Bibliographic record
Abstract
Purpose: The purpose of this research is to investigate the informativeness of dividend payouts with respect to the earnings quality.Design/methodology/approach: A sample of 756 firm-year observations listed on the TSX 300 index during 14 years, from 2011 to 2024, was examined.The quality of earnings was estimated by the accrual-based earnings management and the real earnings management.Findings: Results show that dividend-paying Canadian firms and those with high dividend payouts exhibit higher quality in discretionary accruals but poorer quality in abnormal real earnings management.Neverthless, no significant association was found between increases in the dividend payout ratio and earnings quality. Research implications:The results confirm that dividend signaling theory exhibits certain weaknesses.They show that it is limited in its ability to detect sophisticated forms of earnings management, namely real earnings management.Consequently, while dividend distributions reassure the firm's stakeholders about the authenticity of the financial statements and the absence of accounting manipulations, they do not provide absolute assurance against hidden operational manipulations.Originality/value: This study adds to the empirical literature on the informativeness of dividend payouts by examining whether the dividend policy of a firm could be an indicator of its earnings quality.To the best of our knowledge, no previous study has examined the relationship between the dividend payouts and the real earnings management and this is the first study that examines, in the Canadian context, the effect of the dividend payouts on the earnings management 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".