Wisdom of Crowds: Effect of Analysts Herding Around All Stars

A Dissertation SUBMITTED TO THE FACULTY OF THE

UNIVERSITY OF MINNESOTA BY

Andrew Fluharty

IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN BUSINESS

ADMINISTRATION

Adviser: Pervin Shroff

April, 2020

© Andrew Fluharty, 2020

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Acknowledgements

I thank my dissertation committee: Pervin Shroff (chair), Joshua Madsen, Gerard

McCullough, and Helen Zhang for their invaluable guidance and support. I am grateful to

workshop participants at the University of Minnesota as well as Alexander Nekrasov,

Tjomme Rusticus, Ram Venkataraman, Chandra Kanodia, Xiaoli Guo, Jacob Raleigh,

Jacob Ott, Xinyuan Shao, and Eva Liang for helpful comments and suggestions. Thank

you to Andreas Simon for graciously providing me with his Institutional Investor All-

America Research Team (All Star) data. I am grateful for funding from the Carlson School

of Management at the University of Minnesota.

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Abstract

I examine the effects of herding by financial analysts on the wisdom of crowds. I identify

significant herding of analyst earnings forecasts around forecasts of All Star analysts.

Despite the fact that All Stars (and by extension those that herd with All Stars) are more

accurate forecasters than their peers, I find that consensus forecast accuracy decreases in

the degree of All Star herding, consistent with diminished group performance in the

presence of herding as predicted by the popular wisdom of crowds literature (Surowiecki

2005). This is in contrast to prior studies that focus on herding around the consensus

forecast and how it affects forecasts of individual analysts. Corroborating my main finding,

I also find an increase (decrease) in consensus forecast accuracy in the period after an All

Star discontinues (initiates) coverage of a firm. I further find that All Star herding is

associated with an increased market response to earnings announcements consistent with

herding leading to a greater amount of uncertainty that is resolved by a subsequent earnings

release. Overall, my findings suggest that the detrimental effect of analyst herding on the

wisdom of crowds leads to a deterioration of firms’ information environments.

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Table of Contents

List of Tables ..................................................................................................................... iv List of Figures ..................................................................................................................... v 1. Introduction ................................................................................................................. 1 2. Prior Literature and Hypotheses Development ............................................................ 4

2.1 Analysts and Herding ........................................................................................... 4

2.2 The Wisdom of Crowds, Information Cascades, and Consensus Forecast Accuracy.......................................................................................................................... 7

2.3 All Star Turnover and Its Impact on Information Cascades and Consensus Forecast Accuracy ............................................................................................................ 8

2.4 All Star Herding and Information Asymmetry .................................................. 10

3. Data, Descriptive Statistics, and Herding Measurement ........................................... 11 3.1 Data and Descriptive Statistics .......................................................................... 11

3.2 Herding Measurement ........................................................................................ 13

4. Empirical Analyses ................................................................................................... 14 4.1 All Star Herding and Consensus Forecast Accuracy ......................................... 14

Cross-Sectional Variation in the Impact of All Star Herding ..................... 15

4.2 All Star Turnover in the Consensus .................................................................... 16

4.3 All Star Herding and Information Asymmetry ....................................................... 18

5. Analyst-Level Analysis .............................................................................................. 19 5.1 Factors Associated with All Star Herding Behavior .......................................... 19

5.2 All Star Accuracy ............................................................................................... 21

6. Conclusion ................................................................................................................. 22 References ......................................................................................................................... 24 Appendix ........................................................................................................................... 37

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List of Tables

Table 1: Summary Statistics ............................................................................................. 31 Table 2: Correlation Matrix .............................................................................................. 32 Table 3: All Star Herding and Consensus Accuracy ........................................................ 33 Table 4: Cross-Sectional Analysis .................................................................................... 34 Table 5: All Star Turnover and Consensus Accuracy ....................................................... 35 Table 6: All Star Herding and Earnings Announcement Reactions ................................. 36 Table A1: Factors Associated with the Decision to Herd ................................................. 39 Table A2: Accuracy of All Stars versus Non All Stars .................................................... 40

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List of Figures

Figure 1: Information Cascade Numerical Example ........................................................ 29 Figure 2: Herding Forecast Illustration ............................................................................. 30

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1. Introduction The wisdom of crowds is a widely documented phenomenon in the psychology

literature (e.g., Clemen 1989; Gigone and Hastie 1997; Lorenz et al. 2011; Larrick et al. 2014) where a group of people—a crowd—identifies better solutions to a complex problem or completes a difficult task more effectively and efficiently than an individual, often better than even the most skilled individual. James Surowiecki’s book (2005) by the same title examines this phenomenon and discusses the various features of crowds that make them “wise,” or not-so-wise. One of the biggest roadblocks to crowd wisdom is information cascades, when, instead of relying on their own beliefs, members of the crowd defer to the beliefs of another. When information cascades occur, the crowd’s performance can suffer as the individual members of the crowd are no longer contributing (or are contributing less of) their own value. The role of information cascades has been studied in the finance literature, such as in the context of equity investment decisions in capital markets (Zhou and Lai 2009), initial public offerings (Welch 1992; Amihud et al. 2003) and equity crowdfunding (Vismara 2018).1

In this paper, I identify analyst herding behavior as representing an information cascade, and examine how herding affects the accuracy of firms’ consensus forecasts. A consensus forecast is generated as the average of earnings forecasts issued by equity analysts following a company, and the set of analysts that make these forecasts can be thought of as a “crowd.” If each analyst works independently to issue a forecast then the wisdom of crowds posits that the resulting consensus forecast should be the most accurate. In contrast, herding impedes crowd wisdom, potentially decreasing consensus forecast accuracy.

A substantial literature in accounting and finance finds that analysts do not always generate their forecasts independently. A number of prior studies examine the propensity of analysts to herd around previously issued recommendations (e.g., Graham 1999; Welch 2000; Jegadeesh and Kim 2010) or forecasts (e.g., De Bondt and Forbes 1999; Hong et al. 2000; Clement and Tse 2005). These papers study the drivers of

1 For expositional simplicity, I refer to the effect of information cascades on the wisdom of crowds as the “wisdom of

the crowd(s) effect" or simply as the “wisdom of crowds" throughout the paper.

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herding behavior and the impact of herding on the outputs of individual analysts. In contrast, since the consensus is known to be an important earnings benchmark for firms (e.g., Degeorge et al. 1999), I focus on how herding affects the consensus forecast at the firm level.

