The Circumstances and Legal Consequences of Non‐GAAP Reporting: Evidence from Restatements*
Bibliographic record
Abstract
Abstract Our study examines the circumstances of non‐GAAP financial reporting by 492 U.S. companies that announced restatements from 1995 to 1999. We focus on income statements to analyze the occurrence and resolution of litigation over restatements and explore the role of accounting items in bringing and resolving this litigation. We provide evidence on the pervasiveness of accounting misstatements, describe their nature, and show how, if at all, they affect litigation. We assess the nature of restatements by determining whether regular, recurring earnings from primary operations (core) or other components of earnings (noncore) are misstated, and we assess their pervasiveness by estimating the number of primary accounts misstated. In our sample, companies with core restatements have higher frequencies of intentional misstatements (fraud) and subsequent bankruptcy or delisting. Likewise, these companies have, on average, more material misstatements, more negative security price reactions to restatement announcements, and more negative security price changes over the six months preceding and following restatement announcements. However, controlling for these and other factors, we find a significant association between accounting items and litigation, whether occurrences or resolutions. Specifically, core restatements — driven primarily by misstatements of revenue, a component of core earnings — and more pervasive restatements each play a role, while misstatements of noncore earnings alone do not.
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".