days by
which the two prior forecasts lead the forecast to the cumulative number of days by which the
next two forecasts follow the forecast.17 If an analyst issues more than one forecast for the firm-
year, we sum the numerators and denominators of LFR of the multiple forecasts for that analyst.
We identify the analyst with the highest LFR rank as the lead analyst for the firm-year if the
number of analysts following the firm ranges from five to nine and identify an additional
16 This test addresses three issues: (1) confounding corporate news in the earnings announcement, (2) after-hour announcements (Berkman and Truong 2009), and (3) exclusion of earnings announcement date by prior studies. 17 We use annual earnings forecasts to construct our measure for consistency with our other analyses. Shroff et al. (2013) use forecasts of quarterly earnings.
24
analyst as a lead analyst for each succeeding five-analyst increase in following up to a
maximum of eight lead analysts. These requirements significantly reduce our intra-day returns
sample to 439,503 analyst forecasts with LFR available, including 66,261 forecasts by leaders
and 373,242 forecasts by followers.
In Figure 5 we plot leaders’ and followers’ activities in the 60-trading-day window
pooled over the four earnings announcement events during the year. Because there are fewer
leaders than followers, we convert the raw number of forecasts to a percentage of each group’s
total forecasts to facilitate comparison. The bar charts show that leaders’ and followers’ forecast
patterns during an analyst activity cycle are remarkably similar and closely match the pattern for
all analysts in Figure 1. This similarity suggests that both leaders and followers recognize
information discovery and information analysis as distinct information production phases and
engage in both activities.
Next, we investigate the absolute return responses to leaders’ and followers’ forecasts in
Figure 5 by adding line charts of the mean absolute stock returns measured in the two hours
following leaders’ and followers’ forecasts. The returns for leaders exceed those for followers at
most points of the analyst activity cycle, indicating that leaders have superior private
information or public information processing skills than followers given the same set of public
information. When we compare forecasts over the entire analyst activity cycle, however, we
find that the returns depend more on the information production phase of the activity cycle and
the timing within the phase than on the leader/follower status. For example, the return responses
to followers’ forecasts in most of the information discovery phase are higher than those to
leaders’ in the second half of the information analysis phase and in the post-analysis phase.18
18 The differences are statistically significant at better than the 1% level (untabulated).
25
Finally, we test the associations of forecast timing and absolute returns for leaders and
followers separately and report the results in Table 6. For both groups we find a decline in
return responses over time in the second half of the information discovery phase and in the
information analysis phase.19 We obtain similar results for forecast accuracy improvement and
forecast boldness (untabulated). These findings indicate that forecast timing matters for both
groups and that timing within an information production phase is an incremental determinant of
forecast quality beyond the leader/follower status.
6.2 The length of the information analysis period
In our primary analysis we include Week 2, Days 5 to 9, in the post-analysis phase.
Now, we examine whether the forecast quality patterns in this interval resemble those in the
preceding information analysis phase. The indicator variable Aft05to09 is 1 for days in this
interval and 0 otherwise. We modify the returns model, Equation (5), by adding Aft05to09 and
the interaction between Day and Aft05to09. Equation (6) is the new regression:
.29100905
04001630
2910090504001630||
98
765
43210
ijkijkijkijkijk
ijkijkijkijkijk
ijkijkijkijkijk
toAftDaybtoAftDayb
toAftDaybtoBefDaybDayb
toAftbtoAftbtoAftbtoBefbbReturn
(6)
The estimation results, reported in Table 7, show that the slope coefficient for the
interval of Days 5 to 9 is -0.011, statistically insignificantly different from 0, in contrast to the
significantly negative coefficient for the first week after earnings announcement. We obtain
similar results for Week 2 from the augmented accuracy improvement and forecast boldness
models (untabulated). These results suggest that Week 2 does not belong to the information
19 Similar to Shroff et al. (2013), we require that the firm be followed by at least five analysts to calculate the leader-follower ratio. When we estimate our primary models on a sample of firms with too few analysts to calculate the leader-follower ratio, we still find results consistent with the herding theory, indicating that our timeliness measure provides a measure of forecast quality for a broader sample than the method of Shroff et al.
26
analysis phase and that analyst competition in the information analysis phase ends in Week 1
and does not extend to Week 2.
