Determinants of Nonperforming Loans - The case of Commercial Bank of Ethiopia
Bibliographic record
Abstract
Banks role in the economy of any country is very significant. Lending is risky in that repayment of the principal loan plus interest is not always guaranteed. High levels of Non-performing Loans is as a result of failure to manage loans, this would likely affect the performance of Banks and the country’s economy at large. In view of the critical role banks play in an economy, it is essential to identify problems that affect the performance of these institutions. Nonperforming loan is one of these problems. Therefore, a research on determinants of non-performing loans, the case of Commercial Bank of Ethiopia was conducted. The research seeks to find out the determinants of non-performing loans in the Ethiopian commercial banks. Secondary data that is time series in nature from Commercial Bank of Ethiopia for 40 quarters starting from 2009 quarter one up to 2018 quarter four was used. The data that was collected in the study was quantitative. Autoregressive Distributed Lag (ARDL) model was used to analyze the data and find out whether there exists a relationship between bank specific factors and nonperforming loans in commercial banks in Ethiopia. The study found that Loan to deposit ratio has a positive significant and return on asset has a negative significant long-run relationships with nonperforming loans ratio. However, asset growth rate has positive but statistically insignificant long run relation with nonperforming loans. The study also found that there were no statistically significant short run relationship between the dependent variable of nonperforming loans ratio and the independent variables of loan to deposit ratio, return on asset and asset growth rate at all. This result implies that loan to deposit ratio and return on asset are essential bank’s specific variables that affects the rate of nonperforming loans in the commercial Banks in Ethiopia in the long run. There for banks are recommended to give a serious attention to the health of their asset quality and increase their Marketing and managerial efficiencies to keep their profitability increasing for prevention of loans loss.
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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 | 0.001 |
| 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 | 0.001 |
| 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".