论文标题
量化Covid-19对美国股票市场的影响：多源信息的分析
Quantifying the impact of COVID-19 on the US stock market: An analysis from multi-source information
论文作者
论文摘要
我们开发了一种新型的时间复杂网络方法，以量化美国县一级传播Covid-19的动态。目的是研究当地差异动态，COVID-19案件和死亡的影响以及Google搜索活动对美国股票市场的影响。我们使用常规计量经济学和机器学习（ML）模型。结果表明，在2020年1月至2020年5月之间，Covid-19案件和死亡，其本地差异和Google搜索对异常股票价格产生了影响。此外，还包括有关本地差异的信息可显着提高较长预测视野的股票价格的预测模型的性能。另一方面，尽管一些相关的变量，例如，美国总死亡人数和美国新案件在价格波动，COVID-19案件和死亡，COVID-19和Google搜索活动的当地蔓延以及谷歌搜索活动上表现出因果关系，对价格波动没有影响。
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.
