Security market regulation: antecedents for capital market confidence in frontier markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the capital market effects of corporate governance (CG) practices of a “comply or explain” environment on stock market liquidity in a frontier market. Design/methodology/approach Using secondary data from Nairobi Securities Exchange, the liquidity position is analyzed using panel data random effects regression against CG guidelines. Findings The results show a negative and significant relationship between CG compliance and stock market liquidity, suggesting that regulated CG practices improve market liquidity in Kenya. The results are remarkably robust to different measures of liquidity and supports agency and signaling theory. Practical implications The authors provide evidence to show that security regulation improves stock market liquidity in a frontier market whose characteristics are thought not to favor regulation. Therefore, regulators and stakeholders could be motivated by the benefits of regulation, and this could lead to renewed effort to improve CG compliance. Originality value The findings show that security market regulation through CG guidelines can improve stock market liquidity in frontier markets. This offers regulators and policymakers a strong motivation to enhance security regulation to improve capital market confidence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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 it