  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.
