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Record W4328095895 · doi:10.54691/bcpbm.v39i.4019

Impact of the Coronavirus (COVID-19) on Major Northern Hemisphere Stock Markets

2023· article· en· W4328095895 on OpenAlexaff
Hongyang Sun

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)OutbreakCoronavirusStock (firearms)Stock marketSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Northern Hemisphere2019-20 coronavirus outbreakEconomicsEconomic impact analysisFinancial economicsWestern hemisphereDevelopment economicsBusinessGeographyEconomic geographyVirologyInfectious disease (medical specialty)ClimatologyMedicine

Abstract

fetched live from OpenAlex

As is known, the stock market plays a critical role in the development of economy all over the world even during the epidemic of Coronavirus (COVID-19). The economic risks posed by the pandemic are even more severe by the high degree of interconnectedness within the modern economic system. This paper obtains a large number of theoretical and analytical results on the impact of the outbreak on stock markets in various continents and different countries. Therefore, this paper primarily summarizes and analyzes the general effect of the pandemic on stock markets of the main countries in the Northern Hemisphere. The results of this paper are based on a review of previous literatures. It is shown that the outbreak has a generally non-positive impact on countries economy and finance, albeit a limited one, and it typically has relatively pronounced and severe effects within the initial stage.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.083
GPT teacher head0.314
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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