Schantl Dissertation FINAL - unipub.uni-graz.at

Stefan Schantl

The determinants of the firm coverage and

earnings forecasting behavior of sell-side equity

analysts

Doctoral Thesis

to be awarded the degree of

Doctor of Social and Economic Sciences (Dr. rer.soc.oec.)

at the University of Graz, Austria

Name of first supervisor

Institute of Accounting and Auditing, Prof. Dr. Ralf Ewert

Name of second supervisor

Institute of Accounting and Control, Prof. Dr. Dr.h.c Alfred

Wagenhofer

Graz, April 30, 2014

Author´s Declaration

Unless otherwise indicated in the text or references, or acknowledged above, this thesis is

entirely the product of my own scholarly work. Any inaccuracies of fact or faults in reasoning

are my own and accordingly I take full responsibility. This thesis has not been submitted

either in whole or part, for a degree at this or any other university or institution. This is to

certify that the printed version is equivalent to the submitted electronic one.

g

Graz, April 30, 2014 (Stefan Schantl)

Acknowledgements

I am deeply indebted to my dissertation advisors Ralf Ewert and Alfred Wagenhofer for

introducing me to theoretical accounting research and for their unremitting support during my

endeavors exploring this, for me, new point of view. The numerous cutting edge lectures they

gave throughout the past years as well as their encouraging and constructive feedback on my

ongoing projects inspired (or better incentivized) me to continuously work on my economic

understanding and further my technical skills.

Special thanks go to Paul Fischer, who generously volunteered to be a doctoral thesis

committee member as well as Mirko Heinle and Ro Verrecchia for their perpetual coaching

during and after my stay at the University of Pennsylvania. I explored previously unimagined

perspectives about the economics behind accounting through the discussions with them.

Further, I very much enjoyed the interesting workshops and eye-opening discussions with

Brian Bushee, Chris Ittner, Wayne Guay, Luzi Hail, Thomas Hemmer, Bob Holthausen, Rick

Lambert, Wayne Landsman, and Cathy Schrand.

I moreover want to thank my fellow doctoral students Kristina Berger, Terrence Blackburne,

Anna Boisits, Björn Brand, Michael Carniol, Jacky Chau, Mike Chin, Marina Ebner, Thomas

Gaber, Christian Gross, Jessica Kim-Gina, Brigitte Klotz, Peter Krenn, Sebastian

Kronenberger, Sandra Kukec, Beatrice Michaeli, Konrad Lang, Tanja Sestanj Peric, Spencer

Pierce, Olivers Schinnerl, David Tsui, Dennis Völler, Katrin Weiskirchner-Merten, David

Windisch, Jason Xiao, and Hang Yu for their continuous help throughout my doctoral

studies, for helpful comments on my work and for fruitful and lively discussions and debates.

Financial support from the Doctoral Program in Accounting, Reporting, and Taxation

(DART) funded by the FWF Austrian Science Fund, from the Social and Economic Faculty

of the University of Graz, and from the Austrian state of Styria is much appreciated.

Last but not least I want to thank my parents Inge and Hubert and my friends Carsten,

Dominik, Eva, Florian, and Gernot for their incessant support which was crucial for the

completion of this thesis.

Thank you!

I

Table of Contents

1. Introduction 1

1.1. Methodology 2

1.2. Literature Review 2

1.2.1. The Determinants of Analyst Forecast Properties 4

Analyst Reputation and Accuracy 4

Trading Volume Generation and Overoptimism 5

Career Concerns and Herding 7

Investors’ and Employers’ Inference of Analysts’ Abilities 10

Analysts’ Endowment with Private vs. Public Fundamental Information 11

The Relevance of Non-Fundamental Information 14

1.2.2. The Causes and Consequences of Analyst Coverage Behavior 15

Analyst Information Acquisition Costs and Coverage Initiation 15

Consequences of Changes in Analyst Following 18

1.3. Contribution 20

1.3.1. Analysts’ Career Concerns, Forecast Biasing and Firm Coverage Selection 20

1.3.2. Analyst Information Acquisition and the Informativeness of Forecasts and

Managed Earnings 21

1.3.3. Discretionary Analyst Coverage and Capital Market Characteristics 21

1.4. Structure of the Dissertation 22

2. Analysts’ Career Concerns, Forecast Biasing and Firm Coverage Selection 23

2.1. Introduction 24

2.2. Economic Setting 27

2.3. Model Solution and Equilibrium 31

2.3.1. Analysts’ Forecasting Strategy in the Monopoly Case 31

2.3.2. Analysts’ Forecasting Strategy in the Duopoly Case 32

2.3.3. The Coverage Initiation Threshold 34

II

2.4. Analysts’ Career Concerns and Firm Coverage Selection 36

2.5. Empirical Implications 40

2.6. Conclusion 41

Appendix (Paper 1) 42

Proof of Proposition 1 42

Proof of Corollary 3 45



3. Analyst Information Acquisition and the Informativeness of Forecasts and Managed

Earnings 48




3.4. Analyst Information Acquisition and the Informativeness of Managed Earnings 54

3.5. Conclusion 58





4. Discretionary Analyst Coverage and Capital Market Characteristics 62




4.4. Discretionary Analyst Coverage and Capital Market Characteristics 73

4.5. Extension: Discretionary Analyst Coverage and Market Liquidity 77

4.6. Conclusion 79


III







5. Discussion and Conclusion 87

Bibliography 91

Table of Figures

Figure 1: Prototypical Sequence of Decisions of an Equity Analyst 3

Figure 2: Sequence of Events (Paper 1) 28



Introduction

1

1. Introduction

Sell-side equity analysts are one of the primary information sources for stakeholders in

modern capital markets. Numerous empirical studies claim that reports, especially earnings

forecasts, provided by analysts are informative about the value of a firm (e.g., Givoly &

Lakonishok 1979, Lys & Sohn 1990, Frankel et al. 2006).1 By means of a time-series

regression on the decomposition of cumulative abnormal returns, Beyer et al. (2010) provide

evidence that almost one quarter of the financial information about a firm is disseminated by

equity analysts. Additionally, there is evidence showing that analysts not only provide new

information but also reiterate and reinterpret previously disclosed firm information (e.g.,

Asquith et al. 2005, Chen et al. 2010).2 This interpretation function also seems to matter for

shareholders: analysts who are more responsive to the information in an earnings

announcement and revise their forecasts shortly after the earnings disclosure help to reduce

the post-earnings announcement drift effect (Zhang 2008). Ayers & Freeman (2003), Gleason

& Lee (2003) and Piotroski & Roulstone (2004) present results showing that this effect is

triggered with a larger analyst following since this generally increases the pace of the pricing

process with respect to future earnings of a stock. Chang et al. (2006) and Bowen et al.

(2008) present evidence consistent with the argument that firms with greater analyst coverage

receive better financing contracts. Closely associated with the latter observation is the

evidence of Rajan & Servaes (1997) and James & Karceski (2006), who show that a larger

analyst following yields a more successful initial public offering. Additionally, Alfond &

Berger (1999) and Roulstone (2003) show that a larger analyst following yields an increased

liquidity of a firm’s stock. Altogether, it can be concluded that these empirical observations

yield a lowered cost of equity capital associated with larger analyst following. Furthermore,

firms seem to be aware of the benefits associated with analyst firm coverage since they are

even willing to pay for analyst services (Kirk 2011). It follows that the significance of

analysts in their different roles as information sources and information interpreters cannot be

overemphasized. However, besides the large attention of empiricists over the past twenty

years, the answer to the following question is yet to be found: What determines both analysts’

1 However, there is conflicting evidence on the informativeness of stock recommendations issued by sell-side

security analysts (e.g., Barber et al. 2001, Jegadeesh et al. 2004, Howe et al. 2009). Moreover, the information

role is not constant over time but depends crucially on the timing relative to firm disclosures (Francis et al.

2002, Ivkovic & Jegadeesh 2004, Chen et al. 2010). 2 The importance of information interpretation issues is pointed out by the theoretical contribution of Dutta &

Trueman (2002). They show that the assumption of an uncertain information interpretations leads to a non-

trivial voluntary disclosure strategy.

Introduction

2

forecasting and coverage behavior and how are observable patterns in their decisions

reconcilable to these incentives?

1.1. Methodology

Although the properties of financial analysts’ information as well as the consequences of

their work are empirically well-investigated, theoretical contributions that provide empirical

implications on the sources and consequences of analysts’ distinct incentives are still scarce.

The current cumulative doctoral thesis aims to fill some significant gaps in the theoretical

literature on analyst incentives and provides a number of novel empirical implications.

The advantages of the application of microeconomic models to increase our understanding of

the key determinants of analyst behavior compared to the widely used empirical method are

manifold. First and most importantly, it can be stated that the data availability on analysts’

behavior is still mediocre. This especially holds true for data on the decision of an analyst to

initiate or abandon firm coverage. A second advantage can be found with respect to potential

endogeneity issues which are inevitable with the use of empirical methods: As will be clear

throughout the dissertation, the interactions of analysts with others such as peer analysts,

investors or firm managers are determined by multiple, particularly complex relations and

economic forces. Theoretical models on the other hand enable us to isolate these forces and

ensure a pure analysis of the economics behind the interactions of analysts with other

economic agents.

1.2. Literature Review

In this chapter I provide a brief literature review of existing theoretical and empirical research

on analysts’ forecasting and firm coverage decisions.3 Kothari (2001), Ramnath et al. (2008)

and Beyer et al. (2010) also provide reviews on analyst research, but Kothari (2001)

exclusively summarizes the empirical research, whereas Beyer et al. (2010) impose a

structure which divides theoretical research from empirical evidence. Moreover, Ramnath et

al. (2008) provide only an incomplete discussion of theoretical research on the topic. In

contrast, the approach applied herein involves imposing a topical structure rather than a

methodological one. This approach enables me to reconcile theory and empirical evidence in

my discussion of the literature. Since the main chapters of this doctoral thesis apply formal

3 The focus of this thesis is on analyst forecasting and firm coverage. Consequently, it excludes other reports of

analysts such as price targets or stock recommendations.

Introduction

3

Analyst gathers

information for the

forecasting of

subsequent earnings

Analyst discloses

earnings forecast

Analyst manipulates

private information

signal and formulates

earnings forecast

Analyst observes

private information

signal and decides

whether to release it

Analyst decides

whether to start

collecting information

about a firm

Coverage Initiation Stage (Chapter 1.2.2.)

Forecast Reporting Stage (Chapter 1.2.1.)

t=1 t=2 t=3 t=4 t=5

theoretical modeling, this approach has the advantage of identifying several research issues of

high relevance that need further theoretical guidance.4

In order to add more structure to the literature review, Figure 1 depicts the prototypical

process of an analyst’s decisions.5 First, the analyst decides on whether to start collecting

information, which is subsequently carried out if this optimizes ex-ante his utility.6 After the

information acquisition comes the discretionary reporting stage ( ), in which the analyst

evaluates whether the disclosure of the obtained private information is beneficial for him.

This may happen in conjunction with the action in stage ( ), in which he decides on an

appropriate level of manipulation to complete the formulation of his forecast. In the last stage

he discloses his forecast to his clients.7

The literature review is structured in an inverse fashion and starts with the discussion of a

codetermination of analyst forecasts’ properties by distinct influential factors (i.e., particular

incentives, information endowment). In a second chapter the focus lies on the state of

research concerning analysts’ firm coverage initiation (or abandonment of coverage)

behavior. In addition, in the latter section a summary of the consequences of analyst firm

coverage for the firm is presented in order to provide a sufficiently broad perspective on the

current state of the art of the economic research on analysts.8

4 It shall be noted that for practical reasons the review will not be able to summarize all available contributions

on the topic. Instead, I focus on the most important contributions in the field, whereby the selection is naturally

driven by the author’s subjectivity. 5 For the sake of the readability of the dissertation I use male pronouns to refer to an individual equity analyst or

other stakeholders, although these are interchangeable with pronouns in the female form without any limitations. 6 In this stage he may use some proprietary information on which to base his decision (e.g., see Hayes 1998,

Fischer & Stocken 2010). 7 In further stages the analyst may obtain additional information and may decide whether or not to disclose a

revised version of his forecast. However, this decision can be seen as a repetition of all actions between

and . 8 The reason for the inverse structure is that the drivers behind analyst forecast properties are much better

investigated than those behind coverage initiation or abandonment. Thus, this approach gives the reader a much

better overview over the distinct factors that are influential in analysts’ decisions and enables me to highlight

gaps in the coverage literature.

Figure 1: Prototypical Sequence of Decisions of an Equity Analyst

Introduction

4

1.2.1. The Determinants of Analyst Forecast Properties

Analysts have a variety of objectives that they follow to optimize their overall utility.

However, I identify and subsequently discuss the following factors, which influence the

behavior of analysts with respect to their forecasting strategies: (i) analyst reputation and

accuracy, (ii) the trading volume generation objective and overoptimism, (iii) career concerns

and herding, (iv) investors’ and employers’ inference of analysts’ abilities, and analysts’

endowment with (v) fundamental and (vi) non-fundamental information.

Analyst Reputation and Accuracy

Reputation is assumed to be one of the main driving forces behind analysts’ behavior, which

is the reason why almost all of the discussed models incorporate it in their structure in one

way or another (e.g., Lim 2001, Beyer 2008, Beyer & Guttman 2011). Several empirical

studies proxy reputational concerns by the use of forecast accuracy, which is measured by the

ex-post absolute forecast error. This perspective is established by Stickel (1992), who shows

that members of the Institutional Investor All-American Research Team, a group of analysts

considered to be superior and who thus possess a very good reputation, deliver more accurate

earnings forecasts than non-members of this group.9 Additionally, he also documents a more

pronounced stock market reaction to All-American analyst forecasts.10

In line with the research of Stickel (1992), the reputation incentive is usually theoretically

modeled by assuming that an analyst minimizes either the expected quadratic (Lim 2001) or

absolute forecast error (Beyer 2008).11

Lim (2001) develops a model in which an analyst

chooses the bias of his earnings forecast. He shows that the forecast distortion is positive and

decreases with the predictability of earnings, which he subsequently confirms with an

archival analysis. Beyer (2008) establishes a framework that considers the endogeneity issue

involved in this process: According to existing evidence, managers manipulate earnings such

that the previously published forecast is met or bet. This is rewarded with a market premium

9 Stickel (1992) reasons that there may be a positive relationship between compensation and forecasting

performance under the assumption that All-Star recognition is associated with higher compensation. This

hypothesis is confirmed by Groysberg et al. (2011) by means of internal brokerage house data. 10

Another well-known pattern associated with forecasting accuracy is consistent with experience learning:

Analysts’ forecasting performance measured by ex-post absolute forecast errors increases over time. Notable

empirical contributions in this field of research are Mikhail et al. (1997, 2003), Clement (1999), Jacob et al.

(1999) and Clement et al. (2007). 11

In the absence of other objectives (e.g., the stimulation of trading), these two functions yield the same

solution. However, Basu & Markov (2004) provide empirical evidence pointing to a higher appropriateness of

the absolute rather than the quadratic forecast error loss function, which indicates that other objectives are also

relevant for the forecasting behavior of an equity analyst.

Introduction

5

(Kasznik & McNichols 2002, Matsumoto 2002, Bhojraj et al. 2009).12

However, the model’s

results indicate that a privately informed analyst positively exaggerates his prediction for

most of the possible realizations of reported earnings.

Further, the results provided by Groysberg et al. (2011) together with the ones of Louis et al.

(2013) are consistent with the argument that forecast accuracy is a criterion for the research

house’s interior performance evaluation of an analyst rather than a measure of his reputation

with his clients. Louis et al. (2013) highlight the difference of forecast accuracy and forecast

informativeness in the presence of earnings and earnings guidance management by the firm.

They show that analysts sometimes side with their clients (shareholders) rather than with the

followed firm’s management and that the provision of informative forecasts, which may

predict natural rather than reported earnings, can encourage trading in the stock. This aspect

may be connected to analysts’ trading volume generation objective and the nomination to

equity analyst rankings such as the Institutional Investor All-Star ranking: Analysts providing

information which earns their clients an abnormal rate of return are nominated to be included

in the ranking by these investors. In finance theory, abnormal returns are generated by

underlying investment decisions, where these are based on private information such as the

information that is provided by analysts. Groysberg et al. (2011) provide evidence that

analysts are better compensated if they are recognized to be superior and also if they

stimulate trading.

Trading Volume Generation and Overoptimism

By means of a unique dataset, Groysberg et al. (2011) ascertain that an influential factor for

analysts’ compensation is the amount of trading stimulated with reports on a covered stock

and thus brokerage profits for the investment banking branch of an affiliated analyst. Jackson

(2005) and Beyer & Guttman (2011) provide in-depth theoretical guidance on the issue of

analyst forecasting behavior incentivized by the objective of trading volume generation.13

Jackson (2005) considers a setting in which an analyst trades off the long term incentive to

build a reputation with the short term objective to generate trading volume. His theory

suggests that forecast overoptimism is a result of investors’ uncertainties regarding the type

of the analyst and the existence of short-selling constraints, even if trading commissioning

objectives are restricted to a very low level. He also confirms this with archival evidence.

12

This situation is even exacerbated when pre-forecast firm earnings guidance is present. 13

Another representative of this field of literature is Hayes (1998). However, this paper focuses more on the

relevance of trading volume for the analysts’ information acquisition strategy (see Chapter 1.2.2.).

Introduction

6

Jackson’s (2005) framework considers analysts’ reporting strategies with a high level of

abstraction on the side of the market. Beyer & Guttman (2011) overcome this shortage by

integrating an analyst’s reporting decision with the market model of Grossman & Stiglitz

(1980). The tradeoff of the analyst’s incentives is incurred from Jackson (2005): Every

investor who receives the analyst forecast adjusts his position in the stock. The change in the

market price is also informative to the other investors who do not directly observe the

forecast. Thus, the analyst forecast is private information and causes information asymmetry

across investors. Beyer & Guttman (2011) provide a variety of implications. First, they show

that the (dynamic) forecast bias is an increasing function in the analyst’s private information

and that the bias is within a finite interval. Second, the analyst is, on average, more likely to

bias his forecast upward than downward, which is a direct result of investors’ risk aversion

and the residual uncertainty they have to incur for their investment in the firm. A last

implication is that an analyst with a high level of private information endowment

(represented by a highly precise private estimation signal over earnings in the model) does

not necessarily disclose a more accurate forecast because he knows that he will have a

relatively high impact on the trading in equilibrium, which incentivizes him to deviate more

from a truthful revelation strategy. This partly opposes the common belief that more private

information is necessarily associated with a more accurate forecast.

The empirical side of trading volume incentives of brokerage house affiliated analysts

provides mixed evidence on analysts’ overoptimism bias. Dugar & Nathan’s (1995) results

are such that affiliated analysts are more optimistic in their forecasting than non-affiliated

analysts. Additionally, they seem to not provide more informative forecasts and are, on

average, as accurate as their non-affiliation counterparties. In contrast, Frankel et al. (2006)

claim that analyst forecast informativeness increases with potential brokerage profits, which

partly contradicts the findings of Beyer & Guttman (2011). Additionally, Cowen et al. (2006)

provide evidence showing that analysts affiliated with investment banks that perform a firm’s

underwriting have a lower level of overoptimism in their forecasts than analysts who are

funded by a non-underwriter brokerage house. Thus, the effect is partly counter to the

discussed theoretical studies, which assume that analysts have an interest in stimulating

trading because of their underwriter affiliation. However, even Cowen et al. (2006, 119)

conclude that “optimism is at least partially driven by trading incentives”.

Introduction

7

Career Concerns and Herding

Equity analysts exhibit reputational or career concerns and, as will be clear in this subchapter,

their (relative) ability and performance is an important factor behind this objective. There

exists a solid theoretical base with a number of signaling models on the association of

analysts’ reputational concerns and their attempts to communicate their ability to clients or

employers by means of forecasting strategies which show patterns consistent with herding.

Scharfstein & Stein (1990) develop a theoretical model on the sequential investment

decisions of managers who are concerned about their labor market reputation. In their model

two groups of managers exist (but cannot be distinguished a priori by the labor market), a

good group with a high level and a bad group with a poor level of ability. The ability of each

type is modeled such that the good ones obtain an imperfectly informative signal about a

future state of nature determining the success of a project, whereas the bad type observes a

purely random signal without any information content. Scharfstein & Stein (1990) provide

results showing that good managers want to communicate their type by means of an unbiased

investment decision, whereas bad managers partially mimic the behavior of a good type (i.e.,

they herd in their investment decision) in order to receive a better reputation as if they would

communicate their type based on their (weak) information. The labor market, which

determines the reputation, infers two distinct pieces of information from the observation of an

investment decision, namely whether the investment decision yields a profitable or

unprofitable outcome and whether the behavior of managers is similar to that of others. They

argue that these are two separate pieces of information because a bad decision is not

considered to be a very bad signal over a manager’s ability if the other manager made the

same decision. However, the observation of a profitable decision is of large reputational

value if the peer manager made a contrary one. Graham (1999) adopts Scharfstein & Stein‘s

(1990) model and reinterprets it based on an analyst setting to include pre-existing public

information about the prediction of an uncertain future state in the game. He provides

theoretical implications and consistent evidence that if the public information conflicts with

the private information of a bad analyst, he is more likely to herd because he suspects his

private information to be wrong. This association is mitigated in situations when he has a

high initial reputation and a low real ability. Trueman (1994) develops a signaling structure in

which both types again want to signal a high ability to influence their reputational standing

with their clients and their employers. He shows that there is a general tendency to issue a

forecast that overweighs the prior expectations and/or other publicly available information

and underweights the analyst’s private information. In sum, it can be concluded that the

Introduction

8

information disclosure policy of analysts (or managers) is also shaped by their incentive to

communicate their ability, which ultimately yields a pattern consistent with an overweighing

of prior expectations relative to private information, i.e., herding on priors.

Clarke & Subramanian (2006) develop a theoretical Bayesian learning framework with

multiple periods and analysts. They highlight the importance of prior forecasting performance

for the assessment of analysts’ employment risk, and they consider three types of analysts,

good, bad and intermediate ones: Under- and over-performing analysts rather than

intermediate analysts issue bolder forecasts to manage their employment risk. This is because

high ability analysts will not be fired anyway due to their superiority, which means that they

put more weight on their private signal and are thus bolder than the intermediate analysts. In

contrast, low ability analysts also have an incentive to be bolder than their intermediate

counterparties because with a bold forecasting strategy they introduce noise, which makes it

harder for their employers to evaluate their abilities (i.e., partially pooling with high ability

analysts). Clarke & Subramanian’s (2006) theory of a non-monotonic association between

ability and forecasting policy, which in turn dynamically influences career concerns through

the assessment of the employment risk over time, is further supported by their empirical

investigation.

Existing empirical evidence emphasizes the association between relative performance and

analyst career outcomes, where the former is usually proxied by the relative magnitude of the

ex-post absolute forecast error. Mikhail et al. (1999) investigate the association between

forecast accuracy and the likelihood of analyst turnover, and provide evidence that increases

of absolute forecast accuracy are not associated with a larger probability of termination of

employment. However, they show that analysts which are less accurate than their peers have

a significantly higher risk of being fired, which establishes the claim that career concerns are

associated with the ex-post forecasting performance of an analyst relative to his peer(s). Hong

& Kubik (2003) also study the influence of analyst career concerns on the properties of their

forecasts and present the result that relatively less accurate forecasters are generally less

likely to move up the career ladder, which is in line with the findings of Mikhail et al. (1999).