7. Conclusion
Financial analysts contribute to informational efficiency in the capital markets by
uncovering new information and analyzing public disclosures. Prior research examines the
relative importance of information discovery versus information analysis for analysts as a
group. We extend the literature by examining the timing of individual analysts’ activities within
the information discovery and information analysis phases. Consistent with the reputation-
herding theory, we find that earlier forecasts in an analyst information production phase have
higher forecast quality (as measured by forecast accuracy improvements, forecast boldness, and
the price impact of forecasts) than later forecasts in that phase. In addition, forecast timing
within distinct analyst information production phases is incrementally informative about
forecast quality beyond an analyst’s leader/follower status. These finding enrich our
understanding of how individual analysts contribute to price discovery in the capital markets.
27
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29
Figure 1
Note: Earnings announcements are for the previous fiscal year (Q0) and interim quarters of the current year (Q1 to Q3). Day 0 is the earnings announcement date. Trading days relative to Day 0 are marked. The peak forecast frequency occurs on Day 1. Forecast accuracy is the absolute difference between a forecast and the realization, and is standardized for each firm-year so that the most accurate forecast has a value of 1 and the least accurate forecast has a value of 0. The mean value of forecast accuracy is used if there is more than one forecast for the firm-year on a given trading day.
30
Figure 2
Note: Observations for the prior-year announcement and current-year interim announcements are pooled in this graph. Peers’ outstanding forecasts are proxied by the most recent forecast issued by a peer analyst (the mean estimate is used if there is more than one forecast on that day). A forecast is “bold” if it is outside the interval defined by the analyst’s previous forecast and peers’ expectations. The improvement ratio is the percentage of forecasts from all companies on a given day that are more accurate than
peers’ outstanding forecasts. The bold ratio is the percentage of forecasts that are bold on a given day.
31
Figure 3
Note: This figure plots the daily mean of absolute stock returns in the two hours after an analyst’s forecast. The stock return is set to be missing if the forecast is issued within two hours of an earnings announcement.
32
Figure 4
Note: FRC is the coefficient estimate on Revision in the regression: SurpriseaRevisonaaReturn 210 . Return is the
stock return in the two hours after a forecast and set to be missing if the forecast is issued within two hours of an earnings announcement. Revision is the difference between the analyst’s current and prior forecasts. We control for earnings surprise (Surprise), the difference between announced earnings and the pre-announcement consensus forecast, in the regression. Revision and Surprise are scaled by the stock price at the beginning of the return measurement window. The regression is estimated for each trading day.
33
Figure 5
Note: The bars show the daily percentage of forecasts issued by leader and follower analysts within the respective group. The lines are the daily mean absolute stock returns in the two hours after the forecasts of leaders and followers, respectively. The leader-follower analyst status is determined from clustering patterns in forecasts of current fiscal year earnings following the procedures in Shroff et al. (2013).
34
Table 1 Distribution of Analyst Forecasts around Earnings Announcements
Earnings announcement (EA) event Day
(EA is on Day 0)
Prior Year
First quarter
Second quarter
Third quarter
All
1. Information discovery phase Early information discovery -30 to -16 8.5% 9.8% 13.2% 14.0% 11.7%Late information discovery -15 to -1 14.3% 15.3% 14.3% 15.4% 14.8%
2. Information analysis phase Early information analysis Earnings announcement day 0 13.4% 14.0% 14.3% 13.1% 13.7% First day after 1 35.1% 33.2% 32.5% 31.2% 32.8% Second day after 2 8.1% 7.0% 7.1% 7.3% 7.3% Late Information analysis 3 to 4 4.6% 3.9% 3.6% 4.1% 4.0% 3. Post-analysis phase 5 to 29 16.1% 16.8% 15.1% 14.9% 15.7% Total percentage 100% 100% 100% 100% 100% Total observations 141,902 172,902 188,294 209,848 712,946 Note: The sample includes analyst forecasts of fiscal year t’s earnings issued during fiscal year t, where fiscal year t ends during 1999-2008. During fiscal year t we identify four earnings announcement events and classify each analyst forecast to the closest earnings announcement event (“event,” Day 0). If an earnings announcement is released after the market closes, the following day is considered the event day. We keep forecasts issued between -30 to +29 trading days relative to the event day. If a forecast is issued after the market closes, it is counted as a forecast on its first available trading day. The 60-trading-day window is about 90 calendar days. The four 60-trading-day windows cover most of the typical fiscal year length of 252 trading days.
