Revealing Corporate Financial Misreporting
Bibliographic record
Abstract
ABSTRACT This study examines how frequently firms restate when they materially misstate their financial statements using stock option backdating as the setting. Stock option backdating provides a unique opportunity to study this issue because it is possible to estimate misstatements with publicly available information to a high level of confidence, and the extensive media coverage of backdating notified boards of directors of the significant risk of misstatement. After identifying firms that materially misstated earnings due to stock option backdating with 95 percent (99 percent) probability, we find that only 11.5 percent (16.1 percent) of these firms subsequently restated. Restating firms are larger, have greater board independence, higher litigation risk and ROA, a lower market‐to‐book ratio, less discretionary accruals, and are more likely to have a CFO that was not involved in backdating. Restating firms are also more likely to disclose other adverse news, face securities litigation, and turn over the CFO than firms that appear to materially backdate but do not restate. Since nearly 9 of 10 firms failed to restate, our results give pause to researchers who use restatements as an indicator of misreporting, and to regulators who levy penalties on those who do self‐report.
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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 | 0.009 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".