For Review Only - The FINANCIAL · 2018-05-07 · For Review Only The Effect of CEO Extraversion on Analyst Forecasts: Stereotypes and Similarity Bias March 2018 ABSTRACT: In an experiment

For Review Only

The Effect of CEO Extraversion on Analyst Forecasts:

Stereotypes and Similarity Bias

Journal: The Financial Review

Manuscript ID FIRE-2017-11-168.R1

Manuscript Type: Paper Submitted for Accelerated Review

Keywords: Financial analyst, CEO personality, non-financial information, stereotyping heuristic, similarity bias, extraversion

The Financial Review

For Review Only

The Effect of CEO Extraversion on Analyst Forecasts:

Stereotypes and Similarity Bias

March 2018

ABSTRACT: In an experiment with professional analysts, we study their reliance on CEO

personality information when producing financial forecasts. Drawing on social cognition

research, we suggest analysts apply a stereotyping heuristic, believing that extraverted CEOs are

more successful. The between-subjects results with CEO extraversion as treatment variable

confirm that analysts issue more favorable forecasts (earnings per share, long-term earnings

growth, and target price) for firms led by extraverted CEOs. Increased forecast uncertainty leads

to even stronger stereotyping. Additionally, personality similarity between analysts and CEOs

has a large effect on financial forecasts. Analysts issue more positive forecasts for CEOs similar

to themselves.

Keywords: Financial analyst, CEO personality, nonfinancial information, stereotyping

heuristic, similarity bias, extraversion

JEL Codes: G02, G24, M12

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1. Introduction

As information intermediaries in financial markets, financial analysts collect, analyze, and

interpret value-relevant information about firms. They use financial and nonfinancial information

to produce earnings forecasts, target prices, and stock recommendations based on their

assessment of the quality and investment worthiness of a firm (Schipper, 1991).

Prior studies suggest nonfinancial information is important for supplementing analysis and

for making accurate forecasts (Vanstraelen et al., 2003; Amir et al., 2003), and report that

analysts incorporate nonfinancial information into their analyses. The evaluation of nonfinancial

or qualitative information remains a black box, as little is known about how analysts process this

information (Bradshaw, 2009; Beccalli et al., 2015). While financial accounting information can

be processed through sophisticated estimation models, there is no standard method for evaluating

nonfinancial information (Whitwell et al., 2007). Recently, Brown et al. (2015) call for the

penetration of the black box to advance the analyst literature with regard to nonfinancial

information.

Management quality is one item of nonfinancial information (Bradshaw, 2009), sometimes

even regarded the most important piece of nonfinancial information (Grunert et al., 2005). We

argue that financial analysts use information about CEO personality as a proxy for management

quality (Chatterjee and Hambrick, 2007). However, judgments of management quality based on

CEO personality involve subjectivity and might be influenced by personal perceptions of or

interactions with top managers. This possibility motivates our main research question: How do

financial analysts incorporate personality information into their forecasts of firm performance?

We focus on CEO extraversion as a visible dimension of CEO personality that provides

insights on financial analysts’ appraisal of top management. Research on organizations and

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management devotes considerable attention to the organizational importance of top executives.

They play a decisive role in strategy formation, strategy implementation and leadership.

Following the theory of upper echelons (Hambrick and Mason, 1984), a complementary line of

research across different disciplines examines the impact of specific executive characteristics

(e.g., socio-demographics, functional experience, personalities, attitudes, and values) on the

organization and its outcome. Corporate practices like firm investment activities (Malmendier

and Tate, 2005), financial reporting (Schrand and Zechman, 2012; Olsen et al., 2013), as well as

organizational structure (Nadkarni and Herrmann, 2010) and corporate culture (O’Reilly et al.,

2014) have been shown to relate to CEO characteristics. These and others studies reveal that

observable characteristics of top executives have value-enhancing potential for organizational

outcomes. Consequently, financial analysts tend to use information about the personal qualities of

the CEO for their analyses (Chatterjee and Hambrick, 2007).

We argue that analysts use information about CEO extraversion to predict CEO quality and

firm performance. The personality dimension of extraversion is the first trait perceived by others

at the beginning of an interaction (Koole et al., 2001; McCrae and Costa, 1989). Moreover,

leadership ability has been linked to extraversion (Grant et al., 2011), which makes it an

important attribute in the context of assessing CEO qualities. Drawing on social cognition

research (Bodenhausen and Wyer, 1985; Bodenhausen and Lichtenstein, 1987; Rothbart et al.,

1987), we assume that financial analysts are subject to the judgmental heuristic of stereotyping

when processing information about CEO personality. A stereotype is a mental shortcut by which

specific traits are associated with specific outcomes.

The personality of the analysts themselves may play an important role in the processing of

information about CEO personality. We suspect that personality similarity between the financial


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analyst and the CEO can trigger a similarity bias (Byrne, 1971) and exacerbate the main effect of

stereotyping.

In a large field experiment with 191 financial analysts from 12 European countries, we

explore the relationship between nonfinancial information and profitability forecasts. Eames et al.

(2002) suggest that an experimental approach is often the most appropriate method to explore

cognitive processes. We contacted the heads of equity at large European brokerage firms and

banks, who as research unit directors were in a position to instruct their analysts to participate.

During the experiment, analysts received a short description of a fictitious software firm, its

financial performance over the last three years, and information about the CEO, which is the

treatment variable. Based on this information, we asked analysts to forecast annual earnings per

share (EPS), long-term earnings per share growth (LTG), and target price. Importantly,

participants issued two sets of forecasts, before and after information on the CEO is revealed. To

account for a similarity effect, we asked financial analysts to complete a brief personality

inventory at the beginning of our experiment.

There is a strong effect of CEO personality on all forecast variables. An extraverted CEO is

seen as the more prototypical head of a firm, which is confirmed by participants’ views on

leadership quality, competitive performance, and financial prospects. They expect significantly

higher expected earnings, long-term growth, and price targets when the firm is led by the

extraverted CEO.

Short-term and long-term forecast horizons induce different levels of uncertainty and

complexity in the forecasting process. Financial analysts are known to apply heuristics when

making forecasts (Amir and Ganzach, 1998). With increasing task complexity and uncertainty,

individuals are more likely to use heuristics (Tversky and Kahneman, 1974; Bodenhausen and

Lichtenstein, 1987). We thus expect that analysts’ rely more heavily on stereotyping when


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making longer term forecasts. Indeed, the treatment effect is weakest for the earnings per share

forecast and stronger for the long-term growth and price target.

With regard to the similarity effect, we find that participants issue more positive forecasts

when their own personality is in line with the personality of the CEO. While analysts who are

high in extraversion strongly favor highly extraverted CEOs, less extraverted analysts view

CEOs, who are low in extraversion, more positively.

2. Literature and hypotheses

Our research question—how financial analysts interpret information about CEO personality

and how this manifests in their forecasts—deals with a less well understood aspect of analysts’

decision making. We explore in this section potential underlying cognitive processes in dealing

with this type of information.

2.1 Stereotyping as judgment heuristic

We argue that financial analysts use judgmental heuristics when confronted with information

on CEO personality. Social cognition research explains how and under what conditions people

use heuristics in information processing (Tversky and Kahneman, 1974; Rothbart et al., 1978;

Bodenhausen and Lichtenstein 1987). Heuristics are defined as mental shortcuts or “simplifying

rules of thumb” that facilitate the interpretation of available information. Individuals apply

heuristics in particular when information-processing demands are high. Although heuristics

reduce the complexity and uncertainty of a task, relying on heuristics can lead to cognitive biases

and erroneous conclusions (Tversky and Kahneman, 1974).

An example of a heuristic approach that produces a bias in financial analysts’ judgment is

“scenario thinking” (Sedor, 2002). When analysts receive information about a CEO’s plans to

increase future earnings embedded in scenarios, rather than in an unstructured list, they engage in


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scenario thinking, which causes them to issue over-optimistic forecasts. According to Sedor

(2002) scenario thinking facilitates analysts’ imagination about the implementation of a CEO’s

plans and enhances analysts’ beliefs about the plausibility of those plans and the likelihood that

the CEO is able to achieve the declared goals. Simulating analysts’ forecast process in a

laboratory experiment, Eames et al. (2006) confirm that scenario frames determine how analysts

interpret information. CEO personality might induce analysts to think along the lines of a

scenario, as it invites them to predict the CEO’s behavior and to construct a narrative about how

the future of the company will play out.

A heuristic approach to nonfinancial information such as CEO personality is likely as there is

no standard method for assessing this type of information (Whitwell et al., 2007). Research on

social cognition demonstrates that people apply stereotypes as judgmental heuristics when

assessing others (Bodenhausen and Lichtenstein, 1987). Stereotypes are defined as consistent

social judgements of individual group members based on previously manifested expectations

about this particular group (Lee and James, 2007). According to this definition, stereotypes result

from belonging to a group about which people have particular knowledge, beliefs, and

expectations. Stereotyping reduces the complexity and uncertainty of the world by placing people

or events into a few simple categories (Taylor, 1981). Stereotypes can emerge from the

individual’s interaction with members of a particular group. Attributes perceived as

distinguishing features of the group’s members are likely to become stereotypes.

We assume that financial analysts who frequently interact with CEOs possess a set of

stereotypes about members of the CEO group. They hold particular expectations about what

constitutes a CEO and how he or she should act. Since CEO personal characteristics are

important indicators of behavior and subsequent performance (Hambrick and Mason, 1984), they

provide a basis for the formation of stereotypes about successful CEOs. For instance, investors


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react negatively to the hiring of female CEOs in comparison to male CEOs as stereotypes of the

CEO as a leader are dominantly masculine (Koenig et al., 2011). Investors expect male CEOs to

more closely conform to the masculinity stereotype, which leads to a perceptual bias against

female CEOs. Stereotypes in addition act as a filter to align other available information.

Information consistent with the stereotype will be perceived as affirmative while inconsistent

information will be perceived as irritating and is neglected (Bodenhausen and Lichtenstein,

1987).

2.2 Extraversion as a CEO stereotype

In addition to demographic characteristics like gender or age, stereotypes can also be based

on personality. Personality is often associated with particular behaviors, specifically occupational

behavior and job performance (Barrick and Mount, 1991). Financial analysts might make use of

information about CEO personality to infer a CEO’s competencies and qualities (Chatterjee and

Hambrick, 2007). Executive-level professions like CEO have especially been related to the

personality dimension of extraversion. Two meta-analyses find extraversion to be “the most

consistent correlate of leadership across study settings and leadership criteria” (Judge et al., 2002)

and “the strongest and most consistent correlate of transformational leadership” (Bono and Judge,

2004).

According to Costa and McCrae (1992), extraverted people are described as assertive, active,

sociable, upbeat, energetic, and optimistic. These personal attributes are often perceived as

typical for leaders and individuals tend to imagine leaders as extraverted people (Grant et al.,

2011). In comparison to the general population, extraversion as a personality characteristic occurs

significantly more often among executives (Judge et al., 2002; Bono and Judge, 2004). Since

stereotypes emerge from the high frequency of a characteristic within a particular population

(Kanter, 1977), we assume that extraversion is a stereotypical characteristic of CEOs. This


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generalization leads people to believe the qualities of extraverted executives are greater than of

those who are not extraverted. For instance, in an online survey of over 1,500 senior leaders, only

6% hold that introverted CEOs are better leaders than extraverted, and 65% indicate that

introversion is an obstacle to career development (Jones, 2006).

When analysts apply stereotypes as judgmental heuristics, they expect highly extraverted

CEOs to be more qualified and competent in leading a firm than less extraverted CEOs. We

expect that they subjectively rate the qualities of an extraverted CEO more favorably:

H1a: Financial analysts evaluate the leadership and company quality more favorably for a firm led by a highly extraverted CEO than for a firm led by a less extraverted CEO.

As a consequence, analysts will predict that firms led by highly extraverted CEOs will

perform better. Therefore, we hypothesize for three common financial forecast measures:

H1b: Financial analysts issue higher annual earnings per share (EPS) forecasts, higher long-term EPS growth forecasts, and higher price targets for a firm led by a highly extraverted CEO than for a firm led by less extraverted CEO.

Related research finds that CEO overconfidence leads to more optimistic analyst forecasts

(Kramer and Liao, 2016). While overconfidence is a judgment bias and not a personality trait, it

has been associated with the personality dimension of extraversion (Schaefer et al., 2004;

Durand, et al., 2013). However, when it comes to leadership quality, research shows an

augmented role of extraversion. Extraverts are able to generate confidence among others not just

because of their overconfidence but also because of their positive emotionality and social

dominance (Bono and Judge, 2004; Malhotra et al. 2017). As overconfidence in Kramer and Liao

(2016) is mainly observed via bold statements and forecasts of the CEO, it may affect analyst

forecasts more directly. We shut down this channel, as financial information remains the same

between treatments, and no statements by the CEOs on the status or future of the company are

communicated.


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Stereotyping may have some merit if extraverted CEO’s are indeed more successful.

However, there is insufficient evidence whether this is the case. Green, Jame, and Lock (2014) do

not find better performance for extraverted CEOs in terms of profitability. Gow et al. (2015) even

find a negative impact of extraversion on firm performance. However, other studies suggest

performance benefits from an extraverted CEO via higher strategic flexibility or better

management team integration (Nadkarni and Herrmann, 2010; Araujo-Cabrera et al., 2017). It

has also been shown that extraverted CEOs are more likely to succeed in acquisitions (Malhotra

et al. 2017).

2.3 The effect of forecast uncertainty

Under conditions of heightened uncertainty and complexity, people are more likely to resort

to heuristics. Forecasts of long-term earnings per share growth (LTG) and target prices arguably

represent forecasts under greater complexity and uncertainty than annual earnings per share

(EPS). The forecasting of LTG has been associated with greater required capacities in terms of

time, effort, and resources and greater difficulty in predicting long-term performance (Jung et al.

2012). In addition, analyst forecast bias increases with increasing forecast horizon (De Bondt and

Thaler, 1990; Amir and Ganzach, 1998). This higher bias is associated with lower information

availability as information is only revealed over time, for instance, when an earnings

announcement gets closer. This reduces uncertainty for a short-term measure like annual EPS,

while it remains high for a LTG forecast.

The same holds for the target price forecast since it represents the discounted value of the

firm in the distant future. In addition, the market price is affected by market movements and

exogenous events. There is ample evidence that target price forecasts are inaccurate and overly

optimistic (Bradshaw, 2002; Brav and Lehavy, 2003; Bonini et al., 2010; Bradshaw et al., 2013).

We exploit this variation in complexity and uncertainty to examine analysts’ reliance on


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heuristics. We assume that analysts are more prone to the stereotyping heuristic for LTG

forecasts and price targets than for EPS forecasts. This inclination should be reflected in a

stronger effect of CEO extraversion information on these forecasts. We hypothesize:

H2: The effect of the extraversion stereotyping heuristic increases with an increase in forecast horizon and will be higher for long-term growth and target price forecasts than for annual EPS forecasts.

2.4 The role of similarity

Similarity in terms of personality leads to interpersonal attraction because one perceives

people who are similar to oneself more positively than those who are different. The underlying

psychological explanation for this observation is that similarity functions as a confirmation and

reinforcement of one’s self, values, ideas, and attitudes (Montoya and Horton, 2012). People

perceive those who are similar to themselves as familiar and more likable (Moreland and Zajonc,

1982). Additionally, similarity tends to reduce uncertainty while dissimilarity increases

uncertainty (Berger and Calabrese, 1975). We are interested in examining whether this similarity

effect will amplify the effect of the stereotyping heuristic.

Evidence for the relevance of the similarity effect in the investment context comes from

studies on venture capital decision making. Franke et al. (2006) and Murnieks et al. (2011) show

an interaction between the characteristics of start-up teams and venture capitalists. Similarity in

socio-demographics, profession-related characteristics, and cognitive processing between a start-

up team and the venture capitalist is positively related to the evaluation of the team and the

assessment of the investment opportunity by the venture capitalist. Venture capitalists base their

decisions on shared characteristics with the start-up team as these characteristics are perceived as

an indicator of underlying competence (Aggarwal et al., 2015). We assume that financial analysts

interpret information about CEO personality in a similar way. We thus collect data on the

analysts’ degree of extraversion in order to explore the effect of similarity.


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We assume that personality similarity between the CEO and the financial analyst affects the

analyst’s perception of the CEO positively and is reflected in the analysts’ forecasts of firm

performance. We hypothesize:

H3a: When a firm is led by a highly extraverted CEO, extraverted financial analysts will issue more favorable forecasts (earnings per share, long-term earnings per share growth, target price) and subjective ratings than analysts who are low in extraversion. H3b: When a firm is led by a CEO low in extraversion, extraverted financial analysts will issue less favorable forecasts (earnings per share, earnings per share long-term growth, target price) and subjective ratings than analysts who are low in extraversion.

3. Experimental design

We used a between-subjects design with two different treatments to test our hypotheses on

the association of CEO extraversion with analyst forecasts. Participants were randomly assigned

to either a less or more extraverted CEO treatment. We applied a pre-/post-test design in which

participants submitted forecasts and ratings on management and company quality before and after

the manipulation. Before they received the CEO personality information, analysts could base

their forecasts only on firm data. This ensures that there were no systematic differences between

the groups and that any disparities in the post-forecasts were indeed caused by the manipulation.

3.1 Participants and experimental procedure

Participants were 191 sell-side financial analysts recruited from brokerage firms and banks

across Europe. We gathered contact information from the respective heads of equity, who also

served as the directors of the research units and had authority to instruct analysts to participate.

We explained the motivation of our study to these superiors and asked for support in the form of

instructing their analysts to participate. We followed this procedure to avoid selection effects

occurring with voluntary enrollment. This means, however, that we do not have information on

the full sample of eligible employees and on how superiors made the selection. All nominated


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analysts participated. After arranging an appointment, we visited analysts at their offices to

conduct the study in person.

The experiment was conducted in the presence of a research team member and in most cases

with only one participant at a time.1 By doing so, we prevent mutual influence between

participants and make sure that participants seriously engaged with the experiment. However, the

supervision was light in the sense that nonresponses were possible and that we guaranteed

anonymity of the responses. At the beginning of the experiment, financial analysts learnt that

they were participating in a study on how they value a firm’s intangibles. After a short

introduction, participants provided socio-demographic data and completed Goldberg’s (1992)

extraversion scale. The scale presented ten different adjectives and asked participants to which

extent these adjectives are descriptive of their personality. All items were measured on a 9-point

scale, on which higher values indicated greater extraversion (see Online Appendix A2).

Participants then received the case material comprised of financial data and a short fictitious

company description. The materials were contained in closed, unmarked envelopes from which

the experimenter selected one blind to the treatment condition. Analysts reviewed the material as

long as they liked, and afterwards submitted initial forecasts of annual EPS, long-term EPS

growth, and target price. At the same time, they were asked to provide several subjective ratings

on the quality of the company and its management. These responses were collected before the

treatment manipulation and enable us to assess whether the two groups were equivalent.

As filler task between the pre-treatment and post-treatment forecasts, analysts were offered a

choice of 20 different small muesli packs as a gift for participation. The time spent to select

among the packs was on average five minutes. Then participants were told that a new CEO would

1 In case of more than one participant at a time, there is no communication allowed between participants and they work on the materials in silence. 2 The online appendix is available at the journal webpage (https://financialreview.poole.ncsu.edu).


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be joining the company, and we asked them to read a description of the new CEO’s personality

and leadership style. We randomly assigned participants to experimental conditions and provided

two different versions of this CEO description. The CEO was depicted as either being a highly

extraverted leader or one with low extraversion. The two conditions occur about equally.

After reading the description, the analysts provided a second forecast for annual EPS, long-

term EPS growth, and target price. Participants had access to all first-round information while

making these forecasts except for their prior forecasts. They also submitted a second set of

ratings on company quality and leadership. When these forecasts and ratings were returned, the

experiment ended. Participants completed the study in an average of 25 minutes. Figure 1

summarizes the experimental procedure.

---------------------------------------------- INSERT FIGURE 1 ABOUT HERE

----------------------------------------------

3.2 Case materials

Case materials described a fictitious company named Connexis Software Inc. that participants

were asked to evaluate. We provided information on financial data comprised of financial figures

from the company’s balance sheet, income statement, and cash-flow statement, as well as stock

market data and the evolution of employee figures. In addition to financial data, participants

received a short written company description presenting the firm’s character, vision, and the

treatment of employees, investors, and business partners (for the complete experimental

materials, see Online Appendix A).

Case materials were designed to overcome methodological challenges with eliciting forecasts

in an experimental setting. It was necessary to provide sufficient and appropriate financial data

that enabled the analysts to perform a realistic company analysis. Therefore, we relied on

financial data from a real company to ensure realistic and internally consistent data, and to reduce


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elements that might confuse analysts and might promote measurement error.3 The method of

constructing case materials from financial data of a real company has been applied in previous

studies (e.g., Sedor, 2002). Choosing a company from the software industry is justified by the

high relevance of nonfinancial information for valuation in this industry (Sievers et al., 2013).

In addition to the individual firm data, we included an aggregated industry multiple and

aggregated Dow Jones Stoxx 50 multiple for comparison. In terms of stock market data, we

presented information about the share price, price earnings multiple, and market beta (see Online

Appendix). We pretested the compiled case information with five analysts. According to their

responses, the information was sufficient to perform a company analysis and issue forecasts for

EPS, long-term growth of EPS, and target price.

3.3 CEO extraversion treatments

We manipulated CEO personality by presenting two contrasting CEO characters in a

between-subjects design. The two alternative descriptions of the CEO varied considerably in the

personality dimension of extraversion. One group of participants received information about a

highly extraverted CEO; the other group information about a CEO with low extraversion. For the

descriptions, we refer to the literature to identify behavioral characteristics (Judge and Bono,

2000; Judge et al., 2002; Bono and Judge, 2004; Kahnweiler, 2013). We focus on interaction and

communication with a firm’s stakeholders as the CEO’s central tasks, combined with how the

CEO makes decisions.

Behavioral patterns characterizing the low extraversion CEO included engaged listening and

focused conversations, a preference for written communication, and careful reflection on all

relevant issues to develop substantive solutions. In contrast, the extraverted CEO was

3 We base the case materials on a real software company, Business Objects SA. Financial figures were taken from Business Objects’ balance sheet, income statement, and cash-flow statement. Additional data were retrieved from the Thomson Reuters Investext Database and Thomson Reuters I/B/E/S.


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characterized by an assertive and energetic personality, with a preference for verbal

communication, interaction, and socializing, which the CEO sees as the basis for teamwork (see

Online Appendix).

However, when creating the hypothetical characters, we ensured that the two CEO types do

not differ in their competencies but mainly in their leadership style. For example, we mentioned

that both CEOs have led another software company for the past seven years. Moreover, the

general wording and structure of the descriptions was identical; we solely altered the defining

adjectives and attributes to place the respective CEO on different ends of the extraversion

dimension. We also test whether the tone of the description differs using the word list of

Loughran and McDonald (2011). We find a total of 12 positive words and two negative words for

the description of the high extraversion CEO and 15 positive words and six negative words for

the description of the low extraversion CEO. The positive-negative difference is thus ten for the

high extraversion treatment and nine for the low extraversion treatment. The LM tone measure of

Loughran and McDonald (2015) representing the percentage of positive minus negative words is

3.75% and 3.14%, respectively. We believe that the small difference will not be responsible for

our results.

To validate the experimental design, we conducted a pre-test of the CEO descriptions using

Amazon’s MTurk platform.4 542 MTurkers were exposed to one of the two descriptions and then

evaluated the extraversion of the CEO. For this rating, we used the same extraversion scale as for

the self-rating of analysts. The mean rating for the extraverted CEO was 7.6 and for the less

extraverted CEO 4.9; the difference is highly significant (t=20.8, p<.001). This result confirms

4 Amazon’s Mechanical Turk is a platform to hire workers for usually short tasks (called Human Intelligence Tasks or HITs). MTurkers select and complete HITs that are placed on the platform by requesters. Requesters can set certain requirements that participants should fulfill and pay them a fixed and/or performance-based compensation. MTurk is increasingly used in research and has been shown to produce as reliable data as lab studies with the advantage of featuring a more diverse participant pool (Buhrmester et al. 2011, Horton et al. 2011, Berinsky et al. 2012).


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that perceptions of CEO personality were clearly different and that the manipulation of CEO

extraversion was successful.

3.4 Forecasts and ratings

Participants provided two sets of forecasts for annual EPS, long-term growth of EPS, and the

target price, which were elicited before and after the treatment variation. As we were not certain

how familiar individual analysts are with the numerical prediction of EPS, we anchored their

forecasts by providing a forecast range including a low, mean, and high forecast. These forecasts,

which were also graphically displayed on a line (see Online Appendix), were introduced to

participants as resulting from consensus forecast. Data for these forecasts corresponded to the

real-world data of the company used to construct the case material and were retrieved from

Thomson Reuters I/B/E/S.

In addition to the quantitative forecasts of firm performance, participants were asked for

qualitative evaluations of management quality and firm prospects. They rated the extent to which

14 verbal statements describe the company, using a nine-point scale from “not at all” to

“extremely.” Statements included, for example, “seems to have excellent leadership” or “looks

like it would be a good investment” and were adapted from Walsh and Beatty (2007) (for the full

list see Online Appendix A). The ratings were elicited both before and after the treatment. The

repeated elicitation enables us for both sets of variables – forecasts and ratings – to examine

changes caused by the CEO personality manipulation.

4. Results

4.1 Descriptive statistics

We remove four sample observations because all the forecasts are missing. The remaining

187 analysts are predominantly male (87%), on average 39 years old, and have been working as


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financial analysts for an average of 12 years (see Table 1). We refer to the Global Industry

Classification Standard (GICS) to determine the analysts’ industry specializations. Participants

report a mean (median) of 1.5 (1) industry specializations, with the most common being

industrials (31%), followed by consumer staples (22%), technology (18%), and financials (16%).

By country of residence (not nationality), Germany (35%), the UK (19%), Norway (10%), and

Switzerland (8%) represent the largest share of analysts.

Analyst extraversion is assessed from participants’ self-evaluation with respect to the ten

provided adjectives. Observed correlations between the items are high (between 0.4 and 0.8) and

Cronbach’s alpha of inter-item reliability is 0.92. We construct a measure of extraversion by

taking the mean of the items for each participant. This simple average has very similar weights as

the first component of a principal component analysis and explains 60% of total variance

between items. Financial analysts rate themselves on average at 6.1 on the extraversion scale,

with individual values ranging from 2.3 to 8.3. The distribution is centered above the middle

point of the scale and negatively skewed, indicating that many analysts view themselves as rather

extraverted.5 We nevertheless use a median split to distinguish between more and less extraverted

analysts.

--------------------------------------------- INSERT TABLE 1 ABOUT HERE

---------------------------------------------

Table 1 shows the forecasts and subjective company rating submitted by participants before

the treatment variation. Analysts report a mean of $1.37 for annual EPS, ranging from $0.81 and

$1.75. The EPS long-term growth rate forecast averages 18.6%, ranging from -5% to 40%. The

mean for the target price forecast is $35.51, ranging from $20 to $55. Many analysts submit

5 Only 16% rate themselves below the middle-point of the scale (5). It is possible that rather extraverted people select into the profession of financial analysts. However, the high ratings might also result from the rather positive connotation of several of the adjectives (e.g., energetic, active). These adjectives are rated highest among the total set of adjectives.


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values within the ranges suggested in the case materials but still express their relative optimism

or pessimism. For an aggregated company rating, we take for each analyst the mean across the 14

statements on company quality. Analysts view the company rather favorably with an average

rating of almost six on the nine-point scale, but there exists considerable variation. The ratings

are slightly positively correlated with financial forecasts.

Table 1 also displays means of all variables separately for the treatment groups with a more

or less extraverted CEO. The group means are in all cases close to each other, and the difference

never reaches significance in a standard t-test. Importantly, extraverted analysts are equally likely

to be confronted with an extraverted CEO as with a less extraverted CEO. The treatment groups

thus do not systematically differ in terms of analyst similarity with the CEO. Based on

observables, the randomization between treatments groups appears successful.

4.2 The stereotyping heuristic

As treatment effect, we predict that analysts alter their forecasts for a company after learning

about the personality of its CEO. In particular, in line with the stereotype of successful managers

being extraverted, they evaluate the company more positively when the CEO is described as

extraverted. Table 2 reports forecasts before and after the treatment variation for both groups of

analysts. In the treatment with an extraverted CEO, forecasts become more positive after the

personality description is revealed. While EPS forecasts increase only slightly, differences are

larger and statistically significant for long-term growth and price target. Analysts react to the less

extraverted CEO with forecasts going down. Economically, the effects are larger than for the

extraverted CEO and strongly significant for all forecast items.


---------------------------------------------


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To test for the net treatment effect, we need to compare the reaction of both groups. This

corresponds to a difference-in-differences as changes in forecasts are compared between groups.

Row (7) in Table 2 shows this difference, which is significantly positive for all three forecasts.

Information on an extraverted CEO is interpreted more positively than information on a less

extraverted CEO. On an individual level, 40 participants increase their long-term growth forecast

in the treatment with an extraverted CEO, but only 24 in the less extraverted CEO treatment. The

univariate results provide first evidence for Hypothesis 1b.

We now investigate whether similar changes can be observed for the ratings of firm and

leadership quality. Table 3 shows differences in ratings before and after for each statement

separately by treatment group. For all statements except one, the impression of the company led

by the extraverted CEO is more favorable. However, the strength of the difference varies across

domains. Based on wording and correlations, the statements can be classified as relating to

workforce (quality of employees, treatment of employees), leadership (management and

strategy), business (competition, market performance, and growth), and investment (financial

market performance). We admit this classification is to some extent arbitrary and other choices

can be made.


---------------------------------------------

Results reveal that the treatment effect on the category workforce is relatively weak. Both

CEOs are seen somewhat negatively, perhaps because analysts rather imagine conflicts and

difficulties from working under either CEO. A clearer picture emerges for leadership. Consistent

with the stereotyping heuristic, ratings increase for the extraverted CEO and the change is most

pronounced in this domain. The rating changes for the CEO who is low in extraversion are

negative, and the differences between groups are strongly significant. The strength of the


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treatment effect is even higher for statements on the firm’s business. In this case the effect is

driven by a substantial drop in the ratings of the less extraverted CEO. Analysts seem to believe

that this CEO type is unable to seize opportunities in the market. Finally, the investment category

also shows significant differences in favor of the extraverted CEO. Overall, the findings support

Hypothesis 1a.

To confirm our results in a multivariate setting, we run regressions of the change in forecasts

on a treatment indicator and controls:

�� ,� −�� ,� = � + �� +�� +!� (1)

We run separate OLS regressions for EPS, LTG, and price target over the cross-section of

analysts i. As the dependent variable, we use the difference between the forecast submitted after

and before the treatment variation. A treatment dummy (taking the value of 1 for the extraverted

CEO and 0 otherwise) is our main variable of interest. Controls include demographic

characteristics of analysts, their own extraversion score, specializations, and country fixed

effects. Regressions are estimated using robust standard errors.

Regression results displayed in columns (1), (4), and (7) of Table 4 include analysts’ gender,

age, and self-reported extraversion.6 The treatment effect is positive, significant, and comparable

in magnitude to the univariate results. Gender and age remain insignificant. Interestingly,

extraverted analysts seem to revise their forecasts downward. This result will be important for our

later analysis of analyst-CEO interaction effects. In a next step, we add industry coverage as

indicator variables and country fixed effects (see columns 2, 5, and 8). The large number of these

variables increases R² but does not affect the treatment effect.

---------------------------------------------

6 We do not include experience as it is highly correlated with age (ρ=0.84) and is likely to introduce multicollinearity. In a robustness test, we run the regressions with experience instead of (or in addition to) age and obtain similar results.


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INSERT TABLE 4 ABOUT HERE ---------------------------------------------

Finally, we include the changes in the company ratings in the regression (columns 3, 6, and

9). These changes in analyst perception of firm and leadership quality are one channel of the

treatment effect and are likely to influence the forecasts. Indeed, the coefficients are positive and

significant in all three regressions, and the treatment dummy coefficients are reduced but remain

significant. We conclude that the qualitative ratings can act as a mediator variable in the sense

that forecasts improve due to a more positive opinion about the firm and its CEO.

In a mediation analysis, we explore this result further. We aim to understand if and to which

extent the effect of the treatment on forecasts is mediated by the perception of leadership quality

(Baron and Kenny, 1986). Using the methodology by Emsley et al. (2010), we estimate the direct

effect of the treatment on forecasts and the indirect effect caused by an updated view of

leadership. The results in Figure 2 show that both channels are significant. The indirect effect

amounts to about one quarter to one third of the total effect depending on type of forecast (EPS,

LTG, or price). This implies that forecasts are raised at least in part because the extraverted CEO

is regarded as the better manager.