In addition, they also highlight the fact that analysts who are overly optimistic in their

Introduction

9

forecasts are promoted more frequently after controlling for the already mentioned primary

effect. This is especially the case if they work for the underwriter of the stock.14

Based on the observation that relatively less accurate forecasters have to bear a significantly

larger employment risk, Hong et al. (2000) and Clement & Tse (2005) argue that the

knowledge about this association establishes an incentive for analysts to optimize this

association in expectation, i.e., a pattern that is in line with herding behavior. Further, they

perform studies on the variations of the association between relative forecasting performance

and career risk with the field experience of an analyst. They show that relatively

inexperienced analysts exhibit a higher tendency to bias their forecast in the direction of the

(expected) mean forecast than their more experienced counterparties.15

Thus, it can be

concluded that the group of analysts who have an especially high employment risk tend to

exhibit herding strategies in their forecasts in order to manage this risk.

Based on these empirical observations and the existing theoretical guidance, the following

argument can be established: Analysts who are relatively less accurate in their forecasts of a

firm’s earnings than their peers are punished with a less favorable career outcome (i.e., a

higher risk of termination). This association establishes an implicit incentive of an individual

analyst to choose the forecasting strategy such that the anticipated difference between one’s

own forecast error and the average peer forecast error is minimized. It is straightforward to

assume that all analysts exhibit career concerns. Thus, the relative performance objective is

bi- or multilateral and effectively yields an underreaction of an analyst to his own private

information and an overweighing of the prior expectations with respect to earnings.16

Moreover, this implicit incentive is strongest for those who can lose most from a termination

of employment, namely analysts who are at the beginning of their careers.17

14

Based on the results of the study of Clarke et al. (2007), it can be noted that analyst job changes do not seem

to alter their forecasting behavior. Thus, the summarized incentive seems to be unaltered even if analysts start in

a new workplace. It follows that career concerns do not depend on a career within only one research department. 15

An additional result provided by Clement & Tse (2005) is that bold forecasts are on average more accurate

than herding forecasts. 16

This claim is easily proven by means of a standard noisy rational expectations equilibrium model with two

forecasters obtaining independent private information signals to forecast earnings based on the objective of

relative performance. This model is available upon request from the author. 17

It shall be noted that other information-based reasons may exist for analysts to herd in their forecasts. For

example, Welch (2000) explores the situation of sequential release of stock recommendations by analysts. He

points out a positive association between an early recommendation of an analyst and the later recommendation

of other analysts. It shall be noted that herding, according to Welch (2000), is information based and determined

by the sequence of forecast releases rather than driven by analysts’ incentives. See Chapter 1.2.2. for further

discussion.

Introduction

10

Investors’ and Employers’ Inference of Analysts’ Abilities

In the previous chapter, I summarized theory and evidence on the relevance of career

concerns for analyst forecasting behavior. A related research topic is the literature on the

inference effort of the abilities of analysts performed by their employers and clients. Thus, I

consider the other side of the communication process than in the previous subchapter, namely

that of the recipients.

Theoretical research is provided by Gerardi & Yariv (2008) that utilizes an agency setting

with multiple experts as agents and one decision maker as the principal. The objectives of the

experts include not only the costly acquisition of expertise but also the effort to utilize the

gained expertise for the purpose of a profitability evaluation of a risky project to help the

principal with his investment decision.18

The decision variables of the principal are the

number of contracted experts and the weighting (and/or recognition) of their

recommendations. The model provides the result that the principal hires only at most two

experts and sometimes even decides not to follow their recommendations. If two experts are

selected, the principal wishes to choose the ones who have the most extreme preferences

relative to himself because this eases the weighing of recommendations. However, he may

also choose to sometimes ignore the recommendations provided by the experts. Moreover,

the experts consider this preference dependence in their expertise acquisition strategies.

The empirical literature generally focuses more on the client’s side of this interaction.

However, the results are also partly applicable to the employer’s communication process with

the analyst. It is shown by archival studies that superior analysts are considered to deliver

more informative forecasts, which is why investors react more strongly to them compared to

inferior analysts (e.g., Park & Stice 2000).19

Clement & Tse (2003) build on this viewpoint

and test whether investors would only focus on the accuracy of an analyst’s forecast revision

to assess his abilities. They generate results showing that investors react more strongly to

analysts who are associated with larger brokerage houses and forecasts that are released in the

first half of a fiscal year. Building on the assumption that analysts with a higher ability are

more likely to be employed by the well-funded research departments of larger investment

banks (e.g., Groysberg et al. 2011), it can be reasoned that the affiliation is a criterion for the

assessment of an analyst’s abilities. In an attempt to reconcile their results with prior

research, Chen et al. (2005) develop a simple model on the inference problem of investors

18

The latter objective of the experts contains a notion of career concerns. 19

Cox & Kleiman (2000) provide evidence that All-American analysts have superior skills rather than luck.

Introduction

11

with respect to the ability of an analyst. They provide theoretical and empirically confirmed

results showing that the more forecasting periods already occurred, the more information

investors receive not only about fundamentals but also about the analyst’s ability. This in turn

shapes their pricing behavior such that they place a larger weight on a forecast if the analyst

is “proven” to be accurate over a longer duration.20

The evidence presented in this subsection has an important link to the career concerns

literature discussed above: If an analyst is ignored by his clients due to a lack of accuracy

over a long period, the employer may interpret this as a lack of ability and may decide to

terminate his employment. Thus, the provocation of a market reaction represents another

facet of analyst career concerns.

Analysts’ Endowment with Private vs. Public Fundamental Information

In the previous chapters I already discussed several theoretical papers. Almost all of them,

except Graham (1999), assume a strictly private fundamental information endowment of

analysts, where the observed information may or may not be of value for the forecast

recipients. The relative contribution of public and private fundamental information for the

forecasting of earnings is the main point of this part of the literature discussion. Moreover, it

can be noted that the theoretical side of this branch of literature has a strong focus on the

reconciliation of individual forecast properties with more abstract properties such as the mean

consensus forecast (error) or forecast dispersion, since both constructs are frequently used in

empirical research, i.e., to proxy for earnings expectations (e.g., O’Brien 1988).

In an extension of the theoretical work of Kim & Verrecchia (1991) on patterns in price

variability and expected trading volume, Abarbanell et al. (1995) introduce analyst forecasts

as a source of private information of investors. The forecasts follow an exogenously

predetermined structure and include a common estimation error term and an analyst-specific

estimation error term. The former captures the public information component, whereas the

latter establish differential private informedness of analysts and investors. They show that

analyst forecasts are, at best, a noisy measure of investor’s differential beliefs and that the

relative forecasting properties (i.e., consensus among analysts) are influential in the reactions

of the market to forecasts.21

In particular, both expected trading volume and the variance of

20

Bonner et al. (2007) show that not only past performance or the length of the track record or affiliation can

influence the perceived informativeness of analyst forecasts, but also the sophistication of investors. Moreover,

media coverage of so called “celebrity analysts” also mitigates the effect of a lack of sophistication of investors

on the pricing of a forecast. 21

For a related investigation on consensus vs. informedness, see Holthausen & Verrecchia (1990).

Introduction

12

the price change are shown to increase in forecast dispersion. Abarbanell et al. (1995) is

closely associated with the theoretical investigation of Barron et al. (1998). They go a step

further and disentangle analyst forecast properties from analysts’ information environment.

They utilize the facts that consensus forecast error and forecast dispersion are differentially

influenced by the idiosyncratic and systematic estimation errors in analysts’ private

information signals: “[T]he expected dispersion in forecasts is an increasing function of

uncertainty but a decreasing function of consensus [among analysts], while the expected

squared error in the mean forecast is an increasing function of both uncertainty and

consensus.” (Barron et al. 1998, 422) Based on these properties, the empirical investigation

by Barron et al. (2002) yields unique estimates of the weighing of private and public

information. Their empirical study concludes that the idiosyncratic component increases in its

relative magnitude to the systematic component after the release of an earnings

announcement, which is reconcilable to prior theories on the existence of event-period private

information such as that of Kim & Verrecchia (1997). Further, Kim et al. (2001) develop a

parsimonious model with a finite number of non-strategic analysts and point out that the

mean consensus forecast inefficiently summarizes all of the available information and

overweighs public information relative to analysts’ private information.

Further, the discussed theories are not very explicit about which pieces of information are

summarized under the term “public information”. Several empirical and theoretical studies

investigate analysts’ processing of different sources of public information. Their results can

be summarized as follows:22

· Information impounded in prior stock price changes: As is commonly believed by

accounting and finance scholars, investors impound their private information into

market prices through their trading activities, which generates noisy public

information. Thus, there is considerable information in stock price changes, which can

be inferred by analysts. However, analysts seem to underreact to the information in

these stock returns (Lys & Sohn 1990, Abarbanell 1991), where analysts’ information

extraction ability may mitigate this underreaction (Elgers & Lo 1994).23

Recently,

Clement et al. (2011) provide evidence supporting the argument that the ability to

extract information from stock returns may be a distinct feature of analyst expertise.

22

Furthermore, the recent empirical literature uses components of public information to predict analyst forecast

errors in order to improve their estimates for further purposes such as in investigations of the implied cost of

capital or trading strategy developments (i.e., Hughes et al. 2008, Mohanram & Gode 2013, So 2013). 23

See Chapter 1.2.1. for a discussion of analyst experience.

Introduction

13

· Previously released peer forecasts: In an early and well-known study Stickel (1990)

shows that analysts also use information from previously released peer forecasts, i.e.,

from forecasts of other analysts that also follow the same firm.

· Earnings guidance and other voluntary firm information: A special tension exists

between the provisions of (potentially biased) earnings guidance, the (strategically

chosen) analyst forecast as a proxy for the earnings expectation and (potentially

managed) earnings. In particular, a manager with an interest in the end-of-period

share price may want to first guide expectations downward in order to make the

subsequently disclosed forecast easier to beat with annual earnings (Baginski &

Hassell 1990).24

Versano & Trueman (2013) develop a theoretical model to give some

guidance on this association. They show that only privately but not publicly disclosed

earnings guidance would be chosen to guide analyst expectations downward. Contrary

to Versano & Trueman (2013), empirical evidence shows significant results in support

of the use of public earnings guidance to guide forecasts (a proxy for earnings

expectations) downward (Cotter et al. 2006). Such an interconnectedness of disclosure

may even be beneficial from the analyst’s perspective because it stimulates trading in

the window between the earnings guidance disclosure and the earnings announcement

(Feng & McVay 2010). With respect to the informativeness of earnings guidance,

Lang & Lundholm (1996) show that more informative firm disclosure policies are

associated with more accurate earnings predictions. In line with this result, Chen et al.

(2011) document the fact that firms which commit to stopping the disclosure of

earnings guidance are followed by less accurate analysts. Moreover, Hilary & Shen

(2013) argue that the ability to extract information from management earnings

guidance is also a distinct feature of analyst expertise.

Another issue, which is relevant in a discussion on analysts’ use of public relative to private

information, is analysts’ aggregation of these two types of information. Several studies argue

that analysts would underreact to the available public information and would thus overreact

to their privately obtained information (e.g., Mikhail et al. 2003, Zhang 2008). In a more

recent, jointly theoretical and empirical investigation, Chen & Jiang (2006) establish the

claim of an overweighting or underweighting of private information relative to public

information depending on the influence of other incentives on an analyst’s forecasting

24

The consequences of a setting in which an analyst predicts (potentially) managed earnings are discussed by

Beyer (2008), as summarized already in Chapter 1.2.1. Her economic framework assumes that managers may

want to meet or beat analyst forecasts.

Introduction

14

strategy. In particular, they present data showing that the weighting distribution is skewed

towards positive forecasts relative to the consensus forecast, where the level of skewness

depends on the realization of an analyst’s private information: In more favorable forecasts

private information is overweighted, whereas in less favorable forecasts it is underweighted.

On average this yields an overweighting pattern. They conclude that the strategic incentives

(discussed in Chapter 1.2.1.) are of great relevance for the observable properties of forecasts.

Moreover, their results, which are obtained by means of a probability-based method that is

considered to be statistically very robust, confirms the findings of the underreaction

hypothesis of available public information, i.e., an information-based herding pattern similar

to the one reasoned by Welch (2000).

The Relevance of Non-Fundamental Information

Existing theories argue that the endowment with fundamental information is not the only

possible information which may be relevant for analysts’ behavior. So far, the literature has

assumed the existence of fundamental information about the followed firm such as firm value

and/or earnings, but little research (especially empirical research) has been performed on

non-fundamental information in general and analysts’ use of this class of information in

particular. It shall be noted that all models discussed in this chapter utilize a Kyle (1985) style

market model, which considers single or multiple privately informed speculators and a

market maker under the assumption of general risk neutrality.

There are three notable theoretical contributions on the use of non-fundamental information

for speculative reasons. Madrigal (1996) develops an intertemporal asset market with two

sorts of informed trades, those made by an insider endowed with fundamental information

and those implemented by a speculator endowed with non-fundamental information about

forthcoming (random) liquidity demand. The speculator may not have fundamental

information, but he can better infer it from past rounds of trading than the market maker. It

shall be noted that the non-presence of the insider (who is endowed with fundamental

information) yields a worthlessness of the speculator’s non-fundamental information. By

means of this model, Madrigal (1996) shows that the existence of a speculator trading on

non-fundamental information may incentivize the insider to gather less fundamental

information due to a decreased marginal rate of return of this information. This may even

yield situations with an overall decreased price efficiency in equilibrium. Similar results are

obtained by Yu (1999). He considers the case in which there is only an insider, who is

endowed with two signals, fundamental information and a noisy signal about the demand of

Introduction

15

the liquidity trader. He shows that the profitability of this knowledge depends on the

precision of the latter signal: If it is perfectly informative, the insider can obtain positive

rents, whereas an imperfect signal may even be detrimental for his investment decision and

thus the profitability of his trading orders.

In a setting with a strategic manager who manipulates earnings and has an uncertain interest

in the market price similar to the setting of Fischer & Verrecchia (2000), Fischer & Stocken

(2004) provide some intuition about a strategic manager’s manipulation behavior when the

speculator is endowed with some information about the incentives of the manager. They

show that if the speculator has more information about the manager’s uncertain interest in the

price, the price would be increasingly responsive to earnings, which ex-ante incentivizes the

manager to bias earnings even more. This in turn yields the outcome that the presence of the

non-fundamentally endowed speculator increases the price efficiency in equilibrium only if

the speculator does not know too much about the manager’s objective relative to the

fundamentals of a firm.

The sole theoretical contribution of an information intermediary possessing private

information about a non-fundamental aspect of the market environment is that of Cheynel &

Levine (2012). Their analyst takes the role of an information seller and endogenously chooses

the price of the non-fundamental information, which he subsequently sells to a subgroup of

investors. They show that more precise non-fundamental information is more widely

disseminated among investors for a smaller fee but (again) it is reasoned that price efficiency

does not necessarily increase with the presence of a non-fundamental information seller.25

1.2.2. The Causes and Consequences of Analyst Coverage Behavior

In this chapter, I will summarize and discuss the state of research (ii) on the determinants

behind the decision of an analyst to initiate coverage and start acquiring information about a

firm and (ii) on some of the documented consequences of analyst coverage for the followed

firms.

Analyst Information Acquisition Costs and Coverage Initiation

In the chapters above I treated the amount of public and private information as if it were

exogenously given. In reality analysts actively collect information in order to predict a firm’s

25

As will be discussed below, Hayes (1998) assumes that the analyst, whose aim it is to influence the trading

decision of an investor, is endowed with some proprietary information about the random shocks in the future

share price. He moreover bases his decision to initiate coverage and collect information on this piece of

information.

Introduction

16

earnings. The analyst coverage literature has a strong focus on the information acquisition

logic because it is argued that the likelihood of coverage initiation is identical to the

propensity of an analyst to start collecting information. Notable theoretical contributions are

Hayes (1998), Mittendorf & Zhang (2005), Langberg & Sivaramakrishnan (2008) and

Fischer & Stocken (2010). Hayes (1998) develops a theoretical framework with a strategic

analyst and an investor, where the latter receives some information from the former and

either buys or sells shares. She shows that an analyst, who aims to maximize stock trading,

has an incentive to acquire more information only for well-performing firms, which is in line

with the empirical evidence of McNichols & O’Brien (1997). She argues that this effect

would especially occur in the existence of short-selling constraints.

There are several notable theoretical contributions on analyst coverage initiation. Fischer &

Stocken (2010) develop a cheap talk model with credibility issues between a biased

information sender (e.g., the “analyst” who wants to stimulate trade) and a receiver (e.g., the

“investor”). The analyst first observes a public information signal (e.g., earnings guidance,

information in stock price changes) and then decides whether to initiate coverage and, in the

case that coverage is initiated, how much private information to acquire. Their results

highlight the non-monotonic relationship between public information precision and private

information acquisition incentives. They predict that the introduction of public information

can yield a suboptimal total information endowment of the investor due to the credibility

costs in the cheap talk structure because these costs may be high enough to dissuade the

analyst from initiating coverage. Closely related to Fischer & Stocken (2010) is the theory of

Mittendorf & Zhang (2005), which extends the perspective established in Chapter 1.2.1.

about biased earnings guidance by means of a principal-agent relationship in the presence of

a third party, the analyst. The authors argue that the manipulation of earnings guidance is a

“necessary evil” because it introduces the uncertainty needed to motivate the analyst to

initiate coverage. Coverage is assumed to benefit the manager because the interaction

between manager and analyst is assumed to be part of the manager’s incentive contract, i.e.,

he benefits when there is analyst coverage. In a third setting, a voluntary disclosure model,

Langberg & Sivaramakrishnan (2008) raise the question of the likelihood of voluntarily

disclosed firm information in the potential presence of an analyst. The authors derive

conditions consistent with the argument that good news is disclosed even if it is imprecise,

whereas bad news is only disclosed if it is sufficiently precise. Regarding the role of analysts,

it is pointed out that good news is more likely to be covered than bad news and that analysts

Introduction

17

may have a preference for well-performing firms. This is generally in line with the

implications of Hayes (1998).26

In all of the mentioned theories an analyst has to pay a cost, which is a linear or convex

function of the information precision of the obtained private signal. Empirical research has

identified a variety of factors that shape these costs of information acquisition. These articles

usually use the notion of forecasting complexity or other analyst-specific factors as a

determinant of the analyst’s information acquisition strategy and thus the likelihood of

analyst coverage:

· Firm-specific factors:27

A first factor can be identified as the complexity of a firm’s

organizational structure. Bhushan (1989) and Frankel et al. (2006) argue that the

forecasting complexity and thus the costs of analyst information acquisition increase

in the number of business segments of a firm. Closely related to this first point is a

firm’s international diversification, which can be mentioned as another firm-specific

factor and which has been empirically shown to be a significant factor in the analyst’s

information acquisition strategy: Larger diversification is associated with a lower

accuracy of forecasts since a geographically specialized analyst has to bear larger

marginal costs of information gathering, which decreases his optimal information

precision level (Duru & Reeb 2002). Thus, both the organizational and the

geographical complexity of a firm increase information acquisition costs and thus

decrease the likelihood of analyst coverage. Moreover, Tan et al. (2011) document the

fact that the mandated change from local GAAP to IFRS led to an increase in analyst

following through increased comparability. Under the assumption that IFRS is

superior in terms of financial information quality, this is in line with the observations

of Lang & Lundholm (1996), namely that firms with a better disclosure quality and

policy show higher analyst following because this lowers the costs of information

acquisition. A last result on firm-specific factors is provided by Barth et al. (2002),

who show that the composition of a firm’s assets is also relevant in the information

acquisition strategy of an analyst. In particular, they claim that analysts would be

drawn to firms with a larger stake in intangible assets, which can be assumed to

26

Empirical research by Branson et al. (1998), Irvine (2003) and Das et al. (2006) indicates that an analyst’s

decision to initiate firm coverage has information content for the stock market. The presented theories are not

able to explain this phenomenon. 27

It shall be noted that the firm-specific factors may directly influence earnings’ priors. However, the

argumentation through costs yields qualitatively similar results, due to the assumption that the implications for

analyst information acquisition are the same for ex-ante less precise earnings or higher information acquisition

costs.

Introduction

18

increase a firm’s valuation uncertainty. This result is somewhat counter to those

discussed before since it implies that analysts would prefer firms with larger innate

information acquisition costs. The authors argue that the brokerage profit component

may explain this result since a higher uncertainty may be associated with larger

brokerage profits.

· Coverage portfolio specific factors: Analysts are assumed to face a decision of either

collecting information about a particular firm or gathering information about a whole

industry or geographical area. This tradeoff is especially relevant in the decision on

the firm coverage portfolio of an analyst, i.e., whether he chooses to specialize in an

industry or geographical area. Therefore, it is straightforward to argue that a

segmental or regional specialization is positively associated with forecast accuracy,

which is consistent with empirical observations (Clement 1999, Malloy 2005, Kini et

al. 2009). In a recent study, Sonney (2009) presents evidence showing that analysts

with a country specialization significantly outperform their counterparts with an

industry focus.

A last topic of high relevance for an analyst’s coverage initiation decision is the special event

of an Initial Public Offering (henceforth IPO) of a firm due to the newly created potential of

underwriter profits. Empirical research documents mixed evidence on the association

between the event of an IPO, analyst following and underwriter affiliation. Rajan & Servaes

(1997) and Cliff & Denis (2004) present evidence showing that higher underpricing

stimulates analyst coverage due to overoptimism. In particular underwriter affiliated analysts

are more likely to initiate coverage (Cliff & Denis 2004) and hence issue high target prices

and more favorable information in the case that the aftermarket performance of the firm is

below prior expectations (James & Karceski 2006). Moreover, it has been claimed that this

favorable coverage would be “discounted” by the market (Michaely & Womack 1999).

Contrary to these findings, Bradley et al. (2008) find evidence neither for the relationship

between aftermarket performance and affiliation of coverage-initiating analysts, nor for the

claim that investors would underreact to affiliated analysts’ information releases.

Consequences of Changes in Analyst Following

The existing literature in finance and accounting has documented a number of different

consequences for firms associated with (changes of) analyst firm coverage. These can be

summarized with the following three main consequences:

Introduction

19

· Facilitation of changes in a firm’s cost of capital: Alfond & Berger (1999) and

Roulstone (2003) use simultaneous equations models to show that higher analyst

coverage is associated with more stock liquidity. As is argued by Lambert et al.

(2011), this implies a decrease in a firm’s cost of capital. Another observation is made

by Chang et al. (2006), who argue that larger analyst following leads to better

financing terms and thus to a decrease in the cost of capital.

· Facilitation of changes in a firm’s disclosure policy: By means of a product market

oligopoly setting with a potential analyst following the firms in an industry, Arya &

Mittendorf (2007) provide theoretical guidance on the role of analysts as information

intermediaries. They show that a firm which benefits from analyst coverage (e.g.,

through a decrease of the firm’s cost of capital), initiates a more revealing disclosure

policy even if this means that it would need to reveal information which can be used

“against” it by its industry competitor. In line with this argument, Yu (2008) provides

evidence consistent with the conclusion that a higher analyst following decreases a

manager’s incentive to bias his information and initiate a more truthful and thus more

informative disclosure policy. It is straightforward to reason that this also has an

indirect effect on the cost of capital (Strobl 2013).

