Evidence map›Paper›PMID 34866767›Full record

ArticlePhysica A2022

Impacts of COVID-19 local spread and Google search trend on the US stock market.

Asim K Dey, G M Toufiqul Hoque, Kumer P Das, Irina Panovska

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physica A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Machine learning sentiment analysis, COVID-19 news and stock market reactions.Research in international business and finance · 2023
    Article
  4. Article
  5. 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.

Asim K DeyDepartment of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.
G M Toufiqul HoqueDepartment of Mathematics, Lamar University, Beaumont, TX 77705, USA.
Kumer P DasThe Office of Vice President for Research, Innovation, and Economic Development, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.
Irina PanovskaSchool of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. We use both conventional econometric and Machine Learning (ML) models that incorporate the local spread dynamics, COVID-19 cases and death, and Google search activities to assess if incorporating information about local spreads improves the predictive accuracy of models for the US stock market. The results suggest that COVID-19 cases and deaths, its local spread, and Google searches have impacts on abnormal stock prices between January 2020 to May 2020. Furthermore, incorporating information about local spread significantly improves the performance of forecasting models of the abnormal stock prices at longer forecasting horizons.

Indexed as

Abnormal priceCausalityCovid-19Stock marketTemporal networkVolatility

Identifiers

PMID34866767
PMCPMC8629345

What OpenQuestion holds

Textmetadata
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.