Financial Time Series Prediction Under COVID-19 Pandemic Crisis with Long Short-Term Memory (LSTM) Network

In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small on the order of 10-04 for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis