Limited Attention, Analyst Forecasts, and Price Discovery Abdullah...

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Limited Attention, Analyst Forecasts, and Price Discovery

Abdullah Shahid1 Cornell University [email protected]

Rajib Hasan2

University of Houston-Clear Lake [email protected]

***Preliminary version; please do not quote. ***

ABSTRACT

Post-earnings announcement drift (PEAD), i.e. stock price’s delayed response to earnings

news, is a well-documented evidence of market inefficiency. One explanation for such

phenomenon is that sell-side analysts, an important information intermediary in capital markets,

fail to process earnings news. In this study, we investigate the limited attention of sell-side analysts

to understand why analysts might fail to process earnings news and whether such limited attention

has any implications for PEAD. Specifically, we examine the attention-limiting role of competing

tasks (number of earnings announcements for firms in an analyst’s portfolio and number of

earnings forecasts by an analyst within a short period) and distracting events (number of earnings

announcements for firms outside an analyst’s portfolio within a short period) in influencing

analysts’ forecast accuracy. Then, we test whether limited attention of analysts owing to competing

tasks and distracting events affects firms’ price discovery after earnings news. Using a sample of

5,136 firms and 10,798 analysts over the period 2000-2012, we find the following. First, competing

tasks worsen analysts’ forecast accuracy, whereas distracting events do not seem to affect analysts’

forecast accuracy. Second, competing tasks of analysts delay firms’ price adjustment process after

earnings announcements. Overall, our findings suggest that context-bound (limited) attention of

analysts has implications for their task performance as well as market efficiency.

Key Words: analyst forecast, context-bound rationality, limited attention, market efficiency, post

earnings announcement drift, price discovery

1 Abdullah Shahid acknowledges funding from the Institute for the Social Sciences’ “Creativity, Innovation, and Entrepreneurship” theme project, Cornell University. 2 Rajib Hasan acknowledges funding from the University of Texas-Dallas and comments from the workshop at UHCL business school.

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1.0 Introduction

Prior studies suggest that stock prices underreact to recent earnings news. The most prominent

evidence of such underreaction is post earnings announcement drift3 (PEAD) (e.g., Ball and Brown

1968; Bernard and Thomas 1989; Bernard and Thomas 1990; Chan, Jegadeesh, and Lakonishok

1996). One explanation for market-level underreaction is that analysts, an important information

intermediary for dissemination of earnings news, underreact to such news. Indeed, Abarnabell and

Bernard (1992) show that analysts’ underreaction could potentially explain about half of the

magnitude of the delayed stock price response to earnings news. However, it is not clear why

analysts fail to fully process earnings news and provide more precise earnings forecasts.

In this study, we examine whether and the extent to which analysts’ failure to process earnings

news and to provide accurate forecasts stems from their limited attention. Studies in social sciences

suggest that limited attention is a major reason why individuals might fail to process information.

Limited attention refers to a condition in which individuals fail to pay due attention to the necessary

stimuli in the environment. It could arise, among many possible others, in the following ways.

First, when individuals are faced with competing information processing tasks within a limited

amount of time, they cannot pay appropriate attention to all the tasks at hand (i.e. competing task

hypothesis). Hence, performance on all tasks is likely to suffer. Second, when there are a lot of

distracting stimuli during performance of a task, individuals might get distracted. Hence,

performance could suffer (i.e. distracting event hypothesis). Although analysts are sophisticated

information professionals, they are “decidedly human” and could suffer from processing

limitations that, in turn, affect other less sophisticated market participants (DeBondt and Thaler

1990). So, limited attention of analysts, considered vital actors in helping investors process and

interpret corporate information, could be an important driver of the market-wide underreaction in

stock prices to earnings news.

We test the competing task hypothesis and distracting event hypothesis in the following two

contexts of analysts’ forecast revisions around an earnings announcement respectively: (1) there

are competing earnings announcements for analysts’ portfolio firms4 and/or analysts perform many

3 Note that throughout the paper we use “stock price’s underreaction to earnings news” and PEAD interchangeably. 4 Portfolio firms of an analyst are the firms for which the analyst has provided at least one earnings forecast in the previous quarter. We use the words “cover” and “follow” interchangeably in this paper to mean that a firm is

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earnings forecasts in a day (or within a short period of time); (2) there are earnings announcements

from outside-portfolio firms, i.e. the firms not followed by an analyst. Using these two contexts

we address the following questions: (1) Does the quality (proxied by accuracy) of analysts’

earnings forecasts worsen with the presence of competing and distracting earnings

announcements? (2) Is underreaction to earnings news associated with the presence of competing

and distracting events for analysts?

The first context of analysts’ limited attention, which is used here to test the competing task

hypothesis, is when analysts face competing earnings announcements for their portfolio firms and

/or when analysts perform many earnings forecasts within a short period of time. The context

captures simultaneous processing of multiple earnings news. We expect competing tasks to affect

the quality of analyst forecasts negatively. The reason is that while attending to many tasks at a

time, analysts fail to pay attention to the relevant aspects of all the tasks. Hence, the situation would

lead to an analyst’s failing to properly react to earnings news or provide correct earnings forecasts.

In this context of competing tasks, we have the following specific predictions. First, there is a

negative relationship between competing tasks and analysts’ forecast accuracy. Second, there is a

positive association between the average competing tasks faced (and/or performed) by the analysts

covering a firm and stock price’s underreaction to earnings news of that firm.

The second context of limited attention, which is used here to test the distracting event

hypothesis, is when there are many earnings announcements by outside-portfolio firms on the day

an analyst’s portfolio firm announces earnings. The context may limit attention since it presents

analysts with many irrelevant stimuli. To facilitate flow of discussion, let us term “days with many

earnings announcements by other firms” as “high distraction days”. On high distraction days,

analyst attention could be limited in the following ways (Forster and Lavie 2008). First, distracting

(in other words, less-than-relevant) noise in the workplace could limit attention just like roadside

billboards might distract drivers from the task of driving (Wallace 2003; McEvoy, Stevenson and

Woodward 2007). Likewise, many earnings news on the same day could increase overall noise the

economic environment from which analysts collect information and thus, could distract their

attention away from the relevant task. Second, actions of colleagues could distract an individual.

incorporated in an analyst’s portfolio. Also, an analyst following a firm is called “following analyst” or “covering analyst”.

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High distractions days could increase overall activities and discussions in the analyst colleague

network inside and outside their offices. This could occupy some attention of analysts, disrupting

the rapt attention needed for analysis of relevant news. Our expectations are as follows. First,

analyst forecast accuracy is negatively related with the number of distracting earnings

announcements. Second, market-wide underreaction to earnings news of a firm is positively

associated with the average number of distracting news faced by analysts following the firm.

Using a sample of 5,136 firms and 10,798 analysts over the period from 2000 to 2012 we find

the following for the competing task hypothesis. First, the greater is the number of competing

earnings announcements for portfolio firms on a forecast day, the lower is the forecast accuracy of

an analyst. Moreover, the negative relationship between competing earnings news and forecast

accuracy is larger in magnitude for complicated (proxied by presence of multiple segments of

business) firms. This relationship between competing tasks and forecasting accuracy remains the

same in direction, even after controlling for various firm-specific, analyst-specific and earnings-

news specific factors, and year-quarter fixed effects. The result is robust to varying window of

competing tasks (such as the number of earnings news faced in the week preceding the forecast

day (inclusive)). To find out whether analysts can self-select and focus attention by limiting the

number of tasks to be performed on a particular forecast day, we use the following measures:

number of competing tasks performed on a forecast day and number of competing tasks performed

in the week prior to the forecast day (inclusive). Yet, the negative relationship between forecast

accuracy and competing tasks remain. Second, there is a statistically significant positive

relationship (p-value<0.0001) between the average competing tasks faced by following analysts

on a firm’s earnings announcement day and the delay in price discovery process of the firm

(proxied by absolute cumulative abnormal return in various windows following earnings

announcements of the firm). The result is robust to inclusion of various control variables from the

literature, firm-fixed effects, and quarter-fixed effects. Overall, results provide strong support for

the competing task hypothesis.

Using the same dataset, we find the following for the distracting event hypothesis. First, there

is no significant relationship (p-value>0.60) between the number of outside-portfolio earnings

announcements faced by an analyst on a forecast day and forecast error. The relationship remains

even after controlling for an interaction effect of firm complexity, various firm-specific, analyst-

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specific and earnings-news-specific factors, and year-quarter fixed effects. The results remain

qualitatively similar even after we use the number of earnings-announcements over a week (prior

to the forecast day, inclusive) as an alternative measure for distracting events. Second, there is no

significant relationship between average earnings-announcements by outside-portfolio firms of

analysts on an earnings announcement day and the delay in price discovery process of a firm). The

result is robust to inclusion of various control variables from literature, firm-fixed effects, and

quarter-fixed effects.