Another feature of extant herding literature is that it generally focuses on analysts herding toward the consensus (e.g., Hong et al. 2000; Clement and Tse 2005; Jegadeesh and Kim 2010). An alternate choice for analysts who choose to herd is identified by the behavioral psychology literature as the best-member strategy (Yetton and Bottger 1982) where the members of a group seek out the most knowledgeable member and follow her judgment. Multiple studies have documented and examined groups using a “follow the leader” strategy (e.g., Libby et al. 1987; Henry 1995; Bonner 2004; Larrick et al. 2014). A natural “best member” toward whom analysts might herd is an analyst named to the All-America Research Team by Institutional Investor magazine (All Star). Numerous prior studies have used All Star status as a measure of analyst reputation (e.g., Krigman et al. 2001; Hong and Kubik 2003; Cliff and Denis 2004). Further, Stickel (1992) and Leone and Wu (2007) find that All Star analysts issue more accurate forecasts than non-All Stars and suggest that All Star status is a significant determinant of analysts’ pay.

Using a firm-level measure of herding calculated based on the proximity of earnings forecasts to the most recent All Star relative to the consensus, I document that 12% of forecasts herd around All Stars when a firm is followed by at least one All Star.

To assess the wisdom of crowds effect of All Star herding, I examine consensus forecast accuracy. Intuition suggests that analysts herding around a superior forecaster should produce a superior consensus. However, individual herding forecasts that are accurate relative to other individual forecasts will not necessarily aggregate to form a more accurate consensus forecast. As discussed in the wisdom of crowds literature, this counterintuitive effect occurs because correlated errors will not cancel out in aggregation. Indeed, consistent with the effect of information cascades on the wisdom of crowds, I find a negative relationship between the degree of All Star herding and consensus forecast accuracy.2 This effect is economically significant; if an analyst

2 I additionally study the effect of analysts herding toward the consensus, although the wisdom of crowds does not

offer a prediction for the consensus-level effect of analysts herding around the consensus. I find no relation between the degree of consensus herding and year-end consensus forecast accuracy. I discuss further in section 2.2.

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covering a firm begins herding with an All Star, consensus forecast accuracy decreases by 4%. Cross-sectional analysis demonstrates that the relation between All Star herding and consensus forecast accuracy is magnified for large firms with high analyst coverage. This suggests that All Star herding is most damaging for firms where the wisdom of the crowd has the most value, i.e., when there is a large crowd.

Related tests reveal that consensus accuracy diminishes in the year a new All Star initiates coverage or when a returning analyst gains All Star status. I interpret this as an opportunity for analysts to begin herding which decreases the wisdom of the crowd. On the other hand, consensus accuracy improves in the year following the departure of an All Star. This suggests a change in the coverage environment that forces herders to begin independently generating their forecasts which improves crowd wisdom.

I next address the capital market consequences of herding with respect to firms’ information environments. Prior literature has examined market reactions at earnings announcement dates to understand firm information environments, specifically with respect to pre-announcement information asymmetry (e.g., Atiase and Bamber 1994; Chae 2005; Bushee et al. 2011; Lyle et al. 2020).3 If All-Star herding produces a lower-quality consensus that is detrimental to firms’ information environments, I hypothesize that actual earnings releases result in more pronounced market reactions around earnings announcements with increasing levels of All Star herding. Confirming this hypothesis, I find a significant positive relationship between the degree of All Star herding in the consensus and abnormal trading volume, return volatility, and absolute abnormal returns in the three-day window surrounding subsequent earnings announcements.

Collectively, the findings suggest that analysts utilize a best-member strategy when forecasting earnings. This leads to a significantly less accurate consensus forecast, consistent with the notion that information cascades negatively impact crowd wisdom. As a consequence, market reactions are amplified when earnings are realized suggesting that All Star herding leads to lower quality pre-announcement information and

3 Kim and Verrecchia (1991) posit that a higher level of pre-announcement information asymmetry among market

participants drives differing responses to the release of information at the announcement date. This results in a more pronounced market reaction to new public information.

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contributes to information asymmetry. I make several contributions to extant research. First, I add to the analyst literature

which has largely focused on analyst-level metrics and their effects on the individual analyst. An emerging strand of literature examines analyst attributes and performance at an aggregate level. For example, Hong and Kacperczyk (2010) study the effect of analyst coverage on consensus accuracy, and Merkley et al. (2017) perform a similar analysis at the industry level. I expand this line of research by examining consensus-level effects of analyst behavior and offer new insight into how analyst behavior can bring potentially unanticipated consequences for the consensus forecast. Second, I add to the herding literature which has focused on analyst herding around the consensus and the analyst-level effects of such herding. I document another type of herding—herding around All Stars—and evaluate its effect on the consensus forecast which has more relevance in capital markets relative to individual analyst forecasts.

Finally, I contribute to research on the wisdom of crowds and information cascades. Prior finance studies have examined the role of the wisdom of crowds with particular focus on initial public offerings (Welch 1992; Amihud et al. 2003) and investor behavior in capital markets (Zhou and Lai 2009). To my knowledge, this paper is the first to examine the wisdom of crowds and information cascade effects in the context of financial analysts.

The rest of the paper is organized as follows: Section 2 examines relevant prior literature in motivating the testable hypotheses. Section 3 details the data, descriptive statistics, and herding measurement. I report the empirical results in section 4, section 5 discusses additional analysis performed at the analyst level, and section 6 concludes. Following the conclusion is the appendix which includes variable definitions and analyst-level results.

2. Prior Literature and Hypotheses Development 2.1 Analysts and Herding

Academic studies dating as far back as 1933 (Cowles) have sought to understand the role of financial analysts in capital markets. More recent evidence suggests that analysts are an important cog in the mechanism of information transfer to market

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participants (e.g., Elton et al. 1986; O’Brien 1988; Lys and Sohn 1990; Womack 1996; Francis and Soffer 1997; Loh and Stulz 2011). It stands to reason, then, that a substantial literature seeking to understand the underpinnings of analyst outputs has emerged.4

One major strand of that research focuses on the propensity of analysts to cluster or herd around existing recommendations or forecasts. Trueman (1994), in response to early empirical works which used analyst forecasts to proxy for investor expectations, points out that the underlying assumption is that the forecasts are unbiased representations of analysts’ information. His theoretical model shows that analysts herd toward previously announced forecasts even when it is not justified by their information. Graham (1999), building on Scharfstein and Stein (1990), expands the theoretical foundation for analyst herding predicting that higher levels of herding lead to lower analyst ability and reputation.

Multiple other studies empirically document herding in one form or another. Hong et al. (2000) study analyst terminations and document a negative relationship between analyst experience and herding, consistent with career concerns driving herding behavior. Clement and Tse (2005) find a negative relationship between herding and an analyst’s prior accuracy, brokerage size, and experience and a positive relationship with the number of industries the analyst follows. Welch (2000) finds that analysts’ stock recommendations have a significant influence on subsequent recommendations, and Jegadeesh and Kim (2010) document muted market reactions to such recommendations. A common theme in prior research is that herding behavior negatively affects analyst performance.

