Impacts of COVID-19 local spread and Google search trend on the US stock market.
Physica A
; 589: 126423, 2022 Mar 01.
Article
in English
| MEDLINE | ID: covidwho-1447056
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Physica A
Year:
2022
Document Type:
Article
Affiliation country:
J.physa.2021.126423
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