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Seediscussions,stats,andauthorprofilesforthispublicationat:http://www.researchgate.net/publication/277476365
Perceptionsandprice:EvidencefromCEOpresentationsatIPOroadshows
RESEARCH·MAY2015
DOI:10.13140/RG.2.1.3906.3204
3AUTHORS,INCLUDING:
ElizabethBlankespoor
StanfordUniversity
5PUBLICATIONS18CITATIONS
SEEPROFILE
GregoryMiller
UniversityofMichigan
25PUBLICATIONS1,038CITATIONS
SEEPROFILE
Availablefrom:ElizabethBlankespoor
Retrievedon:21August2015
Perceptions and price:
Evidence from CEO presentations at IPO roadshows
Elizabeth Blankespoor
Stanford University
Graduate School of Business
Bradley E. Hendricks
University of North Carolina at Chapel Hill
Kenan-Flagler Business School
Gregory S. Miller
University of Michigan
Stephen M. Ross School of Business
May 2015
Abstract
This paper examines the relation between rapidly formed basic cognitive impressions of
management and firm valuation. We develop a composite measure of investor perception using
30-second content-filtered video clips of initial public offering (IPO) roadshow presentations.
We show that this measure, designed to capture viewers’ impressions of a CEO’s competence,
trustworthiness, and attractiveness, is positively associated with an IPO firm’s secondary market
value. The result is robust to controls for traditional determinants of firm value. We also show
that firms with highly perceived management are more likely to be matched to high-quality
underwriters. Finally, we examine components of IPO price formation and find that our
composite measure of perception is positively associated with both the price proposed for the
offering and the price revision that occurs from this proposed price to the firm’s secondary
market valuation. Taken together, our results provide evidence that investors’ instinctive
impressions of management are incorporated into investors’ assessments of firm value.
We thank Bill Baber, Mary Barth, Bob Libby, Bill Mayew, Eddie Riedl, and workshop participants at the 2014
Duke/UNC Fall Camp, Georgetown University, London Business School, MIT Sloan School of Management, the
2015 Penn State Accounting Conference, Temple University, the University of Arizona, and the Victor L. Bernard
Memorial Conference for helpful suggestions. We also thank Schinria Islam and the Stanford Graduate School of
Business Behavioral Lab for excellent research assistance and support with survey creation and administration.
Blankespoor ([email protected]) received financial support from the Michelle R. Clayman Faculty Fellowship,
Hendricks ([email protected]) from the Kenan-Flagler Business School and the Deloitte
Foundation, and Miller ([email protected]) from the Michael R. and Mary Kay Hallman Fellowship and Ernst and
Young.
1
1. Introduction
In this study, we examine the relation between investors’ impressions of management and
firm valuation. The psychology literature indicates that basic impressions of others are formed
rapidly and “provide the anchor from which subsequent judgments are made” (Ambady,
Bernieri, and Richeson, 2000). This “thin-slice” literature highlights the wealth of information in
expressive, dynamic behavior, which individuals observe unconsciously and with little effort.
We predict that the impressions created from these observations impact investors’ assessment of
firm value. Following the thin-slice approach, we ask viewers to provide their perceptions of
CEOs after watching 30-second content-filtered video clips of a CEO’s initial public offering
(IPO) roadshow presentation. Consistent with our prediction, we find a positive association
between basic cognitive impressions and measures of firm value from the IPO process. By using
perceptions formed during the first major exposure of management to investors, we have a
unique setting to better understand the capital market consequences of investors’ rapidly formed
impressions.1
A body of research suggests that investors find value in meeting with management.
Surveys of investor relations firms and of analysts document that direct interactions with
management are highly sought after (Bushee and Miller, 2012; Brown, et al., 2015). Empirical
studies find that individual meetings with management are valuable for analysts and investors,
despite the Regulation Fair Disclosure (Reg FD) restrictions around selectively releasing
information (Bushee, Jung, and Miller, 2013; Green, et al., 2014; Soltes, 2014). There is also
evidence of the capital market responding to managers’ affect or emotion as revealed by vocal
cues during conference calls (Mayew and Venkatachalam, 2012). Specific to our setting, Ann
Sherman testified to the U.S. Senate in 2012 that investors primarily attend IPO roadshows “to
1 We use perception, impression, judgment, and assessment interchangeably throughout the draft.
2
get a feel for [management], because [investors] are not just investing in the idea or the product;
[investors] are investing or betting on the management team” (Sherman, 2012). Overall,
investors desire to meet management and appear to respond to the interactions with management.
This evidence, combined with the psychology literature’s documentation of individuals forming
intuitive impressions, suggests that investors could be forming perceptions of management
during these interactions and incorporating the perceptions into firm value.2
However, given the amount of verifiable information about firms and management
available to investors, it is not clear whether investors’ rapid, unreflective impressions of
management influence their valuation judgments. Firm financial reports describe the historical
performance of the firm under management and include detailed manager biographies that
discuss the manager’s prior experience, education, and general background. This information-
rich environment may suggest there is no role for basic cognitive assessments of management.
That is, investors might focus solely on the “hard” information provided in regulatory filings and
other disclosures to form expectations of future cash flows and assess the importance of
management in these expectations. Thus, the impact of basic impressions on firm valuation is an
empirical question.
Regardless of whether the market does use these basic impressions, it is interesting to
consider whether these impressions should rationally be incorporated into firm value. Manager
ability is often cited as one of the most important determinants of firm value (Drucker, 1954). If
the basic impressions of management provide accurate information on managers’ ability, the
impressions should be incorporated into firm valuations. The psychology literature provides
2 Investors could be hoping that managers will provide some additional “hard” information or disclosure beyond
those in the written documents, either knowingly in violation of Reg FD or on accident. In fact, it is likely that
investors hope to get both as these are not mutually exclusive. As discussed later in the document, we have designed
our impressions construct to remove all potential “hard” information.
3
evidence in other settings that rapid, unreflective impressions have accurately predicted
outcomes such as educational, sales, and medical evaluations, as well as political voting and
personal loan funding and default outcomes, especially when the observations involve dynamic
behavior rather than static photos (e.g., Ambady, Bernieri, and Richeson, 2000; Ambady,
Connor, and Hallahan, 1999; Todorov, et al., 2005; Duarte, Siegel, and Young, 2012).
On one hand, the connection between CEOs’ expressive behavior and firm outcomes may
not be as direct as for teaching or sales, implying that these rapid impressions may not be
accurate in the executive setting. In this case, any correlation between impressions and firm
value in the short term would be a result of investors applying a metric that is inappropriate in
this setting. Accordingly, there would be future stock reversal.
On the other hand, a primary component of the CEO’s task is to interact and
communicate with stakeholders such as employees, customers, suppliers, or investors. In this
role, they need to persuade others of their vision and motivate the necessary actions to be taken.
This suggests that rapid impressions of a CEO could predict the abilities of the CEO in a variety
of firm activities and thus be relevant for firm value. Our primary question in this paper is
whether rapid impressions are associated with price, but we also provide initial evidence of
whether the market treatment of this information appears to be rational.
We begin our empirical analysis by examining the association between basic perceptions
of management and secondary market firm valuation for a sample of 224 IPOs filed from 2011
through 2013 on U.S. exchanges. To estimate investors’ perception of management, we asked
naïve participants to view 30-second content-filtered thin slices of CEOs’ roadshow
presentations and assess each CEO’s competence, trustworthiness, and attractiveness on a seven-
4
point Likert scale.3 We use video clips because impressions formed from observing expressive
behavior are more accurate than perceptions based on still photos (Ambady, Connor, and
Hallahan, 1999). In addition, video clips are more representative of the roadshow experience,
which is likely the basis for IPO investors’ impressions of management. Each video clip is rated
by at least 40 participants, and we use the mean rating for each of the CEO’s characteristics to
estimate CEO-specific impressions of competence, trustworthiness, and attractiveness. The mean
of these three characteristics is our summary CEO-specific measure of perception, and it is
designed to capture investors’ overall impression of the CEO at the time of the firm’s IPO.
Our focus on information-rich expressive behavior captured from CEO presentations at
IPO roadshows provides several useful features for creating a clean research design. First, the
IPO roadshow is the first major, and often the only, exposure of management to IPO investors
prior to the market’s initial valuation of the firm, resulting in a clear link between investor
perceptions in that period and valuation (Ernst & Young, 2008).4 Second, the IPO roadshow is an
event common to all IPO firms, mitigating concerns about sample bias due to management
choosing whether and when to interact with shareholders. Third, the use of content-filtered video
clips allows us to base perceptions on rich, dynamic information about CEOs while controlling
for the content of what is being said. Fourth, because these are fundamental human judgments of
individuals, a generated instrument from a general population is a reasonable proxy for investors’
3 Each of these traits is a classic construct used extensively in the psychology and economics literature. A number of
studies find a relation between perceived competence and/or trustworthiness and economic outcomes, such as
political elections (Todorov, et al., 2005), teaching evaluations (Ambady and Rosenthal, 1993), company profits
(Rule and Ambady, 2008, 2011), and personal loan funding and payment outcomes (Duarte, Siegel, and Young,
2012). A manager’s general attractiveness may also impact investors’ assessment of the manager’s value to the firm,
given the evidence of a relation between attractiveness and compensation, confidence, perceived ability, and market
reaction to firm events (Hamermesh and Biddle, 1994; Mobius and Rosenblat, 2006; Halford and Hsu, 2014). 4 The Ernst & Young 2008 survey of institutional investors reports that more than 88% of institutional investors cite
the quality of the roadshow as a key nonfinancial measure in their buying decisions and that the roadshow is
generally “the only time a company’s senior management meets the investor.”
5
judgments of CEOs.5 Fifth, the IPO setting allows us to focus on younger firms where financial
performance is seen as less informative and assessment of management is considered more
important, increasing the power of our tests of the impact of perceptions of management (Kim
and Ritter, 1999; Chemmanur, Simonyan, and Tehranian, 2012).6
Using our summary measure of perception, we find a positive relation between
perceptions of management and the IPO firm’s secondary market value. The relation is robust to
the inclusion of important determinants of price (i.e., firm, offer, and CEO characteristics). These
results imply that investors’ intuitive impressions of management are relevant for firm valuation
independent of specific impacts previously studied.
In addition to the secondary market valuations, we examine how these impressions of
management relate to other portions of the IPO process. One of the first major decisions in the
issuance process is the matching of underwriter and issuer. Fernando, Gatchev, and Spindt
(2005) describe this as a mutual choice based on firm and underwriter characteristics. To the
extent that underwriters use their perceptions of an issuing firm’s management as information
about firm quality, we would expect these perceptions to impact the issuer-underwriter match,
with more positive perceptions of management increasing the likelihood of the issuer being
represented by high-quality underwriters. We find results consistent with this prediction.
Specifically, controlling for the size of the offering and other observable factors associated with
underwriter selection, we find that managers perceived more positively are more likely to be
represented by high-quality underwriters.
5 Consistent with this assumption, we find similar results using subsets of ratings based on rater characteristics. See
additional tests in Section 5. 6 In support of this, Kaplan and Stromberg (2004) examine venture capital firms’ reasons for investing in a given
company and find that 60% cite managerial quality as a reason, while only 27% cite performance to date. Note,
though, that while the IPO setting increases the power of the tests, the findings are less generalizable to other
settings.
6
We then turn to the IPO price-formation process, examining the impact of perceptions of
management on both the price proposed by underwriters prior to the beginning of the roadshow
and the price revision that occurs from the proposed price to the closing price on the firm’s first
day of trading on the secondary market. Similar to the issuer-underwriter matching, we predict
that impressions of management are positively related to the price proposed by underwriters.
However, if reputational concerns constrain underwriters to focus on more objective, verifiable
information when valuing issuers (Roosenboom 2007, 2012), underwriters’ impressions of
management would have a smaller or no impact on the proposed price. In the latter case, we
would expect investors to then impound this soft information into firm valuations during the
book building process (Benveniste and Spindt, 1989). Further, even if underwriters attempt to
include these less objective measures in their initial prices, the roadshow and book building
process likely result in additional information about general impressions of management. This
additional information would then be reflected in the secondary market price. We find a positive
relation between perception and both the proposed price and the price revision. These results
imply that underwriters incorporate their impressions of management into their assessment of
firm value but that this assessment is modified once underwriters get more feedback on the
perceptions from a broad set of investors.7
Finally, we perform several additional analyses and robustness tests. First, we examine
the association between our composite measure Perception and firms’ post-IPO stock
performance to provide descriptive evidence of whether the inclusion of these perceptions in
firm value was a rational decision. In both univariate and multivariate regressions, we fail to find
7 As part of the book building portion of the IPO process, investors that attend IPO roadshows are invited to submit
a series of limit orders for the IPO firm’s shares to the underwriters. Underwriters then use this information to
update the proposed IPO price to better reflect the actual level of demand for the offering, as indicated by these
investors.
7
a statistically significant relationship between Perception and IPO firms’ buy-and-hold abnormal
returns. These results are not conclusive, but they suggest that investors acted rationally when
incorporating their perceptions of management into their valuations. Second, we re-estimate each
of the main regressions in our paper after excluding rater observations in which the rater either
recognized the CEO or exhibited suspicious behavior (e.g., providing the same response to each
request) and find that the results remain qualitatively similar.8 Third, we also re-estimate the
main regressions using ratings from specific demographics to ensure that the relation is not
driven by rater characteristics. Specifically, we sequentially restrict raters to: male raters, female
raters, raters under 30 years old, raters 30 years of age or greater, Caucasian raters, non-
Caucasian raters, raters who have attended at least some college or have a college degree, and
raters who have not attended college. In all cases, we draw similar inferences to those
documented when using the full sample of raters.9 These findings suggest that we are capturing
fundamental human judgments that are not unique to any particular observable demographic.
Our study contributes to several literature streams. First, our study brings additional
evidence to the literature examining whether and how management impacts firm market value. It
is difficult to disentangle management from the firm. A number of studies approach the issue by
testing for changes in investor behavior around a change in management, such as event studies of
abnormal stock returns following unexpected CEO turnover, death, or hospitalization (e.g.,
Johnson, et al., 1985; Bennedsen, Perez-Gonzalez, and Wolfenzon, 2012). Other studies model
the characteristics or skills of management (e.g., education, gender, founder-status, etc.) to see
8 We continue to find a positive relation between investor perceptions of managers and firm valuation (final and
proposed), underwriter matching, and price revision, significant at the 10% level or better in all cases. These
analyses are further discussed in Section 5.2 and tabulated in Table 9. 9 Specifically, we continue to find a positive relation between investor perception of managers and final firm
valuation (market value and book-to-market), underwriter matching, proposed firm valuation (market value and
book-to-market), and price revision across all subsamples, with 28 out of 32 specifications significant at the 10%
level or better.
8
whether these are correlated with firm value (e.g., Cohen and Dean, 2005; Hendricks and Miller,
2013). Our study turns instead to basic investor perceptions of management, in the tightly linked
setting of IPO roadshows, finding evidence of a relation between intuitive impressions of
management and firm value.
Second, our study makes a significant contribution to the IPO literature by being the first
to examine how information learned during the IPO roadshow influences IPO pricing. While
practitioners have suggested that investors learn valuable, nontangible information from
attending an IPO firm’s roadshow (NYSE/NASD, 2003; Sherman, 2012), our study is the first to
provide empirical evidence of the value of roadshow information, focusing on qualitative
information. Our results suggest that investors use roadshows as opportunities to learn important
information about a firm and its management.
Third, our study contributes to the disclosure literature by examining a disclosure channel
that includes a variety of nonverbal components. Prior research has shown the impact of firm
information and management disclosure choices in regulatory filings, press releases, conference
calls, and investor and industry conferences (e.g., Botosan, 1997; Li, 2008; Bushee, Jung, and
Miller, 2011; Blankespoor and deHaan, 2015). Several studies find evidence of an impact of
investors’ and analysts’ one-on-one meetings with management, implying that information may
be conveyed through multiple channels (Bushee, Jung, and Miller, 2013; Solomon and Soltes,
2013; Green, et al., 2014). Consistent with the potential importance of nonverbal behavior,
managerial affect conveyed through vocal cues in conference calls contains information about
financial misreporting and future performance (Hobson, Mayew, and Venkatachalam, 2012;
Mayew and Venkatachalam, 2012). Our study turns to the sensory-rich channel of roadshow
9
video presentations and finds evidence that valuable information about management is conveyed
through their nonverbal behavior.
2. Setting and motivation
2.1. Impressions
A stream of psychology literature examines social perception and the accuracy of
judgments using “thin slices,” or brief excerpts of expressive, dynamic behavior. Expressive
behavior and movements are seen as providing important information about the individual,
including affect, personality, and internal goals and motivations. Nonverbal behaviors
specifically are potentially very valuable for assessments because they are more difficult to
control or suppress but are easily observed (DePaulo, 1992). Essentially, these behaviors
represent a relatively unmanipulated signal about the individual’s true disposition.
Judgments of thin slices of expressive behavior are typically described as automatic or
intuitive processes that require little effort or awareness. For example, there is no evidence of
rater fatigue over time or due to increased cognitive load, and requiring explicit justification for
ratings can often reduce the accuracy of the judgments (Ambady, Bernieri, and Richeson, 2000).
These basic judgments are akin to System 1 thinking processes (Kahneman and Frederick, 2002;
Evans, 2008), which are described as more rapid, intuitive, and universal, rather than System 2
thinking processes that are slower, controlled, and logical. System 1 processes are described as
the primary response in a given situation, which is consistent with the automatic, unconscious
nature of thin-slice judgments.
Although thin-slice judgments are intuitive and unconscious, they are often predictive of
longer-term judgments that are arguably more logical and analytical. Using segments of behavior
ranging from as little as 10 to 60 seconds, studies find evidence that judgments of thin slices of
behavior are associated with final evaluations. For example, impressions of teachers based on
10
observation of a 30-second video clip predicted final course evaluations of teacher effectiveness
(Ambady and Rosenthal, 1993), and judgments of sales personnel based on 20-second video
clips were associated with management evaluations of the employees (Ambady, Krabbenhoft,
and Hogan 2006). In addition to predicting longer-term evaluations, there is evidence that thin-
slice judgments are also associated with performance outcomes. For instance, judgments of
physicians’ expression of concern based on 20-second audio clips were associated with their
history of malpractice (Ambady, et al., 2002), and perceptions of medical and occupational
therapy students based on 15-second video clips predicted clinical performance and final grades
(Rosenblum, et al., 1994; Tickle-Degnen, 1998; Tickle-Degnen and Puccinelli, 1999).10
Overall, this literature provides strong evidence that individuals form instinctive
impressions based on brief segments of human behavior, and that these impressions capture a
fundamental aspect of the perception of others that is associated with longer-term judgments and
outcomes.11 For the setting of firm valuation, the thin-slice findings imply that investors are
likely to form impressions of management based on brief interactions, such as a roadshow
presentation, and that these impressions have the potential to impact assessments of firm value.
2.2. Management impact on firm performance and valuation
Many studies find evidence of managers affecting firm performance and valuation.
Bertrand and Schoar (2003) find that manager fixed effects are related to firm practices and
performance, and Bennedsen, Perez-Gonzalez, and Wolfenzon (2010) show that CEO deaths are
10 We focus here on the literature examining perceptions of expressive behavior because this behavior provides a
richer information set. However, numerous studies also show evidence that instinctive impressions can be formed
based on brief exposure to still photos (e.g., Willis and Todorov, 2006) and that these impressions are predictive for
a variety of outcomes such as political voting, personal loan funding and default, and firm financial success
(Todorov, et al., 2005; Duarte, Siegel, and Young, 2012; Rule and Ambady, 2011). 11 This implies that first impressions remain influential even when subsequent information from repeated
interactions is incorporated (e.g., Lord, Ross, and Lepper, 1979; Rabin and Schrag, 1999). This implication
reinforces the usefulness of examining first impressions since they are shown to persist through repeated
interactions. However, our study does not attempt to answer whether or to what extent first impressions impact later
perceptions.
11
correlated with changes in firm profitability, investment, and growth. Focusing on valuation,
Johnson, et al., (1985) find a relation between executive characteristics and market reaction to
their unexpected deaths, implying that manager talent and responsibility are associated with firm
valuation, and Hayes and Schaefer (1999) find variation in the market reaction to managers’
movements to different jobs that is consistent with managerial characteristics being incorporated
into stock price. Similarly, Adams, Almeida, and Ferreira (2005) find that returns are more
variable for more powerful CEOs, supporting the theory that CEO characteristics can influence
performance and firm valuation.
The impact of management on valuation may be even higher for younger firms, such as
IPO and pre-IPO firms. Management characteristics like education and experience (e.g., Cohen
and Dean, 2005; Higgins and Gulati, 2006), gender (Bigelow, et al., 2014), and founder-status
(Hendricks and Miller, 2013) impact IPO investor interest and valuation. Bernstein, Korteweg,
and Laws (2014) provide further evidence that investors place significant value on information
about the human capital of young firms by using a randomized field experiment to show that
investors respond more strongly to information about the founding team than they do to
information about firm traction or existing lead investors.
An underlying assumption of this literature is that investors somehow observe and
incorporate manager ability into firm valuation, but it is difficult to directly identify investors’
assessments and match them with the relevant firm valuation because investor perception of
management is typically not observable. In addition, even if investor perceptions are observable
at a given point in time, firm values at that given moment are generally the result of repeated
interactions and assessments. To overcome these difficulties, we use basic human impressions
12
from thin slices of managers’ nonverbal behavior displayed during investors’ first major
exposure to the firm’s management.12
2.3. The role of roadshows in the IPO process
Uncertainty is pervasive throughout the IPO process. Potential investors usually know
very little about the issuer prior to the offering and the issuer knows neither the investors who
may be interested nor their level of interest. To reduce this bilateral information asymmetry, an
issuer is required to file a registration statement with the SEC that provides extensive
information about the firm (Leone, Rock, and Willenborg, 2007; Loughran and McDonald,
2013). After this document is filed, issuers enter into a designated quiet period that extends
through the completion of the offering. In the event that an issuer learns new information during
the quiet period, the issuer has a responsibility under the Securities Act of 1933 to communicate
this new information to investors by amending its registration statement. By placing the issuer in
a quiet period, and requiring that all information included in the registration statement be
accurate13, the registration process is designed to provide investors with all the information they
need to make an informed investment decision in a single document.
Having provided this information to investors, the issuing firm’s management team
travels to various financial centers to promote the offering through a series of roadshows (see
12 Several concurrent studies estimate perceptions of CEOs based on photos of the CEO and examine their relation
to a variety of measures, including CEO compensation, firm performance, and market reponse to various events
such as job, earnings, and merger announcements (Graham, Harvey, and Puri, 2014; Halford and Hsu, 2014). In
contrast, our study examines perceptions based on a rich information set that better represents the information
underlying investor perceptions (dynamic videos rather than static photos) within the IPO setting that enables a tight
link between perceptions and firm valuation. 13 While the SEC requires that the information provided be accurate, it does not guarantee it. However, investors
who purchase securities and suffer losses have important recovery rights if they can prove that there was incomplete
or inaccurate disclosure of important information.
13
Fig. 1).14 This process typically involves the firm’s management giving multiple presentations a
day to institutional investors over the final two to three weeks of the registration period. In these
presentations, management is counseled to only make factually accurate statements that coincide
with the registration statements filed with the SEC (Arcella, 2011). Despite the information
being repetitive, Ann Sherman testified to the U.S. Senate in 2012 that investors primarily attend
roadshows to observe the managers and “find value in watching them on their feet” (Sherman,
2012). This view that observing an IPO firm’s management provides investors with valuable
information was also expressed by the NYSE/NASD advisory committee formed in 2003 to
examine the fairness of the IPO process. In considering institutional investors’ selective access to
roadshows, the committee concluded:
[E]ven the opportunity to see and hear senior management may provide
significant information for an investment decision. Many potential investors, both
in the IPO and in the aftermarket, having been excluded from the roadshow, are
not privy to this information. To dispel the perception of unfairness, this must
change. (NYSE/NASD, 2003)
In November 2004, many of the recommendations provided by the NYSE/NASD
advisory committee were included in the SEC’s proposed regulation to enhance the
communication, registration, and offering process under the Securities Act of 1933. This reform,
now referred to as the 2005 Securities Offering Reform, was unanimously adopted on June 29,
2005 with only minor modifications to the proposed legislation. Included in both the proposed
and final reform, issuers that conduct roadshows in conjunction with an equity offering are
required to file an electronic copy of one of their roadshows with the SEC as a free-writing
prospectus. However, IPO firms are allowed to treat the roadshow as an oral communication
14 A roadshow is defined under Rule 433 of the Securities Act of 1933 as an offer (other than a statutory prospectus
or a portion of one filed as part of a registration statement) that contains a presentation made by one or more
members of the issuer’s management team.
14
rather than a free writing prospectus if they make a “bona fide” electronic roadshow available to
unrestricted audiences during the registration period. 15 By classifying roadshows as oral
communications, IPO firms are not required to file the roadshows with the SEC.
The roadshow is an important part of the IPO process because it allows investors to learn
valuable information about an issuing firm. However, it is also important to the underwriter
because it provides an opportunity to gauge the amount of investor demand that exists for a
firm’s offering (Rock, 1986; Benveniste and Spindt, 1989). While the underwriter proposes a
suggested pricing range for a firm’s offering at the beginning of the roadshow period, the
majority of offerings price outside of this proposed range, suggesting that investors’ indications
of interest are often significantly different from underwriters’ expectations (Cornelli and
Goldreich, 2001, 2003; Lowry and Schwert, 2004).
3. Data
3.1. IPO roadshows
We began using video capture software on April 1, 2011 to obtain IPO roadshows from
RetailRoadshow.com, a website that provides public access to online roadshow presentations for
current public offerings. As discussed in Section 2.3, firms offering securities for purchase in the
public markets use roadshows to market the offering to potential investors at the conclusion of
the registration period. To comply with the 2005 Securities Offering Reform, these firms provide
RetailRoadshow with a “bona fide” version of their roadshow during the final weeks of the
15 A bona fide electronic roadshow is defined in the final regulation as “a roadshow that is a written communication
transmitted by graphic means that contains a presentation by one or more officers of an issuer or other persons in an
issuer’s management and, if the issuer is using or conducting more than one roadshow that is a written
communication, includes discussion of the same general areas of information regarding the issuer, such
management, and the securities being offered as such other issuer roadshow or roadshows for the same offering that
are written communications. To be bona fide, the version need not address all of the same subjects or provide the
same information as the other versions of an electronic roadshow. It also need not provide an opportunity for
questions and answers or other interaction, even if other versions of the electronic roadshow do provide such
opportunities.” Refer to Rule 433, “Conditions to permissible post-filing free writing prospectuses,” for additional
details about free writing prospectuses and the criteria for a roadshow to be classified as an oral communication.
15
registration period. During this time, individuals may view the roadshow as often as they like.
However, when the offering is priced, the roadshow presentation is removed from
RetailRoadshow.com and can no longer be viewed.
3.2. Sample Selection
We use the Global New Issues Database within Thomson Financial’s SDC Platinum to
obtain a listing of all U.S. industrial firms that completed an original IPO in the United States
from April 1, 2011 to December 31, 2013. We choose April 1, 2011 to begin our study because
that is the date we began to capture IPO roadshows from RetailRoadshow.com. Consistent with
prior research on IPO firms, we exclude: firms that raised less than $10 million, firms that priced
below $5 per share, limited partnerships, and unit offerings. Our research design also requires
each firm to have historical financial information and first-day stock returns. Accordingly, we
remove firms whose information was either incomplete or missing from Compustat and/or
CRSP. Finally, we exclude firms from our sample whose roadshows did not include video, did
not feature presentations from their management team, or were not captured from
RetailRoadshow.com. As detailed in Table 1, 224 IPO filings remain in our study after applying
these criteria.
3.3. Perceptions of management
To measure perceptions of management, we follow the thin slice research in the
psychology literature and extract a brief portion of each IPO roadshow video to examine. An
underlying assumption of the literature is that each thin slice of management behavior is
representative of the entire behavioral sequence from which it is extracted. To this end, prior
research has generally extracted three samples from a behavioral sequence rather than use a
single excerpt from the behavioral sequence (Ambady, Bernieri, and Richeson, 2000). This
16
approach also enables controlling for linear trends (e.g., fatigue) observable in the behavioral
sequence. Accordingly, we follow this approach and construct a 30-second thin slice using three
10-second excerpts from the first five minutes of each CEO’s roadshow presentation.
Specifically, we take the first excerpt at the beginning of each CEO’s presentation and combine
it with two additional 10-second excerpts taken two and four minutes after the initial 10-second
excerpt has ended.16
Although we only use 30 seconds of excerpts from each video, there is still the concern
that viewers’ impressions may be shaped by factual information about the firm conveyed during
these excerpts. Because we are trying to isolate investor judgment of management independent
of firm performance, we choose to content-filter the video, following Ambady, Krabbenhoft, and
Hogan (2006). Specifically, we use both a lowpass and highpass filter to remove specific
frequencies that aid in word recognition. After this process, the actual words spoken by the CEO
are unintelligible, but the sequence and rhythm of their speech is preserved.
We use Amazon’s Mechanical Turk (MTurk) service to analyze each of the 224 thin
slices created from the roadshow presentations. This online labor market allows requesters to
post jobs to be completed from an on-demand workforce for relatively low pay. While MTurk
was created for human computation tasks, numerous studies provide evidence that MTurk is a
viable alternative to the traditional lab setting for behavioral research in a variety of fields, with
MTurk’s more diverse pool of participants creating a meaningful advantage (e.g., Paolacci,
Chandler, and Ipeirotis, 2010; Buhrmester, Kwang, and Gosling, 2011; Mason and Suri, 2012;
Crump, McDonnell, and Gureckis, 2013). In finance and accounting research, MTurk is starting
16 An alternative to this approach would be to take samples from the beginning, middle, and end of the entire
presentation, rather than just the first five minutes. However, a linear trend such as fatigue would have a more
significant impact on those clips taken from the middle and end portions of longer presentations. Our approach
removes these concerns while still capturing some of the linear trends that might appear in the manager
presentations.
17
to be used as an alternative to traditional lab experiments (e.g., Rennekamp, 2012; Koonce,
Miller, and Winchel, 2014; Asay and Hales, 2015), and there is also potential for this data source
to be used when researchers need to generate a construct not available via archival sources (e.g.,
Duarte, Siegel, and Young, 2012). Our goal is to capture basic human cognitive perceptions, and
thus MTurk’s broad, diverse pool of participants helps ensure that our tests and results are based
on measures of innate processing and not unduly influenced by characteristics of a specific group
of people.
Studies that examine the reliability of data originating from MTurk provide several
recommendations for maximizing data quality. We designed the Human Intelligence Task (HIT)
that we posted to MTurk in accordance with these recommendations. Specifically, we require
that each MTurk worker be located in the United States and have an approval rating of at least
95% on their previous assignments. We also attempt to ensure software compatibility by
including a sample thin-slice video at the beginning of the survey and asking whether the MTurk
worker is able to see and hear the video. MTurk workers that indicated that they were unable to
either see or hear the video were not allowed to complete the HIT. Finally, we included an
attention-check question at both the beginning and the end of the HIT and perform robustness
tests in Section 5 using only responses from MTurk workers that correctly answered these
attention-check questions.17
Table 2 Panel A provides demographic information about the MTurk workers in our
sample. Of the respondents, 87% are between 18 and 50 years old, and slightly over half (53%)
17 Prior to undertaking our data collection on MTurk, we performed a pilot survey in the Stanford GSB Behavioral
Lab to pretest our approach. We had 100 students view a sample of videos and fill out a prototype questionnaire.
