Catch the Heterogeneity: The New Bank-Tailored Integrated Rating
Bibliographic record
Abstract
The purpose of this article is to develop a bank-oriented rating approach, tailored by incorporating the various heterogeneity dimensions characterizing financial institutions, named “Bank-Tailored Integrated Rating” (BTIR). BTIR is able to catch the financial cycle, including the pandemic crisis, and the ongoing change in banking normative from a microeconomic perspective, and it is inherently coherent with the challenging frontier of forecasting tail risk in financial markets in similar ways as in De Nicolò and Lucchetta (2017), although their approach is macroeconomic) since it considers the downside risk in the theoretical framework. The method employed was an innovative integrated rating (IR) statistical and econometrical panel pre-selection analysis that takes into account the characteristics of risk and the greater heterogeneity of the banks. The result is a challenge rating procedure delivering forward-looking preselection requested by the new International Financial Reporting Standard (IFRS-9). The future direction is extremely promising given the increase in idiosyncratic and systemic risks in financial markets.
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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 | 0.000 |
| 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 | 0.000 |
| 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".