Can Employee Stock Options Contribute to Less Risk‐Taking?
Bibliographic record
Abstract
ABSTRACT The executive compensation literature presumes that shareholders offer risk‐averse managers stock options to entice them to take on more risk, resulting in riskier investment decisions and thus a greater return on investment. However, recent empirical work challenges this assumption, and theoretical research even argues that high levels of option‐based compensation for generally under‐diversified managers may actually lead to greater risk aversion. We evaluate the incentive structure of employee stock options by examining the level of R&D investment and the return on that investment conditional on the portfolio “vega,” which captures the sensitivity of option value to stock price volatility. Our results suggest that both investment in R&D and the return on R&D, as measured by future earnings and patent awards, varies concavely with vega. That is, low to moderate levels of vega correspond to increasing investment in and returns on R&D, consistent with vega inducing more profitable investments, but marginal returns decline as vega increases. Collectively, these results, bolstered by several supplemental analyses, suggest that this surprising relation between vega and risky investment is driven by greater risk aversion at higher levels of vega. Overall, our results imply that employee stock options may not always align the incentives of managers and shareholders.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".