Revisiting the CEO Effect Through a Machine Learning Lens
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".