Analysis of empirical determinants of credit risk in the banking sector of the Republic of Serbia
Bibliographic record
Abstract
The aim of this paper is the detection and analysis of empirical determinants of credit risk in the banking sector of the Republic of Serbia. The paper is based on an analysis of results of the application of the linear regression model, during the period from the third quarter of 2008 to the third quarter of 2014. There are three main findings. Firstly, the higher lending activity of banks contributes to the increasing share of high-risk loans in the total withdrawn loans (delayed effect of 3 years). Secondly, the growth of loans as opposed to deposits contributes to the increased exposure of banks to credit risk. Thirdly, the factors that reduce the exposure of banks to credit risk increase profitability, growth of interest rate spread and real GDP growth. Bearing in mind the overall market conditions and dynamics of the economic recovery of the country, there is a general conclusion based on the results that in the coming period the question of non-performing loans (NPLs) in the Republic of Serbia will present a challenge for both lenders and borrowers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".