of annual earnings forecasts - university of floridabear.warrington.ufl.edu/tucker/2014-1...

Analyst information production and the timingof annual earnings forecasts

Sami Keskek • Senyo Tse • Jennifer Wu Tucker

� Springer Science+Business Media New York 2014

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 fore-

casts as measures of forecast quality, we find that in the information discovery phase

that precedes an earnings announcement, earlier forecasts have higher quality than

later forecasts. We also 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, analysts who are more capable participate early in dis-

covering and analyzing information, and therefore earlier forecasts in the infor-

mation discovery and analysis phases are of higher quality than later forecasts in

that phase.

Keywords Financial analysts � Timing � Earnings announcements �Information discovery

JEL Classification G14 � G20 � D82 � D83

S. Keskek

Department of Accounting, Sam Walton College of Business, University of Arkansas, Fayetteville,

AR, USA

e-mail: [email protected]

S. Tse (&)

Department of Accounting, Mays Business School, Texas A&M University, College Station, TX,

USA


J. W. Tucker

Fisher School of Accounting, University of Florida, Gainesville, FL, USA


123

Rev Account Stud

DOI 10.1007/s11142-014-9278-7

1 Introduction

Sell-side security analysts perform two distinct tasks in predicting earnings:

information discovery and information analysis. Chen et al. (2010) conclude that

analysts focus on discovering private information before a corporate earnings

announcement and switch to analyzing information immediately afterwards. Prior

research compares the contribution of analysts as a group in the information

discovery and 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 to better understand 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 agents who are more capable 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 The theory predicts that earlier forecasts in the information

discovery and information analysis phases are issued by analysts who are more

capable and are therefore better 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 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

1 Several studies explore this theory’s predictions about analyst herding behavior. Hong et al. (2000);

Clement and Tse (2005); and Clarke and Subramanian (2006) all examine analyst characteristics

associated with herding and the career consequences of herding. 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.2 Researchers cannot directly observe this competition. We infer the effects of competition from

analysts’ timing patterns and ex post forecast quality.

S. Keskek et al.

123

and hence little new private information to be discovered about the new quarter.

(Prior-period earnings are typically announced 20–30 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 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

et al. (2001) and Shroff et al. (2013), who classify analysts as leaders if their

forecasts prompt a string of forecasts by other analysts (followers). Cooper et al.

(2001) find that leaders have a larger price impact than followers. Shroff et al.

(2013) 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 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 prior studies.

Moreover, those studies do not distinguish among analyst activities in the three

information production phases. We extend Cooper et al. (2001) and Shroff et al.

(2013) 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 that we refer to as ‘‘forecast 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 intraday absolute stock returns immediately after a forecast vary

systematically with the timing of the forecast within an information production

phase. We conduct similar analysis for daily forecast response coefficients to

forecast revisions.

3 We observe such leader and follower forecast patterns in our sample.

The timing of annual earnings forecasts

123

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 Sect. 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 5–29. 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 that forecast quality declines 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 quality, we find that

earlier forecasts have greater price impacts 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 et al. 1997; Clement 1999; Jacob et al. 1999; Clement and

Tse 2003; Bonner et al. 2007). We show that the timing of a 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

S. Keskek et al.

123

activities with roughly similar patterns. That is, leaders 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 highlights 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

et al. 1993; Brennan and Subrahmanyam 1995; Frankel and Li 2004) or information

analysis (Lang and Lundholm 1996; Healy et al. 1999; Francis et al. 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 research design, and Sect. 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

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. 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 accuracy would prefer

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). 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 our scope.


123

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 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 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 quality.

The reputation-herding theory offers different predictions. This theory posits that

agents who are more capable act early and base their estimates on their private

information, whereas less capable agents herd 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 financial analysts who

are more capable and thus to be better 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 reputations. Hong et al. (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 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

S. Keskek et al.

123

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 in the null form: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.

Last, we examine the association of forecast timing with the price impact of

forecasts. If, consistent with the herding theory, analysts who are more capable

participate early and investors rationally anticipate this timing pattern, investors

would respond more strongly to earlier 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 timingpatterns

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

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.6 Trueman (1994, p. 109) argues that ability cannot be the sole determinant of forecast timing. If it were,

then investors could infer analyst ability from timing, removing analysts’ ability to hide low ability and

hence the incentive for delaying forecasts. Thus analysts must have other (exogenous) reasons to release

forecasts at certain dates for the reputation-related timing incentives to function.


