Bank Capital, Securitization and Credit Risk: An Empirical Evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is one of the first attempts to conduct an empirical investigation of the relationship between bank capital, securitization and bank risk-taking in a context of the rapid growth in off-balance-sheet activities. The data come from the Canadian financial sector. Evidence from the 1988-1998 period indicates that: (a) securitization has a negative statistical link with both current Tier 1 and Total risk-based capital ratios, and (b) there exists a positive statistical link between securitization and bank risk-taking. Profit-risk measure is more sensitive than loss-risk measure to the variation in securitization activity. These results seem to agree, during the studied period, with models indicating that banks might be induced to shift to more risky assets under the current capital requirements for credit risk because the regulatory capital levels are considered too high.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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