Empirical Determinants of the Non-Performing Loans in the Cypriot Banking System
Bibliographic record
Abstract
High levels of nonperforming loans (NPLs) weigh heavily on private investment and the ability of banks to meet their basic financing role in society. Using linear regression, the paper examines the factors that affect the level of credit risk of the Cypriot commercial banks as expressed by the percentage of non-performing loans. Like similar studies in the international literature, macroeconomic and institutional/microeconomic factors were utilized to construct and test an appropriate predictive model for NPLs. This empirical study spans the start of the global financial crisis in the fourth quarter of 2008 and the resulting recession of the economy in the second quarter of 2014. All macroeconomic indicators used in the creation and testing of five prediction models were found to affect NPLs significantly, with public debt as a percentage of GDP being the most significantfactor.JEL classification numbers: E32, E44, E51, E52, G10, G21Keywords: Banks, nonperforming loans, business fluctuations, financial stability
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".