Abstract
We examine whether an unsophisticated investor’s own gender interacts with gender of a sell-side equity analyst to affect the investor’s judgment. Prior research shows two potential sources of gender-based discrimination that affect female investors. First, female investors’ advisors offer less risky hence lower return portfolios to female investors than to male investors with similar risk preferences as female investors are perceived as more risk adverse. Second, female equity analysts are subject to greater barriers to enter and advance in investment firms that act as if they believe clients prefer male investment advisors in a male stereotypical occupation. Using two experiments, we use the judge-advisor framework to predict and find that investor’s gender and analyst’s gender jointly influence investor’s judgment. Specifically, female-female analyst-investor pair generates the strongest reaction to analyst’s advice compared to any other analyst-investor pair, everything else equal. Further, we find that efforts to highlight equal gender performance activates gender stereotypes that reduce female investors’ receptivity to female analysts’ advice. By linking the two previously different sources of discrimination we show that they reinforce each other and find that attempts to “level the playing field” by emphasizing gender performance parity may have unexpected results.
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Introduction
This paper examines how unsophisticated investors’ judgment and decision-making are jointly affected by gender of an expert advisor, the sell-side equity analyst, and gender of the advice recipient, the investor, themself. Extant ethics literature has examined barriers faced by female equity analysts when they are being evaluated for entry into the financial services field (Adams et al., 2016; Fang & Huang, 2017) and promotion (Botelho & Abraham, 2017; Lin & Neely, 2017) as well as the effects of overgeneralization of female risk aversion (e.g., Zalata et al., 2019) in professional contexts. We extend this line of research to their joint effects on recipients of such professionals’ advice.
Our focus on individual investors is warranted as they are a significant source of capital in equity markets. With the availability of market microstructure (individual trade) data over the last 15 years (e.g., Barber et al., 2009; Daniel & Hirshleifer, 2015; Elliott et al., 2010; Kelley & Tetlock, 2017; Nolte & Nolte, 2016), research has shown that individual investors (as proxied by the small size of stock purchases and sales transactions) impact market prices and stock returns in both relatively efficient markets such as that of the U.S. (Kelley & Tetlock, 2017; Nolte & Nolte, 2016), Australia (Tian et al., 2015), as well as an emerging market economy (i.e., Indonesia) (Koesrindartoto et al., 2020). Worldwide, the number of individual investors is estimated to be over half a billion (Grout et al., 2009). In the U.S., 52% of households invest in equity securities directly or via mutual funds, owning roughly 37% of total equities in U.S. financial markets (Kolchin, 2019). These individual investors own an estimated average of $40,000 (U.S. dollar, excluding retirement accounts) in equities. Individual investors are also increasingly investing their own retirement accounts given the rise of defined contribution pension plans and government mandates that a portion of or all pension assets be directly managed by beneficiaries (e.g., Ongena & Zalewska, 2018).
Most individual investors are relatively unsophisticated when it comes to investment analysis (Bhattacharya et al., 2003; Mayorga & Trotman, 2016).Footnote 1 As such, they rely on third parties, such as equity analysts, to process information for them and to provide advice based on which they make their investment decisions (Kelly et al., 2012; Mikhail et al., 2007). Individual investors consider sell-side analysts’ research reports among the most influential sources for investment decision-making (e.g., Choi & Robertson, 2020; Kim et al., 2017; Kothari et al., 2009; Merkley et al., 2017).Footnote 2 Indeed, increasing availability of analyst reports through sites such as Yahoo Finance shows that individual investors demand those reports at times of market uncertainty (e.g., earnings announcements) and that they are aware of analyst characteristics (Lawrence et al., 2017). As female investors are likely to live longer than male investors, they are more likely to rely on their investments and their expert advisors for a longer period (Garnick, 2016). However, prior research on financial advisors finds female investors receive less risky portfolio recommendations than male investors with the same stated risk preference, leading to lower returns for female investors (Bhattacharya et al., 2020; Grable & Lytton, 1999; Roszkowski & Grable, 2005; Schubert et al., 1999; Wang, 1994). This paper extends prior ethics and gender literature to examine how an unsophisticated investors’ gender interacts with a sell-side equity analyst’s gender to affect their processing of the analyst’s advice.
We examine this interactive effect in the judge-advisor theoretical framework (e.g., Guntzviller et al., 2020; Yaniv, 2004), which is distinct from the evaluative setting applicable to performance evaluation and promotion decisions examined in prior analyst studies (e.g., Bloomfield et al., 2020).Footnote 3 We suggest that these two settings are different such that the findings from the evaluative setting may not be generalizable. Specifically, in the evaluative setting, research shows that the evaluator’s gender does not decrease evaluator bias that favors stereotypically male evaluatee characteristics (e.g., Bloomfield et al., 2020). We argue that our setting is better characterized within the judge-advisor framework, where the gender of the advice recipient and the advisor may both be important for advisee decision-making. We posit that in a male-dominated industry like financial services, a female analyst’s report could stand out as a unique information cue potentially triggering female investors’ ingroup favoritism (also known as “homophily”, see Rudman & Goodwin, 2004). As ingroup favoritism is stronger among females than males (Cadinu & Galdi, 2012; Rudman & Goodwin, 2004), we predict that female-female analyst-investor pair would generate the biggest reaction to an otherwise identical analyst report, compared to other pairs (e.g., female-male, male-male, male-female analyst-investor pairs).Footnote 4 Our prediction that ingroup favoritism is more likely to arise in a judge-advisor setting (Guntzviller et al., 2020; Yaniv, 2004) is consistent with survey evidence from Baeckstrom et al. (2019) where they conclude that “female investors advised by women report the highest risk tolerance and make the lowest portfolio allocation to risk free assets across the full sample.”
To examine our predictions, in our Experiment 1 we employ 256 online participants with self-report investment experience to make investment judgments based on otherwise identical reports provided by a male or a female analyst.Footnote 5 We find self-identified female investors exhibit a greater reaction to a female analyst’s “buy” report (and to a lesser extent “sell” report) than to an identical report by a male analyst, as well as a greater reaction to female analyst’s reports than male investors. In other words, we find support that female-female analyst-investor pair generates a greater reaction than any other analyst-investor pair. We further find that such ingroup favoritism works unconsciously given attributions about the analyst’s perceived credibility or risk attitudes do not explain the observed experimental differences.
