Silent Suffering: Using Machine Learning to Measure CEO Depression

Sung‐yuan (mark) Cheng and Nargess M. Golshan

Journal of Accounting Research, 2025, vol. 63, issue 2, 689-767

Abstract: We introduce a novel measure of CEO depression by applying machine learning models that analyze vocal acoustic features from CEOs' conference call recordings. Our research was preregistered via the Journal of Accounting Research's registration‐based editorial process. In this study, we validate this measure and examine associated factors. We find that greater firm risk is positively associated with CEO depression, whereas higher job demands are negatively associated with CEO depression. Female and older CEOs show a lower likelihood of depression. Using this novel measure, we then explore the relationship between CEO depression and career outcomes. Although we do not find any evidence that CEO depression is associated with CEO turnover, we find some evidence that turnover‐performance sensitivity is higher among depressed CEOs. We also find limited evidence of higher compensation and higher pay‐performance sensitivity for depressed CEOs. This study provides new insights into the relationship between CEO mental health and career outcomes.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bla:joares:v:63:y:2025:i:2:p:689-767

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Journal of Accounting Research is currently edited by Philip G. Berger, Luzi Hail, Christian Leuz, Haresh Sapra, Douglas J. Skinner, Rodrigo Verdi and Regina Wittenberg Moerman

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