This allowed us to observe individuals completing our survey and to ask them questions about the experience as
they left. The insights from the pilot test allowed us to adjust our process to reduce misunderstandings and thus
enhance the validity of the information collected from the MTurk system. The pretest was not designed to generate
usable observations, and accordingly, none of the data is used in this paper.
18
are male; 74% identify themselves as Caucasian, and 81% have at least some college education
(with 51% having college or graduate degrees). As shown in Fig. 2, we ask the MTurk workers
to use a seven-point Likert scale to provide their impressions about a CEO’s competence,
trustworthiness, and attractiveness after watching each CEO’s roadshow presentation. As Table 2
Panel B describes, the full rating scale is utilized by respondents, with 64% of ratings falling in
the range of 3 to 5, 17.5% below 3, and 18.5% above 5.
Each CEO is rated by at least 40 MTurk workers, and we take the average MTurk worker
rating for each of the CEO’s characteristics to create the following three CEO-specific variables:
Competent, Trustworthy, and Attractive.18 We then calculate the average of these three variables
to create a summary CEO-specific variable, Perception.19 Our motivation for creating Perception
is based on our belief that investors’ perceptions are likely to be formed by more information
than can be encompassed by any single trait. Accordingly, we view Perception as the overall
impression about a CEO at the time of a firm’s IPO.20
Table 2 Panel C provides the distribution of average CEO ratings. Perception ranges
from 3.00 to 5.00, with 79% of the observations falling between 3.50 and 4.50. For the individual
characteristics, Competent (Attractive) has a higher (lower) mean, and Attractive has a larger
standard deviation, ranging from below 2.25 through 5.00. Table 3 Panel A confirms these
statistics, showing a mean Perception of 4.05, mean Competent of 4.72, and mean Attractive of
3.28. Panel B also shows that the different perceptions about CEOs are highly correlated. This
observation is consistent with prior studies showing that perceptions about an individual’s
18 See the Appendix for more details about survey design and implementation, and see Section 5 for robustness tests
related to rating quality. 19 Results for our main tests using the quartile rank of Perception rather than the continuous measure yield the same
inferences. 20 Given this belief, the majority of the commentary included in our empirical analysis is focused on the associations
identified between Perception and the various empirical outcomes examined in this paper.
19
attractiveness are positively related to perceptions about that individual’s competence and
trustworthiness (Eagly, et al., 1991; Etcoff, et al., 2011). Table 3 Panel A also provides
information about the personal characteristics of the CEOs included in our sample. We find that
the average age for a CEO is 51 years old. We also observe that only 4% are female, 14% earned
a degree outside the United States, 59% earned a postgraduate degree, and 36% are founder-
CEOs. This panel also provides information about the roadshow characteristics, revealing that
65% of the retail roadshows are captured from live presentations to investors and that 8% of the
CEOs are seated during their presentations.
4. Empirical results
4.1. Determinants of perception
We begin our empirical analysis by examining the determinants of Perception. Our
motivation for this analysis is to investigate how CEO, roadshow, and firm characteristics
influence perceptions of senior management. To do so, we estimate the following pooled OLS
model:
Perceptioni = β0 + β1 CEO_Agei + β2 Femalei + β3 Foreigni + β4 Grad_Schooli
+ β5 Founderi + β6 Experiencei + β7 Livei + β8 Sittingi + β9 Backgroundi
+ β10 Assetsi + β11 Profitabilityi + β12 R&D_Inteni + β13 Firm_Agei
+ β14 VCi +Fixed Effects + εi (1)
in which Perception is the average of Competent, Trustworthy, and Attractive as described in
Section 3.3. CEO_Age is the natural log of the CEO’s age. Female is an indicator variable that
takes the value of one if the CEO is female. Foreign is an indicator variable that takes the value
of one if the CEO completed a degree from a university outside the United States. Grad_School
is an indicator variable that takes the value of one if the CEO earned a postgraduate degree.
Founder is an indicator variable that takes the value of one if the CEO is also the founder for the
20
IPO firm. Experience is an indicator variable that takes the value of one if the CEO’s prior
employer was a publicly traded firm.
In addition to these CEO-specific characteristics, we also include variables relating to the
roadshow presentation that could influence investor perceptions. Live is an indicator variable and
takes the value of one if the retail roadshow appears to be recorded from an actual presentation
made to institutional investors. Sitting is an indicator variable that takes the value of one if the
CEO is sitting during the presentation. Background is an indicator variable that takes the value of
one if an investment bank’s logo is visible in the background during the CEO’s roadshow
presentation.
Finally, we also include several firm characteristics in our model. If higher-quality CEOs
match with higher-quality firms then it is possible that firm characteristics will be informative
about Perception. Assets is the natural log of the firm’s total assets prior to IPO. Profitability is
the firm’s trailing twelve months net income divided by its total assets prior to IPO.
R&D_Intensity is the firm’s trailing twelve months R&D expenditures divided by its total assets
prior to IPO. Firm_Age is the natural log of the firm’s age at the time of its IPO. VC is an
indicator variable that takes the value of one if the firm has venture backing at the time of IPO.
Finally, we winsorize continuous variables at 1% and 99%, and we include both calendar-year
and industry fixed effects (based on the Fama French 12-industry classifications) in several of
the specifications, as noted in the tables.
Table 4 provides the results of estimating Eq. (1). Columns 1 through 3 of Table 4
present the results of regressing Perception on the CEO-specific, roadshow presentation, or firm
characteristics, respectively. Consistent with impressions about an individual being primarily
determined by individual-specific information, we observe that the adjusted R-squared is much
21
larger in Column 1 than it is for Columns 2 and 3. Further, Column 4 includes all the variables
outlined in Eq. (1) and indicates that five of the six variables that are statistically significant
indicators of Perception are CEO-specific variables. Specifically, CEO_Age, Foreign, and
Founder are all negatively associated with Perception whereas Female and Grad_School are
both positively associated with Perception. Overall, Table 4 provides new insight into how a
CEO’s personal characteristics influence investor perception. In addition, the adjusted R-squared
of only 0.253 highlights the fact that investor perception encompasses more than just observable
characteristics. Because we are interested in fundamental investor perception not explained by
these observable characteristics, we include the CEO and firm characteristics as control variables
in subsequent analyses.
4.2. Perception and firm value
Our main prediction is that impressions of a firm’s CEO are positively associated with firm
value. The underlying premise of this prediction is that if manager ability has the potential to impact
firm value, then perceptions about a firm’s manager should be impounded in that firm’s market
valuation. However, it is possible that investors choose to ignore these rapid System 1 assessments
and focus on more analytical System 2 assessments of management and financial performance,
resulting in no relation between firm valuation and intuitive impressions of managers. Thus, we turn
to regression analysis to empirically estimate the relation.
We use the log of the firm’s market value to proxy for firm value. Value-relevance studies of
IPO firms generally use the total market value of equity or a log transformation of that amount as the
dependent variable. 21 Comparing models that use these two measures, those that use the log
21 While value-relevance studies often use a firm’s price per share as the dependent variable, the IPO literature has
generally used alternative measures to examine questions of value-relevance since underwriters prefer to price each
offering around $15 (Fernando, Krishnamurthy, and Spindt, 2004). This clustering around a single price forces the
explanatory power to come through the correlation between the variable of interest and the number of shares
22
transformation generally provide the best fit (Beatty, Riffe, and Thompson, 2000; Hand, 2003).
Further, its distribution more closely resembles that of a normal distribution, providing it with
attractive econometric properties. Thus, we follow prior research and use the log transformation of
each firm’s total market value of equity as the dependent variable for our tests and estimate the
following pooled OLS regression:
L(MVE_Final)i = β0 + β1 Impressioni + β2 L(Book_Value)i + β3 L(Revenues)i
+ β4 L(Net_Income)i + β5 L(R&D_Expense)i + β6 Firm_Agei + β7 Underwriteri
+ β8 VCi + β9 Big4i + β10 Secondary_Sharesi + β11 Insider_Retentioni
+ β12Mkt_Cond_Leveli + β13-18CEO_Characteristics + Fixed Effects + εi (2)
in which L(MVE_Final) is the natural log of a firm’s market value of equity calculated at the close of
its first day trading on a public exchange. Impression is our primary variable of interest and takes the
value of Perception, Competent, Trustworthy, or Attractive as defined in Section 3.3.
We include several control variables in our model that have been shown to be important
indicators of IPO firm value. Following Aggarwal, Bhagat, and Rangan (2009), we include the log
transformations of each firm’s book value of equity, revenues, net income, and R&D expense for the
12 months prior to their IPO date.22 We also include other nonfinancial measures of firm quality as
suggested by prior research. Specifically, we include Firm_Age calculated as the natural log of 1 plus
the firm’s age at IPO (Fernando, Gatchev, and Spindt, 2005), Underwriter calculated as the average
Carter-Manaster ranking of the firm’s lead underwriters (Leland and Pyle, 1977; Carter and
Manaster, 1990), VC as an indicator variable that takes the value of one if the firm has venture-
capital backing (Barry, et al., 1990; Megginson and Weiss, 1991), Big4 as an indicator variable that
takes the value of one if the firm has a Big4 auditor at the time of IPO (Titman and Trueman, 1986),
outstanding. As a result, value-relevance studies of IPO firms that use a traditional price per share measure generally
provide highly unstable results and offer very little explanatory power (Beatty, Riffe, and Thompson, 2000). 22 Consistent with prior studies, we make the log transformation to these variables by taking the natural log
(1+value) when the original value is positive and –log (1-value) when the value is negative. This transformation is
able to retain the negative values included in the original data while also maintaining the monotonic relationship
among the actual realized values.
23
Secondary_Shares as the percentage of a firm’s shares being offered that are owned by existing
shareholders (Brau, Li, and Shi, 2007), Insider_Retention as the percentage of a firm’s total shares
that are retained by executives and directors after the offering (Jain and Kini, 1994), and
Mkt_Cond_Level as the NASDAQ level at the time of a firm’s IPO (Ritter, 1984; Ljungvist and
Wilhelm, 2003). 23
Table 5 presents the results from estimating Eq. (2). Consistent with our main prediction,
Column 1 shows that the estimated coefficient for Perception is 0.4340 (p-value < 0.01). 24
Importantly, the model includes the six CEO characteristics discussed in Eq. (1), suggesting that our
finding is not simply driven by these other CEO-specific qualities.25 Rather, this finding is consistent
with the NYSE/NASD (2003) IPO Advisory Committee’s statement that “even the opportunity to see
and hear senior management may provide significant information for an investment decision.”
