Determinants of Dividend Policy: The Case of the Casablanca Stock Exchange
Bibliographic record
Abstract
This article investigates the determinants of dividend policy on the Casablanca stock exchange. The variables tested were based on the main theories of dividend policy, and the fixed effect model was used to test panel data over a period of 16 years from 2003 to 2018. The eight independent variables tested were profitability, firm size, retained earnings, firm age, leverage, growth opportunities, price to earnings (P/E) and a dummy variable introduced for financial companies. To corroborate the results, two proxies were used to test the dependent variable: dividend yield and payout ratio. The results led to the identification of three significant determinants of dividend policy, which are firm age, growth opportunities and firm size. The negative correlation between the variables of firm size and firm age with dividend policy is explained by signaling theory. On the other hand, the negative correlation between growth opportunities and dividend payments is predicted by different theories, such as agency theory, financial flexibility theory and life cycle theory. This study provides insights for investors, analysts and researchers into dividend policy determinants on the Casablanca stock exchange based on firms’ characteristic variables.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".