Our study contributes to asset pricing, empirical capital markets in accounting, and new

institutionalism5 in social sciences literature by illustrating that limited attention of analysts from

competing tasks lead to their forecasting inefficiency as well as information processing

inefficiency by the entire market (i.e. the investors in general). At this point, it is to be duly noted

that we do not have sufficient evidence for the distracting event hypothesis for analysts, while

Hirshleifer, Lim and Teoh (2009) show that distracting events (proxied by similar measures) lead

to limited attention of the market in general. However, the finding that the context of analysts

support the competing task hypothesis while providing no sufficient evidence for the distracting

event hypothesis, point, to some extent, to the merit of taking a closer look at the work context in

understanding limited attention of expert actors in the market. The approach encourages us to look

beyond attributing investors’ failure to process some information to analysts’ failure to process the

same information. Rather, we ask “why such failure by analysts” and to find answers we delve

deeper into the constraints from the work-context of analysts. Indeed, in a recent compendium of

scholarly debates, asset pricing scholars have argued that understanding institutional constraints is

where both schools (traditional and behavioral) can have amicable and productive conversations

(Bloomfield 2010).

Our study also contributes to the growing body of literature that examines the impact of limited

attention on financial markets. Hirshleifer and Teoh (2003) argue that limited attention makes it

difficult to process news that requires analysis in conscious thought. They analytically show that

due to presence of limited attention among investors, alternative ways of presenting corporate

5 In various branches of social sciences such as political science and sociology, new institutionalism calls for understanding actions of actors subject to various context-bound constraints (stemming from any formal and information institutional conditions). See Brinton and Nee (1998) for further discussion.

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information could affect price discovery differently. Empirical work finds that stock prices behave

as if investors underreact to earnings news released on Fridays as well as on the days with many

extraneous events (DellaVigna and Pollet 2009; Hirshleifer et al. 2009). Stock prices also do not

incorporate important information such as demographics (DellaVigna and Pollet 2007) and signals

in oil prices (Pollet 2005). In this paper, we provide an explanation for why prices underreact to

earnings news. We find that analysts significantly suffer from limited attention particularly in the

face of competing tasks and such limited attention of analysts has significant association with

market-wide underreaction to earnings news.

Our study is also related to the literature on efficiency of analyst forecasts. Abarnabell and

Bernard (1992) find analysts’ underreaction to past earnings information. Mikhail, Walther, and

Willis (1997) find that accuracy of analysts’ earnings forecasts improves with their firm-specific

forecasting experience. Jacob, Lys, and Neale (1999) show that analysts forecast accuracy is a

function of analyst aptitude, learning-by-doing, and internal environment (i.e. size of the brokerage

house). The contribution of our paper is that we show how limited attention from competing tasks,

a natural aspect of analysts’ task environment, could affect analysts’ efficiency in providing correct

earnings forecast. Moreover, we examine how limited attention of these sophisticated

professionals affects overall information processing efficiency of the market. Hence, in a sense,

we take the debate from behavioral economics and psychology that attention is a precious and

limited cognitive resource (Nisbett and Ross 1980; Kahneman 2013) from mere individuals to

professionals who are known as experts in information processing.

In the remainder of the paper we review the related literature and develop hypotheses (Section

2), discuss data and variables (Section 3), present methods and results (Section 4) and conclude

with summarizing contributions and limitations (Section 5).

2.0 Review of Literature and Development of Hypotheses

This section is organized in the following subsections. First, we briefly discuss the history of

finance literature (particularly, the asset pricing) from market efficiency to the rise of behavioral

finance. Second, we devote a subsection to discuss how new institutionalism perspectives

combined with traditional and behavioral finance, can be useful in further understanding the debate

of market efficiency. Third, we discuss limited attention of analysts and their earnings forecast, a

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case that we empirically examine in the paper. Fourth, we review the prior literature that discuss

the relationship between analyst forecast and price discovery. Fifth, we develop empirical

predictions for the competing task hypothesis and the distracting event hypothesis.

2.1 Market Efficiency, Anomalous Empirical Patterns and the Rise of Behavioral Finance

The neoclassical financial theory, based on assumptions of decision makers’ rationality, common

risk aversion, perfect markets with no frictions, and costless access to information for all market

participants has long dominated finance (Szyska 2010). The efficient market hypothesis (EMH)

and the capital asset pricing model (CAPM) are the foundational theories of the neoclassical

financial theory. EMH argues that market is informationally efficient, i.e. one cannot consistently

attain return in excess of market, since prices already contain all relevant information. The capital

asset pricing model suggest a theoretically appropriate required rate of return on a stock is

determined by its beta, i.e. sensitivity of the stock’s return to a non-diversifiable risk (i.e. the

market risk).

However, finance researchers started finding patterns in stock prices that are not consistent

with the EMH. These patterns are often called “anomalies” in the asset pricing literature. Some

examples of such findings follow. The January effect (Reinganum 1983) shows that much of the

abnormal return to small firms occur during the first two weeks in January. Jaegadeesh and Titman

(1993) find that returns on stock are significantly correlated over three to twelve month time

horizon. These findings suggest that excess returns can be earned using even widely available

information like price. Researchers further show that based on public information such as firm

characteristics (size, book to market ratios, growth in sales, earnings to price ratio, accruals, asset

growth) it is possible to form portfolios that can earn returns in excess of the market (or in excess

of return suggested by the CAPM) (Fama and French 1992; Lakonishaok, Shleifer and Vishny

1994; Sloan 1996). The central anomaly discussed in this paper, PEAD is another such anomalous

empirical pattern. These findings raised serious doubts about the EMH.

At the same time, findings by behavioral economists suggest that the assumptions of decision

maker’s rationality in the neoclassical financial theory is false. Limited in cognitive capacity,

humans are rather found to be relying on various shortcuts and heuristics. Drawing on these

insights from behavioral economics and psychology (such as Daniel Kahneman, Amos Tversky,

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Richard Thaler, and Paul Slovic), some finance researchers started finding biases and irrational

behavior in stock prices, forming the field of behavioral finance. In the early papers of behavioral

finance, for example, DeBondt and Thaler (1985) show investors’ overreaction to news and

Shefrin and Statman (1985) provide evidence that prices show a disposition effect.

From its birth in early 1980s, the field of behavioral finance grew by combining behavioral

and cognitive psychology with conventional economics and finance. Baker and Nofsinger (2010)

summarize the findings of this field into four areas: huristics, framing, emotions, and market

impact. The behavioral finance research in huristics shows that actors use cognitive short-cuts or

rules of thumbs in financing decision making, with some famous huristics being

representativeness, availability, anchoring and adjustments, status quo, loss and regret aversion,

conservatism, ambiguity aversion, and mental accounting. The research in framing shows that if

the same problem is stated differently financial decision makers react differently even though

objective facts of the problem are held constant. The behavioral finance research in emotions show

that emotions such as confidence, illusions about the nature of money, and sense of unfairness play

an important role in people’s financial decisions. The behavioral finance on market impact

examine if cognitive errors and biases of individuals and groups affect market prices in aggregate.

But, one might wonder why individuals and groups suffer from these biases and errors. To

understand this “why” question in greater details, a new institutional perspective can be helpful,

since it would seek to understand the context of the actors in the market. That’s exactly where new

institutionalism perspective can complement this market efficiency debate between traditional and

behavioral finance. We discuss this issue next.

2.2 Towards New Institutionalism of Market Efficiency

New institutionalism is an interdisciplinary research agenda seeking to understand and explain the

interactions between institutions and economic actions of various actors in the society. An

important assumption of the new institutionalists is that actors behave subject to various context-

bound constraints. Such constraints, referred to as “institutions”, can cover all formal and informal

social and economic constraints that shape the choice-set of actors (Nee 1998). New

institutionalists reject neoclassical assumptions, while remaining committed to choice-theoretic

tradition explanation in the social sciences where choices and actions are subject to constraints.

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We argue that a new institutionalism approach towards the debate of market efficiency between

the traditional finance and the behavioral finance is rather complementary, not conflicting. Before

explicating on this point, let us first discuss a very much related research agenda in experimental

capital markets that Bloomfield and his colleagues (Bloomfield et al 2009; Bloomfield and

Rennekamp 2009) have pioneered7. The basic argument behind their research agenda is that

market efficiency should be viewed as an interplay between disciplinary institutions (strong vs

week) and behavioral forces (strong vs. weak). In this framework, strong disciplinary institutions,

for example, would imply competitive and liquid securities market that have very high ability to

eliminate behavioral biases. Here, behavioral forces would have greater impact on market and firm

behavior in absence of strong disciplinary institutions. The following figure by Bloomfield (2010)

presents a 2X2 research framework that can be used to test the key statement of such research

agenda.