Columns 2 through 4 of Table 5 provide the results of regressing the log of market value on
the components of Perception. As shown, the coefficients for Competent, Trustworthy, and Attractive
are all positive, providing evidence of a positive relation between perceptions of management and
firm valuation. Competent and Attractive are both significantly different from zero at the 0.023 level
or better, while Trustworthy is not significantly different from zero. One possible explanation for the
weaker results for Trustworthy is that investors might rely on monitoring mechanisms, such as
regulators and auditors, to ensure that management is not undertaking inappropriate or fraudulent
activities.
4.3. Perception and underwriter matching
23 We thank Jay Ritter for providing the Carter-Manaster rankings and each IPO firm’s founding date on his website
(http://bear.warrington.ufl.edu/ritter/ipodata.htm). 24 Note that since we have a directional prediction for the relation between perception of management and firm
value, we report one-tailed statistical significance for the various impression variables in the firm value, underwriter
matching, and proposed price tests. 25 For brevity, we do not report the coefficients for these CEO characteristics. However, for Column 1 of Table 5,
none of these variables are estimated to be statistically significant determinants of firm value when using
conventional significance levels (p-value < 0.10).
24
We next examine how impressions of a firm’s manager influence underwriter matching and
the IPO price-formation process. Examining relations at these earlier stages of the IPO process
allows us to both validate our primary results of an association between price and investor
perceptions and to better understand how these perceptions enter into price.
IPO firms and underwriters associate by mutual choice, with prior research providing
evidence that the quality of issuing firms is positively associated with underwriter quality
(Chemmanur and Fulghieri, 1994; Fernando, Gatchev, and Spindt, 2005). To the extent that
underwriters rely on their perceptions about a firm’s manager as valuable information about firm
quality or as expectations of the market’s likely assessment of the firm, then these perceptions should
be useful in understanding the underwriter matching that occurs between firms and underwriters.26
Accordingly, we examine this relation by estimating the following pooled OLS regression:
Underwriteri = β0 + β1 Impressioni + β2 Filing_Sizei + β3 Assetsi + β4 Revenuesi
+ β5 Profitabilityi + β6 R&D_Intensityi + β7 Firm_Agei + β8 VCi + β9 Big4i
+ β10 Insider_Retentioni + β11-16CEO_Characteristics + Fixed Effects + εi (3)
in which Underwriter is the average Carter-Manaster ranking of the firm’s lead underwriters,27
Filing_Size is the natural log of the size of the offering, and the remaining variables are as previously
defined.28
Table 6 provides the results from estimating Eq. (3). Consistent with impressions about a
firm’s manager being used by underwriters as an indicator of firm quality, Column 1 reports that the
coefficient for Perception is estimated to be 0.2746 (p-value = 0.017). The importance of this benefit
26 Underwriter assessment of quality and underwriter prediction of market assessments are related concepts. In the
former, the underwriter provides an honest personal assessment of firm quality. In the latter, they assess whether the
market will be interested in the firm at a higher price. Both assessments should help an underwriter predict both
eventual price and the value of being involved in the IPO. 27 We repeat the analysis using the market share of the firm’s lead underwriters, and find very similar results. 28 We model Eq. (3) following the model employed in Fernando, Gatchev, and Spindt (2005). In their model, they
examine how the filing size, firm age, venture backing, profitability, market value of equity, five-year survival
indicator, secondary equity offering indicator, and number of analysts influence underwriter matching. However, we
exclude the final four variables from our model since they are not known at the time of matching. We then
supplement the model with additional variables known at the time of IPO and believed to be important indicators of
firm quality. Specifically, we include Assets, Revenues, R&D Intensity, Big4, and Insider_Retention.
25
is evident when considering that more prestigious underwriters provide issuing firms with all-star
analyst coverage, more reputable syndicates, and higher valuations (Fernando, et al., 2012). In
addition, consistent with the earlier results, Columns 2 through 4 report positive and significant
coefficients for Competent and Attractive (p-values < 0.01 and = 0.081). While prior research has
primarily focused on how a firm’s financial information influences underwriter matching (Fernando,
Gatchev, and Spindt, 2005), our result suggests that an IPO firm’s management team also plays an
important role in attracting prestigious underwriters.29
4.4. Perception and IPO price formation
Section 4.3 provides evidence that underwriter behavior is influenced by the underwriter’s
impressions about a firm’s manager. If true, then we would expect underwriters to include this
information in the valuations that they propose at the beginning of the IPO price-formation process.
However, Roosenboom (2007, 2012) provides evidence that underwriters primarily use financial
models (e.g., dividend discount model, comparable multiples) for their proposed valuations and then
apply an additional discount due to reputational concerns. This valuation methodology may restrict
soft information from being fully reflected in the underwriter’s proposed valuation. In this case, we
would then expect investors to impound this soft information into firm valuations during the
book building process. In addition, predicting market participants’ perceptions is more difficult than
observing them; even if underwriters do incorporate their perceptions of management into the
proposed price, the roadshow and book building process is likely to provide additional information
29 We assume that the perception of management captured during the roadshow is correlated with or representative
of the perception of management that occurs during the underwriter matching. However, it is possible that
underwriter training of management during the IPO process improves the perception of management. If high-quality
underwriters provide better communication training for management than low-quality underwriters, the positive
relation between the perception of the management team and underwriter quality could be partly due to underwriter
training rather than underwriter matching. We control for pre-IPO training possibilities, such as interaction with
venture capital firms and the prior public firm experience of the CEO. In addition, given the inherent, subconscious
nature of the expressive behavior being assessed, it is unlikely that individuals could learn to completely control or
influence their behavior (Ambady, et al. 2000), much less learn this in the few months of underwriter interactions
before an IPO.
26
about investors’ basic perceptions of managers. This information would then be incorporated into
price adjustments during the book building process.30 Accordingly, we examine how investors’
perceptions about a firm’s manager are related to both the underwriter’s proposed valuation and the
revision that occurs to the proposed valuation to arrive at the firm’s secondary market value.
4.4.1. Perception and IPO price formation – proposed valuation
We begin this analysis by examining how Perception is associated with underwriters’
proposed valuations. To do so, we modify Eq. (2) by substituting the underwriter’s proposed
valuation (L(MVE_Proposed)) as the dependent variable in place of the firm’s market value at the
close of its first day of trading on the secondary market (L(MVE_Final)). Table 7 provides the results
from estimating this regression. Consistent with underwriters proposing valuations that reflect
information about their impressions of management, Column 1 of Table 7 reveals that β1 is estimated
to be 0.2906 (p-value = 0.009). Columns 2 through 4 show that the coefficients on Competent,
Trustworthy, and Attractive are also all positive, with Competent and Attractive significantly
different from zero (p-values = 0.031 and < 0.01). Overall, our results suggest that underwriters
incorporate various types of information about the issuing firm, including perceptions of
management, into the proposed price (Kim and Ritter, 1999).
4.4.2. Perception and IPO price formation – price revision
The previous section suggests that underwriters reflect at least some information about
their perceptions of management into the valuations they propose for issuing firms. However, as
previously noted, underwriters face reputational and other pricing pressures that may influence
the extent to which they reflect this information. Further, the incorporation of numerous market
30 This idea is similar to the concept of using multiple raters when the variable of interest involves considerable
judgment, which is what motivates us to use more than 40 raters per video. To show empirical support in our setting
for the benefit of many judges, we re-estimate our main tests using only one randomly selected rater for each CEO
video (rather than 40 raters). Consistent with the concept that more raters improve the average estimation, we do not
find a significant relation between the one randomly selected CEO rating and firm value.
27
participants during the roadshow and book building process may result in additional information
about market perceptions of management. Accordingly, we investigate whether the perceptions
of a firm’s manager are differentially weighted by investors relative to underwriters by
examining the price revision that occurs from the proposed price to the closing market price on
firms’ first day of trading.31 To do so, we estimate the following pooled OLS regression:
Revisioni = β0 + β1 Impressioni + β2 Filing_Size + β3 Assetsi + β4 Revenuesi
+ β5 Profitabilityi + β6 R&D_Intensityi + β7 Firm_Agei + β8 Underwriteri
+ β9 VCi + β10 Big4i + β11 Secondary_Shares + β12 Insider_Retentioni
+ β13 Mkt_Cond_Changei + β14-19CEO_Characteristics + Fixed Effects + εi (4)
in which Revision is defined as the percentage change between an issuing firm’s closing price
per share on its first day of trading on the secondary market and the price per share initially
proposed by the underwriter. Mkt_Cond_Change is the average daily change on the NASDAQ
stock exchange between the date when a price is initially proposed for an offering and the offer
date. This variable captures new information about the macroeconomic conditions that arise
during this period and has been shown to be a powerful determinant of the price revision (Lowry
and Schwert, 2004). All other variables are as previously defined.
Table 8 Panel A provides the results from estimating Eq. (4). β1 is our primary variable of
interest in this equation. Consistent with investors providing additional information about
perceptions of management during the book building process that is incorporated into firm value,
Column 1 indicates that the coefficient for Perception is estimated to be 0.2275 (p-value =
0.027). Turning to the components of Perception, all three (Competent, Trustworthy, and
Attractive) coefficients are estimated to be positive, but only Attractive is significantly different
31 Note that this prediction is two-sided, rather than one-sided like the final market value, underwriter, and proposed
price predictions. Information about investors’ perception of management relative to the underwriters’ estimate
could potentially modify the proposed price upward or downward. Thus, two-tailed p-values are reported for the
Perception variables in this test.
28
from zero (p-value < 0.01). Taken together, these results provide further evidence that investors
use their perceptions of management as significant indicators of firm value.
To gain further insight into this result, we decompose Revision into two components: the
change from the proposed offer price to the final offer price (Price_Update) and the change from
the final offer price to the closing price on the firm’s first day of trading on the secondary market
(Initial_Returns). As shown in Table 8 Panel B, the coefficient between Perception and each of
these two subcomponents (Price_Update and Initial_Returns) is positive, but the relation is only
significantly different from zero for Price_Update (p-value = 0.055). Thus, the positive relation
between Perception and Revision is concentrated in the time period surrounding the roadshow,
which is consistent with roadshow participants forming intuitive impressions during the CEO
presentation and incorporating these impressions into the estimate of firm valuation as part of the
book building process.
5. Additional analyses and robustness tests
5.1. Post-IPO performance
Our finding that perceptions of management are positively associated with firm value
raises the question of whether investors are rationally pricing this information about firms. This
is a difficult question to answer for several reasons. First, there is not an obvious time horizon to
examine whether an unraveling of the valuation premium occurs via poor stock performance.
