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Record W3090298647 · doi:10.1080/23322039.2020.1824362

Does option trading affect idiosyncratic momentum?

2020· article· en· W3090298647 on OpenAlexaff
Songchan Guo, Unyong Pyo

Bibliographic record

VenueCogent Economics & Finance · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsBrock University
Fundersnot available
KeywordsMomentum (technical analysis)EconomicsFinancial economicsSystematic riskEconometrics

Abstract

fetched live from OpenAlex

Portfolios in idiosyncratic momentum are formed on past residuals of the Fama-French three factor model rather than past total returns. This study examines whether the idiosyncratic momentum strategy can sustain excess returns following the emergence of traded options. We compare idiosyncratic momentum returns with traditional momentum returns over different holding periods and over difference in traded options. Our results show that idiosyncratic momentum returns for stocks with options are positive for three, six, and twelve months following the formation date, while traditional momentum returns for those with options are insignificant or even turn to negative. We also find strong evidence that the enhanced information efficiency led by short selling has impacts more on traditional momentum than on idiosyncratic momentum. While traditional momentum disappears on stocks with traded options, idiosyncratic momentum survives and is still anomalous to the efficient market hypothesis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.206
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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