Future earnings growth and dividend payout: Evidence from Malaysia
Bibliographic record
Abstract
This study investigates the effect of dividend payout on firms' future earnings growth (FEG) in Malaysia. We use panel data analysis methodology to determine the effect of dividend payout and other control variables on FEG in 1, 2, 3, 4, and 5 years. Our results show that firm size and payout ratio had significant positive relationship on four out of five dynamic models tested. The remaining factors except of debt ratio are significant at least four out of the five years used in dynamic models in this study. We find evidence that Malaysian firms show mean reversion pattern in their earnings; smaller firms would enjoy greater future earnings growth; increased monitoring from creditors leads to better earnings performance; firms with better investment prospect have greater future growth in earnings; and higher investment in assets leads to higher future earnings growth. The findings show that in Malaysia, managers use dividend as a tool to signal their positive private information about the firms' future prospect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".