INVESTORS LIKE FIRMS THAT EXPENSE EMPLOYEE STOCK OPTIONS AND THEY DISLIKE FIRMS THAT FAIL TO EXPENSE
Bibliographic record
Abstract
During 2002 and 2003, 140 publicly traded US firms announced their intention to recognize an accounting expense when stock options are granted to employees. Many similar firms elected not to expense options. We study the stock market’s reaction. There is no evidence whatsoever that expensing options reduces the stock price. To the contrary, around announcement dates, we find significant price increases for firms electing to expense options and significant price declines for industry/size/performance-matched firms that did not announce expensing at the same moment. The average relative change in market values is 3.65% during a 6 day window around the announcement. The magnitude of the market’s reaction to expensing depends on agency costs, the magnitude of option expenses, and financial reporting costs. The market’s reaction does not seem to be affected by contracting costs (e.g. induced by debt covenants), growth opportunities, or potential political repercussions. Moreover, the decision to expense and the magnitude of the market’s reaction are not signals of future operating performance. The market seems to react favorably to transparent reporting while it penalizes firms that give the appearance of having something to hide.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".