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Record W4414307316 · doi:10.1016/j.jeca.2025.e00437

Stock market sensitivities to European monetary policy

2025· article· en· W4414307316 on OpenAlexvenueno aff
Juan M. Nave, Javier Ruiz

Bibliographic record

VenueThe Journal of Economic Asymmetries · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónUniversidad de Castilla-La ManchaFederación Española de Enfermedades RarasEuropean CommissionMinisterio de Ciencia, Innovación y Universidades
KeywordsMonetary policyStock (firearms)Proxy (statistics)Stock marketFinancial marketTransmission channel

Abstract

fetched live from OpenAlex

In this paper, we analyze the transmission of common monetary policy shocks in the euro area to its main stock markets. To this end, we implement SVAR models where the ECB monetary policy is modeled as a function of euro area aggregate economic factors and global economic conditions, which we proxy using US economic variables. Our results suggest, in line with economic theory, that the transmission of monetary policy to euro area stock markets exhibits heterogeneity driven by differences in the characteristics of listed firms. To investigate the sources of this heterogeneity, we test the hypothesis that the sectoral composition of financial markets explains the variation in responses. However, our findings provide evidence against this hypothesis – differences in the reaction of stock markets to monetary policy shocks are not fully accounted for by their sectoral composition.

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)0.002
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.239
Teacher spread0.205 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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