Issuing a Convertible Bond with \nCall-Spread Overlay: Incorporating \nthe Effects of Convertible Arbitrage
Bibliographic record
Abstract
In recent years companies issuing convertible bonds enter into some transactions simul- \ntaneously in order to mitigate some of the negative impacts of issuing convertible bonds \nsuch as the dilution of existing shares. One of the popular concurrent transactions is a \ncall-spread overlay which is intended to reduce the dilution impact. This thesis explores \nthe motivation for using these combined transactions from the perspective of the issuers, \ninvestors, and underwriters. We apply a binomial method to price the convertible bonds \nwith call-spread which are subject to default risk. Based on previous empirical studies \nconvertible bond issuers experience a drop in their stock price due to the activities of \nconvertible bond arbitrageurs when the issuance of convertible bonds is announced. We \npropose a model to estimate the drop in the stock price due to convertible bond arbitrage \nactivities, at the time of planning the issue and designing the security that will be offered. \nWe examine the features of the model with simulated and real-world data.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".