The extant herding literature has two largely universal attributes. One, it focuses on consequences for individual analysts, and two, it generally studies herding toward the consensus. In contrast, I focus on analysts herding toward All Stars, and examine the effect of this herding on consensus forecast accuracy rather than individual analyst forecast accuracy.

First, I argue that studying the consensus-level effect of analyst herding is important. To use a sports metaphor: While it is certainly important to understand the individual

4 See Ramnath et al. (2008) for a survey of the financial analyst literature through 2008.

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players in the game, it is also critical to understand how the players mesh together to form a team. In the context of analysts, though the group covering a firm does not work together as a team, their outputs are aggregated to form the consensus forecast—a benchmark that is fundamentally important in capital markets (Degeorge et al. 1999; Dechow et al. 2003; Brown and Caylor 2004).5 Evidence shows that firms whose earnings surpass this benchmark are rewarded (Bartov et al. 2002), while firms that miss it are severely penalized (Skinner and Sloan 2002).6 An entire strand of literature investigates how managers manipulate or “manage” their accounting reports to meet or beat the consensus (e.g., Dechow et al. 2003; Black and Christensen 2009, Davis et al. 2009). Surveys of managers indicate that the consensus forecast is one of the key benchmarks managers strive to meet or beat (Graham et al. 2005). Thus, my focus is on herding and its effect on the consensus forecast; this is the aggregate-level forecast rather than the forecasts of individual analysts which has been the focus of much of the prior literature.

Second, hypothesizing that analysts follow a best-member strategy in their herding behavior, I focus on analysts herding around All Stars rather than the consensus. As discussed in the behavioral psychology literature, a best-member strategy is one where a member of a group defers to the judgment of another member deemed to be superior (Yetton and Bottger 1982; Libby et al. 1987; Henry 1995). An intuitive choice of a best member in the group of analysts covering a firm is one named to the Institutional Investor All-America Research Team (All Star).

All Star status has been used as a proxy for analyst reputation in numerous prior studies. For instance, Krigman et al. (2001) and Cliff and Denis (2004) find that firms engaged in initial public offerings buy analyst coverage from influential (All Star) analysts. Hong and Kubik (2003) state, “an analyst’s place in the profession depends critically on (being named to the team).” Further, evidence suggests that All Star

5 A recent working paper by Fang and Hope (2020) studies the prevalence of teamwork in the financial analyst

industry and finds that reports and forecasts are often generated by teams working together at their brokerage. My paper examines the group of analysts covering the same firm (the consensus) but working at separate brokerages and is thus not directly related to that study.

6 In their working paper, Michaely et al. (2019) motivate their research by what they deem a “fixation on the consensus” by investors.

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analysts issue more accurate forecasts than non-All Stars, and All Star status is a significant determinant of analysts’ pay (Stickel 1992; Leone and Wu 2007).

This paper fills a gap in extant research by addressing the following questions: Do analysts issue forecasts that herd around All Stars? How does analyst herding around All Stars impact the consensus forecast accuracy? What are the capital market implications of All Star Herding?

2.2 The Wisdom of Crowds, Information Cascades, and Consensus

Forecast Accuracy

The wisdom of crowds is a phenomenon that has been studied in behavioral psychology literature (e.g., Clemen 1989; Gigone and Hastie 1997; Lorenz et al. 2011; Larrick et al. 2014). It has recently started to garner mainstream interest. James Surowiecki’s popular best-seller, The Wisdom of Crowds (2005), offers a multitude of anecdotes ranging from guessing games to locating a submarine lost at sea that illustrate the power of crowd wisdom. Under a set of common conditions, the average response of a crowd often will outperform even its most skilled member.

Surowiecki also discusses certain features of crowds that can destroy their collective wisdom. One of the biggest roadblocks to crowd success he explores is that of information cascades. Information cascades occur when members of the group cease relying on their own beliefs and instead choose to defer to the judgment of another group member. The benefits of crowd wisdom begin to break down and performance of the group suffers.

Finance literature models information cascades in a theoretical framework where agents’ actions converge toward those of another due to ignoring private information signals (e.g., Welch 1992; Amihud et al. 2003; Zhou and Lai 2009; Vismara 2018). Hirshleifer and Teoh (2003) present a review of theory and evidence of herd behavior in capital markets linked to information cascades. I identify analysts herding toward All Stars as representing an information cascade that can damage the wisdom of the crowd of analysts who generate the consensus forecast (Figure 1 presents a simple example to illustrate this effect). As a consequence, the beneficial effect of the wisdom

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of crowds on consensus forecast accuracy is likely to diminish resulting in a less accurate consensus. Therefore, H1, the information cascade hypothesis, predicts that:

H1: Consensus forecast accuracy decreases in the level of All

Star herding in the consensus.

Potentially related to All Star herding is analysts herding around the consensus. On one hand, intuition suggests that this represents analysts deferring to the wisdom of the crowd which might lead to a more accurate consensus at year-end. On the other hand, analysts who choose to herd around the existing consensus are not contributing their own value which might lead to a less accurate consensus at year-end. In view of these competing effects, I have no prediction regarding the relationship between the degree of analyst herding around the existing consensus and year-end consensus forecast accuracy. For completeness, however, I examine the impact of analyst herding around the consensus as a variation of H1. I discuss further in section 4.1.

2.3 All Star Turnover and Its Impact on Information Cascades and

Consensus Forecast Accuracy

One criticism of extant herding literature is that what researchers identify as herding behavior may really reflect independent forecasts based on similar information leading to similar forecasts. To address this concern, I examine the effects of All Stars initiating or discontinuing coverage—events which should have no impact on independently generated forecasts of other analysts. If a new All Star initiates coverage (and there is no herding), consensus forecast accuracy should increase due to the addition of a superior forecaster, all else equal.7 An observed decrease in consensus forecast accuracy would be consistent with the increased effect of herding on the wisdom of the crowd. In the same vein, if an existing All Star discontinues coverage (and there is no herding), we should observe a decrease in consensus forecast accuracy with the

7 Stickel (1992) and Leone and Wu (2007) document that All Stars issue more accurate forecasts than non-All Stars.

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departure of a superior forecaster, all else equal; an observed increase would be consistent with the decreased effect of herding on the wisdom of the crowd.

To further alleviate endogeneity concerns surrounding coverage changes, I refine the above analysis of changes in coverage by All Stars. I identify changes in All Star status when: (i) a continuing analyst is named an All Star, or (ii) a continuing analyst is no longer named as an All Star. Since the only change in this setting is the change in status and not a change in the population of analysts covering a firm, it represents an arguably exogenous event that should not affect the information of analysts in the consensus. Thus, in the absence of herding, forecast accuracy should not be affected by an existing analyst gaining or losing All Star status. However, an observed decrease (increase) in forecast accuracy when a continuing analyst is (no longer) named an All Star would be consistent with herding impacting the wisdom of the crowd.

