Corporate Governance and Dividend Policy : An Empirical Analysis From Borsa Istanbul Corporate Governance Index (XKURY)
Bibliographic record
Abstract
The aim of this research is to analyze the potential relationship between corporate governance and dividend policy. To conduct this research, a sample of 19 corporations from the Borsa Istanbul (BIST) Corporate Governance Index (XKURY), which is composed of listed companies who accomplished a certain level of Corporate Governance Principles over the period of 2007-2014, were selected. OLS (Ordinary Least Squares) panel regression analysis has been performed. The potential relationship between ownership structure and dividend policy has also been analyzed by utilizing the independent variables of ownership concentration, managerial ownership and total foreign ownership. In addition to our independent variables, we also included return on equity (ROE) and firm size to our research in order to increase the explanatory power of our model. This study finds an insignificant relationship between corporate governance and dividend policy. On the other hand, we obtained significant positive relationship between total foreign ownership and dividend policy and significant negative relationships between ownership concentration and dividend policy and managerial ownership and dividend policy. Finally, we obtained significant negative association between return on equity (ROE) and dividend policy and significant positive association between firm size and dividend policy.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".