Weak Behavioral Forces

Strong Behavioral Forces

Inst

itut

ions

Strong Disciplinary Institutions

(Cell 1) (Cell 2)

Weak Disciplinary Institutions

(Cell 3) (Cell 4)

Figure 1: A Research Design for Behavioral Finance Studies by Bloomfield (2010). Note: Bloomfield (2010) clarifies the figure by noting “This research design clarifies the interaction between the strength of behavioral forces on individual decision making and the ability of the finance institution in which individuals make decision to eliminate the behavioral forces in aggregate phenomena”.

7 See Bloomfield (2010) and Bloomfield and Anderson (2010) for a more detailed description of this research agenda in experimental capital markets.

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A novel proposition in itself, this framework, however, misses an important issue that new

institutionalists would like to know in greater details: the sources of these behavioral forces.

Particularly, institutionalists would inquire about the institutional and extra-institutional sources

of these behavioral forces and in doing so, would examine the context-bound economic actions of

pertinent actors and the relationships among their actions. This perspective of looking emphatically

at the sources of behavioral forces is, indeed, not entirely new to finance. We observe that the

emerging fields of neuroeconomics and neurofinance have been investigating the fundamental

biological and psychological mechanisms underlying the individual biases and irrational behavior

(Peterson 2010). Thus, a new institutionalist framework, reduced to simple representation, would

look like Figure 2. In this framework, three factors, i.e. “actors’ context and connections”,

“behavioral forces”, and “disciplinary market institutions” interact with each other to give rise to

aggregate phenomena such as market efficiency or market inefficiency. We further add that

aggregate market phenomena is not at a mere receiver in this interactive framework; rather,

aggregate market phenomena give back to the rest in a dynamic manner.

One would argue whether we could fit “actors’ context and connections” into the disciplinary

market institutions, defined by long-standing laws, practices and organizations (Bloomfield and

Rennekemp 2009). Such an argument may sound like the hypothetical story of a person who loses

a watch (for not-yet-known reasons) in a dark room and cannot find it, and the search-party blames

the darkness of the room for such loss and to be successful, starts looking for the watch in a brighter

room. So, we argue that such an attempt (of fitting sources into disciplinary institutions) would

Disciplinary Market

Institutions

Behavioral Forces

Actors’ Context

and Connectedness

Aggregate Market Phenomena

Figure 2: A New Institutionalist’s Framework for Aggregate Market Phenomena

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hinder a careful understanding of the sources of behavioral biases and apparent irrationality of

actors. While it may be true that disciplinary market institutions may mediate/foster the effect the

effects of behavioral forces on aggregate market phenomenon, such disciplinary institutions are

not necessarily the sources of behavioral biases. In other words, the context of various actors in

the market and their connectedness cannot be entirely subsumed by the disciplinary market

institutions.

Let us further explicate this point by taking the case of limited attention. One argument in this

area is that scarcity captures attention (Mullainathan and Shafir 2013). Scarcity of food, for

example, severely limits an individual’s thoughts to foods. Scarcity of time, for example, could

make an individual further hurried for time. Now let us consider the role of such concept of limited

attention of actors for the aggregate market phenomenon. In doing that consider the finding

(Hirshleifer et al. 2009) that on the days of many earnings announcements investors feel scarcity

of time and thus underreact to earnings news of a firm. And, in absence of strong disciplinary

institutions such as arbitrageurs, such underreaction shows up as a systematic irrational behavior

in the aggregate market phenomena. Should we say that arbitrageurs are the reasons for investors’

inattention? In another example, let’s say during Fridays, sports, lunch hours, festivals, and

elections, investors further fail to pay attention to the news relevant for stock pricing and in absence

of active arbitrage activities (i.e. presence of weak disciplinary institutions) individual investors’

inattention shows up as a systematic aggregate market phenomena. Should we attribute the sources

of inattention to the disciplinary institutions only? While features of the disciplinary institutions

are definitely of high import, another area of investigation would be to look at the context of the

actors and the connectedness among the actors to understand the causes of inattention. It is to note

that we add “connectedness among actors” along with the context of actors to emphasize that

actions of capital market actors (and so is case for any phenomena that is “social”) have

theoretically as well as empirically shown mutual independence8; so context-bound actions of one

actor could end up affecting another actor due to their relevant connectedness.

In a similar vein, we turn our attention to a particular context-bound rationality of analysts, a

very important information intermediary whose actions have been found to have implications for

8 The bulk of literature proving this point in various branches of social sciences is quite huge. We do not provide more discussion about it for the sake of brevity.

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investors, i.e. the market in general. In particular, we discuss limited attention from competing

tasks and distracting events and the role of such limited attention in analyst earnings forecast.

2.3 Limited Attention and Analyst Earnings Forecast

Limited attention refers to the neglect of relevant information in performing a task. Studies in

psychology show how individuals’ task performance suffers due to limited attention. These studies

relate to the school of “behaviorism” in psychology. This school, introduced by John D. Watson

in 1913, underscores that environmental stimuli, not the internal proclivities, determine human

behavior. The idea is that we could predict a person’s behavior based on the external stimuli

presented to her (e.g., Daniel, Hirshleifer, and Teoh 2002). Limited attention could arise due to

competing tasks and distracting events.

Limited attention could stem from a situation in which an individual is performing multiple

tasks simultaneously (i.e., competing tasks). The situation occurs particularly when the tasks

require “controlled processing”. A task is said to require “controlled processing” if individuals

need to engage in conscious thinking before giving a response (i.e., individual response is not

automatic, Shiffrin and Schneider 1977). Studies in psychology and ergonomics provide various

reasons why competing tasks could limit attention and affect performance. First, multiple difficult

tasks could use up resources quickly. The multiple resources models argue that conduct of

competing difficult tasks create a competition for resources among the tasks. So, individuals have

to allocate and exchange resources among the tasks. This could create interferences. Such

inferences could lead to individuals not paying due attention to all the relevant aspects of the tasks.

Hence, efficiency could suffer. When analysts have competing earnings news to process, they face

a similar situation, because processing earnings news is a complex professional task and multiple

earnings news creates a stretch on resources. Second, the single channel theories suggest that in

performance of multiple tasks in a time-sharing situation, interferences could arise at various

levels, leading to limited attention. Two prominent levels at which interferences occur are as

follows: (1) when individuals perceive the stimuli; (2) when individuals generate the response

(Broadbent 1958; Briggs, Peter, and Fisher 1972). The idea is that interferences arise because

multiple tasks quickly saturate the channel capacity (i.e., the individual capacity needed to perform

a task). In situations of saturated channel capacity, individuals could switch to serial (i.e. one after

another) processing from parallel (i.e. simultaneous) processing. However, failure to do that at any

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levels could result in interferences and thus, limit attention. Likewise, competing news could

saturate analysts’ channel capacity and thus, could lead to limited attention. Third, there are studies

which show how task interruptions, in general, could lead to poor performance. Research shows

that occurrences of a second task affect operators in the nuclear power industry negatively and

lead to incidents that shut-down the operation. Studies find in context of a telephone company

sales office that occurrence of a second task creates interference in the performance of the first

task, leading to increased duration for execution of the first task (Cellier and Eyrolle 1992). Hence,

if analysts face multiple tasks, i.e. multiple earnings news for his/her portfolio firms, the quality

of his/her earnings forecast revisions could suffer.

Limited attention could also arise from a situation in which individuals are faced with

distracting events. The reason is that such events could distract attention away from the relevant

tasks. There is ample evidence in social sciences that show irrelevant stimuli distract attention.

Some examples follow. “Stroop Test” is a famous one (Stroop 1935). The test shows that people

take longer time to read the name of a color if the print color of the word does not match with the

color name. For example, if “blue” is printed in red color it would take readers longer to read.

Wallace and Vodanovich (2003) find that electrical workers who are more distracted by the events

in daily life face greater number of accidents at work. Roadside billboards distract attention of the

drivers (Wallance 2003). According to McEvoy, Stevenson, and Woodward (2007) more than 10%

of the drivers (out of 1,367 sample after accidents) indicate that at the time of accidents they were

absentminded due to various distracting events (such as sight of a person, event, or object outside

the car or animal or insect in inside the car). Forster and Lavie (2008) argue that activities of

colleagues or work-environment in general could distract individuals as well. While the exact

forms of distractions caused by irrelevant stimuli are not known, the mounting evidence shows

that irrelevant stimuli do limit attention. Moreover, studies in “inattentional blindness” and

“dichotic listening” show that individuals cannot attend to and retain multiple stimuli at the same

time (Cherry 1953; Moray 1959; Simons and Chabris 1999). So, once irrelevant stimuli somehow

catch a person’s attention, it is likely that s/he will be distracted from the relevant. Analysts could

face similar distracting stimuli when the environment in which analysts operate has lots of

activities going on. One such example is when there are many earnings announcements by other

firms on the day an analyst’s portfolio firm announces earnings or an analyst provide a forecast

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revision. Days with such high distracting events could negatively affect the quality of analysts’

earnings forecast revisions.