The preceding gives rise to two sets of hypotheses, H2A and H2B (the All Star arrival hypotheses), and H2C and H2D (the All Star departure hypotheses). They are as follows:8

H2A: Consensus forecast accuracy decreases in the period

following the arrival of an All Star into coverage.

H2B: Consensus forecast accuracy decreases in the period

following a continuing analyst gaining All Star status.

H2C: Consensus forecast accuracy increases in the period

following the departure of an All Star from coverage.

H2D: Consensus forecast accuracy increases in the period

following a continuing analyst losing All Star status.

8 For expositional simplicity, I collectively refer to new All Stars, dropped All Stars, and changes in All Star status

in the consensus as ”All Star turnover” throughout the paper.

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2.4 All Star Herding and Information Asymmetry

If All Star herding decreases the quality of the consensus and, by extension, firms’ information environments, we should observe effects in capital markets. Market reactions at earnings announcement dates have been used in prior literature to understand firm information environments, particularly with respect to pre-announcement information asymmetry. For example, extending Beaver (1968), the model in Kim and Verrecchia (1991) shows that increasing levels of pre-announcement information asymmetry among market participants lead to differing responses to the release of new public information. Specifically, low quality pre-announcement information results in a more pronounced market reaction to a subsequent earnings release. Prior empirical research also examines market reactions around earnings announcements to study information asymmetry in a variety of settings (e.g., Atiase and Bamber 1994; Chae 2005; Kanagaretnam et al. 2007; Bushee et al. 2011; Lyle et al. 2020). Similar to these studies, if All Star herding produces a lower-quality consensus that is detrimental to firms’ information environments, I hypothesize that actual earnings releases will result in more pronounced market reactions around subsequent earnings announcements with increasing levels of All Star herding (the information asymmetry hypotheses).

Abnormal trading volume at earnings announcement dates is likely to be high when there is pre-announcement information asymmetry (e.g., Beaver 1968; Kim and Verrecchia 1991; Chae 2005). Since All Star herding likely increases pre-announcement information asymmetry, I expect to find a positive relationship between announcement abnormal volume and All Star herding. Thus,

H3A: Three-day abnormal volume around the earnings

announcement date increases in the degree of All Star

herding in the consensus.

Lyle et al. (2020) examine return volatility at earnings announcements and suggest that it is tightly linked to information processing in the market. They reason that if an

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earnings announcement causes investors to revise their beliefs, prices will change and volatility increases. Because a low quality consensus should drive greater revision of beliefs at an earnings announcement, I expect to find a positive relationship between announcement return volatility and All Star herding. Thus,

H3B: Three-day abnormal return volatility around the

earnings announcement date increases in the degree of All

Star herding in the consensus.

Bushee et al. (2011) find that absolute abnormal announcement returns increase in the degree of information transferred via announcements in the setting of conference presentations. Because the earnings announcement is more informative relative to the lower quality consensus brought on by All Star herding (i.e., the relative information transfer is greater), I expect more revision of beliefs by investors which should be reflected in absolute abnormal returns at the announcement date. This implies a positive relationship between announcement-window absolute abnormal returns and All Star herding. Thus,

H3C: Three-day absolute abnormal returns around the

earnings announcement date increase in the degree of All

Star herding in the consensus.

3. Data, Descriptive Statistics, and Herding Measurement

3.1 Data and Descriptive Statistics I obtain analyst earnings forecasts and actuals data from I/B/E/S from 1992 through

2009.9 After dropping observations missing valid analyst codes, earnings forecast values, and/or forecast dates, I merge in the All Star data. Analyst forecast observations receive All Star classification based on the annual publication date of the Institutional

9 The sample is determined by All Star data availability. I am grateful to Andreas Simon for providing me with his

All Star data.

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Investor All-America Research Team which occurs in October.10 These observations are merged with CRSP daily and monthly data and Compustat data over the same time period.11 I require non-missing price and return data from CRSP and non-missing total assets, net income, and sales data from Compustat.

After calculating coverage, herding, and related measures at the analyst level, I condense these observations to the firm-year level (consensus level).12 Consensus-level measures are calculated using the most recent forecast for each analyst that was issued within 90 days of the fiscal year-end as is customary in the analyst literature. Though results are robust to the alternate strategy of calculating consensus measures without the 90-day recency restriction, I choose the 90-day specification to avoid stale forecasts contaminating the consensus. Any remaining observations missing necessary data items to run the tests are excluded from the sample; this includes singleton observations (i.e., fixed effect groups with only one observation) which are dropped to avoid introducing bias in regression standard errors (Correia 2015). The final sample consists of 57,132 firm years.

Descriptive statistics are presented in panel A of table 1. Variable definitions can be found in the appendix. In the full sample, the mean rate of All Star herding is 5% of forecasts while 3% herd with the consensus. The average number of analysts in coverage is 7.13, consistent with prior literature (e.g., Bradshaw et al. 2016). To provide insight that is generalizable, the sample includes all firms with required data during the sample period, including firms with no All Stars in coverage. If the sample is restricted to only firms with at least one All Star in coverage (i.e., firms where analysts have an opportunity to herd with an All Star) the rate of All Star herding increases to 12% of

10 Since the publication date varies within the month of October from year to year, I use the period from October 1

through September 30 of the following year to classify All Stars; this coincides with the “post-publication period” described by Leone and Wu (2007). Results are robust to calendar year classification and to classifying based on whether an analyst was ever named an All Star, though the latter approach precludes testing H2C to assess the effect of a continuing analyst losing All Star status on the following year’s consensus forecast accuracy.

11 I use unadjusted forecast and actuals data from I/B/E/S and follow method 3 described by “A Note on Unadjusted IBES Data” from Wharton Research Data Services (WRDS) for linking I/B/E/S forecasts and actuals to CRSP and properly adjusting for stock splits and reverse splits. Compustat data is merged based on the “ICLINK” program offered by WRDS that links the firm identification variables in CRSP to those in Compustat. To prevent improper matches, I reject merges with an “ICSCORE” of 2 or greater.

12 I discuss the herding measures in detail in the following section.

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forecasts and the average number of analysts covering each firm increases to 12.0; the rate of consensus herding declines slightly to 2%.

Panel B of Table 1 presents descriptive statistics by year for the number of analysts in coverage and herding measures. Coverage levels dip in the mid-1990s, increase through the late 90s, and, after a lull during the dot-com bubble burst of the early 2000s, moderately increase through the first decade of the new millennium with a decrease around the financial crisis in 2007. Interestingly, the rate of All Star herding decreases almost monotonically throughout the period. One explanation is that Regulation Fair Disclosure may reduce the perceived benefits analysts expect to obtain by herding with an All Star, though this relationship would warrant additional investigation. Small increases in herding around 2002 and 2007 are likely related to the dot-com bubble burst and sub-prime mortgage crisis, respectively, and are consistent with herding behavior being driven by career or reputation concerns as in Graham (1999), Hong et al. (2000), and Hong and Kubik (2003).

