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

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.

Fig. 1

All 3-node and 4-node connected network motifs.

Fig. 2

A changing network shown over three time steps.

Fig. 3

Local spread of COVID-19. (a) Shows US counties with 5 or more Coronavirus cases ($\gamma =5$) on April 11, 2020. (b) Represents the corresponding spread network ($\lambda =5$, $\delta =100$) with 514 nodes and 3831 edges.

Fig. 4

Time plots of abnormal price ($AP$) and volatility ($Vol$) from January 13 2020 to May 29 2020.

Fig. 5

(Spearman) Correlations between Covid-19 and abnormal S&P 500. Correlations of eight COVID-19 variables in each lags are summarized in a box plot.

Fig. 6

Abnormal price prediction for March 2020 to May 2020 with 1, and 2 day horizons.

Fig. 7

Time plots of $AP$ and $Vol$ from January 13 2020 to May 29 2020.