35
Table 2 Descriptive Statistics
Information discovery phase
Day -30 to Day -1 Information analysis phase
Day 0 to Day 4 Post-analysis phase
Day 5 to Day 29 Entire
period Early
[-30, -16] Late
[-15, -1] Early-Late
(t-stat.) Entire
period Early
[0, +2] Late
[+3, +4] Early-Late
(t-stat.)
Improve 0.50 0.51 0.50 0.01*** (2.66)
0.52 0.53 0.45 0.08*** (25.74)
0.48
Obs. 179,999 80,485 99,514 383,137 305,761 77,376 110,223 Bold 0.58 0.59 0.57 0.02***
(7.65) 0.59 0.60 0.51 0.09***
(30.71) 0.57
Obs. 179,999 80,485 99,514 383,137 305,761 77,376 110,223 |Return| (%) 1.70 1.70 1.70 0.00
(0.24) 1.71 1.78 1.23 0.55***
(44.82) 1.40
Obs. 159,122 70,115 89,007 307,901 245,770 62,131 91,702 Note: Improve is an indicator variable that takes the value of 1 if the forecast is more accurate than peers’ outstanding forecasts, proxied by the most recent forecast issued by a peer analyst (the mean estimate is used if more than one peer forecast is issued on that day). Bold is an indicator variable that takes the value of 1 if the forecast is outside the interval defined by the analyst’s previous forecast and peers’ expectations. |Return| is the absolute stock return in the two hours after a forecast and is set to be missing if the forecast is issued within two hours of an earnings announcement. The numbers in parentheses are the t-statistics to test the hypothesis that the difference in means is zero.
36
Table 3 Forecast Timing and Forecast Properties
Logit model:
ijkijkijkijkijk
ijkijkijk
ijk
ijk
toAftDayatoAftDaya
DayatoAftatoAftaaF
Bold
Improve
29050400
29050400Prob
54
3210
Improve Bold Coefficient Coefficient
sum Coefficient
Coefficient
sum Intercept -0.022
(-1.34) 0.237***
(13.13)
Aft00to04 0.322*** (12.60)
0.418*** (18.63)
Aft05to29 -0.208*** (-6.46)
-0.156*** (-5.39)
Day -0.002*** (-3.50)
-0.005*** (-6.41)
Day×Aft00to04 -0.189*** (-17.80)
-0.192*** (-18.75)
-0.249*** (-26.39)
-0.254*** (-28.28)
Day×Aft05to29 0.012*** (9.06)
0.009*** (8.53)
0.018*** (18.21)
0.013*** (14.09)
Pseudo R2 1% 1%
Note; The estimations use all annual analyst forecasts (k) for a given firm (i) around an earnings announcement event (j, such as 2007Q1) and that have a prior forecast by any other analyst for calculating forecast accuracy improvement or boldness. Improve is an indicator variable that takes the value of 1 if the forecast is more accurate than peers’ outstanding forecasts, proxied by the most recent forecast issued by a peer analyst (the mean estimate is used if more than one peer forecast is issued on that day). Bold is an indicator variable that takes the value of 1 if the forecast is outside the interval defined by the analyst’s previous forecast and peers’ outstanding forecasts. Day is the number of trading days relative to the closest earnings announcement date and its value is negative for observations before the earnings announcement, 0 for the announcement date, and positive for observations after the announcement date. The information discovery phase (Days –30 to –1) is the baseline period in the estimation. We use the indicator variables Aft00to04 for the information analysis phase and Aft05to29 for the post-analysis phase. The slope coefficient for the information analysis phase is the sum of coefficients on Day and Day×Aft00to04 and the slope coefficient for the post-analysis period is the sum of coefficients on Day and Day×Aft05to29, as indicated in Columns 2 and 4. The estimations use 673,359 observations with standard errors clustered by analyst and year. We report z statistics in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
37
Table 4 Forecast Timing and Absolute Stock Returns
ijkijkijkijkijkijkijkijk
ijkijkijkijk
toAftDayatoAftDayatoBefDayaDaya
toAftatoAftatoBefaaReturn
290504001630
290504001630||
7654
3210
Coefficient Coefficient
sum Coefficient Coefficient
sum Intercept 0.016***
(9.64) 0.015***
(9.97)
Bef30to16 0.002 (0.98)
Aft00to04 0.004*** (4.13)
0.005*** (5.81)
Aft05to29 -0.005*** (-5.81)
-0.004*** (-5.00)
Day -0.004 (-1.27)
-0.026*** (-6.03)
Day×Bef30to16 0.024** (2.48)
-0.002 (-0.30)
Day×Aft00to04 -0.264*** (-11.71)
-0.269*** (-12.13)
-0.243*** (-11.60)
-0.269*** (-12.13)
Day×Aft05to29 0.022*** (6.98)
0.018*** (7.24)
