The Cost of Investor Protection: Bank Loan Contracting During SEC Investigations
Bibliographic record
Abstract
ABSTRACT In examining the loan contracting implications of SEC investigations, we document that banks charge higher loan spreads when borrowers are under investigation, with the rise in interest rates varying predictably with lender characteristics. Further, our evidence implies that the debt pricing impact of SEC investigations is amplified for borrowers suffering worse credit quality and information asymmetry as well as those relying more on bank loans. These findings suggest that banks perceive increased risk for borrowers under SEC scrutiny while also leveraging their knowledge of the investigations to extract rents. Supplemental analyses reveal tighter nonspread loan terms and a higher likelihood of amending existing loan contracts during SEC investigations. Additionally, the tightening of loan terms reverses for investigations that conclude without enforcement actions. Overall, our research identifies an economic cost of SEC investigations and alerts regulators to these costs when deciding whether to launch an investigation. Data Availability: All data used are available from the sources indicated in the paper. JEL Classifications: M41; D82; G21; K22.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".