Further, to determine that the initial valuation was irrational, a research design would need to
show that the unravelling was not driven by changes in investors’ perception of management, an
extremely difficult construct to capture empirically.
Despite these limitations, we examine the association between Perception and the post-
IPO stock performance of our sample of firms. Specifically, we regress firms’ one-year buy-and-
29
hold abnormal returns (BHAR) on Perception.32 While admittedly ad hoc, we choose a one-year
time horizon for two reasons. First, our sample concludes at the end of 2013, making one-year
the longest horizon we are able to examine for the entire sample. Second, using a one-year
horizon allows two prominent features that impact the secondary market pricing of IPOs to
expire, namely, insiders’ lockup provisions (Field and Hanka, 2001) and underwriters’
overallotment options (Lewellen, 2006). By allowing sufficient time for these features to expire,
we remove concerns that the final price is not a true market price. The results from this
univariate regression (untabulated) fail to identify a statistically significant relationship between
Perception and BHAR (coefficient = 0.014, t-stat = 0.16). When including other known
determinants of post-IPO performance in the model (e.g., underwriter quality, VC backing,
revenues, market conditions), the point-estimate remains close to zero (coefficient = 0.035, t-stat
= 0.36). Similar results are found when examining one-month, three-month, or six-month time
horizons. Thus, our tests fail to find any evidence that investors acted irrationally when
incorporating their perceptions of management into firm value.
5.2. Data quality
Prior research indicates several data-quality concerns may arise when using MTurk to
perform behavioral research (Mason and Suri, 2012; Crump, McDonnell, and Gureckis, 2013).
In this section, we examine whether these concerns are present in our data and investigate their
impact on our results.
We used every rating that we received from the MTurk workers in our calculation of
Perception, Competent, Trustworthy, and Attractive. However, some of the MTurk worker
ratings may be compromised either because the rater recognized the CEO, wasn’t paying close
32 To determine each firm’s BHAR, we determine the gross buy-and-hold stock returns for each IPO firm and
subtract the returns earned by that firm’s Fama-French 10x10 portfolio (i.e., the matrix of 100 portfolios formed on
deciles for the market value of equity and the book-to-market ratio) over the same one-year period.
30
attention, or was engaged in otherwise suspicious behavior (Crump, McDonnell, and Gureckis,
2013). This suspicious behavior may include providing the same response to each request or
providing responses that are uncorrelated with the group average. Accordingly, we exclude the
data observations that manifest these behaviors from the data and re-estimate each of the main
regressions in our paper.
Table 9 provides the results of re-estimating the main regressions in our paper after
excluding abnormal ratings provided by MTurk workers. Panel A provides the results after
excluding all ratings where an MTurk worker indicated having recognized the CEO in the video.
Panel B provides the results after excluding all ratings where MTurk workers failed to answer
our two attention-check questions correctly. Panel C provides the results after removing all
ratings where MTurk workers indicated the same value for a characteristic for each of the videos
that they rated. Panel D provides the results after excluding all raters whose ratings were not
positively correlated with the group average (p-value < 0.10). Panel E provides the results after
removing all ratings that were excluded in Panels A-D. In each panel, we note that the qualitative
inferences made when using the restricted set of ratings are identical to those made when using
the complete set of ratings. While the statistical inferences are similar, we note that the
coefficients estimated for Perception_Net (Panel E) are slightly lower than those estimated for
Perception (Tables 5–8) in each of the regressions.
5.3. Rater characteristics
Thin-slice judgments (and System 1 thinking processes in general) are described as
unconscious assessments made without effort or awareness, independent of intelligence or
working memory (Ambady, Bernieri, and Richeson, 2000; Evans, 2008). Because these are
fundamental, universal perceptions, we don’t expect results to be driven by rater characteristics.
31
In addition, we follow the content analysis literature’s recommendation to use a large number of
raters when capturing variables that involve judgment (Neuendorf, 2002), with the goal of
estimating a common perception. Thus, we are hesitant to estimate perceptions using
significantly smaller groups of raters. Nevertheless, we repeat our analyses using ratings from
various subsamples of raters to examine whether we observe this fundamental assumption in our
setting. We find qualitatively similar inferences for our main tests when using subsamples
limited to any of the following demographics: male raters, female raters, raters under 30 years
old, raters 30 years of age or greater, Caucasian raters, non-Caucasian raters, raters that have
attended at least some college or have a college degree, and raters that have not attended college.
These findings provide support that we are capturing fundamental human judgments that are not
unique to any particular demographic.
6. Conclusion
This study examines how investors’ perceptions of management are associated with firm
valuation. Specifically, we examine whether basic judgments of management, formed from
watching 30-second content-filtered video excerpts of a CEO’s IPO roadshow presentation, are
correlated with investors’ assessment of firm value, controlling for known determinants of firm
value. We find that a composite measure of perception based on competence, trustworthiness,
and attractiveness is positively associated with an IPO firm’s secondary market value and
underwriter quality. We also examine how this information influences IPO price formation and
show that our composite measure of perception is positively associated with both the price
proposed for the offering and the price revision that occurs from this proposed price to the
closing price on the firm’s first day of trading on the secondary market.
We contribute to the existing literature in several areas. First, we provide evidence that
impressions of management are associated with timely measures of firm value. Second, we
32
contribute to the IPO literature by providing the first empirical evidence that investors learn
valuable, non-tangible information from attending an IPO firm’s roadshow. Third, our study
contributes to the disclosure literature by providing evidence that valuable information about
management is conveyed through visual and audial nonverbal behavior.
Although we have a unique setting that allows us to match perception with concurrent
valuation, two limitations remain. First, when obtaining ratings of management, we remove
contextual information about the firm, firm performance, the history of the CEO, and even the
fact that the presentation is part of an IPO. Our goal is to focus on the most basic human
judgments, irrespective of additional information. However, this means that we are not able to
capture a measure of investor perception that is influenced by the content of conversations or
historical information about management. Second, we rely on prior literature’s findings that thin-
slice judgments are intuitive, automatic, and not easily influenced by outside factors such as
cognitive load or intelligence. However, to the extent that these inherent judgments could be
influenced by financial incentives, our measure of perception would be incomplete because our
raters are not making investment decisions based on their assessments.
33
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Appendix. Survey design and implementation
When creating the MTurk survey, we employ several techniques common in survey design to
reduce concerns about bias in the responses. First, we organize CEO videos by randomly
assigning each video to one of twenty groups or cells, and we then randomly assign each of our
respondents to view and rate one cell or group of videos. In this way, each video is watched by at
least 40 respondents (and on average, 45).33 In addition, respondents are only allowed to view
and rate one group of videos, reducing concerns about rater fatigue or differences in rater
learning over time. Second, within each group of videos, we randomize the order of the videos’
appearance to the respondent to minimize the potential for different responses based on when
CEO videos are viewed during the rating exercise. Third, we randomize the order of the three
characteristic questions (Competent, Trustworthy, and Attractive) for each rater to avoid
systematically different responses due to the ordering of the traits. (We do, however, leave the
question order the same for all videos rated by a given rater to avoid unnecessary confusion
during a series of videos and questions.) Fourth, we provide a practice video and questions
before the sample videos to familiarize respondents with the format, and we require raters to
confirm their ability to see and hear the practice video before allowing the survey to begin. In
addition, during the survey, we do not allow the rater to progress to the questions until the time
required to view the video has elapsed. In this way, we minimize the risk that respondents ignore
the video and respond with random ratings.34
33 Respondents are required to view and rate all videos in the group to obtain credit for completing the survey, and
we receive only complete responses. Thus, the difference in raters per cell is a result of the random assignment of
raters. On average, each cell will be assigned 45 raters. However, in practice, cells were assigned between 43 and 47
raters. Accordingly, all cells (and thus videos) were rated by at least 43 respondents. 34 Also see robustness tests in Section 5.2 eliminating ratings that are potentially of lower quality.
39
Figure 1. IPO Timeline
Figure 2. Survey question
t-2 t-1 t t+1
Firm files registration
statement with SEC
Initial pricing range
is announced
The final offer price
is announced
Price observed on
secondary market
IPO Roadshow
40
Table 1. Final sample
Notes: Panel A details our sample selection process and reports the final number of firms included in our
empirical analyses. Section 3.2 provides additional information about our sample selection process.
Notes: Panel B details the distribution of our final sample reporting both the issuing year and Fama-French
12-industry classification.
Panel A: Sample Selection Process
Observations
360
(55)
(41)
(18)
(22)
224
SDC Platinum listing of U.S. Industrials that completed an original initial public
offering between April 1, 2011 - December 31, 2013.
Details
Less: Firms that raised proceeds less than $10 million or have a final offer price
below $5 per share
Final Sample
Less: Audio-only roadshows, roadshows without manager presentations, or
roadshows that were not captured from RetailRoadshow
Less: IPOs with incomplete financial information (CRSP/Compustat)
Less: Limited Partnerships or Unit offerings
Panel B: Sample Distribution
Industry 2011 2012 2013 Total
Consumer Non-Durables 1 3 2 6
Consumer Durables 0 1 2 3
Manufacturing 2 5 3 10
Oil & Gas 4 5 4 13
Chemicals 1 0 3 4
Business Equipment 19 28 23 70
Telecommunications 1 1 2 4
Wholesale 7 10 11 28
Healthcare 5 11 36 52
Other 5 8 21 34
Total 45 72 107 224
41
Table 2. Mechanical Turk workers
Notes: Panel A provides the demographic and background characteristics of the MTurk workers
that analyzed the CEO presentations in our study. Section 3.3 provides additional information about
our use of Amazon’s Mechanical Turk system.
Panel A: Characteristics of Mechanical Turk Workers
Frequency Percent
Gender
Male 473 52.6%
Female 427 47.4%
Total 900 100%
Age
18-29 406 45.1%
30-49 378 42.0%
50+ 116 12.9%
Total 900 100%
Education
Some high school or less 9 1.0%
High school graduate or equivalent 112 12.4%
Trade, technical, or vocational training 49 5.4%
Some college credit, no degree 274 30.5%
College graduate 350 38.9%
Some postgraduate work 25 2.8%
Post graduate degree 81 9.0%
Total 900 100%
Ethnicity
Caucasian 662 73.6%
African American 76 8.4%
Asian 66 7.3%
Hispanic 60 6.7%
Other 36 4.0%
Total 900 100%
42
Table 2. Mechanical Turk workers, continued
Notes: Panel B provides the distribution of ratings provided by Mechanical Turk workers. The Appendix
provides additional information about the survey techniques used in obtaining these responses.
