Corporate Governance, Ownership Structure and Stock Market Liquidity in Saudi Arabia: A Conceptual Research Framework
Bibliographic record
Abstract
This paper aimed to provide a review of the literature concerning the effects of corporate governance and ownership structure on the devices of market microstructure. It provided a clear overview of empirical archival studies in literature regarding the way corporate governance and ownership structure mechanisms influence market liquidity, with focusing on the Saudi institutional setting. It aimed to pinpoint the differences and similarities in empirical outcomes of studies and determine the areas that call for further exploration. On the basis of the thorough review of literature and the theoretical basis, our study proposed a conceptual research framework. The framework is based on the premise that effective corporate governance can lead to enhanced disclosure quality, which in turn, lead to mitigating the information asymmetry and ultimately, enhanced market liquidity. Although theoretical studies argued the presence of the relationship between corporate governance, ownership and liquidity, we find that outcomes from empirical studies are still mixed. Majority of extant studies, with majority in the context of the U.S. firms, provide ambiguous results, making it challenging to reconcile the differences among them. Our paper provides important guidance for both new and experienced researchers, and it has implications for stock exchange authorities in terms of adopting effective regulatory policies and efficient trading systems to tackle information asymmetry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".