About: Quantifying the impact of Covid-19 on the US stock market: An analysis from multi-source information

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Quantifying the impact of Covid-19 on the US stock market: An analysis from multi-source information
Creator	Das, Kumer Kumer, Asim Toufiqul Hoque, G
source	ArXiv
abstract	We investigate the impact of Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities on the US stock market. We develop a temporal complex network to quantify US county level spread dynamics of Covid-19. We conduct the analysis by using the following sequence of methods: Spearman's rank correlation, Granger causality, Random Forest (RF) model, and EGARCH (1,1) model. The results suggest that Covid-19 cases and deaths, its local spread spreads, and Google searches have impacts on the abnormal stock price between January 2020 to May 2020. However, although a few of Covid-19 variables, e.g., US total deaths and US new cases exhibit causal relationship on price volatility, EGARCH model suggests that Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities do not have impacts on price volatility.
has issue date	2020-08-25(xsd:dateTime)
has license	arxiv
sha1sum (hex)	2e4444a27fd4a95c1fac557519e921c7ef6bc78e
resource representing a document's title	Quantifying the impact of Covid-19 on the US stock market: An analysis from multi-source information
resource representing a document's body	covid:2e4444a27fd4a95c1fac557519e921c7ef6bc78e#body_text
is schema:about of	named entity 'cases' named entity 'Random Forest' named entity 'Covid-19' named entity 'temporal' named entity 'arXiv' »more»

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