Table 2 presents Pearson correlations for the variables used in the analysis. Though expected, one relation that is important to note is the high degree of correlation between the # Analysts and LNSIZE variables. This relationship contributes to an unexpected relation between # Analysts and consensus forecast accuracy in later analysis. I discuss further in section 4.1.

3.2 Herding Measurement Herders are identified at the analyst level with reference to the forecasts an analyst

is able to view as of the forecast date (i.e., the forecasts made previous to that forecast date). For each forecast date, a consensus is calculated as a rolling average of the most recent forecast made by each analyst in the preceding 90 days.13 This is compared to the most recent All Star forecast. The difference between these two values is the distance between the current consensus and All Star forecast as of the date an analyst makes her

13 A forecast is considered to be current through its review date in I/B/E/S. If no review date is provided, the forecast

date is used for determining recency.

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forecast (“distance”).14 An analyst is deemed to be herding with an All Star (consensus) if her forecast is within 5% of the distance with respect to the most recent All Star (consensus) forecast, and not herding otherwise.15 Figure 2 illustrates an example.

The All Star herding measure used for analysis is the percentage of forecasts in the

year-end consensus that herded with an All Star. In order to document the effect of All

Star herding in the most generalizable manner, firms which have no All Stars in

coverage are assigned values of 0. Main findings are qualitatively unchanged when

restricting the sample to firms that have at least one All Star in coverage.

4. Empirical Analyses 4.1 All Star Herding and Consensus Forecast Accuracy

In order to address H1, the information cascade hypothesis, I estimate the regression of consensus forecast accuracy on herding as follows:

Accuracyi,t =α + β · Herding Measurei,t + γ · Controlsi,t

+ δ · Firm FE + η · Year FE + εi,t

Accuracy is the consensus forecast accuracy for firm i in year t, and Herding Measure is either % Star Herd or % Cons Herd for firm i in year t. Controls are variables related to consensus forecast accuracy measured for firm i in year t and are consistent with those used in previous literature studying analysts on an aggregate level (e.g., Hong and Kacperczyk 2010; Merkley et al. 2017, 2019; Michaely et al. 2019). The control variables include # Analysts, LNSIZE, LNBM, ROA, Earnings Vol, Return Vol, and RET12. All variables are defined in the appendix. Each regression includes

14 When multiple All Stars cover a firm, the most recent forecast by any All Star is used to determine herding. In the

rare case that more than one All Star releases a forecast on the same day, I use the average of these forecasts. 15 Results are not sensitive to the choice of 5% bandwidth and are robust to identifying herders based on the gamut

of bandwidths up to identifying herders based on being closer to the current All Star or consensus forecast (i.e., a 50% bandwidth) as well as using 1% of the rolling consensus as the bandwidth with overlaps identified as herding with both the consensus and an All Star or neither. In the rare case where the most recent All Star and consensus forecasts are identical and a subsequent identical forecast is made, an analyst is deemed to not be herding, though results are robust to classifying such an analyst as a herder.

(1)

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firm and year fixed effects, and standard errors are clustered by firm.16

Table 3 tabulates results from panel regressions of equation 1. Column 1 presents the results using % Star Herd as the herding measure, column 2 uses % Cons Herd, and column 3 includes both measures. Columns 1 and 3 indicate a significant negative relationship (-0.612, p<0.01 and -0.614, p<0.01, respectively) between % Star Herd and Accuracy with a t-statistic of -3.00 in each of the specifications.17 This is consistent with the information cascade hypothesis, that All Star herding reduces consensus forecast accuracy. The effect of All Star herding is also economically significant. For an average firm in the sample, one analyst choosing to herd with an All Star results in a 4% decrease in consensus forecast accuracy.18 Columns 2 and 3 depict an insignificant relationship between % Cons Herd and Accuracy, consistent with the notion that the wisdom of crowds does not offer a prediction for how herding toward the consensus might affect year-end consensus forecast accuracy.19

Collectively, the results in Table 3 support the information cascade hypothesis. Consensus forecast accuracy decreases in the level of All Star herding in the consensus. I next turn to cross-sectional variation among firms that may alter the wisdom of the crowd effect of All Star herding.

Cross-Sectional Variation in the Impact of All Star Herding

Given evidence that All Star herding is negatively associated with consensus forecast accuracy, I turn to cross-sectional variation in the relationship to shed light on settings where All Star herding might have differing impacts on consensus forecast

16 Continuous variables are winsorized at the 1st and 99th percentile. 17 Note that the measure for consensus forecast accuracy is negatively transformed, consistent with Merkley et al.

(2019). This transformation is made so that an increase (decrease) in accuracy is reflected as a positive (negative) change.

18 This corresponds to an increase in % Star Herd of 0.14. 19 Another relationship of note is that between # Analysts and Accuracy. Intuition suggests that higher coverage

levels should result in increased forecast accuracy; this is confirmed by prior research (Hong and Kacperczyk 2010; Merkley et al. 2017). This relationship appears to be driven by the strong correlation between # Analysts and LNSIZE. If the analysis is repeated excluding LNSIZE, # Analysts is significantly (p<0.01) positively related to consensus forecast accuracy; other correlations are unchanged. Repeating the analysis by excluding # Analysts retains the significance between LNSIZE and Accuracy.

16

accuracy. While All Star herding is, on average, detrimental to the wisdom of the crowd as it relates to the consensus forecast, it stands to reason that there are settings where the effect is more pronounced and others where it is muted.

I predict that herding will be more damaging for firms that should experience greater benefits from the wisdom of crowds. The power of crowd wisdom is greater when the crowd size is large and when there are many sources of information available for analysts to contribute their own value to the consensus. Thus, larger firms with more analysts in coverage should benefit the most from independently generated forecasts that enhance crowd wisdom. By the same token, an information cascade may be more likely to pick up steam and cause more damage to these firms. All Star herding will more negatively impact consensus forecast accuracy as a result.

To test this prediction, I examine firm years where I expect the most benefit from crowd wisdom and accordingly, more detriment from the information cascades brought on by All Star herding. I interact % Star Herd with indicator variables for above average coverage and size—High Coverage and Large, respectively—and re-estimate equation 1.

Table 4 tabulates the results of this analysis. Column 1 reports results for firms with above average analyst coverage, while column 2 reports results for firms which are in larger in size than average. As expected, the coefficients on the interaction terms are negative and significant for both High Coverage (-0.833, p<0.05) and Large (-1.185, p<0.05).20 This is consistent with the notion that All Star herding is more detrimental to consensus forecast accuracy for firms where analysts making independent forecasts can contribute the most value through the effect of crowd wisdom, and further suggests that All Star herding is an important factor affecting consensus forecast accuracy.