To further demonstrate that it is ingroup favoritism at work, in our Experiment 2 we combine the 134 participants in the “sell” condition from Experiment 1 with an additional 137 online participants who self-report investment experience to test the effect of a gender prime. Priming gender, even if the prime’s content indicates equal (or better) ability and performance by women, can activate gender stereotypes and make salient the lack of fit between a female analyst and a stereotypical equity analyst who is male (Heilman, 2012; Rudman & Phelan, 2010). Using a news article (“gender news prime”) emphasizing female analysts’ similar levels of risk aversion and performance to male analysts (i.e., priming gender), we find that female investors with the prime react significantly less to the female analyst’s report than without the prime, whereas results for all other investor-analyst pairs stay the same whether the prime is present or not.
Our study has implications for investors and analysts. Female investors’ greater reactivity to female analysts’ reports makes it more likely that female investors will undertake a more active management style for their investments (Baeckstrom et al., 2019), thus ameliorating one of the root causes of female investors’ portfolio underperformance. Thus, our evidence implies that one means to overcome the historical underperformance of female investors’ advisor-recommended portfolios would be to have more investment advice authored by female analysts. Our study debunks Wall Street investment firm managements’ belief that both female and male investors prefer male sell-side analysts (Roth, 2004a, 2004b), thus, preference for male analysts in recruitment and promotion decisions based on the so-called “client needs” is likely unfounded. However, we show that the potential performance gains for female investors are reduced when gender stereotype is activated, even though the content of the prime intends to “level the playing field” by emphasizing the equal performance of male and female analysts.
The remainder of this paper proceeds as follows. First, we review studies on gender and ethics leading to our hypotheses. Second, we present the research design including the manipulations as well as the measurements of constructs in our experiments. We next present our two experiments’ results. We conclude with a discussion of the results and their implications for female investors and female equity analysts.
Background, Theory, and Hypotheses Development
Ingroup Favoritism in the Judge-Advisor Context
Research on gender and ethics has documented perceived discrimination and actual biases towards female professionals working in accounting, finance, and management (Bohnet et al., 2016; Cohen et al., 2020; Hardies et al., 2020; Hoyt et al., 2009; Kanze et al., 2018; Koch et al., 2015; Koenig et al., 2011; Latu et al., 2011; Leicht et al., 2017). With women representing less than 20% of sell-side equity analysts (Adams et al., 2016; Bertrand & Hallock, 2001; Bertrand et al., 2010; Brown et al., 2015), the skewed gender composition has led to stereotypes that associate characteristics of equity analysts with those of men (Bloomfield et al., 2020; von Hippel et al., 2015), and consequently, biases against female analysts despite similar (or superior) performance of female analysts in terms of accuracy and returns compared to male analysts (Kumar, 2010; Li et al., 2013).Footnote 6 These stereotypes are associated with female analysts needing to be better educated than male analysts to enter the profession (Adams et al., 2016; Fang & Huang, 2017), and elite male analysts receiving a higher base salary and larger pay increases than female analysts (Lin & Neely, 2017). As such, females need to outperform males to be deemed equal performers, yet even that may not be sufficient for promotion (Botelho & Abraham, 2017).
Consistent with Messick’s (2009) statement that “making personnel decisions on the basis of race, gender, ethnicity, or other ‘irrelevant’ factors is unethical” (p. 73, emphasis added), the existing literature largely examines gender and ethical issues in the performance evaluations context, including evaluating leadership (Hoyt et al., 2009; Koenig et al., 2011), assignment of audit client portfolio (Hardies et al., 2020), as well as evaluating sell-side equity analysts for promotion (Bloomfield et al., 2020) and compensation (Lin & Neely, 2017) among others (see meta-analysis by Koch et al., 2015). Our study extends prior gender and ethics literature beyond the evaluation setting to a judge-advisor setting (Yaniv, 2004; Zaleskiewicz et al., 2016; Baeckstrom et al., 2018; for a review of the judge-advisor paradigm, see Guntzviller et al., 2020), where individual investors rely on and process the advice provided by an expert (i.e., an equity analyst) to make investment decisions.
If investors were acting as evaluators of analysts, rather than as advice recipients, we would expect that evaluator (i.e., investor) gender would not matter, as research shows male and female evaluators hold the same gender stereotypes, especially for evaluations in a male-dominated domain (e.g., Latu et al., 2011; Rudman & Phelan, 2010; Penner et al., 2012; Leicht et al., 2017; Heilman et al., 2019; Bloomfield et al., 2020; see also Koch et al., 2015 for a meta-analysis). In other words, for performance evaluations in a male-dominated setting, the evidence suggests that evaluator’s gender does not interact with evaluatee’s gender. However, the advisor-advisee relationship that we believe best characterizes equity analysts and individual investors relationship is inherently different from the evaluator-evaluatee relationship in a performance evaluation for compensation and/or promotion context (e.g., Bloomfield et al., 2020). Instead of evaluating the analyst who writes the report, investors are acting as information processors (i.e., advice recipients) who rely on the report.Footnote 7
Research in the judge-advisor setting suggests that identity of the advisor and identity of the advisee both matter for the advisee’s judgment and decision. For example, applying the judge-advisor framework in an audit setting, Kadous et al. (2013) find that auditors’ receptivity to a specialist’s advice is contingent on whether the auditor herself is an expert as well as characteristics of the specialist. Survey evidence from Baeckstrom et al. (2019) suggests that gender of the investor and gender of the financial advisor both matter in portfolio allocation decisions.Footnote 8 Thus, we consider how the advisee (i.e., the investor)’s gender interacts with the advisor (i.e., the equity analyst)’s gender to impact individual investor’s judgment and decision-making.
In a male-dominated industry such as the investment industry, we expect that female investors will exhibit ingroup favoritism (i.e., homophilous preference) in their reaction to analyst reports, that is, they exhibit a preference for advice from those of the same gender (Greenberg & Mollick, 2017; Rudman & Goodwin, 2004).Footnote 9 Research finds females’ attitudes tend to reveal pro-female sentiments (Carpenter, 2001; Richeson & Ambady, 2001; Skowronski & Lawrence, 2001) and that ingroup favoritism is particularly strong in females (Cadinu & Galdi, 2012; Rudman & Goodwin, 2004). Such ingroup favoritism among females has been found in other settings in the male-dominated investment industry. For instance, female-led start-ups are more successful with female angel investors than the observably similar male-led start-ups (Greenberg and Mollick, 2017; Solal, 2019; Ewens & Townsend, 2020). Female investors are also found to be willing to invest in a riskier portfolio if their advisor is female rather than male (Baeckstrom et al., 2019).