· Changes in the pricing of earnings: The existence of one (or multiple) analyst(s) that

provide firm information to (a part of) the market can have implications for the

pricing of earnings which are reported by the firm. First, it is assumed that analysts

provide new information prior to an earnings release (Asquith et al. 2005). However,

empirical studies show mixed evidence on the relation between the informativeness of

analyst earnings forecasts and reported earnings: Francis et al. (2002) and Frankel et

al. (2006) argue that they are complements (i.e., forecasts reinforce earnings), whereas

Chen et al. (2010) advocate the viewpoint that they are substitutes (i.e., forecasts pre-

empt earnings). Second, other empirical research by Dempsey (1989), Ayers &

Freeman (2003) and Gleason & Lee (2003) provide evidence that higher coverage

accelerates the pricing of earnings information released by the firm. These studies

analyze various reasons for this effect such as an increased motivation for investors to

“be prepared” for the interpretation of an earnings announcement (Dempsey 1989) or

the delegation of this interpretation role to analysts, where more analysts are assumed

to yield a higher responsiveness of the market to corporate financial information

releases due to the increased information processing capacity of the market (Ayers &

Freeman 2003).

Introduction

20

1.3. Contribution

The current dissertation provides three main contributions to the previously discussed

literature, which are based on the interactions of analysts with three major interest groups

(and involve the consideration of particular decisions which have been summarized in the

sequence of decisions in Figure 1), namely (i) peer analysts, i.e., analysts, who follow the

same firm and provide similar services (stages and ), (ii) the followed firm (stage

) and (iii) their clients (stage ), respectively. It is shown that the behavior of an

individual analyst is shaped by and shapes others’ actions in important ways.

1.3.1. Analysts’ Career Concerns, Forecast Biasing and Firm Coverage

Selection

Prior research has shown that career concerns influence equity analysts’ (and other economic

agents’) behavior. In particular, it is argued that intensified analyst career concerns yield

patterns in forecasts consistent with herding on prior expectations (e.g., Scharfstein & Stein

1990, Trueman 1994, Hong et al. 2000, Hong & Kubik 2003).

The first paper of this dissertation contributes to the literature by taking analysts’ herding

incentives as given and analyzing a setting with two equity analysts, who (simultaneously)

decide on the coverage of a firm by means of the provision of earnings forecasts. The

coverage is rewarded by a partially stochastic outcome, which is split in two halves in the

case that both analysts decide to follow the firm and is influenced by the realized forecasts in

a positive manner. However, both analysts obtain private proprietary information on the

stochastic elements of their halves of the reward and decide whether to start gathering

fundamental earnings information and to subsequently release a manipulated forecast. In the

case that they both decide to cover the firm, their performance is not only evaluated by

absolute forecast accuracy (as is the case when an analyst is the sole follower of a firm) but

with absolute forecast precision relative to the respective other analyst.

By means of this setting I am able to show that career concerns impact an individual analyst’s

coverage decision in a non-trivial way. In particular, the inherent effects in the model are

such that increased analyst career concerns keep the analyst from following a firm (i) if the

stock in question is relatively illiquid (i.e., trading volume is not very responsive to changes

in the forecast) and (ii) if the average private information quality of analysts is relatively

high. In addition, the paper presents the result that the decision against the coverage of a firm

(based on non-fundamental information) has information value to the peer analyst.

Introduction

21

1.3.2. Analyst Information Acquisition and the Informativeness of Forecasts

and Managed Earnings

Empirical studies show mixed evidence on the question of whether the informativeness of

pre-earnings analyst information and the information content of (potentially) managed

earnings announcements are substitutes (Chen et al. 2010) or complements (Francis et al.

2002, Frankel et al. 2006). However, since the informativeness of earnings announcements is

of great interest for standard setters, the issue of whether analyst information pre-empts or

reinforces the information content of earnings is of general importance.

In a second setting I show that an analyst who obtains a noisy signal over natural earnings

and wants to predict manipulated earnings chooses his information acquisition effort in such

a way that he matches the noise in the endogenous reporting bias. This in turn also shapes a

manager’s costly earnings management strategy, which optimizes the uncertain objective to

influence the price and moreover the meet-or-beat-analyst-forecast incentive. The paper

provides an intuition behind the empirical results and qualifies meet-or-beat incentives as the

reason for the so called interpretation or disciplining role of analyst earnings forecasts. It is

moreover shown that this role dominates the information role if information acquisition costs

are sufficiently high such that the weight in front of the analyst forecast in a regression of

price on earnings and the forecast shows a negative sign.

1.3.3. Discretionary Analyst Coverage and Capital Market Characteristics

The empirical as well as analytical literature on analyst coverage initiation or abandonment

imposes the presumption that analysts, incentivized by a tradeoff between stimulating

informed trade and reputational costs, would always disclose earnings forecasts if they have

previously gathered information (e.g., Hayes 1998, Cowen et al. 2006, Beyer & Guttman

2011). For example, the contributions of Hayes (1998), Mittendorf & Zhang (2005) and

Fischer & Stocken (2010) assume that once an analyst has started collecting information he

always discloses the information that he acquired and either does or does not distort it (i.e., an

application of an ex-ante coverage perspective). However, it is straightforward to assume that

an analyst has some discretion about whether he wants to withhold or release the already

acquired information, which is more consistent with an ex-post coverage concept.

The third contribution of this dissertation focuses on the discretionary coverage decision of

an analyst, who may provide a private information signal to a subset of the market and thus

chooses whether or not to create private information asymmetry among investors. As a

consequence, informed investors choose their stock trade order based on this information,

Introduction

22

whereas investors who are not clients of the analyst may infer some of the communicated

information from the market price. It is shown that the analyst’s coverage strategy, which

optimizes the tradeoff between the stimulation of informed trade and the costs stemming

from reputational concerns, is crucially dependent on the market’s properties and on the fact

that the analyst only initiates coverage and provides forecasted information if the information

is sufficiently extreme. In particular, it is shown that the likelihood of analyst coverage is a

non-monotonic function of the risk attitude of investors and the market penetration of the

analyst’s information: The likelihood of analyst coverage decreases with investors’ risk

tolerance and with the market penetration of the information (i.e., the fraction of the market

that the analyst directly supplies with the forecasting information) if the liquidity shock is

minor but reputational costs are significant, and increases otherwise.

In an extension with an imperfectly competitive capital market in which a few large traders

are aware that their demand order impacts the share price it is shown that analysts are more

likely to follow more liquid stocks. Prior empirical research such as that of Roulstone (2003)

shows that analyst following increases the liquidity of a stock. However, it has been unclear

whether analysts would consciously pick stocks with larger market liquidity.28

Moreover, this

result runs counter to the argumentation of studies such as those of Bhushan (1989), Ackert &

Athanassakos (2003) as well as Ljungqvist et al. (2007), who advocate the viewpoint that

analysts would prefer to supply large institutional investors with private information. Such an

argument is contradictive because large investors, whose trades substantially impact the stock

price, trade less based on their private information. This consequently impairs the liquidity in

the market.

1.4. Structure of the Dissertation

The cumulative dissertation proceeds such that the following chapters investigate the

previously derived research questions: (i) Analysts’ Career Concerns, Forecast Biasing and

Firm Coverage Selection (Chapter 2), (ii) Analyst Information Acquisition and the

Informativeness of Forecasts and Manipulated Earnings (Chapter 3) and (iii) Discretionary

Analyst Coverage and Capital Market Characteristics (Chapter 4). Chapter 5 provides a

comparative discussion and concludes the dissertation.

28

Beyer et al. (2010, 328) reason as follows: “Further, the direction of causality, that is, whether analyst

following leads to changes in liquidity or changes in cost of capital for firms, or vice versa, is not clear.”

Analysts’ Career Concerns, Forecast Biasing and Firm Coverage Selection

23

2. Analysts’ Career Concerns, Forecast Biasing and Firm

Coverage Selection

Chapter Abstract

In this paper I develop a rational expectations equilibrium model with two equity analysts,

who simultaneously decide whether or not to initiate coverage of a firm by providing

manipulated earnings forecasts. In line with empirical evidence on analysts’ career concerns,

I assume that analysts exhibit herding in the case that another analyst also follows a firm in

order to manage the risk of termination of employment. The main result of the paper is that

the likelihood of coverage initiation is a non-monotonic function of analyst career concerns.

In particular, it is shown that the association between coverage initiation and career concerns

is negative if the average analyst private information precision is sufficiently large and if the

responsiveness of the reward to the forecast is relatively small. In addition, I show that the

coverage initiation decision has information content to stakeholders such as peer analysts.

Keywords: analyst following, herding, forecast bias, non-fundamental information

JEL: D82, G20, M41


24

2.1. Introduction

Equity analyst coverage is of great importance for firms because a larger analyst following is

associated with benefits for the firms, such as a more efficient pricing process (Ayers &

Freeman 2003, Barth & Hutton 2004), a higher market liquidity (Alfond & Berger 1999,

Roulstone 2003), and better equity financing terms (Chang et al. 2006), which altogether

decrease the firm’s cost of equity capital. Companies seem to be aware of the benefits, since

they are even willing to pay analysts for their services (Kirk 2011). Thus, analyst coverage

has a variety of consequences for the firm being covered. However, not a lot has been said

about the factors behind an analyst’s decision to initiate coverage.29

Due to the limited

availability of data on analysts’ decisions against the coverage of a firm, the investigation of

the issue of the causes of analyst following is a theoretical one.

In this paper I develop a symmetric and simultaneous rational expectations equilibrium model

to investigate the influence of analysts’ career concerns as a cause for coverage initiation. In

particular, I consider the decision of two peer analysts to initiate coverage of a particular firm

by means of earnings forecasting. I assume that each analyst receives a partly random reward

(i.e., brokerage profits and subscription fees net of information acquisition and other costs

such as opportunity costs), which is an increasing function of the analyst’s forecast.

Moreover, the analyst observes part of the random reward (i.e., non-fundamental or

proprietary information) and bases his decision on this observation.30

The key feature of the

model is introduced with the assumption that an individual analyst, in the presence of another

analyst who can serve as a forecasting performance benchmark for the first analyst’s

employer, has career concerns and realizes a herding pattern in his forecast because he also

optimizes the ex-ante expected difference of forecast errors (henceforth referred to as

“relative forecast accuracy”).31

A large set of prior research papers argues that analysts’ career concerns would have a

significant effect on their behavior. Scharfstein & Stein (1990) show theoretically that

managers who have reputational concerns partly ignore their private information and make a

29

Only the effect of the provision of earnings guidance on analyst following is already theoretically (e.g.,

Mittendorf & Zhang 2005, Arya & Mittendorf 2007, Fischer & Stocken 2010) as well as empirically (e.g., Chen

et al. 2011) well investigated. 30

The existence of non-fundamental information is in line with the assumption of Cheynel & Levine (2012),

who show that analysts can obtain a positive price from investors for information about random liquidity

demand. Moreover, it is also in line with the analyst’s endowment with proprietary market information in the

model of Hayes (1998). 31

In the other case, where there is only one follower of a particular firm, he optimizes absolute forecast

accuracy, which is in line with the work of Beyer (2008).


25

decision in favor of the consensus investment decision. Trueman (1994) points out that

herding in analyst forecasts can be a consequence of their objective to communicate their

abilities in order to enjoy a better standing with their clients and employers. Empirical

research provides evidence on this relation and shows that analysts are more likely to be

punished with an unfavorable career outcome for being bold in their forecasts relative to their

peer’s forecasting. In particular, existing evidence shows that relative forecasting boldness

yields an increased risk of termination of employment (Hong et al. 2000) or a decreased

probability of being promoted (Hong & Kubik 2003).32

It can be followed that existing

research documents the explicit association between analyst career concerns and the ex-ante

incentive to herd due to analysts’ career concerns. However, the mentioned theories focus on

the signaling aspect of career concerns but do not consider whether these also shape the

observability of forecasts in the first place.33

This is the claim examined by the current paper.

I contribute to the existing literature in three ways. First and most importantly the paper adds

to the theoretical literature on the decision of an analyst to initiate coverage of a firm.

Mittendorf & Zhang (2005) investigate a game between a strategic analyst, who considers

following a firm, and a strategic firm manager, whose aim is to guide expectations downward

with a manipulated management earnings forecast so that the analyst forecast is more likely

to be beaten in the earnings disclosure stage. They show that biased earnings guidance

ensures that the analyst is incentivized to collect information about a firm. Arya & Mittendorf

(2007) establish a firm competition framework with an analyst, whose presence creates an

incentive for firms to have a more revealing disclosure policy. Fischer & Stocken (2010)

focus on an information acquisition and communication game with a biased analyst providing

information to a decision maker, where the latter imposes credibility costs on the former. As

in Mittendorf & Zhang (2005), Fischer & Stocken (2010) focus on the provision of earnings

guidance and its consequences for the strategic behavior of an analyst and show that the

decision maker may be strictly worse off in situations in which the firm provides accurate

public information because this may demotivate the analyst to provide additional information.

This paper is different from Mittendorf & Zhang (2005), Arya & Mittendorf (2007) and

Fischer & Stocken (2010) in a variety of ways. First, their papers have a strong focus on the

strategic interaction between an analyst and either the followed firm or an information

recipient, whereas I investigate the anticipated strategic interaction between peer analysts.

32

Graham (1999), Welch (2000) and Clement & Tse (2005) also find robust evidence on the existence of

analysts’ career concerns and their influence on herding in forecasts. 33

In other words, the incentive to herd may have unintended consequences for analysts’ stock picking behavior.


26

The fact that this strategic interconnectedness can be important is shown by the already

mentioned career concerns papers but also by other empirical studies.34

A second important

difference of the theoretical coverage initiation papers from mine is that the former solely

focus on the role of a firm’s disclosure policy and its effect on analyst coverage. On the

contrary, I identify analysts’ career concerns introduced by forecast herding incentives to be

an influential force in the coverage initiation decision. In particular, career concerns are

shown to impact on analyst coverage in a non-monotonic way due to the existence of two

potentially countervailing effects, the standard deviation of forecast errors effect and the peer

forecast bias effect: It is shown that analysts’ career concerns decrease the likelihood of

analyst coverage if the private earnings information precision is sufficiently large and the

reward-of-coverage response to the analyst forecast is sufficiently low. This effect is most

likely to be observed in cases in which large brokerage houses with a financially well-

endowed, affiliated analyst research department initiate coverage of IPO stocks whose prices

may not be sufficiently responsive to their forecasts (e.g., due to a low liquidity in the

market).

Research on the partly conflicting incentives of analysts is closely related to the first stream

of literature: Equity analysts usually trade off their reputation objective with one that

provokes market reactions and possibly contributes to the brokerage profits of the affiliated

investment houses (e.g., Hayes 1998, Jackson 2005, Beyer & Guttman 2011). The overall

conclusion of this branch of literature is that trading volume generation is a notable source for

the well-known bias in analysts’ forecasts. Hayes (1998) is the only contributor within this

branch of research to consider a notion of analyst coverage. She additionally considers a

similar sequence of events, where she also first assumes the existence of some preliminary

information about the profitability of covering a firm on which the analyst bases his coverage

initiation strategy. The crucial difference between this paper and Hayes (1998) is that I

consider a strategic interaction of two analysts rather than an interaction between an investor

and an analyst. In line with Hayes (1998) I provide some guidance on the relevance of firm

characteristics for analyst following. However, a notably different result to the one obtained

by Hayes (1998) is that increases in private earnings information precision do not necessarily

incentivize analysts to establish a strategy with a higher likelihood of coverage initiation in

the presence of career concerns. Conversely, the individual analyst would choose to “collude”

34

For example, Stickel (1992) and Groysberg et al. (2011) provide evidence consistent with the argument that a

relatively higher number of accurate analysts are rewarded with an enhanced reputation through Institutional

Investor All-Star recognition, which in turn yields higher brokerage profits and higher compensation for the

individual analyst.


27

with the other analyst and settle on a lower level of forecast accuracy when analysts’ career

concerns are introduced into the structure and are assumed to be sufficiently strong.

This paper contributes to the literature in a third way, namely that distinct features (more

precisely, reward features) of the model can be reconciled with the evidence of two distinct

sets of empirical studies. First, the model developed herein confirms that one aspect of higher

market liquidity, namely a larger response to the forecast, incentivizes analysts to follow a

firm. Empirical studies on the impact of analyst following on market liquidity reasoned this

relationship and used simultaneous models to overcome the endogeneity issue (Alfond &

Berger 1999, Roulstone 2003). The second aspect is based on the assumption that analysts

earn a partly stochastic reward of coverage and that a sole follower earns the overall reward,

whereas two followers only earn half of it. Since the latter knows that his peer also observes

part of the noise of his potential share and may decide against coverage initiation he revises

his ex-ante expectations downward in the coverage initiation stage. It follows that the

knowledge about the coverage decision has information value for stakeholders such as

(potential) peer analysts if the existence and observation of non-fundamental information is

assumed. The claim that coverage initiation has information value to stakeholders is not

completely new; Branson et al. (1998), Irvine (2003) and Das et al. (2006) provide archival

evidence that investors react to analyst coverage initiation announcements by means of

subsequent trading. However, I show that in my setting it is the non-coverage that has

information value to peer analysts and I moreover provide the general result that if the part of

the reward for which information is directly or indirectly available increases, analysts may

not necessarily be more willing to initiate coverage due to the downward revision in

conjunction with the non-coverage of the peer analyst.

This paper proceeds as follows: Chapter 2.2 discusses the economic setting. In Chapter 2.3

the model is solved and the unique coverage initiation and forecasting equilibrium is proven.

The comparative statics on the likelihood of analyst coverage are presented in Chapter 2.4,

whereas empirical implications are discussed in Chapter 2.5. The conclusion is displayed in

Chapter 2.6.

2.2. Economic Setting

In this section I describe the economic setting. In particular, I assume the existence of two

analysts with career concerns, who simultaneously choose their coverage and

forecasting policies. Figure 1 provides the sequence of events.


28

Each analyst observes

and announces the

decision to initiate firm

coverage

Each analyst chooses

forecast and

discloses it publicly

Earnings are

disclosed Each analyst observes

private earnings

forecasting signal

Each analyst observes

whether the respective

other analyst

announced that he

would cover the firm

I study a market for coverage initiation which yields a partly stochastic payoff for an analyst

who decides to initiate the coverage of a particular firm and to disclose a forecast on

forthcoming earnings. Analyst receives a profit or loss of covering a firm and disclosing a

forecast denoted by (henceforth “reward”), which shall be defined as the net sum of

brokerage profits, information acquisition and processing costs and costs for business

acquisition and other.35

It is moreover assumed that firm coverage may not always yield a

profit when related costs are considered. In particular, this argument holds true for cases in

which a large initial investment in knowledge is necessary to be able to compete in a

contested market, as is the case in the equity research business. In addition, the portfolio

choice of an analyst can be also highly relevant for the determination of the reward of one

selected project (e.g., Clement 1999, Kini et al. 2009): The benefits of allocating limited

resources to the coverage of a particular firm may be outweighed by the opportunity costs of

not following another (potentially more profitable) firm. Therefore, there are several

economic arguments for the existence of economic loss in the project selection of equity

analysts.

However, an analyst’s reward is assumed to be of the form

, where is a positive constant and denotes the average response

of the reward to the earnings forecast of analyst , . The assumption that excludes

cases in which the average forecast overoptimism bias induced by the reward approaches

infinity.36

This is in line with the results of the theoretical investigations provided by Beyer

(2008) and Beyer & Guttman (2011): They show that the bias in analyst forecasts is restricted

from above and below and is crucially influenced by the trading volume objective.37

35

See Beyer & Guttman (2011) for a theoretical investigation on brokerage profits as an objective of an analyst

who shapes his forecasting strategy. 36

Random terms are denoted with a tilde, whereas conjectures are denoted with a hat. 37

Consistent empirical evidence on the market impact of analyst forecasts is provided by Lys & Sohn (1990)

and Gleason & Lee (2003).

Figure 2: Sequence of Events (Paper 1)


29

Uncertainty is introduced in the payoff structure through the stochastic variables and ,

which are both assumed to be independently standard normally distributed.

captures the relative weight on these two stochastic shocks.38

The assumption of standard

normal distribution is without loss of generality but eases the updating and the uniqueness

proof. The existence of these two shocks and the relative weight is relevant due to the

assumption that analyst observes in the first stage and bases his coverage initiation

decision on the realization of this non-fundamental information. An example for such a piece

of information is an analyst’s estimation of the expected demand of liquidity traders and the

resulting expected market reaction, net of transaction costs. The claim that analysts may have

non-fundamental information of this kind is also made by Cheynel & Levine (2012), who

show that such a piece of information may be of value to others (e.g., shareholders or peer

analysts).39

However, as will be shown below, if the realization of is above a certain

threshold, analyst decides to initiate coverage and announces his decision publicly. At the

same time, analyst also discloses whether he will offer a forecast on the firm’s earnings.

After both have announced their decision, both check whether the respective other analyst

emulates his actions. This introduces the possibility of three cases, one in which there are two

analysts following a firm, another in which only one analyst covers a firm’s earnings, and a

third where no analyst initiates coverage.40

In the next step analyst gathers information and observes a private signal over forthcoming

earnings, , where denotes earnings, which are assumed to be normally

distributed with zero mean and precision (inverse of variance) .41

The s are assumed to be

independently and identically normally distributed with zero mean and precision , and

capture the forecasting errors of the private signals due to lack of information or ability of

analysts.42

After analyst observes his private signal, he formulates his forecast, which

maximizes his utility in the respective case.

As mentioned above, the described setting yields two cases with two different objective

functions and forecast disclosures, and one in which there is a payoff of zero and no forecast

38

The reason for the assumption of two independent random variables that are weighed by is to introduce

some residual uncertainty, which remains after the observation of one of the two variables. This makes

economic sense since it cannot be argued that economic profits are ex-ante certain. However, the model’s

results, with the exception of the one obtained in Corollary 4 (ii), remain qualitatively similar. 39

The fact that investors are interested to pay a fee for non-fundamental information is not necessarily

connected with a higher efficiency of prices, which is shown by Madrigal (1996). 40

It shall be noted that the one analyst case is a feasible situation in the described game because of the

randomization of the rewards and the associated non-fundamental information asymmetry among analysts. 41

The assumption of a mean of zero is without loss of generality. 42

The information gathering stage is assumed for the sake of simplicity to be exogenous.


30

disclosure. In the first case (henceforth the “monopoly case”, abbreviated with “ ”), analyst

is the only follower of a firm and receives the total reward, or .

Moreover, he also considers the expected absolute forecast error as a loss, or ,

which is in line with the evidence of Basu & Markov (2004) on the appropriateness of the

absolute rather than the quadratic forecast error loss function. It follows that the utility of

analyst in the monopoly case is

. (1)

The second case (henceforth the “duopoly case”, abbreviated with “ ”) describes the

situation in which analyst competes with analyst and earns . In this case, I

assume that the forecasting decision is also shaped by analyst ’s career concerns because

there is another analyst , who now provides a benchmark for the employer of with respect

to ’s performance in the forecasting task. Moreover, a considerable body of empirical

research shows that analysts who underperform with respect to the accuracy of their forecasts

relative to that of their peers’ have to face a strictly worse career outcome regarding the risk

of termination or the chance of being promoted (e.g., Mikhail et al. 1999, Hong et al. 2000,

Hong & Kubik 2003, Groysberg et al. 2011). In line with previous theoretical articles such as

those of Scharfstein & Stein (1990) and Trueman (1994), this yields an implicit incentive for

the ex-ante management of the forecast towards that of a peer, who may also follow a

particular firm. In other words an incentive exists to issue a forecast with a herding pattern

that puts more weight on the prior expectations and less on an analyst’s private information.43

Herding is introduced here via a special cost term in the duopoly case which consists of the

absolute of the forecast error net of analyst ’s forecast error, or

, which I henceforth refer to as relative forecast accuracy.44

captures the average career concerns of analysts, where a higher is interpreted as

larger career concerns. The intuition behind the limit of is that the primary objective of a

minimization of one’s own absolute forecast error should also remain present in the duopoly

case, which is generally in line with methodological choices of empirical studies (e.g., Hong

et al. 2000). The utility of analyst in the duopoly case is as follows:

43

It shall be noted that a similar structure can be imposed with the countervailing “anti-herding” pattern found

by Bernhardt et al. (2006) or Chen & Jiang (2006). The results of such a structure are qualitatively identical. 44

The choice of the mean consensus forecast error (which also includes ’s forecast) instead of the peer

analyst’s forecast error as is suggested by Clarke & Subramanian (2006) yields qualitatively similar results.


