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Record W4391880970 · doi:10.1111/fire.12380

Sentiment and the cross‐section of expected stock returns

2024· article· en· W4391880970 on OpenAlex

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

VenueFinancial Review · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCapital asset pricing modelStock (firearms)Market sentimentStock marketEconomicsEconometricsSentiment analysisFinancial economicsBETA (programming language)Monetary economicsComputer scienceArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

Abstract The asset pricing Literature suggests market sentiment is a state variable. This study shows that market sentiment is positively priced at the cross‐section of stock returns, conditional on aggregate investors’ sentiment. We estimate individual stock sentiment beta and find that, following low‐sentiment periods, stocks in the highest sentiment beta quintile generate a 0.74% higher monthly return than stocks in the lowest sentiment beta quintile. However, this return spread is insignificant following medium‐ or high‐sentiment periods. This finding is consistent with the argument that overpricing following high‐sentiment periods is more prevalent than underpricing following low‐sentiment periods due to short‐sale constraints.

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)0.000
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: Review · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.369

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 studies0.0000.000
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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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