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

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.

Keywords: Personality, Big Five, Machine Learning, Risk, Audit Fees

JEL Classification: G41, G30, M12, M42