Bibliographic record
Abstract
I build a dynamic capital structure model that allows the firm to renegotiate debt with its creditors. Renegotiations between creditors and equity holders are not always successful as debt forgiveness by some creditors increases the value of other creditors ’ debt claims. Rationally anticipating that the firm’s assets are insufficient to cover the creditors ’ claims under these externalities debtholders refuse to participate in a restructuring and the firm is inefficiently liquidated. The probability of successful renegotiations increases in the value of the firms assets at the time of restructuring, the concentration of the debt structure, and in the costs of liquidating the firm’s assets. Anticipating the outcome of the debt restructuring I solve for the firm’s optimal capital structure in a dynamic tradeoff model. Contrasting the classical tradeoff theory optimal leverage is non-monotonic in bankruptcy costs. When bankruptcy costs are low and debt is held by multiple creditors renegotiations will fail and optimal leverage is decreasing in bankruptcy costs in line with the trade-off theory. High bankruptcy costs increase the probability that renegotiations succeed making debt more attractive resulting in higher optimal leverage. Firms with low bankruptcy costs will optimally have a concentrated debt structure while firms with high bankruptcy costs maximize ex-ante firm value with dispersed debt. Preliminary and incomplete 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".