Ingroup favoritism research also suggests that ingroup effects are motivated by preferential treatment towards members of the same group rather than hostility towards outgroup members (Brewer, 1999). Thus, female investors’ ingroup favoritism could unconsciously (Krendl et al., 2008) result in a stronger response among female-female analyst-investor pair compared to other combinations (e.g., the same report by a male analyst processed by a male or female investor, or the same report by a female analyst processed by a male investor). Male investors, not having (or having a lower level of) this ingroup affinity (Cadinu & Galdi, 2012; Rudman & Goodwin, 2004), likely would not exhibit a stronger reaction to a male analyst’s report than other analyst-investor pairs and if anything, considering the male industry stereotype, they would exhibit a smaller reaction to the female analyst’s report than other pairs.Footnote 10 This analysis leads to our two initial hypotheses:
Hypothesis 1
Female investors’ belief change in a company’s investment prospects based on a female analyst’s report is greater than female investors’ belief change based on an otherwise identical male analyst’s report.
Hypothesis 2
Female investors’ belief change in a company’s investment prospects based on a female analyst’s report is greater than male investors’ belief change based on the same female analyst’s report.
Based on this literature we also have theory-based expectations about male investors’ reaction to male and female analyst’s reports. However, male investors are not the focus of our research, hence we do not state those expectations as hypotheses albeit we do test for differences and report the results.
Gender Prime and Gender Stereotype in the Analyst Context
Research finds that gender-based differences in risk aversion among professionals are virtually eliminated for tasks directly related to their professional context (Beckmann & Menkhoff, 2008; Johnson & Powell, 1994; Masters & Meier, 1988). Consistent with the view that familiarity with risk changes behavior towards risk (Dwyer et al., 2002; Slovic, 2000), Kumar (2010) finds female analysts provide bolder forecasts than male ones, and Bollen and Posavac (2018) find male and female finance professionals feature similar risk preferences (see also Croson & Gneezy, 2009).Footnote 11 Despite these findings, third parties (e.g., investors who receive analyst reports) may be influenced by both the broader gender stereotype that women are more risk averse and the more specific, occupational stereotypes that equate characteristics of successful equity analysts with those of men.
Individual’s beliefs about any social group (e.g., equity analysts) derive from their experiences with “typical” group members. When “typical” group members are overwhelmingly from a certain social group (e.g., men), this noticed difference results in gender-based role stereotyping (Eagly, 1987; Wood & Eagly, 2012) that provides quick and easy means to assess others’ abilities (Schneider, 2004; Yzerbyt & Demoulin, 2010). Indeed, prior research shows gender stereotypes are ubiquitous (Eagly et al., 2020) and that the social category of gender is fundamental to human cognition and social organization (Eagly et al., 2020). Further, gender stereotypes lead to general expectations for differential performance that are regularly incorporated into the decision-making process (Hoyt et al., 2009; Leicht et al., 2017; Piliavin & Martin, 1978).
Research finds that, across different domains, gender stereotypes implicitly impact the expectations one holds about the qualities of individual males and females (see Ellemers, 2018 for a review of that literature). Research shows that when females are in positions that are stereotyped as “men’s jobs”, females’ performance is often devalued and their competence denied (e.g., Bohnet et al., 2016; Rudman & Phelan, 2010; Swim et al., 1989). These results are attributed to inconsistencies between stereotypical perceptions of how females perform and the qualities that are thought necessary to perform a job typically held by men (Dipboye, 1985; Heilman, 1995). For example, Lee and James (2007) find that investors react more negatively to the announcement of a female CEO than the announcement of a male CEO. Eagly et al. (1991) find female leaders tend to receive lower evaluations than their male counterparts for the same behaviors (see also Heilman, 2001; Heilman et al., 2004; Kulich et al., 2011). Given males represent 80% of equity analysts and make 84% of all forecasts (Kumar, 2010), equity analyst positions are considered as stereotypically male (see Bloomfield et al., 2020 for extensive support for this conclusion).Footnote 12
Highlighting gender in an environment where there is a strong gender stereotype is likely to trigger activation of such stereotypes to a greater extent than if not primed (Rudman & Phelan, 2010) even if the prime is a positive piece of information about females’ performance in a male-dominated environment. Rudman and Phelan (2010) report that priming gendered roles in an occupation results in females responding with stronger gender stereotypes relative to those who were not primed. The priming did not affect males’ reactions. Bohnet et al. (2016) report that making gender salient and then separately assessing only one gender’s work trigger even greater stereotype-based reactions.
Hence, we expect female investors to exhibit a lessened response to a female analyst’s report when a gender news prime is present, as it would activate dominant gender stereotypes (Heilman et al., 2019; Krendl et al., 2008; Latu et al., 2011; Leicht et al., 2017; Penner et al., 2012). The presence (absence) of the prime should have no effect on how either gender reacts to a male analyst’s report given a male analyst’s report reflects the expected male occupational stereotype. Hence, we hypothesize that:
Hypothesis 3
The presence of a gender news prime will result in a smaller belief change about a company’s investment prospects by female investors in response to a female analyst’s report compared to the absence of such a prime.
Hypothesis 4
The presence or absence of a gender news prime will not affect male and female investors’ belief changes about a company’s investment prospects in response to a male analyst’s report.
For male investors, if the content of the prime (i.e., equal performance of female and male analysts) causes them to revise stereotyped beliefs about female analyst’s risk aversion and/or performance in a male-dominated industry, then male investors who are exposed to the gender prime would react more strongly to the female analyst’s report. As male investors are not the focus of our research, we do not state this as formal hypothesis, but we report the test’s results.
