Impact of Ownership Structure and Dividends on Firm Risk and Market Liquidity
Bibliographic record
Abstract
This article examines the impact of ownership structure and dividend payouts on idiosyncratic risk and market liquidity using agency, signaling, and bankruptcy theories from an emerging market perspective. The evidence shows that family firms dominate and have concentrated ownership, and dividend payouts are lower among family firms than their counterparts. The idiosyncratic risk is high among firms with higher family ownership concentration. The family ownership concentration and control positively influence the (firm) risk, dividends positively affect the market liquidity, and risk relates negatively to the market liquidity, supporting the entrenchment of the minority shareholders’ proposition that a significant payout leads to a decrease in information asymmetry and a lower level of risk. The study further supports the proposition that information asymmetries are central to elucidating the dynamics of dividend payouts and their effects on firm risk and market liquidity. The evidence confirms that family ownership concentration affects policy decisions, especially ownership control. The paper’s originality lies in factoring ownership concentration when analyzing how payouts affect firm risk and market liquidity from an emerging markets perspective where controlling shareholders enjoy substantial private benefits, whereas minority shareholders have limited protection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".