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CEO power and stock price crash risk in China: Do female directors' critical mass and ownership structure matter?

Shahab, Yasir, Ntim, Collins G., Ullah, Farid, Yugang, Chen, Ye, Zhiwei·2020·International Review of Financial Analysis

Abstract

Abstract This study investigates how CEO power is associated with stock price crash risk. We further examine the moderating roles of female directors' critical mass and ownership structure on the relationship between CEO power and stock price crash risk. Employing one of the largest datasets to-date of Chinese listed firms over the 2005–2015 period (13,421 firm-year observations), we find that CEO's power to increase the likelihood of stock price crash risk is significantly mitigated when the percentage of: (a) female directors; and (b) ownership by blockholders and institutions, is high within firms. We interpret our findings within a theoretical framework that draws insights from neo-institutional, managerial power and critical mass theories. The findings are robust to the use of alternative measures, estimation methods and endogeneity issues.

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Metadata

Authors
Shahab, Y., Ntim, C. G., Ullah, F., Yugang, C., & Ye, Z.
Venue
International Review of Financial Analysis
Publisher
Elsevier BV
Published
2020-03
Volume
68
Pages
101457
ISSN
1057-5219 (ISSN-L)
OpenAlex
W2999407275
Cited by
169
References
4
FWCI
45.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: CEO power and stock price crash risk in China: Do female directors' critical mass and ownership structure matter? https://app.cochat.ai/papers/10.1016/j.irfa.2020.101457
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Shahab, Y., Ntim, C. G., Ullah, F., Yugang, C., & Ye, Z. (2020). CEO power and stock price crash risk in China: Do female directors' critical mass and ownership structure matter? International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2020.101457

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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.