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Gender and Earnings Conference Calls
Bill Francis
Lally School of Management
Rensselaer Polytechnic Institute
Thomas Shohfi
Lally School of Management
Rensselaer Polytechnic Institute
Daqi Xin
Lally School of Management
Rensselaer Polytechnic Institute
January 2019
.
We thank the Donald Shohfi Financial Research Fund for computing support.
Gender and Earnings Conference Calls
January 2019
Abstract
Using a sample of more than 65,000 earnings conference call transcripts from 2007 to
2016, we examine analyst and management gender differences in participation and behavior during
conference calls. We find that female analysts are less likely to participate in earnings conference
calls. In addition, female analysts appear later in the Q&A session to ask questions, are granted
fewer follow-up opportunities, and speak less compared with their male counterparts. Female
executives also have shorter discourses than male executives and female analysts’ questions elicit
more executives’ positive sentiment. Female executives use less numeric content when answering
analysts’ question but their tone is less uncertain. Subsequent to conference calls, the EPS
magnitude of forecast revision is larger for female analysts.
2
“Forget the board room. Women’s voices are barely even present on conference calls.”
-Bloomberg1
1. Introduction
Despite females accounting for nearly half of the total labor force in the U.S., women are
underrepresented in the most powerful positions in the business world.2 Catalyst.org reports that
females accounted for 5.7% of CEOs and 21.2% of board positions among S&P 500 firms in 2017.3
Only 1.9% of total mutual fund assets are managed by women exclusively compared with 74% by
men only (Lutton and Davis, 2015). The glass ceiling still exists persistently in reality and in mind
(Bertrand 2017; Cohen et al., 2017). To explain the gender gap in business professions, especially
the underrepresentation of women as business leaders, an emerging literature examines gender
differences in individual and corporate decision-making (Barber and Odean, 2001; Adams and
Ferreira, 2009; Francoeur, Labelle, and Sinclair-Desgagné, 2008; Huang and Kisgen, 2013; Levi,
Li and Zhang, 2014). In general, women are found to be more conservative (Johnson and Powell,
1994; Croson and Gneezy, 2009; Faccio, Marchica and Mura, 2016), more ethically-oriented
(Franke, Crown and Spake, 1997), and less competitive (Gneezy, Niederle and Rustichini, 2003;
Gneezy and Rustichini, 2004; Niederle and Vesterlund, 2007). Recent studies also examine gender
gaps in various occupations in the business world including analysts (Kumar, 2010; Fang and
Huang, 2017), loan officers (Beck, Behr, and Guettler, 2012), and auditors (Ittonen, Miettinen, and
Vähämaa, 2010).
Although women are significantly outnumbered in various business professions by their
male counterparts, whether they are better performers and whether they are treated fairly are still
1 https://www.bloomberg.com/news/articles/2018-09-13/men-get-the-first-last-and-every-other-word-on-earnings-
calls 2 https://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS?locations=US 3 http://www.catalyst.org/knowledge/women-ceos-sp-500
3
inconclusive. In this paper, we investigate various gender-related communication issues in the
setting of earnings conference calls. In particular, we examine four research questions: (1) whether
female analysts are discriminated against in earnings conference calls participation; (2) whether
female analysts and executives behave differently from their male counterparts; (3) how female
analysts react differently to information acquired from conference calls compared with male
analysts and; (4) how the markets interpret female and male participants’ behavior differently.
Women are not only solely outnumbered in the board room where participants have face-
to-face communication—their voice is drowned out in conference calls. Earnings conference calls
represent a unique and valuable setting to study gender issues but have yet been leveraged
specifically for this purpose in the literature.4 First, in conference calls, two parties—analysts and
executives—participate together, which makes conference calls different from other disclosure
venues in which only one party is involved. Prior research leverages this analyst-manager
interaction environment and shows that narratives in earnings conference calls convey “soft”
information. For example, Larcker and Zakolyukina (2012) classify CEO and CFO narratives from
conference call transcripts into “deceptive” and “trustful” parts based on psychological and
linguistic word lists and find that the deception measure can predict subsequent financial
restatements. By systematically inferring gender within a participant’s first name, we can directly
observe the interaction between analysts and management with various gender combinations.
Second, during the question-and-answer (Q&A) session, analysts and managers interact in
real time without rehearsal or scripting. Conference calls are a stressful environment for managers
because of potential interrogation by analysts. Matsumoto, Pronk and Roelofsen (2011) argue that
the spontaneous nature of the Q&A part of a conference call leads to more information disclosure
4 See Milian, Smith and Alfonso (2017) as an exception. The authors study positive tone in language during earnings
conference calls and find that female analysts exhibit significantly more favorable language.
4
by managers because they prefer to withhold bad news in prepared statements. Literature shows
that women have low preference for competition and perform poorly compared with men under
competition (Gneezy, Niederle and Rustichini, 2003; Niederle and Vesterlund, 2007). If women
generally experience stronger emotions and more nervousness and fear when faced with
unfavorable outcomes (Croson and Gneezy, 2009), we expect to observe gender differences in
behavioral patterns during earnings conference calls.
Third, compared with other information dissemination venues of analysts and executives,
conference calls make gender more visible to participants and are more likely to reflect any
possible gender gap. Investors are expected to pay less attention to analyst gender when analyst
reports, stock recommendation or earnings forecasts are issued. However, female voice is highly
distinguishable in conference calls, which may elicit different gender perceptions for all
participants (Sturm et al., 2014; Jannati et al. 2018).
We collect more than 65,000 conference call transcripts from Capital IQ from 2007 to
2016. Using multiple algorithms based on first names, we determine the gender of analysts who
participate in conference calls. First, we follow Mayew (2008) to use I/B/E/S to identify an analyst
population who are interested in asking questions in conference calls and show that female analysts
are less likely participate in conference calls. This relatively lower likelihood of participation
persists is robust to the number of covering analysts, analyst professional characteristics (e.g.
experience, all-star status), and across industries. Moreover, we find evidence that management
discriminates against female analysts in conference call participation by deferring their positions
in Q&A session and allowing fewer follow-up questions. Both female analyst and executives have
shorter discourses than their male executives. Regarding sentiment, female analysts’ questions
elicit more positive sentiment from executives. Female executives use less numeric content when
5
answering analysts’ question but their tone is more affirmative. Markets react similarly to male
and female executives’ conference call participation. Following calls, female analysts make
forecast revision with larger magnitude.
This paper contributes to the literature in three aspects. First, we extend earnings
conference call research by introducing gender effects in analyst-management communication.
While prior research on earnings conference calls focuses on incremental information and roles of
various participants, this paper focuses on gender differences in participant behavior. Given gender
differences in financial markets and the unique communication and disclosure form of earnings
conference calls, we enlarge the scope of earnings conference calls from an information
perspective.
Second, we add to the gender research in the financial and accounting literatures. Extant
research documents gender differences in risk attitudes, competition preferences, performance etc.
(Faccio, Marchica and Mura, 2016; Post and Byron, 2015; Fang and Huang, 2017), but the results
are mixed. For example, Kumar (2010) argues that gender differences do not exist because some
females self-select into the competitive financial industry and they are less representative of the
general female population. We use earnings conference calls as a setting in which participants are
under certain pressure or constraints to examine their behavior. In addition, the interaction between
analysts and managers with the same or different genders provides a unique opportunity to directly
observe gender effects in financial markets.
The rest of the paper proceeds as follows. We review literature and develop hypotheses in
Section 2. Sections 3 describes data. In Section 4, we present analysis of gender effect in analyst-
manager interaction on conference call. Sections 5 concludes.
6
2. Literature Review and Hypothesis Development
2.1 Gender and Corporate Decisions
The theoretical foundation of gender’s effect on corporate decisions is the behavioral
difference from the perspective of risk attitude and moral standard (Ho et al., 2015). Given the
literature in general social sciences, women are more risk-averse than men (Byrnes, Miller and
Schafer, 1999; Powell and Ansic, 1997; Croson and Gneezy, 2009). Faccio, Marchica, and Mura
(2016) find that firms with female CEOs have lower leverage, less earnings volatility, and a higher
probability of survival. Huang and Kisgen (2013) document that males are overconfident about
significant corporate decisions compared to females. Specifically, female executives conduct
fewer acquisitions and issue debt less often. Accordingly, markets react more strongly to female
executives’ announcements. Moreover, female executives give a wider range of earnings estimates
than male executives. Gul, Srinidhi, and Ng (2011) find that the presence of a woman in a firm’s
board of directors leads to consistently higher stock price informativeness. They further show that
the channel of this relationship is through more public firm-specific disclosure in large firms and
by facilitating more private information collection in small firms. Regarding financial reporting,
female CFOs are more conservative (Francis, Hasan, Park and Wu, 2015), produce higher quality
earnings (Srinidhi, Gul, and Tsui, 2011;Krishnan and Parsons, 2015), and conduct less earnings
management (Barua, Davidson, Rama, and Thiruvadi, 2010; Peni and Vahamaa, 2010).
