The Effect of Banking Concentration on Non-Performing Loans: The Case of Albania
Bibliographic record
Abstract
Purpose: 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.Originality/Value: The research provides empirical results encouraging further investigations on the subject matter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".