Predicting the European stock market during COVID-19: A machine learning approach

Abstract

This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.

Keywords: Coronavirus; Europe; Least Absolute Shrinkage and Selection Operator (LASSO); Stock markets.