The Effect of Banking Concentration on Non-Performing Loans: The Case of Albania
Bibliographic record
Abstract
The article examines the correlation among banking concentration and nonperforming loans using datasets of the Albanian banking sector during 2005-2017. We investigated the non-performing loans affected by market structural variables, banking variables and macroeconomic variables. Approach/Methodology/Design: We test the loan concentration impact on nonperforming loans through linear regression models. Findings: The Albanian banking sector proved the ambiguous results and the sound correlation in long run among concentration and non-performing loans. Outcome confirmed the negative effect of return on assets and the average interest rate for non-performing loans. Meanwhile the total loans, exchange rates and Gross Domestic Product is affected positively by the non-performing loans. Practical Implications: The Albanian banking sector operated to moderate concentration despite banks' mergers recently. It has linked with the increasing non-performing loans ratio past to the last quarter of 2008. We demonstrated the empirical impacts that they ought to be taken into consideration by the banking sector.
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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".