Stock Market Prediction During COVID Using Stacked LSTM

Abstract

In the field of computation, the art of predicting the stock market has always been a tough nut to crack for researchers. This is because stock prices are highly influential values. The prices depend on many factors, ranging from physical to physiological, rational and irrational, from geopolitical stability to the sentiments of the investors – all play a crucial role. Investors anticipate market conditions in the future for a successful investment. Hence considering the past stock prices as an embodiment of the factors mentioned above, we propose a stacked long-short-term-memory (LSTM) model to predict the closing index of stock prices during this highly uncertain pandemic period using root mean square error (RSME) as the performance indicator. The model is optimized to improve the prediction accuracy in order to achieve high performance stock forecasting. The dataset considered is from NIFTY 50 scaling across four sectors, namely – auto, bank, healthcare and metal from a duration of 30th January 2020 to 31st March 2022. This paper aims to consider the historical data to analyze future patterns and insights.