Does Dividend Policy Affect Firm Earnings? Empirical Evidence from Nigeria
Bibliographic record
Abstract
This study examines the effect of dividend policy on firm’s returns using data of seventeen (17) manufacturing firms listed on the Nigerian stock Exchange. Employing descriptive statistics, correlation analysis and panel regression technique, where the fixed effect regression was adopted, the findings reveal that current dividend payout, growth opportunity of firms and dividend per share have positive and significant effect on earnings per share, with that of growth having an overwhelming influence. Current dividend payout and dividend per share are both significant at the 5percent level. One lagged dividend payout (previous dividend payout), cash flow and leverage have positive but not significant influence on EPS, while the impact of size is negative and not significant. The study recommends the implementation of effective and result-oriented dividend policies by financial managers of firms as well as sound investment, effective regulatory and supervisory framework by capital market regulators in order to enhance firms’ earnings and performance in Nigeria.
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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 | 0.002 | 0.013 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".