In addition, a gender gap also exists in ethical issues. Gender socialization theory argues
that personality differences between men and women are the result of divergent social expectations
and learning social rules differently. Gilligan (1982) finds that men and women are different in the
way they address moral dilemmas. Franke, Crown, and Spake (1997) conduct a meta-analysis and
find that women have higher standards for ethical business practices. Bernardi and Arnold (1997)
use a Defining Issues Test to measure moral development of managers in five of the Big Six
7
accounting firms and find that female managers are more morally developed. More directly, female
executives are associated with less securities fraud (Cumming, Leung, and Rui, 2015).
2.2 Conference Calls
Earnings conference calls are one of the most important venues to communicate with
institutional investors (Brown, Call, Clement, and Sharp, 2016). The majority of conference calls
are held immediately following a quarterly earnings release. A conference call usually starts with
a presentation session in which each participating executive discusses current operations and
forward-looking statements. After presentation, analysts and investors can ask managers questions
regarding the firm. Prior studies show that conference calls provide value-relevant information to
capital markets (Frankel et al. 1999; Bushee et al. 2004; Kimbrough, 2005). Matsumoto, Pronk,
and Roelofsen (2011) find that both presentation and discussion sessions have incremental
information over press releases and that discussions sessions are particularly more informative.
They further show that the informativeness of a Q&A session is associated with the number of
analysts following the firm. Their findings suggest analysts’ active role may contribute to the
informativeness of conference calls. Further, Bowen et al. (2002) show that conference calls
increase analysts’ forecast accuracy and decrease forecast dispersion. Mayew (2008) shows that
firms discriminate against unfavorable analysts by providing analysts who issue favorable stock
recommendation with more opportunities to ask question during conference calls. Further, Mayew,
Sharp, and Venkatachalam (2013) find that analysts who participate in conference calls by asking
questions issue more accurate and timelier earnings forecasts than non-participating analysts,
suggesting participating analysts may possess superior information.
One stream of literature examines soft information embedded in conference calls.
Linguistic cues contain soft information which is incremental to press releases (Matsumoto et al.
8
2011). For example, Allee and DeAngelis (2015) document that tone dispersion, the degree to
which tone is spread evenly in a narrative, is associated with firm performance, managers’ financial
reporting choices, and managers’ incentive to influence perception of the firm. Mayew and
Venkatachalam (2012) show that managers’ affective states in conference calls can predict future
firm performance and the effect is more prominent in the Q&A session when managers are under
greater scrutiny by analysts. Davis, Ge, Matsumoto, and Zhang (2015) show that there exists a
manager-specific component in the tone of earnings conference calls that cannot be explained by
current performance, future performance, or strategic incentives. They further add that this
manager-specific factor is related to demographic characteristics including career experience and
charitable organization involvement. The authors argue that tone of executives in earnings
conference calls is associated with their level of optimism. However, they only document weak
evidence that female executive use less favorable language.
2.3 Hypothesis Development
Gender discrimination is ubiquitous among male-dominated industries. For example,
Jacobi and Schweers (2017) examines the oral argument at the U.S. Supreme Court and show that
females Justices are disproportionately interrupted by both their male counterparts and male
advocates. Equity analysts are a male-dominated occupation. Given the extensive gender
discrimination and “old boys network”, establishing connections for female analysts is potentially
more difficult. Fang and Huang (2017) document that females account for only 12% of all analysts
in the 1993-2009 period. Although they find females analysts are equally likely to be selected as
Institutional Investor all-star analysts, performance improvements and recommendation impact for
female analysts are much lower compared to their male counterparts. They further show that these
connections mitigate the negative influence of forecast error on reputation for male analysts but
9
intensifies the negative effect for female analysts. Consequently, the disparity in the effectiveness
of connections can lower the intention of female analysts to make connections with managers.
Moreover, because managers have discretion over analysts’ conference call participation (Mayew,
2008), connections are a key determinant of conference call participation. Consistent with this
argument, Brown, Call, Clement and Sharp (2015) survey 365 analysts and find that analysts avoid
asking difficult questions in a conference call to maintain a good relationship with management
and leave harsh questions to private communication instead. Along the same line, Soltes (2014)
argues that public interaction between management and analysts is an approach to maintain a
relationship.
From the other perspective, female analysts are frequently documented as better performers.
Kumar (2010) proposes a self-selection hypothesis that female analysts are not representative of
common female characteristics such as higher risk aversion and lower preference for
competitiveness but are self-selected into the male-dominated profession due to their superior
ability. Consistent with the self-selection hypothesis, he finds that female analysts issue bolder and
more accurate forecasts. Female analysts are more likely to cover large stocks with higher
institutional ownership even in the early stage of career. He further shows that the market reacts,
both in the short and long term, more strongly to female analysts’ forecast revisions even when
they attract less media coverage. In addition, Kumar (2010) documents female analysts are more
likely to be promoted to a prestigious brokerage firm and less likely to receive a demotion to a less
prestigious one. Li, Sullivan, Xu, and Gao (2013) find that the markets render the same level of
importance to male and female analysts’ recommendations in terms of abnormal returns but less
idiosyncratic risk is generated by female analysts’ recommendation portfolios. They find no
evidence of gender discrimination measured by the likelihood of brokerage firm upward mobility
10
and find that female analysts are more likely to be selected as star analysts by both Institutional
Investor and the Wall Street Journal.
From the perspective of managers, coverage from prestigious analysts is valuable due to
increased credibility and stronger market impact (Stickel, 1992; Gleason and Lee, 2003; Park and
Stice, 2000). Mayew (2008) documents the role of analyst reputation as a deterrent of
discrimination given the high cost of discrimination against prestigious and unfavorable analysts.
In particular, even though managers have incentives to limit conference call access for unfavorable
analysts, recommendation downgrades are not associated with a decrease in conference call
participation for prestigious analysts. Given the superior performance of female analysts (Kumar,
2010) and their higher probability of being voted as all-star analysts (Green, Jegadeesh, and Tang,
2009), management may increase their conference call access. Since the relative importance of
gender discrimination in earnings conference calls is undetermined, we propose:
H1a: Female analysts are less likely to participate in earnings conference calls.
H1b: Female analysts are more likely to participate in earnings conference calls.
Firm are very sensitive about information disclosure in conference calls given that both
solid and soft information is disseminated to the public (Zhou, 2018; Suslava, 2017).5 To avoid
disclosing unfavorable information, management regularly chooses to not answer certain analysts’
questions (Hollander, Pronk, and Roelofsen, 2010) or disproportionately prioritize optimistic
analysts (Cohen, Lou, and Malloy, 2016). Given the time limit of conference calls, managers may
not be able give a thorough answer to questions asked by analysts appearing late in the queue
compared to questions asked earlier. According to firms’ Investor Relations Officers (IROs),
priority in the question queue is usually given to analysts who have a long coverage history with
5 For example, Elon Musk, the CEO of Tesla, Inc., said the questions from analysts are “boring, bonehead questions”
in its 2018 Q1 earnings conference call on May 2nd, 2018. The stock price plunged 5.6% on the following day.
11
the firm (Brown et al., 2017). Consequently, asking the first question in a conference call sends a
strong signal of firm’s special attention and connection with analysts (Cen, Chen, Dasgupta, and
Ragunathan, 2018; Call et al. 2018). Recent evidence shows that the Q&A session of earnings
conference calls is less spontaneous than it seems to be: sell-side analysts provide question to be
asked in conference calls to Investor Relations Officers (IROs) in advance (Brown et al., 2018).
The coordinated nature of Q&A session further entails a deep relationship between two parties.
Because of the lower benefits from connection to management for female analysts (Fang and
Huang, 2017) and potential in-group bias (Jannati et al., 2018), female analysts may be less capable
of building connections. Therefore, we propose:
H2: Females analysts are less likely to ask the first question and to have follow-up
interactions in the question-and-answer session on earnings conference calls.
Analysts benefit from connections with management both from the perspective of research
informativeness (Green, Jame, Markov, and Subasi, 2014) and compensation (Groysberg, Healy,
and Maber, 2011). Under Regulation FD, although firms must open conference calls to all
interested members of the general public (Bushee et al., 2004), the complementing role of public
information to private information (i.e. mosaic theory) on earnings conference calls remains
crucial for analysts (Mayew, 2008). Since management has discretion to decide who can ask
questions (Mayew, 2008), analysts’ connections with managers are crucial for analyst success.
Analysts value their reputation from recognition such as “all-star” status which is voted on by
influential institutional investors (Fang and Huang, 2017). Connections of analysts are also
associated with their quality of opinion and career advancement. Sell-side analysts have strong
incentives to curry favor of buy-side clients (Groysberg, Healy, and Maber, 2011). A considerable
amount of compensation paid by buy-side clients to sell-side firms is for corporate access (Brown
12
et al., 2018). Moreover, the limited time allocated to each analyst in a conference call and analysts’
concern of “tipping their hands” suggest conference call participation is more of a relationship
manifestation (Brown et al., 2015; Brown et al., 2016; Chen and Matsumoto, 2006). Management
often provides “call-backs” to well-connected analysts (Brown et al. 2018). To retain this
connection with management, analysts must not interrogate executives and/or cast them in a
negative light. “Assuming you want management to continue speaking with you, you have to avoid
making the C-suite lose face on the call…if you have difficult questions and you want management
to speak openly, you have to do that off-line.” (Soltes, 2014).
