Intervention Analysis and Machine Learning to Evaluate the Impact of COVID-19 on Stock Prices

Abstract

The purpose of this study is to evaluate the impact of the COVID-19 outbreak on composite and individual stock prices in China, the USA, South Korea, and Indonesia by using an intervention model and comparing the results of its predictions with a machine learning model, i.e. neural network (NN) and deep learning neural network (DLNN). This intervention model can be used not only to find out the magnitude of the effect of COVID-19 on the stock price, but also the period of the effect. The composite stock price data used are KS11, 000001.SS, DJI, and JKSE, while the individual stock price data used are TLKM and EXCL. The data used is the daily stock data. The analysis shows that COVID-19 hurts stock prices both in countries that have passed the peak period and are still in the peak period of COVID-19. The impact is not directly after the first case of COVID-19 in each country. The lowest stock price occurred at the end of March 2020 in each country. Different conditions were shown by individual stock prices in the telecommunications sector that showed a positive trend after the end of April 2020. Generally, for all stock prices, intervention models are better for forecasting in-sample data and explanation impact COVID-19 on stock price, whereas machine learning models are better for forecasting out-of-sample data.