----------------------------------------------

We conclude that in line with Hypotheses 1a and 1b, the description of an extraverted CEO

induces more optimistic forecasts and more favorable company ratings. Part of the forecast

increase is driven by higher ratings of leadership quality consistent with a stereotyping heuristic.

4.3 Forecast uncertainty

According to our second hypothesis, the stereotyping effect will be stronger for long-term

EPS growth and target price forecasts than for annual EPS forecasts because of the difference in


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the forecast horizon and forecast uncertainty. Confronted with higher uncertainty and complexity,

people tend to rely more on heuristics. The results in Table 2 and 4 already conveyed that the

statistical significance of the treatment effect is weakest for the EPS forecast. However, the

economic significance of the effect is impossible to judge from significance levels alone.

To derive comparable statistics, we consider the percentage change in the forecasts, the

change in terms of standard deviations, and the change in terms of provided forecast ranges (i.e.,

the ranges depicted in the question as consensus forecast). We consider these alternatives as the

nature of the three forecasts is different and it is not clear what the right comparison is. Table 5

displays absolute changes in the forecasts, which is by how much participants on average revise

their forecasts. The revision amounts to 5% or 0.45 standard deviations for EPS, to 17% or 0.56

standard deviations for LTG, and to 10% or 0.58 standard deviations for the target price. This

already shows that participants adjust their LTG forecasts and price targets more strongly.


---------------------------------------------

Looking at the treatment groups separately, analysts increase their forecasts after learning

about an extraverted CEO much more for LTG and price targets than for EPS. With less

extraverted CEOs, downward revisions are smaller for EPS in percentage terms and closer to the

other forecasts when expressed in standard deviation or percentage of provided forecast range. In

conjunction, this results in a treatment effect that is much smaller for EPS than for the other

forecasts.

For an additional test of effect size we perform a multivariate analysis of variance

(MANOVA) of the changes in forecasts depending on the treatment. We find that the effect size

is greater for long-term growth forecasts (partial η² = .096) and target prices (partial η² = .084)

than for EPS forecasts (partial η² = .035). The reaction to the CEO personality information is


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again smaller for EPS forecasts. Overall, the results suggest, in line with Hypothesis 2, that when

making forecasts under greater uncertainty and a longer forecast horizon, financial analysts rely

more strongly on the stereotyping heuristic.

4.4 Similarity Bias

The similarity bias suggests that people view other people more positively if they share

certain characteristics, for example, personality traits. We expect extraverted analysts to react

more positively to an extraverted CEO and likewise less extraverted analysts to evaluate the CEO

more positively who is low in extraversion. We classify analysts as extraverted if their self-rating

on the extraversion scale is above the median.

Figure 3 shows the changes in forecasts (EPS, LTG, and price target) separately for the cases

of matching personalities and opposite personalities. Personalities match if either the analyst and

the CEO are both more extraverted or both less extraverted. Non-matching personalities covers

the remaining cases. Matching personalities on average result in an upward revision of forecasts

while opposite personalities induce a downward revision of forecasts. The magnitude of the

effect is about as large as the treatment effect, but it is orthogonal to the treatment effect as

matching personalities occur in both treatments. As a consequence, the similarity bias is likely to

exacerbate the treatment effect for extraverted analysts and to reduce it for less extraverted

analysts.


---------------------------------------------- To explore this further, Table 6 differentiates between results by analyst personality and CEO

personality. Extraverted analysts make a strong downward revision in their forecasts if they learn

the CEO is low in extraversion. Except for EPS, they also make the strongest upward revision

when confronted with an extraverted CEO. Together this response induces a very strong


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treatment effect, which is about twice as large as for the full sample. In contrast, less extraverted

analysts seem not to react to the extraversion treatment; the treatment effect is even slightly

negative for two of the three forecasts. It appears that the positive stereotype of an extraverted

CEO is for this group counterbalanced by the dissimilarity of personalities, resulting in a zero net

effect.

The rightmost column of Table 6 shows the similarity effect by treatment. It should be

positive for the extraverted CEO treatment and negative for the less extraverted CEO treatment

(as we subtract less extraverted analyst forecast changes from those of extraverted analysts). This

holds true in five out of six occasions for which the effect is also significant. Taking into account

that the samples become somewhat small in this two-way split (n about 45), our Hypotheses 3a

and 3b about the presence of a similarity bias are generally confirmed.

---------------------------------------------- INSERT TABLE 6 ABOUT HERE

----------------------------------------------

As a next step, we explore whether the similarity effect is also visible in the ratings of

company and leadership quality. As Table 7 shows, analysts perceive companies more favorably

when they learn about a CEO that has a personality similar to theirs. All changes in sub-ratings

(except workforce) are positive with the highest increase observed for leadership quality. CEOs

with contrasting personalities are viewed negatively, all sub-ratings decrease in this case. The

overall similarity effect is in magnitude and statistical significance strongest for leadership

quality, suggesting that analysts associate their own personality with better leadership. This effect

is strong enough to overturn the stereotyping heuristic for the group of analysts who are low in

extraversion. They perceive the less extraverted CEO as slightly superior in the aggregated rating

and in the leadership rating. The strong treatment effect thus mainly comes from the extraverted

analysts who vastly favor their personality type.


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----------------------------------------------

We finally turn to a multivariate analysis to observe the interplay of the effects. Table 8

displays a regression of changes in forecasts on explanatory variables (as in Table 4), now

including a similarity dummy taking a value of one if analyst personality and CEO personality

are similar and zero otherwise. The regression results show that the treatment effect barely

changes, which is not surprising as treatment effect and similarity effect are orthogonal. The

coefficient for the similarity dummy itself is positive and strongly significant. Interestingly, its

magnitude is about the same as the one for the treatment effect. The remaining control variables

show the same pattern as before; notably analyst extraversion has a negative overall impact on

the change in forecasts, and the change in the subjective company rating has a positive impact.


----------------------------------------------

To examine the interaction effect more closely, we define an interaction term between analyst

extraversion and the treatment group. This interaction isolates the treatment effect for the

extraverted analysts and reveals whether similarity exacerbates the treatment effect. Table 9

shows results of this regression specification. Most importantly, the treatment effect disappears,

which means that it is not present for less extraverted analysts. Coefficients are in fact close to

zero and in most cases negative. As seen from the univariate results before, the similarity bias

neutralizes the treatment effect. However, it is not strong enough to fully reverse the stereotype of

extraverted CEOs as successful, as otherwise the treatment effect for the less extraverted should


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be more negative. For analyst extraversion as such we again observe the negative effect reported

before.7

The interaction term introduced in the regressions has a significant and strongly positive

effect on the changes in forecasts. It confirms that the treatment effect is concentrated in the

extraverted analysts. The magnitude of the interaction effect is about twice as large as the

treatment effect estimated on the full sample. We conclude that the similarity between the analyst

personality and the personality described in the CEO portrayal augments the extraversion

stereotype of CEOs. Together with the other evidence, we view this result as a further

confirmation of our Hypotheses 3a and 3b.


----------------------------------------------

5. Robustness tests

We perform several additional tests to establish the robustness of our results. In the main

analyses, we use the changes in forecasts as the dependent variables. An alternative would have

been to consider the level of the forecasts after the treatment variation. Given that participants are

randomly assigned to treatments, the pre-treatment forecasts should play no role, and they are

about equal across the two groups anyway (see Table 1). We thus repeat the analysis of Table 4

now with the ex-post forecasts as the dependent variable. As Table B.1 in the Online Appendix B

shows, the economic magnitude of the treatment effect remains about the same although

statistical significance is somewhat reduced. This is due to the higher idiosyncratic variation in

the forecast level variable, becoming also apparent in the lower R² in these regressions. In

particular, the introduction of the company rating reduces the treatment effect, which speaks

7 The larger magnitude of the coefficient is due to the dummy used here instead of the continuous variable in the regressions before.


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again in favor of a mediation of the treatment effect through the perception of company quality.

In an (unreported) mediation analysis, we are able to confirm this channel.

To tame the analyst specific effects within the previous regression, we include pre-treatment

forecasts in the regression. This specification is closest to an analyst fixed effect given that real

fixed effects cannot be used in purely cross-sectional regressions. Table B.2 in the Online

Appendix reveals that the coefficients for the pre-treatment forecasts range between 0.82 and

0.93. In this specific context, it can be interpreted as pre-treatment forecasts explaining roughly

80 to 90% of the post-treatment forecasts. This makes sense, as the CEO personality is only one

additional piece of information to a large set of information analysts receive. The treatment effect

in these regressions is very similar to the results presented in Table 4, which is not surprising as

the regressions including the ex-ante forecasts represent quasi-changes regressions. Nevertheless,

the different format provides some reassurance for our previous results.

For the similarity effect, we reproduce Table 8 with the ex-post forecasts as dependent

variable. As Table B.3 shows the effect remains intact in the level regressions. Results for

regressions including the pre-treatment forecasts are very similar (unreported). We also obtain

similar results for the interaction effect (unreported). We conclude that our results hold for

regressions in forecast levels as opposed to changes in forecasts.

The similarity effect was established using a binary variable indicating whether analysts are

more or less extraverted relative to the median. An alternative is to exploit the continuous nature

of analyst extraversion. We construct a variable that equals the extraversion rating for the group

confronting the extraverted CEO and the value of 9 minus the rating for the group with the less

extraverted CEO. The higher this variable, the more similar is the analyst in terms of extraversion

to the respective CEO under the assumption that the two CEO types reside on either end of the


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scale.8 Table B.4 shows a positive similarity effect also for the continuous measure of similarity.

To interpret the magnitude of coefficients, one needs to know that the binary measure used before

corresponds on average to a 2-point change on the extraversion scale. Multiplying coefficients by

two yields a similar effect size as established in the main analysis. A downside to the continuous

measure is that treatment effect and similarity effect are no longer orthogonal. Similarity ratings

are in general higher for the extraverted CEO treatment and thus the measure picks up part of the

treatment effect, which is no longer significant in these regressions.

Since the case materials present a company belonging to the software industry, we test

whether analysts who cover this industry evaluate the company differently. In total, 34 analysts

report to analyze technology companies (18% of the sample). We first compare the means of the

pre-treatment forecasts between technology analysts and other analysts. There is no significant

difference for any of the three forecast measures. We then examine the specialization indicators

in our main regressions more closely. The technology dummy is never significant in the

regressions.9 The treatment effect is not different for technology analysts (measured by an

interaction term). If anything, it is a little bit stronger although due to the low number of analysts

covering the industry statistical significance cannot be established.

