Factors Influencing the Lending Behavior of Commercial Banks in Nepal
Bibliographic record
Abstract
This study examines the major factors affecting the lending behavior of commercial banks in Nepal. It is based on analysis of the data from five investing banks encompassing the ten-year period from 2014/15 to 2023/24. The research employed both descriptive and causal-comparative research designs. The study is based on the secondary data extracted from annual reports and financial statements. Statistical analysis was carried out using SPSS version 27. The sampled banks are Prabhu Bank, Nepal Investment Mega Bank, NMB Bank, NIC Asia Bank, and Kumari Bank Limited. The analysis is concerned with how several independent variables Cash Reserve Ratio (CRR), Capital Adequacy Ratio (CAR), Interest Rate Spread (IRS), Total Deposits (TD), and Inflation Rate (INF) are related to the dependent variable, Loans and Advances (LA). The results disclose a moderate negative association between CRR and LA, implythat higher reserves reduce investing activity. In counterpoint, CAR shows a strong positive connection with investing, involving that well-capitalized banks are more capable of amplifying credit. IRS displays only a poor relationship, while TD has a strong positive influence, meaning banks with larger deposit bases attend to issue more loans. Similarly, inflation (INF) is moderately negatively related to investing, indicating that higher inflation can diminish credit growth. Overall, the findings show that CAR significantly enhances investing, whereas IRS has a negative effect. Total deposits have a strong positive impact, while CRR and inflation are not statistically significant predictors. The study concludes that CRR, IRS, TD, and INF play major roles in creating the investment behavior of Nepalese commercial banks.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".