The Price Effects of Event‐Risk Protection: The Results from a Natural Experiment
Bibliographic record
Abstract
Prior studies have generally agreed that bond prices reflect both the event risk faced by the holders of a particular issuer’s debt securities and the degree of protection from event risk contained within the terms of the bond. Therefore, it has been found that bonds without event risk protection command a premium over comparable bonds with “change in control puts” or similar protective covenants. Although prior studies have reached this common conclusion, each has struggled with a common problem. The decision to include protective covenants is endogenous. Bonds facing the highest event risk are likely to be those for which event risk protection is most highly valued. Therefore a determination of the pricing effect of event risk protection must somehow control for the differing degree of event risk faced by the different bonds included in any given study’s sample. While various proxies for the likelihood of an event, a leveraged buyout or recapitalization, have been developed, they are by their nature imprecise. A recent event, a series of court decisions regarding the proposed leveraged buyout of Bell Canada, the largest LBO transaction announced in history, provides a natural experiment for evaluating the pricing effect of event risk protection in corporate bonds that avoids the endogeniety problem. We report the results of that experiment in this paper.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".