Corporate Governance Mechanisms and Stock Price Volatility: The Mediating Role of Dividend Policy: Evidence From Emerging Markets
Bibliographic record
Abstract
This study is novel research aims to investigate the impact of corporate governance mechanisms on stock price volatility in the Egyptian stock exchange through the role of the dividend policy as a mediating variable. The study examines certain corporate governance mechanisms such as: board independence, board size, number of board meetings, CEO duality, and audit committee. The study used quarterly data on EGX 30 for the period 2012-2021. It was based on a sample of 25 stocks traded in the Egyptian stock exchange, not including the stocks of the financial sector. Leverage and size used as a controllable variables. Results revealed that corporate governance has an impact on stock price volatility and dividend policy. Board independence, board size, board meetings and audit committee have a significant negative impact on stock price volatility of listed Egyptian companies. A good corporate governance practices is a good sign to reduce the fluctuations in stock prices. However, CEO duality and size has no impact on stock price volatility. Leverage has a positive significant impact on stock price volatility. Accordingly, CEO duality and leverage is a sign of poor corporate governance. Egyptian investors need to consider the issue of corporate governance practices alongside with the risk associated with expected return when taking investment decisions. A new dimension which added to investigate the interrelationship between corporate governance mechanisms and stock price volatility is dividend policy as mediator factor. Dividend policy is the outcome of good corporate governance practices and has an impact on stock price volatility. Corporate governance is important for stimulating the dividend payments which in turn affect stock 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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".