Moreover, men and women have different views on the purpose of conversation. Women
seek social connections and relationships in communication while men exhibit power (Leaper,
1991). Consequently, women are more expressive and politer in conversation while men are
aggressive (Basow and Rubenfeld, 2003). Research in linguistic documents conversation between
females as more fluent and affirmative compared to mixed-gender pairs and male-only pairs
(Hirschman, 1994).
Therefore, male analysts may be less concerned about the “conversation in harmony” and
may manifest their ability to the public by asking tough questions related to weaknesses of the
firm, which entails managers to explain with more words reflecting negative sentiment. On the
contrary, female analysts are expected to initiate a relatively relaxed conversation with
management in accordance with the “theater” nature of conference calls (Brown et al., 2018).
When both questioner and answerer are female, the cooperation attribute of the conversation will
be stronger given that females usually exhibit strong in-group favoritism (Rudman and Goodwin,
2004). Thus, we have:
13
H3: Female analysts’ interaction with management on earnings conference calls is
shorter than male analysts’ interaction with management. Interactions are shortest if the
manager is also female.
H4: The tone of female analysts’ interaction with management on earnings conference
calls is less negative than the tone of male analysts’ interaction with management. The tone is
least negative when both the analyst and the manager are female.
Women are less resistant to pressure (Gneezy, Niederle, and Rustichini, 2003; Niederle
and Vesterlund, 2007) and evoke more negative feelings when anticipating negative outcomes
(Croson, and Gneezy, 2009). Given lower resilience to pressure and high ethical standards, when
faced with interrogation from analysts, especially male analysts, female managers will reveal more
information truthfully. Therefore, we propose:
H5a: Female managers exhibit less uncertainty in their narratives.
H5b: Female managers use more numeric information in their narratives.
Women are more conservative than men (Byrnes, Miller and Schafer, 1999; Powell and
Ansic, 1997; Croson and Gneezy, 2009; Niederle and Vesterlund, 2007) and conservative
individuals are more likely to exhibit status quo bias (Samuelson and Zeckhauser, 1988;
Kahneman et al. 1991). However, female analysts are found to rely more on independent research
relative to earnings news and are less likely to issue forecast revisions than men after earnings
announcements (Green, Jegadeesh, and Tang, 2009). If female analysts have less access to
earnings conference calls, they may be more sensitive to new information obtained therefrom. In
addition, female analysts issue bolder forecast revision because of their superior ability and low
employment risk (Kumar, 2010). We expect female analysts will issue bolder forecast revision
than male analysts.
14
H6: Female analysts’ forecast revision magnitude is larger than that of male analysts.
3. Data
3.1 Sample selection
Earnings conference call transcripts of Standard and Poor’s 500 (S&P 500) constituent
firms are collected via Capital IQ from 2007 to 2016. Additionally, we also collect transcripts of
another 2,700 firms which are not included in S&P 500 index but appear in the Center for Research
in Security Prices (CRSP) database. Our sample construction starts with 81,677 earnings
conference call transcripts for 3,346 unique publicly traded companies. Firms without data in
I/B/E/S or CRSP are removed. For each transcript, we record the call date, time stamp, names of
firm executives, names of analysts participating in the question-and-answer (Q&A) session, and
analyst affiliation. We follow Mayew (2008) to use I/B/E/S as the universe of analysts who are
potentially interested in attending conference calls and construct a corresponding I/B/E/S sample.
For the initial I/B/E/S sample, we require each firm-quarter-analyst observation to have both an
outstanding earnings forecast and an outstanding stock recommendation. We refer to this as the
“full I/B/E/S sample.” Earnings forecasts must be issued within one year of a given fiscal quarter
end for an analyst to be considered as actively following the firm.
To determine analyst gender, we need to obtain analyst full analyst first names. I/B/E/S
only provides each analyst’s last name and first initial (item “ANALYST” in I/B/E/S).
Observations with missing brokerage ID (ESTIMID in I/B/E/S) or analyst name are removed. In
addition, forecasts made by research teams are eliminated.6 To ensure the accuracy of analyst
gender determined from first name, we remove analysts for which two or more analysts (indicated
6 Analyst names for forecast issued by team are record in I/B/E/S as two or more last names (e.g.,
“GERRY/ADKINS”, “RESEARCH DEPT”).
15
by analyst code in I/B/E/S) share the same analyst last name in the same brokerage (Bradley,
Gokkaya, and Liu, 2017). Next, to determine the first name of analysts in I/B/E/S, we match the
analysts on the transcripts with the analysts in the I/B/E/S at the brokerage level. We also remove
observations for which a firm is covered by only one analyst for a fiscal quarter end. Only the most
recent forecasts prior to an earnings conference calls are used. We apply the R package, gender
and a Python package, gender-guesser, to determine the gender of analyst based on the first name.7
For androgynous names, we further use the dataset on gender-api.co.8 All of these tools use
publicly available government databases and social network data to construct name-gender
databases. For executives who appear in conference calls, we match names with Execucomp
records which have gender and other information. Among 224,452 call-executive observations,
1,592 (0.7%) are unidentified. 9 Finally, we complement missing analyst and executive gender by
manually searching a variety of sources including Capital IQ, LinkedIn, Bloomberg, Seeking
Alpha, etc. We successfully identify the full name and gender for 5,687 analysts (99.8% of 5,722
unique analysts corresponding to all conference calls) in I/B/E/S. The final I/B/E/S sample
includes 708,592 analyst-firm-quarter observations related to 2,876 firms and 65,850 conference
calls. For the call-analyst sample, we identify analyst gender for 97% of observations.
In order to investigate the dynamics of analyst-management conversation in each
conference call, we parse all conference call transcripts into question-answer blocks. Each
conference call transcript is scanned from the beginning to the end to identify these blocks.
Conversation is defined as continuous back-and-forth comments between the analyst and
executives in which call no conference call operator speaks out in between. One difficulty is to
7 https://cran.r-project.org/web/packages/gender/gender.pdf and https://pypi.python.org/pypi/gender-guesser/ 8 https://gender-api.com/en/ 9 Unidentified company participants are recorded as “Unidentified Company Representative”, “Unknown
Executive”, “Attendees”, “Unknown Speaker”, etc.
16
identify the target of an analyst’s question. A conversation block may not follow a “one question-
one answer” pattern (i.e., analyst-executive (A-E) pattern). For example, one question from an
analyst may be answered by two executives in turns (i.e., A-E1-E2 or A-E1-A-E2). To simplify
various conversation patterns, the executive who answers the question first is considered the
“target” of an analyst’s question.10 Eventually, each continuous interaction between one analyst
and one or more executives generates one record in our conference call sample. The conference
call sample contain 495,816 conversation-level observations.
3.2 Variables
We use the initial I/B/E/S sample to construct variables about analyst characteristics. To
measure analyst forecast performance, we follow Clement (1999) to construct a forecast accuracy
measure which is equal to the negative value of the absolute forecast error demeaned by same
quarter-firm forecast average:
𝑓𝑜𝑟𝑒_𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦𝑖,𝑗,𝑡 = −(𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝑖,𝑗,𝑡 − 𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝑗,𝑡 )/𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝐽,𝑡
where 𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝑖,𝑗,𝑡 is the absolute forecast error (the absolute difference between the last
earnings per share (EPS) forecast and actual EPS) for analyst i of firm j in quarter t and
𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝐽,𝑡 is the mean absolute forecast error (average 𝑓𝑜𝑟𝑒_𝑒𝑟𝑟𝑜𝑟𝑖,𝑗,𝑡 across all analysts
covering firm j in quarter t. Positive (negative) fore_accuracy indicates an analyst’s forecast is
more (less) accurate than the forecasts of the same firm in the same quarter. This measure of
forecast accuracy is relative to other analysts and eliminates heteroskedasticity across firm-quarter
(Ke and Yu, 2006).
10 The method could lead to misidentification of analyst’s target executive for several reasons. First, an analyst may
not have a target executive to whom the question is asked. Second, an analyst may not indicate an executive to
answer the question and the executive who answers the question is not the one who is expected by the analyst.
Third, the executive who speaks out first may ask another executive to take the question.
17
If analyst ability varies systematically with gender, the relationship between analyst gender
and conference call or market outcomes will be biased. To account for analyst ability, we follow
Clement (1999) to include variables which are related to analyst ability. AllStar is an indicator
variable for Institutional Investor All-American analysts in a given year. GenExp is the number of
years between the conference call data and the date on which the analyst issued a forecast on
I/B/E/S for the first time. FirmExp is the number of years between the conference call data and the
date on which the analyst issued a forecast for the firm on I/B/E/S for the first time.