Research Design and Method
Both experiments utilize a 2 × 2 × 2 research design. In Experiment 1, we manipulate the type of analyst’s report (“buy” or “sell”) and the analyst’s reported gender. We elicit participant’s gender through self-identification.Footnote 13 In Experiment 2, we use “sell” report conditions from Experiment 1 and employ a news item (present or absent) that primes gender (i.e., explicitly triggering the male industry stereotype) in addition to the analyst gender manipulation.Footnote 14
Overview of Research Instrument
The research instrument for Experiment 1 includes the following materials. First, we provide background information on a publicly traded company, drawing from the financial and trading data of an actual public company in North America. We elicit participants’ initial perception of the company’s investment prospects given the background information provided (denoted as the “pre-report judgment”). The information provided is designed to be neutral such that both types of analyst’s reports (“buy” and “sell”), given in the next part of the instrument, are plausible.Footnote 15 Next, we randomly assign participants to an analyst’s report containing either a “buy” or a “sell” recommendation. Participants are given the opportunity to review the background information provided previously. Participants then provide judgment on the company’s investment prospects again (denoted as the “post-report judgment”). Next, we ask several questions about participants’ perception of the analyst’s risk attitude and credibility. Finally, participants complete a post-experimental questionnaire including comprehension checks and demographic questions.
Analyst’s Report
After participants read the background company information, we provide a short introduction to (re)familiarize participants with the role of sell-side equity analysts and the analyst’s report. In addition, we inform participants that the brokerage with which the analyst is affiliated does not have an investment banking relationship with the covered company, nor was it involved in the initial public offering of the covered company.Footnote 16 The analyst’s report includes the analyst’s recommendation, target price and earnings per share (“EPS”) forecast, a risk assessment and justification for the risk assessment, and a summary statement on the outlook of the covered company. Each of these types of information has been found to be incrementally informative in explaining stock price movements (Beyer et al., 2010). All reports contain identical descriptions of the analyst’s education background, work experience and job title.
We manipulate gender of the analyst report writer by using validated male and female names based on survey results in Milkman et al. (2012). They found the names “Steven Smith” and “Claire Smith” had a 100% rate of gender and race recognition as Caucasian male and Caucasian female, and the highest net gender recognition rates among their set of tested names. In addition, we use equal-sized, generic black-and-white silhouette portraits of a male (female) wearing a suit and tie (a business suit and blouse). As silhouette portraits, the pictures do not contain any facial features or expressions while reinforcing gender of the analyst. We use silhouette pictures instead of real pictures of analysts to avoid introducing other variables such as the perceived attractiveness of the analyst, which is known to influence decisions in other contexts (Eagly et al., 1991) but is not the focus of our study. See Online Appendix A for a copy of the “buy” report by male versus female analyst used in Experiment 1.
In Experiment 1 we manipulate report type (“buy” versus “sell”) by building a consistent profile for each report type. That is, a “buy” (“sell”) report always has optimistic (pessimistic) price and EPS estimations, low (high) risk assessment and summary statements indicating strong (limited) growth potential. We keep the distance between the current price (EPS) and estimated future price (EPS) equal but of opposite sign for “buy” versus “sell” reports.
Dependent Variables
Investor’s beliefs about the company’s prospects as a potential investment are elicited twice, after the background information on the company is provided (“pre-report judgment”) and after the analyst’s report is made available (“post-report judgment”). We use multiple measures to elicit beliefs about the company’s prospects as a potential investment: riskiness of the company’s shares as an investment (reverse coded), attractiveness of the company’s shares as an investment, likelihood to invest in the company’s shares, and price appreciation potential of the company’s shares.Footnote 17 See Table 1 for the wording of relevant question items.
For the four items elicited on scales, the difference between the pre-report judgment and post-report judgment is denoted as the change in investors’ beliefs about the company’s investment prospects. These belief change measures are the source of our dependent variable and allow each participant to be their own control.Footnote 18 This approach reduces the likelihood of the plausible alternative hypothesis that results are driven by differences in participants’ pre-report judgments rather than information contained in the analysts’ reports (Shadish et al., 2002).
Experiment 1
We developed an initial experimental instrument and pilot tested it with undergraduate students while running an extensive pretest with highly experienced finance professionals.Footnote 19 We revised the initial instrumental based on feedback from those professionals. We collect data to test all four hypotheses concurrently, but we separate the presentation of results into Experiments 1 and 2 for clarity of exposition.
Participants
We recruit four hundred and sixty-four individuals who claim investment experience through the Prolific platform to participate in the anonymous online study.Footnote 20 Mean (median) time for participants to complete the study is 26 min (16 min).Footnote 21 Ninety percent of participants spent between 8 and 42 min to complete the study. Therefore, we consider those participants who completed the entire study under 10 min combined with any other indication of lack of attention (such as failed comprehension check) as not providing meaningful responses. Thus, we limit our analysis to 393 participants (85%) out of the 464 potential participants, as documented in Table 2, Panel A, in the column labeled “Experiments 1 and 2”. These 393 participants spent on average 26 min to complete the study. On average, participants have 16 years of work experience and 9 years of investment experience.Footnote 22 We find significant (p < 0.05) differences in work experience, investment experience, plan to invest in future, and knowledge about analysts’ reports (including self-rated familiarity and experience with analysts’ reports) between male and female participants. See Table 2 Panel B for additional demographic data. Where these background differences make a difference in statistical inferences, we note it in our analysis.Footnote 23
Finally, our female participants rated themselves as significantly (p < 0.05) more risk averse than male participants, consistent with literature finding gender-based differences in risk aversion among the general population (e.g., Eckel & Grossman, 2002). Of the 393 participants, 256 took part in Experiment 1. Experiment 2 utilized the 134 “sell” report participants from Experiment 1 for “gender news prime absent” condition in Experiment 2 and used the remaining 137 participants for “gender news prime present” condition. Hence, Experiment 2 had 271 participants.
Measurement of Dependent Variable and Multivariate Analysis
Table 2, Panel C presents descriptive statistics of our four individual belief change measures. We perform a principal component factor analysis on the four belief change measurements (i.e., change in riskiness, change in attractiveness, change in price increase potential, and change in likelihood to invest) to ensure that investors’ belief change in the firm’s investment prospects conceptually captures one construct.Footnote 24 Factor analysis using the four items shows only one common factor with an eigenvalue greater than 1.00 and accounting for 72.5% of the variance. All four items load to the common factor at 0.75 or greater, with a Cronbach’s alpha of 0.87 suggesting the scale is reliable (Nunnally, 1978). Thus, we create the composite dependent variable “belief change about the company’s investment prospects”, by taking the average of these four change items (see Table 2 Panel C).Footnote 25 The univariate comparisons show that investors react significantly to the analyst’s report by revising their beliefs about the company’s investment prospects in the same direction as the analyst’s report recommends.
