Does auditor style influence <scp>non‐GAAP</scp> earnings disclosure?
Bibliographic record
Abstract
Abstract Regulators and practitioners have concerns that the lack of standardization in non‐GAAP disclosure can make it challenging for users to process non‐GAAP earnings and use it in decision‐making. We examine whether auditor style extends beyond mandatory disclosures to induce similarity in non‐GAAP earnings disclosures. Specifically, we find that clients audited by the same auditor are more likely to disclose non‐GAAP earnings in a similar manner. We assess disclosure similarity using (1) the decision to disclose non‐GAAP earnings, (2) the disclosure prominence of non‐GAAP earnings in the earnings press release, (3) the discussion of non‐GAAP earnings in the management discussion and analysis of the annual report, (4) the choice to exclude recurring items, and (5) the receipt of SEC comment letters related to non‐GAAP earnings. We find that the association between auditor style and non‐GAAP disclosure is determined by Big 4 accounting firms and clients audited by the same audit office. The results are stronger for larger audit offices and smaller clients. We provide evidence that auditors facilitate non‐GAAP disclosure, which can improve compliance with SEC requirements and increase the standardization of non‐GAAP earnings disclosures. Our results are relevant to current policy discussions regarding auditor involvement in unaudited non‐GAAP earnings reporting.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".