Bibliographic record
Abstract
Recent studies and some policy experts have posited that dividends indicate higher‐quality earnings. In this study, we test this conjecture by comparing the dividend policies of firms accused of accounting fraud to those of firms not accused of accounting fraud. Specifically, we examine whether alleged fraud firms are as likely to be dividend payers as non‐fraud firms, and whether managers of dividend‐paying fraud firms increase dividends at the same rate as managers of non‐fraud firms. Our data reveal that dividend paying status is negatively associated with the probability of committing accounting fraud. In addition, we also find that, during the alleged fraud period, the earnings–dividends relation is weaker for the alleged fraud firms relative to firms not accused of fraud. Finally, using propensity score match tests, the data provide evidence that managers of alleged fraud firms increase dividends less often than managers of firms not accused of fraud, consistent with the alleged fraud firms not being able to match the dividend policies of firms not accused of fraud. Overall, our results suggest that dividends, especially dividend increases, are associated with higher earnings 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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".