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Gender, learning, and earnings estimate accuracy

Author

Listed:
  • Bhagwat, Vineet
  • Shirley, Sara E.
  • Stark, Jeffrey R.

Abstract

We analyze the underlying source of gender differences in earnings estimates on a crowdsourcing platform, Estimize, to understand the mechanisms driving analyst ability. Estimates made by females are more accurate than those made by males. This outperformance is not consistent with explanations based on females’ innate ability to process information, females utilizing more up-to-date information, superior stock selection among females, copycat estimates, gender bias, or survivorship bias. Instead, our evidence is consistent with females learning more quickly through making estimates, leading to their outperformance.

Suggested Citation

  • Bhagwat, Vineet & Shirley, Sara E. & Stark, Jeffrey R., 2023. "Gender, learning, and earnings estimate accuracy," Journal of Financial Markets, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:finmar:v:62:y:2023:i:c:s1386418122000489
    DOI: 10.1016/j.finmar.2022.100756
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    More about this item

    Keywords

    Analyst accuracy; Gender; Gender differences; Learning; Female analyst;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G40 - Financial Economics - - Behavioral Finance - - - General

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