CEO turnover after poor performance: turnaround or scapegoating?
Bibliographic record
Abstract
This paper explores whether firms that dismiss their Chief Executive Officers (CEOs), due to poor corporate performance, exhibit better performance after the CEO turnover, or whether the CEO dismissal merely serves a scapegoating function. We examine whether companies that were in the eye of the public due to disappointing results recover after dismissing their CEO. We match firms in the same industry, by size, and Altman Z-Score and compare our turnover sample with this matched group of firms that did not dismiss the CEO. Our results suggest that CEO turnovers do not translate into better operating performance, or firm valuation (Tobin’s Q). However, we do find that, after some delay, the market reacts positively to CEO dismissals due to bad performance: Underperforming firms that fire their CEOs exhibit positive and significant abnormal returns, while their counterparts, who retain their CEOs, exhibit negative abnormal returns. \n \nKey words: CEO Turnover, Scapegoating, Performance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".