Impact of Net Stable Funding Ratio Regulations on Net Interest Margin: A Multi-Country Comparative Analysis
Bibliographic record
Abstract
We empirically investigate the impact of liquidity framework proposed under Basel III, namely Net Stable Funding Ratio on Net Interest Margin for 385 banks in SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) along with five developed countries i.e. Australia, Canada, China, Japan and United State over 2003-2013. The NSFR in Basel III liquidity necessity intended to limit funding risk emerging from maturity conflicts between assets and liabilities of overall countries. The results indicate that there is also a gap between developing and developed countries to managing the stability of their funding source as well as liquidity of its assets is a benefit to them and is also transformed into net interest margin by comparison of developing and developed countries. In addition, this study also proved the findings of previous researches in developed countries that are relevant to bank determinants and net interest margin in the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".