Do Restatements Improve the Persistence of Earnings and Its Components?
Bibliographic record
Abstract
Abstract We examine the persistence of earnings in the pre‐ and postrestatements periods and find that restatements generally improve the persistence of earnings. We also examine how the persistence of earnings is influenced by restatements that are voluntarily initiated by managers (voluntary restatements) and those forced onto firms by outsiders (mandated restatements). Our analysis shows that voluntary restatements are followed by improvement in the persistence of earnings and that mandated restatements are not followed by improvement in earnings persistence. We find results that are consistent with the main finding when we decompose earnings into accruals and free cash flows. We use a difference‐in‐difference research design and confirm that the improvement in the postrestatement persistence of earnings components exceeds that of control firms only for voluntary restatements. Further, we show that our results are robust after controlling for endogeneity of voluntary restatements by including a two‐stage model using the Heckman ( ) method where we first estimate the likelihood of manipulation detection and analyze change in persistence conditional on the first stage analysis. The improvement in earnings persistence around voluntary restatements is not driven by the level of earnings decomposition or a subgroup of voluntary restatements. The results support our hypothesis that voluntary restatements have distinctly different economic consequences from mandated restatements.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".