123

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 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 intraday 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. (2010) 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 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. (2010) 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 2 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 0–4, as the ‘‘information analysis’’

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.’’

S. Keskek et al.

123

phase and set the indicator variable Aft00to04 to 1 for days in this interval and 0 for

other days. We group week 2, days 5–9, with the subsequent weeks and refer to days

5–29 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.

3.3 Analyst forecast timing patterns

We observe variation in analyst forecasting activity during the year. In Fig. 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 10 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 20–25 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

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.


123

Tab

le1

Distributionofanalystforecastsaroundearningsannouncements

Day

(EA

isonday

0)

Earningsannouncement(EA)event

Prioryear

First

quarter

Secondquarter

Thirdquarter

All

1.Inform

ationdiscoveryphase

Early

inform

ationdiscovery

-30to

-16

8.5

%9.8

%13.2

%14.0

%11.7

%

Lateinform

ationdiscovery

-15to

-1

14.3

%15.3

%14.3

%15.4

%14.8

%

2.Inform

ationanalysisphase

Early

inform

ationanalysis

Earningsannouncementday

013.4

%14.0

%14.3

%13.1

%13.7

%

First

day

after

135.1

%33.2

%32.5

%31.2

%32.8

%

Secondday

after

28.1

%7.0

%7.1

%7.3

%7.3

%

LateInform

ationanalysis

3–4

4.6

%3.9

%3.6

%4.1

%4.0

%

3.Post-analysisphase

5–29

16.1

%16.8

%15.1

%14.9

%15.7

%

Totalpercentage

100%

100%

100%

100%

100%

Totalobservations

141,902

172,902

188,294

209,848

712,946

Thesampleincludes

analystforecastsoffiscalyeart’searningsissued

duringfiscalyeart,wherefiscalyeartendsduring1999–2008.Duringfiscalyeart,weidentify

four

earningsannouncementeventsandclassify

each

analystforecastto

theclosestearningsannouncementevent(‘‘event,’’day

0).Ifan

earningsannouncementisreleased

afterthemarketcloses,thefollowingday

isconsidered

theeventday.Wekeepforecastsissued

between-30and?29tradingdaysrelativeto

theeventday.Ifaforecast

isissued

afterthemarketcloses,itiscountedas

aforecastonitsfirstavailabletradingday.The60-trading-day

windowisabout90calendar

days.Thefour60-trading-day

windowscover

mostofthetypical

fiscal

yearlength

of252tradingdays

S. Keskek et al.

123

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 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

Fig. 1 Forecast frequency and standardized forecast accuracy around earnings announcementsthroughout the fiscal year. Earnings announcements are for the previous fiscal year (Q0) and interimquarters of the current year (Q1 to Q3). Day 0 is the earnings announcement date. Trading days relative today 0 are marked. The peak forecast frequency occurs on day 1. Forecast accuracy is the absolutedifference between a forecast and the realization and is standardized for each firm-year so that the mostaccurate forecast has a value of 1 and the least accurate forecast has a value of 0. The mean value offorecast accuracy is used if there is more than one forecast for the firm-year on a given trading day


123

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

ProbImproveijk

Boldijk

!¼ F

a0 þ a1Aft00to04ijk þ a2Aft05to29ijk þ a3Dayijk

þ a4Dayijk � Aft00to04ijk þ a5Dayijk � Aft05to29ijk

!:

ð1ÞOur 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 a3coefficient, whereas the tradeoff theory predicts no association and thus an

insignificant a3. Similarly, the herding theory predicts a negative coefficient of

a3 ? a4 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:

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.11 Our proxy for peers’ outstanding forecasts is consistent with Brown and Caylor (2005, see 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 Fig. 1.

S. Keskek et al.

123

(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 Eq. (2):

jReturnijkj ¼ b0 þ b1Aft00to04ijk þ b2Aft05to29ijk

þ b3Dayijk þ b4Dayijk � Aft00to04ijk þ b5Dayijk � Aft05to29ijk þ eijk:

ð2ÞReturn is the 2-h intraday 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 intraday returns data are from the TAQ database. We eliminate forecasts that

are within 2 h 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 Eq. (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 consensus forecast, scaled by the stock price at the beginning of the

return window. We estimate Eq. (3) for each of the 60 trading days.