We also examine participants’ pre-report judgment to rule out potential ceiling effect or floor effect. As the mean (median) rating of the pre-report company’s investment prospects is 6.00 (6.25) on a scale of 0 to 10 we conclude there is plenty of room for judgment revisions, either upwards or downwards. Finally, we establish that our investors react consistently with the well-known effect that investors’ belief revisions will be greater following a “sell” report as compared to a “buy” report (Asquith et al., 2005; Barber et al., 2007; Hirst et al., 1995). We find that the absolute value of investor’s change in beliefs for a “sell” report is significantly larger than their belief changes in response to an analyst’s “buy” report (2.04 vs. 0.76, t = 6.86, p < 0.001 results untabulated).
Tests of Hypotheses
Given our replication of prior literature that investors react differently to “buy” and “sell” type reports, we examine Hypotheses 1 and 2 within the context of the “buy” and “sell” reports separately. Table 3 Panel A reports the descriptive statistics for our eight experimental cells broken down by report type (“sell” versus “buy”) and within each report type by investor gender (male versus female) and analyst gender (male versus female). See Fig. 1 for a graphical presentation of means. Table 3, Panel B reports the overall ANOVA analysis showing three marginally significant interactions, most importantly the three-way interaction of Analyst Gender by Investor Gender by Report Type (p < 0.055 two-tailed test) that indicates all three factors that are important to the investment decision.
Experiment 1 results. We measure belief revision as the average difference between each participant’s pre-analyst report and post-analyst report assessments of the company’s investment prospects. Participants indicate on a scale from 0 to 10 how attractive is the company’s stock as an investment, the price increase potential of the company’s stock, their likelihood to invest in the company’s stock, and the riskiness of the investment. The belief change score is an average of the four question’s response differences between pre-report and post-report. Higher positive values and lower negative values indicate greater belief revision in the positive and negative directions, respectively. We randomly assign participants to one of four analyst report conditions: a “buy” report issued by a female analyst; a “buy” report issued by a male analyst; a “sell” report issued by a female analyst; and a “sell” report issued by a male analyst. Participants identify their own gender in the post-experimental questionnaire
In Table 3, Panel C we test our first hypothesis that female investors’ reactions (i.e., their belief changes) to a female analyst’s report are greater than their reactions to an otherwise identical male analyst’s report. This test directly examines the ingroup favoritism argument that females will react more strongly to female (versus male) provided advice in a male-dominated domain, hence exhibiting homophilous reaction. We find strong support for our Hypothesis 1 in the “buy” report condition (t = 2.62, p < 0.02 two-tailed test), and directionally consistent but not significant (t = 1.22, p = 0.22, two-tailed) support in the “sell” report condition. Finally, to complete the analysis, we show that male investors do not differ in their response to male versus female analyst’s reports, which is consistent with prior findings that ingroup favoritism is only exhibited by females in a male-dominated environment.Footnote 26
In Panel D we test our second hypothesis that a female investor’s belief change when provided with a female analyst’s report is greater than a male investor’s belief change given the identical female analyst’s report. We find, consistent with Hypothesis 2, that female investors react to a greater extent to a female analyst’s report in both “buy” (t = 2.27, p < 0.03 two-tailed test) and “sell” (t = 1.84, p < 0.07) conditions than male investors. Again, this finding is consistent with the ingroup favoritism argument that female investors exhibit a greater reaction to a female analyst’s report than male investors do in this stereotypically male setting.Footnote 27
Perception of Analyst’s Credibility and Risk Attitude in Experiment 1
In this analysis we seek to understand whether participants react to analyst gender through conscious, deliberate processing of the report writer’s gender.Footnote 28 To that end, we ask participants how credible the analyst is. Consistent with prior literature in accounting (Kadous et al., 2009; Mercer, 2005) and psychology (Berlo et al., 1969; McCroskey, 1966), we measure perceived source credibility by asking participants to assess analyst’s competence, knowledge, and qualification for providing the report, as well as analyst’s trustworthiness, honesty, and truthfulness in his (her) report (see Table 1, Panel B). In addition, consistent with Hirst et al. (2007), we ask participants to assess the overall believability of the report and the analyst.
Descriptive statistics for the individual items by analyst report type are found in Table 4, Panel A. Directionally, the descriptive statistics show no pattern that would support differences in perceived credibility driving our results (directional changes appear to be inconsistent across measures). MANOVA analysis (untabulated) using the six (or eight) items shows there are no significant main or interaction effects for our three independent variables on these judgments. Further, we find no difference in participants’ perception of male and female analysts’ credibility in “buy” and “sell” reports separately. Factor analysis of these six items shows existence of only one factor with eigenvalue greater than 1.00 and factor loadings well above 0.50. Reliability analysis shows a Cronbach’s alpha of 0.88 for the six items, suggesting that the scale is reliable (Nunnally, 1978). Consequently, we form a composite measure of perceived analyst credibility by taking the average of participants’ responses to the six questions (see Table 4, Panel B), consistent with Mercer (2005) and Hirst et al. (2007). The untabulated results show insignificant three-way ANOVAs (i.e., a 2 × 2 × 2) and two-way ANOVAs for the “buy” and “sell” reports separately (i.e., two 2 × 2 ANOVAs).Footnote 29
To understand whether investors’ reactions to analyst’s gender is due to conscious attributions of differential risk aversion between male and female analysts, we measure our investors’ perception of analyst’s risk-related attitudes. We ask participants to assess analyst’s risk attitude, how pessimistic or optimistic the analyst’s report is, how consistent the report is with participants’ expectations, and the incentives for the analyst to issue such a report. For exact questions employed, see Table 1, Panel C. As can be seen in the descriptive statistics in Table 5, Panel A, the direction of the individual items in the descriptive statistics shows no discernible pattern. Following similar procedures as with the creditability analysis, multivariate analyses (i.e., MANOVA) finds no significant main or interaction effects for our three independent variables. Factor analysis of the four items shows the existence of only one factor with eigenvalue greater than 1.00; however, the factor loadings are relatively low (all less then 0.50), and the reliability analysis shows a low Cronbach’s alpha for the four items (Nunnally, 1978).Footnote 30 Hence, we used the individual risk attitude measures rather than a composite in our univariate analyses. To illustrate we tabulate the results of perceived analyst risk attitude measure by gender (see Table 5, Panel B).
