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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 and Irina Panovska

Physica A: Statistical Mechanics and its Applications, 2022, vol. 589, issue C

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: Covid-19; Stock market; Temporal network; Abnormal price; Volatility; Causality (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121006968

DOI: 10.1016/j.physa.2021.126423

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