Revenue Manipulation and Restatements by Loss Firms
Bibliographic record
Abstract
SUMMARY: This paper investigates the relation between the extent of a firm’s past and expected future losses or negative cash flows and the ex ante probability that it will manipulate revenues. When a firm has a string of losses or negative cash flows, traditional valuation models do not yield reliable estimates of firm value, and traditional price-earnings ratios are not meaningful. Evidence suggests that market participants tend to value loss firms on the basis of the level and growth in revenues, rather than cash flows and earnings, thereby motivating these firms to overstate revenue. In fact, empirical results indicate that there is a positive relation between the number of years that firms exhibit and/or anticipate losses or negative cash flows and investment in receivables after controlling for credit policy. We further show that the ex ante likelihood that firms manipulate revenue in violation of GAAP is positively associated with the history of past and expected future losses or negative cash flows, as well as with the investment in accounts receivable (adjusted for credit policy). Our results suggest another indicator of manipulation that may be used by auditors and regulators in identifying firms that are more likely to overstate revenues.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".