Does Financial Statement Comparability Facilitate <scp>SEC</scp> Oversight?*
Bibliographic record
Abstract
ABSTRACT This study examines the impact of cross‐firm financial statement comparability on regulatory oversight of accounting quality. Required to review each firm's periodic filings at least once every three years, the SEC learns about the degree to which a firm's accounting system is comparable to those of its peers. We posit that the SEC's ex ante knowledge about financial statement comparability, gleaned from prior‐year filing reviews, facilitates its evaluation of firms' accounting quality during the current‐year filing review. Consistent with the notion that comparable accounting systems enhance regulators' ability to identify discretionary accounting deviations, we find that the likelihood of the SEC issuing a comment letter for higher abnormal accruals increases with financial statement comparability. Further analysis reveals that the regulatory benefits from higher financial statement comparability are more salient when the SEC faces higher monitoring constraints in filing reviews. Moreover, our finding shows that comparable accounting numbers across firms help the SEC detect severe accounting violations that necessitate restatements. Overall, we provide novel evidence suggesting that higher financial statement comparability improves the efficacy of the SEC's oversight of accounting quality by reducing the information costs associated with cross‐firm comparisons.
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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 | 0.030 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".