Credit Risk Management and US Bank-Holding Companies: An Empirical Investigation
Bibliographic record
Abstract
This paper empirically evaluates the impact of ownership structure on the cost of credit in US banks. It does so by comparing their grouped option-adjusted credit spreads on the outstanding debt issues. As the overall risk of the creditors is reflected in the yield spread of the firms’ outstanding bonds, separately classifying bank-holding companies and stand-alone banks and controlling risk ratings, maturities, and issue sizes enables us to compare the yield spreads tied to ownership structure. After computing the option-adjusted yield spreads of outstanding operating and holding company bonds, we used these values in a master regression equation to test the statistical and economic significance of the binary variable separating the option-adjusted spreads of the two sets. Our work finds that when the S&P ranks and maturities are controlled, US bank-holding companies finances with higher cost of credit compared with stand-alone banks, although holding companies add a layer of liability protection due to the legal separation between the assets and the owners. This suggests that certain characteristics of US bank-holding companies, such as higher leverage and higher systematic risk levels, make them riskier compared with traditional stand-alone banks, offsetting the benefits of forming a holding company.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".