How Important Is Corporate Governance? Evidence from Machine Learning
Chicago Booth Research Paper No. 22-16
University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2022-137
Rock Center for Corporate Governance at Stanford University Working Paper Forthcoming
71 Pages Posted: 29 Sep 2022 Last revised: 4 Apr 2023
Date Written: March 31, 2023
We use machine learning to assess the predictive ability of over a hundred corporate governance features for firm outcomes, including financial-statement restatements, class-action lawsuits, business failures, operating performance, firm value, stock returns, and credit ratings. We discover that adding corporate governance features does not improve the predictive accuracy of models over that of models constructed using only firm characteristics. Our results confirm the challenges in constructing measures of corporate governance with predictive value suggested in prior research. These results also raise doubts about the existence of strong causal effects of corporate governance on firm outcomes studied in prior research.
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