Long-term Examination of Bank Crashes Using Panel Logistic Regression: Turkish Banks Failure Case
Why this work is in the frame
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Bibliographic record
Abstract
Crises in the financial sector over the last two decades have shown the importance of early warning systems, especially for bank failures. This study aims to develop an early warning system for Turkish commercial bank failures using panel data from 2002 to 2012. The data was analyzed using pooled logistic regression versus random panel logistic regression. The dependent variable was the bank failure, defined as the return-on assets ratio. Factor analysis was used to construct independent variables of financial ratios. The meaningful factors were found as: Interest income and expenditures, Equity, Other income and expenditures, Balance sheet, Deposit, Due, Asset quality. When the focus is sensitivity, the best prediction performance was obtained using random-effect logistic regression.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 it