4. Empirical findings
4.1 Analyst gender distribution
Table 1 reports the call-analyst level analyst gender distribution by year (Panel A), Global
Industry Classification Standard (GICS) sector (Panel B) and brokerage affiliation (Panel C).11 In
Panel A, the number of quarterly earnings conference calls exhibits a steady increasing trend
except for 2016.12 Total number of unique analysts is lower in the later years compared with
earlier. We find a slightly decreasing trend for female analyst participation. The percentage of
female analyst participation and the percentage of unique female analysts indicate that for those
participating analysts in our sample, the likelihood of participation of female and male analysts are
similar. Panel B shows the gender distribution across 11 GICS sectors. Female analysts are more
concentrated in Consumer Staples and Consumer Discretionary, followed by Health Care, Real
Estate, and Utilities. The evidence is consistent with the self-selection hypothesis that female
analysts choose sectors in which they have more expertise (Kumar, 2010). In Panel C, we follow
Green et al. (2009) to rank brokerage firms in I/B/E/S database based on the number of affiliated
11 The number of observations is this sample is less than the conversation sample for two reasons. First, each
analyst-call has only one observation. Second, analysts without determinable gender are not included. 12 The relatively small number of conference calls in 2007 is due to data availability in Capital IQ.
18
analysts in each year and separate top 10 and other brokerages. Analysts without an affiliated
brokerage found in I/B/E/S are either with buy-side institutions, media outlets, and other
institutions (Call et al., 2018). We find that the proportion of female analysts who are in I/B/E/S
is higher than that of non-IBES analysts. In I/B/E/S analysts, women account for 16.59% analysts
in top 10 brokerages compared with 11.52% in other brokerages. Green et al. (2009) argue that the
relative high representation of female analysts in large brokerages is because of the emphasis on
the employee diversity and better working conditions which are attractive to women.
[Insert Table 1 here]
4.2 Descriptive Statistics
Table 2 presents descriptive statistics for conference call level variables. On average, a firm
has market capitalization is $7 billion (MktCap), market-to-book ratio of 2.89 (MB), a leverage
ratio of 2.59 (Leverage) and return on asset of 0.01 (ROA). 22% (58%) of firms are S&P 500 (S&P
1500) constituents. Institutional ownership accounts for 67% of total shares (InstOwn). Each firm
is covered by 7.24 analysts (NumAna). The average standardized unexpected earning (SUE) is
0.033. Mean stock recommendation consensus is 0.721 (RecCon).
Regarding conference call characteristics, the total number of words spoken in the Q&A
session is 3,808 at the mean (TotalWords). On average, 7.6 conversations (ConverCall) are
conducted by 7.2 analysts (AnaCount) and 3.4 executives (ExeCount). Among analysts, the median
number of analysts in I/B/E/S is 5 (IBESCount). 95.6% of conference calls have at least one I/B/E/S
analyst (IBESPart). The number (percentage) of female analysts is 0.768 (9.7) at the mean. 3
executives attend a conference call at the median. Average number of female executives is 0.44
(FemaleExeCount) and the mean percentage of female executives is 12.6% (FemaleExePct).
Since one duty of Investor Relations Officers (IROs) are organizing conference calls and they are
19
therefore frequent participants but do not answer analysts’ questions, we limit executives to only
CEOs and CFOs who are regarded as the most important corporate participants. 58.1% (56.6%) of
conference calls have CEO (CFO) attended (CEOPart and CFOPart) and 50.8% have both CEO
and CFO attended (CEOCFOPart). We find that the percentage of female CEO or CFO is 6%
(FemaleCEOCFOPct) compared with 12.6% of female firm participants. This is consistent with
previously reported evidence that about percentage of female IROs are are much higher than other
executives (Brown et al., 2018).
[Insert Table 2 here]
4.3 Univariate analysis
4.3.1 Gender difference in narratives
We first compare the mean of a series of analyst-call level variables between male and
female analysts. Table 3 Panel A shows the results. Variable definition is summarized in Appendix
B. Female analysts are much more likely to be all-star analysts (AllStar), are hired by large broker
firms (BrokerSize), have less general experience (GenExp) but similar firm-specific experience
(FirmExp), cover fewer industries (IndCover) and companies (CompCover), are more accurate in
earnings forecasts (ForeAcc), and have less favorable stock recommendation (Rec) and shorter
recommendation horizons (RecHorizon). Results are consistent with prior studies (Mayew, 2008;
Kumar, 2010). Regarding analyst participation characteristics, we report four variables: the order
of analyst question in Q&A session (Order), first questioner dummy (First), the number of
conversations between analyst and managers (ConverAna), and abnormal conversation length
(AbnLength). Following Call et al. (2018), AbnLength is defined as:
𝐴𝑏𝑛𝐿𝑒𝑛𝑔𝑡ℎ =𝑊𝑜𝑟𝑑𝑠 𝑖𝑛 𝑎𝑙𝑙 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑎𝑡𝑖𝑜𝑛𝑠 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑎𝑛𝑎𝑙𝑦𝑠𝑡
(𝑊𝑜𝑟𝑑𝑠 𝑖𝑛 𝑄&𝐴 𝑠𝑒𝑠𝑠𝑖𝑜𝑛
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑛𝑔 𝑎𝑛𝑎𝑙𝑦𝑠𝑡𝑠)
− 1
20
Specifically, female analysts appear later in the Q&A queue, are less likely to ask the first question,
are less likely to have a follow-up interaction with executives, and have shorter conversations,
consistent with H2 and H3.
Narrative sentiment is measured for analysts and managers separately with the Loughran
and McDonald (2011) (LM) dictionaries in each conversation block. 13 Prior research has
established that the LM dictionary is an effective measure of financial statement sentiment. Given
that the LM dictionary is specially designed for financial statements and conference call transcripts
are derived from verbal communication, we also use Harvard General Inquirer (Harvard GI)
dictionary to measure sentiment. 14 To capture the general sentiment in analyst-management
interaction, we construct a net tone measure, which is the difference between positive and negative
tone (net and netGI). Positive net tone indicates that an interaction exhibits more positive sentiment
than negative sentiment. Each tone variable is the number of words in each tone dictionary divided
by the total number of words spoken in percentage. In addition, we follow Zhou (2018) to examine
the percentage of number of in the narratives (number). Numbers are expected to contain more
value-relevant information than lexical content (Zhou, 2018). In addition, we include three
variables related to conversation characteristics: the percentage of interruption (Interrupt),
percentage of hesitation (Hesit), and the number of back-and-forth comments for the call-analyst
(Rally) in each interaction block. Prior work finds that females are more likely to be interrupted
and men are likely to be the interrupter on the Supreme Court (Jacobi and Schweers, 2017). In a
conference call, being interrupted indicates managers (analysts) strongly disagree with the
analyst’s (manager’s) comments. In the example given in Appendix C Panel A, the CEO of Tesla
Inc., Elon Musk, interrupted analyst Galileo Russell’s comment. A more oppressive comment will
13 The Loughran and McDonald (2011) dictionaries can be found at http://sraf.nd.edu/ 14 The Harvard General Inquirer dictionaries can be accessed at http://www.wjh.harvard.edu/~inquirer/
21
be more likely to be interrupted. Similarly, a pleasant conversation or well-rehearsed speech is
expected to involve less hesitation. Larcker and Zakolyukina (2012) use the speech hesitation of
executives as an indicator of deception. Appendix C Panel B exhibits an example that an manager
hesitates for several times when tackling with question unwanted. Capital IQ uses ellipsis (…) at
the end a sentence to indicate that speakers cut off each other and two hyphens (--) to indicate non-
continuous speech. Results are shown in Table 3 Panel B. Female analysts’ questions are more
concise in that they use 18 (12.2%) fewer words than male analysts. Interestingly, female analysts
use both more positive and negative words but do not exhibit a difference in net tone compared
with male analysts. With Harvard GI dictionary, female analysts exhibit more positive sentiment,
similar negative sentiment and in total more net positive sentiment. Less numeric content is
included within female analysts’ inquiries. When analysts interrogating about value-relevant
information is more challenging for executives to handle, this evidence is in line with H4 that
female analysts possess a stronger wish to establish a harmonious conversation with managers in
the conference call. In the same vein, female analysts are less likely to be interrupted by managers
and exhibit less hesitation.15 Last, the conversation between female analysts and mangers on
average has fewer rounds of back-and-forth comments. It also suggests that female analysts are
less likely to initiate a cutthroat conversation with managers. It also suggests that a superior
forecast ability could be a result of better relationship with firm management for female analyst
(Kumar, 2010). The evidence is consistent with H3 and H4 so far.
We then report the same set of variables for managers in Table 3 Panel C. Female
executives in general give shorter answers to analysts’ questions with 47 (13%) fewer words
relative to their male counterparts. Different from analysts’ narrative, female managers convey
15 Using the number of instead of percentage of interruption and hesitation instances yields similar results.
22
more positive sentiment as measured by both LM and Harvard GI dictionaries. Moreover, a lower
percentage of uncertain and weak modal words but higher percentage of strong modal words also
indicate female managers’ tone are more affirmative, consistent with H5a. The results contrasts to
weak evidence documented by Davis et al. (2015) that female executives use less favorable
language. 16 However, female executives use less numeric information in their responses to
analyst’s questions, inconsistent with H5b. Male executives are less likely to be interrupted but
hesitate more than female executives.
In sum, the univariate analyst results are consistent with the argument that male analysts’
questions are more aggressive and invoke more oppositions. In contrast, female analysts’ questions
are more concise and pleasant. For executives, women are interrupted more, consistent with the
evidence in the court (Jacobi and Schweers, 2017). However, both female analysts and executives
are more fluent with their discourse, indicating a better preparation and high level of confidence.
