Firm Accrual Quality Following Restatements: A Signaling View
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We consider whether and how firms improve their financial reporting credibility following a restatement by comparing two alternative views. The compliance view predicts that firms simply correct errors to comply with regulations; the signaling view predicts that improvements are broader to allow firms to signal higher reporting quality and thereby reduce information uncertainty. We find that accrual quality improves significantly following the restatement and that this improvement is observed for both earnings and non‐earnings error restatements. We also find that the extent of real earnings’ management decreases significantly. Further, we find that improvements in accrual quality are higher for firms with CEO turnover and higher incentives to improve, but lower for firms switching to an auditor of lower quality. Collectively, our findings suggest that firms signal improved reporting credibility following a restatement through higher accruals quality and lower real earnings management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it