Do Covenants of Bonds Outstanding Affect the Choice of Covenants of New Issues? Evidence from the U.S. Corporate Bonds
Bibliographic record
Abstract
This paper investigates the relation between debt covenants of a firm’s bonds outstanding and covenants of its newly issued bonds. On the one hand, since covenants are priced and costly, newly issued bonds may not include covenants that have been used in bonds outstanding, suggesting a negative relation between covenants of bonds outstanding and those of new issues. On the other hand, since firms tend to use boilerplate language in debt indentures, similar covenants of bonds outstanding are likely to be used repeatedly in the contracts of new issues, indicating a positive relation. Based on the U.S. public corporate bonds data from 1990 to 2014, this paper provides empirical evidence that covenants of a firm’s new issues are positively related to covenants of its bonds outstanding, suggesting boilerplate language is widely used in corporate bond contracts. Results also show that use of boilerplate language is significantly related to issuers’ financial condition and economic cycle. Issuers with stable financial condition, as measured by commercial paper ratings, tend to use boilerplate language more frequently. And during the Dot-Com bubble period, boilerplate language is used more prevalently than during the financial crisis period.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".