We further test for the effect of financial analysts’ years of experience. Kaustia et al. (2008)

demonstrate that experience reduces the effect of heuristics. In our main regressions, age is

included, which is very highly correlated with experience. Replacing age by experience does not

change the negligible and insignificant effect on analysts’ forecasts. However, this direct effect of

8 Another possibility is to use the average ratings of the MTurk pre-test to classify the CEOs in terms of extraversion (7.6 and 4.9, respectively). One can then calculate the absolute distance between each analyst and the confronted CEO. Results for this distance measure are qualitatively similar to those for the proposed continuous measure. 9 Other specializations obtain statistical significance in some of the regressions. However due to the number of variables (10), it can be expected that such significance occurs without being causally related to the forecasts.


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experience is not of primary interest in this context but rather its interaction with the stereotyping

heuristic and the similarity bias. We split the sample at the median in more experienced and less

experienced analysts (up to ten years of working in the profession). Interactions of experience

with the treatment effect and the similarity effect are not consistent across forecast items. As

none of the interactions obtains statistical significance, the evidence remains inconclusive.

6. Conclusion and discussion

We conducted an experiment with practitioners from the financial industry to examine how

analysts interpret information about the personality of a CEO. We manipulated the extraversion

dimension of personality, as it is easily identifiable and a stereotypical characteristic often

associated with successful CEOs. In line with this stereotype, we find that analysts supply higher

financial forecasts for a company led by an extraverted CEO. Specifically, analysts issue higher

forecasts for annual earnings per share, long-term earnings per share growth, and target price, and

view the company, its leadership quality, and its business prospects more positively.

Similarity in personality between an analyst and the CEO contributes to these positive

perceptions. We demonstrate that, when a firm is led by an extraverted CEO, extraverted

financial analysts issue more optimistic forecasts than do less extraverted analysts. Likewise, low

extraversion analysts issue more favorable forecasts for a company led by a less extraverted

CEO. This similarity effect and the stereotyping effect coexist and work in the same direction for

extraverted analysts and in opposite directions for less extraverted analysts. It leads extraverted

analysts to strongly favor extraverted CEOs while the net effect is close to zero for less

extraverted analysts.

We contribute to the exploration of Bradshaw’s “black box” of how financial analysts’ deal

with qualitative management information. In general, research on how information is used by


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financial analysts as part of their decision making is still limited. We believe this line of inquiry

provides much potential for future research.

Among the limitations of the current study is that we still know very little about the actual

performance of CEOs with different personalities. It thus remains unclear whether the stereotype

of a successful extraverted CEO is a useful heuristic or a judgmental bias. Keeping all

quantitative information on the firm and all factual information on CEO competence constant, we

cannot fully exclude that there is informational value in CEO personality and leadership style.

We therefore refer to a stereotyping heuristic and not to a stereotyping bias.

With regard to the similarity effect, we can be more certain. In this case, the forecast outcome

is shown to depend on the personality of the analyst, which under normal circumstances is totally

unrelated to the future of the company. An analyst should thus ignore closeness in personality to

the CEO. Such feelings of similarity are not limited to personality, but might extend to gender,

ethnicity, or political affiliation as recently revealed by Jannati et al. (2016) in a study on

earnings forecasts and earnings surprises. We thus conclude that there is a broad applicability of

this similarity bias or in-group bias in financial judgment.

While our focus is on extraversion, future research can explore whether the same effect holds

true for other personality dimensions. Agreeableness, for example, has been examined in the

context of transformational leadership (Rubin et al. 2005), building trust, and engaging in honest

relationships with employees (Keller 2000). Narcissism, sometimes seen as the opposite of

agreeableness has been explored as a feature of CEO personality (Strelan 2007; Paulhus and

Williams 2002). Interestingly, CEO narcissism is associated with both a dark side of CEO

behavior (Resick et al. 2009; Spain et al. 2014) and distinct visionary impulse essential to

effective leadership (Rosenthal and Pittinsky 2006). It remains, therefore, open what stereotype

analysts may hold about narcissist CEOs.


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Our results are relevant both for financial analysts and their employers as well as for firms

interacting with analysts and investors. While analysts should raise their awareness on how their

subjective perception of a CEO will influence their financial judgments, firms might want to

consider how a CEO presents her-/himself in public, especially given the growing personalization

of CEOs and extended communication channels. What makes this more difficult is that the same

information content might be perceived differently by different analysts, not only due to factual

disagreement but also due to felt closeness to the CEO. Investor relations representatives are well

advised to control the provision of CEO-specific information and to track its reception.

Our study calls for a shift of attention towards the receiver of that information, the analyst.

Not all determinants of analyst forecasts reside in the company as the object of analysis, but also

in the analyst as originator of the forecasts. Financial professionals are often unaware of the

cognitive factors that affect their information processing and subsequently their decision making

(Kaustia et al. 2008). We expose two of these factors, the stereotyping heuristic and the similarity

bias. Although de-biasing is notoriously hard in practice, the identification of the cognitive

processes is the first step in this direction.


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References

Aggarwal, R., D. Kryscynski, and H. Singh, 2015. Evaluating venture technical competence in venture capitalist investment decisions, Management Science 61(11), 2685-2706.

Amir, E., and Y. Ganzach, 1998. Overreaction and underreaction in analysts' forecasts, Journal of

Economic Behavior & Organization 37(3), 333-347. Amir, E., B. Lev, and T. Sougiannis. 2003. Do financial analysts get intangibles? European

Accounting Review 12 (4), 635-659. Araujo-Cabrera, Y., M. A. Suarez-Acosta, and T. Aguiar-Quintana, 2017. Exploring the

influence of CEO extraversion and openness to experience on firm performance: The mediating role of top management team behavioral integration, Journal of Leadership &

Organizational Studies 24(2), 201-215. Baron, R.M., and D.A. Kenny, 1986. Moderator-mediator variables distinction in social

psychological research: Conceptual, strategic, and statistical considerations, Journal of

Personality and Social Psychology 51(6), 1173–82. Barrick, M.R., and M.K. Mount, 1991. The big five personality dimensions and job performance:

A meta‐analysis, Personnel Psychology 44(1), 1-26. Beccalli, E., P. Miller, and T. O'Leary, 2015. How analysts process information: Technical and

financial disclosures in the microprocessor industry, European Accounting Review 24(3), 519-549.

Berger, C.R., and R.J. Calabrese, 1975. Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication, Human Communication

Research 1(2), 99-112. Berinsky, A.J., Huber, G.A., and G.S. Lenz, 2012. Evaluating online labor markets for

experimental research: Amazon.com's Mechanical Turk, Political Analysis 20(3), 351-368.

Bodenhausen, G.V., and M. Lichtenstein, 1987. Social stereotypes and information-processing strategies: The impact of task complexity, Journal of Personality and Social Psychology 52(5), 871-880.

Bodenhausen, G.V., and R.S. Wyer, 1985. Effects of stereotypes in decision making and information-processing strategies, Journal of Personality and Social Psychology 48(2), 267-282.

Bonini, S., L. Zanetti, R. Bianchini, and A. Salvi, 2010. Target price accuracy in equity research,

Journal of Business Finance & Accounting 37(9‐10), 1177-1217. Bono, J.E., and T.A. Judge, 2004. Personality and transformational and transactional leadership:

A meta-analysis, Journal of Applied Psychology 89(5), 901-910. Bradshaw, M.T., 2002. The use of target prices to justify sell-side analysts' stock

recommendations, Accounting Horizons 16(1), 27-41. Bradshaw, M.T., 2009. Analyst information processing, financial regulation, and academic

research, The Accounting Review 84(4), 1073-1083. Bradshaw, M.T., 2011. Analysts’ forecasts: what do we know after decades of work? Working

Paper, http://ssrn.com/abstract=1880339. Bradshaw, M.T., L.D. Brown, and K. Huang, 2013. Do sell-side analysts exhibit differential

target price forecasting ability? Review of Accounting Studies 18(4), 930-955. Brav, A., and R. Lehavy, 2003. An empirical analysis of analysts' target prices: Short-term

informativeness and long-term dynamics, Journal of Finance 58(5), 1933-1968.


123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Review Only

- 32 -

Brown, L.D., A.C. Call, M.B. Clement, and N.Y. Sharp, 2015. Inside the “black box” of sell‐side financial analysts, Journal of Accounting Research 53(1), 1-47.

Buhrmester, M., T. Kwang, and S.D. Gosling, 2011. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science 6(1), 3-5.

Chatterjee, A., and D.C. Hambrick, 2007. It's all about me: Narcissistic chief executive officers and their effects on company strategy and performance, Administrative Science Quarterly 52(3), 351-386.

Costa, P.T., and R.R. McCrae, 1992. Neo personality inventory-revised (NEO PI-R), Psychological Assessment Resources Odessa, FL.

De Bondt, W.F., and R.H. Thaler, 1990. Do security analysts overreact? The American Economic

Review 80(2), 52-57. Durand, R., R. Newby, K. Tant, S. Trepongkaruna, 2013. Overconfidence, overreaction and

personality. Review of Behavioral Finance 5(2), 104-133. Eames, M. J., S.M. Glover, and J.J. Kennedy, 2002. The association between trading

recommendations and broker‐analysts’ earnings forecasts, Journal of Accounting

Research 40(1), 85-104. Eames, M. J., S.M. Glover, and J.J. Kennedy, 2006. Stock recommendations as a source of bias

in earnings forecasts, Behavioral Research in Accounting 18(1), 37–51. Franke, N., M. Gruber, D. Harhoff, and J. Henkel, 2006. What you are is what you like—

similarity biases in venture capitalists' evaluations of start-up teams, Journal of Business

Venturing 21(6), 802-826. Goldberg, L.R., 1992. The development of markers for the Big-Five factor structure,

Psychological Assessment 4 (1), 26-42. Gow, I., S. Kaplan, D. Larcker, and A. Zakolyukina, 2015. CEO personality and firm policies,

Working Paper, http://www.nber.org/papers/w22435. Grant, A.M., F. Gino, and D.A. Hofmann, 2011. Reversing the extraverted leadership advantage:

The role of employee proactivity, Academy of Management Journal 54(3), 528-550. Green, T.C., R.E. Jame, and B. Lock, 2014. Executive extraversion: Career and firm outcomes,

Working Paper, http://ssrn.com/abstract=2503663. Grunert, J., L. Norden, and M. Weber, 2005. The role of non-financial factors in internal credit

ratings, Journal of Banking and Finance 29(2), 509-531. Hambrick, D.C., and P.A. Mason, 1984. Upper echelons: The organization as a reflection of its

top managers, Academy of Management Review 9(2), 193-206. Horton, J.J., D.G. Rand, and R.J. Zeckhauser, 2011. The online laboratory: Conducting

experiments in a real labor market, 14(3), 399–425. Jones, D., 2006. Not all successful CEOs are extroverts, USA Today, June 7. Janatti, S., A. Kumar, A. Niessen-Ruenzi, and J. Wolfers, 2016. In-Group Bias in Financial

Markets, Working Paper, https://ssrn.com/abstract=2884218. Judge, T.A., and J.E. Bono, 2000. Five-factor model of personality and transformational

leadership, Journal of Applied Psychology 85(5), 751-765. Judge, T.A., J.E. Bono, R. Ilies, and M.W. Gerhardt, 2002. Personality and leadership: a

qualitative and quantitative review. Journal of Applied Psychology 87(4), 765-780. Jung, B., P.B. Shane, and Y.S. Yang, 2012. Do financial analysts' long-term growth forecasts

matter? Evidence from stock recommendations and career outcomes, Journal of

Accounting and Economics 53(1), 55-76. Kahnweiler, J.B., 2013. Quiet Influence: The Introvert's Guide to Making a Difference (Berrett-

Koehler Publishers, San Francisco).