31

. (2)

An additional assumption has to be introduced to ensure the existence of a unique

equilibrium: I assume that the ex-ante expected utility of analyst in the monopoly case is

always positive before he observes any information, i.e., he would always initiate coverage

where he wouldn’t have to share potential profits or losses of the coverage of the firm and if

the other analyst were to be completely absent. This is a strong assumption, but the

motivation behind it is a rational one and in line with an out-of-equilibrium belief: An analyst

would simply not want to acquire a non-fundamental private information signal about the

potential reward and above all a signal about earnings if the net present value of a coverage

project wouldn’t be positive in the case that he were to receive all of the potential reward.

This assumption leads to a minimum level of , , which will be shown to be strictly

positive for the proposed model structure.

In the final stage, forecasts are disclosed simultaneously and subsequently earnings (as well

as forecast errors) are realized. All distributional assumptions are common knowledge. In this

paper I focus on a pure strategy Bayesian Nash equilibrium with linear forecasting strategies

with conjectures and for monopoly and duopoly cases,

respectively. In addition, the conjectures are assumed to be self-fulfilling.

2.3. Model Solution and Equilibrium

In this section I derive the model solution and prove its uniqueness. The solution to the

proposed setting proceeds by means of backward induction. It shall be noted that the

presented equilibrium will be symmetric (i.e., each analyst imposes the same strategies) due

to identical private earnings information qualities, career concerns and rewards. These

assumptions are necessary to rule out some of the mathematical complexity from the

proposed model structure.

2.3.1. Analysts’ Forecasting Strategy in the Monopoly Case

In this section I derive the solution to the forecasting problem in the event that analyst is the

sole follower of the firm. In the first step the forecast solves the conditional expectation of the

utility defined in equation (1), or

. (3)

The conditional expectation in (3) also takes into consideration the information that the

observation of by analyst kept him from initiating coverage (i.e., the realization


32

is below a certain threshold ).45

This has information value to analyst . This

feature will be important in the derivation of the coverage initiation threshold but does not

influence the forecasting strategy due to the specific form of the reward. The solution of the

endogenous forecast follows the proof of Beyer (2008). Consequently, the problem in (1) is

proportional (i.e., they yield the same solution) to that of the following expression:

.

denotes the probability density function conditional upon the observation of .

The first order condition by means of the standard formula for a differentiation under the

integral sign is obtained and simplified to

, which can be rearranged such that . The left side is the

cumulative distribution function of all values below , whereas the right side is always

larger than since . It follows that , where denotes the

expectations operator, and consequently that by means of the

standard formula for the cumulative distribution function of a normally distributed random

variable.46

After the derivation of the conditional expectation and the conditional variance,

the forecast of analyst in the event that he is the only follower is

, (4)

where and . The term is strictly positive under the

assumptions made and captures the average upward bias of the forecast of analyst in the

monopoly case. Since the second order condition of the problem is always negative, a

maximum is obtained with forecasting strategy .

2.3.2. Analysts’ Forecasting Strategy in the Duopoly Case

The solution of analyst ’s forecasting strategy in the duopoly case is similar to that in the

monopoly case. For this case the problem can be formulated as follows:

. (5)

45

The coverage initiation threshold is endogenously derived below. 46

The Gaussian error function is denoted by .


33

The procedures to derive the solution of this problem in (5) are listed in the Appendix. The

resulting forecast is as follows:

, (6)

where and . Important relations, which will be useful to

prove the uniqueness of the equilibrium, are that and that . Again, the

forecasting strategy is always an optimal solution to the maximization problem in (5).

The forecasting strategy resembles those described in prior theories on analyst herding (e.g.,

Scharfstein & Stein 1990, Trueman 1994): Increasing levels of yield a higher underreaction

to the private forecasting signal and shift the weight towards the priors (which are assumed

to be zero). Moreover, the reward-induced upward biases have certain properties, which are

summarized in Corollary 1.

Corollary 1: The forecast biases have the following properties:

(i) the reward-driven upward bias in the monopoly case is larger than that in the duopoly

case ( ) and

(ii) the upward bias in the duopoly case increases in analyst career concerns ( ).

Proof: The proof is straightforward and thus omitted.

Corollary 1 (i) presents the observation that the reward-induced upward bias is generally

larger in the monopoly case than in the duopoly case. This observation is crucially influenced

by the reward response: The market is assumed to react more strongly to the analyst forecast

if there is no other analyst disclosing a forecast on the firm’s earnings. This incentivizes the

analyst to introduce a larger upward bias in the forecast in the case that he is the sole

follower. More importantly, the upward bias in the duopoly case increases in analyst career

concerns , as is argued by Corollary 1 (ii). Thus, herding initiates an underreaction not only

to the private information but also to a larger upward bias. However, Corollary 1 (i) shows

that even with highly distinct career concerns, the herding bias is smaller than the monopoly

bias. Under the assumption of increasing career concerns the difference between the

overoptimism biases in the respective cases decreases in their magnitude, or

.


34

2.3.3. The Coverage Initiation Threshold

In the last step, the condition which implicitly defines the coverage initiation threshold

, is derived. Due to the absolute forecast error loss functions in the respective case,

the expectation of a folded normally distributed random variable has to be derived for each

respective case. The problem of analyst in the coverage initiation stage is as follows:

. (7)

The probability of the non-initiation of analyst is captured by the cumulative distribution

function . Moreover, analyst already knows at the coverage

initiation stage that analyst will not initiate coverage as long as the perceived reward is

below a certain threshold . He uses this knowledge to update his beliefs accordingly, which

is summarized by Corollary 2. 47

Corollary 2: The anticipated non-coverage of analyst has information value to analyst .

Proof: The proof is straightforward and thus omitted.

Under the assumption of non-fundamental information endowment of analysts, Corollary 2

shows that stakeholders such as a peer analyst can infer (or more precisely anticipate) analyst

’s private information in the decision to initiate coverage of a particular firm. This adds to

the literature on the information value of analyst coverage initiation, which consists of the

empirical studies of Branson et al. (1998), Irvine (2003) and Das et al. (2006). However,

these studies provide evidence of a significant return effect as a reaction to analyst coverage

initiation announcements, whereas I show that it is the non-coverage of the peer analyst that

contains information used by to update his beliefs.

The derivation of the equilibrium condition for the coverage initiation applies the formulae

for the expected value of a folded normal distribution and a truncated standard normal

distribution, and is summarized in the Appendix. It shall be noted that the condition in (7)

holds with equality to obtain the equilibrium threshold level, which has the property that

holds true in equilibrium. As is shown in the Appendix, a unique coverage

initiation threshold exists under the above-mentioned assumptions. Again, an

47

In general, this has some similarities to the voluntary disclosure framework of Verrecchia (1983). However,

the author neither considers non-fundamental information endowment of the sender nor a peer analyst setting

with coverage initiation.


35

important condition is that the total ex-ante expected reward has to be large enough, or

, to incentivize the analyst to acquire fundamental and non-

fundamental information signals.48

This condition stems from the assumption that the

expected utility before observation of a part of the reward is positive in the monopoly case.

Proposition 1 summarizes the equilibrium.

Proposition 1: Given that , a unique equilibrium exists consisting of forecasting

strategies for the respective cases and a coverage initiation threshold with the following

properties:

(i) the forecasting strategy in the monopoly case is and ,

(ii) the forecasting strategy in the duopoly case is and and

(iii) the threshold

is defined by the

following condition:

. (8)

Proof: The remaining parts of the proof are in the Appendix.

The equilibrium defined in Proposition 1 has an important property, which needs further

clarification: The threshold , which is implicitly defined by condition (8), can take any

sign, i.e., it can be positive or negative. Thus, in the tradeoff between the financial factor (the

reward) and his reputational and/or career concern incentives, an analyst would be willing to

engage in a coverage project which may not be profitable in financial terms. Moreover, it is

not possible to argue which one of the cases is more likely. In the next chapter the

equilibrium coverage initiation threshold will be analyzed and the inherent associations

explained.

48

It shall be noted that is always positive due to the assumption of the zero mean of earnings. However, the

results remain qualitatively similar with a non-zero mean.


36

2.4. Analysts’ Career Concerns and Firm Coverage Selection

In this section I present the economic effects in the model and explain their influence on the

behavior of the coverage initiation threshold , where threshold increases are interpreted

with a smaller likelihood of a following by an individual analyst.

The associations in the developed model are mostly driven by a tradeoff between two distinct

effects, one in which both the forecast error standard deviation and one’s own overoptimism

bias are influential (henceforth “standard deviation effect”) and another in which the peer

forecast overoptimism bias is crucial (henceforth “peer effect”). Both effects are obtained as a

result of two model components: The responsiveness of the reward to the individual forecast

and the relative forecast error loss function.49

In particular, the standard deviation effect is represented by the weighted expected cost term,

or . Most of the comparative statics

discussed below, except the main result of this paper, are dominated by the standard deviation

effect. However, the second effect, the peer effect, introduces a countervailing force into the

model. It is represented by the term , and thus occurs solely due to the

herding incentive, which is introduced into the model by the relative forecast accuracy

maximization objective. The analysis of the distinct comparative statics begins with the

variation of the properties of analysts’ information environment and endowment. These

effects are summarized in Corollary 3.

Corollary 3: The coverage initiation threshold

(i) strictly decreases in the earnings precision and

(ii) decreases in the average private information precision if career concerns are

sufficiently low, or , and may increase or decrease otherwise.

Proof: The proof is in the Appendix.

The results in Corollary 3 mostly stem from the behavior of the a priori standard deviations of

the forecast errors, and , for the monopoly and duopoly cases, respectively, and thus

from the standard deviation effect. Corollary 3 (i) provides a first result, namely the

49

For the analysis of I use the implicit function theorem. Every association is described with the claimed

inherent effects, where the behavior of each effect is directly interpreted in conjunction with the sign it has on

the coverage initiation threshold and/or on the likelihood of analyst coverage. For example, when the standard

deviation effect increases in an exogenous variable, this increases the threshold and decreases the likelihood of

analyst coverage.


37

unambiguously positive relationship between the predictability of earnings and the likelihood

of analyst coverage. The result is a standard one and can be found in several other theoretical

articles (e.g., Hayes 1998, Fischer & Stocken 2010).50

It is obtained because both analysts

shift more weight onto the prior expectation and less weight onto the private information

when earnings become more precise. Consequently, the ex-ante expected standard deviation

of forecast errors decreases, which directly decreases the threshold and thus increases the

likelihood of analyst coverage. Moreover, in the duopoly case both analysts “collude” to put

even more weight on priors, which means that the effect is stronger in the duopoly case than

in the monopoly case. However, the current model also considers the peer effect, which

behaves counter to the standard deviation effect but is not strong enough to even ambiguously

render the overall association.

The result that a larger earnings precision is associated with a larger likelihood of analyst

coverage is only partly confirmed by empirical evidence. For example, Tan et al. (2011)

document that the mandated change from local GAAP to IFRS in 2005 – this change is

commonly believed to improve a firm’s mandatory reporting and disclosure quality by

academics and practitioners – would have led to an increased following by globally active

analysts due to a larger level of comparability. Contrary to Tan et al. (2011), Barth et al.

(2002) argue that firms with a higher proportion of intangible assets are followed by a larger

number of analysts because equity analysts would be drawn to the larger measurement risk

and thus a higher earnings uncertainty.

The result in Corollary 3 (ii) is profoundly more sophisticated and states that the likelihood of

analyst coverage increases in the average private information precision if analysts’ career

concerns are sufficiently weak. Contrary to the common beliefs about analyst coverage

initiation and the theoretical evidence of Hayes (1998), the result shows that a larger average

resource endowment and/or ability of analysts, which can both be assumed to lead to more

precise earnings estimation signals, do not necessarily yield a larger incentive to initiate

coverage in the presence of analyst career concerns. The reason for this effect lies in the

behavior of a part of the expected standard deviation of the relative forecast error, namely :

An increase in the private information precision increases both of the analysts’ predictive

accuracy of uncertain earnings (and each other’s conjecture with respect to their private

information endowment) and incentivizes them to allocate more weight to their private

50

Fischer & Stocken (2010) do not directly state this result, but it follows from the information acquisition

strategies in all of their discussed model variants.


38

information signal. This in turn triggers the strength of the herding incentives, which

effectively means that the reallocation of weight towards the analyst’s private information is

undermined by herding. Since this is a symmetric effect (i.e., both analysts have conflicting

incentives) this yields an ambiguous condition. A restricted strength of career concerns

( ) provides a sufficient condition to obtain an unambiguously positive effect. It shall be

noted that the peer effect is again not strong enough to dominate the standard deviation effect.

Corollary 4 highlights the behavior of the coverage initiation threshold when reward

properties vary.

Corollary 4: The coverage initiation threshold

(i) strictly decreases in the reward responsiveness and

(ii) increases in the observable share of the reward if the ex-ante expected reward is

sufficiently large, or , and decreases if .


Corollary 4 (i) establishes the association between the reward responsiveness of analyst

forecasts and the coverage initiation policy. It is shown that an analyst is more likely to

initiate coverage if the reward is more responsive to changes in the forecast. In general, a

larger reward response increases the incentive to include a higher upward bias in the forecast.

It follows that the ex-ante expected forecast errors increase in the respective cases, which in

turn initiates a lower threshold. However, this effect is completely offset by the increased

benefits of a larger reward. What remains is the net effect, which is captured by the standard

deviation effect through the parameters and : Here changes in the responsiveness yield

a negative effect with respect to the coverage initiation threshold . Additionally, the

increase in the likelihood of coverage is even triggered by the otherwise countervailing peer

effect.

It is straightforward to reason that a larger responsiveness of the reward (i.e., trading volume)

is not only influenced by the informativeness of forecasts but also by other factors such as the

liquidity of the stock. Prior empirical research by Alfond & Berger (1999) and Roulstone

(2003) provides results consistent with the effect that a larger analyst following leads to a

higher liquidity of the followed stock. The current model implies an endogenous relationship

between liquidity and the likelihood of analyst coverage initiation, which is addressed by

both studies through the use of a simultaneous equations approach in their empirical


39

investigations. The model’s result stated by Corollary 4 (i) reinforces the validity of this

methodological choice.

Corollary 4 (ii) summarizes another result on the relevance of the structure of the reward for

analyst coverage initiation: Analysts learn only a part, , of the uncertain reward, and the

larger this share, the lower the coverage threshold if is sufficiently small. Non-

monotonicity is introduced by the specific reward setting imposed in this paper: An analyst

receives only part of the reward if there is another analyst to also follow the firm, whereas the

total reward is appropriated to a sole follower. Furthermore, an analyst will potentially be the

sole follower when the other analyst observes a relatively bad signal on the share that he

would receive if he were to also initiate coverage (i.e., the signal realization is below the

endogenous coverage initiation threshold). Since the sole follower anticipates this in the

coverage initiation decision, he revises his expectations of the realization of the respective

reward share downwards. Larger levels of the constant yield the case that the threshold

is shaped to be unambiguously negative and the effect of on the likelihood of coverage

unambiguously positive.

Therefore, this proves that the more an analyst learns about the reward, the less likely it can

be that he initiates coverage because the non-coverage of the peer analyst is interpreted as a

very bad signal, given that the ex-ante expected reward is sufficiently large. This is a

relatively technical result but provides an important insight, namely that the announcement

by an analyst that he will initiate coverage of a firm by means of earnings forecasting may

have information value for others and may be a sign that an analyst possesses non-

fundamental information, which may be also valuable for other stakeholders of the firm. As

the last step of the analysis the main result of the paper is exposed in Corollary 5.

Corollary 5: The coverage initiation threshold increases in analysts’ career concerns if

the average private information precision is sufficiently large and the responsiveness of the

reward to an analyst forecast sufficiently small, or and , and decreases

otherwise.


Corollary 5 documents the behavior of the threshold with respect to analysts’ career

concerns and thus the main result of this paper. In particular, it is claimed that the threshold

is a non-monotonic function of . In this non-monotonicity, the parts in the expected

utility function stemming from the duopoly case of both the standard deviation effect and the


40

peer effect are contributive. It is straightforward to show that the standard deviation effect

unambiguously decreases in career concerns, which in turn decreases the threshold level

and increases the likelihood of coverage. The second effect, the peer effect, is conditionally

counter to the standard deviation effect if . In the cases in which the overall

association between career concerns and the likelihood of analyst coverage initiation are

positive. Furthermore, Corollary 5 shows that analyst following may even be deterred by

increases in analyst career concerns if not only the average private information precision is

sufficiently large ( ) but if also the average reward response is sufficiently small ( ).

2.5. Empirical Implications

The model developed and analyzed above has several empirical implications, which will be

discussed in this section of the paper. It shall be noted that some of the predictions have

already been confirmed by existing empirical research, whereas others (including the main

result of this paper) are yet to be investigated. Due to the assumptions that analysts announce

their decision to initiate coverage and disclose their forecasts simultaneously, and that the

reward may also be derived from brokerage profit commissions, the implications of the

presented model are most likely to be testable with firms performing an Initial Public

Offering and with analysts affiliated to one of the underwriters that accompany this process.

A first implication of the model represents the opportunity for brokerage profit stimulation.

Firms that perform poorly in the aftermarket are more likely to be covered with favorable

analyst reports. The favorability comes into play because affiliated analysts should be less

likely to issue unfavorable news about a firm which is a client of the affiliated underwriter

due to future brokerage deals from increases of capital. Existing empirical research provides

evidence which is in line with this prediction: Underwriter-affiliated analysts in particular are

more likely to initiate coverage and publish favorable information such as high price targets

or strong buy recommendations if the IPO firm’s stock shows poor aftermarket performance

(e.g., Rajan & Servaes 1997, Cliff & Denis 2004, James & Karceski 2006). In addition,

Michaely & Womack (1999) argue that the information in this favorable coverage would be

discounted by the market participants.

Further predictions can be made based on the overoptimism biases in the respective cases,

which are essential for the results of this paper. It has been shown by Corollary 4 (i) that the

reward response to forecasts crucially influences not only the forecast bias but also an

analyst’s coverage initiation strategy through the bias. As mentioned above, an influential

factor that may shape the reward response can be identified as the stock’s liquidity:


41

Observable forecast biases are positively related to market liquidity and are partly the result

of self-selection. In general, the self-selection part of the prediction is in line with the

theoretical result of Hayes (1998) and also confirmed by the evidence provided by

McNichols & O’Brien (1997). However, what has still not been empirically investigated is

how market properties such as liquidity mediate between overoptimism bias and coverage

initiation.

Another prediction, which is closely related to the latter, is the observation that the forecast

bias is always strictly larger in the monopoly case than in the duopoly case, as is proposed by

Corollary 1 (i). This result is obtained because the reward response is assumed to be halved in

the latter case, whereas a single analyst following a firm obtains the maximum available

response. Under the presumption that a larger analyst following yields to a decrease in the

responsiveness of the trading volume to each individual forecast, this result is in line with the

evidence presented by Lys & Soo (1995). They show that the ex-post forecast error is a

decreasing function of the number of analysts following the respective firm, where the

number of following analysts should represent forecasting competition among peer analysts.

A last empirical implication, for which to the best of my knowledge empirical evidence is

still missing, can be derived in conjunction with the main result of the model: Analyst

coverage initiation is shown to be a non-monotonic function of analysts’ career concerns due

to the tradeoff between the standard deviation effect and the peer effect. The following claim

can be formulated: Coverage by analysts with larger career concerns is more likely if the

private information precision is sufficiently large and the reward response is sufficiently low.

It shall be noted that such a situation corresponds to a setting in which a brokerage house

with a large resource endowment allocated to the analyst research department covers a stock

with a relatively low level of liquidity.

2.6. Conclusion

This paper develops a symmetric and simultaneous move rational expectations equilibrium

model with two analysts who strategically initiate coverage and choose their forecasts

depending on whether only one or both initiate coverage. The crucial feature of the model is

that they both exhibit career concerns in the case that the peer analyst also initiates coverage.

As is documented by theoretical and empirical investigations on career concerns, analysts are

assumed to herd in their forecasts in the presence of another analyst who also follows a

particular firm.


42

The paper shows that analyst career concerns have a non-monotonic effect on analysts’

coverage initiation strategy and that the likelihood of coverage is a decreasing function of

reputational concerns if the average private earnings information precision of the analysts is

sufficiently large and if the reward response to analyst forecasts is low. It is possible that this

association, which is the result of the tradeoff between two distinct effects, namely the

standard deviation effect and the peer effect, is most likely to be observed with analysts who

are affiliated to large brokerage houses that decide on the coverage of low liquidity stocks.

The model also provides other highly relevant results. As reasoned by prior empirical

research, endogeneity exists with respect to liquidity and analyst following, where high

liquidity (here represented by a high reward response) increases the likelihood of coverage.

Moreover, the model shows two further results, which partly oppose prior research and

commonly held views. The first is that increased private earnings information precision

(which represents higher resource endowment and superior forecasting skills) does not

necessarily yield a larger likelihood of analyst coverage due to the existence of career

concerns. Second, I show that more information about the reward does not necessarily yield

more frequent following because of the information value of non-coverage of a potential peer

analyst.

There are several limitations of this paper, which arise mostly from the associated

mathematical complexity of the necessary theoretical structure. First and most notably is the

exogenous nature of analysts’ reward of coverage and, closely related to this, the market

consequences of coverage initiation. It is straightforward to argue that the endowment with

non-fundamental private information has pricing and thus reward implications as has been

claimed by prior empirical research (Branson et al. 1998, Irvine 2003, Das et al. 2006).

Second, I neither present a variant of the model in which there is asymmetric information

endowment and/or differential preferences across analysts, nor do I discuss a sequential

coverage initiation and/or forecast disclosure structure. Thus, several extension opportunities

of the developed framework exist which are left for future research.

Appendix (Paper 1)

Proof of Proposition 1

Parts of the proof of Proposition 1 are in the main text. However, some more clarifications

are needed. These are presented in the following four Lemmas.


43

Lemma 1: The derivations of the conditional expectation and variance in the monopoly case

use the well-known formulae of the first two moments of a multivariate normal distribution

and can be written as follows:

and

. Plugging in these moments in condition and

rearranging yields the forecast in equation (4).

Lemma 2: The solution of the problem depicted in equation (5) uses the property that a sum

of normally distributed independent random variables is again normally distributed. Thus, we

can define and perform the same procedures as in the monopoly case.

The problem is rewritten to the following expression:

The first order condition of this expression with respect to is

, which can be rearranged to .

