COVID-19 and commodity effects monitoring using financial & machine learning models

Abstract

This article focuses on examining the effects of the COVID-19 pandemic and gold prices on the stock market. It primarily analyzes the relationship between COVID-19 cases and stock market prices, along with the impact on various commodity elements such as gold, oil, Chinese RMB, and US Dollar prices. These commodity elements are considered essential indicators of a country's financial health, and the study investigates how the increase in COVID-19 cases affects these financial elements. The research incorporates financial models, machine learning algorithms, and a financial Gaussian mixture model for data analysis and comparison. The findings shed light on the correlation between the virus, trading outcomes, and the importance of Karachi Stock Exchange-100 index data in preventing market crashes. The study also explores the implications of emergencies on the finance sector and provides insights for future financial predictions and the impact of social disasters on the economy.

Keywords

• VAR model
• Machine learning
• Commodity effect
• Global financial analysis
• COVID-19