论文标题
对Covid-19建模的回归方法及其对股票市场的影响
Regression Approach for Modeling COVID-19 Spread and its Impact On Stock Market
论文作者
论文摘要
该论文研究了对Covid-19建模的不同回归方法及其对股票市场的影响。 Logistic曲线模型与贝叶斯回归一起用于冠状病毒扩散的预测分析。使用回归方法研究了Covid-19的影响，并将其与其他危机影响进行了比较。在实际分析中，重要的是要发现每天的冠状病毒病例的最大病例，这一点意味着估计冠状病毒在该地区的冠状病毒散布的半个时间。获得的结果表明，不同原因的不同危机对同一股票产生不同的影响。分别分析其影响很重要。贝叶斯推论使分析危机影响的不确定性成为可能。
The paper studies different regression approaches for modeling COVID-19 spread and its impact on the stock market. The logistic curve model was used with Bayesian regression for predictive analytics of the coronavirus spread. The impact of COVID-19 was studied using regression approach and compared to other crises influence. In practical analytics, it is important to find the maximum of coronavirus cases per day, this point means the estimated half time of coronavirus spread in the region under investigation. The obtained results show that different crises with different reasons have different impact on the same stocks. It is important to analyze their impact separately. Bayesian inference makes it possible to analyze the uncertainty of crisis impacts.
