Bibliographic record
Abstract
Purpose The purpose of this study is to present theory and empirical evidence on whether changes in leverage are systematically associated with changes in the CEO's risk incentives over time.Design/methodology/approach – A model is developed to explain the dynamic relationship between leverage and managers’ risk incentives, and empirically tested with data on executive stock option grants. The model relies on the observation that the risk sensitivity of a call option does not monotonically increase or decrease in the value of the underlying stock.Findings – It is found that changes in the CEO's risk incentives are not systematically correlated with changes in the firm's leverage over time.Research limitations/implications – The near‐universal practice of setting option exercise prices near the prevailing stock price at the date of grant effectively undoes most of the effects of financial leverage, and therefore executives’ incentives to take equity risk are not correlated with firm leverage.Practical implications – For reasonable parameter values, this risk incentive‐maximizing stock price lies very close to the option's exercise price. This finding provides evidence that stock options plans granted approximately at‐the‐money encourage maximum risk‐taking by managers in a dynamic setting.Originality/value – This paper develops theory and evidence to explain why executive stock options are usually granted at‐the‐money.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".