4.3.2 Gender interaction in narratives
Since an executive’s response is a function of an analyst’s question, to examine the gender
effect in the interaction between analysts and executives, we double-sort variables regarding
conversations characteristics based on analyst gender and executive gender and report the results
in Table 4. Table 4 Panel A reports the number of words in each analyst’s narrative. Female
analysts speak less irrespective of interacting executive gender compared with male analysts.
When the interacting executive is female, the questions of both male and female analysts are
shorter. When female analysts ask female executive questions, the number of words, 123.69 on
average, spoken by analysts is the least.
16 Davis et al. (2015) use Diction wordlist, Henry (2006) dictionary and LM dictionary but only find significant
results with Diction wordlist.
23
Panel B reports the net tone of analysts’ questions. When interacting executives are male,
male and female analysts exhibit similar sentiment in questions; however, when the interacting
executive is female, female analysts’ tone is more positive. The significant difference-in-difference
indicates that the conversation between female analysts and female executives is most relaxed. In
addition, the univariate results also imply that male analysts exert more pressure on female
executives but female analyst do the opposite. In sum, the evidence is consistent with H3 and H4.
Results in Panel C about numeric information indicate the female analysts’ questions
involve less quantitative information, especially when the executive is also female. Similarly,
female executives disclose less numeric information, especially when replying to female analysts’
inquiries. One explanation is that women have a higher preference for soft information in
conference calls.
4.4 Multivariate Analysis
4.4.1 Conference call participation
We first examine female analysts’ conference call participation. We follow Mayew (2008)
to model the conference call participation probability of analyst i following firm j in quarter t
participating on an earnings conference call. We estimate the following pooled cross-sectional
logit regression model. Standard errors are clustered at the analyst level:
𝑃𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑒𝑖,𝑗,𝑡 = 𝛽0 + 𝛽1𝐹𝑒𝑚𝑎𝑙𝑒𝐴𝑛𝑎𝑖,𝑗,𝑡 + 𝛽2𝑅𝑒𝑐𝑖,𝑗,𝑡 + 𝛽3𝐴𝑙𝑙𝑆𝑡𝑎𝑟𝑖,𝑗,𝑡 + 𝛽4𝐹𝑜𝑟𝑒𝐴𝑐𝑐𝑖,𝑗,𝑡
+𝛽5𝐺𝑒𝑛𝐸𝑥𝑝𝑖,𝑗,𝑡 + 𝛽6𝐹𝑖𝑟𝑚𝐸𝑥𝑝𝑖,𝑗,𝑡 + 𝛽7𝐼𝑛𝑑𝐶𝑜𝑣𝑖,𝑗,𝑡 + 𝛽8𝐶𝑜𝑚𝑝𝐶𝑜𝑣𝑖,𝑗,𝑡
+𝛽9𝐵𝑟𝑜𝑘𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡 + 𝛽10𝐹𝑜𝑟𝑒𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑖,𝑗,𝑡 + 𝛽11𝑁𝑢𝑚𝐴𝑛𝑎𝑖,𝑗,𝑡
+𝛽0𝑇𝑜𝑡𝑎𝑙𝑊𝑜𝑟𝑑𝑠𝑖,𝑗,𝑡 + 𝜖𝑖,𝑗,𝑡
The dependent variable, Participate, is an indicator variable if an analyst asks a question in a
conference call. We examine two specifications with year, industry and brokerage fixed effects. In
24
Model 1, we include nine control variables regarding analyst characteristics. We further two
control variables about conference call participation competition in Model 2. Results are shown in
Table 5. Conference call participation probability is decreasing for female analysts (-0.096,
p<0.01). In terms of odds ratio, the participation odds for female analysts is 0.9 times that of male
analysts. Consistent with literature, the likelihood of conference call participation increases with
stock recommendation favorableness (Rec), all-star designation (AllStar), prior forecast accuracy
(ForeAcc), firm-specific experience (FirmExp), and length of Q&A session (TotalWords).
Interestingly, general analyst experience is negatively related to the participation likelihood.
Mayew (2008) argues that analysts with more general experience may have lower demand for
firm-specific information. Analysts covering more companies (CompCover) and issuing less
timely coverage (ForeHorizon) have lower participation probability. High analyst coverage
(NumAna) also lowers the participation opportunity. In sum, we find support for H1a.
4.4.2 Conference call prioritization
Next, we examine whether management prioritizes female analysts and provides them with
more interaction opportunities in conference calls. Three variables used to examine prioritization
are First, Order and ConverAna. To eliminate the influence of number of participants, Order is
scaled to the (0,1] interval. Table 6 Panel A reports the results. The logit model estimation in
Column 1 does not indicate gender differences in the likelihood of asking the first question. For
initial question position results in Column 2, female analysts are found to appear later in the Q&A
queue. Column 3 reports the Poisson model results for the number of conversations. We include
the initial question position because analysts who ask question early in the queue are more likely
to have a follow-up opportunity. We find that female analysts have fewer non-continuous
conversations with managers in conference calls. In sum, female analysts are given less priority in
25
interactions with managers during conference calls and H2 is mainly supported. Panel B reports
the Interrupt and Hesit for analysts. Female analyst are interrupted less and also hesitate less,
consistent with the notion that female analyst exert less pressure on executives.
4.4.3 Analyst-management interaction length
We next test H3. The abnormal interaction length, AbnLength, is a proxy for question and
answer intensity. In Table 7, we regress AbnLength on two female analyst dummy (Model 1),
female executive dummy (Model 2), and with their interactions term (Model 3). Interactions
involving either female analysts or executives are shorter compared to males. However, when both
the analyst and executive are female, the impact on abnormal interaction length is not significant.
In sum, H3 is partially supported.
4.4.4 Analyst-management interaction textual characteristics
Table 8 shows the OLS regression results of executives’ net tone (Model 1 and Model 2),
percentage of uncertain words (Model 3), and percentage of numeric contents (Model 4).
Executives’ responses to female analysts’ questions exhibit more positive sentiment but the
interaction term of the two female dummies is insignificant. H4 is partially supported. In addition,
female analysts are associated with less uncertain sentiment in the executive’s narrative. H5a is
supported. However, executives use less numeric contents to answer female analysts’ questions.
Perhaps female analysts’ question are more related to qualitative issues which do not entail much
numeric information. H5b is not supported.
4.4.5 Post-call forecast revision
If female analysts are more skillful and have lower employment risk, we expect that their
forecast revisions will have a larger magnitude compared with male analysts (Kumar, 2010). We
use the absolute value of the difference between next quarter EPS forecast after the conference call
26
and next quarter EPS forecast prior to conference call scaled by stock price at this quarter end,
AbsRev, to examine the post-call forecast revision. Given that about half of analyst do not have
AbsRev, we construct a dummy variable Rev_dummy which is equal to 1 if an analyst revises
his/her EPS forecast for the next quarter. Analyst tone is added as an additional control. Table 9
shows regression results. Consistent with H6, we find that female analysts make larger EPS
forecast revisions after conference calls. However, there is no gender difference in the likelihood
of a forecast revision. Net tone is negatively related to post-call revision magnitude. The negative
sign of the interaction term of female analyst and net tone implies that when the analyst tone is
more positive, female analysts’ forecast magnitude and likelihood decrease. One explanation is
that the less access to conference calls increases the sensitivity of female analysts to information
obtained from conference calls.
5. Conclusion
Women are different from men in various aspects. In this paper, we examine gender
differences in analysts’ and executives’ earnings conference call participation. Earnings
conference calls provide a unique opportunity to investigate gender interactions between analysts
and executive in a real-time environment.
We find that female analysts are less likely participate in conference calls. In addition,
female analysts appear later in the Q&A session to ask questions, have less opportunities of follow-
ups, and speak less compared with their male counterparts. Both female analysts and executives
have shorter discourses than their male counterparts. Regarding sentiment, female analyst and
executive exhibit more favorable tone. Female executives use less numeric content when
answering analysts’ question but their tone is more affirmative. Subsequent to calls, female
analysts make forecast revisions with a larger magnitude.
27
In sum, our results indicate that the playground of earnings conference calls is still
dominated by men. Compared with men, women are less active in conference call participation
although they possess superior ability and are hired by large brokerage houses. Female exhibit a
preference for soft information and exert less pressure in conference calls. Further research should
consider other analyst behaviors such as analyst report.
28
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34
Table 1 - Gender distribution
Table 1 reports the gender distribution of analysts for 469,472 call-analyst observations. year is the calendar
year of the conference call date. Sectors are GICS sectors. Top 10 brokerages are the largest 10 brokerages
in terms of the number of analysts affiliated in I/B/E/S. %calls is the percentage of conference calls.
NumAnalysts is the number of unique analysts. %FemaleParticipation is the percentage of conference call
participation by female analysts. %Female is the percentage of unique female analysts in I/B/E/S.