Return ¼ a0 þ a1Revisonþ a2Surpriseþ e: ð3ÞWe then regress the FRC estimates on the information production phase indicators

and forecast timing variables in Eq. (4) with each trading day, t, being one

observation:

FRCt ¼ c0 þ c1Aft00to04t þ c2Aft05to29t

þ c3Dayt þ c4Dayt � Aft00to04t þ c5Dayt � Aft05to29t þ et:ð4Þ

The herding theory predicts a positive association between timeliness and return

response in the information discovery phase and therefore negative coefficients of

b3 in Eq. (2) and c3 in Eq. (4), whereas the tradeoff theory predicts no association

and thus insignificant b3 and c3. Similarly, the herding theory predicts negative

coefficients of b3 ? b4 in Eq. (2) and c3 ? c4 in Eq. (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


123

Table

2Descriptivestatistics

Inform

ationdiscoveryphase

Day

-30to

Day

-1

Inform

ationanalysisphase

Day

0to

Day

4

Post-analysisphase

Day

5to

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

Improve

isan

indicatorvariablethattakes

thevalueof1iftheforecastismore

accuratethan

peers’outstandingforecasts,proxiedbythemostrecentforecastissued

bya

peeranalyst.(Themeanestimateisusedifmore

than

onepeerforecastisissued

onthatday.)Bold

isan

indicatorvariablethattakes

thevalueof1iftheforecastisoutside

theintervaldefined

bytheanalyst’spreviousforecastandpeers’expectations.|Return|istheabsolutestock

return

inthe2hafteraforecastandissetto

bemissingifthe

forecastis

issued

within

2hofan

earningsannouncement.Thenumbersin

parentheses

arethet-statistics

totestthehypothesisthat

thedifference

inmeansis

zero

S. Keskek et al.

123

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

0–2)—a typical three-day window for event studies—and the late period (days 3 and

4).

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 Fig. 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 announce-

ments, the measure peaks at 53 % about 25 days before the upcoming announce-

ment. 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 % 5 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 to its lowest level of 50 % in 3 or 4 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 analysts 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


123

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 9 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 9 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 improve on the accuracy of peers’ outstanding forecasts and

Fig. 2 Proportion of forecasts that are more accurate than peers’ outstanding forecasts or that are bold.Observations for the prior-year announcement and current-year interim announcements are pooled in thisgraph. Peers’ outstanding forecasts are proxied by the most recent forecast issued by a peer analyst. (Themean estimate is used if there is more than one forecast on that day.) A forecast is ‘‘bold’’ if it is outsidethe interval defined by the analyst’s previous forecast and peers’ expectations. The improvement ratio isthe 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

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.

S. Keskek et al.

123

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 (Anilowski et al. 2007; Lansford et al. 2013).

Forecasts issued soon after the earnings announcement may improve on the

accuracy of peers’ outstanding forecasts or be bold because they incorporate

Table 3 Forecast timing and forecast properties Logit model: ProbImproveijk

Boldijk

!¼

Fa0 þ a1Aft00to04ijk þ a2Aft05to29ijk þ a3Dayijk

þ a4Dayijk � Aft00to04ijk þ a5Dayijk � Aft05to29ijk þ eijk

!

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 9 Aft00to04 -0.189***

(-17.80)

-0.192***

(-18.75)

-0.249***

(-26.39)

-0.254***

(-28.28)

Day 9 Aft05to29 0.012***

(9.06)

0.009***

(8.53)

0.018***

(18.21)

0.013***

(14.09)

Pseudo R2 1 % 1 %

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

Day9 Aft00to04, and the slope coefficient for the post-analysis period is the sum of coefficients on Day

and Day 9 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

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).


123

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 9 Aft00to04

is -0.269 with a t-statistic of -12.13, 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 Eq. (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:

jReturnijkj ¼ b0 þ b1Bef30to16ijk þ b2Aft00to04ijk þ b3Aft05to29ijk þ b4Dayijk

þ b5Dayijk � Bef30to16ijk þ b6Dayijk � Aft00to04ijk þ b7Dayijk

� Aft05to29ijk þ eijk: ð5ÞWe 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 9 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 Fig. 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 5 days, and then

S. Keskek et al.

123

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.

