Bibliographic record
Abstract
Purpose – This paper aims to examine the nexus between hedging, which reduces the volatility of corporate assets, and the anomaly of debt overhang, whereby corporate management is motivated to reject positive net present value (NPV) projects. The question of whether hedging ameliorates or aggravates debt overhang is addressed. Design/methodology/approach – The Black–Scholes isomorphism between common shares and call options is exploited to determine the allocation of a project’s NPV between debt- and stock-holders. The effect of hedging on this NPV-partitioning is then gauged to determine the resulting likelihood of debt overhang. Findings – If the volatility of corporate assets is below a critical maximum, hedging ameliorates debt overhang consistent with extant theoretical research. However, above that critical value of volatility, hedging aggravates debt overhang. Originality/value – The novel result of this note, namely, hedging may exacerbate debt overhang, is demonstrated both analytically and intuitively. The latter is explained by allusion to a second agency-theoretic conflict between debt- versus stock-holders, namely, risk shifting. The disparate effects of hedging on debt overhang imply a non-monotonic relationship between metrics for these two variables, which is a phenomenon that extant empirical studies have failed to take into account.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".