Country Governance, Market Concentration and Financial Market Dynamics for Banks Stability in Pakistan
Bibliographic record
Abstract
Considering the country governance, market concentration and financial market dynamics are key explanatory indicators, this study has examined the stability trends in commercial banks of Pakistan. Overall sample of 28 banks is considered, adding both conventional and Islamic banks into consideration for the panel regression models like fixed effect and random effect. Findings for overall sample indicates that both stability measures in the form of z-score ROA and ROE are significantly and negatively affected by poor control over corruption, regulatory quality, market concentration, financial market development and increasing non-performing loans. For conventional banking, key determinants of financial stability are control over corruption, political instability, market structure and credit risk. For Islamic banking firms, corruption and government effectiveness, capital adequacy ratio, market structure and financial market development are significant determinants, affecting Z measures of stability. However, through lending interest rate, we do not find any significant relationship with both stability measures. Study findings are very useful for country officials, risk officers, and other stakeholders in financial markets who want to explore the relationship between country governance and financial market dynamics in the economy of Pakistan. In addition, study has experienced various limitations like non-consideration of bank-based and macroeconomic risk factors, international trends in banking and their influence on domestic banks of Pakistan, which could be reconsidered in coming research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".