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Record W4206079329 · doi:10.1093/rof/rfac001

How Do Options Add Value? Evidence from the Convertible Bond Market

2022· article· en· W4206079329 on OpenAlexaff
Inmoo Lee, Rex Wang Renjie, Patrick Verwijmeren

Bibliographic record

VenueEuropean Finance Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsConvertible bondIssuerConvertible arbitrageConvertibleBusinessEmbedded optionValuation of optionsValue (mathematics)Stock (firearms)BondFinancial economicsEconomicsFinanceCapital asset pricing modelComputer scienceArbitrage pricing theory

Abstract

fetched live from OpenAlex

Abstract This paper studies the value relevance of the options market by focusing on convertible bond pricing. Pricing convertible bonds requires essentially the same set of information necessary to price options. Using a regression discontinuity design based on minimum stock price requirements for option listings, we find that the availability of stock options helps issuers attract more convertible bond buyers and reduces convertible issuers’ cost of financing. Our results highlight that the availability of individual stock options can add value to security issuers.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.336
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.061
GPT teacher head0.227
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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