In untabulated 2 × 2 ANOVA analyses the interaction of investor gender and analyst gender is (not) significant in the (“sell”) “buy” report. The form of this significant interaction is one where male investors view female analysts as more risk seeking than male analysts. This is contrary to the stereotypical gender-based risk aversion perception of females being more risk adverse than males and hence does not provide a theory consistent explanation based on differential gender-based risk aversion.
Overall, in Experiment 1 we find support for female investors reacting stronger to a female analyst’s report than to a male analyst’s identical report (Hypothesis 1) and for female investors reacting stronger than male investors to a female analyst’s report (Hypothesis 2). The supplemental results suggest that conscious attribution of perceived differences in gender-based analyst credibility or gender-based differential risk sensitivity does not explain these results.
Experiment 2
Another conscious attribution that can account for Experiment 1’s results is that female investors are more aware than male investors that female analysts frequently outperform male analysts (i.e., Kumar, 2010) leading to female investors’ greater receptivity to female analyst’s advice. Even if this specific knowledge is unknown by female investors, it is likely that they would hold general beliefs that females must outperform males to advance in a male-dominated industry (e.g., Botelho & Abraham, 2017). Thus, our second experiment informs all investors that female and male analysts perform equally well at their analysis task and examines whether the ingroup favoritism result continues to be exhibited.
Participants and Method – Experiment 2
To be efficient in our data collection, we collected data for Experiments 1 and 2 at the same time. As such, we focused on the analyst report condition that was found to be more reactive in our pretesting (i.e., the “sell” report) with undergraduate business students (see footnote 18) albeit the pretest results are overall consistent with our Hypotheses 1 and 2. Hence, in Experiment 2, we collect data from 137 additional participants, who read a news article about the equal performance of female and male analysts before being randomly assigned to either a male or a female analyst’s “sell” report condition. Combining these participants with the 134 participants who had been randomly assigned the “sell” report conditions in Experiment 1, we had 271 participants (see Table 2 for demographics) in a 2 (analyst gender) × 2 (participant gender) × 2 (presence vs. absence of gender news prime) experimental design.
The gender news prime was adapted from an actual news article (https://www.cnbc.com/2018/03/08/research-finds-female-analysts-are-more-accurate-make-bolder-calls.html). See Online Appendix B for the news prime. We pretested with upper year undergraduate business students to ensure that the prime was viewed as neutral to positive regarding female analysts’ ability relative to male analysts. All other materials and methods in Experiment 2 are the same as those described in Experiment 1.
Tests of Hypotheses
Table 6, Panel A presents the descriptive statistics associated with Experiment 2. Figure 2 shows them graphically. We follow the same analytical procedures to build our composite belief change dependent variable given similar loadings and reliabilities as reported in Experiment 1. We address each of Hypotheses 3 and 4 in turn.
Experiment 2 “sell” report results with (without) gender news prime. See Fig. 1 for basic experimental details. In the “with gender news prime” conditions, participants read a news article about female analysts’ similar risk aversion and performance results compared to male analysts before random assignment to a female or male-authored analyst report
Hypothesis 3 examines whether female investors’ reaction to a female analyst’s report is lessened when the gender prime is present than when the prime is absent. Table 6, Panel B shows that female investors react substantially less (t = 2.68, p < 0.01) to the female analyst’s “sell” report when primed than they do in the absence of the news prime. This finding supports the posited effect that activating profession-based gender stereotypes will cause female investors to rely less on female analysts consistent with the investment industry’s male gender stereotype.
Based on ingroup favoritism being driven by preference for the ingroup rather than negativity towards others, Hypothesis 4 predicts the gender prime will make no difference to male and female investors’ interpretation of a male analyst’s report. Table 6, Panel C reports that male and female investors react no differently to the male analyst’s report with or without the presence of the prime. Table 6, Panel D also shows that the marginally significant difference between female and male investors to a female analyst’s “sell” report (i.e., Hypothesis 2) disappears in the presence of the prime consistent with the prime activating the investment industry’s male stereotype in both male and female investors.
Finally, to complete the pairwise analysis, Panel E explores whether male investors exhibit a stronger reaction to the female analyst’s report in the presence of a gender prime. Such a reaction would occur if the prime activates male investors’ realization that their stereotypical risk aversion attribution to female analysts was incorrect leading them to react stronger to a female analyst’s report. We find that the presence of the prime results in no change in the male investors’ responsiveness to the female analyst’s report compared to without the prime (t = 0.52. n.s.).
Overall, Experiment 2 provides further evidence that the effects in Experiment 1 were due to ingroup favoritism. The news prime triggers gender stereotypes leading female investors to act in accordance with dominant male stereotype of the financial services industry, significantly decreasing their reliance on female analyst’s advice. It also highlights the potential unintended effects of emphasizing gender performance equality in a male-dominated domain as it may result in attenuated female response to female provided information, defeating the potentially positive effects of ingroup favoritism for the minority group. Given the extensive literature that suggests females in male-dominated occupations are on average better performers to overcome the resistance encountered (e.g., Botelho & Abraham, 2017), our findings suggest the seemingly innocuous broadcast of performance equality across gender groups may result in unexpected consequences.
Conclusion, Implications, and Limitations
In this study, we employ experimental methods to examine how gender of the advisee and the advisor interact to affect advice-taking in an investment setting. Specifically, we examine how an individual investor’s gender interacts with an equity analyst’s gender to affect investor’s receptivity to the investment advice. This allows us to examine two types of discrimination that appear in the investment setting, the provision of advice to female investors that reflects a lower return than to male investors with the same stated level of risk, as well as the apparently high barriers for female analysts to enter and advance in an overwhelmingly male investment industry. Our findings support our contention that the two types of discrimination are mutually reinforcing.
We find that in the male-dominated investment industry, female investors exhibit ingroup favoritism towards female analysts by responding more strongly to a female analyst’s report. Consistent with survey evidence that female investors have more aggressive investment portfolios when their financial advisor is female than when their advisor is male (Baeckstrom et al., 2019), our evidence suggests that female investors would be more receptive to investment advice when it is provided by a female analyst than by a male analyst. This highlights a “Catch-22” situation faced by female investors in that they would likely benefit from a greater amount of female analyst advice if investment firms hired and promoted more female analysts, but the investment firms have historically preferred male analysts based on the belief that investors – regardless of their gender – prefer male analysts to female analysts (Roth, 2004a, 2004b). Evidence from our study contradicts such belief, and as such, there is no basis for preferring male to equally competent female analysts in recruitment and retention. We note that a conscious commitment to overcoming prior discriminatory practices (whether intentional or not) might be more likely to happen today as investment firms struggle to attract female investors as a client base (Clempner et al., 2020; Dagher, 2019).
