Bibliographic record
Abstract
Abstract This paper examines three levels of regulation and compliance imposed by the Federal Reserve, the Securities and Exchange Commission (SEC), and stock exchanges, on risk profile of banking institutions in the United States. SEC‐registered and exchanged‐listed bank holding companies have more concentrated and lower quality loan portfolios; they grow faster through acquisitions and are more likely to execute M&A exit strategy themselves. Also, bank holding companies that commit to higher levels of compliance are better capitalized but have lower return on equity capital. Lower profitability is partially compensated with higher payout ratio, suggesting that regulatory frameworks to some extent ameliorate agency issues. Multi‐tiered regulation of banking institutions yields a separating equilibrium, in which banks choose level of compliance to match their business strategies. Three‐tiered regulatory framework enables market segmentation and matching of capital providers with desired risk profile. Switch to higher levels of compliance is accompanied by risk profile increase without a better risk‐return tradeoff, suggesting managerial agency cost explanation. SEC‐compliant banks are likely to migrate to exchanges or downshift to FDIC disclosure only. Both SEC and exchange‐listed banks are likely to execute growth‐by‐acquisitions but listed bank holding companies are only marginally more likely to default, suggesting more efficient risk‐taking or reliance on government support when market conditions decline. SEC compliance is the most volatile of three disclosure regimes. My study does not conclude that excessive regulation yields negative effects. It does not address assessment of changes in systematic risk and externalities focusing instead on firm‐level effects. Finally, it suggests the need to regulate the market for corporate control, which appears to be one major risk‐taking transmission mechanism in the US private banking market.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".