<scp>Risk‐Taking</scp> Incentives and Earnings Management: New Evidence*
Bibliographic record
Abstract
ABSTRACT We reexamine the positive association between stock option vega and earnings management previously documented by Armstrong, Larcker, Ormazabal, and Taylor (2013; henceforth, ALOT). In contrast to ALOT, prior empirical research and practitioner literature emphasizes earnings management's goals of increasing stock price and reducing volatility. Specifically, we assess whether the association is robust to (i) employing discretionary accruals that are less prone to misspecification, (ii) focusing on a more recent time period, and (iii) including additional controls for period‐specific factors. Our main findings are as follows. First, we fail to find a positive association between vega and earnings management after controlling for performance‐related misspecification in discretionary accruals. Second, we find no association between vega and earnings management in a more recent time period, suggesting the results of ALOT may be sensitive to period‐specific factors. Last, the positive association vanishes when we control for year fixed effects, growth opportunities, or monitoring, suggesting the original results of ALOT's research may be sensitive to correlated, omitted variables. Overall, our results question the extent to which vega incentivizes earnings management. Our results may be of interest to boards of directors in designing executive compensation contracts, to regulators in crafting policies that maintain high levels of financial reporting quality, and to researchers seeking to identify settings where earnings management incentives are most salient.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".