Determinants of Differentiation of Cost of Risk (CoR) among Polish Banks during COVID-19 Pandemic
Bibliographic record
Abstract
The aim of the paper is to assess the evolution of the cost of credit risk (CoR) of Polish banks as a result of the COVID-19 pandemic in the first three quarters of 2020 as well as its microeconomic determinants. We analysed the structural diversity of the sample of the 13 largest Polish commercial banks in terms of the evolution of their CoR. For this purpose, a diagraphic method of Jan Czekanowski was used. It allowed us to distinguish two groups of banks displaying features characteristic of multi-object structures and three groups consisting of individual banks characterized by atypical CoR developments, significantly different from the structures of objects classified to the first and second groups. In the second part of the research, in order to identify the determinants of the observed trends, a multiple regression model was used in which the explanatory variable was the dynamics of CoR in the first three quarters of 2020. The parameters of return on capital (ROE) at the end of 2019, Non-Performing Loans (NPLs) at the end of 2019 and the dynamics of write-offs in the period 2017–2019 proved to be important explanatory variables.
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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 | 0.002 |
| 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 | 0.000 | 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".