We also find that priming gender in a male-dominated profession eliminates female investors’ greater receptivity towards female analyst’s advice. These findings suggest that highlighting gender in male-dominated professions may have costs undocumented by previous literature (Caleo & Heilman, 2019; Milkman et al., 2012; Moss-Racusin et al., 2012). Thus, how to highlight female’s achievements without priming gender stereotypes should be further explored (Howard et al., 2015; Tomlin et al., 2019). The most promising solution may be through greater representation of female professionals in previously male-dominated industries such as finance. A growing number of female analysts may slowly shift the stereotype such that characteristics of equity analysts become less associated with characteristics of men, and thus highlighting female’s performance would not contradict the dominant stereotypes.
While the financial services industry is important enough (e.g., Clempner & Moynihan, 2019; Clempner et al., 2020) to warrant research on its specific concerns, we believe the judge-advisor theory-based analysis that we use in this study could provide the basis for identification of other similar settings. Essentially, our analysis suggests that two conditions need to be present, a male stereotypical industry context and service recipients of both genders that need to make economic decisions based on the advice received. Hence, one might consider settings in the skilled trades (e.g., vehicle mechanics, plumbers, electricians, etc.) where economically important service decisions need to be made by both genders but where the advisee lacks the knowledge to diagnose the problem and develop solutions. However, there may well be context specific issues in those settings that may need to be considered before generalizing our setting’s conclusions.
Finally, this paper also contributes to psychology research on gender. Our findings suggest that the advice recipient’s gender and the advisor’s gender interact to influence the advice recipient’s receptivity to the advice’s content. Psychology research to date has documented that gender of the message sender (or evaluatee) affects responses of the message recipient (or evaluator) (e.g., Milkman et al., 2012) but has not explored the interaction of advisor’s gender and advisee’s gender, nor whether the content of the advice matters.
Of course, there are limitations to our research. First, we use only one company in our experiment. To the extent that participants’ reaction to this company is not typical, and to the extent that the experimental set-up differs from the “real world”, the results may not generalize. However, an archival-based analyst researcher and several experienced finance professionals who collectively have read hundreds of analyst reports opined that our instrument contains key aspects of actual analyst reports, albeit with less detail. Second, our analyst’s gender manipulation employs Caucasian names. We leave to future research to see if results generalize to other racial groups. Third, given the use of a third-party provider of unsophisticated investors who claim to have investment experience, we may have introduced some unknown bias into our experiment. We note that prior researchers have found online participants to be equally, if not more, attentive, and representative of unsophisticated investors (Krische, 2019; Leiby et al., 2019), and we follow their advice to screen for the most suitable participants. We encourage future research to replicate our findings using other samples. Offsetting these limitations are the advantages that the experiential method provides us over archival research (even if the data were available which we understand that at present it is not), as it allows for strong experimental controls over differences in the backgrounds of the advice provider (e.g., age, experience, number of companies followed) and the depth of the report’s analysis that varies significantly in the investment setting.
Notes
Consistent with Krische (2019), we refer to individual investors as “relatively unsophisticated”, as opposed to professional investors. We use the term “individual investors” and “unsophisticated investors” interchangeably.
Drake et al. (2020) suggest that analysts who only post online (what they call “social media analysts”) may be affecting the business model of sell-side analysts by reducing the impact of analyst reports. However, the features of “social media analysts” that determine the extent of investor reliance are very similar to those of sell-side analysts: report detail and analyst expertise. Hence, the gender effects we discuss in this paper would likely to be just as relevant in this alternative analyst domain. To date, robot-based investment analysts (“robo-advisors”) have not been shown to have much impact on markets (Coleman et al., 2020).
Accounting ethics researchers have also examined perceived gender discrimination in public accounting firms and industry (e.g., Cohen et al., 2020; Dalton et al., 2014) as well as how accounting-based performance measurement systems can reinforce gender discrimination (Maas and Torres-Gonzalez 2011).
This judgment-based literature (e.g., Rudman & Goodwin, 2004) predicts ex ante the effects of ingroup favoritism (or homophily) on judgments. This effect stands in contrast to the post hoc use of “client homophily” as a justification for the overrepresentation of male investment analysts in the investment community (Roth 2004a, 2004b). We use the former in developing hypotheses in this study.
Our online participants have comparable knowledge to unsophisticated investors (Elliott et al., 2007). All our participants meet the FASB’s (and the IASB’s) criteria that users of financial reports “have a reasonable understanding of business and economic activities and are willing to study the information with reasonable diligence” (Elliott et al., 2007; Financial Accounting Standards Board (FASB), 2010).
Green et al. (2009), using a less rigorous research design, find that female analysts are less accurate forecasters than their male counterparts but conclude that they “outperform men in other aspects of job performance” including being designated an “all star” analyst (p. 65). Li et al. (2013) find that female analysts’ recommendations lead to similar abnormal returns as male analysts’ but with lower idiosyncratic risks.
We can characterize our setting via signaling theory (e.g., for review see Connelly et al. 2011). We hold the content of the signal (i.e., the report content), the sender’s (the analyst) decision to send a signal (i.e., always sent), and its medium (textual and graphic) all constant. We focus on the issue of how the signal’s receiver (i.e., the investor) interprets (processes) a signal that contains exact same information content that differs only by gender of the sender.
Other studies employing a judge-advisor framework include Boo et al. (2020) who examine advice-taking in an auditor whistle-blowing context.
Eckel and Grossman’s (2002) research on the general population finds that the overestimation of other females’ risk aversion by males surveyed is greater than the overestimation by females surveyed, albeit both overestimate females’ actual risk aversion (see also Bajtelsmit & Bernasek, 1996; Siegrist et al., 2002). Hence, if male investors give credence to greater perceived female risk aversion, they are likely to especially underreact to female analyst’s sell reports.
Supporting the professional context argument, Wu et al. (2018) find female and male executives do not differentially impact bank’s risk-taking. Overall prior research concludes that male and female have only a few differences that separate them and those tend to be quite small (Dobbins & Platz, 1986; Donnell & Hall, 1980; Eagly & Johnson, 1990; Gipson et al., 2017; Powell, 1990).