In Table 5 we report regression results for Eq. (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 9 Aft00to04 is -0.281, statistically significantly negative, indicating a

substantial decline in return responses as each day passes in the information

Table 4 Forecast timing and absolute stock returns

jReturnijkj ¼ a0 þ a1Bef30to16ijk þ a2Aft00to04ijk þ a3Aft05to29ijk þ a4Dayijk þ a5Dayijk

� Bef30to16ijk þ a6Dayijk � Aft00to04ijk þ a7Dayijk � Aft05to29ijk þ eijk


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 9 Bef30to16 0.024**

(2.48)

-0.002

(-0.30)

Day 9 Aft00to04 -0.264***

(-11.71)

-0.269***

(-12.13)

-0.243***

(-11.60)

-0.269***

(-12.13)

Day 9 Aft05to29 0.022***

(6.98)

0.018***

(7.24)

0.044***

(7.29)

0.018***

(7.24)

Adjusted R2 1 % 1 %

|Return| is the absolute stock return in the 2 h 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 2 h 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 9

Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on Day and

Day 9 Aft00to04. The slope coefficient for the post-analysis phase is the sum of coefficients on Day and

Day 9 Aft05to29. The coefficients on Day and its interaction terms are multiplied by 100 for presen-

tation. 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


123

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 Eq. (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 5 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 guidance or on the next 2 days. Our

original sample of observations with available intraday returns is reduced to 467,590

Fig. 3 Absolute stock returns around analyst forecasts. This figure plots the daily mean of absolute stockreturns in the 2 h after an analyst’s forecast. The stock return is set to be missing if the forecast is issuedwithin 2 h of an earnings announcement

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 entire information analysis phase, suggesting that investors

sometimes value information discovery more highly than they do information analysis.

S. Keskek et al.

123

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 2-h window and eliminate

forecasts that are within 2 h of the earnings announcement to avoid the confounding

effect of earnings announcement. As a robustness check, we repeat our analysis

Table 5 Forecast timing and forecast response coefficients

FRCt ¼ a0 þ a1Bef30to16t þ a2Aft00to04t þ a3Aft05to29t þ a4Dayt þ a5Dayt � Bef30to16t þ a6Dayt

� Aft00to04t þ a7Dayt � Aft05to29t þ et


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 9 Bef30to16 0.022

(0.97)

-0.025

(-1.55)

Day 9 Aft00to04 -0.270***

(-3.03)

-0.281***

(-3.16)

-0.234***

(-2.71)

-0.281***

(-3.31)

Day 9 Aft05to29 0.043***

(4.35)

0.032***

(4.04)

0.078***

(4.43)

0.032***

(4.23)

Adjusted R2 61 % 61 %

Forecast response coefficient (FRC) is the estimated coefficient on Revision in the model

Return ¼ a0 þ a1Revisonþ a2Surpriseþ e. Return is the stock return in the 2 h 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 2 h 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


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 9 Bef30to16. The slope coefficient for the information analysis phase is the sum of coefficients on

Day and Day 9 Aft00to04. The slope coefficient for the post-analysis phase is the sum of coefficients on

Day and Day 9 Aft05to29. We report t-statistics in parentheses. ***, **, and * indicate statistical

significance at the 1, 5, and 10 % level, respectively


123

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 versus 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. (2013), 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

Fig. 4 Forecast timing and forecast response coefficients. FRC is the coefficient estimate on Revision inthe regression: Return ¼ a0 þ a1Revisonþ a2Surpriseþ e. Return is the stock return in the 2 h after aforecast and set to be missing if the forecast is issued within 2 h of an earnings announcement. Revision isthe 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, inthe regression. Revision and Surprise are scaled by the stock price at the beginning of the returnmeasurement window. The regression is estimated for each trading day

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.

S. Keskek et al.

123

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 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 intraday returns sample to

439,503 analyst forecasts with LFR available, including 66,261 forecasts by leaders

and 373,242 forecasts by followers.

In Fig. 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 Fig. 1. This

similarity suggests that both leaders and followers recognize information discovery

and information analysis as distinct information production activities and do both.

Next, we investigate the absolute return responses to leaders’ and followers’

forecasts in Fig. 5 by adding line charts of the mean absolute stock returns measured

in the 2 h 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

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.

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.18 The differences are statistically significant at better than the 1 % level (untabulated).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.


