What Do Credit Markets Tell Us About the Speed of Leverage Adjustment?
Bibliographic record
Abstract
This paper proposes a new methodology to infer investors' expectations about the speed of leverage adjustment implicit in the prices of credit instruments. On average, the credit markets imply a fairly rapid annual speed of adjustment of 26% toward a firm's predicted leverage. The speed varies considerably across partitions formed by the differential implications of the pecking order, market timing, and trade-off theories of capital structure. This finding suggests that investors' expectations are formed in accordance with all three theories. We also show that the addition of firm fixed effects in the predicted leverage model gives noisier estimates of investors' expectations of future leverage, and that a firm's initial leverage is a poor estimate of its future leverage. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1871 . This paper was accepted by Jerome Detemple, finance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".