Pay for Performance: A Comparative Analysis of Machine Learning Models for CEO Compensation Prediction

Abstract

The objective of our study is to demonstrate the feasibility of predicting chief executive officer (CEO) compensation by exploring various machine learning methods. In our analysis, we examine six models: k-nearest neighbors, random forest, decision tree, extra trees, extreme gradient boosting, and support vector machines regressors. We find that XGBoost, random forest, and extra trees regressors exhibit the highest predictive power with the lowest error. Decision tree feature importance analyses identify firm size, CEO age, tangibility, cash holding, and Tobin's Q as key factors in predicting CEO compensation. We also conduct ordinary least squares regressions and find that the significance levels of the coefficients are comparable to the feature importances from the machine learning analysis. Our feature-grouping analysis shows that firms' financial performance and economic characteristics play the most significant role in determining CEO compensation, followed by board characteristics. The analysis further indicates that the predictive power of random forest and extra trees is stronger for forecasting next year's compensation than for predicting the current year's. These findings are valuable for compensation consultants and stakeholders involved in benchmarking decisions.