Net Stable Funding Ratio (NSFR) and Bank Performance: A Study of the Indian Banks
Bibliographic record
Abstract
The present study examines the impact of the Net Stable Funding Ratio (NSFR) on the performance of Indian commercial banks from 2010 to 2021. The study further investigates how the relationship between liquidity and performance varies under the influence of bank-specific factors such as ownership structure (Promoter vs. Institutional investors). Bank performance is evaluated using a two-fold approach—Profitability measures (NIMs and ROA) and NPA levels of banks. Using the Dynamic panel data regression technique, we find that the relationship between NSFR and NIMs is negative, implying that bank NIMs tend to decline as banks comply with NSFR regulation. Furthermore, the study demonstrates that the inverse relationship between NSFR and bank NIMs becomes more profound when promoters’ stakes are high. Finally, the results highlight that for banks with higher institutional holdings, NPA levels witness an upward trend as the NSFR ratio increases. From a policy perspective, study results will help policymakers understand how changes in liquidity levels impact the wider banking sector and guide them on the overall direction in which to progress with the reforms.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".