The Debt Market Role of Asset Valuation Uncertainty
Bibliographic record
Abstract
ABSTRACT We collect data on ranges of hypothetical asset liquidation values disclosed in U.S. Bankruptcy Court filings. We use this historical information to construct a firm-specific measure, “RecRisk,” which captures asset recovery risk through the uncertainty surrounding asset valuations in liquidation events. We document that higher RecRisk is associated with smaller syndicated loan amounts as a percentage of available collateral, more and tighter performance covenants, and increased loan spreads for borrowers with high credit risk. High RecRisk borrowers also experience lower secondary loan market prices and reduced liquidity for loans with high credit risk. When borrowers become financially distressed, high RecRisk is further associated with declining loan prices and reduced ownership by Collateralized Loan Obligations, the dominant investors in the leveraged loan market. Overall, our results indicate that loan contract terms and prices reflect recovery risk faced by lenders. Data Availability: Data are available from the sources cited in the text. The authors can provide the RecRisk measure at the firm-year level upon request. JEL Classifications: M41; G32; G34; G12; G21; G33.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".