Incentives and Opportunities to Manage Earnings around Option Grants*
Bibliographic record
Abstract
This study examines discretionary accruals imbedded in quarterly earnings announcements that precede executive stock option grants. Prior research indicates that managers attempt to increase the value of their option pay (by depressing the option's exercise price) through a variety of strategies including timing voluntary disclosures, influencing option grant dates, or managing accruals. This study extends the research by jointly examining managerial incentives and opportunities to pursue an accruals-based strategy. We find evidence that discretionary accruals are lower when option pay is high and when concurrent firm performance is poor (incentive factors), but only when firms issue grants following earnings announcements relatively infrequently (opportunity factor). For firms that follow a predictable grant schedule, managers behave as if they believe that investors will discount earnings-based signals preceding the grant. Our results suggest that the decision to pursue an option-related strategy is influenced by economic tradeoffs. From a policy perspective, our results have relevance for the ongoing debate over option compensation practices, appropriate disclosure to investors, and the quality of corporate earnings.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".