The Simultaneous Effect of Corporate Ownership on Dividends and Capital Structure: Malaysian Evidence
Bibliographic record
Abstract
Most of the researchers analyzed the impact of ownership structure on dividends and capital structure decisions separately. Drawing upon preceding empirical studies, the interdependence between dividends and capital structure raises the potential of the endogeneity bias when interdependent factors are segmented. Therefore, this study examined the effect of corporate ownership structure on capital structure and dividend policy simultaneously. This study utilized 407 Malaysian-listed firms over the period from 2012 to 2016 and adopted simultaneous modelling using 2SLS and 3SLS regression techniques. The results changed markedly in sign, magnitude and significance when moving from OLS estimator to 2SLS and 3SLS estimators. The findings show that both dividend and capital structure policies have positive interdependence. The substantial, family, government and foreign ownership affect dividends positively and capital structure negatively. The study provides various theoretical and practical implications to improve corporate governance and corporate financial policies. This study contributes to the growing literature on corporate finance and corporate ownership. Particularly, it provides simultaneous investigation on the effect of family, government and foreign ownership on dividends and capital structure for Malaysian firms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".