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Record W4297323550 · doi:10.1287/mnsc.2022.4557

Investor Attention and Option Returns

2022· article· en· W4297323550 on OpenAlexaffabout
Siu Kai Choy, Jason Zhanshun Wei

Bibliographic record

VenueManagement Science · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPortfolioAsideFinancial economicsEconomicsBehavioral economicsMargin (machine learning)Capital asset pricing modelMarket portfolioAsset (computer security)Finance

Abstract

fetched live from OpenAlex

This paper examines the attention effect in the options market. We show that option investors (especially retail investors) buy more calls and puts on both daily winner and loser stocks, and this buying pressure leads to an overvaluation, as shown in subsequent lower hedged returns. The overvaluation is due to a combination of differences of opinion, risk aversion, and margin requirements. The economic magnitude is large. For instance, a zero-financing portfolio involving options on loser stocks renders an alpha of 2.90% per month. Aside from contributing to the broad literature of investor attention versus asset returns, our study also sheds light on an important yet largely neglected topic: the impact of margins on option trading and pricing. This paper was accepted by Lukas Schmid, finance. Funding: Financial support from King’s College London, the University of Toronto, and the Social Sciences and Humanities Research Council of Canada [Grant 435-2018-0514] is gratefully acknowledged. Supplemental Material: Data and the internet appendices are available at https://doi.org/10.1287/mnsc.2022.4557 .

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)0.001
metaresearch head score (Gemma)0.000
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 score0.774
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.205
Teacher spread0.178 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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