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Record W4411156150 · doi:10.1111/jfir.70001

The predictive power of option prices for stock returns and nonfundamental shocks

2025· article· en· W4411156150 on OpenAlexaff
Asli Eksi, Saurabh Roy

Bibliographic record

VenueThe Journal of Financial Research · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPredictive powerStock (firearms)EconomicsEconometricsFinancial economicsPhysicsHistory

Abstract

fetched live from OpenAlex

Abstract Option prices can predict stock returns. Previous studies suggest that this is because option traders have private information about the fundamentals of the stock. We show another reason is that option traders can anticipate the reversal of nonfundamental shocks to stock price. We use S&P 500 index inclusion as an example of a nonfundamental shock. We document that index inclusion increases the implied volatility skew of added stocks, which then predicts the reversal of the stock price shock in the following months.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.329
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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