Revisiting the CEO Effect Through a Machine Learning Lens
利用机器学习模型和样本外预测分析，重新检验CEO对企业绩效的影响，发现传统基于样本内数据的方法外部有效性有限，CEO的预测效应在样本外测试中不显著。
An important debated topic in strategic management concerns the so-called “chief executive officer (CEO) effect,” which quantifies the impact that CEOs have on the performance of the firms that 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 demonstrate that the results of the approach from the literature have limited external validity. More specifically, we convey that the analyses are purely based on in-sample data and that the predictive effects of CEOs are not substantive when out-of-sample test data sets are used. Although our main analysis relies on optimized distributed gradient boosting, we also conduct extensive robustness tests spanning close to 100 models with alternative algorithms and specifications, all of which yield consistent results. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03625 .