Legal environment and financial restatements : evidence from Canada and the U.S
Bibliographic record
Abstract
This paper compares the differences in the determinants and consequences of accounting restatements between Canada and the U.S. We have four findings. First, Canadian firms make fewer restatements than their U.S. counterparts. Second, for both Canadian and U.S. firms, the discretionary accruals of restated firm-years are significantly higher than non-restated firm-years, but the difference is more pronounced in Canada than the U.S. Third, institutional investors and Big 4 auditors mitigate the discretionary accruals in restated firm-years relative to non-restated firm-years, but the effects are limited to the U.S. firms only. Fourth, investors react negatively to both Canadian and U.S. restatement announcements. Furthermore, restated firms experience a greater likelihood of auditor and CEO turnover than non-restated firms in both countries. Overall, our findings are consistent with the notion that poorer litigious environment in Canada relative to the U.S. is associated with a fewer detections of accounting misstatements and a poorer earnings quality in those restated firms of Canada. Our findings also lend support to the argument that firm-level governance complements country-level legal enforcement in monitoring corporate financial reporting practice. Keywords: legal environment; restatement; discretionary accruals; market reaction; auditor change; CEO turnover JEL Classifications: G38; M41; M48; K22; K41
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".