The Price Effects of Event‐Risk Protection: The Results from a Natural Experiment
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Bibliographic record
Abstract
Prior studies conclude that bond prices reflect both an issuer's event risk and a bond's contractual protections from event risk. Therefore, it is assumed that the market requires a higher return for unprotected bonds than for comparable protected bonds. These prior studies, however, struggle with the problem of isolating the pricing effect by controlling for comparability. Issuers will differ from each other on a number of other attributes that could affect their bond prices. The issue of comparability eludes a simple modeling solution given the indefiniteness and multiplicity of variables that could cause the market to distinguish one issuer from another. Recent court decisions regarding the buyout of Bell Canada Enterprises provide a natural experiment for evaluating the pricing effect of event‐risk protection that mitigates this comparability problem. Based on this experiment, we find support for the prior conclusions that an exogenous shift in event‐risk protection is priced by the market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it