The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Self‐Selection and the Forecasting Abilities of Female Equity Analysts
Abstract
This paper investigates whether there are systematic differences between the forecasting style and abilities of female and male analysts, and whether market participants recognize these differences. My key conjecture is that only female analysts with superior forecasting abilities enter the profession due to a perception of discrimination in the analyst labor market. Consistent with this conjecture, I find that female analysts issue bolder and more accurate forecasts and their accuracy is higher in market segments in which their concentration is lower. Further, the stock market participants are aware of the male–female skill differences. They respond more strongly to the forecast revisions by female analysts even though those analysts get less media coverage. The short‐term market reaction is incomplete, however, because it is followed by a strong post‐revision drift. The perception of abilities is similar in the analyst labor market, where female analysts are more likely to move up to high‐status brokerage firms, while their downward career mobility is lower. Collectively, these results indicate that female analysts have better‐than‐average skill due to self‐selection and market participants are at least partially able to recognize their superior abilities.
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Chava, S. (2014) · Management Science · doi:10.1287/mnsc.2013.1863 · Cited by 1,851
Military experience and corporate tax avoidance
Law, K. K. F., & Mills, L. F. (2017) · Review of Accounting Studies · doi:10.1007/s11142-016-9373-z · Cited by 334
Decision fatigue and heuristic analyst forecasts
Hirshleifer, D., Levi, Y., Lourie, B., & Teoh, S. H. (2019) · Journal of Financial Economics · doi:10.1016/j.jfineco.2019.01.005 · Cited by 248
Metadata
- Authors
- KUMAR, A.
- Venue
- Journal of Accounting Research
- Publisher
- Wiley
- Published
- 2010-03-16
- Volume
- 48
- Issue
- 2
- Pages
- 393-435
- ISSN
- 0021-8456 (ISSN-L)
- 1475-679X
- OpenAlex
- W2130896076
- Cited by
- 263
- References
- 31
- FWCI
- 10.08
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.