Reporting misstatements as revisions: An evaluation of managers' use of materiality discretion
Bibliographic record
Abstract
Abstract In recent years, firms reporting revisions of prior financial statements outnumber those reporting restatements. Misstatements that are material to prior periods are required to be reported as restatements, whereas immaterial errors can be reported as revisions. Based on SEC guidance and widely used materiality benchmarks, I find a significant percentage (29%) of revisions are suspect in that they meet at least one materiality criterion. These suspect revisions are 15% to 29% more likely to be reported when managers have a strong incentive to avoid restatements—when they face the threat of a compensation clawback for reporting a restatement. This result is especially salient when the clawback policy does not require misconduct for recoupment and when the error correction significantly reduces prior period net income. Overall, this evidence suggests that some managers use materiality discretion opportunistically to report misstatements as revisions instead of restatements.
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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 | 0.063 | 0.314 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".