MODELING COVID-19 SPREAD AND ITS IMPACT ON STOCK MARKET USING DIFFERENT TYPES OF DATA

Bohdan Pavlyshenko

Abstract


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 COVID-19 spread. Bayesian approach makes it possible to use informative prior distributions formed by experts that allows considering the results as a compromise between historical data and expert opinion. 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. The impact of COVID-19 on the stock market using time series of visits on Wikipedia pages related to coronavirus was studied. Regression approach for modeling COVID-19 crises and other crises impact on stock market were investigated. The analysis of semantic structure of tweets related to coronavirus using graph theory and frequent itemsets and association rules theory was carried out.

Keywords: coronavirus, COVID-19, Bayesian regression, stock market, predictive analytics.


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References


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DOI: http://dx.doi.org/10.30970/eli.14.1

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