The Effect of Using a Lattice Model to Estimate Reported Option Values
Bibliographic record
Abstract
Abstract Statement of Financial Accounting Standards 123R suggests that lattice valuation models may improve the estimates of reported employee stock option values relative to the more commonly used Black–Scholes (BS) model. However, lattice model critics have expressed concerns that managers may use lattice models' flexibility to opportunistically understate option values. In this study, we investigate a sample of firms that recently adopted a lattice model to value employee stock options to provide evidence on this issue by identifying the determinants of lattice model adoption and examining the effect of lattice model use on reported option values. We report three main results. First, we find that firms are more likely to adopt a lattice model when it is more likely to produce lower values than the BS model and when managers have incentives to lower stock option expense. Second, we find that firms adopting a lattice model increase understatement of reported option values more than firms that continue to use the BS model and that the incremental understatement is due to use of the lattice model. Third, we conduct several tests to examine whether the valuation effect of lattice model use is consistent with efforts to correct for documented shortcomings in the BS model and find no evidence that this is the case. Taken together, the evidence in this study suggests that firms adopt and implement lattice models primarily to lower reported option values.
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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 | 0.021 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".