Gender and Earnings Conference Calls

Bill Francis

Lally School of Management

Rensselaer Polytechnic Institute

francb@rpi.edu

Thomas Shohfi

Lally School of Management

Rensselaer Polytechnic Institute

shohft@rpi.edu

Daqi Xin

Lally School of Management

Rensselaer Polytechnic Institute

xind@rpi.edu

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

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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.

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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

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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.

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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

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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.

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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

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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

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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.

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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

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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:

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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.

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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”).

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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.

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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.

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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.