Revisiting the CEO Effect Through a Machine Learning Lens

• 成果类型: Article
• 署名作者: Shimao, Hajime; Kim, Sung Joo; Khern-Am-Nuai, Warut; Cohen, Maxime C.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Purdue University System; Purdue University; McGill University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.03625
• 发表日期: 2025
• 关键词: CEO effect; Machine Learning; predictive analytics

摘要

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.