MétaCan
Menu
Back to cohort
Record W3121251767 · doi:10.1017/s002210902000085x

Moment Risk Premia and Stock Return Predictability

2020· article· en· W3121251767 on OpenAlex
Zhenzhen Fan, Xiao Xiao, Hao Zhou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Financial and Quantitative Analysis · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPredictabilityRisk premiumEconometricsEconomicsMoment (physics)Capital asset pricing modelStock (firearms)Volatility risk premiumFinancial economicsMathematicsVolatility (finance)StatisticsImplied volatilityPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract We study the predictive power of option-implied moment risk premia embedded in the conventional variance risk premium. We find that although the second-moment risk premium predicts market returns in short horizons with positive coefficients, the third-moment (fourth-moment) risk premium predicts market returns in medium horizons with negative (positive) coefficients. Combining the higher-moment risk premia with the second-moment risk premium improves the stock return predictability over multiple horizons, both in sample and out of sample. The finding is economically significant in an asset-allocation exercise and survives a series of robustness checks.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.110
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.245
Teacher spread0.203 · 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