4.2 All Star Turnover in the Consensus To test H2A and H2B (H2C and H2D), the All Star arrival (departure) hypotheses,

I limit the sample to firms with no All Stars in the previous (current) year. These

20 Results are qualitatively unchanged when defining High Coverage and Large as having analyst coverage and size in

the top quartile, respectively.

17

restrictions limit noise brought about by existing All Stars in coverage toward whom analysts might already be herding or toward whom they may continue to herd in the future. I then estimate consensus forecast accuracy using regressions of the following form:

Accuracyi,t =α + β · Turnover Measurei,t + γ · Controlsi,t

+ δ · Firm FE + η · Y ear FE + εi,t

Accuracy, Controls, fixed effects, and clustering are consistent with equation 1.

Turnover Measure is an indicator variable that takes a value of 1 for firm i experiencing

a turnover event in year t and 0 otherwise. For testing All Star arrival hypothesis H2A,

the turnover measure is New All Star when an All Star initiates coverage during the

current year; for H2B, the turnover measure is New Status when an existing analyst

covering the firm gains All Star status. The turnover measure for testing the first All Star

departure hypothesis, H2C, is All Star Drop when an All Star discontinues coverage from

the previous year, and the turnover measure for testing the second All Star departure

hypothesis, H2D, is Lost Status when a continuing analyst loses All Star status from the

previous year.

Table 5 presents the results of panel regressions of equation 2. Columns 1 and 2 address the All Star arrival hypotheses that predict a decrease in consensus accuracy with an All Star arrival. The coefficients on both New All Star (-0.462, p<0.05) and New Status (-1.375, p<0.01) are negative and significant, consistent with H2A and H2B, respectively. I interpret this as consistent with the wisdom of the crowd effect of All Star herding. Analysts covering firms with no All Stars previously had no choice but to independently generate their forecasts. A new All Star arriving or an existing analyst being named an All Star represents a shock for other analysts’ forecasting decisions giving them a target toward whom they can begin herding; this initiates an information cascade and diminishes crowd wisdom.

Column 3 of Table 5 tabulates results addressing the first All Star departure hypothesis, H2C. The coefficient on All Star Drop is positive and significant (0.375, p<0.05), consistent with an improvement in consensus accuracy after the departure of

(2)

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an All Star analyst. The departure of an All Star from coverage acts as a shock to the forecasting decisions of analysts who may have been herding with the All Star. They must begin independently generating their forecasts and the information cascade ends, improving consensus accuracy.

The results in Table 5 column 4 show that the relation between Lost Status and Accuracy is in the predicted direction; however, the coefficient on Lost Status is statistically insignificant. Thus, H2D lacks support. A possible explanation for this finding is that a lost status event is not significant enough to alter the way that fellow analysts view a colleague. As a result, they may not change their herding behavior. Put another way, if an analyst is identified as superior and others begin herding, losing All Star status does not draw others’ attention to the same degree as a new status event. If this is the case, the information cascade continues and crowd wisdom remains unchanged.

Overall, the results in Table 5 are generally consistent with the All Star arrival and departure hypotheses and with All Star herding bringing information cascades that damage consensus forecast accuracy. When a target toward whom other analysts may herd is added to the consensus, analysts begin herding and crowd wisdom suffers. On the flip side, when a herding target is removed, the information cascade is ended and crowd wisdom improves.

4.3 All Star Herding and Information Asymmetry

To understand the impact of All Star herding on information asymmetry in capital markets, I study market reactions at subsequent earnings announcement dates to test H3A, H3B, and H3C, the information asymmetry hypotheses. I regress measures of market reactions in the 3-day window surrounding the earnings announcement date on All Star herding as follows:

Market Reactioni,t =α + β · % Star Herdi,t + γ · Controlsi,t

+ δ · Y ear FE + εi,t

The market reaction measures are Abn Volume, 3-Day Return Vol, or Abs Abn

(3)

19

Return for firm i in the 3-day window surrounding the earnings announcement for year t. The control variables include Abs Earnings Surprise and the interaction term, % Herd X Abs Earn Surprise. These variables control for the magnitude of the earnings surprise and the role All Star herding plays in the magnitude of the surprise; they are included to address the concern that an observed relationship between All Star herding and information asymmetry is driven only by the role that All Star herding plays in increasing the magnitude of the surprise. Additional controls include LNSIZE, LNBM, ROA, Earnings Vol, Return Vol, and RET12. Each regression includes year fixed effects and standard errors are clustered by firm. If herding increases preannouncement information asymmetry, I expect a significant positive association between % Star Herd and the market response to a subsequent earnings release.

Table 6 tabulates the results of panel regressions of equation 3. Columns 1, 2, and 3 demonstrate a significant positive relationship between % Star Herd and Abn Volume (0.134, p<0.01), 3-Day Return Vol (0.003, p<0.01), and Abs Abn Return (0.003, p<0.1), respectively. These findings collectively support the information asymmetry hypotheses, H3A-C.

I interpret these results in two ways. First, they contribute to existing evidence that analysts play an important role as information intermediaries in capital markets. Second, they are consistent with the notion that All Star herding degrades the quality of information contained in the consensus forecast which leads to an amplified response when earnings are released. Overall, the findings suggest that All Star herding reflects the presence of information cascades among the crowd of analysts that increase information asymmetry in capital markets.

5. Analyst-Level Analysis 5.1 Factors Associated with All Star Herding Behavior

Though the main goal of this paper is to document that herding toward All Stars occurs among financial analysts and to understand the consequences of this behavior in the context that is important to capital market participants – its effects on the consensus forecast – factors affecting the choice to herd are nevertheless interesting. As such, in this section, I explore potential drivers of herding behavior at an analyst level.

20

I begin with the sample of analyst forecasts which are aggregated to form the firm-year sample. This sample includes 400,335 analyst-firm-year observations. The dependent variable is Star Herd, an indicator variable that takes a value of 1 if the analyst herds with an All Star and 0 otherwise. Though the focus of this paper is on All Star herding, I additionally include Cons Herd as a dependent variable to explore whether there are factors that differentiate that target toward which analysts choose to herd. Explanatory variables include analyst-level measures associated with forecasting behavior and the firm-level variables used in the earlier analysis. The analyst-level variables include # Firms Forecasted, Specialist (as in Gilson et al. 2001), Large Broker (as in Hong et al. 2000), Firm Experience, Tot Experience, Star-Cons Dist, and ABS Dist. Variable definitions are included in the appendix.

Table A1 in the appendix reports the results of panel regressions of the herding indicators on the explanatory variables expected to impact the decision to herd. Columns 1 and 3 tabulate the results of OLS regressions with Star Herd and Cons Herd as the dependent variables, respectively; columns 2 and 4 present the results of logit regressions with Star Herd and Cons Herd, respectively.

