Effect of Dividend Policies on Firm Value: Evidence from quoted firms in Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the possible effects of dividend policy on firm value. The study covers 10 quoted companies studied for the period of 1995-2015. In so doing, the methodology adopted is the ordinary least square regression analysis for primary data analyses and multiple regression analysis for the secondary data analyses with models MPS (Market Price Per Share) as dependent variable, EPS (Earnings Per Share) and DPS (Dividend Per Share) as independent variables. The co-efficient of determination is R2 to evaluate the data collected from the ten studied companies and the Nigerian stock exchange. The study shows the relevance of dividend, dividend as a signaling model and proves that firm value is greatly influenced by dividend policy as far as public limited companies are concerned.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it