Gender, learning, and earnings estimate accuracy

Vineet Bhagwat, Sara E. Shirley and Jeffrey R. Stark

Journal of Financial Markets, 2023, vol. 62, issue C

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.

Keywords: Analyst accuracy; Gender; Gender differences; Learning; Female analyst (search for similar items in EconPapers)
JEL-codes: G00 G10 G14 G24 G40 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:62:y:2023:i:c:s1386418122000489

DOI: 10.1016/j.finmar.2022.100756

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