Notes: Panel C provides the distribution of rating averages of the 224 CEO presentations included in our
sample. Each CEO is rated by at least 40 MTurk workers, and we take the average MTurk rating for each of the
CEO’s characteristics to create Competent, Trustworthy, and Attractive. Perception is the average of
Competent, Trustworthy, and Attractive.
Panel B: Distribution of ratings provided by Mechanical Turk Workers
Rating Competent Trust Attractive Combined
1 209 468 1,555 2,232
2 438 914 1,699 3,051
3 1,073 1,656 2,146 4,875
4 2,396 2,769 2,573 7,738
5 2,947 2,421 1,366 6,734
6 2,191 1,440 590 4,221
7 822 408 147 1,377
Total 10,076 10,076 10,076 30,228
Panel C: Distribution of rating averages by CEO
Value Perception Competent Trustworthy Attractive
Less than 2.25 0 0 0 6
2.25 - 2.50 0 0 0 24
2.75 - 2.50 0 0 0 20
2.75 - 3.00 0 0 0 34
3.00 - 3.25 5 0 0 37
3.25 - 3.50 12 0 11 24
3.50 - 3.75 39 0 24 28
3.75 - 4.00 41 5 50 17
4.00 - 4.25 57 20 50 13
4.25 - 4.50 41 39 43 8
4.50 - 4.75 21 55 27 8
4.75 - 5.00 8 53 13 5
5.00 - 5.25 0 44 6 0
Greater than 5.25 0 8 0 0
Total 224 224 224 224
43
Table 3. Descriptive statistics
Notes: Table 3 provides descriptive statistics for our sample of firms. The data used in this study is
collected from a variety of sources including Compustat, CRSP, the SEC EDGAR database, and
Jay Ritter’s IPO database. The motivations and descriptions for all variables appear in Section 4 of
this paper.
Variable Obs Mean Std. Dev Q1 Median Q3
Perception 224 4.05 0.39 3.76 4.09 4.32
Competent 224 4.71 0.35 4.47 4.73 4.98
Trustworthy 224 4.16 0.41 3.86 4.13 4.47
Attractive 224 3.28 0.66 2.80 3.18 3.73
L(MVE_Proposed) 224 6.54 0.97 5.81 6.31 7.15
Revision 224 0.23 0.52 -0.16 0.12 0.51
Price_Update 224 -0.01 0.22 -0.17 0.00 0.13
Initial_Returns 224 0.21 0.29 0.01 0.14 0.32
L(MVE_Final) 224 6.67 1.11 5.94 6.58 7.34
Filing_Size 224 5.01 0.8 4.4 4.78 5.42
Assets 224 5.25 1.71 4.03 4.90 6.40
Revenues 224 1.01 0.81 0.41 0.86 1.47
Profitability 224 -0.27 0.68 -0.30 -0.03 0.04
R&D_Intensity 224 0.26 0.42 0.00 0.09 0.34
Firm_Age 224 2.62 0.89 2.08 2.48 3.16
Underwriter 224 8.27 0.77 8.00 8.50 8.75
VC 224 0.48 0.50 0.00 0.00 1.00
Big4 224 0.88 0.32 1.00 1.00 1.00
Secondary_Shares 224 0.15 0.26 0.00 0.00 0.20
Insider_Retention 224 0.41 0.25 0.17 0.45 0.59
Mkt_Cond_Level 224 3,120 421 2,754 2,998 3,481
Mkt_Cond_Change 224 0.08 0.09 0.04 0.08 0.14
CEO_Age 224 3.93 0.15 3.84 3.95 4.04
Female 224 0.04 0.19 0.00 0.00 0.00
Foreign 224 0.14 0.35 0.00 0.00 0.00
Grad_School 224 0.59 0.49 0.00 1.00 1.00
Founder 224 0.36 0.48 0.00 0.00 1.00
Experience 224 0.48 0.50 0.00 0.00 1.00
Live 224 0.65 0.48 0.00 1.00 1.00
Sitting 224 0.08 0.27 0.00 0.00 0.00
Background 224 0.15 0.36 0.00 0.00 0.00
44
Table 4. Determinants of Perception
Notes: Table 4 presents the results from an OLS regression of Perception on several CEO, firm, and
offering characteristics. Perception is defined as the average of Competent, Trustworthy, and Attractive.
See Section 4 for all other variable definitions. *** designates two-tailed statistical significance at 1%, **
at 5%, and * at 10%.
VARIABLES (1) (2) (3) (4)
CEO_Age -1.2286*** -1.1941***
(0.000) (0.000)
Female 0.5141*** 0.4761***
(0.000) (0.000)
Foreign -0.1111* -0.1332**
(0.064) (0.042)
Grad_School 0.0724 0.0902*
(0.144) (0.097)
Founder -0.1375*** -0.1311**
(0.004) (0.012)
Experience -0.0451 -0.0467
(0.338) (0.344)
Live -0.0084 0.0666
(0.881) (0.218)
Sitting -0.1717 -0.0964
(0.139) (0.369)
Background -0.0716 -0.1699**
(0.324) (0.017)
Assets -0.0020 0.0196
(0.929) (0.331)
Profitability -0.0827 -0.0491
(0.210) (0.454)
R&D_Inten -0.0188 0.0505
(0.882) (0.695)
Firm_Age 0.0363 0.0235
(0.289) (0.448)
VC 0.0890 0.0727
(0.267) (0.260)
Industry Fixed Effects Excluded Excluded Included Included
Observations 224 224 224 224
Adjusted R-squared 0.238 0.009 0.018 0.253
Perception
45
Table 5. Perception and firm value
Notes: Table 5 presents the results from an OLS regression of L(MVE_Final) on several CEO, firm, and
offering characteristics. L(MVE_Final) is defined as the natural log of the firm’s market value of common
equity calculated at the end of its first day trading on the secondary market. Perception is our primary
variable of interest and is defined as the average of Competent, Trustworthy, and Attractive. See Section 4
for all other variable definitions. *** designates one-tailed statistical significance at 1%, ** at 5%, and * at
10% for Perception, Competent, Trustworthy, and Attractive, and two-tailed significance for control
variables.
VARIABLES (1) (2) (3) (4)
Perception 0.4340***
(0.002)
Competent 0.3187**
(0.023)
Trustworthy 0.1620
(0.141)
Attractive 0.3216***
(0.000)
L(Book_Val_Post) 0.0647*** 0.0646*** 0.0624*** 0.0626***
(0.005) (0.005) (0.008) (0.007)
L(Revenues) 0.3278*** 0.3324*** 0.3315*** 0.3358***
(0.000) (0.000) (0.000) (0.000)
L(Net_Income) -0.0205 -0.0235 -0.0223 -0.0206
(0.375) (0.316) (0.348) (0.369)
L(R&D_Expense) 0.1375** 0.1330** 0.1380** 0.1414**
(0.028) (0.034) (0.030) (0.024)
Firm_Age -0.0771 -0.0836 -0.0750 -0.0636
(0.350) (0.321) (0.374) (0.441)
Underwriter 0.1825*** 0.1833** 0.2041*** 0.1898***
(0.010) (0.011) (0.005) (0.007)
VC 0.0016 0.0193 0.0110 0.0088
(0.990) (0.887) (0.935) (0.945)
Big4 0.3448** 0.3543** 0.3207** 0.3070**
(0.017) (0.015) (0.027) (0.030)
Secondary_Shares 0.3820* 0.3817* 0.3702* 0.2984
(0.068) (0.069) (0.087) (0.143)
Insider_Retention -0.1119 -0.1137 -0.1207 -0.1019
(0.610) (0.611) (0.590) (0.636)
Mkt_Cond_Level 0.0007*** 0.0007*** 0.0007*** 0.0006***
(0.003) (0.003) (0.003) (0.004)
CEO Characteristics Included Included Included Included
Fixed Effects Time, Industry Time, Industry Time, Industry Time, Industry
Observations 224 224 224 224
Adjusted R-squared 0.564 0.555 0.549 0.571
L(MVE_Final)
46
Table 6. Perception and underwriter matching
Notes: Table 6 presents the results from an OLS regression of Underwriter on several CEO, firm, and
offering characteristics. Underwriter is calculated as the average Carter-Manaster IPO ranking for the
firm’s lead underwriters. Perception is our primary variable of interest and is defined as the average of
Competent, Trustworthy, and Attractive. See Section 4 for all other variable definitions. *** designates
one-tailed statistical significance at 1%, ** at 5%, and * at 10% for Perception, Competent, Trustworthy,
and Attractive, and two-tailed significance for control variables.
Panel A:
VARIABLES (1) (2) (3) (4)
Perception 0.2746**
(0.017)
Competent 0.3673***
(0.007)
Trustworthy 0.1511
(0.119)
Attractive 0.1151*
(0.081)
Filing_Size 0.1696 0.1584 0.1889 0.1792
(0.166) (0.183) (0.138) (0.156)
Assets 0.1069 0.1083 0.1011 0.1105
(0.170) (0.161) (0.205) (0.157)
Revenues 0.0064 0.0059 0.0135 0.0181
(0.924) (0.929) (0.842) (0.783)
Profitability 0.4418*** 0.4483*** 0.4329*** 0.4360***
(0.002) (0.002) (0.003) (0.003)
R&D_Intensity 0.3810 0.3942 0.3782 0.3865
(0.164) (0.146) (0.170) (0.168)
Firm_Age 0.0252 0.0119 0.0256 0.0346
(0.700) (0.857) (0.701) (0.597)
VC 0.2743** 0.2763** 0.2761** 0.2844**
(0.020) (0.018) (0.023) (0.017)
Big4 0.4822*** 0.5056*** 0.4781*** 0.4619***
(0.006) (0.003) (0.007) (0.010)
Secondary_Shares 0.1289 0.1573 0.1304 0.0804
(0.400) (0.313) (0.396) (0.593)
Insider_Retention 0.4174** 0.4102** 0.4119** 0.4217**
(0.041) (0.042) (0.048) (0.041)
CEO Characteristics Included Included Included Included
Fixed Effects Time, Industry Time, Industry Time, Industry Time, Industry
Observations 224 224 224 224
Adjusted R-squared 0.340 0.351 0.330 0.331
Underwriter
47
Table 7. Perception and proposed IPO price
Notes: Table 7 presents the results from an OLS regression of L(MVE_ Proposed) on several CEO, firm,
and offering characteristics. L(MVE_ Proposed) is defined as the natural log of the firm’s market value of
common equity calculated at the midpoint of the initial pricing range proposed for the offering. Perception
is our primary variable of interest and is defined as the average of Competent, Trustworthy, and Attractive.
See Section 4 for all other variable definitions. *** designates one-tailed statistical significance at 1%, **
at 5%, and * at 10%.