123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Review Only

- 33 -

Kang, J.K., 1997. Why is there a home bias? An analysis of foreign portfolio equity ownership in Japan. Journal of Financial Economics 46(1), 3-28.

Kanter, R. M. 1977. Men and Women of the Corporation (Basic Books, New York). Kaustia, M., E. Alho, V. Puttonen, 2008. How much does expertise reduce behavioral biases?

The case of anchoring effects in stock return estimates, Financial Management 37(3), 391-412.

Keller, T., 2000. Images of the familiar: Individual differences and implicit leadership theories, The Leadership Quarterly 10(4), 589-607.

Koenig, A.M., A.H. Eagly, A.A. Mitchell, and T. Ristikari, 2011. Are leader stereotypes masculine? A meta-analysis of three research paradigms, Psychological Bulletin 137(4), 616-642.

Koole, S. L., A. Dijksterhuis, and A. van Knippenberg, 2001. What's in a name: implicit self-esteem and the automatic self, Journal of Personality and Social Psychology 80(4), 669-685.

Kramer, L.A., and C.M. Liao, 2016. The spillover effects of management overconfidence on analyst forecasts, Journal of Behavioral and Experimental Finance 12, 79-92.

Lee, P.M., and E.H. James, 2007. She'‐e‐os: Gender effects and investor reactions to the announcements of top executive appointments, Strategic Management Journal 28(3), 227-241.

Libby, R., R. Bloomfield, and M.W. Nelson, 2002. Experimental research in financial accounting, Accounting, Organizations and Society 27(8), 775-810.

Loughran, T., and B. McDonald, 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-ks, The Journal of Finance 66(1), 35-65.

Loughran, T., and B. McDonald, 2015. The use of word lists in textual analysis, Journal of

Behavioral Finance 16(1), 1-11. Malhotra, S., T.H. Reus, P.C. Zhu, and E.M. Roelofson, (2017). The acquisitive nature of

extraverted CEOs, Administrative Science Quarterly, forthcoming. Malmendier, U., and G. Tate, 2005. CEO overconfidence and corporate investment, The Journal

of Finance 60(6), 2661-2700. McCrae, R.R., and P.T. Costa, 1989. The structure of interpersonal traits: Wiggins's circumplex

and the five-factor model, Journal of Personality and Social Psychology 56(4), 586-595. Montoya, R.M., and R.S. Horton, 2012. A meta-analytic investigation of the processes

underlying the similarity-attraction effect, Journal of Social and Personal Relationships 30(1), 64-94.

Moreland, R.L., and R.B. Zajonc, 1982. Exposure effects in person perception: Familiarity, similarity, and attraction. Journal of Experimental Social Psychology 18(5), 395-415.

Murnieks, C.Y., J.M. Haynie, R.E. Wiltbank, and T. Harting, 2011. ‘I Like How You Think’: Similarity as an interaction bias in the investor–entrepreneur dyad, Journal of

Management Studies 48(7), 1533-1561. Nadkarni, S., and P. Herrmann, 2010. CEO personality, strategic flexibility, and firm

performance: The case of the Indian business process outsourcing industry, Academy of

Management Journal 53(5), 1050-1073. O’Reilly, C.A., D.F. Caldwell, J.A. Chatman, and B. Doerr, 2014. The promise and problems of

organizational culture: CEO personality, culture, and firm performance, Group and

Organization Management 39(6), 595-625. Olsen, K.J., K.K. Dworkis, and S.M. Young, 2013. CEO narcissism and accounting: A picture of

profits, Journal of Management Accounting Research 26(2), 243-267.


123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

For Review Only

- 34 -

Paulhus, D.L., and K.M. Williams, 2002. The dark triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality 36(6), 556-563.

Resick, C.J., D.S. Whitman, S.M. Weingarden, and N.J. Hiller, 2009. The bright-side and the dark-side of CEO personality: Examining core self-evaluations, narcissism, transformational leadership, and strategic influence, Journal of Applied Psychology 94(6), 1365-1381.

Rosenthal, S.A., and T.L. Pittinsky, 2006. Narcissistic leadership, The Leadership Quarterly 17(6), 617-633.

Rothbart, M., S. Fulero, C. Jensen, J. Howard, and P. Birrell, 1978. From individual to group impressions: Availability heuristics in stereotype formation, Journal of Experimental

Social Psychology 14(3), 237-255. Rubin, R.S., D.C. Munz, and W.H. Bommer, 2005. Leading from within: The effects of emotion

recognition and personality on transformational leadership behavior, Academy of

Management Journal 48(5), 845-858. Schaefer, P.S., C.C. Williams, A.S. Goodie, and W.K. Campbell, 2004. Overconfidence and the

Big Five, Journal of Research in Personality 38(5), 473-480. Schipper, K., 1991. Analysts’ forecasts, Accounting Horizons 5(4), 105-121. Schrand, C.M., and S.L. Zechman, 2012. Executive overconfidence and the slippery slope to

financial misreporting, Journal of Accounting and Economics 53(1), 311-329. Sedor, L. M., 2002. An explanation for unintentional optimism in analysts' earnings forecasts,

The Accounting Review 77(4), 731-753. Sievers, S., C. F. Mokwa, and G. Keienburg, 2013. The relevance of financial versus non-

financial information for the valuation of venture capital-backed firms, European

Accounting Review 22(3), 467-511. Spain, S.M., P. Harms, and J.M. LeBreton, 2014. The dark side of personality at work, Journal of

Organizational Behavior 35(S1), S41-S60. Strelan, P., 2007. Who forgives others, themselves, and situations? The roles of narcissism, guilt,

self-esteem, and agreeableness, Personality and Individual Differences 42(2), 259-269. Tversky, A., and D. Kahneman, 1974. Judgment under uncertainty: Heuristics and biases.

Science 185(4157), 1124-1131. Vanstraelen, A., M.T. Zarzeski, and S.W. Robb, 2003. Corporate nonfinancial disclosure

practices and financial analyst forecast ability across three European countries, Journal of

International Financial Management and Accounting 14(3), 249-278. Wash, G., and S.E. Beatty, 2007. Customer-based corporate reputation of a service firm: Scale

development and validation, Journal of the Academy of Marketing Science 35(1), 127-143.

Whitwell, G.J., B.A. Lukas, and P. Hill, 2007. Stock analysts' assessments of the shareholder value of intangible assets, Journal of Business Research 60(1), 84-90.


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Tables

Table 1

Descriptive Statistics

The table shows descriptive statistics (no. of observations, mean, standard deviation, minimum, and maximum) for participant characteristics and forecasts before the experimental manipulation. Gender is an indicator variable taking the value of 1 for male and 0 for female participants. Age and experience as financial analyst are expressed in years. Industry specializations is the number of covered industries. Analyst extraversion is the average self-rating across ten adjectives on a scale from 1 (low extraversion) to 9 (high extraversion). The aggregated company rating is the average rating of the company across 14 different statements on a scale from 1 to 9 with a higher number representing a more positive evaluation. EPS is the forecasted earnings per share; LTG is the forecasted long-term growth of EPS in %; and target price is the forecasted target price. For both treatment groups, means are reported and the t-value of a t-test for differences between the groups. Differences are significant at *p<0.1, **p<0.05, and ***p<0.01.

Full sample

Treatment Extr. CEO

Treatment L.extr. CEO Difference

n Mean Std.dev. Min Max Mean Mean t-stat

Gender (male = 1)

187 0.87 0.34 0 1 0.87 0.87 0.03

Age 187 38.95 8.96 20 60 38.82 39.08 –0.19

Experience 186 11.79 7.79 0 34 12.08 11.49 0.52

Industry specializations

186 1.53 1.25 1 9 1.57 1.49 0.46

Analyst Extraversion

180 6.06 1.18 2.30 8.30 6.07 6.06 0.07

Company Rating

180 5.95 1.27 1.21 8.64 5.92 5.98 –0.32

EPS (pre treatment)

187 1.37 0.16 0.81 1.75 1.37 1.37 0.16

LTG (pre treatment)

186 18.64 6.29 -5.00 40.00 18.52 18.75 –0.25

Target price (pre treatment)

186 35.51 6.05 20.00 55.00 35.49 35.53 –0.04


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Table 2

Treatment Effect

The table shows the mean forecasts for the two treatment groups before and after participants receive CEO personality information. Rows (3) and (6) show differences before and after within groups. Row (7) shows the difference in these differences, the treatment effect. Significance levels of two-sided t-tests are displayed with *p<0.1, **p<0.05, and ***p<0.01.

n EPS LTG Target Price

(1) Extraverted CEO (pre treatment) 94 1.37 18.52 35.49

(2) Extraverted CEO (post treatment) 94 1.38 19.72 36.63

(3) Difference (2) – (1) 94 0.01 1.21** 1.14**

(4) Less Extrav. CEO (pre treatment) 93 1.37 18.75 35.53

(5) Less Extrav. CEO (post treatment) 93 1.33 16.72 33.65

(6) Difference (5) – (4) 93 –0.03*** –2.03*** –1.88***

(7) Difference (3) – (6) 187 0.04** 3.23*** 3.02***


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Table 3

Changes in Company Ratings

Ratings on company and leadership quality are elicited on a scale from 1 (“not at all descriptive”) to 9 (“extremely descriptive”) for 14 different statements. The table shows for all statements the changes in ratings after the treatment variation separately for both treatment groups. It also displays changes in the aggregated company rating and the domains “workforce,” “leadership,” “business,” and “investment.” The rightmost column provides the differences between groups and the t-value of a two-sided t-test. Differences are significant at *p<0.1, **p<0.05, and ***p<0.01.

