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Record W4389614100 · doi:10.1086/724421

Loans to Chapter 11 Firms: Contract Design, Repayment Risk, and Pricing

2023· article· en· W4389614100 on OpenAlexaff
B. Espen Eckbo, Kai Li, Wei Wang

Bibliographic record

VenueThe Journal of Law and Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsLoanDebtorBankruptcyDefaultNon-performing loanBusinessNon-conforming loanCross-collateralizationDebtPossession (linguistics)Actuarial scienceCollateralFinanceCreditor

Abstract

fetched live from OpenAlex

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])

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.229
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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