Robust Models of CEO Turnover: New Evidence on Relative Performance Evaluation

Abstract

We examine the robustness of empirical models of CEO turnover and revisit prominent findings in the literature regarding the CEO turnover-performance relation. We find that the procedure used to categorize turnover events and the method used to construct performance metrics can have large effects on model inferences. We show that common modeling choices have potentially serious shortcomings including (a) ignoring important information, (b) relying on information that may be systematically biased towards the hypothesis of interest, and (c) weighing extreme observations more heavily than the underlying theory would suggest. We identify a small set of robustness checks and model permutations that are likely to span the reasonable set in many turnover modeling contexts. Using these checks, we show that the widely documented significant sensitivity of CEO turnover to a firm’s abnormal stock performance is an extremely robust result. In contrast, the surprising recent evidence that CEO turnover depends on industry returns is fragile and non-robust, and in general there is no convincing evidence of an independent role of industry stock returns in predicting CEO turnover. We conclude that efforts to explain the general presence of an industry factor in turnover are misguided and that a simple efficient learning view of turnover with full relative performance evaluation is a reasonable description of the CEO turnover process.