Publication detail

Measuring Executive Personality Using Machine-Learning Algorithms: A New Approach and Audit Fee-Based Validation Tests.

Author(s): Jiří Novák M.Sc., Ph.D., Deloitte Corporate Chair, Hrazdil, Karel; Rogo, Rafael; Wiedman, Christine; and Zhang, Ray
Type: Articles in journals with impact factor
Year: 2020
Number: 3
ISSN / ISBN: 1468-5957
Published in: Journal of Business Finance & Accounting
Publishing place: 519–44
Keywords: big five, machine learning, personality, risk tolerance
JEL codes: M12, M42, G30
Suggested Citation:
Grants: GACR 15-13040S Accounting Earnings Quality, Insider Trading Profitability and Stock Price Informativeness
Abstract: We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs’ and CFOs’ responses to questions raised by analysts during conference calls. We obtain the Big Five personality traits – openness, conscientiousness, extraversion, agreeableness and neuroticism – based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk‐tolerance measure varies with existing inherent and behavioural‐based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised‐vested options) in predictable ways. Second, we show that variation in firm‐year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk‐tolerance – as an innate personality trait – on audit fees is incremental to the effect of increased risk appetite from equity risk‐taking incentives (Vega). Measuring executive personality using machine‐learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct.




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