Material weakness disclosures and restatement announcements: The joint and order effects
Bibliographic record
Abstract
Abstract We examine differences in stock price, option volatility, and litigation reactions to restatement announcements that are associated with a material weakness (MW) disclosure. Contrasted with restatements that are not associated with any MW disclosure, our analyses reveal that firms that announce both a restatement and an associated MW experience significantly more negative market returns, greater implied volatility, and higher likelihood of class action lawsuits. Separating the restatements into timely reporters , where the MW precedes the restatement, and non‐timely reporters , where the MW is concurrent with or follows the restatement, we find that timely reporters experience more negative returns at the time of the restatement, relative to non‐timely reporters, suggesting that investors perceive the early MW disclosure to signal more pervasive control‐related problems. Interestingly, we find that timely and non‐timely reporters are equally likely to be sued, consistent with the argument that wrongdoing (through either a timely or non‐timely MW disclosure) provides stronger grounds for establishing scienter. However, timely reporters appear to secure more favorable litigation outcomes: they face higher likelihood of lawsuit dismissals and pay much lower settlements, compared to non‐timely reporters. Overall, our evidence provides new insights into how market participants incorporate information about internal control weaknesses into their perceptions regarding the economic implications of financial restatements, and financial reporting quality.
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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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".