Revisiting the CEO Effect Through a Machine Learning Lens

An important debated topic in strategic management concerns the so-called "CEO effect," which quantifies the impact CEOs have on the performance of the firms they lead. Prior literature has empirically investigated the CEO effect and found support for both theses: a significant effect and no effect at all. We note, however, that virtually all prior studies have relied on an empirical specification that leverages in-sample data, which could be unreliable in certain circumstances. In this paper, we utilize machine learning models and predictive analytics based on out-of-sample data to revisit the CEO effect. In particular, we operationalize the CEO effect as the gain in the out-of-sample predictive accuracy by adding the CEO information to the model input in addition to the firm information. By analyzing 1,245 firms and 1,779 CEOs over 20 years, we find that the CEO effect is not substantive.