123

6.2 The length of the information analysis period

In our primary analysis we include week 2, days 5–9, 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, Eq. (5),

by adding Aft05to09 and the interaction between Day and Aft05to09. Equation (6) is

the new regression:

jReturnijkj ¼ b0 þ b1Bef30to16ijk þ b2Aft00to04ijk þ b3Aft05to09ijk þ b4Aft10to29ijk

þ b5Dayijk þ b6Dayijk �Bef30to16ijk þ b7Dayijk �Aft00to04ijk

þ b8Dayijk �Aft05to09ijk þ b9Dayijk �Aft10to29ijk þ eijk: ð6ÞThe estimation results, reported in Table 7, show that the slope coefficient for the

interval of days 5–9 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 in the information analysis phase and that analyst

competition in this phase ends in week 1.

Fig. 5 Forecast distribution and absolute returns for leader and follower analysts. The bars show thedaily percentage of forecasts issued by leader and follower analysts within the respective group. The linesare the daily mean absolute stock returns in the 2 h after the forecasts of leaders and followers,respectively. The leader–follower analyst status is determined from clustering patterns in forecasts ofcurrent fiscal year earnings following the procedures in Shroff et al. (2013)

S. Keskek et al.

123

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

Table 6 Forecast timing and absolute stock returns for lead and follower analysts

jReturnijkj ¼ a0 þ a1Bef30to16ijk þ a2Aft00to04ijk þ a3Aft05to29ijk þ a4Dayijk þ a5Dayijk

� Bef30to16ijk þ a6Dayijk � Aft00to04ijk þ a7Dayijk � Aft05to29ijk þ eijk

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 9 Bef30to16 0.026*

(1.71)

-0.007

(-0.65)

0.018*

(1.87)

-0.004

(-0.49)

Day 9 Aft00to04 -0.236***

(-8.21)

-0.269***

(-9.84)

-0.217***

(-10.65)

-0.239***

(-10.70)

Day 9 Aft05to29 0.050***

(4.68)

0.017***

(3.28)

0.039***

(5.34)

0.017***

(5.74)

Adjusted R2 1 % 1 %




relative to the closest earnings announcement date, and its value is negative for observations before 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 9 Bef30to16. The slope coef-

ficient for the information analysis phase is the sum of coefficients on Day and Day 9 Aft00to04. The

slope coefficient for the post-analysis phase is the sum of coefficients on Day and Day 9 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. (2013). 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


123

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

Table 7 Absolute stock returns and forecast timing in week 2 after the earnings announcement

jReturnijkj ¼ a0 þ a1Bef30to16ijk þ a2Aft00to04ijk þ a3Aft05to09ijk þ a4Aft10to29ijk þ a5Dayijk

þ a6Dayijk � Bef30to16ijk þ a7Dayijk � Aft00to04ijk þ a8Dayijk � Aft05to09ijk

þ a9Dayijk � Aft10to29þ eijk

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 9 Bef30to16 0.024**

(2.48)

-0.002

(-0.30)

Day 9 Aft00to04 -0.243***

(-11.60)

-0.269***

(-12.13)

Day 9 Aft05to09 0.015

(1.25)

-0.011

(-0.90)

Day 9 Aft10to29 0.045***

(6.89)

0.019***

(5.32)

Adjusted R2 1 %




relative to the closest earnings announcement date, and its value is negative for observations before 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 5–9, 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 9 Bef30to16. The slope coefficient for the information analysis phase is

the sum of coefficients on Day and Day 9 Aft00to04. The slope coefficient for week 2 is the sum of

coefficients on Day and Day 9 Aft05to09. The slope coefficient for the remaining post-analysis phase is

the sum of coefficients on Day and Day 9 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

S. Keskek et al.

123

about forecast quality beyond an analyst’s leader/follower status. These finding

enrich the understanding of how individual analysts contribute to price discovery in

the capital markets.

Acknowledgments 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.

References

Anilowski, C., Feng, M., & Skinner, D. (2007). Does earnings guidance affect market returns? The nature

and information content of aggregate earnings guidance. Journal of Accounting and Economics, 44,

36–63.

Berkman, H., & Truong, C. (2009). Event day 0? After-hours earnings announcements. Journal of

Accounting Research, 47(1), 71–103.

Bonner, S., Hugon, A., & Walther, B. (2007). Investor reaction to celebrity analysis: The case of earnings

forecast revisions. Journal of Accounting Research, 45(3), 481–513.

Brennan, M. J., Jegadeesh, N., & Swaminathan, B. (1993). Investment analysis and the adjustment of

stock prices to common information. Review of Financial Studies, 6, 799–824.

