The Cost of Intermediary Market Power for Distressed Borrowers
Bibliographic record
Abstract
Distressed firms need urgent financing to preserve operations and avoid inefficient liquidation, but they borrow in concentrated markets shaped by existing-creditor blocking power and a small group of specialized lenders. We show that these borrowers pay exceptionally high loan spreads even after removing compensation for credit risk, liquidity risk, and non-risk loan-making costs. To quantify and decompose lender market power, we develop and estimate a dynamic game-theoretic model of distressed lending with latent demand heterogeneity, endogenous lender participation, creditor blocking power, and tacit collusion sustained by repeated syndication. Using granular facility-level data on debtor-in-possession (DIP) and highly speculative loans, we find that lender market power explains 533 bps of risk-adjusted spreads in the DIP market and 300 bps in the highly speculative loan market, including about 140 bps from tacit collusion in each market. Smaller borrowers are especially vulnerable because their weaker bargaining positions and less elastic demand strengthen specialized lenders' incentives to collude. Lender market power is therefore a major source of financial distress costs, reducing survival-critical liquidity by 16--20\% and thereby worsening asset-value destruction.
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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 | 0.017 |
| 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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".