Statement ∆ rating

extr. CEO ∆ rating

less extr. CEO Difference (t-value)

Looks like a good company to work for –0.26 –0.92 0.67 (2.73***)

Seems to treat its people well –0.40 –0.36 –0.05 (0.18)

Has management who seems to pay attention

to the needs of its employees –0.30 –0.57 0.27 (0.94)

Seems to have good employees –0.12 –0.45 0.33 (1.67*)

Seems to maintain high standards in the way

that it treats people –0.08 –0.14 0.07 (0.31)

Sub-rating “workforce” –0.21 –0.47 0.26 (1.50)

Seems to have excellent leadership 0.75 –0.12 0.87 (2.92***)

Seems to be well-managed 0.48 –0.21 0.69 (2.34**)

Seems to have a clear strategy for its future 0.36 –0.27 0.63 (2.23**)

Sub-rating “leadership” 0.53 –0.16 0.69 (2.92***)

Tends to outperform competitors 0.35 –0.50 0.85 (3.40***)

Seems to recognize and take advantage of

market opportunities 0.46 –1.35 1.81 (6.53***)

Looks like it has strong prospects for future

growth 0.23 –1.18 1.42 (5.10***)

Sub-rating “business” 0.35 –1.02 1.37 (5.82***)

Looks like it would be a good investment 0.44 –0.43 0.87 (3.45***)

Appears to make financially sound decisions 0.25 0.23 0.02 (0.08)

Seems to increase shareholder value 0.30 –0.46 0.75 (2.76***)

Sub–rating “investment” 0.34 –0.21 0.55 (2.37**)

Company rating (aggregate) 0.20 –0.43 0.63 (3.51***)


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Table 4

Changes in Forecasts

The table shows results of OLS regressions with changes in forecasts as the dependent variable. Columns (1) – (3) have results for changes in earnings per share (EPS), columns (4) – (6) for changes in long-term growth of EPS (LGT), and columns (7) – (9) for changes in price targets. Independent variables are a treatment indicator (=1 for an extraverted CEO), analysts’ gender and age, their aggregated self-rating of extraversion (on a scale from 1-9), the change in the subjective company rating (after – before treatment), and covered industry specializations as indicator variables. Further, country fixed effects for the country of occupation are included. The table shows coefficients and in parentheses robust standard errors. Coefficients are significant at *p<0.1, **p<0.05, and ***p<0.01.

∆ EPS ∆ LTG ∆ Price target

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment Extr. CEO

0.045 (0.02)***

0.038 (0.02)**

0.028 (0.02)*

3.270 (0.75)***

3.302 (0.74)***

2.327 (0.74)***

3.098 (0.73)***

3.272 (0.77)***

1.877 (0.67)***

Gender 0.038 (0.03)

0.028 (0.03)

0.027 (0.03)

0.433 (1.18)

0.136 (1.21)

0.018 (1.25)

–0.142 (0.99)

–0.167 (1.13)

–0.279 (1.10)

Age 0.000 (0.00)

0.000 (0.00)

0.000 (0.00)

–0.031 (0.04)

–0.028 (0.04)

–0.031 (0.04)

0.008 (0.04)

–0.008 (0.04)

–0.015 (0.04)


–0.029 (0.01)***

–0.028 (0.01)***

–0.026 (0.01)***

–0.819 (0.35)**

–0.931 (0.40)**

–0.880 (0.37)**

–1.010 (0.35)***

–1.113 (0.42)***

–1.044 (0.36)***

∆ Company Rating

0.023 (0.01)***

1.563 (0.32)***

2.308 (0.34)***

Constant 0.11 (0.05)**

0.125 (0.06)**

0.12 (0.05)**

3.750 (2.36)

4.237 (2.62)

5.143 (2.54)**

4.008 (2.56)

3.792 (2.85)

5.070 (2.50)**

Specializations No Yes Yes No Yes Yes No Yes Yes Country FE No Yes Yes No Yes Yes No Yes Yes R² 0.14 0.25 0.30 0.14 0.27 0.37 0.14 0.25 0.48 Observations 180 180 173 179 179 172 179 179 172


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Table 5

Magnitude of Forecast Revisions

The table shows absolute changes in forecasts (EPS, LTG, and price target), changes in forecast by treatment group, and the treatment effect. Values are expressed as percentage change (in %), in terms of standard deviation (in std. dev.), and as percentage of the range provided as the forecast consensus (see Appendix, Figure A.7).

EPS LTG Target Price

Abs. ∆ Forecast in % (full sample) 5.19% 17.34% 10.14%

∆ Forecast in % (treatment extr. CEO) 0.72% 9.89% 3.21%

∆ Forecast in % (treatment less extr. CEO) –2.15% –8.65% –4.42%

Treatment effect in % 2.87% 18.55% 7.63%

Abs. ∆ Forecast in std. dev. (full sample) 0.454 0.562 0.588

∆ Forecast in std. dev. (treatment extr. CEO) 0.052 0.192 0.188

∆ Forecast in std. dev. (treatment less extr. CEO) –0.220 –0.322 0.310

Treatment effect in std. dev. 0.272 0.514 0.498

Abs. ∆ Forecast in % of range (full sample) 16.31% 16.83% 18.33%

∆ Forecast in % of range (treatment extr. CEO) 1.86% 5.75% 5.87%

∆ Forecast in % of range (treatment less extr. CEO) –7.89% –9.65% –9.67%

Treatment effect in % of range 9.76% 15.39% 15.54%


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Table 6

Treatment Effect and Similarity Effect

The table shows changes in forecasts 2x2 by analyst personality (columns) and CEO personality (rows). The respective differences show the treatment effect and similarity effect. Panel A displays changes in earnings per share (EPS), Panel B changes in long-term growth (LTG), and Panel C changes in price targets.

PANEL A: ∆ EPS Analyst Personality Similarity Effect CEO personality Extraverted Less Extraverted

Extraverted 0.005 0.016 –0.011 Less Extraverted –0.090 0.021 –0.111***

Treatment Effect 0.095*** –0.005

PANEL B: ∆ LTG Analyst Personality Similarity Effect CEO personality Extraverted Less Extraverted

Extraverted 2.29 0.30 1.99* Less Extraverted –4.31 0.20 –4.51***

Treatment Effect 6.59*** 0.09

PANEL C: ∆ Price Target Analyst Personality Similarity Effect CEO personality Extraverted Less Extraverted

Extraverted 2.18 0.26 1.92** Less Extraverted –5.03 1.12 –6.15***

Treatment Effect 7.21*** –0.86


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Table 7

Similarity and Changes in Company Ratings

Statements on company and leadership quality are aggregated into the domains “workforce,” “leadership,” “business,” and “investment” (see Table 3). The table shows for all sub-ratings the changes in numeric ratings for the group of analysts with similar personality as the CEO and with dissimilar personality as the CEO. It also displays changes in the aggregated company rating. The rightmost column provides the differences between groups and the t-value of a two-sided t-test. Differences are significant at *p<0.1, **p<0.05, and ***p<0.01.

Statement

∆ rating similar

personality

∆ rating contrasting personality

Difference (t-value)

Sub-rating “workforce” –0.02 –0.69 0.67 (3.90***)

Sub-rating “leadership” 0.75 –0.38 1.12 (4.92***)

Sub-rating “business” 0.12 –0.74 0.86 (3.41***)

Sub–rating “investment” 0.58 –0.44 1.02 (4.56***)

Company rating (aggregate) 0.31 –0.56 0.87 (4.91***)


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Table 8

Similarity and Changes in Forecasts

The table shows results of OLS regressions with changes in forecasts as the dependent variable. Columns (1) – (3) have results for changes in earnings per share (EPS), columns (4) – (6) for changes in long-term growth of EPS (LGT), and columns (7) – (9) for changes in price targets. Independent variables are as in Table 4, including a similarity dummy with a value of one when analysts and CEO have similar personality and zero otherwise. The table shows coefficients and in parentheses robust standard errors. Coefficients are significant at *p<0.1, **p<0.05, and ***p<0.01.


(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment Extr. CEO

0.046 (0.02)***

0.039 (0.02)**

0.033 (0.02)**

3.358 (0.71)***

3.349 (0.71)***

2.643 (0.76)***

3.205 (0.68)***

3.327 (0.71)***

2.190 (0.70)***

Similarity 0.049

(0.02)*** 0.054

(0.02)*** 0.039

(0.02)** 3.292

(0.72)*** 3.254

(0.83)*** 2.409

(0.89)*** 4.008

(0.69)*** 3.903

(0.70)*** 2.388

(0.68)***

Gender 0.038 (0.03)

0.024 (0.03)

0.024 (0.03)

0.395 (1.04)

–0.077 (1.03)

–0.191 (1.12)

–0.188 (0.82)

–0.423 (0.91)

–0.486 (1.00)

Age 0.000 (0.00)

0.000 (0.00)

0.000 (0.00)

–0.048 (0.03)

–0.040 (0.03)

–0.036 (0.04)

–0.013 (0.04)

–0.023 (0.04)

–0.020 (0.04)


–0.029 (0.01)***

–0.027 (0.01)***

–0.026 (0.01)***

–0.769 (0.32)**

–0.870 (0.37)**

–0.852 (0.36)**

–0.949 (0.31)***

–1.039 (0.38)***

–1.016 (0.35)***

∆ Company Rating

0.017

(0.01)**

1.195 (0.34)***

1.944

(0.35)***

Constant 0.092

(0.05)* 0.107

(0.06)* 0.102

(0.05)* 2.503 (2.24)

3.049 (2.46)

3.983 (2.54)

2.489 (2.27)

2.368 (2.43)

3.92 (2.38)

Specializations No Yes Yes No Yes Yes No Yes Yes Country FE No Yes Yes No Yes Yes No Yes Yes R² 0.18 0.30 0.33 0.23 0.36 0.41 0.29 0.38 0.52 Observations 180 180 173 179 179 172 179 179 172


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Table 9

Interaction Effect between Treatment and Analyst Extraversion

The table shows results of OLS regressions with changes in forecasts as the dependent variable. Columns (1) – (3) have results for

changes in earnings per share (EPS), columns (4) – (6) for changes in long-term growth of EPS (LGT), and columns (7) – (9) for changes

in price targets. Independent variables are a treatment indicator (=1 for an extraverted CEO), an analyst extraversion indicator (0/1

variable created from the continuous analyst extraversion by median split), and the interaction between the two. Further control variables

are as before. The table shows coefficients and in parentheses robust standard errors. Coefficients are significant at *p<0.1, **p<0.05, and

***p<0.01.


(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment Extr. CEO

-0.005 (0.02)

-0.006 (0.02)

-0.019 (0.02)

0.095 (0.84)

-0.007 (0.86)

-0.023 (1.00)

-0.863 (0.79)

-0.898 (0.81)

-0.703 (0.84)

Analyst Extr. (0/1)

-0.111 (0.02)***

-0.111 (0.02)***

-0.115 (0.02)***

-4.508 (0.99)***

-4.449 (1.00)***

-4.572 (1.21)***

-6.148 (0.97)***

-6.128 (0.97)***

-6.318 (1.13)***

Treatment X Analyst Extr.

0.100 (0.03)***

0.101 (0.03)***

0.112 (0.03)***

6.498 (1.45)***

6.69 (1.45)***

6.655 (1.70)***

8.068 (1.36)***

8.137 (1.38)***

7.967 (1.42)***

Gender 0.024 (0.03)

0.012 (0.03)

0.023 (1.04)

-0.447 (1.04)

-0.642 (0.81)

-0.897 (0.89)

Age -0.001 (0.00)

-0.001 (0.00)

-0.061 (0.03)*

-0.056 (0.04)

-0.024 (0.04)

-0.036 (0.04)

Constant 0.021 (0.02)

0.021 (0.04)

0.052 (0.05)

0.201 (0.68)

2.527 (1.67)

2.777 (1.97)

1.123 (0.61)*

2.587 (1.87)

2.148 (2.01)

Specializations No No Yes No No Yes No No Yes Country FE No No Yes No No Yes No No Yes R² 0.16 0.16 0.29 0.20 0.21 0.34 0.28 0.28 0.38 Observations 180 180 180 179 179 179 179 179 179


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Figures

Figure 1

Experimental Procedure

The figure shows the sequence of tasks in the experiment. First participants complete demographic questions (gender, age, experience, and industry coverage) and the extraversion scale. Then they receive the case materials on the company that they are supposed to evaluate. After studying the materials, they provide a first set of forecasts and company ratings. Then a filler task is inserted for distraction, followed by the random assignment to one of the treatment. The experiment ends with a second set of forecasts and ratings.