2.4 Prior Evidence of Analyst Forecast Quality and Price Discovery

There is a large literature on analyst forecast accuracy. Briefly put, the literature has investigated

analyst-specific characteristics (e.g. experience), brokerage house characteristics (particularly,

size), following-firm characteristics (e.g. firm complexity, analyst following), and various decision

making errors (e.g. overweight of information, underreaction, and faulty input models). However,

Katherine Schipper’s (1991) commentary suggests that there is a dearth of research on

understanding the decision-context of analysts, a gap our paper attempts to fulfill.

Since analysts are a major information intermediary and play a vital role in interpretation and

dissemination of information in capital markets, researchers have also devoted attention to

understanding the role of analysts’ forecasts in price discovery. Chan et al. (1996) show that a

moving average of the forecast revisions over the last six months can significantly predict firms’

returns over the next 6-12 months. Park and Stice (2000) find that market response to analyst

forecast revisions depends on the prior usefulness of analyst forecasts. Our paper further extends

this literature by examining how the limited attention of analysts delays price discovery.

2.5 Empirical Predictions

Given two prominent sources of limited attention pointed out in the relevant literature we divide

our empirical predictions into two categories: (1) competing task hypothesis; (2) distracting event

hypothesis.

2.5.1 Competing Task Hypothesis

The first context of limited attention the number of competing tasks an analyst faces his/her

portfolio. Here, by the number of competing tasks we mean the number of earnings

announcements that an analyst faces for his portfolio firms on a day. As the literature review

suggests this context demands analyst attention to multiple tasks and hence, in such a context,

analyst earnings forecast quality is likely to be worse. At this point, one could argue that such

prediction does not consider the idea that what an analysts face may not necessarily matter if the

analyst can prioritize tasks rationally, i.e. analysts resort to ‘serial processing’ in a way so that

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forecast accuracy does not suffer due to competing tasks faced by the analysts. Our argument is

that even if an analyst can switch to such ‘serial processing’ in the face of competing tasks, the

mere thought that there are tasks pending could create a disturbance in the performance of the task

at hand. However, to be thorough in laying the hypothesis we also consider the number of tasks an

analyst actually performs as an additional definition of limited attention arising from competing

tasks

By analyst earnings forecast quality we consider the ‘accuracy’ aspect of earnings forecast.

Hence, we hypothesize the following.

H1A: There is a negative relationship between the level of competing tasks faced and/or performed

by analysts and the accuracy of analysts’ earnings forecast, ceteris paribus.

If analysts fail to process relevant information because of competing tasks, their limited

attention is also likely to be a factor contributing to market-wide underreaction to earnings news,

i.e. PEAD. We argue that the higher the overall analyst (i.e. all the analysts who follow/cover a

firm) limited attention due to competing tasks, the greater will be the market underreaction to

earnings news of a firm. Hence, we hypothesize the following.

H1B: There is a positive association between the market underreaction to earnings news and the

limited attention due to competing tasks faced and/or performed by all the analysts that follow a

firm, ceteris paribus.

2.5.2 Distracting Event Hypothesis

The second prominent source of limited attention is distracting events. Here, we consider the

following distracting events that are closely related to analysts’ work environment: number of

earnings announcements by non-portfolio firms. Consistent with the literature review, we argue

that the higher the level of such distraction on a day (i.e. forecast day), the lower will be the quality

(i.e. accuracy) of analyst forecasts. Hence we hypothesize the following.

H2A: There is a negative relationship between distracting events and analyst forecast accuracy,

ceteris paribus.

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If analysts fail to process relevant information because of distracting events, their limited

attention is also likely to be a factor contributing to market-wide underreaction to earnings news.

We argue that the higher the overall analyst (i.e. all the analysts that follow a firm) limited attention

due to distracting events, the greater will be the market underreaction to earnings news of a firm.

Hence, we hypothesize the following.

H2B: There is a positive association between the market underreaction to earnings news and the

limited attention due to distracting events faced by all the analysts that follow a firm, ceteris

paribus.

3.0 Data

We use data from WRDS database for this study. Our sample covers the period from 2000 to 2012.

Variables are winsorized on both sides of the distribution (1% and 99%). The final sample has

5,136 distinct firms, 10,798 distinct analysts, and 155,443 firm-quarters covered. Next we define

the variables used in the study.

3.1 Dependent Variables

The dependent variables are analyst earnings forecast accuracy (for ease in measure and

interpretation, we rather calculate forecast error, FE in short) and price drift (absolute value of

abnormal return, ABSRET in short) following earnings announcement of a firm. They are defined

below.

��,�,�: Forecast error of analyst ‘i’ for forecast made about the earnings of firm ‘j’ for quarter ‘q’

is calculated by the following formula where �� is the actual earnings of firm ‘j’ for quarter ‘q’,

��,�,� is the forecast made my analyst ‘i’ for the quarter ‘q’ earnings of firm ‘j’, and ��,� is the

closing day’s (t) stock price on the day of forecast.

��,�,� = |��,� − ��,�,�

��,�|

��,�: Absolute value of abnormal return of firm ‘j’ on trading day ‘t’ is calculated after

return is adjusted for Cahart four factors, which are momentum factor and Fama-French three

factors (overall market factors and factors related to firm size and book-to-market equity). We

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cumulate this absolute abnormal return over various post-announcement windows such as (+2,

+6), (+7,+11) and (+12 to +16) following earnings announcement to find out ABS CAR, i.e.

absolute cumulative abnormal returns. It should be noted that day ‘0’ refers to the earnings

announcement day; so , day +2 refers to 2nd trading day after the earnings announcement day, day

+12 refers to the 12th trading day after the earnings announcement day.

3.2 Key Independent Variables

The key independent variables measures competing tasks (analyst-level and firm-level) and

distracting events (analyst-level and firm-level).

Competing Task Measures: We use two measures of analyst-level competing tasks (i.e.

COMPTASK1, and CTFACED1) and two measures of firm-level competing tasks (i.e.

AVG_COMPTASK1 and AVG_CTFACED1). These measures are defined below.

COMPTASK1: This is an analyst-level competing measure. It is calculated by the number of

earnings forecasts made by analyst ‘i’ on a particular day. In other words, this is a measure of

competing tasks performed.

CTFACED1: This analyst-level competing task measure is calculated by the number of earnings

announcements by the portfolio firms of analyst ‘i’ on a forecast day. In other words, this is a

measure of competing tasks faced.

AVG_COMPTASK1: This is a firm-level competing task measure. It is calculated as the average

COMPTASK1 (on the day of firm’s j’s quarterly earnings announcement) for all the covering

analysts for firm ‘j’.

AVG_CTFACED1: This is a firm level competing task measure. It is calculated as the average

CTFACED1 (on the day of firm’s j’s quarterly earnings announcement) for all the covering


Distracting Event Measures: We use one analyst-level distracting event measure and one firm-

level distracting event measure. They are defined below.

DISTRACT1: This is an analyst-level distracting event measure. It is calculated by the number of

earnings announcements by firms outside the portfolio of analyst ‘i’ on a forecast day.

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AVG_DISTRACT1: This is a firm-level distracting event measure. It is calculated by the average

DISTRACT1 of all following analysts of firm ‘j’ on the day of quarterly earnings announcement

of firm ‘j’.

3.3 Control Variables

Consistent with prior literature we use a number of control variables in the models used for testing

hypotheses (to be discussed in Section 4.0). The control variables can be categorized into the

following: firm and earnings characteristics, analyst characteristics, and information environment.

We also discuss (or provide footnotes) as to how these controls also proxy for the various

components in Figure 2, while we focus on the influence of limited attention from competing tasks

and distracting events.

Firm and Earnings Characteristics: We use the following firm characteristics as control variables

in the study: firm size (SIZE), number of segments (NUMSEG), incidence of restructuring

(RESTRUCTURE), incidence of merger and acquisition (MERGE), incidence of special items

reporting (SPECIAL), institutional ownership (INST), size of earnings news (UE), type of earnings

news (BDNEWS), and quarter of the earnings (4THQTR). The variables are measured as follows.

