Forecasting NIFTY 50 in Volatile Markets Using RNN–LSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic

Abstract

The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period.