Panel A. Conference call gender distribution by year
year %calls NumAnalysts %FemaleParticipation %Female
2007 2.09% 3826 14.56% 12.31%
2008 8.38% 9037 12.33% 11.31%
2009 9.06% 9069 11.88% 10.59%
2010 10.13% 7939 11.72% 10.91%
2011 11.39% 8260 11.21% 10.34%
2012 11.98% 6519 10.60% 11.03%
2013 12.34% 6314 10.31% 10.99%
2014 12.77% 5845 10.20% 11.57%
2015 12.76% 5870 10.43% 11.47%
2016 9.10% 4819 10.31% 12.33%
Panel B. Conference call gender distribution by sector
GICS sector %calls NumAnalysts %FemaleParticipation %Female
Consumer Discretionary 16.30% 6159 18.55% 15.57%
Consumer Staples 3.91% 1577 24.29% 18.52%
Energy 6.57% 2860 6.95% 8.15%
Financials 12.40% 3763 8.93% 8.53%
Health Care 12.49% 3834 12.40% 14.08%
Industrials 15.22% 5470 7.96% 7.88%
Information Technology 19.49% 6676 6.75% 7.97%
Materials 5.21% 2583 7.21% 8.59%
Real Estate 4.28% 1545 9.94% 10.16%
Telecommunication Services 1.27% 606 9.72% 6.93%
Utilities 2.85% 887 7.99% 12.40%
Panel C. Conference call analyst gender distribution by brokerage firms
Brokerage type NumAnalysts %FemaleParticipation %Female
Top 10 brokerages 5944 12.71% 16.59%
Other brokerages 11443 9.81% 11.52%
Non-I/B/E/S 14829 12.20% 9.30%
35
Table 2 - Descriptive statistics
Table 2 reports the descriptive statistics for I/B/E/S sample and conference call sample. See Appendix for
variable definitions.
Variable N mean Q1 median Q3
MktCap 65289 7080.721 475.528 1439.758 4679.769
Leverage 65240 2.587 1.238 1.556 2.341
MB 65241 2.887 1.171 1.943 3.420
ROA 65228 0.010 0.001 0.017 0.043
SP500 65294 0.221 0.000 0.000 0.000
SP1500 65294 0.584 0.000 1.000 1.000
InstOwn 65294 0.665 0.522 0.738 0.879
NumAna 65294 7.239 4.000 7.000 10.000
SUE 60775 0.033 -0.059 0.057 0.242
RecCon 63918 0.721 0.390 0.730 1.000
TotalWords 65294 3808.429 2508.000 3738.000 4974.000
ConverCall 65294 7.594 5.000 7.000 10.000
AnaCount 65294 7.190 4.000 7.000 9.000
IBESCount 65294 5.140 3.000 5.000 7.000
IBESPart 65294 0.956 1.000 1.000 1.000
FemaleAnaCount 65294 0.768 0.000 0.000 1.000
FemaleAnaPct 65109 0.100 0.000 0.000 0.167
ExeCount 65266 3.409 3.000 3.000 4.000
FemaleExeCount 65266 0.439 0.000 0.000 1.000
FemaleExePct 65266 0.126 0.000 0.000 0.250
CEOPart 65266 0.581 0.000 1.000 1.000
CFOPart 65266 0.566 0.000 1.000 1.000
CEOCFOPart 65266 0.508 0.000 1.000 1.000
FemaleCEOCFOPct 41676 0.063 0.000 0.000 0.000
36
Table 3 - Analyst gender difference in conference calls
Table 3 reports analyst gender difference-in-mean test in call-analyst level variables (N=455,806) and
conversation level variables (N=479,959). Sentiment variables in Panel are in percentage. ***, **, and *
indicate statistical significance at the 1%, 5%, and 10% levels, respectively. See Appendix B for variable
definitions.
Panel A. Call-analyst level variables
Male Female Difference t-stat
AllStar 0.16 0.24 -0.08 -36.06***
BrokerSize 62.27 67.68 -5.40 -19.18***
GenExp 9.52 8.79 0.73 22.02***
FirmExp 4.50 4.50 -0.00 -0.19
CompCover 16.97 15.46 1.51 35.83***
IndCover 3.18 2.83 0.35 28.34***
ForeAcc 0.13 0.14 -0.01 -2.67**
Rec 0.77 0.69 0.09 16.06***
RecHorizon 465.66 446.42 19.23 7.00***
ConverAna 1.05 1.04 0.01 11.05***
TotalWordsAna 156.96 136.22 20.74 47.14***
PctWordsAna 4.23 3.54 0.69 38.36***
Order 5.05 5.28 -0.24 -14.35***
First 0.14 0.13 0.01 7.42***
ConverAna 1.05 1.04 0.01 11.05***
AbnLength 0.64 -2.78 3.42 16.47***
Rally 3.37 3.08 0.29 28.01***
Panel B. Conversation-level analyst narrative variables
Male Female Difference t-stat
WordsAna 148.88 130.71 18.17 46.04***
numberAna 0.76 0.64 0.12 23.12***
positiveAna 1.09 1.16 -0.07 -12.59***
negativeAna 1.29 1.35 -0.07 -11.20***
netAna -0.20 -0.19 -0.01 -0.80
uncertainAna 1.64 1.59 0.05 7.31***
litigiousAna 0.13 0.11 0.02 9.12***
weakAna 1.15 1.15 0.01 0.92
aggregateAna 2.92 2.94 -0.02 -2.42*
strongAna 0.21 0.24 -0.03 -11.30***
constrainingAna 0.06 0.06 0.00 2.93**
positiveGIAna 3.09 3.13 -0.04 -4.45***
negativeGIAna 0.93 0.92 0.00 0.27
netGIAna 2.17 2.21 -0.04 -3.98***
InterruptAna 0.06 0.05 0.02 11.77***
HesitAna 0.85 0.66 0.20 29.58***
37
Panel C. Conversation-level executive narrative variables
Male Female Difference t-stat
WordsExe 361.36 313.96 47.40 33.65***
positiveExe 1.45 1.50 -0.05 -7.88***
negativeExe 0.82 0.77 0.05 10.47***
netExe 0.63 0.73 -0.10 -12.23***
uncertainExe 0.93 0.84 0.08 17.81***
litigiousExe 0.13 0.12 0.01 4.86***
weakExe 0.38 0.34 0.04 15.49***
aggregateExe 1.74 1.61 0.13 19.12***
strongExe 0.62 0.65 -0.03 -8.94***
constrainingExe 0.11 0.11 0.00 3.10**
positiveGIExe 3.23 3.29 -0.06 -6.47***
negativeGIExe 0.95 0.92 0.04 7.38***
netGIExe 2.28 2.38 -0.10 -9.28***
numberExe 2.39 2.03 0.35 20.48***
InterruptExePct 0.02 0.02 -0.00 -2.98**
HesitExePct 0.56 0.51 0.05 11.76***
38
Table 4 – Number of conversations, length of interaction and tone comparison: Double-
sorts
Panel A: Number of words spoken
WordsAna
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 150.518 145.562 -4.956 0.000***
[2] Female analyst 132.695 123.690 -9.005 0.000***
Diff-in-diff
[2] - [1] -17.823 -21.872 -4.049 0.006***
p-value 0.000*** 0.000***
WordsExe
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 364.439 319.117 -45.322 0.000***
[2] Female analyst 346.472 295.928 -50.544 0.000***
Diff-in-diff
[2] - [1] -17.966 -23.189 -5.223 0.188
p-value 0.000*** 0.000***
WordsConver
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 514.957 464.679 -50.278 0.000***
[2] Female analyst 479.167 419.618 -59.550 0.000***
Diff-in-diff
[2] - [1] -35.790 -45.061 -9.272 0.057*
p-value 0.000*** 0.000***
39
Panel B: Tone of conversation
netAna
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst -0.194 -0.248 -0.054 0.000***
[2] Female analyst -0.207 -0.032 0.175 0.000***
Diff-in-diff
[2] - [1] -0.012 0.216 0.228 0.000***
p-value 0.119 0.000***
netExe
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 0.631 0.712 0.081 0.000***
[2] Female analyst 0.689 0.883 0.193 0.000***
Diff-in-diff
[2] - [1] 0.058 0.170 0.112 0.000***
p-value 0.000*** 0.000***
Panel C: Numerical contents
numberAna
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 0.751 0.751 0.000 0.961
[2] Female analyst 0.634 0.577 -0.057 0.001***
Diff-in-diff
[2] - [1] -0.117 -0.174 -0.057 0.002***
p-value 0.000*** 0.000***
numberExe
Male
executive
Female
executive [2] - [1] p-value
[1] Male analyst 2.376 2.044 -0.332 0.000***
[2] Female analyst 2.467 1.971 -0.496 0.000***
Diff-in-diff
[2] - [1] 0.091 -0.073 -0.164 0.001***
p-value 0.000*** 0.117
40
Table 5 - Analyst gender and conference call participation
Table 5 reports the regression results of a logit model. Participate is an indicator variable if an analyst
attends the quarterly conference call of the firm-quarter he/she covers. Standard error is clustered at firm
level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. See
Appendix B for variable definitions.
