Impact of Liquidity Coverage Ratio on Performance of Select Indian Banks
Bibliographic record
Abstract
The post-crisis liquidity framework improves banking stability by imposing stricter liquidity requirements. However, consistent bank performance continues to be an essential factor in achieving this goal. This study examines the impact of the liquidity coverage ratio (LCR) on the profitability and non-performing assets (NPAs) of Indian banks using annual data from 2010 to 2019. By applying the dynamic panel data regression technique, we found that compliance with the minimum level of the LCR reduces the net interest margins (NIMs) of banks due to a narrower interest spread, thereby impacting banks profitability. Moreover, the NPAs of the banks tend to grow with an increase in LCR. The study’s findings have far-reaching implications for policymakers. Indian policymakers/regulators need to understand the strategies used by banks to meet liquidity standards and, if necessary, revisit the policy framework to achieve better compliance results. The study’s framework establishes a foundation that can be used for conducting similar research in other complex geographies such as India.
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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 | 0.001 | 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".