Insider Trading and CEO Pay-Gap Induced Turnover
Bibliographic record
Abstract
We explore how insider trading returns, disparities in executive pay, and CEO turnover are interrelated. Our findings reveal both independent and interactive effects for insider trading returns, the CEO pay gap, and the likelihood of CEO turnover. First, an increase in abnormal returns from insider purchases lowers the probability of a CEO’s turnover, while an increase in abnormal returns from insider sales increases the likelihood of a CEO’s dismissal. Second, the CEO pay gap negatively affects the probability of CEO turnover for insider purchases, but it does not have a similar effect on insider sales. Third, the interaction between insider abnormal returns and any CEO pay disparity influences the impact of these returns on CEO turnover. Specifically, this interaction diminishes the positive effect of insider selling on the probability of a CEO’s dismissal, offsets the negative effect of insider purchasing on CEO dismissal, and, finally, amplifies the negative impact of CEO pay disparity on the probability of a CEO’s dismissal during periods witnessing insider purchases.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".