��,�: Firm size is measured as natural logarithm of the product of closing stock price and

number of shares outstanding of firm ‘j’ in quarter ‘q’.

��,�: Number of segments is the number of business segments reported in the quarterly

financial statements of firm ‘j’ in quarter ‘q’.

��,�: Incidence of restructuring is a dummy variable that is assigned a value of ‘1’

if firm ‘j’ reported restructured cost of quarter ‘q’ and else, it takes on a value of ‘0’.

��,�: Incidence of mergers and acquisitions for firm ‘j’ is a dummy variable that is assigned

a value of ‘1’ if firm ‘j’ reported any mergers/acquisitions in financial statements of quarter ‘q’;

else, the variable takes on a value of ‘0’.

��,�: Incidence of special items for firm ‘j’ is a dummy variable that is assigned a value

of ‘1’ if firm ‘j’ reported any special items in financial statements of quarter ‘q’; else, it takes on a

value of ‘0’.

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��,�: Institutional ownership is measured by percentage of institutional ownership of firm ‘j’

at the end of quarter ‘q’.

��,�: Unexpected earnings of firm ‘j’ in quarter ‘q’ (��,�) is calculated as the actual earnings

minus the latest analyst earnings revision prior to earnings announcement.

��,�: Bad news indicator variable, ��,� for firm ‘j’ in quarter ‘q’ equals ‘1’ if

��,� < 0 and 0 otherwise.

4��: Quarter of the earnings, 4�� is an indicator variable that equals ‘1’ if the

earnings is for the 4th quarter of the fiscal year of firm ‘j’ and ‘0’ otherwise.

Analyst Characteristics: We use the following analyst characteristics as control variables for the

study: experience (EXPERIENCE) and size of the analyst’s brokerage house (BRSIZE). The

variables are measured as follows.

��,�: The experience of analyst ‘i’ in forecasting earnings of firm ‘j’, is calculated as

the number of prior quarters of revisions made for firm ‘j’ by analyst ‘i’.

��,�: Size of the brokerage house of analyst ‘i’ in quarter ‘q’ is the number of analysts

employed by the analyst’s brokerage house in that quarter.

Information Environment: We use the following variables to proxy for firm information

environment: analyst following (NANALYST) and management guidance (GUIDE). The

variables are measured as follows.

��,�: Number of analysts following the firm, �� is calculated as the number

distinct analysts who provided at least one forecast for firm ‘j’ in quarter ‘q’.

��,�: Management guidance indicator variable, ��,� equals ‘1’ if the firm ‘j’ provided

guidance for quarter ‘q’ and ‘0’ otherwise.

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4.0 Models and Results

First we discuss the descriptive statistics and correlations among the study variables. Then, we

discuss models and results into two subsections: competing task hypothesis and distracting event

hypothesis. All tables containing results are provided in the Appendix.

4.1 Descriptive Statistics

Table 1 presents the descriptive statistics of the key study variables. Some notable aspects from

descriptive statistics are discussed next. We find that on an average analysts are less than accurate.

Also the magnitude of the standard deviation of forecast error is almost 7 times the mean forecast

error. There is a large deviation in forecast errors since mean and median (50th percentile) error is

largely distant from each other. COMPTASK1, a measure of limited attention from competing

tasks performed on a forecast day has a mean of 1.44 with a median of 1, suggesting a wide

variation among analysts in the number of competing tasks they (analysts) perform on a forecasting

day. CTFACED1, a measure of limited attention due to competing tasks faced, has a mean 1.9

times its median. This suggests that analysts also vary in the number of competing tasks they face

for their portfolio firms. DISTRACT1, a measure of distraction of analysts from earnings

announcements by outside-portfolio firms, does not have a wide variation (mean is closer to

median). This makes sense, since an individual analyst’s portfolio is very small compared to the

universe of firms in the US capital markets as well as in our sample.

Table 1 further shows that various control variables used in this study (firm and earnings

characteristics, analyst characteristics, and information environment) have variation in them as

well. Some notable aspects are discussed next. In only about 7% of the firm-quarters in our sample

managers provide guidance. Average institutional shareholding for sample firms is about 70%

whereas at 10th percentile such shareholding is about 30%. In about 27% of the firm-quarters,

earnings announcement is bad news. In about 12.9% of firm-quarters there are incidences of

mergers and acquisitions and in about 9% of the firm-quarters there are incidences of restructuring

charges. On an average we observe more than 5 segments (NUMSEG) per firm-quarter, with some

variation in the measure (1 segment in 10th percentile and 11 segments in the 90th percentile). An

average analyst in the sample has about 11 quarters of firm-specific forecasting experience and an

average brokerage house employs about 47 analysts (with a median of 40 analysts).

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Table 2 presents results of spearman correlation analysis among the study variables. Some

notable findings from this analysis are discussed next. There is a significantly positive correlation

between forecast error (FE) and number of competing tasks performed by analysts on a forecast

day (COMPTASK1). Similarly, FE has a significantly positive relationship with DISTRACT1, i.e.

the number of earnings announcement for outside-portfolio firms of analysts on a forecast day.

The number of competing tasks faced (CTFACED1) has a negative relationship with forecast

error, which, in bivariate comparison weaken our competing task hypothesis and contradict with

the relationship between COMPTASK1 and FE. However, one must note that analysts’ forecast

errors are significantly correlated with various control variables as well. For example, firm size

(SIZE) and incidences of mergers and acquisitions (MERGE) are significantly negatively

correlated with forecast error. Also, extent of disciplinary institutions (or better information

environment), as proxied by management guidance and institutional ownership, is significantly

negatively correlation with forecast error. We find (in unreported results) that pearson correlation

provide qualitatively similar results (the direction of the relationship is mostly similar but

magnitude different).

Overall, the results of Table 2 correlation analysis suggest that we must consider the entire

gamut of pertinent factors in multivariate models to find careful results for our hypothesized

relations.

4.2 Models and Results for the Competing Task Hypothesis

We use the following models (Model 1 and Model 2) to test the relationship between competing

tasks and analyst earnings forecast error. The variables are defined in Section 3. We further control

for year-quarter fixed effects to proxy for any time-dependent aggregate market phenomena9.

��,�,� = �� + ��1�,� + ��,� + �� 1�,� × ��,� +

�� ,� + ��,� + ��,� + ��4�� + ��,� +

��,� + ��,� + ��,� + ��,� + �� ,� +

��,� + �� ,� + �� + � ---(Model 1)

9 In the future version of this paper we will consider more specific incidences of aggregate market phenomena, for example, market liquidity (which may also be considered as disciplinary market institutions, in the definition of Bloomfield (2010)).

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��,�,� = �� + ��1�,� + ��,� + �� 1�,� × ��,� +

�� ,� + ��,� + ��,� + ��4�� + ��,� +

��,� + ��,� + ��,� + ��,� + �� ,� +

��,� + �� ,� + �� + � ---(Model 2)

In Model 1, the measure of competing tasks is COMPTASK1, i.e. the number of forecasting

tasks performed by an analyst on a day the analyst makes a forecast. The results of Model 1 are

presented in Table 3. We find that the coefficient on COMPTASK1 is positive and statistically

significant (p-value<0.001), suggesting that the larger the number of competing tasks performed,

the lower is the forecast accuracy of analysts. And, statistically significant (p-value<0.01) positive

interaction between firm complexity (NUMSEG) and competing task measure suggests that higher

number of competing tasks particularly make forecast accuracy of complex firms even worse.

The results of Model 2 are provided in Table 4. The coefficient on CTFACED1 is positive and

statistically significant (p-value<0.05), suggesting that competing earnings processing tasks faced

on a forecast day reduces forecast accuracy. This provides support for the Part A (i.e. Hypothesis

1A) of the competing task hypothesis. Overall, we find that both competing tasks performed and

competing tasks faced reduce analysts’ earnings forecast accuracy.

Now, we use the following models (i.e. Model 3 and Model 4) to test the relationship between

average competing tasks (both performed and faced) of analysts of a firm on its earnings

announcement day and the delay in price discovery following the earnings announcement. The

variables used are defined in Section 3.

�� ,�,� = �� + ��_��1�,�,�� + ��,�,�� + ��,� +

�� 4��,� + ��,� + ��,� + ��,� + ��,� +

��,� + ��,� + ��,� + �� ,� + �� +

�� + � ---(Model 3)

�� ,�,� = �� + ��_��1�,�,�� + ��,�,�� + ��,� +

�� 4��,� + ��,� + ��,� + ��,� + ��,� +

��,� + ��,� + ��,� + �� ,� + �� +

�� + � ---(Model 4)

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Here, use various measure of the subscript ‘t’, i.e. the trading days such as the following

windows: [+2,+6], [+7,+11], and [+12,+16]. We also use firm-fixed effects and quarter-fixed

effects.

