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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.
--------------------------------------------- INSERT TABLE 2 ABOUT HERE
---------------------------------------------
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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.
--------------------------------------------- INSERT TABLE 3 ABOUT HERE
---------------------------------------------
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.
---------------------------------------------- INSERT FIGURE 2 ABOUT HERE
----------------------------------------------
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.
--------------------------------------------- INSERT TABLE 5 ABOUT HERE
---------------------------------------------
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.
---------------------------------------------- INSERT FIGURE 3 ABOUT HERE
---------------------------------------------- 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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---------------------------------------------- INSERT TABLE 7 ABOUT HERE
----------------------------------------------
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.
---------------------------------------------- INSERT TABLE 8 ABOUT HERE
----------------------------------------------
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.
---------------------------------------------- INSERT TABLE 9 ABOUT HERE
----------------------------------------------
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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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)
Analyst Extraversion
–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.
∆ EPS ∆ LTG ∆ Price target
(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)
Analyst Extraversion
–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.
∆ EPS ∆ LTG ∆ Price target
(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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Ex-post EPS Ex-post LTG Ex-post price target
(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)*
Specializations No Yes Yes No Yes Yes No Yes Yes
Country FE No Yes Yes No Yes Yes No Yes Yes
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
Table B.2: 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 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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Ex-post EPS Ex-post LTG Ex-post price target
(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)***
Specializations No Yes Yes No Yes Yes No Yes Yes
Country FE No Yes Yes No Yes Yes No Yes Yes
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
Table B.3: 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 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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∆ EPS ∆ LTG ∆ Price target
(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)
Specializations No Yes Yes No Yes Yes No Yes Yes
Country FE No Yes Yes No Yes Yes No Yes Yes
R² 0.18 0.29 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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