The conditional expectation and variance are

and , respectively. Imposing the conjectures yields

and . Since

the model assumes symmetry across analysts it follows that and that

. This property yields . Plugging this in the equation for

and solving for results in .

Lemma 3: The derivation of the conditional expectation of the utility of analyst in the

coverage initiation stage uses the standard formula of a folded normal distribution with a non-

zero mean. The condition in (7) can now be rewritten to


44

.

Based on the property that this reduces to

.

To derive the threshold this condition must hold with equality, and since

due to symmetry the equilibrium condition reduces to

.

Lemma 4: For the uniqueness proof of the coverage initiation threshold it is sufficient to

prove monotonicity of in . The first order condition of with respect to after

some algebraic manipulations and by means of the definition of the variance of a truncated

standard normally distributed random variable reduces to

.

In order to establish that the first order condition is unambiguously positive it is necessary to

show that . Recall that it is assumed that the ex-ante utility of an

analyst in the monopoly case is positive. This utility is and

can be rewritten to a minimum level of , which is subsequently

imposed. In the next step is rearranged to


45

.

The right hand side of this condition has the following properties:

and

.

is one limit of , if the right hand side is monotonically decreasing in . Since this is the

issue in question, it is sufficient to show that . After observation that as well as

are decreasing functions in can be imposed, which - after some algebraic

manipulations - yields the following condition:

It is important to note that .

With this property in mind it is a straightforward exercise to show that this condition holds

true for all . However, it shall be noted that may be positive or negative, whereas

is strictly negative. This proves the existence of the unique coverage initiation threshold

. ■

Proof of Corollary 3

The proofs of Corollaries 3 through 5 are performed by means of the implicit function

theorem. It is already known from the uniqueness proof that . Thus, every effect in

an exogenous variable , , is proportional (i.e., they have the same sign) to .

Corollary 3 captures the effects based on the information environment and endowment of

analysts. The first order condition with respect to , , can be written as follows:

.


46

This condition is positive because and the overall effect is

negative, or , which proves (i).

The proof of result (ii) again starts with the direct effect:

.

This condition can be positive or negative due to the ambiguity of the term

, which is positive if . ■


The first order condition of with respect to the reward responsiveness parameter is as

follows:

.

As is straightforward to see, the condition is positive which implies a negative relation

between and .

The first order condition with respect to the observable share of the reward is

. This condition can be positive or negative. However, a unique threshold

can be found in the reward constant , . Recall, that . Again is rewritten

as in Lemma 4 and plugged into the above first order condition, which results in

.

The limits of this rewritten first order condition with respect to are

and .

It follows from Lemma 4 that the latter condition is positive. To prove monotonicity, it is

sufficient to show that since . Thus, , where can


47

be positive or negative. After algebraic manipulations and by means of the formula of the

variance of a truncated standard normally distributed random variable this condition is

rewritten to , which is

always positive. It follows that there must exist a unique threshold such that for

and for . ■


The proof of Corollary 5 proceeds like the previous ones and begins with the first order

condition of with respect to :

.

The overall effect of with respect to is proportional to

. The limits of

with respect to a variation in are as follows:

and

. The former limit is always positive if

. Under this assumption the latter limit must be negative. Moreover,

, which is unambiguously negative. This establishes the existence of a

threshold . Moreover, can be shown to be always negative if . ■

Analyst Information Acquisition and the Informativeness of Forecasts and Managed Earnings

48

3. Analyst Information Acquisition and the Informativeness of

Forecasts and Managed Earnings

Chapter Abstract

Mixed empirical evidence exists on whether information provided by analysts such as

earnings forecasts complements or rather substitutes the information contained in corporate

earnings. In this paper I develop a model in which an analyst endogenously acquires costly

information to forecast the fundamental information contained in a subsequently released and

strategically manipulated earnings announcement. By means of this setting I show that the

analyst forecast fulfills two roles, an information role (i.e., a substitutive relationship) and an

interpretative role, i.e., it complements earnings in that the market uses the forecast to back

out a part of the reporting bias. Both roles are established through the incentives of the

manager: The information role of analyst information is present due to the assumption of an

uncertain price interest of the manager, whereas the interpretation role is established through

the manager’s incentive to meet or beat the analyst’s forecast. In a regression of share price

on earnings and the forecast it can be shown that a positive forecast response coefficient and

thus the dominance of the substitutive role of analyst forecasts is only obtained if information

acquisition costs are sufficiently low; otherwise the complementary relationship prevails.

Keywords: analyst forecasting, earnings manipulation, relative informativeness

JEL: D83, M41


49

3.1. Introduction

Sell-side equity analysts are crucial for the supply of investors with information about a

firm’s financial performance. Beyer et al. (2010) show that analysts are the second most

important information source after the firm itself. Benefits of a larger analyst following are

manifold and range from a faster pricing process of firm information (e.g., Ayers & Freeman

2003, Gleason & Lee 2003) to better equity financing terms (e.g., Chang et al. 2006).

However, a long-lasting, mostly empirically driven debate exists as to whetther previously

disclosed analyst forecasts substitute or rather complement the informativeness of

subsequently released earnings announcements. In particular, Lang & Lundholm (1996),

Francis et al. (2002) and Frankel et al. (2006) provide evidence supporting the argument that

the informativeness of an earnings announcement is positively related to the price reaction to

an analyst earnings forecast, where this can be found either when the analyst disclosure is

preceded by or precedes firm disclosure. Contrary to the above mentioned complementarity

studies, Chen et al. (2010) address additional endogeneity problems in this context and

highlight an overall negative association between analyst reports and earnings

announcements, arguing for a pre-emptive rather than a complementing relationship. Their

results are consistent with the effects described in sequential disclosure models (e.g.,

Holthausen & Verrecchia 1988, Kim & Verrecchia 1991, Demski & Feltham 1994). Under

which conditions a complementing or a substitutive association dominates is the research

question of the present paper.

By means of a theoretical model with an analyst who endogenously chooses his information

acquisition effort and a firm manager who manipulates earnings based on an uncertain price

interest and meet-or-beat analyst forecast incentives (henceforth “MOB incentives”) I show

that the analyst’s information takes two roles in the presence of endogenous earnings

management: First it provides fundamental information and competes with the earnings

announcement in the firm valuation process, which represents a substitutive or pre-emptive

association (i.e., information role). Further, the information is also utilized by the market to

rule out a part of the reporting bias contained in earnings, which yields a complementary

relationship (i.e., interpretation role). It is shown that both roles can adversely dominate the

valuation process under certain conditions: If information acquisition costs are low, then the

pre-emptive relationship dominates, whereas when these are high the analyst forecast mainly

complements the pricing of reported earnings.


50

Besides providing some guidance for the mixed empirical evidence, I also contribute to the

literature on analyst information acquisition and communication. Hayes (1998) establishes

the claim that trading commission incentives affect the production of information. She finds

that analysts are more likely to initiate firm coverage and acquire information if the firm in

question is financially successful. This is even triggered in the presence of short selling

constraints. Fischer & Stocken (2010) use a cheap talk structure to show how an analyst

information acquisition strategy is affected by publicly available information prior to an

analyst’s coverage initiation. They show that public information may deter analysts from

covering a firm due to the existence of credibility costs. Contrary to the mentioned papers, I

consider an ex-ante setting to highlight the reliance of an analyst’s information acquisition

efforts on the uncertain parts of the reporting bias contained in future earnings. It is shown

that analysts decrease the depth of firm coverage (i.e., they acquire less information) if there

is less earnings uncertainty and less uncertainty about the incentives of the manager. This is

generally in line with the observations of Barth et al. (2002), who argue that analysts would

be drawn to uncertainty.

Another field of contribution is the interaction between a strategic analyst and a strategic firm

manager. As is argued by the archival studies by Lys & Sohn (1990), Ivkovic & Jagadeesh

(2004) and Asquith et al. (2005), analyst forecasts are highly relevant before an earnings

announcement because the announcement is jointly processed with the forecast. In particular,

forecasts are believed to provide an additional source of earnings expectation (O’Brien 1988),

which in turn incentivizes the manager to try to meet-or-beat the analyst forecast by means of

an earnings bias because this is rewarded with a market premium (e.g., Kasznik & McNichols

2002, Matsumoto 2002, Bhojraj et al. 2009). Hence, an endogenous relationship exists

between analysts’ forecasting decisions and managers’ reporting decisions. Theoretical

insight into this endogenous interrelation is provided by Beyer (2008). In her model the

analyst faces the decision of whether to revise a forecast based on more precise private

information about natural earnings or to withhold the information in order to not motivate

earnings management. Contrary to Beyer (2008), the primary focus of my model is not the

voluntary revision decision, but the information gathering of the analyst, given that the

manager manipulates natural earnings driven by both MOB incentives and an uncertain price

interest, where the latter is not considered by Beyer (2008). Contrary to this paper and those

of Fischer & Verrecchia (2000) and Ewert & Wagenhofer (2005) I show that increases of

marginal reporting bias costs (e.g., through larger litigation costs) may not increase the


51

Market processes

earnings in

conjunction with

analyst forecast and

sets the market price

accordingly

Manager observes

natural earnings and

the analyst’s forecast

and chooses a level of

bias; manipulated

earnings are disclosed

Analyst disclosures

information

Analyst acquires

information in order to

maximize his expected

reputation

credibility of and consequently the price responsiveness to reported earnings in the presence

of MOB incentives.

The paper proceeds as follows: In Chapter 3.2. I present the economic setting, which is then

solved in the form of a linear, unique equilibrium in Chapter 3.3. The subsequent chapter

provides some comparative statics and Chapter 3.5. summarizes and concludes.


In this chapter I develop a rational expectations equilibrium model in which an analyst

forecasts a managed earnings announcement. The analyst chooses his level of information

gathering based on forecast error minimization to maximize his long term reputation. The

manager observes the forecast and natural earnings and chooses a level of bias that optimizes

her objectives to influence the share price and to meet or beat the analyst forecast. Hence the

properties of the analyst’s earnings forecast and of reported earnings are dependent on each

other through the incentives of analyst and manager. The sequence of events is presented in

Figure 3.

A corporate manager observes “natural earnings” which are assumed to be normally

distributed with zero mean and precision and the analyst earnings forecast . Subsequent to

the observation of the two information signals, he chooses a level of bias, , before he

further discloses the manipulated earnings announcement . The bias maximizes the

following utility function:

. (1)

captures the direct marginal disutility of biasing and represents the manager’s

uncertain interest in the price resulting from, for example, stock or stock option-based



52

compensation.51

However, the price manipulation incentive is assumed to be normally

distributed with mean zero and precision . The mean of the uncertain price interest of the

manager is assumed to be zero without loss of generality.52

An important feature of the model

is the incentive parameter , which captures the manager’s MOB incentive. The function

is quadratic to facilitate the computations and to obtain a closed form solution. The manager

incurs a cost not only when he misses the forecast but also when he exaggerates too much.

This follows Beyer (2008) and is based on the idea that the clean surplus condition must be

fulfilled and that the bias of this period has to revert back in later periods. Meeting the

market’s expectations, which are present through the analyst’s forecast, is assumed to be a

core incentive for firms with a strong interest in their financial market performance. Prior

empirical research shows that firms that meet the market’s expectations are rewarded with a

premium but also have to deal with overly optimistic expectations (e.g., Kasznik &

McNichols 2002, Matsumoto 2002, Bhojraj et al. 2009).

In the first stage, prior to the reporting decision of the manager, the analyst decides whether

he wants to follow a firm and how much information he gathers to maximize the forecast’s

accuracy. Thus, the analyst wants to predict the manipulated report of the manager, who in

turn includes the analyst’s information through the bias in his signal. The analyst publishes a

forecast , where is normally distributed with mean zero and (endogenously

chosen) precision .53

To obtain the optimal precision, the analyst solves optimization

problem

, (2)

where denotes the cost of information gathering. The first part of the analyst’s

objective function benchmarks the accurateness of his forecast with the managed earnings

precision and induces the analyst to collect information. The minimization of forecast errors

relative to a benchmark is a widely accepted way to model reputation incentives in analytical

models on analyst disclosure decisions (e.g., Lim 2001, Fischer & Stocken 2010). The reason

for assuming this association is that higher forecast accuracy should yield better ranking

51

The assumption of an uncertain price interest is in the spirit of Fischer & Verrecchia (2000). 52

All random variables are assumed to be stochastically independent and all distributional assumptions are

common knowledge. 53

The large economic literature on analyst forecasting argues that analysts would always include a bias in their

forecasts. However, the emphasis of this paper lies on the analyst’s information acquisition activity rather than

on his biasing decision. If a notion of biasing would be considered in the discussed setting, it would represent an

adjustment for the private information’s inaccuracy rather than a distortion. The economics discussed in this

paper remain unchanged in such a setting and the results are qualitatively similar.


53

positions, which in turn shapes an analyst’s compensation in a positive way (e.g., Stickel

1992, Groysberg et al. 2011).

After the disclosures of analyst and manager, a risk-neutral, perfectly competitive market

values the firm at its conditional expectation of natural earnings:54

. (3)

This market price not only incorporates every available piece of information but also

rationally conjectures the biasing strategy of each manager type, , and the

information gathering strategy of the analyst, , or .

Moreover, the conjectures are assumed to be self-fulfilling.


In this chapter I solve the model and prove the uniqueness of the equilibrium solution. For

this purpose I introduce the following linear conditions for the report’s bias and the price,

respectively:

(4)

and

. (5)

The model is solved by backward induction and thus starts with the pricing. Since this is a

straightforward exercise, it is omitted. However, prior to the market pricing the manager

conjectures the impact of earnings on the price and solves problem (1). The following

expression for the reporting bias can be obtained:

. (6)

It shall be noted that the term represents the analyst forecast’s error. Applying the

linear conjecture defined in equation (4) results in and .

Using these parameters to solve for the price yields the following pricing function:

.

Imposing the linear conjecture of (5) on this expression yields

54

An intermediate pricing stage in which only the analyst forecast is processed is irrelevant for the proposed

model and is thus omitted.


54

, (7)

and

. (8)

It is straightforward to see that cannot be explicitly solved but only implicitly presented

and that is dependent on . Moreover, it can be noted that both parameters are influenced

by . In the next and last step the analyst’s information acquisition decision solves the

following optimization problem in which the expectation of the forecast error is already

presented in a solved form:

.

Since is a function of , the implicit function theorem is applied to obtain the first order

condition of with respect to . The resulting expression can be simplified by using the

property in (7) and the function can be rearranged to obtain equilibrium condition

, (9)

where . The uniqueness of the obtained solution depends on the

uniqueness of and , which are both implicitly defined by conditions (7) and (9),

respectively. In the Appendix the existence of the unique equilibrium is proven and it is

shown that and are jointly monotonic. Proposition 1 summarizes the equilibrium.

Proposition 1: There exists a unique, linear equilibrium in which and , which

is defined by the pricing parameters in (7) and (8) conjectured by (5), the reporting bias

conjecture by (4), where the parameters are and , and the

information acquisition effort defined in (9).


3.4. Analyst Information Acquisition and the Informativeness of Managed

Earnings

In this section I present some comparative statics and properties of the above developed

equilibrium solution. In doing so, I will especially point out the interrelation between the

price responsiveness to manipulated earnings ( ) and the analyst’s level of information

acquisition ( ) in order to forecast the fundamental information contained in earnings. The


55

discussion starts with a general statement on the relation between analyst information

acquisition and the price-earnings relation .

Remark 1: (i) The price-earnings relation decreases in the information acquisition effort

, or . (ii) The level of information acquisition increases in the price-earnings-

relation , or .

Remark 1 (i) summarizes the observation that the price responsiveness to earnings decreases

in the analyst’s information precision. This is a standard result obtained through Bayesian

updating and well-known from prior literature (e.g., Holthausen & Verrecchia 1988, Kim &

Verrecchia 1991, Demski & Feltham 1994). However, since the earnings response coefficient

is also contained in the reporting bias (Fischer & Verrecchia 2000), a decrease of the

coefficient also decreases the manager’s incentive to manipulate earnings. Hence, this is in

line with Yu (2008), who shows that a greater analyst coverage (and thus a larger aggregate

) decreases the manager’s incentive to manage earnings based on his price interest.

However, Remark 1 (ii) states that an analyst acquires more information if earnings are more

value relevant, which generally establishes the main tension in the interaction between the

analyst, the manager, and the market.

This tension exists in the presented setting because the analyst’s fundamental information

acquisition is influenced by his prediction of the noise added by the earnings bias. Since the

latter is endogenous and a distinct part of the analyst’s objective function, he anticipates the

effect of additional information acquisition on the bias and considers it in choosing the level

of costly information gathering.55

This aspect of the model establishes the information role of

analyst forecasts and thus suggests a pre-emptive association between analyst information

and the informativeness of an earnings announcement (e.g., Chen et al. 2010). Furthermore,

the value relevance of earnings is also decisive for the manager’s bias such that an increased

information acquisition level also increases the precision of the earnings signal since it

decreases the magnitude of the bias in equilibrium.

However, the decision of the analyst shapes the reporting bias in a second way because of the

existence of MOB incentives: These serve as an additional source of biasing costs which are

imposed on the manager for not meeting the analyst forecast. In particular, this incentivizes

55

In general, this is similar to the analyst forecast manipulation mechanism developed in Lim (2001), because

the manipulation in his setting also directly influences the forecast’s precision. However, Lim (2001) considers

neither a strategic manager, nor a difference between natural and manipulated earnings, which influences the

strategic decision of an analyst in the way discussed in this paper.


56

the manager to include the analyst forecast’s error into the bias. In the valuation process of

the market this additional source of noise in the reporting bias is partly ruled out since

earnings are jointly processed with the analyst forecast. This generally yields a

complementing role of the analyst forecast and is henceforth referred to as the interpretation

role of analyst information (e.g., Lang & Lundholm 1996, Francis et al. 2002, Frankel et al.

2006). In his decision to acquire information the analyst considers these two aspects, which

yields the behavior of the analyst (and the earnings response coefficient) summarized in

Corollary 1.

Corollary 1: The analyst’s information acquisition effort (the earnings response

coefficient )

(i) decreases (decreases) in the earnings precision ,

(ii) decreases (increases) in the precision about the uncertain price interest of the manager ,

(iii) decreases (increases) in the manager’s MOB incentive

(iv) and is ambiguous (is ambiguous) in the manager’s direct biasing costs .


The analyst’s information acquisition effort is shaped by a dependence on the manager’s bias,

where more uncertainty generally incentivizes the analyst to acquire more information, as is

evident by Corollary 1, parts (i) and (ii). This is in line with the empirical observation of

Barth et al. (2002), who argue that analysts would be drawn to uncertainty. However, these

two associations are obtained for different reasons: An increase in the earnings precision

directly decreases the earnings response coefficient, which in turn decreases the noisy part in

the reporting bias. In comparison to the analyst forecast, the earnings signal becomes more

precise, which discourages the analyst from collecting information. This is established

because the relative impact of the analyst on the earnings response coefficient decreases with

increasing earnings precision.

The second effect, namely that a lower uncertainty with respect to the manager’s price

interest yields a decreased analyst information acquisition level, stems from two partially

countervailing effects. First, the earnings announcement receives a larger weight in the

pricing, which in turn increases the endogenous part of the bias (see Corollary 1 (ii) for the

behavior of the earnings response coefficient). However, the second, dominant effect is

present through the expectations of the analyst: A decreasing uncertainty with respect to the

manager’s price interestedness overall decreases the magnitude of the noisy part of the bias.

Due to the multiplicative association, the overall association is such that increased certainty


57

about the manager’s price incentives discourages the analyst from acquiring more

information.

The results of Corollary 1 (iii) on the behavior of the value relevance of earnings and the

analyst’s information acquisition effort with respect to the MOB concerns of the manager can

be explained as follows: The higher the weight on the MOB incentive relative to the direct

marginal costs of manipulation , the higher the weight on the analyst forecast’s error in the

earnings bias and the lower the weight on the (noisy) capital market part of the bias. In turn,

the lower the weight, the more value relevant are earnings since this part of the bias can be

ruled out by the market due to the publicity of the analyst forecast. However, the overall

noise of the earnings announcement decreases such that the analyst strategically countersteers

by expanding less effort.

Corollary 1 (iv) contains another interesting result: Contrary to the classical effect

documented by Fischer & Verrecchia (2000) and Ewert & Wagenhofer (2005) that biasing

costs (i.e., increased penalty for earnings manipulation) would increase the value relevance of

an earnings announcement, the proof shows that the presence of MOB incentives renders this

effect ambiguous. This is the case because the analyst observes an overall decrease of the

reporting bias and particularly a lower weight on the noisy price interest part, which directly

increases the precision of earnings relative to the forecast. As a reaction, the analyst cuts back

on his information gathering activities to outbalance a part of the increased earnings

information precision. Unfortunately, due to the complexity of the model no clear cut

threshold values can be found for which the conditions are unambiguous. However, it can be

stated that the earnings response coefficient may decrease and the information acquisition

effort may increase in the biasing costs if both are already very large.

Another interesting feature of my model is that the forecast response coefficient can be

either positive or negative. This is due to the existence of the manager’s MOB incentive

because he automatically includes the analyst forecast’s error in his bias. The joint processing

of forecast and reported earnings enables the market to rule out a part of the reporting bias

(namely the one that is caused by the presence of MOB incentives). This yields a negative

part in the coefficient and establishes the interpretation role of analyst information. The

positive part of the coefficient evolves naturally because the analyst’s forecast is a

fundamental information source for the market. The next corollary is devoted to discussing

the two roles of the analyst forecast in the presented model by means of the evident

ambiguity of the forecast response coefficient .


58

Corollary 2: There exists a unique threshold such that if and

if .


The forecast response coefficient best represents the two roles of the analyst forecast in the

pricing of earnings: On the one hand the analyst provides information to the market which

pre-empts the information content of earnings; on the other hand the forecasting information

imposes an additional cost on the manager to fall short of the analyst forecast. This in turn

leads to the situation that the manager incorporates the forecast error of the analyst into

reported earnings. In conjunction with the analyst forecast, the market is able to infer a part of

the reporting bias and is thus able to set the price such that the earnings report is more value

relevant. It is shown by Corollary 2 that the sign of the forecast response coefficient, and thus

whether the interpretation (negative sign) or the information role (positive sign) dominates

depends on the analyst’s costs of information acquisition: If costs are low (high) the

association between the relative informativeness of earnings and analyst forecast is

complementary (substitutive).

By means of this result I am able to shed some light on the mixed evidence presented by

several empirical studies on the role of analyst information for the informativeness of

earnings information: Lang & Lundholm (1996), Francis et al. (2002), and Frankel et al.

(2006) provide empirical evidence supporting a complementing association between earnings

and forecast value relevance, whereas Chen et al. (2010) focus on the involved endogeneity

problem and show substitutive results. However, under the assumption of bilateral

endogeneity, as has been claimed by Chen et al. (2010) the results can be reconciled to the

presented model’s properties. The result contained in Corollary 2 suggests that a

predominantly complementary association can exist (a) if the manager has MOB incentives

and (b) if analyst information acquisition costs are sufficiently high.

3.5. Conclusion

In this paper I highlight the interdependence of an analyst’s decision to acquire information

based on reputation incentives and a manager’s decision to manipulate earnings based on an

uncertain price interest and the incentives to meet-or-beat the analyst’s forecast. The model’s

purpose is to provide theoretical guidance to reconcile and understand the mixed empirical

evidence on the association between the informativeness of analyst information and the

informativeness of (potentially manipulated) earnings announcements. It is argued that the

forecasting information serves two roles, where one is the information role (suggesting a


59

substitutive relationship) and the other is the interpretation role (supporting a complementary

relationship). It is shown that the latter is established through the existence of MOB

incentives since the manager includes the analyst forecast’s error into the reporting bias,

which is subsequently ruled out by the market and yields a negative sign in the pricing

equation.

