Dividend policy and share price volatility: empirical evidence from Vietnam
Bibliographic record
Abstract
This paper was conducted to examine the relationship between dividend policy and share price volatility of companies listed on Hochiminh Stock Exchange (HOSE) in Vietnam. Data set used in this research was compiled from financial statements of 260 listed firms on HOSE from 2009 to 2018. Three statistical approaches employed to address econometrics issues as well as to improve the accuracy of the regression coefficients like fixed effects model (FEM), random effects model (REM) and general method of movement (GMM). Based on the results from GMM, the association between share price volatility and dividend yield, dividend payout ratio has been explored. The findings show a positive relationship between dividend yield and stock price volatilities and a negative relationship between dividend payout ratio and stock price volatility. In addition, it is found that a firm's growth rate, leverage and earnings volatility had positive influences on share price volatility while firm's size had negative effect on share price volatility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".