Evidence map›Paper›PMID 37420440›Full record

ArticleEntropy (Basel, Switzerland)2022

Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic.

Semei Coronado, Jose N Martinez, Victor Gualajara, Omar Rojas

Open access · goldAbstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.9field-weighted citation impact, top 10% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Bibliometric Analysis of Granger Causality Studies.Entropy (Basel, Switzerland) · 2023
    Article
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

4 authors at 4 institutions in 3 countries.

Semei CoronadoPalomar College, San Marcos, CA 92069, USA.ORCID 0000-0002-7945-7155
Jose N MartinezAccounting, Finance and Economics Department, California State University, Dominguez Hills, Carson, CA 90747, USA.
Victor GualajaraDepartamento de Métodos Cuantitativos, Centro Universitario de Ciencias Económico Administrativas, Universidad de Guadalajara, Guadalajara 44100, Jalisco, Mexico.ORCID 0000-0002-5405-3779
Omar RojasFacultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Zapopan 45010, Jalisco, Mexico.ORCID 0000-0002-0681-3833
Airlangga University · IDCalifornia State University, Dominguez Hills · USPalomar College · USUniversidad de Guadalajara · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets' volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America.

Indexed as

Granger causalityMODWTtransfer entropy

Identifiers

PMID37420440
PMCPMC9601440
OpenAlexW4303699729

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.