By means of the developed model it is moreover shown that the dominance of the

information vs. the interpretation role crucially depends on the analyst’s costs of information

acquisition: If these are low then the information role dominates and yields a positive forecast

response coefficient (i.e., a pre-emptive association), where the opposite is true for high

information acquisition costs (i.e., a complementary relationship). Moreover, it is shown that

analysts generally acquire more information if there is more uncertainty in the setting or if

MOB incentives are not very distinct. Contrary to existing theories on earnings management,

I additionally show that increases of marginal biasing costs may not increase the credibility of

and consequently the price responsiveness to reported earnings in the presence of MOB

incentives.

Appendix (Paper 2)


The uniqueness proof of the equilibrium summarized in Proposition 1 starts with defining the

following two functions which follow from rearranging equations (7) and (9):

and

.

The proof of joint monotonicity is performed by the derivation of the determinant of matrix

. The derivation of the first order conditions is a tedious but

straightforward exercise and yields the following solution after algebraic manipulations by

means of equations (7) and (9):

.


60

The derivation of the determinant yields .

Thus, and are jointly monotonic. Moreover, observe that the implicit definition of in

equation (7) suggests that must be strictly positive if is non-negative, which is ruled out

by assumption. In particular, must be in the interval . For deriving the limits let

in the right hand side of equation (7) approach zero and positive infinity respectively, or

and . This can be done because is

monotonic in , which can also be proven by means of the implicit function theorem

( ). Moreover, it is straightforward to derive the limits of

by means of : since is a finite

expression and since . Together with the joint

monotonicity property this proves the existence of unique levels of and . ■


The proof of Corollary 1 applies the implicit function theorem with two equilibrium

conditions. In particular, the effect of changes in an exogenous variable can be derived by

solving the following system of equations:

.

The solutions can be obtained by means of Cramer’s rule and can be presented as follows

(after some algebraic manipulations):

,

,

,

,


61

,

,

,

.

In addition, the first order conditions with respect to the information acquisition cost are as

follows:

and

.

These will be needed for the proof of Corollary 2. ■


The sign of can be shown to depend on the magnitude of the marginal information

acquisition costs . Since and it is sufficient to provide the

behavior of in . First, note that and that

. Second, the first order condition of with respect to is

.

It follows that there must exist a unique level such that if and

if . ■

Discretionary Analyst Coverage and Capital Market Characteristics

62

4. Discretionary Analyst Coverage and Capital Market

Characteristics

Chapter Abstract

In this paper I develop a model with an equity analyst and two groups of investors competing

over information in a perfectly competitive capital market, one of which the analyst may

provide private information to if he decides to initiate coverage. Thus, the analyst serves as a

potential source of information asymmetry among investors, which in turn is thought of as a

source of stock trade by traditional asset pricing models. After observing fundamental

information the analyst, incentivized by a tradeoff of trading volume generation and

reputational concerns, establishes a coverage policy in which coverage is always initiated

when reputational costs are sufficiently low or when the forecasting information is

sufficiently extreme and when reputational costs are large. The main contribution of the

perfectly competitive market setting is to show that the likelihood of analyst coverage is

shaped in a non-monotonic way (a) by the (potential) market penetration of his information

and (b) by the risk aversion of investors. In an extension with an imperfectly competitive

market I show that analyst coverage is less likely for stocks with lower market liquidity.

Keywords: selective disclosure, institutional ownership, market liquidity

JEL: D82, G20


63

4.1. Introduction

Sell-side equity analysts provide fundamental information to their clients by means of

forecasts and other information reports such as recommendations or target prices and thus

serve an important information role in modern capital markets (e.g., Gleason & Lee 2003,

Frankel et al. 2006). Prior theoretical as well as empirical research has claimed that the

institutional setting in which analysts are embedded motivates them to (discretionarily)

release reports that stimulate trade on a stock – especially when analysts are affiliated to

brokerage houses (e.g., Cowen et al. 2006, Groysberg et al. 2011). Prior theoretical research

has had a strong focus on highlighting the relevance of analysts’ tradeoff of strategic trading

commissioning incentives with reputational costs for the properties of their forecasts (Jackson

2005, Beyer & Guttman 2011), whereas it is not completely clear how these objectives

impact their coverage policy.56

In particular, it is yet to be studied how the characteristics of

the financial market and its participants shape an analyst’s decision to initiate or abandon the

coverage of a stock if the analyst is interested in stimulating trade by means of providing

private information.

Analyst firm coverage is of high general relevance for the covered firms because this is

associated with various benefits. In particular, existing empirical evidence shows that a larger

number of analysts following a firm yields an accelerated pricing process of earnings

announcements (Ayers & Freeman 2003, Barth & Hutton 2004, Piotroski & Roulstone 2004),

better equity financing (Chang et al. 2006, Bowen et al. 2008) or a more successful initial

public offering (Rajan & Servaes 1997, James & Karceski 2006). Further, firms seem to be

aware of these benefits and hire paid-for analysts to provide firm coverage (Kirk 2011).

In this paper I develop a rational expectations equilibrium model which utilizes the logic

developed in the voluntary disclosure literature and applies it to an analyst setting. In

particular, I consider the case in which an analyst decides on whether or not to initiate

(abandon) coverage and release (withhold) an exogenously given earnings forecasting signal

to a subgroup of risk averse investors (henceforth referred to as “informed”). The other

investor group (henceforth referred to as “uninformed”) obtains a noisy measure of the

forecasting signal through the observation of the share price.57

Furthermore, the analyst is

assumed to serve as the only source of private information and thus information asymmetry

56

The only exception is Hayes (1998), which will be discussed below. 57

The results of this paper are also applicable to the setting in which analysts stop the coverage of a firm. The

evidence presented by Rao et al. (2001), Tucker (2010), and Mola et al. (2012) documents that the situation in

which analysts stop the coverage of a firm occurs frequently. However, for convenience I use either “coverage

initiation” or “likelihood of analyst coverage” for the remainder of this paper.


64

but in turn incentivizes all investors in the market to trade a firm’s stock (e.g., Grossman &

Stiglitz 1980, Kim & Verrecchia 1991). In order to maximize his utility the analyst

discretionarily decides on whether or not to disclose an exogenously given forecast. In this

decision he trades off the incentive to stimulate informed trade in the firm’s stock with the

objective to minimize reputational costs (caused by the inaccuracy of his forecast).58

If

reputational costs are significantly high, the natural outcome of such a setting is that an

analyst discloses only certain realizations of forecasts, namely the ones that are sufficiently

“extreme”, in order to stimulate trade. This corresponds to the argument brought up by

McNichols & O’Brien (1997) that analysts would self-select in their coverage to provide only

a subset of all available private information realizations. The selectivity of disclosure

represents the “likelihood of analyst coverage” in the developed model here. This definition

of analyst coverage is generally in line with a large number of empirical studies that define

analyst coverage as equivalent to the observability of analyst information within a certain

time period (e.g., Bhushan 1989, O’Brien & Bhushan 1990, Ackert & Athanasassakos 2003,

Ljungqvist et al. 2007). However, the concept used here of analyst coverage applies an ex-

post point of view, whereas prior theoretical research has exclusively focused on an ex-ante

perspective (e.g., Hayes 1998, Arya & Mittendorf 2007, Fischer & Stocken 2010). In other

words, the inherent presumption of the existing theoretical literature is that an analyst would

commit to disclose every realization of fundamental information, even though – and this is

shown in the current paper – it might not optimize his utility.59

The basic setting, in which I assume that investors are price takers, contributes to the

literature by supplementing our understanding of the dependence of analysts’ coverage

behavior on the properties of the capital market of a stock through the strategic incentive to

stimulate trade. The most closely related paper is that of Hayes (1998), who develops a

theoretical model with an affiliated analyst who provides financial information to one

investor. She shows that the properties of the firm as well as an analyst’s incentives are

decisive in the analyst’s information acquisition and coverage decision. In particular, she

provides theoretical results showing that financially well-performing stocks are more likely to

be covered, wherby this effect is triggered by the presence of short selling constraints.

Contrary to Hayes (1998) and most other theoretical papers on the subject of analyst coverage

58

Reputational costs can be redefined as an analyst’s proprietary costs and, as is known from the voluntary

disclosure literature, such “proprietary costs” can be crucial in the decision of an information sender to provide

information to a recipient (e.g., Verrecchia 1983, Wagenhofer 1990). 59

Such a perspective is supported by a large set of empirical studies analyzing the frequently occurring event

that analysts stop the coverage of a firm (e.g., Rao et al. 2001, Mola et al. 2012).


65

initiation, I disentangle the information acquisition and the forecast disclosure decisions of an

analyst and focus on the latter (e.g., Mittendorf & Zhang 2005, Arya & Mittendorf 2007,

Fischer & Stocken 2010). However, Hayes (1998) considers a highly abstracted trading and

pricing process, in which a single investor chooses to revise his stake in the firm in response

to the analyst’s information under the assumption of an exogenously given share price.

Contrary to Hayes (1998) and in line with the setting of Beyer & Guttman (2011) – who

indicate the dependence of the earnings forecast bias on the trading volume objective – I

assume the existence of a capital market with multiple investors in which the analyst, who is

interested in stimulating informed trade, may decide to disclose a forecast that causes private

information asymmetry among investors. In the considered market not only is the share price

endogenous, but also the incremental information that is gleaned from it by uninformed

investors. Contrary to Beyer & Guttman (2011) I assume the properties of the disclosed

forecasting signal to be exogenously given and focus on the likelihood of the provision of

information by analysts.

By means of the competitive setting I am able to show that an analyst’s coverage decision is

non-monotonic in the characteristics of the capital market. This non-monotonicity is based on

the well-known tension in market models with revealing prices, namely that the degree to

which informed investors trade on their private information influences the usefulness of the

share price for uninformed investors’ demand choice. This latter aspect yields a residual

uncertainty from the viewpoint of the analyst since he knows the fundamental information

upfront but not the realization of the random liquidity demand. Depending on this tension and

the existing residual uncertainty, I provide the result that analysts are less likely to cover a

firm with increasing market penetration of their information (i.e., the fraction of investors

they supply with private information) if their reputational costs are sufficiently large and (a)

if they already reach the majority of traders investing in the stock or (b) if they consider a

firm with a very revealing market price but know that they will have a relatively low

information outreach. In all other cases, increasing market penetration by an analyst yields an

ex-ante higher likelihood of analyst coverage. A second result is obtained from analyzing the

dependence of the likelihood of analyst coverage on the degree of risk tolerance of investors.

It is shown that the likelihood of analyst following increases in the risk tolerance in most

situations. However, in the case in which the market price is very useful for the updating of

uninformed investors and if reputation is at stake, increased risk tolerance may even deter

analyst coverage.


66

In an extension of the basic setting, I relax the assumption of perfect investor competition

over information by changing the assumption on the composition of investors: In this setting I

assume the existence of only a few (risk-neutral) informed investors instead of a large

number of (risk-averse) traders. These investors are aware that their trading order has a

positive influence on the share price and they conjecture this in their trading decision. The

resulting setting is an imperfectly competitive market, as discussed by papers such as Kyle

(1989) or Lambert et al. (2011). Several empirical papers argue that the information demand

of large traders such as institutional investors motivates analysts to provide information (e.g.,

Bhushan 1989, El-Gazzar 1998). O’Brien & Bhushan (1990) and Ackert & Athanasassakos

(2003) point to the involved endogeneity because analysts are drawn to stocks for which they

can stimulate trade to generate commissions for the affiliated investment bank or brokerage

house. An aspect that has not been considered by these studies is the fact that the presence of

large informed traders impairs the market liquidity due to their impact on prices. By means of

the framework developed here I am able to show that the likelihood of analyst coverage is

lower if informed institutional demand has a higher influence on the share price. I thus

provide the theoretical result that analysts are less likely to cover a firm if market liquidity is

lowered by the presence of large stockholders such as institutional investors.60

This paper proceeds as follows: Chapter 4.2. establishes the economic setting. In Chapter 4.3.

the model is solved under the assumption of a perfectly competitive capital market and the

unique discretionary coverage initiation equilibrium is proven. Comparative statics are

discussed in Chapter 4.4. In Chapter 4.5. I relax the assumption of perfect investor

competition over information and derive the unique equilibrium, which is subsequently

discussed. Chapter 4.6. discusses the results and concludes.


In this section I describe the economic setting of the one-shot game between an equity analyst

and two groups of investors . The sequence of events, which is also summarized in

Figure 4, is as follows: Initially, the analyst is endowed with an exogenously given earnings

forecasting signal , where earnings and estimation error are normally

distributed with mean zero and precisions and , respectively.61

The analyst, whose

objective it is to trade off the maximization of absolute informed trading with the

minimization of reputational costs, decides whether he discloses his earnings forecast to

60

Beyer et al. (2010) state that the causal relationship between analyst following and market liquidity remains

unclear. Therefore, this paper gives some insight on this subject. 61

The assumption of a zero mean of earnings is without loss of generality.


67

The analyst observes

the realization of the

forecasting signal

Both and

formulate their

demand orders, the

liquidity demand is

realized and the share

price is set accordingly

The group

observes the analyst

forecast in the case of

coverage

The analyst decides

whether or not to

initiate coverage

The analyst gathers

information about a

firm’s forthcoming

earnings

investor group (henceforth referred to as “informed”), and thus creates private

information asymmetry among investors or withholds his information. 62

The informed

investors trade on the analyst’s information, whereas “uninformed investors” infer

some information from the share price and also choose a demand order.63

The subgroup of potentially informed investors is of size , whereas group

consists of investors. Both groups of investors are assumed to consist of a very large

number of members (i.e., countably infinite) such that every individual investor, informed or

not, takes the price as given.64

The overall number of investors is denoted by . All

investors in the proposed setting are assumed to have a non-random initial endowment of the

firm’s shares, which is normalized to zero without loss of generality. Moreover, they are also

assumed to be risk-averse with identical constant absolute risk tolerance and they choose

their demand order such that their negative exponential utility function is optimized, or

, where denotes individual investor ’s wealth.65

For the sake

of simplicity I assume that investors try to conjecture earnings instead of firm value.66

Additionally, and as is standard in noisy rational expectations market models, I assume the

existence of liquidity traders, whose aggregate demand order is random and normally

distributed with mean zero and precision . Thus, when uninformed investors observe the

price they receive a noisy estimate of the analyst’s forecast, where this incremental

information signal is represented by information signal . This signal is derived

from the conjecture with respect to the share price. In the basic setting I impose the following

linear conjecture on the share price: . Thus, . Moreover, all random

62

Throughout this paper the act of forecast disclosure is synonymous with coverage initiation. 63

The dashed part of the timeline represents the exogenously assumed information acquisition phase. Moreover,

after the formation of the price, earnings are publicly disclosed and the forecast error of the analyst is realized. 64

This assumption will be relaxed in Chapter 4.5. 65

A model variant that distinguishes between the risk tolerances of informed and uninformed investors yields

qualitatively similar results. 66

A version with firm value instead of earnings is more tedious and yields qualitatively similar results.



68

variables are independent from each other and their distributional assumptions are common

knowledge.

The assumption that the information provided by the analyst is private rather than public is

identical to the one imposed by the work of Beyer & Guttman (2011) and can be derived

from the existence of fees that investors have to pay in order to obtain the information from

the analyst, where these are not explicitly modeled either in Beyer & Guttman (2011) or in

the current setting. Further, an analyst who is interested in the generation of trading volume

has an implicit incentive to limit the availability of his information to a subgroup of investors

since public information would not yield informed trading in the considered setting.

Additionally, since the analyst’s information would not yield abnormal returns for any

investor, this would also not increase his chances to be nominated for an equity analyst

ranking. However, the interest in the stimulation of trade as well as the relation between

abnormal returns and the nomination for rankings have both been shown to influence an

analyst’s objective, i.e., through his compensation contract (e.g., Dugar & Nathan 1995,

Cowen et al. 2006, Ljungqvist et al. 2006, Groysberg et al. 2011).

The focus of this paper lies on the analyst’s decision to initiate coverage of a firm and

provide an (unbiased) forecast . For this purpose I assume that the analyst trades off the

incentive to stimulate trading with the costs of reputational loss associated with a potential

inaccuracy of the provided forecast. There is a variety of empirical evidence supporting such

a tradeoff. A first stream of research investigates the association between forecast accuracy

and the incentives of an analyst and establishes the argument that forecast accuracy is

significantly associated with the reputational and career concerns of analysts. For example,

Stickel (1992) shows that members of the Institutional Investor All-American Research

Team, a group of analysts considered to be superior and who thus enjoy a very good

reputation, deliver more accurate earnings forecasts. Mikhail et al. (1999) provide evidence

that lower relative forecast accuracy yields a significantly worse career outcome. This effect

is also supported by the evidence of Groysberg et al. (2011) on the compensation schemes

used in large brokerage houses that also provide analyst services.

A second branch of literature argues that analysts, especially those with an affiliation to a

brokerage house, would have an incentive to stimulate trade to earn the affiliated bank

commissions (e.g., Cowen et al. 2006). Again, Groysberg et al. (2011) provide evidence that

the trading volume, which is generated by an affiliated analyst’s report, is an important factor

that drives his compensation. Several existing theoretical models recognize the tradeoff


69

between the short term goal to generate trade and the long term goal to build a good

reputation (e.g., Hayes 1998, Jackson 2005, Beyer & Guttman 2011). However, they do not

consider the case of strategic coverage initiation with endogenous trading on a firm’s stock.

Following Beyer & Guttman (2011), the analyst’s utility in the case that he initiates coverage

is assumed to be of the form

, (1)

where is a positive constant and denotes the marginal costs of reputational damage from

being inaccurate.67

There are several consequences of the analyst’s objective function which need further

discussion. First and most foremost, the coverage initiation strategy of the analyst is such that

the analyst will disclose all realizations of if the marginal reputational costs are

sufficiently low, whereas he will be more selective in his decision if these are high. Under

these circumstances his strategy is symmetric around the ex-ante expected forecast, i.e., he

will always disclose realizations of which are in the intervals and , where

denotes the coverage initiation threshold. If the analyst decides against coverage initiation, no

trading occurs because informed and uninformed investors alike know that the analyst did not

decide to cover the firm. Hence, there is no information asymmetry in the market, which

implies that the analyst initiates coverage .68

Consequently, in the model

developed here analyst coverage is defined as the likelihood of the observability of analyst

information. This definition differs from earlier articles on analyst coverage: In the existing

literature the analyst always discloses the information he gathered by expending costly effort

(e.g., Mittendorf & Zhang 2005, Fischer & Stocken 2010). This is, however, in line with most

of the empirical studies investigating analyst following since they usually define analyst

coverage as equal to the observability of information (e.g., Bhushan 1989, O’Brien &

Bhushan 1990, Ackert & Athanasassakos 2003, Ljungqvist et al. 2007, Mola et al. 2012).

A further aspect is the exogenous nature of the forecasting signal and the assumption that

the analyst reports it truthfully. This case is considered by Beyer & Guttman (2011). They

67

The analyst’s objective function presented in (1) includes the expected value of absolute trading orders rather

than the difference between the trading orders of two rounds of trade. This corresponds to a setting in which

neither the analyst nor other sources of private or public information provide any information to investors before

the considered round of trade. This is a simplifying assumption to make the economic setting more tractable.

However, it follows the trade-based forecast bias setting considered by Beyer & Guttman (2011). 68

A similar perspective is taken by McNichols & O’Brien (1997), who argue that analysts would self-select to

initiate coverage and disclose information about successful firms. Contrary to their arguments, my model

implies that not only very positive but also very negative information is disclosed by analysts.


70

provide analytical results which show that the well-known upward bias in analyst forecasts is

the result of their trading volume objective. However, the obtained bias in their model

remains only a (commonly known) function of the analyst’s private information. In other

words, informed investors are able to perfectly infer the private information from the

observation of the forecast. Ceteris paribus, the information content of the forecast is

constant, since investors can perfectly adjust for any level of distortion. Since I am interested

in the implications of a capital market orientation of an analyst for his coverage initiation

strategy, the assumption of truthful forecast disclosure is without loss of generality.

A last set of assumptions has to be imposed in order to obtain a linear market solution: First,

in the case that the analyst initiates coverage and discloses his information I assume that

uninformed investors are not able to distinguish whether the information impounded in the

price comes from the analyst or from another information source of informed investors.

Further, if uninformed investors cannot infer any information from prices, they know for sure

that the analyst did not initiate coverage.69

Consequently, in the non-coverage case informed

and uninformed investors do not trade, due to missing information asymmetry. Otherwise, the

analyst’s coverage initiation decision would have information value to uninformed investors

and they would use this information jointly with the incremental information signal inferred

from the price in the updating of their expectations.70

It follows that I neglect this

“information value of coverage initiation” which is documented by several studies (Branson

et al. 1998, Irvine 2003, Das et al. 2006). It can be reasoned that this information effect

undermines the (potential) information asymmetry in the market and should intuitively

trigger the association obtained here with respect to the likelihood of analyst coverage: If the

likelihood of coverage is already very low (i.e., threshold is high) only very extreme

realizations of would incentivize the analyst to disclose it. Since uninformed investors are

aware of this fact, they know, by observing the price at the same time, almost as much as

informed investors. Hence, informed investors trade less on the information provided by the

analyst, which should decrease ex-ante expected, absolute demand. This in turn discourages

the analyst further from providing information. Consequently, it is reasonable to assume that

the coverage information effect rather represents a second order effect, whereas I want to

69

Alternatively, one may assume that uninformed investors are naïve about the discretion of analysts. The study

of Malmendier & Shanthikumar (2007) supports such an assumption. They provide evidence showing that small

traders (such as the ones assumed in this paper) are notoriously naïve about the strategic behavior of analysts

and ignore any strategic intentions in their decisions. 70

A simple variant of the updating problem of the uninformed investor can be obtained upon request from the

author.


71

focus on the first-order effects. As will be shown below, these turn out to be non-monotonic

in the exogenous variables. Thus, it can be followed that a non-linear pricing process would

only trigger the non-monotonicity of the primary effects.


In this section I solve the proposed setting and prove the uniqueness of the equilibrium. The

solution of the market in case of coverage initiation follows standard procedures that are

frequently used in the rational expectations literature. Thus, the problem of each informed

investor is to solve the following maximization problem in the case of coverage initiation by

the analyst:

.

This problem is straightforward to solve since is a noisy measure of and thus neglected

by investors. Thus, the solution with respect to is simply

. (2)

Recall the assumption that the uninformed investors do not observe the coverage initiation

decision of the analyst but only the information contained in the price. Thus, the problem of

an uninformed investor can be presented as follows:

.

The optimization yields the following demand order after replugging in :

. (3)

The demand orders in equations (2) and (3) can now be plugged into the market clearing

condition and rearranged with respect to , which yields share price

.