0.044*** (7.29)
0.018*** (7.24)
Adjusted R2 1% 1% Note: |Return| is the absolute stock return in the two hours after an analyst forecast or in the first two trading hours on the next trading day if the forecast is made after the stock market closes. The variable is set to be missing if the forecast is issued within two hours of an earnings announcement. Day is the number of trading days relative to the closest earnings announcement date and its value is negative for observations before the earnings announcement, 0 for the announcement date, and positive for observations after the announcement date. The full information discovery phase (Days –30 to –1) is the baseline period in the first estimation and the late information discovery phase (Days –15 to –1) is the baseline period in the second estimation. We use the indicator variables Bef30to16 for the early information discovery period, Aft00to04 for the information analysis phase, and Aft05to29 for the post-analysis phase. The slope coefficient for the early information discovery phase is the sum of coefficients on Day and Day×Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on Day and Day×Aft00to04. The slope coefficient for the post-analysis phase is the sum of coefficients on Day and Day×Aft05to29. The coefficients on Day and its interaction terms are multiplied by 100 for presentation. The estimations use 558,725 observations with standard errors clustered by analyst and year. We report t-statistics in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
38
Table 5 Forecast Timing and Forecast Response Coefficients
tttttttt
tttt
toAftDayatoAftDayatoBefDayaDaya
toAftatoAftatoBefaaFRC
290504001630
290504001630
7654
3210
Coefficient Coefficient sum
Coefficient Coefficient sum
Intercept 1.051*** (9.97)
0.827*** (5.66)
Bef30to16 -0.153 (-0.38)
Aft00to04 0.048 (0.20)
0.272 (1.07)
Aft05to29 -0.933*** (-5.23)
-0.709*** (-3.53)
Day -0.011* (-1.88)
-0.047*** (-2.92)
Day×Bef30to16 0.022 (0.97)
-0.025 (-1.55)
Day×Aft00to04 -0.270*** (-3.03)
-0.281*** (-3.16)
-0.234*** (-2.71)
-0.281*** (-3.31)
Day×Aft05to29 0.043*** (4.35)
0.032*** (4.04)
0.078*** (4.43)
0.032*** (4.23)
Adjusted R2 61% 61% Note: Forecast response coefficient (FRC) is the estimated coefficient on Revision in the model
SurpriseaRevisonaaReturn 210 . Return is the stock return in the two hours after an analyst
forecast or in the first two trading hours on the next trading day if the forecast is made after the stock market closes. The variable is set to be missing if the forecast is issued within two hours of an earnings announcement. Revision is the difference between the analyst’s current and prior forecasts. Surprise is the difference between earnings and the pre-announcement consensus forecast. Revision and Surprise are deflated by the stock price at the beginning of the return measurement window. We estimate this regression on each trading day and obtain the daily FRC estimates. The 60 daily FRC estimates are regressed on the information production phase indicators, Day, and the interactions. Day is the number of trading days relative to the closest earnings announcement date and its value is negative for observations before the earnings announcement, 0 for the announcement date, and positive for observations after the announcement date. The full information discovery phase (Days –30 to –1) is the baseline period in the first estimation and the late information discovery period (Days –15 to –1) is the baseline period in the second estimation. We use the indicator variables Bef30to16 for the early information discovery period, Aft00to04 for the information analysis phase, and Aft05to29 for the post-analysis phase. The slope coefficient for the early information discovery phase is the sum of coefficients on Day and Day×Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on Day and Day×Aft00to04. The slope coefficient for the post-analysis phase is the sum of coefficients on Day and Day×Aft05to29. We report t-statistics in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
39
Table 6 Forecast Timing and Absolute Stock Returns for Lead and Follower Analysts
ijkijkijkijkijkijkijkijk
ijkijkijkijk