The results of Model 3 are presented in Table 5. We find that in various windows following

earnings announcement, absolute cumulative abnormal return of stocks is positively and

significantly (p-value<0.001) associated with average competing tasks performed by following

analysts on the earnings announcement day. This suggests that analysts’ limited attention from

competing tasks performed positively contributes to the delay in price adjustment process.

The results of Model 4 are presented in Table 6. We also use firm-fixed effects and quarter

fixed effects. The coefficient of the firm-level competing task measure (AVG_CTFACED1) is

found positively and significantly (p-value<0.01) related with the absolute cumulative abnormal

return of the [+7,+11] window. So, the positive association of competing tasks with delay in post-

earning price adjustment process is stronger when we consider tasks performed.

4.3 Models and Results for the Distracting Event Hypothesis

We use the following model (Model 5) to test the relationship between distracting events and

analysts’ earnings forecast error. The variables used are defined in Section 3. The results for Model

5 are presented in Table 7.

��,�,� = �� + ��1�,� + ��,� + �� 1�,� × ��,� +

�� ,� + ��,� + ��,� + ��4�� + ��,� +

��,� + ��,� + ��,� + ��,� + �� ,� +

��,� + �� ,� + �� + � ---(Model 6)

We are particularly interested in the coefficient on DISTRACT1, which is found positive but

statistically not significant (p-value>0.006). This suggests that distracting events, i.e. earnings

announcements of firms outside their portfolio do not distract analysts; even if distraction occurs,

such distraction does not affect analysts’ task performance, i.e. earnings forecast accuracy.

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We use the following model (i.e. Model 7) to test the relationship between average distracting

events for analysts of a firm on its earnings announcement day and the delay in price discovery in

post-earnings announcement. The variables used are defined in Section 3.

�� ,�,� = �� + ��_��1�,�,�� + ��,�,�� + ��,� +

�� 4��,� + ��,� + ��,� + ��,� + ��,� +

��,� + ��,� + ��,� + �� ,� + �� +

�� + � ---(Model 7)

The results for Model 7 are presented in Table 8. We are interested in the coefficient of

AVG_DISTRACT1, which is found statistically insignificant. This finding suggests that

distracting events like the earnings announcements of firms outside portfolio of analysts are not

likely to be associated with the delay in price discovery process. Overall, we do not find sufficient

evidence to support the distracting event hypothesis for analysts. However, we find strong

evidence to support the competing task hypothesis.

5.0 Additional Analysis and Plan for Further Robustness Checks

In this section we discuss some additional analyses as well as plans for performing some more

robustness checks.

5.1 Varied Windows for Competing Tasks and Distracting Events Definitions

We use varied windows for measuring both competing tasks and distracting events. While our key

measures of competing tasks and distracting events focus on one day, the alternative measures

focus on a 7-day window. The measures are defined below.

Alternative Competing Task Measures:

COMPTASK7: This is an analyst-level competing task measure. It is calculated by the number of

earnings forecasts made by analyst ‘i’ in the last 7 days (forecast day inclusive).

CTFACED7: This analyst-level competing task measure is calculated by the number of earnings

announcements by the portfolio firms of analyst ‘i’ over a week prior to the forecast day

(inclusive).

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AVG_COMPTASK7: This is a firm-level competing task measure. It is calculated as the average

COMPTASK7 (on the day of firm’s j’s quarterly earnings announcement) for all the following


AVG_CTFACED7: This is a firm level competing task measure. It is calculated as the average

CTFACED7 (on the day of firm’s j’s quarterly earnings announcement) for all the following


Alternative Distracting Event Measures:

DISTRACT7: This is an analyst-level distracting event measure. It is calculated by the number of

earnings announcements by the firms outside the portfolio of analyst ‘i’ in the week prior to a

forecast day (inclusive).

AVG_DISTRACT7: This is a firm-level distracting event measure. It is calculated by the average

DISTRACT1 of all following analysts of firm ‘j’ on the day of quarterly earnings announcement

of firm ‘j’.

The additional analyses of competing task hypotheses using the above-noted alternative

measures are presented in Table 9, Table 10, and Table 1. Results show that the alternative

measures yield qualitatively similar findings for competing task hypothesis, i.e. competing tasks

reduce forecast accuracy and positively contributes to the delay in post-earnings price adjustment

process. Similarly, we find (in unreported results) qualitatively similar results for distracting

events, i.e. such events do not affect analyst earnings forecast accuracy and post-earnings price

adjustment delay.

5.2 Further Issues and Plan for Robustness Checks

We have not explicitly addressed some issues that the readers may have found to be justifiably

important: self-selection by analysts and relevance of distracting events. We discuss these two

issues next.

One might wonder whether analysts self-select into performing tasks so that they can manage

better performance. This is very much likely. However, we argue that even if analysts do so we

use both “competing tasks faced” as well as “competing tasks performed” as measures of

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competing tasks. We rather find that competing tasks performed lead to even worse forecasting

accuracy. One may argue that self-selection may work in even subtler ways, such as analysts

perform some forecasting tasks better than others since they might strategically, based on their

interest, prioritize forecasting tasks. We argue that such an event is quite likely. In the next version

of this paper, we will provide evidence on this issue. However, even if analysts do so, the results

that limited attention from competing tasks affect their forecasting performance on an aggregate

remains robust.

One might argue that the measure of distracting events is too wholesale and does not consider

the possibility that not all distracting events are equally distracting. For example, earnings

announcement of some firms may contain information for other firms. Such concern is valid.

Albeit, some firms may be linked through their supply chain or same industry memberships. We

will address this issues in robustness tests in the future version of this paper.

6.0 Conclusions and Limitations

We draw upon the context of analysts to show that a deeper understanding of the work contexts of

actors in the capital markets could be a productive route for further understanding the behavioral

forces and institutions that contribute to market (in)efficiency. Specifically, we empirically

illustrate the attention limiting role of competing tasks and distracting events faced by analysts in

understanding underreaction to earning news, i.e. post-earnings-announcement drift (PEAD), a

long-standing evidence casting doubts on market efficiency. We find that, competing tasks indeed

limit analyst attention, evidenced by a reduction in their forecast accuracy. We also show that

competing tasks contribute to the market-wide underreaction to earnings news. However, we do

not find any effect of distracting events (proxied by measures previously used in the literature for

examining limited attention of investors in general) on analysts’ forecasting performance as well

as price discovery process.

Evidence of this paper that competing tasks limit attention but wholesale distracting events do

not, emphasizes the merit of taking a new institutionalism approach and thus looking closely into

work context of actors in the capital markets. While previous literature finds investors’ inattention

using distracting events measures, our results show that expert information professionals like

analysts, do not get distracted by such wholesale events. We need to closely examine the contexts

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in which these professionals perform their tasks, to better understand the sources of their limited

attention.

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Appendix

Table 1

Descriptive Statistics of the Key Study Variables

Variable N Mean St. Dev.

1st Pctl 10th Pctl

Lower Quartile

50th Pctl

Upper Quartile

90th Pctl

99th Pctl

FE 396,707 0.011 0.073 0.000 0.000 0.000 0.002 0.004 0.013 0.143

COMPTASK1 399,027 1.435 1.364 1.000 1.000 1.000 1.000 1.000 2.000 7.000

CTFACED1 107,121 1.960 1.511 1.000 1.000 1.000 1.000 2.000 4.000 8.000

DISTRACT1 132,824 141.874 95.058 5.000 22.000 60.000 129.000 215.000 268.000 350.000

NUMSEG 399,027 5.610 4.037 1.000 1.000 3.000 5.000 8.000 11.000 18.000

SIZE 397,530 7.988 1.737 4.051 5.763 6.775 7.959 9.228 10.224 12.047

MERGE 399,027 0.129 0.335 0.000 0.000 0.000 0.000 0.000 1.000 1.000

RESTRUCTURE 399,027 0.089 0.285 0.000 0.000 0.000 0.000 0.000 0.000 1.000

4THQTR 399,027 0.244 0.429 0.000 0.000 0.000 0.000 0.000 1.000 1.000

EXPERIENCE 399,027 10.922 11.101 0.000 0.000 2.000 7.000 16.000 27.000 46.000

NANALYST 399,027 13.444 7.895 1.000 4.000 7.000 12.000 18.000 24.000 34.000

BRSIZE 399,027 47.023 33.496 1.000 8.000 18.000 40.000 76.000 94.000 129.000

BDNEWS 399,027 0.276 0.447 0.000 0.000 0.000 0.000 1.000 1.000 1.000

SPECIAL 399,027 0.468 0.499 0.000 0.000 0.000 0.000 1.000 1.000 1.000

UE 397,982 0.011 0.239 -0.690 -0.070 -0.010 0.010 0.050 0.125 0.610

GUIDANCE 399,027 0.076 0.265 0.000 0.000 0.000 0.000 0.000 0.000 1.000

INST 399,027 0.698 0.251 0.000 0.329 0.583 0.763 0.880 0.966 1.000

ABSRET 31,003 0.038 0.047 0.000 0.004 0.009 0.023 0.049 0.088 0.218

ABS CAR [+2, +6] 31,003 0.100 0.092 0.016 0.031 0.047 0.076 0.122 0.191 0.440

ABS CAR [+7, +11] 31,003 0.091 0.089 0.014 0.028 0.042 0.067 0.109 0.176 0.435

ABS CAR [+12, +16] 31,003 0.092 0.091 0.014 0.027 0.041 0.066 0.109 0.178 0.448

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Table 3

Relationship between Competing Tasks Performed on A Forecast Day and Forecast Error