In our pilot test on students, we confirm that equity analysts are perceived as stereotypically male. When we identify the analyst with an initial as opposed to a first name, more than 70% of our participants erroneously recall the analyst in the case to be male, even though the options “unspecified” and “I do not remember” are available.
We elicit gender by asking as part of post-experiment questions “My gender is:” and providing responses of “Female, Male, Other, I prefer not to say”. To allow for non-binary self-identification, we asked a follow-up question if participants chose “other”: “You answered ‘Other’ to the question ‘What is your gender’. Please specify your gender.” This allows participants to describe their gender in their own words. We elicit gender after the dependent variables to avoid priming effects.
In Experiment 2, we use “sell” conditions (male vs. female analyst) from Experiment 1. We discuss details of Experiment 2 later including differences from these procedures.
Our instrument was reviewed by several experienced finance professionals to ensure this is the case.
We obtain stronger results when we use the final judgment for each of the four measures, individually or averaged across the four, as the dependent variable.
We used the initial instrument in a pilot study employing undergraduate business students as participants. The results were generally consistent with those reported in Experiment 1. However, unlike our Experiment 1 results, the pilot study found stronger effects in the “sell” report condition than in the “buy” report condition. This finding contributed to our selection of the “sell” report in Experiment 2.
Prolific Academic is a UK-based crowdsourcing platform designed for academic research. Research reports Prolific as having a more diverse and honest set of participants compared with other crowdsourcing platforms (Goodman & Paolacci, 2017; Peer et al., 2017). Participants are paid £2.5 ($3.3 USD) for their participation, which, given the actual time to complete the study, translates to £9.4 ($12.4 USD) per hour. Following suggestions from Leiby et al. (2019), we use multiple screening criteria: the individual resides in United States or Canada; the individual has made investments in the common stock or shares of a company; the individual has invested in stock market in the past; when evaluating a company’s stock as a potential investment, the individual examines a company’s financial statements (“sometimes”, “most of the time”, or “always”); the individual has obtained at least 98% approval rate in their past studies. Our study was approved by the research ethics board (i.e., IRB) of the authors’ university.
There is no difference in terms of time spent on the study between participants who are provided with a “buy” versus a “sell” type report. Further, there is no difference in comprehension check pass rates across report type.
We measure participants’ investment experience and their financial literacy following suggestions from Krische (2019). Using the quiz scores as a covariate yields qualitatively similar results as reported in the paper. On average, participants correctly answer 60% of the accounting knowledge questions.
We include work experience and investment experience as covariates in our analysis. Other subjective measures elicited post experiment, such as self-rated risk attitudes, may vary with the experimental conditions due to priming, particularly in female population (Chatard et al., 2007; Schmader, 2002; Steele & Ambady, 2006). Consistent with the self-rating literature, untabulated results find that both male and female participants’ self-rated risk attitudes differ significantly between those who viewed “buy” type report versus those who viewed “sell” type report.
Like the change measures, we perform a principal component factor analysis on the pre-report and post-report judgements separately (i.e., riskiness, attractiveness, price increase potential, and likelihood to invest). Factor analysis using the four items shows only one common factor with an eigenvalue greater than 1.00, with the four items loading at 0.62 or greater. Cronbach’s alpha for the four items is 0.84 (pre-report) and 0.90 (post-report) suggesting the scale is reliable (Nunnally, 1978).
Decomposing the results by individual measure leads to a similar pattern of results with some fluctuations in levels of statistical significance depending on the test.
Our finding is inconsistent with the alternative plausible hypothesis that investors erroneously generalize females’ risk aversion stereotypes to professional equity analysts (e.g., Eckel and Grossman 2002). In that case, we would have expected investors to react less to a female analyst’s report than to a male analyst’s report. We provide additional evidence that this alternative plausible hypothesis is not supported in the subsection “Perception of analyst’s credibility and risk attitude in Experiment 1” as well as in Experiment 2.
Separate 2 × 2 ANOVAs for “buy” and “sell” type report are also consistent with the strength of the results in direct tests of Hypotheses 1 and 2 (results untabulated). The ANOVA for the “buy” report shows an interaction between analyst gender and investor gender [F(1, 118) = 3.80, p < 0.055] ,whereas the weaker pattern of direct tests are reflected in the non-significant interaction for the “sell” report [F(1, 130) = 1.26, n.s.].
This would be consistent with investors engage in “taste-based discrimination” (Becker, 1957), where investors directly experience disutility from female analyst’s reports.
When we include controls (e.g., investment experience), participant gender becomes marginally significant (p < 0.09). The ANCOVA results remain unchanged when controls are included.
Before defaulting to individual item testing, we dropped the item “how consistent the report is with participants’ expectations” that had the highest uniqueness of the four items (unique variance = 0.9214). Dropping this item resulted in a Cronbach’s alpha of 0.5808 for the remaining three items, still well below the threshold of 0.80 (Nunnally, 1978) for a consistent measure and 0.70 for an acceptable measure.
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Acknowledgements
We would like to thank our Editor, Professor Charles H. Cho, and two anonymous reviewers for their valuable comments and suggestions. We appreciate comments on previous versions of this paper from faculty and students at the Smith Doctoral Symposium, Smith’s Social and Behavioral Accounting Brown Bag, 2018 Canadian Academic Accounting Association Annual Conference, and 2018 American Accounting Association Annual Meeting. We thank Jeremy Douthit (discussant), Pujawati (Estha) Gondowijoyo (discussant), Till-Arne Hahn, Kerry Humphreys, Bertrand Malsch, Pam Murphy, Ken Trotman, Sara Wick (discussant), and Mike Wynes for detailed feedback as well as feedback from the workshop participants at the European Network for Experimental Accounting Research (ENEAR), University of Bristol, Hong Kong Baptist University, and University of New South Wales. Yi Luo would also like to pay a tribute to the memory of Zhubao Yang, who very sadly passed away after the paper was accepted. Her courage and wisdom inspired this paper on gender. I dedicate this paper to you.
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Luo, Y., Salterio, S.E. The Effect of Gender on Investors’ Judgments and Decision-Making. J Bus Ethics 179, 237–258 (2022). https://doi.org/10.1007/s10551-021-04806-3
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DOI: https://doi.org/10.1007/s10551-021-04806-3