Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis

Abstract

This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making.