(1) (2)
VARIABLES participate participate
FemaleAna -0.0964*** -0.0955***
(0.0256) (0.0254)
Rec 0.2953*** 0.3031***
(0.0083) (0.0079)
AllStar 0.2485*** 0.2666***
(0.0226) (0.0231)
ForeAcc 0.0602*** 0.0729***
(0.0054) (0.0054)
GenExp -0.0162*** -0.0183***
(0.0017) (0.0017)
FirmExp 0.0183*** 0.0263***
(0.0024) (0.0023)
IndCover 0.0075 -0.0019
(0.0054) (0.0052)
CompCover -0.0161*** -0.0158***
(0.0012) (0.0012)
BrokerSize 0.0005 0.0005
(0.0004) (0.0004)
ForeHorizon -0.0071*** -0.0072***
(0.0001) (0.0001)
NumAna -0.0398***
(0.0011)
TotalWords 0.1440***
(0.0046)
Constant 1.9275*** 1.6377***
(0.5528) (0.5729)
Observations 647,701 647,701
Year FE YES YES
Industry FE YES YES
Brokerage FE YES YES
Pseudo R2 0.104 0.124
41
Table 6 - Participation prioritization of conference calls
Table 6 reports the call-analyst level logit (Model 1), OLS (Model 2), and Poisson (Model 3) regression
results for conference call prioritization. First is an indicator variable which equals 1 if an analyst is the
first one to ask question. Order is the scaled analyst position in Q&A session. ConverAna is the number of
non-continuous conversations for the analyst. Standard error is clustered at firm level. ***, **, and *
indicate statistical significance at the 1%, 5%, and 10% levels, respectively. See Appendix B for variable
definitions.
Panel A. Analyst participation prioritization
(1) (2) (3)
VARIABLES First Order ConverAna
FemaleAna 0.0067 -0.0128*** -0.0060***
(0.0282) (0.0031) (0.0017)
Rec 0.2201*** -0.0373*** 0.0025***
(0.0098) (0.0013) (0.0007)
log(AnaCount) -1.6608*** -0.1695*** -0.0667***
(0.0136) (0.0020) (0.0050)
log(TotalWords) 0.0006 0.0008* 0.0088***
(0.0022) (0.0004) (0.0015)
Order -0.1020***
(0.0024)
Constant 1.0379*** 0.8261*** 0.1637***
(0.0372) (0.0060) (0.0140)
Observations 431,694 431,694 431,694
Firm controls YES YES YES
Year-quarter FE YES YES YES
Industry FE YES YES YES
Pseudo-R2 0.0640 0.000955
Adjusted R2 0.0286
Panel B. Analyst interruptions and hesitations
(1) (2)
VARIABLES InterruptAna HesitAna
FemaleAna -0.1448*** -0.1817***
(0.0418) (0.0205)
Rec -0.0074 -0.0052
(0.0129) (0.0060)
log(AnaCount) -0.9795*** -0.7125***
(0.1077) (0.0402)
log(TotalWords) 0.9666*** 0.6510***
(0.0592) (0.0209)
Order 0.3474*** -0.1370***
(0.0327) (0.0145)
Constant -25.4085*** -4.5177***
42
(3.5268) (0.2980)
Observations 283,657 283,657
Firm controls YES YES
Year-quarter FE YES YES
Industry FE YES YES
Pseudo-R2 0.0492 0.105
43
Table 7 - Gender and abnormal interaction length
Table 7 reports OLS regression results for abnormal interaction length. Abnormal interaction length is the
standardized difference between the participant’s actual length of interactions and the average interaction
length for the call (Call, et al., 2018). Standard error is clustered at firm level. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively. See Appendix B for variable definitions.
(1) (2) (3)
VARIABLES AbnLength AbnLength AbnLength
FemaleAna -3.1699*** -3.1574***
(0.4428) (0.4589)
FemaleExe -1.6872*** -1.6780***
(0.3315) (0.3206)
FemaleAna×FemaleExe 0.2182
(1.3893)
Rec 2.1691*** 2.2625*** 2.1212***
(0.1530) (0.1536) (0.1534)
log(AnaCount) -0.0684 1.4673*** 1.8107***
(0.2881) (0.2004) (0.2217)
log(TotalWords) 0.8124*** -1.9691*** -2.3497***
(0.2817) (0.1453) (0.1536)
Constant -5.4532*** 13.6960*** 16.9245***
(2.0680) (1.1064) (1.2675)
Observations 430,388 431,957 426,123
Firm controls YES YES YES
Year-quarter FE YES YES YES
Industry FE YES YES YES
Adjusted R2 0.00199 0.00188 0.00229
44
Table 8 - Analyst gender and executive narrative sentiment
Table 8 reports OLS regression results of executive narrative sentiment. Standard error is clustered at firm
level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. See
Appendix B for variable definitions.
(1) (2) (3) (4)
VARIABLES netExe netExe uncertainExe numberExe
FemaleAna 0.0234** 0.0183*
(0.0102) (0.0107)
FemaleExe 0.0466* -0.0524*** -0.2441***
(0.0259) (0.0131) (0.0527)
FemaleAna×FemaleExe 0.0493
(0.0413)
Rec 0.0306*** 0.0305*** -0.0038** 0.0102
(0.0033) (0.0034) (0.0018) (0.0079)
AnaCountLog 0.0159 0.0676* -0.0234 -1.0161***
(0.0315) (0.0354) (0.0198) (0.0934)
TotalWordsLog 0.0158*** -0.0444** 0.0096 1.1095***
(0.0056) (0.0193) (0.0111) (0.0549)
Constant 0.3671*** 0.7726*** 0.9220*** -5.2590***
(0.1309) (0.1782) (0.0842) (0.6658)
Observations 453,829 447,525 454,884 454,884
Firm controls YES YES YES YES
Year-quarter FE YES YES YES YES
Industry FE YES YES YES YES
Adjusted R2 0.0392 0.0384 0.0263 0.0419
45
Table 9 - Forecast revision
Table 9 reports OLS (Column 1 and 2) and logit (Column 3) regression results for forecast revision. AbsRev
is the absolute value of the difference between next quarter EPS forecast after conference call and next
quarter EPS forecast prior to conference call, scaled by stock price at this quarter end. Rev_dummy is an
indicator which is equal to 1 if an analyst revise the EPS forecast for next quarter. Standared error is
clustered at firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. See Appendix B for variable definitions.
(1) (1) (2)
VARIABLES AbsRev AbsRev Rev_dummy
FemaleAna 0.0134*** 0.0130*** -0.0098
(0.0042) (0.0042) (0.0241)
netAna -0.0044*** -0.0041*** 0.0041
(0.0005) (0.0005) (0.0026)
FemaleAna×netAna -0.0023* -0.0334***
(0.0013) (0.0079)
Rec 0.0556*** 0.0556*** 0.8208***
(0.0017) (0.0017) (0.0128)
log(AnaCount) -0.0655*** -0.0656*** 0.0875*
(0.0119) (0.0119) (0.0485)
log(TotalWords) 0.0033 0.0033 0.0417***
(0.0030) (0.0030) (0.0116)
Constant 0.3051*** 0.3053*** -1.7846***
(0.0882) (0.0882) (0.2618)
Observations 431,694 431,694 431,694
Firm controls YES YES YES
Year-quarter FE YES YES YES
Industry FE YES YES YES
Adjusted R2 0.102 0.102
Pseudo-R2 0.0864
46
Appendix A – Sample construction
Conference
calls
Forecasts / Recommendations Firms analysts
Merge with I/B/E/S to
obtain quarterly
forecasts/recommendation
related to conference calls
70,224 4,100,455 3,002 6,325
Remove observations
without at least one
corresponding quarterly
earnings forecast issued
within 365 days prior to
conference call. Remove
estimates without analyst
name, brokerage ID
(ESTIMID). Remove
estimates made by team
(i.e., analyst name is
“RESEARCH
DEPARTMENT” or two
last names separated by
“/”)
70,023 3,008,705 2,997 5,800
Drop observations for
which two or more
analysts have
the same first initial and
last name at the same
brokerage
69,995 2,988,095 2,997 5,733
Remove observations for
which firm is covered by
only one analysts for a
fiscal quarter end
66,813 2,978,585 2,892 5,722
Remove observations with
no Compustat/CRSP data
65,888 2,927,685 2,878 5,701
Keep the last quarterly
forecast prior to
conference call date.
65,850 708,592 2,876 5,687
47
Appendix B – Variable Definitions
Variables Definition
Conference call level variables
TotalWords The number of words spoken in question-and-answer portion
of conference call
ConverCall The number of conversations between analysts and
executives in a conference call
AnaCount Number of analysts in the conference call
IBESCount Number of IBES analysts in the conference call
IBESPart Indicator variable equal to 1 if at least one IBES analyst
participates
FemaleAnaCount Number of female analysts in the conference call
FemaleAnaPct Percentage of female analysts in the conference call
ExeCount Number of executives in the conference call
FemaleExeCount Number of female executives in the conference call
FemaleExePct Percentage of female executives in the conference call
CEOPart Indicator equal to 1 if CEO attends the conference call
CFOPart Indicator equal to 1 if CFO attends the conference call
CEOCFOPart Indicator equal to 1 if both CEO and CFO attend the
conference call
MktCap The market value of equity, in million dollars
Leverage Book value of debt and equity divided by the market value of
equity.