Brennan, M., & Subrahmanyam, A. (1995). Investment analysis and price formation in securities markets.

Journal of Financial Economics, 38, 361–381.

Brown, L. D., & Caylor, M. L. (2005). A temporal analysis of quarterly earnings thresholds: propensities

and valuation consequences. The Accounting Review, 80(2), 423–440.

Chen, X., Cheng, Q., & Lo, K. (2010). On the relationship between analyst reports and corporate

disclosures: exploring the roles of information discovery and interpretation. Journal of Accounting

and Economics, 49, 206–226.

Chen, Q., & Jiang, W. (2006). Analysts’ weighting of private and public information. Review of Financial

Studies, 19(1), 319–355.

Clarke, J., & Subramanian, A. (2006). Dynamic forecasting behavior by analysts: Theory and evidence.


Clement, M. B. (1999). Analyst forecast accuracy: Do ability, resources, and portfolio complexity matter?

Journal of Accounting and Economics, 27, 285–303.

Clement, M. B., & Tse, S. Y. (2003). Do investors respond to analysts’ forecast revisions as if forecast

accuracy is all that matters? The Accounting Review, 78(1), 227–249.

Clement, M. B., & Tse, S. Y. (2005). Financial analyst characteristics and herding behavior in

forecasting. Journal of Finance, 40(1), 307–341.

Cooper, R. A., Day, T. E., & Lewis, C. M. (2001). Following the leader: A study of individual analysts’

earnings forecasts. Journal of Financial Economics, 61, 383–416.

Francis, J., Schipper, K., & Vincent, L. (2002). Earnings announcements and competing information.


Frankel, R., & Li, X. (2004). Characteristics of a firm’s information environment and the information

asymmetry between insiders and outsiders. Journal of Accounting and Economics, 37(2), 229–259.

Gleason, C. A., & Lee, C. M. C. (2003). Analyst forecast revisions and market price discovery. The

Accounting Review, 78(1), 193–225.

Graham, John R. (1999). Herding among investment newsletters: Theory and evidence. Journal of

Finance, 54(1), 237–268.

Gul, F., & Lundholm, R. (1995). Endogenous timing and the clustering of agents’ decisions. The Journal

of Political Economy, 103(5), 1039–1066.

Guttman, I. (2010). The timing of analysts’ earnings forecasts. The Accounting Review, 85(2), 513–545.

Healy, P. M., Hutton, A. P., & Palepu, K. G. (1999). Stock performance and intermediation changes

surrounding sustained increases in disclosure. Contemporary Accounting Research, 16(3), 485–520.

Hong, H., Kubik, J. D., & Solomon, A. (2000). Security analysts’ career concerns and herding of earnings

forecasts. RAND Journal of Economics, 31(1), 121–144.


123

Ivkovic, Z., & Jegadeesh, N. (2004). The timing and value of forecast and recommendation revisions.


Jacob, J., Lys, T. Z., & Neale, M. A. (1999). Expertise in forecasting performance of security analysts.


Lang, M., & Lundholm, R. (1996). Corporate disclosure policy and analyst behavior. The Accounting

Review, 71(4), 467–492.

Lansford, B., Lev, B., & Tucker, J. (2013). Causes and consequences of disaggregating earnings

guidance. Journal of Business, Finance and Accounting, 40(1–2), 26–54.

Livnat, J., & Zhang, Y. (2012). Information interpretation or information discovery: Which role of

analysts do investors value more? Review of Accounting Studies, 17, 612–641.

Mikhail, M., Walther, B., & Willis, R. (1997). Do security analysts improve their performance with

experience? Journal of Accounting Research, 35, 131–166.

Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. American Economic Review,

80(3), 465–479.

Shroff, P., Venkataraman, R., & Xin, B. (2013). Timeliness of analysts’ forecasts: The information

content of delayed forecasts. Contemporary Accounting Research (forthcoming).

Stickel, S. E. (1992). Reputation and performance among security analysts. Journal of Finance, 47(5),

1811–1836.

Trueman, B. (1994). Analyst forecasts and herding behavior. Review of Financial Studies, 7(1), 97–124.

Zhang, Y. (2008). Analyst responsiveness and the post-earnings-announcement drift. Journal of

Accounting and Economics, 46, 201–215.

S. Keskek et al.

123

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