Variables (1) (2) (3) (4)
Perception 0.2906***
(0.009)
Competent 0.2369**
(0.031)
Trustworthy 0.1048
(0.181)
Attractive 0.2065***
(0.004)
L(Book_Value) 0.0369* 0.0370* 0.0351* 0.0354*
(0.071) (0.070) (0.090) (0.086)
L(Revenues) 0.3312*** 0.3339*** 0.3346*** 0.3360***
(0.000) (0.000) (0.000) (0.000)
L(Net_Income) -0.0124 -0.0142 -0.0141 -0.0133
(0.551) (0.498) (0.511) (0.524)
L(R&D_Expense) 0.1178** 0.1147** 0.1184** 0.1211**
(0.034) (0.041) (0.035) (0.029)
Firm_Age -0.0038 -0.0084 -0.0029 0.0010
(0.956) (0.905) (0.968) (0.989)
Underwriter 0.1172* 0.1164* 0.1316** 0.1202*
(0.064) (0.067) (0.045) (0.060)
VC 0.0214 0.0323 0.0299 0.0277
(0.857) (0.788) (0.804) (0.812)
Big4 0.1915 0.1998* 0.1730 0.1659
(0.102) (0.088) (0.136) (0.153)
Insider_Retention -0.3506** -0.3512** -0.3577** -0.3529**
(0.047) (0.050) (0.045) (0.042)
Mkt_Cond_Level 0.0006*** 0.0006*** 0.0006*** 0.0006***
(0.003) (0.002) (0.003) (0.003)
Remaining Variables Included Included Included Included
Time Fixed Effects Included Included Included Included
Industry Fixed Effects Included Included Included Included
Observations 224 224 224 224
Adjusted R-squared 0.598 0.595 0.589 0.601
L(MVE_Proposed)
48
Table 8. Perception and IPO price revision
Notes: Table 8 Panel A presents the results from an OLS regression of Total_Revision on several CEO,
firm, and offering characteristics. Revision is defined as the percentage change between the price per share
initially proposed for the offering and the IPO firm’s closing price per share after its first day of trading on
the secondary market. Perception is our primary variable of interest and is defined as the average of
Competent, Trustworthy, and Attractive. See Section 4 for all other variable definitions. *** designates
two-tailed statistical significance at 1%, ** at 5%, and * at 10%.
Panel A: Determinants of the Total Revision
VARIABLES (1) (2) (3) (4)
Perception 0.2275**
(0.027)
Competent 0.1387
(0.190)
Trustworthy 0.0893
(0.358)
Attractive 0.1812***
(0.005)
Filing_Size 0.1871** 0.1918** 0.2009** 0.1857**
(0.031) (0.026) (0.020) (0.033)
Assets -0.1798*** -0.1793*** -0.1845*** -0.1753***
(0.000) (0.000) (0.000) (0.000)
Revenues -0.0544 -0.0449 -0.0454 -0.0523
(0.252) (0.351) (0.350) (0.265)
Profitability 0.1953** 0.1889** 0.1805** 0.1970**
(0.027) (0.037) (0.040) (0.024)
R&D_Intensity -0.2385* -0.2317 -0.2437* -0.2364*
(0.089) (0.104) (0.086) (0.083)
Firm_Age -0.0495 -0.0513 -0.0479 -0.0404
(0.218) (0.217) (0.243) (0.318)
Underwriter 0.0577 0.0622 0.0715 0.0593
(0.213) (0.196) (0.136) (0.192)
VC 0.0193 0.0222 0.0182 0.0291
(0.836) (0.815) (0.850) (0.751)
Big4 0.2730*** 0.2714*** 0.2598*** 0.2541***
(0.001) (0.001) (0.001) (0.001)
Secondary_Shares 0.0230 0.0099 0.0103 -0.0146
(0.855) (0.937) (0.935) (0.908)
Insider_Retention 0.2946* 0.2910* 0.2861* 0.2990*
(0.054) (0.058) (0.065) (0.053)
Mkt_Cond_Change 1.0101** 0.9430** 0.9739** 1.1216**
(0.032) (0.044) (0.038) (0.015)
CEO Characteristics Included Included Included Included
Fixed Effects Time, Industry Time, Industry Time, Industry Time, Industry
Observations 224 224 224 224
Adjusted R-squared 0.260 0.245 0.242 0.273
Revision
49
Table 8. Perception and IPO price revision, continued
Notes: Table 8 Panel B presents the results from an OLS regression of Price_Revision and Underpricing on several CEO, firm, and offering characteristics.
Price_Revision is defined as the percentage change between the price per share initially proposed for the offering and the final offer price. Underpricing is
defined as the percentage change between the final offer price and the IPO firm’s closing price per share after its first day of trading on the secondary
market. Perception is our primary variable of interest and is defined as the average of Competent, Trustworthy, and Attractive. See Section 4 for all other
variable definitions. *** designates two-tailed statistical significance at 1%, ** at 5%, and * at 10%.
Panel B: Determinants of the Price Revision and IPO Underpricing
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8)
Perception 0.0729* 0.0710
(0.055) (0.223)
Competent 0.0198 0.0709
(0.611) (0.246)
Trustworthy 0.0078 0.0500
(0.831) (0.360)
Attractive 0.0773*** 0.0363
(0.002) (0.254)
Other Controls Included Included Included Included Included Included Included Included
Time Fixed Effects Included Included Included Included Included Included Included Included
Industry Fixed Effects Included Included Included Included Included Included Included Included
Observations 224 224 224 224 224 224 224 224
Adjusted R-squared 0.255 0.244 0.243 0.279 0.314 0.314 0.312 0.312
Price_Update Initial_Returns
50
Table 9. Robustness tests using alternative measures of Perception
Notes: Panel A presents the results from running Eq. (2), (3), and (4) modified to include Perception_Recognized rather than Perception.
Perception_Recognized is defined as the average of Competent, Trustworthy, and Attractive after excluding all ratings that indicated to recognize the CEO. See
Section 4 for all other variable definitions. *** designates one-tailed statistical significance at 1%, ** at 5%, and * at 10% for all but the Revision test, where
two-tailed significance is designated.
Notes: Panel B presents the results from running Eq. (2), (3), and (4) modified to include Perception_Attention rather than Perception. Perception_Attention is
defined as the average of Competent, Trustworthy, and Attractive after excluding all raters that did not correctly answer the two attention-check questions
included in the survey. See Section 4 for all other variable definitions. *** designates one-tailed statistical significance at 1%, ** at 5%, and * at 10% for all
but the Revision test, where two-tailed significance is designated.
Notes: Panel C presents the results from running Eq. (2), (3), and (4) modified to include Perception_Constant rather than Perception. Perception_Constant is
defined as the average of Competent, Trustworthy, and Attractive after excluding all raters from MTurk workers that indicated the same value for a
characteristic for each of the videos that they rated. See Section 4 for all other variable definitions. *** designates one-tailed statistical significance at 1%, ** at
5%, and * at 10% for all but the Revision test, where two-tailed significance is designated.
Panel A: Robustness - Recognized the speaker
VARIABLES L(MVE_Final) BTM_Final Underwriter L(MVE_Proposed) BTM_Proposed Revision
Perception_Recognized 0.4409*** -0.1354*** 0.2727** 0.2976*** -0.1101*** 0.2272**
(0.002) (0.003) (0.018) (0.009) (0.002) (0.028)
Ratings Retained 99.3% 99.3% 99.3% 99.3% 99.3% 99.3%
Adjusted R-squared 0.565 0.191 0.340 0.599 0.207 0.260
Panel B: Robustness - Incorrectly answered the attention check questions
VARIABLES L(MVE_Final) BTM_Final Underwriter L(MVE_Proposed) BTM_Proposed Revision
Perception_Attention 0.3956*** -0.1131*** 0.2847** 0.2660** -0.0889*** 0.2217**
(0.004) (0.006) (0.015) (0.014) (0.007) (0.027)
Ratings Retained 84.4% 84.4% 84.4% 84.4% 84.4% 84.4%
Adjusted R-squared 0.562 0.181 0.342 0.597 0.197 0.260
Panel C: Robustness - Provided a constant rating
VARIABLES L(MVE_Final) BTM_Final Underwriter L(MVE_Proposed) BTM_Proposed Revision
Perception_Constant 0.4132*** -0.1254*** 0.2748** 0.2805*** -0.1031*** 0.2083**
(0.003) (0.003) (0.015) (0.010) (0.003) (0.034)
Ratings Retained 94.2% 94.2% 94.2% 94.2% 94.2% 94.2%
Adjusted R-squared 0.564 0.188 0.341 0.598 0.205 0.258
51
Table 9. Robustness tests using alternative measures of Perception, continued
Notes: Panel D presents the results from running Eq. (2), (3), and (4) modified to include Perception_Uncorrelated rather than Perception.
Perception_Uncorrelated is defined as the average of Competent, Trustworthy, and Attractive after excluding all raters whose responses were uncorrelated with
the group average (p-value < 0.10). See Section 4 for all other variable definitions. *** designates one-tailed statistical significance at 1%, ** at 5%, and * at
10% for all but the Revision test, where two-tailed significance is designated.
Notes: Panel E presents the results from running Eq. (2), (3), and (4) modified to include Perception_Net rather than Perception. Perception_Net is defined as
the average of Competent, Trustworthy, and Attractive after excluding all ratings that were excluded in Panels A-D. See Section 4 for all other variable
definitions. *** designates one-tailed statistical significance at 1%, ** at 5%, and * at 10% for all but the Revision test, where two-tailed significance is
designated.
Panel D: Robustness - Uncorrelated to the average rating
VARIABLES L(MVE_Final) BTM_Final Underwriter L(MVE_Proposed) BTM_Proposed Revision
Perception_Uncorrelated 0.4105*** -0.1268*** 0.2317** 0.2774*** -0.1014*** 0.2083**
(0.002) (0.003) (0.026) (0.008) (0.003) (0.027)
Ratings Retained 93.3% 93.3% 93.3% 93.3% 93.3% 93.3%
Adjusted R-squared 0.564 0.190 0.337 0.599 0.205 0.259
Panel E: Robustness - Violated criteria specified in Panels A-D
VARIABLES L(MVE_Final) BTM_Final Underwriter L(MVE_Proposed) BTM_Proposed Revision
Perception_Net 0.3518*** -0.1043*** 0.2406** 0.2434** -0.0811*** 0.1890**
(0.006) (0.008) (0.021) (0.015) (0.009) (0.034)
Ratings Retained 74.5% 74.5% 74.5% 74.5% 74.5% 74.5%
Adjusted R-squared 0.561 0.181 0.340 0.597 0.197 0.257