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The Effect of Gender on Investors’ Judgments and Decision-Making

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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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Notes

  1. 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.

  2. 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).

  3. 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).

  4. 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.

  5. 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).

  6. 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.

  7. 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.

  8. Other studies employing a judge-advisor framework include Boo et al. (2020) who examine advice-taking in an auditor whistle-blowing context.

  9. Homophilious preferences can arise due to many in and out group factors, not only gender (see Rudman et al., 2002; Dasgupta 2004).

  10. 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.

  11. 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).

  12. 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.

  13. 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.

  14. 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.

  15. Our instrument was reviewed by several experienced finance professionals to ensure this is the case.

  16. We provide this information to hold constant factors that prior research suggests could influence investor’s reactions to analysts’ reports (Hirst et al., 1995; Michaely & Womack, 1999).

  17. Those measures are taken from previous experimental literature on analysts’ reports: see Hirst et al. (1995), Kadous et al. (2009), Kelly et al. (2012).

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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).

  25. 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.

  26. 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.

  27. 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.].

  28. This would be consistent with investors engage in “taste-based discrimination” (Becker, 1957), where investors directly experience disutility from female analyst’s reports.

  29. 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.

  30. 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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  1. Ted Rogers School of Management, Ryerson University, 55 Dundas St W, Toronto, ON, M5G 2C3, Canada

    Yi Luo

  2. Stephen J. R. Smith School of Business, Queen’s University, Kingston, ON, K7L 3N6, Canada

    Steven E. Salterio

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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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