MB Ratio of market value of equity to book value of equity.
ROA Net income over the last twelve months divided by total
book value of assets
SP500 Indicator variable equal to 1 if a firm is a component of
Standard and Poor’s 500 index and 0 otherwise.
SP1500 Indicator variable equal to 1 if a firm is a component of
Standard and Poor’s 1500 index and 0 otherwise.
InstOwn Percentage of aggregate institutional ownership in shares
outstanding of firm in the Thomson Reuters 13-F filing
immediately prior to conference call date.
NumAna The number of analysts issuing one-quarter-ahead or two
two-quarter-ahead forecast and having an outstanding stock
recommendation for the current fiscal quarter
SUE Actual quarterly EPS minus consensus EPS forecast, scaled
by the stock price at the quarter end
RecCon Mean stock recommendation scaled into [-2, +2] interval as
of the conference call date. -2 indicates strong sell and +2
indicates strong buy.
Runup Fama-French 4-factor adjusted cumulative return during the
[-42, -2] window relative to the conference call date
CAR Fama-French 4-factor adjusted cumulative return during the
[-1, +1] event window relative to the conference call date
48
PEAD Fama-French 4-factor adjusted post-earnings announcement
drift over a [+2, +60] window relative to the conference call
date
Analyst-firm-quarter level variable
Participate Indicator variable equal to 1 if an analyst asks a question in
firm’s quarterly earnings conference call and 0 otherwise.
Call-individual level variables
Rec I/B/E/S stock recommendation score prior to the conference
call in [-2, +2] interval. 2 indicates strong buy, 1 indicates
buy, 0 indicates hold, -1 indicates sell, and -2 indicates
strong sell.
AllStar Indicator variable equal to 1 if an analyst is voted as
Institutional Investor All-American research team in the
prior calendar year of the conference call.
ForeAcc The negative value of the absolute forecast error demeaned
by same quarter-firm average forecast for previous quarter
BrokerSize The number of analysts hired by affiliated brokerage firm of
an analyst in the prior calendar year of the conference call.
GenExp The number of years between the analyst’s first forecast date
for the firm and the conference call date.
FirmExp The number of years between the first forecast date of an
analysts and the conference call date.
CompCover The number of firms covered by an analyst in the prior
calendar year of the conference call.
IndCover The number of Fama-French 48 industries covered by an
analyst in the prior calendar year of the conference call.
RecHorizon Number of days between most recent forecast and
conference call date
AbsRev The absolute value of the difference between next quarter
EPS forecast after conference call and next quarter EPS
forecast prior to conference call, scaled by stock price at this
quarter end
Rev_dummy Indicator which is equal to 1 if an analyst revise the EPS
forecast for next quarter
RecRev Recommendation revision equal to the first stock
recommendation issued in a [0,21]-day window relative to
the conference call minus the most recent recommendation
before the conference call based on the IBES stock
recommendation code. Revisions less than or equal to -2 are
assigned -2 and revisions greater than or equal to 2 are
assigned 2. CEO Indicator equal to 1 if the executive is the CEO in most recent
fiscal year
CFO Indicator equal to 1 if the executive is the CFO in most recent
fiscal year
AgeExe Age of the executive in the most recent fiscal year
49
Tenure Number of years the executive has become CEO, scaled by
365
Compensation Total compensation for the executive in the most recent
fiscal year (tdc1 in Execucomp)
ConverAna Number of conversations for the analyst in a conference call
TotalWordsAna Number of words spoken by the analyst in the call
PctWordsAna Proportion of words spoken by the analyst in the call as a
percentage of total words in call
TotalWordsExe Number of words spoken by the executive in the call
PctWordsExc Proportion of words spoken by the executive in the call as a
percentage of total words in call
Conversation-level variables
First Indicator equal to 1 if this is the first conversation in the call
Order The order of this conversation in the call
WordsConver Number of words spoken in this conversation
AbnLength Abnormal interaction length for each participant, measured
as the standardized difference between the participant’s
actual length of interactions and the average interaction
length for the call
Rally Number of back-and-forth comments between the analyst
and executive in this conversation
FemaleAna Indicator equal to 1 of if the analyst is female
FemaleExe Indicator equal to 1 of if the executive is female
InterruptAna Number of times the analyst is interrupted by another
conference call participant in this conversation
InterruptExe Number of times the executive is interrupted by another
conference call participant in this conversation
HesitAna Number of times the analyst self-corrects or has a broken
thought in this conversation
HesitExe Number of times the executive self-corrects or has a broken
thought in this conversation
WordsAna Number of words spoken by the analyst in this conversation
WordsExe Number of words spoken by the executive in this
conversation
numberAna Percentage of numbers the analyst speaks in this
conversation
numberExe Percentage of numbers the executive speaks in this
conversation
positiveAna The percentage of positive words in the analyst’s narrative in
this conversation based on Loughran and McDonald (2011)
dictionary
positiveExe The percentage of positive words in the executive’s narrative
in this conversation based on Loughran and McDonald
(2011) dictionary
50
negativeAna The percentage of negative words in the analyst’s narrative
in this conversation based on Loughran and McDonald
(2011) dictionary
negativeExe The percentage of negative words in the executive’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
netAna The percentage of positive minus negative words in the
analyst’s narrative in this conversation based on Loughran
and McDonald (2011) dictionary
netExe The percentage of positive minus negative words in the
executive’s narrative in this conversation based on Loughran
and McDonald (2011) dictionary
uncertainAna The percentage of uncertain words in the analyst’s narrative
in this conversation based on Loughran and McDonald
(2011) dictionary
uncertainExe The percentage of uncertain words in the executive’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
litigiousAna The percentage of litigious words in the analyst’s narrative in
this conversation based on Loughran and McDonald (2011)
dictionary
litigiousExe The percentage of litigious words in the executive’s narrative
in this conversation based on Loughran and McDonald
(2011) dictionary
weakAna The percentage of weak modal words in the analyst’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
weakExe The percentage of weak modal words in the executive’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
aggregateAna The percentage of aggregate words in the analyst’s narrative
in this conversation. Aggregate words are the union of
uncertain, weak modal and negative words (Loughran and
McDonald, 2013)
aggregateExe The percentage of aggregate words in the executive’s
narrative in this conversation. Aggregate words are the union
of uncertain, weak modal and negative words (Loughran and
McDonald, 2013)
strongAna The percentage of strong modal words in the analyst’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
strongExe The percentage of strong modal words in the executive’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
51
constrainingAna The percentage of constraining words in the analyst’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
constrainingExe The percentage of constraining words in the executive’s
narrative in this conversation based on Loughran and
McDonald (2011) dictionary
negativeGIAna The percentage of negative words in the analyst’s narrative
in this conversation based on Harvard General Inquirer
dictionary
negativeExe The percentage of negative words in the executive’s
narrative in this conversation based on Harvard General
Inquirer dictionary
positiveGIAna The percentage of negative words in the analyst’s narrative
in this conversation based on Harvard General Inquirer
dictionary
positiveGIExe The percentage of positive words in the executive’s narrative
in this conversation based on Harvard General Inquirer
dictionary
netGIAna The percentage of positive minus negative words in the
analyst’s narrative in this conversation based on Harvard
General Inquirer dictionary
netGIExe The percentage of positive minus negative words in the
executive’s narrative in this conversation based on Harvard
General Inquirer dictionary
52
Appendix C – Interruption and hesitation in conference call transcripts
Panel A: An excerpt of the transcript of Tesla’s 2018 Q1 call
Galileo Russell “So I'm also wondering, are you guys going to let Porsche beat you to market with a 350-kilowatt hour
Supercharger? Because I know you've mentioned a V3...”
Elon Musk “We'll keep going until you ask questions that are not boring.”
Panel B: An excerpt of the transcript of Tesla’s 2015 Q3 call:
Adam Michael Jonas
Elon, just thinking longer term here. Assuming Tesla establishes itself as a leader in autonomous transport, do you
see a business case for selling autonomous cars to ride-sharing firms? Or can Tesla cut out the middleman and offer
on-demand electric mobility services directly from the company's own platform?
Elon R. Musk
I think we'd have to say no comment.
Adam Michael Jonas
I mean, Elon, it's kind of unusual for you to punt on strategic questions of a long-term nature. Is this a dumb
question? Or a funny question?
Elon R. Musk
No, I think it's quite a smart question, actually, but there's still no comment.
Adam Michael Jonas
Why -- all right, okay, I won't antagonize. Let's move on. I mean, it's just odd because you normally -- or I've never
heard you punt like that, that's all. But in any case, is it because of a competitive sensitivity? Or is it because the
concept itself is just too in-flux?
Elon R. Musk
I think there's a right time to make announcements, and this is not that time.
Adam Michael Jonas
Fair play. All right can I ask one on Autopilot?
Elon R. Musk
And nor -- I mean, nor is our strategy fully baked here. So to -- for us to state what it would be, it's not fully baked.
So there's no -- we'd prefer to announce something when it's -- when we're -- we think we've got the full story
understood.
Adam Michael Jonas
So saying it's not fully baked implies there's something in the oven, but just, okay.
Elon R. Musk
Okay, can we -- we kind of need to move on.