This analysis reveals some consistent themes. In both specifications, there exists a positive relationship between the number of firms for which an analyst issues a forecast and the decision to herd. This is consistent with herding being a low-effort forecasting decision chosen by analysts who are busy. The negative relation between Large Broker and the decision to herd suggests that analysts at more prestigious brokerages with more resources at their disposal are less likely to engage in herding behavior. Negative relationships noted between the decision to herd and Firm Experience and Tot Experience are consistent with more experienced analysts who are presumably more skilled being more likely to issue forecasts that are independently generated; these findings are also consistent with Clement and Tse (2005).

One particularly interesting finding relates to the distance between All Star and consensus forecasts. I find a negative relationship between Star Herd and the signed distance measure, Star-Cons Dist, and a positive relation between Cons Herd and Star-Cons Dist. Since positive (negative) values of Star-Cons Dist represent instances where the All Star is more optimistic (pessimistic) than the consensus, this relationship

21

suggests that analysts will choose the more pessimistic option when herding. This is surprising since analysts are known to be, on average, optimistic (e.g., Dechow et al. 2000). The positive relation between both herding indicators and the unsigned distance measure, ABS(Dist), suggests that analysts are more likely to make the choice to herd when there is more disagreement between an All Star and the consensus. One interpretation of these findings is that analysts view optimistic All Star forecasts which deviate substantially from the consensus as indicative of currying favor with management as in Das et al. (1998) and Feng and McVay (2010). If this is the case, herding with the consensus could be viewed as a superior herding strategy. On the other hand, Kadous et al. (2009) find that investors attribute bold forecasts made by reputable analysts as proof of ability. It follows that analysts may view pessimistic All Star forecasts which deviate substantially from the consensus as a reflection of the All Star’s superior skill; they might choose to herd with that All Star as a result.

Other relationships seem to carry less apparent explanations. It is also important to note that some of the observed relationships could simply be statistically inevitable due to the construction of the herding measures, so any interpretations of these findings should be taken with this in mind. The results in Table A1 are intended to shed light on factors that may influence analysts’ choice to herd, rather than to provide a comprehensive understanding of the drivers of analysts’ herding behavior or to make any causal inferences with respect to these findings.

5.2 All Star Accuracy To address the concern that the firm-level results are driven by the inclusion of All

Stars who achieved their status for reasons other than their forecasting skill, I perform additional analysis at the analyst-firm-year level to confirm that All Stars are more accurate forecasters than their peers as shown by prior research (Stickel 1992; Leone and Wu 2007).21

21 The sample used for this analysis is the same as the sample used in section 5.1. It includes the 400,335 analyst

firm years which are aggregated to firm year in the main analyses.

22

For each firm year, I calculate the average accuracy of analysts covering the firm.22 For each analyst-firm-year observation, I take the difference between the analyst’s forecast accuracy and the average analyst’s accuracy; a positive (negative) difference indicates an analyst who is more (less) accurate than the average analyst covering the firm. Finally, I transform these differences to percentages for comparability across firm years such that an analyst with average accuracy is assigned a value of 0.

Table A2 in the appendix presents the results of t -tests of the difference in relative accuracy between All Stars and non All Stars. On average, All Stars are 4.78% more accurate than an average analyst while non All Stars are 0.54% less accurate than average. This is a significant difference (p<0.01) of 5.32% between All Star and non All Star accuracy, and confirms that the All Stars in the sample are superior forecasters relative to non All Stars.

6. Conclusion The objective of this study is to examine the role of information cascades and the

wisdom of crowds in the “crowd” of analysts who form the consensus forecast, a benchmark that is exceedingly important for firms, their managers, and their investors. With that goal in mind, I document significant herding of analyst forecasts around those of All Stars and find deleterious effects to consensus accuracy and the firm information environment related to the degree of All Star herding in the consensus. I additionally find evidence of information cascades beginning when a target for potential herders is newly identified or added to the consensus, and a reversal of this effect when the target of these herders is no longer available. These findings are consistent with the presence of information cascades among financial analysts and the subsequent detrimental effect they have on crowd wisdom. In additional analyses, I explore factors associated with an analyst’s decision to herd that lends further insight into this behavior.

I provide what I believe to be the first documentation of All Star herding behavior in financial analysts. The herding literature to date has focused on drivers of herding

22 Note that this measure is different than consensus forecast accuracy. It is the average of individual analysts’

accuracy (analogous to the mean error), while consensus forecast accuracy is the accuracy of the group of analysts covering a firm (analogous to the error of the mean).

23

behavior and the propensity of analysts to herd toward the consensus, and the bulk of extant analyst literature has focused on the individual-level effects of analyst behavior. I expand the analyst literature by studying the aggregate effects of analyst behavior in both the consensus and in capital markets that rely on the consensus.

I contribute to the wisdom of crowds and information cascades literature and link it to the financial literature. I add to the information environment literature by providing additional evidence that analysts and the consensus forecast are important factors in information dissemination in capital markets and by documenting a channel through which that information may be degraded. Finally, I advance the information asymmetry literature by studying features of the consensus forecast that can magnify information asymmetry. These findings also offer a word of caution to researchers studying topics such as earnings management or others that rely on empirical measures for earnings expectations which often simply take the consensus forecast as given.

In addition to the academic implications, the findings are relevant to investors in capital markets. I provide new evidence that calls into question the reliability and consistency of the consensus forecast that is treated as the main benchmark for judging firm performance. The results should also be relevant to brokerages and investment banks who employ these analysts and to regulators who oversee the industry.

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Figure 1: Information Cascade Numerical Example

Figure 1 uses a simple numerical example to illustrate how herding can reduce consensus forecast accuracy while improving the forecast accuracy of individual analysts choosing to herd. The illustration in the top panel depicts the spread of six analysts’ forecasts around a hypothetical earnings announcement with no herding. The bottom panel illustration depicts the spread of the same six analysts’ forecasts around a hypothetical earnings announcement where analysts 1, 2, and 5 have chosen to herd with the All Star. Note that absolute forecast error is reduced for all three of the herding analysts - from $10 to $1 for Analysts 1 and 5, and from $5 to $1 for Analyst 2 - while the consensus forecast error has increased from $0.17 under the no-herding regime, to $0.50 under the herding regime. Note that figure is not drawn to scale.

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Figure 2: Herding Forecast Illustration

Figure 2 illustrates the process used to identify herders. For each forecast date, the distance, D, between the most recent All Star forecast and the current consensus is computed. An analyst is deemed to be herding with an All Star (consensus) if her forecast is within 5% of the distance with respect to the most recent All Star (consensus) forecast, and not herding otherwise. Note that the figure is not drawn to scale

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Table 1: Summary Statistics Panel A - All Years

Panel B - Average by Year

Panel A depicts summary statistics for the measures of All Star herding and the control variables for all firms in my sample from 1992 through 2009. For the full sample, n = 57,132; for the sample restricted to firms with at least one All Star, n = 19,428. Panel B depicts averages of the same measures for all firms partitioned by year. Variables are defined in the appendix.

