Default Risk Estimation, Bank Credit Risk, and Corporate Governance
Bibliographic record
Abstract
This study explores the relationship between credit risks of banks and the corporate governance structures of these banks from the perspective of creditors. The cumulative default probabilities are estimated for a sample of US commercial and savings banks to measure their risk taking behavior. The results show that one year and five year cumulative default probabilities are time‐varying, with a significant jump observed in the year prior to the financial crisis of 2008–09. Generally speaking, corporate governance structures have a greater impact on US commercial banks than on savings institutions. We provide evidence that, after controlling for firm specific characteristics, commercial banks with larger boards and older CFOs are associated with significantly lower credit risk levels. Lower ownership by institutional investors and more independent boards also have lower credit risk levels, although these effects are somewhat less significant. For all the banks in our sample, large board size, older CFO, and less busy directors are associated with lower credit risk levels. When we restrict the sample to consider the joint effects of the governance variables, the results on board size and busy directors are maintained.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".