Basel accord capital regulations and financial risk management: Empirical evidence from Pakistan’s financial institutions
Bibliographic record
Abstract
The Pakistani banking sector has shown tremendous growth in the last two decades and witnessed strategic reforms including the implementation of Basel regulations. The objective of this study is to investigate the effect of Basel capital regulations on the various proxies of the financial performance of the Pakistani commercial banks. This study uses three different proxies to assess the effectiveness of the Basel capital regulations on the financial performance of Pakistani commercial banks from 2006 to 2018 and quantifies the effect of different Basel accords on the banking sector of Pakistan using the dynamic panel data estimation technique. In addition, the effect of the Global Financial Crisis (2008) on the financial performance of Pakistani banks has also been evaluated. The results indicate that Basel II and Basel III capital regulations have affected the banks’ profitability differently. Capital regulations of Basel II have increased the performance while capital requirements of Basel III have not affected the financial performance of Pakistani banks, pointing towards the ineffectiveness of Basel III capital regulations. Besides, there has been no change observed in the financial performance of Pakistani banks during the Global Financial Crisis (2008). Overall, the results of the Generalized Method of Moments (GMM) technique show that Basel capital regulations enhance the financial performance of the Pakistani banking sector.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".