Comparative Analysis of Earnings Rate, Retention Rate, and Dividend Yield as Determinants of Capital Growth
Bibliographic record
Abstract
This work compares the effect of earnings rate, retention rate, and dividend yield on the capital growth of Nigeria’s emerging and large companies. The study begins with a detailed characterization of emerging companies in the Nigerian context, then, an extensive review of both theoretical and empirical studies surrounding dividend policy. Thereafter, three contextual arguments or positions were identified to provide the framework for divergence or convergence with the outcome of this work. The variables were also specified as follows: growth in equity capital (GEC), earnings rate (ER), retention rate (RR), and dividend yield (DY). Two procedures were adopted: z-test of two means, aimed at providing additional basis for predicting the outcome of our estimates. To do this, ten years time series data were collected from the annual financial reports of ten emerging and twenty large companies quoted on the Nigeria Stock Exchange, thus, pooling 100 data items for emerging companies and 200 data items for large companies. Then, the means of the respective variables for each size of company were compared, particularly, to establish any significant growth pattern explained later in the second procedure. The second procedure involves specification of panel data regression model with random effect, one for each of the two classes of companies. The results show statistically significant and consistent differences between the two categories of companies in the means of GEC and ER. Secondly, for the emerging companies, only RR significantly contributed to changes in GEC. Thirdly, for the large companies, none of the independent variables significantly explained changes in GEC. It was concluded that the study did not demonstrate any pattern in dividend/retention behavior in emerging or large companies that is consistent with any of dividend policy positions, leading us to conclude that no single dividend policy magic wand is a recipe for all managers at all times.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".