The Materiality of Accounting Errors: Evidence from SEC Comment Letters
Bibliographic record
Abstract
ABSTRACT We gain unique insights into materiality judgments about accounting errors by examining SEC comment letter correspondence. We document that managers typically use multiple quantitative benchmarks in their materiality analyses, with earnings being the most common benchmark. In most of the cases we review, managers deem the error immaterial despite its exceeding the traditional “5 percent of earnings” rule of thumb, often in multiple periods and by a large degree. Instead of attempting to conceal these overages, managers tend to forthrightly acknowledge them, often asserting that the benchmark is abnormally low during the violation period. We find that 17–26 percent of these “low benchmark” assertions are suspect (although none of these “low benchmark” assertions are challenged by the SEC). We also document substantial variation in the extent to which qualitative factors are mentioned as considerations. The SEC generally is deferential toward managers' arguments and judgments but is more likely to challenge immateriality claims when managers admit there are qualitative factors that indicate errors are material.
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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 | 0.116 | 0.690 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".