Bibliographic record
Abstract
Purpose The study has three inter-related goals: first, to examine empirically the transfer of wealth from creditors to shareholders that occurs when firms default on debt covenants, second, to develop a framework for measuring the impact of overinvestment by firms in covenant default and, third, to provide an estimate of the cost to creditors of managerial risk-shifting. Design/methodology/approach The paper examines the relationship between market volatility and investment of financially distressed firms in a real options framework. The debt is taken to be exogenously determined. Numerical methods are employed to assess the impact of volatility on investment. Findings The study finds that the risk-shifting motivations of shareholders offset the anticipated negative relationship between investment and volatility as firms violate covenants and become financially distressed. Research limitations/implications The debt violation data in this study are extracted from filings by SEC registrants posted on the EDGAR website. The laws and regulations affecting firms in other jurisdictions may differ, possibly affecting the options open to firms in financial distress. Practical implications Prior research has found that covenant violations rarely lead to accelerated payment or bankruptcy. The present study suggests that monitoring should include investment scrutiny procedures to minimize risk-shifting behavior. Social implications The results offer a cautionary tale for creditors about the negative value created by risk-shifting when firms are in financial distress. They indicate a need for close monitoring of a firm's investment decisions post-violation. Originality/value This study appears to be the first to provide evidence of changes in the relationship between investment and volatility as a firm violates debt covenants and become financially distressed.
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".