Evidence map›Paper›PMID 32942766›Full record

ArticleInternational journal of environmental research and public health2020

Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis.

Ștefan Cristian Gherghina, Daniel Ștefan Armeanu, Camelia Cătălina Joldeș

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
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  9. The Impact of COVID-19 Pandemic on Stock Market: Evidence from Romania.International journal of environmental research and public health · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ștefan Cristian GherghinaDepartment of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania.ORCID 0000-0003-2911-6480
Daniel Ștefan ArmeanuDepartment of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania.
Camelia Cătălina JoldeșDepartment of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths' cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.

Indexed as

Economic DevelopmentBetacoronavirusChinaCommerceCoronavirus InfectionsCOVID-19FranceGermanyHumansItalyPandemicsPneumonia, ViralRomaniaSARS-CoV-2SpainUnited KingdomARDL modelCOVID-19Granger causalitystock market

Identifiers

PMID32942766
PMCPMC7558856

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.