Voluntary Disclosure of Disaggregated Balance Sheet and Cash Flow Information around Restatements
Bibliographic record
Abstract
SYNOPSIS We examine changes in voluntary disclosure of balance sheet and cash flow (BS/CF) information in earnings releases after restatement announcements. We consider these disclosures to be particularly relevant in the restatement context since they help investors interpret accruals and assess reporting quality at a time when information uncertainty is high. We find that BS/CF disclosures drop significantly for at least five quarters following restatement announcements, particularly for severe restatements and those restatements more likely to lead to litigation, and less for firms likely to benefit from reputation-repairing activities. We next consider the impact of BS/CF changes on earnings informativeness and find significantly lower post-restatement earnings response coefficients for firms ceasing BS/CF disclosure, but not otherwise. Overall, we argue that litigation concerns provide a strong disincentive for disclosure following restatement announcements. Our findings add to a growing literature on the importance of disaggregated BS/CF information in interpreting accruals.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".