toAftDayatoAftDayatoBefDayaDaya
toAftatoAftatoBefaaReturn
290504001630
290504001630||
7654
3210
Leader
Follower
Intercept 0.016*** (9.83)
0.015*** (9.38)
Bef30to16 0.000 (0.19)
0.001 (0.58)
Aft00to04 0.004*** (3.48)
0.004*** (4.78)
Aft05to29 -0.003** (-2.27)
-0.004*** (-4.53)
Day -0.033*** (-3.37)
-0.022*** (-4.28)
Day×Bef30to16 0.026* (1.71)
-0.007 (-0.65)
0.018* (1.87)
-0.004 (-0.49)
Day×Aft00to04 -0.236*** (-8.21)
-0.269*** (-9.84)
-0.217*** (-10.65)
-0.239*** (-10.70)
Day×Aft05to29 0.050*** (4.68)
0.017*** (3.28)
0.039*** (5.34)
0.017*** (5.74)
Adjusted R2 1% 1%
Note: |Return| is the absolute stock return in the two hours after an analyst forecast or in the first two trading hours on the next trading day if the forecast is made after the stock market closes. The variable is set to be missing if the forecast is issued within two hours of an earnings announcement. Day is the number of trading days relative to the closest earnings announcement date and its value is negative for observations before the earnings announcement, 0 for the announcement date, and positive for observations after the announcement date. The late information discovery period (Days –15 to –1) is the baseline period. We use the indicator variables Bef30to16 for the early information discovery period, Aft00to04 for the information analysis phase, and Aft05to29 for the post-analysis phase. The slope coefficient for the early information discovery phase is the sum of coefficients on Day and Day×Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on Day and Day×Aft00to04. The slope coefficient for the post-analysis phase is the sum of coefficients on Day and Day×Aft05to29. The coefficients on Day and its interaction terms are multiplied by 100. We identify leader and follower analysts using the procedures in Shroff et al. (2012). The leader (follower) estimation uses 66,261 (373,242) observations with standard errors clustered by analyst and year. We report t statistics in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
40
Table 7 Absolute Stock Returns and Forecast Timing in Week 2 after the Earnings Announcement
ijkijk
ijkijkijkijkijkijk
ijkijkijkijkijkijk
toAftDaya
toAftDayatoAftDayatoBefDaya
DayatoAftatoAftatoAftatoBefaaReturn
2910
090504001630
2910090504001630||
9
876
543210
Coefficient Coefficient sum Intercept 0.015***
(9.97)
Bef30to16 0.002 (0.98)
Aft00to04 0.005*** (5.81)
Aft05to09 -0.002** (-2.04)
Aft10to29 -0.004*** (-3.98)
Day -0.026*** (-6.03)
Day×Bef30to16 0.024** (2.48)
-0.002 (-0.30)
Day×Aft00to04 -0.243*** (-11.60)
-0.269*** (-12.13)
Day×Aft05to09 0.015 (1.25)
-0.011 (-0.90)
Day×Aft10to29 0.045*** (6.89)
0.019*** (5.32)
Adjusted R2 1% Note: |Return| is the absolute stock return in the two hours after an analyst forecast or in the first two trading hours on the next trading day if the forecast is made after the stock market closes. The variable is set to be missing if the forecast is issued within two hours of an earnings announcement. Day is the number of trading days relative to the closest earnings announcement date and its value is negative for observations before the earnings announcement, 0 for the announcement date, and positive for observations after the announcement date. The late information discovery phase (Days –15 to –1) is the baseline period in the estimation. We use the indicator variables Bef30to16 for the early information discovery period, Aft00to04 for the information analysis phase, Aft05to09 for Week 2, Days 5 to 9, and Aft10to29 for the remaining post-analysis phase. The slope coefficient for the early information discovery phase is the sum of coefficients on Day and Day×Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on Day and Day×Aft00to04. The slope coefficient for Week 2 is the sum of coefficients on Day and Day×Aft05to09. The slope coefficient for the remaining post-analysis phase is the sum of coefficients on Day and Day×Aft10to29. The coefficients on Day and its interaction terms are multiplied by 100 for presentation. The estimations use 558,725 observations with standard errors clustered by analyst and year. We report t-statistics in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
