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

doi:10.1016/j.physa.2021.126423

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

Physica A. 2022 Mar 1:589:126423. doi: 10.1016/j.physa.2021.126423. Epub 2021 Sep 30.

Authors

Asim K Dey^{1

2}, G M Toufiqul Hoque³, Kumer P Das⁴, Irina Panovska⁵

Affiliations

¹ Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.
² Department of Electrical and & Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
³ Department of Mathematics, Lamar University, Beaumont, TX 77705, USA.
⁴ The Office of Vice President for Research, Innovation, and Economic Development, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.
⁵ School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.

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.

Keywords: Abnormal price; Causality; Covid-19; Stock market; Temporal network; Volatility.