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Record W1918907778 · doi:10.1017/s0022109015000046

Religion and Stock Price Crash Risk

2015· article· en· W1918907778 on OpenAlex
Jeffrey L. Callen, Xiaohua Fang

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 · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReligiosityHoarding (animal behavior)Corporate governanceStock priceBusinessShareholderStock (firearms)CrashMonetary economicsFinancial economicsEconomicsFinanceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract This study examines whether religiosity at the county level is associated with future stock price crash risk. We find robust evidence that firms headquartered in counties with higher levels of religiosity exhibit lower levels of future stock price crash risk. This finding is consistent with the view that religion, as a set of social norms, helps to curb bad-news-hoarding activities by managers. Our evidence further shows that the negative relation between religiosity and future crash risk is stronger for riskier firms and for firms with weaker governance mechanisms measured by shareholder takeover rights and dedicated institutional ownership.

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)0.002
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 score0.116
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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