Panel A: Relationship between competing tasks performed on a forecast day and forecast error

Panel B: Relationship of forecast error with competing tasks performed on a forecast day and task complexity interaction

Parameter Estimate t-Stat p-Value Estimate t-Stat p-Value

Intercept 0.0757 8.38 <.0001 0.07652 8.49 <.0001

COMPTASK1 0.0005 3.63 0.0003 0.00014 0.94 0.3487

NUMSEG 0.0006 3.45 0.0007 0.00043 3.06 0.0025

COMPTASK1*NUMSEG 0.00006 2.81 0.0053

SIZE -0.0071 -10.49 <.0001 -0.00712 -10.52 <.0001

MERGE -0.0039 -3.74 0.0002 -0.00394 -3.74 0.0002

RESTRUCTURE 0.0045 3.42 0.0007 0.00454 3.44 0.0007

4THQTR -0.0009 -0.10 0.9191 -0.00084 -0.09 0.9248

EXPERIENCE 0.0001 5.20 <.0001 0.00014 5.18 <.0001

NANALYST 0.0005 4.35 <.0001 0.00047 4.38 <.0001

BRSIZE 0 -3.03 0.0027 -0.00002 -3.07 0.0024

BADNEWS -0.0155 -5.71 <.0001 -0.01552 -5.71 <.0001

SPECIAL 0.0043 5.12 <.0001 0.00426 5.1 <.0001

UE -0.1444 -7.86 <.0001 -0.14438 -7.86 <.0001

GUIDE -0.0032 -5.38 <.0001 -0.00322 -5.39 <.0001

INST -0.021 -9.48 <.0001 -0.02097 -9.47 <.0001

N 396,514 396,514

Adjusted R-Squared 0.234 0.234

Cluster Industry Industry

Fixed Effects Year Quarter Year Quarter

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Table 4 Relationship between Competing Tasks Faced on A Forecast Day and Forecast Error

Panel A: Relationship between

competing tasks faced on a forecast day and forecast error

Panel B: Relationship of forecast error with competing tasks faced on a forecast day and task complexity interaction


Intercept 0.0747 9.87 <.0001 0.0780 8.90 <.0001

CTFACED1 0.0062 2.10 0.0363 -0.0095 -2.02 0.0443

NUMSEG 0.0006 2.71 0.0071 -0.0007 -0.72 0.4741

CTFACED1*NUMSEG 0.0013 1.48 0.1399

SIZE -0.0076 -9.69 <.0001 -0.0076 -9.69 <.0001

MERGE -0.0040 -3.38 0.0008 -0.0040 -3.38 0.0008

RESTRUCTURE 0.0028 2.46 0.0145 0.0028 2.46 0.0145

4THQTR 0.0059 1.58 0.1144 0.0059 1.58 0.1144

EXPERIENCE 0.0001 2.65 0.0087 0.0001 2.65 0.0087

NANALYST 0.0004 4.34 <.0001 0.0004 4.34 <.0001

BRSIZE 0.0000 -3.03 0.0027 0.0000 -3.03 0.0027

BADNEWS 0.0112 6.18 <.0001 0.0112 6.18 <.0001

SPECIAL 0.0051 4.61 <.0001 0.0051 4.61 <.0001

UE 0.0000 -1.72 0.0874 0.0000 -1.72 0.0875

GUIDE -0.0052 -6.65 <.0001 -0.0052 -6.65 <.0001

INST -0.0199 -6.44 <.0001 -0.0199 -6.44 <.0001

N 106,541 106,541

Adjusted R-squared 0.044 0.044


Fixed Effects Year-Quarter Year-Quarter

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Table 5 Relationship between Average Competing Tasks Performed by Covering Analysts on Earnings

Announcement Day and Price Discovery

ABS CAR [+2, +6] ABS CAR [+7, +11] ABS CAR [+12, +16]

Parameter Est. t-Stat p-value Est. t-Stat p-value Est. t-Stat p-value

Intercept 0.224 11.86 <.0001 0.257 7.26 <.0001 0.228 9.61 <.0001

AVG_COMPTASK1 0.001 4.16 <.0001 0.001 6.59 <.0001 0.002 6.78 <.0001 ABSRET 0.429 20.84 <.0001 0.391 17.59 <.0001 0.385 16.76 <.0001

SIZE -0.019 -50.71 <.0001 -0.018 -48.48 <.0001 -0.018 -45.94 <.0001

4THQTR 0.000 -0.01 0.989 -0.049 -1.40 0.161 -0.020 -0.86 0.391

NANALYST 0.001 18.61 <.0001 0.001 18.48 <.0001 0.001 17.69 <.0001

MERGE -0.017 -14.690 <.0001 -0.016 -16.160 <.0001 -0.015 -12.670 <.0001

RESTRUCTURE 0.002 1.300 0.194 0.003 2.950 0.003 0.003 2.520 0.012

BADNEWS -0.002 -1.260 0.209 -0.002 -1.480 0.139 -0.004 -3.060 0.002

SPECIAL 0.007 6.400 <.0001 0.005 5.190 <.0001 0.006 5.320 <.0001

UE -0.035 -8.930 <.0001 -0.034 -8.110 <.0001 -0.034 -8.910 <.0001

GUIDE -0.012 -10.890 <.0001 -0.011 -12.360 <.0001 -0.012 -11.670 <.0001

INST -0.024 -14.520 <.0001 -0.025 -14.910 <.0001 -0.024 -14.360 <.0001

N 30,661 30,661 30,661

Adjusted R-square 0.224 0.213 0.196

Firm Fixed Effects Yes Yes Yes

Quarter Fixed Effects Yes Yes Yes

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Table 6 Relationship between Average Competing Tasks Faced by Covering Analysts on Earnings

Announcement Day and Price Discovery


Parameter Est. t-Stat p-Value Est. t-Stat p-Value Est. t-Stat p-Value

Intercept 0.238 5.95 <.0001 0.229 5.74 <.0001 0.224 4.68 <.0001 AVG_CTFACED1 0.006 1.27 0.206 0.014 2.90 0.004 0.006 1.56 0.119 ABSRET 0.281 11.55 <.0001 0.270 10.08 <.0001 0.230 11.23 <.0001

SIZE -0.017 -29.49 <.0001 -0.016 -30.59 <.0001 -0.016 -28.83 <.0001

4THQTR -0.032 -0.82 0.411 -0.050 -1.28 0.200 -0.038 -0.81 0.417

NANALYST 0.001 12.16 <.0001 0.001 12.67 <.0001 0.001 12.10 <.0001

MERGE -0.016 -9.59 <.0001 -0.011 -7.90 <.0001 -0.011 -7.05 <.0001

RESTRUCTURE 0.003 1.73 0.084 0.003 1.78 0.075 0.002 1.39 0.166

BADNEWS -0.002 -1.31 0.190 -0.004 -1.73 0.083 -0.006 -3.56 0.000

SPECIAL 0.004 2.56 0.011 0.002 1.21 0.226 0.002 1.46 0.143

UE -0.033 -6.90 <.0001 -0.043 -5.11 <.0001 -0.036 -6.22 <.0001

GUIDE -0.012 -8.47 <.0001 -0.011 -9.74 <.0001 -0.012 -9.85 <.0001

INST -0.021 -7.41 <.0001 -0.019 -7.82 <.0001 -0.017 -6.67 <.0001

N 10,753 10,753 10,753




of 43

Table 7 Relationship between Distracting Events (Earnings Announcements outside Portfolio) on A Day