The conjecture with respect to the price is now introduced to derive , which ultimately

yields the unique solution . Plugging this solution into the above price and the

uninformed demand order leads to the following final expressions:

and (4)


72

(5)

where and . In a last

step, the analyst’s coverage initiation threshold is derived. The analyst initiates coverage

and discloses his private information signal whenever

. (6)

The conditional expectation of the absolute informed demand represents a folded sum of

normally distributed random variables. Thus, the derivation of the conditional expectation

applies the standard formula of the expectation of a folded normally distributed random

variable. The details are presented in the Appendix. As was already discussed above, the use

of folded conditional expectations in the described analyst game yields a situation in which

the analyst does not want to initiate coverage and thus withholds his information if

for certain values of . For the derivation of the threshold , inequality (6) must hold

with equality. The equilibrium condition can then be presented in the following form:71

. (7)

The first term is the result of the conditional expectation of the aggregate absolute demand

orders of informed investors, whereas the second term represents the conditionally expected

quadratic forecast error. In the Appendix it is shown that there exists a unique level if

and only if the marginal reputational costs are sufficiently large, or . Proposition 1

summarizes the unique linear equilibrium.

Proposition 1: There exists a unique coverage initiation and capital market equilibrium

which can be characterized as follows:

(i) the demand orders of investors and in the case of analyst forecast

disclosure are

and

, respectively;

(ii) the equilibrium price in the case of forecast disclosure is and

71

denotes the error function of a standard Gaussian.


73

(iii) the analyst' s coverage initiation strategy is such that (a) he always initiates coverage if

and (b) there must exist a unique threshold if that is defined by equation

(7) in which , where the analyst initiates coverage if or if .


The equilibrium described in Proposition 1 has certain properties which are subsequently

discussed in Chapter 4.4.

4.4. Discretionary Analyst Coverage and Capital Market Characteristics

In this section I provide some comparative statics on the likelihood of analyst coverage. It

shall be noted that increases in the threshold are interpreted with a lower likelihood of

analyst coverage.

There are two main components of the equilibrium summarized by Proposition 1, which

codetermine the overall behavior of the conditionally expected absolute informed demand.

First, there is the (expected) degree to which an informed investor trades based on the

analyst’s information, i.e., the magnitude of the position an informed investor takes in the

stock. In general, the larger the position of informed investors, the higher the likelihood of

analyst coverage. Moreover, the second component comes from the assessment of the analyst

with respect to the residual uncertainty induced through the liquidity trader’s random

demand. This effect is a byproduct of the use of the absolute informed demand in the

objective function of the analyst: The random demand as a part of the endogenous price is

directly included in the informed investor’s demand order and thus also shapes the analyst’s

coverage behavior. If the residual uncertainty decreases, so does the willingness of the

analyst to provide information.

It shall be noted that the subsequently presented non-monotonic effects of the behavior of the

coverage initiation threshold with respect to the exogenous variables , and stem from

the varying information content of the price for the updating of uninformed investors.

Further, this underlying causality codetermines both already mentioned components, the size

of the informed trade and the residual uncertainty. The analysis begins by investigating the

behavior of the threshold with respect to the number of informed investors, given that the

overall number of investors is assumed to be constant, i.e., the market penetration or

dissemination of an analyst’s information.

Corollary 1: Given that the overall number of investors ( ) is constant, the likelihood of

analyst coverage is a non-monotonic function of the market penetration :


74

(i) the threshold strictly decreases in iff and ;

(ii) in all other cases, there exists a threshold such that increases in if

and decreases in if .


Corollary 1 (i) shows that the likelihood of analyst coverage increases in the market

penetration of the analyst’s information when the absolute number of informed investors as a

fraction of uninformed investors is small and when prices are not very revealing due to a

relatively large noise caused by liquidity demand. In contrast, Corollary 1 (ii) provides the

result that in large market penetration cases or in situations with highly revealing market

prices the likelihood of analyst coverage increases in the market penetration only if the

analyst’s expected reputational costs are not too high.

The causal relationship underlying Corollary 1 can be divided into three parts. The first part

is the effect that the existence of more privately informed investors means that each

individual investor takes a smaller position in the firm because the overall advantage of being

privately informed decreases. This association is well-known and is discussed by the seminal

work of Grossman & Stiglitz (1980), who refer to this relationship as decreasing marginal

return on information. In the updating of the analyst this is represented by a monotonically

decreasing conditional expectation of informed demand with respect to for non-negative

values of (or ) and an opposite association for negative values of

. Thus, the absolute effect is a decrease of the conditional expectation of informed demand

and thus a lowering of the likelihood of analyst coverage (an increasing ).

Moreover, an additional effect is present in the model because an increase in the market

penetration also increases the weight on the conditional expectation of absolute informed

demand in the objective function of the analyst, which yields a decrease of the threshold.

However, the effect derived from the position of an informed investor in the stock may be

partly or fully offset by the change in the residual uncertainty: The more informed investors

there are, the more useful the price for the demand decision of each individual uninformed

investor becomes. This would yield a negative effect with respect to analyst coverage, but

since the individual demand order of an informed investor relies less on the obtained private

information, the behavior of the residual uncertainty is ambiguous. In particular, the residual


75

uncertainty always increases in if the group of informed investors is at least as large as the

group of the uninformed (or ), or if the group is relatively small but the price

informativeness is large. In this case, which is formally summarized by Corollary 1 (ii), the

residual uncertainty of informed demand serves as a countervailing force to the other two

effects and thus induces a dependence of the direction of the overall relationship on the extent

of marginal reputational costs: If the marginal reputational damage caused by imprecision of

the forecast is sufficiently large (or ), the analyst is less likely to follow a firm and

provide information the higher the fraction of investors he directly influences with his

information.

The result in Corollary 1 is of high relevance due to its empirical implications since the

market penetration of individual analysts is in reality heterogeneous: Analysts affiliated with

large investment banks are believed to be superior (represented by the nomination for equity

analyst rankings) and their reports are frequently shown to have a larger market impact than

those of analysts from smaller, unaffiliated research agencies (e.g., Park & Stice 2000,

Gleason & Lee 2003). Due to the perception of superiority these analysts may reach a higher

fraction of investors with their information. Increases in the (already high) information

outreach may even deter analyst coverage ex-ante if reputational costs are sufficiently large.

In the subsequent corollary I present some results on the behavior of the likelihood of analyst

coverage with respect to the risk attitude of investors.

Corollary 2: The likelihood of analyst coverage is a non-monotonic function of investors’

risk tolerance :

(i) the threshold decreases in if ;

(ii) if , there exists a threshold such that increases in

if and decreases in if .


In Corollary 2 I state that the behavior of the threshold with respect to the risk tolerance

crucially depends on the noise introduced by the liquidity demand and thus on whether the

share price is very informative about forthcoming earnings. If this is the case then the

coverage likelihood additionally depends on the level of marginal reputational costs incurred

by the inaccuracy of the provided forecasting signal. This effect is constituted as follows: A

larger risk tolerance increases the willingness of informed investors to face larger uncertainty


76

by taking a relatively larger position in the firm. This increase of informed trade in turn

increases the informativeness of the share price for uninformed investors’ demand orders,

which in turn demotivates informed investors from trading on their private information. It

follows that the residual uncertainty contained in informed demand orders is unambiguously

increased by an increase of risk tolerance in the market, whereas the conditional expectation

of the magnitude of informed demand may increase or decrease depending on the size of the

uncertainty of liquidity traders’ aggregate demand and moreover the reputational concerns of

the analyst.

At first glance, variations in the risk tolerance of investors and the effect on an analyst’s

likelihood of coverage may not seem empirically measureable. However, it is commonly

believed that different groups of investors have different degrees of risk tolerance and that

large traders such as institutional investors are able to diversify their portfolio such that their

risk attitude may be closer to risk neutrality than that of smaller investors (e.g., Bushee &

Goodman 2007). In this light an empirical investigation examining the effect of investor risk

tolerance on the coverage behavior of an analyst can represent the average degree of risk

aversion by using the (potential) ownership structure. In this sense the result contained in

Corollary 2 is partly in line with that presented by the empirical investigation of Bhushan

(1989), who documents a positive association between institutional ownership and analyst

following. However, as is noted by O’Brien & Bhushan (1990), this result has to be

interpreted with caution since Bhushan does not control for the potential endogeneity with

respect to the self-selective behavior of investors. Moreover, the aspect of reputation is not

included in his analysis, where this would be implied by Corollary 2 (ii).

As was shown by Corollaries 1 and 2, the role of the informativeness of prices for the

decision of uninformed investors is a crucial element for the likelihood of analyst coverage.

Hence, Corollary 3 shall be devoted to the analysis of the behavior of the analyst regarding

the noise induced by the liquidity demand.

Corollary 3: The coverage initiation threshold increases in for most cases.


A last result of the dependence of an analyst’s coverage strategy on capital market

characteristics is provided in Corollary 3: A more revealing price yields a decreasing

likelihood of analyst coverage since it discourages informed investors in taking a large


77

position in the stock. However, this also undermines the residual uncertainty, except in cases

in which there is not a lot of fundamental uncertainty and the information asymmetry is

sufficiently beneficial for informed investors.72

Unfortunately, the result in Corollary 3 is a

rather theoretical one due to the difficulty associated with measuring the randomness of

demand orders that are not information based. Thus, so far the empirical literature does not

provide evidence on this relationship.

4.5. Extension: Discretionary Analyst Coverage and Market Liquidity

The equilibrium in Proposition 1 provides several novel empirical implications and leads us

to the conclusion that the characteristics of the capital market (or its participants) determine

the coverage behavior of analysts in non-trivial ways. The described market setting assumes

perfect investor competition over private information due to the assumption that the two

groups of investors consist of a very large numbers of members (i.e., they are both countably

infinite). This is a very restrictive assumption especially in an analyst setting because sell-

side equity analysts are assumed to have a strong preference for providing institutional

investors with information since these in turn nominate them for rankings (e.g., Ackert &

Athanassakos 2003, Ljungqvist et al. 2007).73

This type of investor exists in a much lower

number compared to individual speculators and is usually of a respectable size in terms of

capital endowment. Since the latter in particular shows a tendency to blocktrade, an

institutional investor certainly conjectures his (positive) impact on the share price in his

decision to choose his demand order. Prior theoretical research by Kyle (1989) and Lambert

et al. (2011) argues that this knowledge would impair the liquidity in the market. Ceteris

paribus, there exists an endogenous situation between analyst following, institutional

ownership and market liquidity. Despite the highly relevant empirical study performed by

Roulstone (2003) – who shows that a larger analyst following yields increased liquidity – the

direction of the involved endogeneity remains unclear.74

For the purpose of addressing the issue of the endogeneity associated with discretionary

analyst coverage and liquidity (which is referred to by the use of subscript in the

subsequent model setup) several changes in the economic setting have to be made. First, I

assume that the group that is provided with information by the analyst ( ) only consists of

72

I prove in the Appendix that only one case exists in which the threshold decreases in , that is (i) if the

analyst’s information is not very disseminated, (ii) if the forecasting precision is mediocre, (iii) if the earnings

precision is sufficiently large and (iv) if the marginal reputational damage is sufficiently small. 73

Groysberg et al. (2011) show that a higher rank is significantly associated with increased compensation. 74

In their review, Beyer et al. (2010, 328) conclude: “Further, the direction of causality, that is, whether analyst

following leads to changes in liquidity or changes in cost of capital for firms, or vice versa, is not clear.”


78

a few large institutional investors. To simplify the situation I moreover assume that these

investors are risk-neutral and optimize their utility, , by choosing .75

Second,

the following conjectures are applied on informed demand and the share price:

and . The risk attitude and the optimization problem of

the uninformed investors remains unchanged, whereas the incremental information signal that

they observe is derived in the Appendix and is of the structure .

The described setting yields a unique discretionary analyst coverage and imperfect investor

competition equilibrium which is summarized in Proposition 2.

Proposition 2: There exists a unique imperfectly competitive capital market and

discretionary analyst coverage equilibrium:

(i) The capital market equilibrium consists of informed and uninformed demand orders,

prices and the incremental information of prices , , and

, respectively.

(ii) The analyst’s coverage initiation strategy is such that (a) he always initiates coverage if

and (b) there must exist a unique threshold if ,

where the analyst initiates coverage if or if .


As in earlier models of imperfect investor competition, captures the depth of the market

and can thus be interpreted as a measure for market illiquidity (e.g., Kyle 1985, 1989,

Lambert et al. 2011). Further, the effect of market illiquidity on the likelihood of analyst

coverage described in Proposition 2 (ii) is presented by Corollary 4.

Corollary 4: The coverage initiation threshold increases in .


When informed investors are aware that their demand order has an impact on the price, they

tend to trade less on their private information, i.e., they give up a part of the information

advantage that they have relative to uninformed investors. It is commonly believed by

financial market scholars that this impairs the liquidity in the market. As is shown by

75

The assumption of risk-neutrality is without loss of generality but decreases the complexity of the

mathematical problem. Identical results can be obtained with risk-aversion as in the basic setup. However, the

assumption corresponds to the effects documented by Bushee & Goodman (2007): Large institutional investors

are more likely to trade based on information than based on risk.


79

Corollary 4, market illiquidity also impacts the analyst’s decision to provide information to

the market. This is because the extent to which informed investors trade on the analyst’s

information is lower with higher market illiquidity and consequently the price becomes less

informative. This in turn decreases the residual uncertainty because the overall trading in the

market is lower. Overall this yields a positive association between the likelihood of analyst

coverage and market liquidity.

This result is somewhat puzzling because it highlights the special tension underlying several

empirical studies on institutional ownership and analyst following, which argue that analysts

would have a distinct incentive to provide institutional investors with information (e.g.,

Ackert & Athanassakos 2003, Ljungqvist et al. 2007). Contrary to this common belief, I show

that the presence of informed large traders impairs the market liquidity and discourages

analysts from following a firm and providing information.

4.6. Conclusion

This paper develops a theoretical model to study the dependence of the likelihood of analyst

coverage on the characteristics of capital markets. In particular, it is shown that when an

analyst trades off the incentives to stimulate informed trade with the reputational costs of

providing inaccurate information he establishes a coverage policy in which every piece of

information is disclosed if marginal reputational costs are low, whereas only extremely good

or bad information is provided if the marginal reputational damage is above a certain level. In

a competitive investor setting it is shown that the likelihood of analyst coverage is a non-

monotonic function of (a) the market penetration of the provided forecast and (b) the risk

aversion of investors. Due to a special interest of the empirical literature in the causal

relationship between analyst following, institutional ownership and market liquidity I provide

an extension with an imperfectly competitive financial market, in which a few large informed

investors conjecture their impact on the share price and anticipate this in the choice of their

informed demand order. In turn this conjecture impairs the liquidity in the market. In the

obtained equilibrium I am able to provide the result that the likelihood of analyst coverage

increases with the market’s liquidity.

The economic setting introduced here is not without limitations. The most notable is that in

order to establish a linear equilibrium I neglect the possibility of an “information value of

coverage initiation” as has been documented by Branson et al. (1998), Irvine (2003) and Das

et al. (2006). This effect would be present in the discussed framework since uninformed

investors infer a noisy measure of the analyst forecast from the share price and additionally


80

know that the analyst must have initiated coverage. They use these two pieces of information

jointly when updating, which would ultimately yield a non-linear expectation of earnings and

consequently also a non-linear share price. A second limitation is the exogeneity with respect

to the market penetration of the analyst’s forecasting information: To a certain extent this is a

choice variable of the analyst because he also chooses the price of his information. These and

further issues are left for future research endeavors.

Appendix (Paper 3)


The proof of the competitive equilibrium summarized in Proposition 1 is performed stepwise

by means of two Lemmas.

Lemma 1: The derivation of equilibrium condition (7), which implicitly defines the coverage

initiation threshold , uses the demand orders presented in equations (2) and (5) and the share

price in (4). The analyst initiates coverage and discloses information whenever

.

The folded conditional expectation of the demand order in inequality (6) can be derived by

the following standard formula:

,

where . The

conditional mean and variance of the informed demand order are and

, respectively. Moreover,

. A crucial feature of the proposed model is the symmetry of the

equilibrium solution with respect to the equilibrium threshold since

. This technical property can be applied to present the equilibrium condition as

follows:

.


81

Given that (or alternatively ) this inequality must hold with equality to obtain

the condition presented in (7).

Lemma 2: For the uniqueness proof it is sufficient to show that the equilibrium condition in

equation (7) increases in and is negative for some values of as approaches . For this

purpose I define the following function:

.

First, the limit of when approaches zero is .

This condition is positive if and negative otherwise. Moreover,

. Additionally, the first order condition of with respect to is

.

Thus, it must be true that if and if . ■


The proofs of Corollaries 1 through 3 apply the implicit function theorem. As is already

known . Thus, the first order condition of with respect to an exogenous variable

must have the same sign as . Moreover, an important feature is that .

The Proof of Corollary 1 begins by plugging in into . The first order

condition can now be presented as follows:

where

.

The sign of the first order condition is proportional to the one of . The first term in

is unambiguously positive. However, the second term can be positive or negative


82

because the sign of

is not determined by the assumptions made and since

. However, it is straightforward to show

that the term is unambiguously positive (and thus also ) if and only if

and . In all other cases can be positive or

negative and a condition can be found with respect to the magnitude of . Since it is

sufficient to analyze the behavior of with respect to to obtain a unique threshold

. The limits of with respect to are

and . Moreover,

if . Thus, there must exist a unique level of

such that is negative if and positive . ■


The first order condition of with respect to is

,

where

.

Again the condition depends on , in which the first term is unambiguously positive

and the second term is only positive if which is


83

the case if . If , it is straightforward to show that

there exists a unique threshold yielding the effects summarized in Corollary 2 (ii). ■


The first order condition of with respect to is as follows:

.

This condition is unambiguously negative if

which is the case for all parameter

constellations except the following: (i) , (ii) and (iii)

. In this case it is

straightforward to prove that there exists a unique threshold such that the condition is

non-negative if . ■


The Proof of Proposition 2 proceeds by means of three Lemmas.

Lemma 3: The capital market equilibrium for the imperfect investor competition case begins

with the derivation of the incremental information signal:

.

An informed investor’s decision problem is

. It shall be noted that, since and are jointly observed by investor

, the realization can be inferred. Thus, this problem is equivalent to the solution of

. The demand order can be solved and rewritten to


84

obtain , or and after imposing the

conjectures. Moreover, the demand order of an uninformed investor is identical to the one

presented in equation (3). Thus, the market clearing condition can be applied and rewritten

with respect to the share price

.

The weight in the incremental information is now derived by solving

with respect to , which yields . Plugging this solution into yields the

following solution for the price:

,

where and

. Imposing the available conjectures of

the price and the informed demand ( ) and after plugging in

and the following two equations are obtained: and . The

latter is rearranged to . Thus, every component of the market solution depends

on the uniqueness of . However, an explicit solution for is not available but the

parameter is implicitly defined by equality

.

Following the equilibrium proof in Lambert et al. (2011) Decartes’ rule of signs can be

applied which implies a single or no positive root of with respect to .76

Since

and there must exist a unique positive , .

This proves the uniqueness of the capital market equilibrium since it does not depend on the

coverage initiation threshold .

76

The equilibrium condition is a third-order polynomial instead of a fourth-order polynomial which is the case

in Lambert et al. (2011). This is due to the assumption of risk neutrality of informed investors.


85

Lemma 4: The derivation of the coverage initiation threshold follows the same procedures as

the ones used in Lemma 1. Thus, only the resulting expression shall be presented:

.

Lemma 5: For the proof of the uniqueness of the coverage threshold first note the above

inequality must hold with equality which further implicitly defines threshold by means of

the following equality:

.

This expression has the following limits in : and

. Moreover,

. Thus, there exists a unique threshold whenever . ■


The Proof of Corollary 4 again utilizes the implicit function theorem and the effect of with

respect to depends on the sign of . This first order condition is


86

.

This condition is ambiguous if

since the first term is negative and the expression in the last bracket is positive. This is the

case if . It is straightforward to see that equilibrium condition

decreases in . Thus,

plugging in into the equilibrium condition yields

which is a contradiction. Thus, it must be true that

and therefore that . Hence, . ■

Discussion and Conclusion

87

5. Discussion and Conclusion

In this dissertation I provide theoretical guidance on several issues that were previously

observed by empirical studies or that extend the prior theoretical literature in a novel way

such that implications for future empirical research can be derived:

· In the career concerns paper (Chapter 2) I show that analysts who herd in their

forecasts (i.e., due to the existence of career concerns) choose their coverage initiation

strategy such that they may be more or less likely to initiate coverage when their

career concerns vary, where the strategy is based on privately observed proprietary

reward-from-coverage information. In particular, I show that analysts are less likely to

initiate coverage and collect information if their career concerns increase, provided

that the average analyst private information precision is sufficiently large, and if the

responsiveness of the reward (i.e., the sum of trading volume commissions,

information acquisition costs and opportunity costs) to the forecast is relatively small.

In addition, I provide the result that the non-coverage of an analyst has information

content for a peer analyst.

· By means of an information acquisition model (Chapter 3) I reasoned that managed

earnings can be more or less informative for the market provided that there is a

previously released (endogenous) analyst forecast. In line with prior research such as

that of Chen et al. (2010), I argue that analyst forecasts fulfill two roles, an

information role stemming from the (additional) noise in the reporting bias and an

interpretation role induced by managers’ meet-or-beat analyst forecast incentives. It is

shown that analyst forecasts and managed earnings are complements if an analyst’s

information acquisition costs are high, whereas they are substitutes if costs are low.

· The discretionary coverage framework (Chapter 4) shows that the presumption of the

existing literature that analysts would always disclose the fundamental information

which they previously acquired and thus would always initiate coverage if they have

earnings information may not hold true in the presence of a tradeoff between the

objective to stimulate trade and reputational costs. In particular, I provide theoretical

proof that analysts release all fundamental information if reputational costs are

insignificant, whereas they only disclose sufficiently extreme information if these

costs are of significance. Additionally, it is shown that an analyst is less likely to

cover a firm if he penetrates a larger share of the equity market with his information,


88

if he already reaches out to a sufficiently large number of shareholders relative to the

overall equity, and if reputational concerns are very distinct. Moreover, an extension

provides the insight that market illiquidity deters analysts from following a firm, as is

assumed by empirical studies such as those of O’Brien & Bhushan (1990) and Ackert

& Athanassakos (2003).

In sum, the results in the distinct papers suggest that not only the analyst’s own objectives

such as trading volume generation or career concerns determine his firm coverage and

forecasting behavior of sell-side equity analysts in non-trivial ways, but also the incentives of

others such as those of peer analysts or firm managers. Hence, the incentives of analysts and

those of other stakeholder of a firm that they (may) follow are interrelated in subtle but non-

trivial ways.

The papers in this thesis all include distinct phases in the coverage initiation stage, as is

depicted in Figure 1 in the literature review (see Chapter 1). The information acquisition

article (Chapter 3) models an analyst who always wants to initiate coverage and to provide a

forecast. However, his information-gathering strategy crucially depends on the noise

contained in the reporting bias – if there is more noise, then the analyst needs to collect more

fundamental information to outweigh the increased ex-ante expected forecast error. However,

the papers in Chapters 2 and 4 use different definitions of analyst coverage initiation (or

alternatively stop of coverage): The latter paper’s analysts decide on the collection of

information by means of proprietary signals about the profitability of firm coverage, but

release all possible realizations of the fundamental earnings forecasting signal (i.e., an ex-

ante coverage policy), whereas the former paper’s analyst discretionarily decides on whether

to disclose an earnings signal and thus on firm coverage after the realization of the respective

signal (i.e., an ex-post coverage policy). The ex-ante coverage strategy has been the

perspective of all theoretical contributions to the field of analyst coverage (e.g., Hayes 1998,

Mittendorf & Zhang 2005, Arya & Mittendorf 2005, Fischer & Stocken 2010). However,

conceptionally similar to the ex-post perspective is the contribution of Beyer (2008), in which

the analyst discretionarily decides on the revision of a previously disclosed forecasting signal

after he observes a new (and more accurate) private information signal.

