CEO Hedging Opportunities and the Weighting of Performance Measures in Compensation Contracts
Bibliographic record
Abstract
ABSTRACT This study examines the rather controversial practice of managerial hedging, which allows CEOs to delink their compensation from stock price performance. We presume that boards are aware of these practices and adjust the weights placed on accounting‐based and stock‐based performance measures in executive compensation contracts to mitigate the problem. Empirically, we find that, in the presence of managerial hedging opportunities, accounting‐based performance measures receive more weight, whereas stock‐based performance measures receive less weight in determining executive compensation. Moreover, these results are more pronounced when managerial hedging needs are high. Regarding the effects of earnings management resulting from accounting‐based incentives, we find that good auditing and strong governance mechanisms strengthen the benefit of placing more weight on accounting‐based performance measures. Taken together, our findings suggest that corporate boards shift the relative weights of performance measures in compensation contracts in response to managerial hedging opportunities, which is consistent with optimal contracting.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".