Loans to Chapter 11 Firms: Contract Design, Repayment Risk, and Pricing
Bibliographic record
Abstract
With a hand-collected set of 545 debtor-in-possession (DIP) loan facilities for 2002–19, we show that these short-term loans are highly overcollateralized and contain a comprehensive set of restrictive covenants, mandatory prepayments, and restructuring milestones—all of which help produce a repayment risk near 0. Nevertheless, the all-in spread drawn averages 658 basis points—almost five times the average spread on matched investment-grade loans and nearly double the average spread on matched leveraged loans issued by highly risky firms outside of bankruptcy. Textual analysis of court documents shows lack of outside lenders’ participation in the loan solicitation process, but spreads are somewhat lower when outside interest is high. We discuss alternative interpretations of the high DIP loan spreads, ranging from monitoring-cost compensation to rent extraction as DIP loan providers with strong bargaining power share in the preservation of going-concern value helped by the last-resort loan. “The question this debtor had to ask, is: Is this [debtor-in-possession loan] better than a liquidation?” (Mark Ellenberg, in Kary [2009]) “In the Great Recession default cycle, no [debtor-in-possession loans] defaulted.” (David Keisman, in Adler [2016])
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".