Managing Stock Option Expense: The Manipulation of Option‐Pricing Model Assumptions*
Bibliographic record
Abstract
Abstract This paper examines whether firms that voluntarily recognize stock option expense in their financial statements manage that expense downward more than firms that do not recognize the expense by adjusting option‐pricing model assumptions. To examine this issue, I collect option‐pricing model assumptions from fiscal year 2002 for both a sample of firms that voluntarily recognize stock option expense (“recognizing firms”) and a sample of control firms that do not (“disclosing firms”). The empirical results suggest that recognizing firms manage the recognized stock‐based compensation expense reported in their financial statements downward more than do firms that only disclose the expense. Additional analyses reveal that recognizing firms assume a lower level of volatility than disclosing firms in the option‐pricing model calculations; however, I find no evidence that recognizing firms manage the dividend yield and risk‐free interest rate assumptions more than disclosing firms. The Financial Accounting Standards Board (FASB) recently issued Statement of Financial Accounting Standards No. 123(R), which requires the expensing of the fair value of stock options, so these results may be of interest to capital‐market participants and the FASB as they assess the reliability of stock option expense as determined by option‐pricing models.
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 | 0.008 | 0.047 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".