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Figure 2

Mediation Analysis

The figure displays results of a mediation analysis of the treatment effect on analyst forecasts, with leadership quality as the mediator variable. TE is the total effect, DE the direct effect, and IE the indirect effect. The indirect effect results from a multiplication of the effect of the treatment on leadership and leadership on forecasts.


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Figure 3. Effect of Similar Personalities

The figure shows the changes in forecasts (EPS, LTG, and price target) for the case of matching personalities (striped bars) or opposite personalities (solid bars). If the analyst and the CEO are both extraverted or the analyst and the CEO are both less extraverted, personalities match. If personalities of the CEO and analyst are different, they do not match. The unit of changes corresponds to the way the respective forecasts are measured.


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ONLINE APPENDIX

A. Experimental Materials

Figure A.1: Measure for analyst extraversion. Ten adjectives have to be rated according to how

well they describe a participant’s personality.


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Figure A.2: Financial information and employee information about Connexis Software Inc.

provided in the experiment.


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Figure A.3: Verbal company information used in the experiment.


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Figure A.4: Aggregated industry and market multiples as well as stock market information

provided in the experiment.


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Figure A.5: Description of the CEO with low extraversion.


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Figure A.6: Description of the CEO with high extraversion.


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Figure A.7: Forecast elicitation in the experiment. The display is the same for the forecasts

before and after the treatment.


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Figure A.8: Qualitative ratings of management and firm quality.


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B. Robustness Tests

Ex-post EPS Ex-post LTG Ex-post price target

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment

Extr. CEO

0.052

(0.02)**

0.045

(0.03)*

0.032

(0.03)

3.157

(1.08)***

3.259

(1.14)***

2.087

(1.24)*

3.087

(1.05)***

3.104

(1.16)***

1.612

(1.23)

Gender 0.001

(0.04)

–0.005

(0.04)

–0.001

(0.04)

–0.071

(1.41)

–0.116

(1.64)

–0.44

(1.60)

–1.952

(1.31)

–2.948

(1.63)*

–2.764

(1.40)*

Age –0.002

(0.00)

–0.002

(0.00)

–0.002

(0.00)

–0.082

(0.06)

–0.045

(0.06)

–0.041

(0.06)

–0.079

(0.06)

–0.076

(0.07)

–0.095

(0.06)

Analyst

Extraversion 0.008

(0.01)

0.011

(0.01)

0.011

(0.01)

–0.101

(0.47)

–0.26

(0.55)

–0.105

(0.51)

0.207

(0.52)

0.232

(0.61)

0.317

(0.55)

∆ Company

Rating 0.019

(0.01)

1.769

(0.47)***

1.981

(0.48)***

Constant 1.370

(0.09)***

1.334

(0.10)***

1.354

(0.09)***

20.411

(3.54)***

18.904

(3.79)***

19.31

(3.44)***

36.942

(3.54)***

36.380

(4.11)***

38.047

(3.54)***

Specializations No Yes Yes No Yes Yes No Yes Yes

Country FE No Yes Yes No Yes Yes No Yes Yes

R² 0.04 0.15 0.17 0.06 0.15 0.22 0.06 0.13 0.22

Observations 180 180 173 179 179 172 179 179 172

Table B.1: The table shows results of OLS regressions with after-treatment forecasts as the dependent variable. Columns (1) – (3) have

results for earnings per share (EPS), columns (4) – (6) for long-term growth of EPS (LGT), and columns (7) – (9) for price targets.

Independent variables are as in Table 4. The table shows coefficients and in parentheses robust standard errors. Coefficients are

significant at *p<0.1, **p<0.05, and ***p<0.01.


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(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment

Extr. CEO

0.046

(0.02)***

0.039

(0.02)**

0.028

(0.02)*

3.252

(0.73)***

3.296

(0.73)***

2.288

(0.72)***

3.096

(0.73)***

3.254

(0.76)***

1.858

(0.68)***

Gender 0.032

(0.03)

0.023

(0.03)

0.023

(0.03)

0.353

(1.09)

0.098

(1.15)

–0.057

(1.18)

–0.382

(0.97)

–0.454

(1.14)

–0.453

(1.09)

Age 0.000

(0.00)

0.000

(0.00)

0.000

(0.00)

–0.039

(0.04)

–0.031

(0.04)

–0.032

(0.04)

–0.003

(0.04)

–0.015

(0.04)

–0.020

(0.04)

Analyst

Extraversion –0.023

(0.01)***

–0.022

(0.01)***

–0.021

(0.01)***

–0.706

(0.33)**

–0.830

(0.38)**

–0.752

(0.34)**

–0.849

(0.34)**

–0.974

(0.40)**

–0.948

(0.33)***

∆ Company

Rating 0.023

(0.01)***

1.597

(0.32)***

2.285

(0.34)***

Ex-ante

forecasts 0.826

(0.06)***

0.836

(0.07)***

0.859

(0.06)***

0.843

(0.09)***

0.849

(0.08)***

0.835

(0.08)***

0.868

(0.08)***

0.897

(0.08)***

0.930

(0.08)***

Constant 0.329

(0.09)***

0.323

(0.10)***

0.295

(0.10)***

6.369

(2.63)**

6.454

(2.85)**

7.483

(2.67)***

8.368

(3.46)**

7.151

(4.11)*

7.381

(3.83)*



R² 0.61 0.66 0.70 0.57 0.64 0.69 0.56 0.61 0.72

Observations 180 180 173 179 179 172 179 179 172



Independent variables are as in Table 4 including the pre-treatment forecasts of the respective forecast variable, e.g., pre-treatment EPS

forecast for the EPS regressions in columns (1) – (3). The table shows coefficients and in parentheses robust standard errors. Coefficients

are significant at *p<0.1, **p<0.05, and ***p<0.01.


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(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment

Extr. CEO

0.053

(0.02)**

0.045

(0.03)*

0.039

(0.03)

3.277

(1.03)***

3.331

(1.09)***

2.650

(1.23)**

3.188

(1.02)***

3.161

(1.13)***

1.951

(1.25)

Similarity 0.050

(0.02)**

0.062

(0.02)**

0.050

(0.03)*

4.471

(1.04)***

5.068

(1.17)***

4.301

(1.24)***

3.775

(1.05)***

4.012

(1.08)***

2.585

(1.14)**

Gender 0.001

(0.04)

–0.009

(0.04)

–0.006

(0.04)

–0.123

(1.32)

–0.448

(1.49)

–0.813

(1.47)

–1.996

(1.17)*

–3.211

(1.43)**

–2.988

(1.32)**

Age –0.003

(0.00)*

–0.002

(0.00)

–0.002

(0.00)

–0.106

(0.05)*

–0.064

(0.06)

–0.050

(0.05)

–0.100

(0.06)

–0.091

(0.06)

–0.100

(0.06)

Analyst

Extraversion

0.009

(0.01)

0.012

(0.01)

0.011

(0.01)

–0.033

(0.43)

–0.165

(0.50)

–0.056

(0.49)

0.264

(0.49)

0.307

(0.55)

0.347

(0.53)

∆ Company

Rating

0.011

(0.02)

1.113

(0.50)**

1.587

(0.51)***

Constant 1.351

(0.09)***

1.314

(0.09)***

1.331

(0.09)***

18.716

(3.16)***

17.054

(3.36)***

17.239

(3.27)***

35.512

(3.30)***

34.915

(3.76)***

36.802

(3.56)***



R² 0.07 0.18 0.19 0.15 0.26 0.28 0.13 0.20 0.24

Observations 180 180 173 179 179 172 179 179 172



Independent variables are as in Table 8. The table shows coefficients and in parentheses robust standard errors. Coefficients are

significant at *p<0.1, **p<0.05, and ***p<0.01.


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(1) (2) (3) (4) (5) (6) (7) (8) (9)

Treatment

Extr. CEO

–0.018

(0.02)

–0.026

(0.02)

–0.002

(0.02)

–0.676

(0.90)

–0.815

(1.06)

–0.100

(1.16)

–1.741

(0.97)*

–1.444

(0.99)

0.154

(1.11)

Similarity

(continuous)

0.020

(0.01)***

0.020

(0.01)***

0.010

(0.01)

1.264

(0.30)***

1.315

(0.35)***

0.863

(0.41)**

1.550

(0.30)***

1.506

(0.31)***

0.613

(0.36)*

Gender 0.031

(0.03)

0.018

(0.03)

0.022

(0.03)

–0.012

(1.05)

–0.498

(1.06)

–0.423

(1.16)

–0.687

(0.84)

–0.893

(0.94)

–0.592

(1.07)

Age 0.000

(0.00)

0.000

(0.00)

0.000

(0.00)

–0.053

(0.03)

–0.055

(0.04)

–0.046

(0.04)

–0.019

(0.04)

–0.039

(0.04)

–0.025

(0.04)

Analyst

Extraversion

–0.029

(0.01)***

–0.027

(0.01)***

–0.026

(0.01)***

–0.779

(0.32)**

–0.856

(0.37)**

–0.853

(0.37)**

–0.961

(0.31)***

–1.027

(0.39)***

–1.025

(0.36)***

∆ Company

Rating

0.019

(0.01)**

1.174

(0.38)***

2.031

(0.40)***

Constant 0.068

(0.05)

0.091

(0.05)*

0.100

(0.05)*

1.045

(2.42)

1.981

(2.53)

3.453

(2.79)

0.689

(2.31)

1.208

(2.36)

3.870

(2.60)



R² 0.18 0.29 0.31 0.21 0.35 0.39 0.26 0.36 0.49

Observations 180 180 173 179 179 172 179 179 172

Table B.4: The table shows results of OLS regressions with changes in forecasts as the dependent variable. Columns (1) – (3) have

results for changes in earnings per share (EPS), columns (4) – (6) for changes in long-term growth of EPS (LGT), and columns (7) – (9)

for changes in price targets. Independent variables are as in Table 8, with the binary similarity variable replaced by a continuous variable

of similarity. This variable equals “analyst extraversion” if the treatment is extraverted CEO and “9 – analyst extraversion” if the

treatment is less extraverted CEO. The table shows coefficients and in parentheses robust standard errors. Coefficients are significant at

*p<0.1, **p<0.05, and ***p<0.01.


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For Review Only - The FINANCIAL · 2018-05-07 · For Review Only The Effect of CEO Extraversion on Analyst Forecasts: Stereotypes and Similarity Bias March 2018 ABSTRACT: In an experiment