Credit Risk Determinants in Selected Ethiopian Commercial Banks: A Panel Data Analysis
Bibliographic record
Abstract
The study aims to investigate the factors that contribute to credit risk in Ethiopian commercial banks, considering both macroeconomic and bank-specific factors. The research utilized multiple regression models, a quantitative research approach, and explanatory research designs. A purposive sample technique was used to select 10 commercial banks for the study, and secondary data from audited financial reports were analyzed. The findings of the study reveal a significant positive relationship between credit risk and several variables, including bank size, profitability, efficiency, capital adequacy, and inflation. Conversely, there is an inverse relationship between credit risk and both loan growth and currency rates. Surprisingly, the study found that neither GDP nor interest rates have a significant impact on credit risk. Based on these findings, the study provides recommendations for Ethiopian commercial banks. It suggests maintaining adequate levels of capital, avoiding business in sectors influenced by inflationary pressures, carefully evaluating non-interest income, and adjusting lending policies as necessary. Furthermore, the study advises periodically examining the relationships between GDP growth, interest rates, and credit risk. It also emphasizes the importance of adapting credit risk management practices to changing market conditions and staying vigilant toward emerging trends.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".