A qualitative analysis of bank credit risk disclosure: Evidence from the Canadian and Italian banking sectors
Bibliographic record
Abstract
Abstract This paper aims to analyze bank credit risk disclosure practices in two different geographical contexts characterized by a homogeneous regulatory framework (Canada and Italy), by means of a qualitative content analysis methodology. We employ an innovative approach, which allows us to investigate both the qualitative and quantitative profiles of disclosures. Unlike an entirely quantitative approach, this comprehensive methodology allows us to analyze in depth the disclosure practices of Canadian and Italian banks and detect their commonalities, differences, points of strength, and weaknesses. Our results show that although there are some variations in the disclosure practices of Canadian and Italian banks, the quality of their disclosures is not significantly different. Among the most relevant differences, it emerges that while Italian banks provide more comprehensive disclosures, Canadian banks offer a more holistic view on credit risk. We contribute to the scant literature on credit risk disclosure by identifying room for improvement for both Canadian and Italian banks.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".