Further, the papers considered here also differ in how forecast biasing is considered: For the

sake of simplicity in the discretionary coverage paper I assume that there is no distortion in a

released forecast (only the strategy to disclose the forecast introduces distortion), whereas the

other two parts apply different definitions of biasing. The information acquisition paper


89

considers a distortion only in the acquisition of fundamental information, which is generally

in line with the manipulation concept proposed by Lim (2001). In the career concerns paper

the analysts are assumed to bias their forecasts in a distinct stage after they have gathered

private earnings information. In this sense, the latter paper is similar to approaches presented

by theoretical endeavors such as those ones of Beyer (2008) and Beyer & Guttman (2011).

However, both approaches yield distortions which impact the properties (e.g., accuracy) of

the observable forecast.

Another relevant aspect is whether analyst information is considered to be public or private.

For the validity of the models in Chapters 2 and 3 such a distinction is irrelevant as long as

the peer analyst or the firm manager have access to an analyst’s earnings forecast at a given

point in time. However, the discretionary analyst coverage model in Chapter 4 relies on the

assumption that analyst information provision causes private information asymmetry among

investors due to the necessity of a distinct interest of the analyst in the capital market. In

general, this is in line with Beyer & Guttman (2011) and well-known market microstructure

frameworks such as those of Grossman & Stiglitz (1980) and Kim & Verrecchia (1991). An

opponent to this viewpoint is Roulstone (2003), who provides empirical evidence supporting

the publishing of analyst information. An argument against the complete publishing of

analyst information can be found in the literature: According to Groysberg et al. (2011)

individual analysts earn more if they are nominated for rankings such as the Institutional

Investor All-Star equity research ranking. Such rankings are based on the mechanism that the

nominating investors earn abnormal returns as a consequence of trading on information that

the aforementioned analyst provides (e.g., Stickel 1992). It is straightforward to reason that

abnormal returns are more likely to be obtained with private rather than with public

information. In addition, it can be observed that analysts limit the availability of their

information by demanding a fee in exchange. Given these two observations, it is more likely

that analyst information represents private rather than public information.

This dissertation has several limitations which may form the basis for future theoretical

research projects. First and foremost, a general weakness exists in the theoretical analyst

coverage and forecasting literature, namely that all papers focus on the decision of an analyst

with respect to a particular firm instead of taking a portfolio perspective. The latter seems to

be highly relevant for the behavior of analysts since several empirical studies provide

evidence showing that analysts develop preferences with respect to categories such as

industry or geographical location (e.g., Malloy 2005, Kini et al. 2009, Sonney 2009). Next,


90

due to technical complexity and as is discussed in the discretionary coverage paper it is not

clear how the signaling value of analyst coverage initiation or abandonment codetermines the

analyst’s coverage and forecasting behavior. First, empirical evidence in this direction is

provided by Branson et al. (1998), Irvine (2003), and Das et al. (2006). A last issue, which

remains unclear, is how the opportunity to use multiple information signals (forecasts,

recommendations, target prices) codetermines the properties of each distinct information

signal. It can be concluded that there is still room for future research endeavors on analysts’

behavior.

Bibliography

91

Bibliography

Abarbanell, J.S.,1991. Do analysts’ earnings forecasts incorporate information in prior stock

price changes? Journal of Accounting and Economics 14 (2), 147–165.

Abarbanell, J.S., W.N. Lanen & R.E. Verrecchia, 1995. Analysts’ forecasts as proxies for

investor beliefs in empirical research. Journal of Accounting and Economics 20 (1), 31-60.

Ackert, L.F. & G. Athanassakos, 2003. A simultaneous equations analysis of analysts’

forecast bias, analyst following, and institutional ownership. Journal of Business Finance and

Accounting 30 (7-8), 1017-1042.

Alfond, A.W. & P.G. Berger, 1999. A simultaneous equations analysis of forecast accuracy,

analyst following, and trading volume. Journal of Accounting, Auditing and Finance 14 (3),

219-240.

Arya, A. & B. Mittendorf, 2007. The interaction among disclosure, competition between

firms, and analyst following. Journal of Accounting and Economics 43 (2-3), 321-339.

Asquith, P., M.B. Mikhail, & A.S. Au, 2005. Information content of equity reports. Journal of

Financial Economics 75 (2), 245-282.

Ayers, B.J. & R.N. Freeman, 2003. Evidence that analyst following and institutional

ownership accelerate the pricing of future earnings. Review of Accounting Studies 8 (1), 47-

67.

Ayers, B.C., O.Z. Li, & P.E. Yeung, 2011. Investor trading and the post-earnings-

announcement drift. The Accounting Review 86 (2), 385-416.

Baginski, S.P. & J.M. Hassell, 1990. The market interpretation of management earnings

forecasts as a predictor of subsequent financial analyst forecast revision. The Accounting

Review 65 (1), 175-190.

Barber, B., R. Lehavy, M. McNichols & B. Trueman, 2001. Can investors profit from the

prophets? Security analyst recommendations and stock returns. Journal of Finance 56 (2),

531-563.

Barron, O.E., D. Byard & O. Kim, 2002. Changes in analysts’ information around earnings

announcements. The Accounting Review 77 (4), 821-846.

Barron, O.E., O. Kim, S.C. Lim & D.E. Stevens, 1998. Using analysts’ forecasts to measure

properties of analysts’ information environment. The Accounting Review 73 (4), 421-433.

Barth, M.E. & A.P. Hutton, 2004. Analyst earnings forecast revisions and the pricing of

accruals. Review of Accounting Studies 9 (1), 59-96.

Barth, M.E., R. Kasnik & M.F. McNichols, 2002. Analyst coverage and intangible assets.

Journal of Accounting Research 39 (1), 1-34.

Basu, S. & S. Markov, 2004. Loss function assumptions in rational expectations tests of

financial analysts’ earnings forecasts. Journal of Accounting and Economics 38 (1), 171-203.

Bernhardt, D., M. Campello & E. Kutsoati, 2006. Who herds? Journal of Financial

Economics 80 (3), 657-675.

Beyer, A., 2008. Financial analysts’ forecast revisions and managers’ reporting behavior.

Journal of Accounting and Economics 46 (2-3), 334-348.

Bibliography

92

Beyer, A., D.A. Cohen, T.Z. Lys & B.R. Walther, 2010. The financial reporting environment:

Review of the recent literature. Journal of Accounting and Economics 50 (2-3), 296-343.

Beyer, A. & I. Guttman, 2011. The effect of trading volume on analysts’ forecast bias. The

Accounting Review 86 (2), 451-481.

Bhojraj, S., P. Hribar, M. Picconi & J. McInnis, 2009. Making sense of cents: An

examination of firms that marginally miss or bear analyst forecasts. Journal of Finance 64 (5),

2361-2388.

Bhushan, R., 1989. Firm characteristics and analyst following. Journal of Accounting and

Economics 11 (2-3), 255-274.

Bonner, S.E., A. Hugon & B.R. Walther, 2007. Investor reaction to celebrity analysts: The

case of earnings forecast revisions. Journal of Accounting Research 45 (3), 481-513.

Bowen, R., X. Chen & Q. Cheng, 2008. Analyst coverage and the cost of raising equity

capital: Evidence from underpricing of seasoned equity offerings. Contemporary Accounting

Research 25 (3), 657–699.

Bradley, D.J., B.D. Jordan & J.R. Ritter, 2008. Analyst behavior following IPOs: The

“bubble period” evidence. Review of Financial Studies 21 (1), 101-133.

Branson, B.C., D.M. Guffey & D.P. Pagach, 1998. Information conveyed in announcements

of analyst coverage. Contemporary Accounting Research 15 (2). 119-143.

Bushee, B.J. & T.H. Goodman, 2007. Which institutional investors trade based on private

information about earnings and returns? Journal of Accounting Research 45 (2), 289-321.

Chang, X., S. Dasgupta & G. Hilary, 2006. Analyst coverage and financing decisions. Journal

of Finance 61 (6), 3009-3048.

Chen, Q., J. Francis & W. Jiang, 2005. Investor learning about analyst predictive ability.

Journal of Accounting and Economics 39 (1), 3-24.

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

of Financial Studies 19 (1), 319-355.

Chen, S., D. Matsumoto & S. Rajgopal, 2011. Is silence golden? An empirical analysis of

firms that stop giving quarterly earnings guidance. Journal of Accounting and Economics 51

(1-2), 134-150.

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

disclosures: Exploring the roles of information discovery and interpretation. Journal of

Accounting and Economics 49 (3), 206-226.

Cheynel, E. & C.B. Levine, 2012. Analysts’ sale and distribution of non fundamental

information. Review of Accounting Studies 17 (2), 352-388.

Clarke, J., A. Khorana, A. Patel & P.R. Rau, 2007. The impact of all-star analyst job changes

on their coverage choices and investment banking deal flow. Journal of Financial Economics

84 (3), 713-737.

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

evidence. Journal of Financial Economics 80 (1), 81-113.

Clement, M.B., 1999. Analyst forecast accuracy: Do ability, resources, and portfolio

complexity matter? Journal of Accounting and Economics 27 (3), 285-303.

Bibliography

93

Clement, M.B., J. Hales & Y. Xue, 2011. Understanding analysts’ use of stock returns and

other analysts’ revisions when forecasting earnings. Journal of Accounting and Economics 51

(3), 279-299.

Clement, M.B., L. Koonce & T.J. Lopez, 2007. The roles of task-specific forecasting

experience and innate ability in understanding analyst forecasting performance. Journal of

Accounting and Economics 44 (3), 378-398.

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

forecasting. Journal of Finance 60 (1), 307-341.

Cliff, M.T. & D.J. Denis, 2004. Do initial public offering firms purchase analyst coverage

with underpricing? Journal of Finance 59 (6), 2871-2901.

Cotter, J., I. Tuna & P.D. Wysocki, 2006. Expectations management and beatable targets:

How do analysts react to explicit earnings guidance? Contemporary Accounting Research 23

(3), 593-624.

Cowen, A., B. Groysberg & P. Healy, 2006. Which types of analyst firms are more

optimistic? Journal of Accounting and Economics 41 (1-2), 119-146.

Cox, A.A. & R.T. Kleiman, 2000. A stochastic model of superstardom: Evidence from

Institutional Investor’s All-American Research Team. Review of Financial Economics 9 (1),

43-53.

Das, S., R.-J. Guo & H. Zhang, 2006. Analysts’ selective coverage and subsequent

performance of newly public firms. Journal of Finance 61 (3), 1159-1185.

Dempsey, S.J., 1989. Predisclosure information search incentives, analyst following, and

earnings announcement price response. The Accounting Review 64 (4), 748-757.

Demski, J. & G. Feltham, 1994. Market response to financial reports. Journal of Accounting

and Economics 17 (1-2), 3–40.

Dugar, A. & S. Nathan, 1995. The effect of investment banking relationships on financial

analysts’ earnings forecasts and investment recommendations. Contemporary Accounting

Research 12 (1), 131-160.

Duru, A. & D.M. Reeb, 2002. International diversification and analysts’ forecast accuracy

and bias. The Accounting Review 77 (2), 415-433.

Dutta, S. & B. Trueman, 2002. The interpretation of information and corporate disclosure

strategies. Review of Accounting Studies 7 (1), 75-96.

El-Gazzar, S.M., 1998. Predisclosure information and institutional ownership: A cross-

sectional examination of market revaluations during earnings announcement periods. The


Elgers, P.T. & M.H. Lo, 1994. Reductions in analysts’ annual earnings forecast errors using

information in prior earnings and security returns. Journal of Accounting Research 32 (2),

290-303.

Ewert, R. & A. Wagenhofer, 2005. Economic effects of tightening accounting standards to

restrict earnings management. The Accounting Review 80 (4), 1101-1124.

Feng, M. & S. McVay, 2010. Analysts’ incentives to overweight management guidance when

revising their short-term earnings forecasts. The Accounting Review 85 (5), 1617-1646.

Bibliography

94

Fischer, P.E. & P.C. Stocken, 2004. Effect of investor speculation on earnings management.

Journal of Accounting Research 42 (5), 843-870.

Fischer, P.E. & P.C. Stocken, 2010. Analyst information acquisition and communication. The


Fischer, P.E. & R.E. Verrecchia, 2000. Reporting bias. The Accounting Review 75 (2), 229-

245.

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

information. Journal of Accounting and Economics 33 (3), 313-342.

Frankel, R., S.P. Kothari & J. Weber, 2006. Determinants of the informativeness of analyst

research. Journal of Accounting and Economics 41 (1-2), 29-54.

Gerardi, D. & L. Yariv, 2008. Costly expertise. American Economic Review: Papers &

Proceedings 98 (2), 187-193.

Givoly, D. & J. Lakonishok, 1979. The information content of financial analysts’ forecasts of

earnings. Journal of Accounting and Economics 1 (3), 165-185.

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


Gleason, K., I. Mathur & M. Peterson, 2000. Analysis of intraday herding behavior among

the sector ETFs’. Journal of Empirical Finance 11 (5), 681-694.

Graham, J.R., 1999. Herding among investment newsletters: Theory and evidence. Journal of

Finance 54 (1), 237-268.

Grossman, S.J. & J.E. Stiglitz, 1980. On the impossibility of informationally efficient

markets. American Economic Review 70 (3), 393-408.

Groysberg, B., P.M. Healy & D.A. Maber, 2011. What drives sell-side analyst compensation

at high-status investment banks? Journal of Accounting Research 49 (4), 969-1000.

Hayes, R.M., 1998. The impact of trading commission incentives on analysts’ stock coverage

decisions and earnings forecasts. Journal of Accounting Research 36 (2), 299-320.

Hilary, G. & R. Shen, 2013. The role of analysts in intra-industry information transfer. The


Holthausen, R.W. & R.E. Verrecchia, 1988. The effect of sequential information releases on

the variance of price changes in an intertemporal multi-asset market. Journal of Accounting

Research 26 (1), 82-106.

Holthausen, R.W. & R.E. Verrecchia, 1990. The effect of informedness and consensus on

price and volume behavior. The Accounting Review 65 (1), 191-208.

Hong, H. & J.D. Kubik, 2003. Analyzing the analysts: Career concerns and biased earnings

forecasts. Journal of Finance 58 (1), 313-351.

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

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

Howe, J.S. E. Unlu & X. Yan, 2009. The predictive content of aggregate analyst

recommendations. Journal of Accounting Research 47 (3), 799-821.

Hughes, J., J. Liu & W. Su, 2008. On the relation between predictable market returns and

predictable analyst forecast errors. Review of Accounting Studies 13 (2-3), 266-291.

Bibliography

95

Irvine, P.J., 2003. The incremental impact of analyst initiation of coverage. Journal of

Corporate Finance 9 (4), 431-451.

Ivkovic, Z. & N. Jegadeesh, 2004. The timing and the value of forecast and recommendation

revisions. Journal of Financial Economics 73 (3), 433-463.

Jackson, A.R., 2005. Trade generation, reputation and sell-side analysts. Journal of Finance

60 (2), 673-717.

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

analysts. Journal of Accounting and Economics 28 (1), 51-82.

James, C. & J. Karceski, 2006. Strength of analyst coverage following IPOs. Journal of

Financial Economics 82 (1), 1-34.

Jegadeesh, N., J. Kim, S.D. Krische & C.M.C. Lee, 2004. Analyzing the analysts: When do

recommendations add value? Journal of Finance 59 (3), 1083-1124.

Kasznik, R. & M.F. McNichols, 2002. Does meeting earnings expectations matter? Evidence

from analyst forecast revisions and share prices. Journal of Accounting Research 40 (3), 727-

759.

Kim, O., S.C. Lim & K.W. Shaw, 2001. The inefficiency of the mean analyst forecast as a

summary forecast of earnings. Journal of Accounting Research 39 (2), 329-336.

Kim, O. & R.E. Verrecchia, 1991. Trading volume and price reactions to public

announcements. Journal of Accounting Research 29 (2), 302-321.

Kim, O. & R.E. Verrecchia, 1997. Pre-announcement and event-period private information.

Journal of Accounting and Economics 24 (3), 395-419.

Kini, O., S. Mian, M. Rebello & A. Venkateswaran, 2009. On the structure of analyst

research portfolios and forecast accuracy. Journal of Accounting Research 47 (4), 867-909.

Kirk, M., 2011. Research for sale: Determinants and consequences of paid-for analyst

research. Journal of Financial Economics 100 (1), 182-200.

Kothari, S.P., 2001. Capital markets research in accounting. Journal of Accounting and

Economics 31 (1-3), 105-231.

Kyle, A.S., 1985. Continuous auctions and insider trading. Econometrica 53 (6), 1315-1336.

Kyle, A.S., 1989. Informed speculation with imperfect competition. Review of Economic

Studies 56 (3), 317-356.

Lambert, R.A., C. Leuz & R.E. Verrecchia, 2007. Accounting information, disclosure, and

the cost of capital. Journal of Accounting Research 45 (2), 385-420.

Lambert, R.A., C. Leuz & R.E. Verrecchia, 2011. Information asymmetry, information

precision, and the cost of capital. Review of Finance 16 (1), 1-29.

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

Accounting Review 71 (4), 467–492.

Langberg, N. & K. Sivaramakrishnan, 2008. Voluntary disclosures and information

production by analysts. Journal of Accounting and Economics 46 (1), 78-100.

Lim, T, 2001. Rationality and analysts’ forecast bias. Journal of Finance 56 (1), 369-385.

Ljungqvist, A., F. Marston, L.T. Starks, K.D. Wei & H. Yan, 2007. Conflicts of interest in

sell-side research and the moderating role of institutional investors. Journal of Financial

Economics 85 (2), 420-456.

Bibliography

96

Louis, H., A.X. Sun & O. Urcan, 2013. Do analysts sacrifice forecast accuracy for

informativeness ? Management Science 59 (7), 1688-1708.

Lys, T. & S. Sohn, 1990. The association between revisions of financial analysts’ earnings

forecasts and security price changes. Journal of Accounting and Economics 13 (4), 341-363.

Lys, T. & L.G. Soo, 1995. Analysts’ forecast precision as a response to competition. Journal

of Accounting, Auditing and Finance 10 (4), 751-765.

Madrigal, V., 1996. Non-fundamental speculation. Journal of Finance 51 (2), 553–578.

Malloy, C.J., 2005. The geography of equity analysis. Journal of Finance 60 (2), 719-755.

Malmendier, U. & D. Shanthikumar, 2007. Are small investors naïve about incentives?

Journal of Financial Economics 85 (2), 457-489.

Markov, S. & A. Tamayo, 2006. Predictability in financial analyst forecast errors: Learning

or irrationality? Journal of Accounting Research 44 (4), 725-761.

Matsumoto, D.A., 2002. Mangement’s incentives to avoid negative earnings surprises. The


McNichols, M. & P.C. O’Brien, 1997. Self-selection and analyst coverage. Journal of

Accounting Research 35 (Supp.), 167-199.

Michaely, R. & K. Womack, 1999. Conflict of interest and the credibility of underwriter

analyst recommendations. Review of Financial Studies 12 (4), 653-686.

Mikhail, M.B., B.R. Walther & R.H. Willis, 1997. Do security analysts improve their

performance with experience? Journal of Accounting Research 35 (Supp.), 131-157.

Mikhail, M.B., B.R. Walther & R.H. Willis, 1999. Does forecast accuracy matter to security

analysts? The Accounting Review 74 (2), 185-200.

Mikhail, M.B., B.R. Walther & R.H. Willis, 2003. The effect of experience on security

analyst underreaction. Journal of Accounting and Economics 35 (1), 101-116.

Mittendorf, B. & Y. Zhang, 2005. The role of biased earnings guidance in creating a healthy

tension between managers and analysts. The Accounting Review 80 (4), 1193-1209.

Mohanram, P. & D. Gode, 2013. Removing predictable analyst forecast errors to improve

implied cost of equity estimates. Review of Accounting Studies 18 (2), 443-478.

Mola, S., P.R. Rau & A. Khorana, 2013. Is there life after the complete loss of analyst

coverage? The Accounting Review 88 (2), 667-705.

O’Brien, P.C., 1988. Analysts’ forecasts as earnings expectations. Journal of Accounting and

Economics 10 (1), 53-83.

O'Brien, P.C. & R. Bhushan, 1990. Analyst following and institutional ownership. Journal of

Accounting Research 28 (Supp.), 55-76.

Park, C.W. & E.J. Stice, 2000. Analyst forecasting ability and the stock price reaction to

forecast revisions. Review of Accounting Studies 5 (3), 259-272.

Piotroski, J.D. & D.T. Roulstone, 2004. The influence of analysts, institutional investors, and

insiders on the incorporation of market, industry, and firm-specific information into stock

prices. The Accounting Review 79 (4), 1119-1151.

Rajan, R. & H. Servaes, 1997. Analyst following of Initial Public Offerings. Journal of

Finance 52 (2), 507-529.

Bibliography

97

Ramnath, S., S. Rock & P. Shane, 2008. The financial analyst forecasting literature: A

taxonomy with suggestions for future research. International Journal of Forecasting 24 (1),

34-75.

Rao, H., H.R. Greve & G.F. Davis, 2001. Fool’s gold: Social proof in the initiation and

abandonment of coverage by Wall Street analysts. Administrative Science Quarterly 46 (3),

502-526.

Roulstone, D.T., 2003. Analyst following and market liquidity. Contemporary Accounting

Research 20 (3), 552-578.

Scharfstein, D. & J. Stein, 1990. Herd behavior and investment. American Economic Review

80 (3), 465-479.

Sonney, F., 2009. Financial analysts’ performance: Sector versus country specialization.

Review of Financial Studies 22 (5), 2087-2131.

Stickel, S.E., 1990. Predicting individual analyst earnings forecasts. Journal of Accounting

Research 28 (2), 409-417.

Stickel, S.E., 1992. Reputation and performance among security analysts. Journal of Finance

47 (5), 1811-1836.

Strobl, G., 2013. Earnings manipulation and the cost of capital. Journal of Accounting

Research 51 (2), 449-473.

Tan, H., S. Wang & M. Welker, 2011. Analyst following and forecast accuracy after

mandated IFRS adoptions. Journal of Accounting Research 49 (5), 1307-1357.

Trueman, B., 1994. Analyst forecasts and herding behavior. Review of Financial Studies 7

(1), 97-124.

Tucker, J.W., 2010. Is silence golden? Earnings warnings and subsequent changes in analyst

following. Journal of Accounting, Auditing and Finance 25 (3), 431-456.

Verrecchia, R.E., 1983. Discretionary disclosure. Journal of Accounting and Economics 5

(5), 179-194.

Versano, T. & B. Trueman, 2013. Expectations management. Working Paper, Yale

University School of Management & UCLA Anderson School of Management, Version: May

9.

Wagenhofer, A., 1990. Voluntary disclosure with a strategic opponent. Journal of Accounting

and Economics 12 (4), 341-363.

Welch, I., 2000. Herding among security analysts. Journal of Financial Economics 58 (3),

369-396.

Yu, F., 1999. What is the value of knowing uninformed trades? Economics Letters 64 (1),

87–98.

Yu, F., 2008. Analyst coverage and earnings management. Journal of Financial Economics

88 (2), 245-271.

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

of Accounting and Economics 46 (1), 201-215.

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