and Forecast Error

Panel A: Relationship between distracting events on a forecast day

and forecast error

Panel B: Relationship of forecast error with distracting events

faced on a forecast day and task complexity interaction


Intercept 0.068077 11.43 <.0001 0.068079 11.43 <.0001

DISTRACT1 0.000003 0.47 0.6355 0.000003 0.47 0.6378

NUMSEG 0.000639 2.71 0.0072 0.001473 2.17 0.0310

DISTRACT1*NUMSEG -0.000834 -1.34 0.1830

SIZE -0.007610 -9.71 <.0001 -0.007611 -9.71 <.0001

MERGE -0.004017 -3.45 0.0007 -0.004018 -3.45 0.0007

RESTRUCTURE 0.002805 2.46 0.0145 0.002804 2.46 0.0146

4THQTR 0.005883 1.58 0.1145 0.005885 1.58 0.1144

EXPERIENCE 0.000100 2.64 0.0088 0.000100 2.64 0.0088

NANALYST 0.000427 4.40 <.0001 0.000427 4.40 <.0001

BRSIZE -0.000032 -3.02 0.0028 -0.000032 -3.02 0.0028

BADNEWS 0.011165 6.18 <.0001 0.011165 6.18 <.0001

SPECIAL 0.005128 4.66 <.0001 0.005127 4.66 <.0001

UE 0.000000 -1.73 0.0842 0.000000 -1.73 0.0842

GUIDE -0.005165 -6.64 <.0001 -0.005166 -6.64 <.0001

INST -0.019937 -6.46 <.0001 -0.019933 -6.45 <.0001

N 52,146 52,146

Adjusted R-Squared 0.04352 0.0435


Fixed Effects Year-Qtr Year-Qtr

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Table 8 Relationship between Average Distracting Events (Earnings Announcements outside Portfolio)

by Covering Analysts on Earnings Announcement Day and Price Discovery


Parameter Est. t-Stat p-Value Est. t-Stat p-Value Est. t-Stat p-Value

Intercept 0.249 6.63 <.0001 0.252 6.67 <.0001 0.235 5.15 <.0001

AVG_DISTRACT1 -0.000013 -1.98 0.048 -0.00001 -1.62 0.105 -0.00001 -1.33 0.185

ABSRET 0.281 11.50 <.0001 0.270 10.03 <.0001 0.230 11.21 <.0001

SIZE -0.017 -29.64 <.0001 -0.016 -30.68 <.0001 -0.016 -28.90 <.0001

4THQTR -0.034 -0.90 0.367 -0.056 -1.49 0.135 -0.040 -0.89 0.376

NANALYST 0.001 12.01 <.0001 0.001 12.57 <.0001 0.001 12.06 <.0001

MERGE -0.015 -9.49 <.0001 -0.011 -7.80 <.0001 -0.011 -6.97 <.0001

RESTRUCTURE 0.003 1.70 0.090 0.003 1.72 0.086 0.002 1.36 0.175

BADNEWS -0.002 -1.25 0.212 -0.003 -1.67 0.095 -0.006 -3.52 0.000

SPECIAL 0.004 2.60 0.009 0.002 1.28 0.201 0.002 1.50 0.133

UE -0.033 -6.88 <.0001 -0.043 -5.10 <.0001 -0.036 -6.22 <.0001

GUIDE -0.012 -8.50 <.0001 -0.011 -9.84 <.0001 -0.013 -9.90 <.0001

INST -0.021 -7.33 <.0001 -0.018 -7.78 <.0001 -0.017 -6.65 <.0001

N 10,753 10,753 10,753



Qtr. Fixed Effects Yes Yes Yes

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Table 9 Relationship between Competing Tasks Performed over A Week and Forecast Error

Panel A: Relationship between competing tasks performed over a week and forecast error

Panel B: Relationship of forecast error with competing tasks performed over a week and task complexity interaction


Intercept 0.0675 11.02 <.0001 0.0667 10.90 <.0001

COMPTASK7 0.0007 2.78 0.0058 0.0012 2.07 0.0397

NUMSEG 0.0007 2.85 0.0047 0.0008 2.66 0.0083

COMPTASK7*NUMSEG -0.0001 -0.93 0.3552

SIZE -0.0078 -9.34 <.0001 -0.0078 -9.34 <.0001

MERGE -0.0050 -3.69 0.0003 -0.0050 -3.69 0.0003

RESTRUCTURE 0.0043 3.04 0.0026 0.0043 3.03 0.0027

4THQTR 0.0052 1.25 0.211 0.0052 1.25 0.2123

EXPERIENCE 0.0001 3.21 0.0015 0.0001 3.21 0.0015

NANALYST 0.0005 4.46 <.0001 0.0005 4.46 <.0001

BRSIZE 0.0000 -4.23 <.0001 0.0000 -4.23 <.0001

BADNEWS 0.0134 6.47 <.0001 0.0134 6.46 <.0001

SPECIAL 0.0059 5.79 <.0001 0.0059 5.79 <.0001

UE 0.0000 -0.76 0.4508 0.0000 -0.76 0.4506

GUIDE -0.0038 -5.19 <.0001 -0.0037 -5.16 <.0001

INST -0.0210 -7.95 <.0001 -0.0210 -7.95 <.0001

N 106,541 106,541




of 43

Table 10 Relationship between Competing Tasks Faced over A Week and Forecast Error

Panel A: Relationship between competing tasks faced over a

week and forecast error

Panel B: Relationship of forecast error with competing tasks faced over a

week and task complexity interaction

Estimate t-Stat p-Value Estimate t-Stat p-Value

Intercept 0.0675 11.02 <.0001 0.0667 10.90 <.0001

CTFACED7 0.0007 2.78 0.0058 0.0012 2.07 0.0397

NUMSEG 0.0007 2.85 0.0047 0.0008 2.66 0.0083

CTFACED7*NUMSEG -0.0001 -0.93 0.3552

SIZE -0.0078 -9.34 <.0001 -0.0078 -9.34 <.0001

MERGE -0.0050 -3.69 0.0003 -0.0050 -3.69 0.0003

RESTRUCTURE 0.0043 3.04 0.0026 0.0043 3.03 0.0027

4THQTR 0.0052 1.25 0.211 0.0052 1.25 0.2123

EXPERIENCE 0.0001 3.21 0.0015 0.0001 3.21 0.0015

NANALYST 0.0005 4.46 <.0001 0.0005 4.46 <.0001

BRSIZE 0.0000 -4.23 <.0001 0.0000 -4.23 <.0001

BADNEWS 0.0134 6.47 <.0001 0.0134 6.46 <.0001

SPECIAL 0.0059 5.79 <.0001 0.0059 5.79 <.0001

UE 0.0000 -0.76 0.4508 0.0000 -0.76 0.4506

GUIDE -0.0038 -5.19 <.0001 -0.0037 -5.16 <.0001

INST -0.0210 -7.95 <.0001 -0.0210 -7.95 <.0001

N 106,541 106,541




of 43

Table 11 Relationship between Average Competing Tasks Performed by Covering Analysts in the Week

Leading to Earnings Announcement and Price Discovery


Parameter Est. t-Stat p-value Est. t-Stat p-value Est. t-Stat p-value

Intercept 0.225 11.92 <.0001 0.259 7.30 <.0001 0.229 9.69 <.0001

AVG_COMPTASK7 0.000 4.44 <.0001 0.000 6.42 <.0001 0.000 6.51 <.0001

ABSRET 0.430 20.85 <.0001 0.392 17.61 <.0001 0.386 16.79 <.0001 SIZE -0.019 -50.67 <.0001 -0.018 -48.44 <.0001 -0.018 -46.02 <.0001

4THQTR 0.000 -0.01 0.993 -0.049 -1.40 0.162 -0.020 -0.85 0.396

NANALYST 0.001 18.66 <.0001 0.001 18.56 <.0001 0.001 17.80 <.0001

MERGE -0.017 -14.68 <.0001 -0.016 -16.15 <.0001 -0.015 -12.66 <.0001

RESTRUCTURE 0.002 1.31 0.189 0.003 2.97 0.003 0.003 2.52 0.012

BADNEWS -0.002 -1.26 0.207 -0.002 -1.49 0.136 -0.004 -3.07 0.002

SPECIAL 0.007 6.42 <.0001 0.005 5.22 <.0001 0.006 5.35 <.0001

UE -0.035 -8.93 <.0001 -0.034 -8.11 <.0001 -0.034 -8.91 <.0001

GUIDE -0.012 -11.12 <.0001 -0.012 -12.73 <.0001 -0.012 -12.20 <.0001

INST -0.024 -14.49 <.0001 -0.025 -14.86 <.0001 -0.024 -14.31 <.0001

N 30,661 30,661 30,661




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