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.
We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. The objective is to study the effects of the local spread dynamics, COVID-19 cases and death, and Google search activities on the US stock market. We use both conventional econometric and Machine Learning (ML) models . 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. In addition, incorporating information about local spread significantly improves the performance of forecasting models of the abnormal stock prices at longer forecasting horizons. On the other hand, although a few COVID-19 related variables, e.g., US total deaths and US new cases exhibit causal relationships on price volatility, COVID-19 cases and deaths, local spread of COVID-19 , and Google search activities do not have impacts on price volatility.
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