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Record W4376171438 · doi:10.54254/2754-1169/3/2022885

How Does the Russia–Ukraine War Affect the Stock Market?--An Empirical Study

2023· article· en· W4376171438 on OpenAlexaboutno aff
Junyan Yu, Guoxuan Chen

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsnot available
Fundersnot available
KeywordsStock marketIndex (typography)Stock (firearms)GeopoliticsQuarter (Canadian coin)Stock market indexSpanish Civil WarEconomicsAffect (linguistics)EconomyBusinessPolitical scienceGeographyLawPsychology

Abstract

fetched live from OpenAlex

The Russia–Ukraine war is one of the most topical affairs of the first quarter of 2022. This study investigates the responses of stock market indexes and commodities markets to the ongoing war between Russia and the Ukraine. Using daily adjusted index prices from 1 April, 2021 to 19 April, 2022, and drawing data regarding Russian exports from the United Nations’ Comtrade database, we observe a negative relationship between the level of reliance on Russian exports and the change in daily (compound) returns. Our results indicate that this relationship was stronger during the two-week period after 22 April, 2022, than when a long-er impact window (22 February – 19 April, 2022) is used. These results are consistent with existing studies on other major geopolitical events.

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 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)0.000
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 score0.123
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.033
GPT teacher head0.332
Teacher spread0.298 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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