TITLE:
Using Machine Learning Models to Predict Economic Recession Caused by COVID-19
AUTHORS:
Wenxi Xiu
KEYWORDS:
COVID-19, Economic Recession, Machine Learning Models
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.13 No.1,
February
8,
2024
ABSTRACT: Beginning in early 2020, there was a severe global epidemic, which was
named COVID-19. Its outbreak has severely disrupted the global economy. This
paper attempts to predict the economic fluctuations caused by COVID-19. I
collect data about unemployment rate, inflation rate, Producer Price Indices,
house price, population, stock market value, treasury bill rate, corporate and
government bond yields, net export, savings rate, average price-earnings ratio
of the stock market, total credit, loan rate, personal and government
consumption expenditures and investment, and so forth from the United States
spanning from 1983:1 to 2023:2 and make prediction of the Gross Domestic
Product (GDP) using a VAR model, five machine learning models, including
gradient boosted regression model, random forest regression model, K-nearest
neighbors regression model, linear regression model and support vector machine
regression model, along with a deep learning model which is Long Short Term
Memory model. The predictive effectiveness of those models is measured by mean
absolute error and the results shows that the gradient boosted regression model
after hyperparameter optimization, whose error is minimized to about 75, is the
best at predicting the US economy. This model also exhibit superior performance
in predicting the Italian economy, which to some extent shows its widespread
usage.