Regression Approach for Modeling COVID-19 Spread and its Impact On Stock Market

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 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.

• The paper combines logistic curve models with Bayesian regression to offer probabilistic predictions of COVID-19 case growth.
• It leverages OLS and Bayesian techniques to compare the pandemic’s impact on stock market dynamics against earlier financial crises.
• The findings underscore the value of uncertainty quantification for enhancing predictive frameworks in both epidemiological and financial risk management.

Overview of the Regression Approach for Modeling COVID-19 Spread and Its Impact on the Stock Market

The paper entitled "Regression Approach for Modeling COVID-19 Spread and Its Impact on Stock Market," authored by Bohdan M. Pavlyshenko, investigates the utility of various regression methodologies for both predicting the spread of COVID-19 and evaluating its effects on financial markets. This research employs a logistic curve model in conjunction with Bayesian regression to provide probabilistic predictions of COVID-19 case counts. In parallel, it explores the differential impacts of the COVID-19 pandemic compared to previous financial crises on stock price dynamics using Ordinary Least Squares (OLS) and Bayesian regression techniques.

Bayesian Regression and COVID-19 Spread

The study leverages a logistic curve model enhanced by Bayesian regression to predict the spread of COVID-19. Bayesian methods offer the advantage of integrating expert opinion through informative priors and accounting comprehensively for uncertainty, which is critical given the initial dearth of historical data on the pandemic. Parameters such as the maximum number of coronavirus cases and the spread rate are estimated using Bayesian inference, supported by Monte Carlo sampling methods like Gibbs and Hamiltonian sampling. These probabilistic models yield posterior distributions for each parameter, enabling nuanced risk and uncertainty assessments. The paper details the application of this methodology across multiple regions, emphasizing the necessity of identifying the peak of daily coronavirus cases to determine the spread's half-time.

Impact Assessment on Stock Markets

The investigation extends to the stock market by assessing how COVID-19 has influenced stock prices, drawing comparisons to the financial crises of 2008 and the market downturn in 2018. By applying Bayesian regression, the research deduces the probability distributions of crisis impact weights on stock indices such as the S&P 500. This approach facilitates an understanding of the pandemic's relative impact vis-à-vis other financial downturns, highlighting the substantial uncertainty associated with the shorter COVID-19 period of analysis. Such uncertainty is quantified through the standard deviation of weight probability density functions, providing valuable insights into investor behavior and aiding in the formulation of risk assessments and portfolio management strategies.

Implications and Future Directions

This paper's methodological contributions underscore the importance of incorporating Bayesian approaches in crisis modeling, especially in contexts characterized by limited historical data and high uncertainty. By simultaneously considering logistic growth models and Bayesian inference, the study enhances predictive analytic frameworks applicable to epidemiological and financial domains. Future research can build on this foundation by expanding the dataset as the pandemic evolves and refining predictive models to better accommodate the unique characteristics of COVID-19 compared to prior crises. The findings have significant implications for both theoretical modeling and practical applications in financial risk management, illustrating the tangible benefits of integrating probabilistic modeling techniques in complex, dynamic environments.