Research

Managerial overconfidence and pay-for-luck
Wei, X.A., 2025. Managerial overconfidence and pay-for-luck. International Review of Financial Analysis, p.104607.

This research presented on 2025 Southern Finance Association Annual Conference (Orlando, Florida, US), 2025 Financial Management Association (FMA) Annual Conference (Vancouver, Canada). Thanks all anonymous reviewers and participants for valuable comments and insights.

“It is not compensation that is in the interest of shareholders in any way” (Andreani et al., 2024)
Abstract: This paper examines how CEO overconfidence amplifies the “pay-for-luck” phenomenon in executive compensation. Using a decomposition of firm performance into exogenous “luck” and firm-specific “skill” components, we find that overconfident CEOs receive disproportionately higher rewards for positive market shocks while avoiding equivalent penalties for negative shocks. To address endogeneity concerns, we instrument CEO overconfidence using the industry-level density of overconfident CEOs and Lewbel’s (2012) internal IV approach. Our results remain robust across alternative overconfidence measures, empirical specifications, and governance conditions. Further analysis suggests that overconfident CEOs engage in greater risk-taking behaviors and higher R&D investments which reinforce the effects of CEO overconfidence on pay-for-luck. Additionally, we find that stronger corporate governance and DoDD-Frank Act mitigates the extent of overconfident CEOs’ pay-for-luck. These findings contribute to the literature on executive compensation and behavioral corporate finance, offering implications for incentive design and governance reforms.

Banking topics
with Dr. Shengfeng (Frank) Mei and Dr. Shure Shi

CEO personal behaviour topics
with Dr. Conghui (Jocelyn) Chen and Dr. Rongxin Chen

Macroprudential Policies topics
with Dr. Shuren Shi

CEO compensation topics
with Dr. Yujia Chang

CEO tenure topics
with Dr. Jinghan Guan, Prof. Alaa Zalata, and Dr. shakeel Ahmed

Chinese policy topics
with Dr. Chen Yang

Machine Learning, deep learning, and marco-ecnomics forcasting topics
with Dr. Yujia Chang, Prof. Tapas Mishra and Prof. Mario Brito