Restating Internal Control Reports Following Financial Statement Restatements: Determinants and Consequences*
Bibliographic record
Abstract
ABSTRACT After restating their financial statements, companies may voluntarily restate their previously issued internal control (IC) reports for the financial statement (FS) misstatement periods, changing them from “effective” to “ineffective.” This paper examines the determinants and consequences of IC restatements, which have been of concern to financial statement users. When announcing these IC restatements, companies often provide a detailed explanation of the IC problems and a discussion of their plans to remediate these problems. We find that companies with less severe IC problems that can be remediated more quickly are more likely to restate their IC reports. Moreover, these results are driven by companies with a higher need for external financing, suggesting that IC restaters voluntarily restate their IC reports in order to inform investors about their less severe IC material weaknesses and their plans to improve IC quality. Finally, we find that, relative to other FS restatement companies, IC restaters have a lower likelihood of CFO turnover and auditor resignation following the FS restatement. Taken together, our results suggest that voluntary IC restatements are used by IC restaters as a means to separate themselves from other FS restatement companies with more severe control problems and slower remediation plans. Our findings also indicate that studies investigating IC quality and related disclosures need to distinguish between IC restaters and other FS restatement companies because of the different characteristics and consequences between the two groups.
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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 | 0.014 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".