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.

More on this topic

BFI Working Paper·Apr 15, 2025

Large Language Models, Small Labor Market Effects

Anders Humlum and Emilie Vestergaard
Topics: Technology & Innovation
BFI Working Paper·Jan 28, 2025

Drive Down the Cost: Learning by Doing and Government Policies in the Global EV Battery Industry

Panle Jia Barwick, Hyuk-soo Kwon, Shanjun Li Nahim, and Bin Zahur
Topics: Energy & Environment, Technology & Innovation
BFI Working Paper·Dec 10, 2024

Learning Fundamentals from Text

Alex G. Kim, Maximilian Muhn, Valeri Nikolaev, and Yijing Zhang
Topics: Technology & Innovation