Market-to-book ratio and conditional conservatism: firms’ voluntary expensing of employee stock options
Bibliographic record
Abstract
Purpose – This paper aims to examine the relationship between firms’ decisions to expense employee stock options (ESOs) under the voluntary period of Statement of Financial Accounting Standard No. 123 (SFAS 123) and their market-to-book (MTB-1) ratio and conditional conservatism. Conservatism is chosen because, even though expensing of ESOs is a conservative accounting treatment, it is not obvious how the decision would be related to the MTB-1 ratio and conditional conservatism, particularly because the MTB-1 is a long-run measure of conservatism and the decision to voluntarily expense is examined over two years. The setting provides a unique opportunity to assess how two major proxies of conservatism are related. Design/methodology/approach – Using firms listed in the S&P 1500 index, firms that expensed ESOs are compared to firms that disclosed only in the financial statements. The main comparison uses a logistic regression. Findings – During the period when expensing ESOs was voluntary, SFAS 123, the MTB-1 ratio was negatively associated with ESO expense recognition, but conditional conservatism was positively associated with ESO expense recognition. The former is attributed to incentives of conservatism and the latter to the tenet of conservatism tending to reduce income. Research limitations/implications – The findings add to the literature/controversy on the negative relationship between the MTB-1 ratio and conditional conservatism. More important, they support using more than one measure of conservatism in studies that involve accounting conservatism. A limitation is that the findings are specific to voluntary ESO expense recognition. Originality/value – This is the first study to examine how conservatism affects the choice to recognize an item on the financial statements or disclosure only. In addition, the study shows that firms were willing to incur real costs from their financial reporting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".