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Table 2: Correlation Matrix

This table depicts a correlation matrix for the measures of All Star herding, consensus herding, and the control variables for all firms in the sample from 1992 through 2009. ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively. Variables are defined in the appendix.

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Table 3: All Star Herding and Consensus Accuracy

Accuracyi,t =α + β · Herding Measurei,t + γ · Controlsi,t

+ δ · Firm FE + η · Year FE + εi,t

This table reports the results of regression (1) of consensus forecast accuracy on the percentage of forecasts in the consensus that herded with an All Star and the percentage of forecasts in the consensus that herded with the consensus as of the date of their forecast. Variables are defined in the appendix. All regressions include firm and year fixed effects, t-statistics reported in parentheses are based on standard errors clustered by firm, and ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.

(1)

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Table 4: Cross-Sectional Analysis

This table reports the results of regressions of consensus forecast accuracy on the degree of All Star herding in the consensus across cross-sectional factors hypothesized to vary the degree or impact of herding in the consensus. All regressions include firm and year fixed effects, t-statistics reported in parentheses are based on standard errors clustered by firm. ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.

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Table 5: All Star Turnover and Consensus Accuracy

Accuracyi,t =α + β · Turnover Measurei,t + γ · Controlsi,t

+ δ · Firm FE + η · Year FE + εi,t

This table reports the results of regression (2) of consensus forecast accuracy on variables for turnover of All Stars in the consensus. Columns 1 and 2 present results testing the All Star arrival hypotheses where the turnover measures are New All Star and New Status, respectively. The sample for columns 1 and 2 includes all firms with no All Star in coverage in year t-1. Columns 3 and 4 present results testing the All Star departure hypotheses where the turnover measures are All Star Drop and Lost Status, respectively. The sample for columns 3 and 4 includes all firms with no All Star in coverage in year t. Variables are defined in the appendix. All regressions include firm and year fixed effects, t-statistics reported in parentheses are based on standard errors clustered by firm, and ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.

(2)

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Table 6: All Star Herding and Earnings Announcement Reactions

Market Reactioni,t =α + β · % Star Herdi,t + γ · Controlsi,t

+ δ · Year FE + εi,t

This table reports the results of regression (3) of abnormal volume, return volatility, and absolute abnormal returns in the three-day window surrounding the earnings announcement date on the percentage of forecasts in the consensus that herded with an All Star. Variables are defined in the Appendix. All regressions include firm and year fixed effects, t -statistics reported in parentheses are based on standard errors clustered by firm, and ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.

(3)

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Appendix

Variable Definitions Variable Definition

Dependent Variables

Accuracy Absolute forecast error as a percentage of price; the absolute difference between the consensus forecast and the actual earnings per share scaled by stock price at the end of the prior fiscal year multiplied by -100.

Abn Volume Three day volume around the earnings announcement date scaled by average three day volume during the preceding fiscal year.

3-Day Return Vol The standard deviation of three day returns around the earnings announcement date.

Abs Abn Return The absolute value of three day returns around the earnings announcement date less value-weighted market returns in the same three day window.

Independent Variables

% Star Herd The percentage of forecasts in the year-end consensus forecast that herded with an All Star forecast.

% Cons Herd The percentage of forecasts in the year-end consensus forecast that herded with the consensus forecast.

# Analysts The number of analysts who issued a forecast in the last 90 days of the fiscal year.

LNSIZE The natural log of the market value of equity available to common shareholders of the firm.

LNBM The natural log of the ratio of the book value of equity to the market value of equity.

ROA Profitability measured as income before extraordinary items scaled by total assets.

Earnings Vol Earnings volatility; the standard deviation of ROA over the last 5 years.

Return Vol Return volatility; the standard deviation of daily percentage stock returns over the last 12 months.

RET12 The average monthly return over the last 12 months.

High Coverage An indicator variable that takes a value of 1 if a firm has above average # Analysts and 0 otherwise.

Large An indicator variable that takes a value of 1 if a firm is above average LNSIZE and 0 otherwise.

New All Star An indicator variable that takes a value of 1 if an All Star initiates coverage in the current year and 0 otherwise.

New Status An indicator variable that takes a value of 1 if a continuing analyst gains All Star status in the current year.

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Variable (cont) Definition (cont)

Independent Variables (cont)

All Star Drop An indicator variable that takes a value of 1 if an All Star discontinues coverage in the current year and 0 otherwise.

Lost Status An indicator variable that takes a value of 1 if a continuing analyst loses All Star status from the previous year.

Abs Earnings Surprise The absolute difference between the consensus forecast and actual earnings per share scaled by stock price at the end of the prior fiscal year.

Analyst-Level Variables

Star Herd An indicator variable that takes a value of 1 if the analyst herds with an All Star and 0 otherwise.

Cons Herd An indicator variable that takes a value of 1 if the analyst herds with the consensus and 0 otherwise.

# Firms Forecasted The number of firms the analyst issued a forecast for during the year.

Specialist As defined in Gilson et al. (2001); an indicator variable that takes a value of 1 if the analyst issues forecasts for 5 or more firms in the same I/B/E/S industry during the year, and 0 otherwise.

Large Broker A “high-status” broker as defined in Hong et al. (2000); an indicator variable that takes a value of 1 if the analyst works for a broker that employs 25 or more analysts during the year, and 0 otherwise.

Firm Experience The number of years the analyst has issued forecasts for the firm in I/B/E/S

Tot Experience The total number of years the analyst has issued forecasts in I/B/E/S.

Star-Cons Dist The signed difference between the most recent All Star forecast and the consensus forecast, measured at the consensus level. A positive (negative) value indicates that the All Star was more optimistic (pessimistic) than the consensus.

ABS(Dist) The absolute value of Star-Cons Dist; the absolute difference between the most recent All Star forecast and the consensus forecast.

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Table A1: Factors Associated with the Decision to Herd

This table reports the results of regressions of the choice to herd on multiple analyst- and firm-level factors hypothesized to relate to this decision. Columns 1 and 3 (2 and 4) present the results of OLS (logit) regressions with Star Herd and Cons Herd as the dependent variables, respectively. Variables are defined in the Appendix. OLS regressions include year fixed effects, and t -statistics reported in parentheses are based on standard errors clustered by firm. ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.

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Table A2: Accuracy of All Stars versus Non All Stars

This table reports t-tests of the percentage difference in relative accuracy of All Stars versus non All Stars. Amounts are presented as whole-number percentages. t-statistics reported in parentheses are based on standard errors clustered by firm. ***, **, and * indicate 1%, 5%, and 10% two-tailed statistical significance, respectively.