Cash Flow Restatements: Stock Market Reaction to Overstated versus Understated Restatements
Bibliographic record
Abstract
The Securities and Exchange Commission has become increasingly concerned with the rising number of restatements to statements of cash flows (SCFs). Regulators and practitioners are generally more focused on the overstatement of operating cash flows, while the understatement of operating cash flows is often overlooked but may have the same (or more) negative economic consequences. We examine market reactions to cash flow restatements (CFRs) where firms overstate or understate cash flows from 2000 to 2013. This study finds that 41% of firms overstated operating cash flows, while a surprising 48% understated operating cash flows. While we find that the market does not react to overstated operating cash flows or overstated total cash flows (TCFs), we find a negative response to understated operating cash flows and understated TCFs. Interestingly, the market penalizes these firms more for understating rather than overstating cash flows. There is a CFR disclosure post-announcement drift in abnormal returns that occurs for both understated operating and understated TCFs. We provide evidence that the often-overlooked understated CFRs may have “real” economic consequences and that they should be evaluated further and given the same consideration as overstatements by auditors, regulators, and investors.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".