Hybrid Model of Multifactor Analysis with RNN-LSTM to Predict Stock Price

Abstract

Prediction on the stock market is one of the most difficult tasks to do in real life. There are so many aspects on which the stock market depends—physical factors versus psychological, rational, and irrational behavior, etc. Proposed research work consists of different aspects on which stock markets are based on. It consists of three models to forecast a stock price on State Bank of India (SBI) stock data. In the current research, we proposed a hybrid model followed by recurrent neural network-long short-term memory (RNN-LSTM) to predict a next-day closing price of SBI. A hybrid model is the combination of two different aspects related to the prediction of stock price. The first technique used other companies’ stock data to predict the target company’s next-day closing price. Other companies lie in the same sector so that they are correlated to each other. For training and testing, we have used multilayer perceptron (MLP) regression model. It is a neural network model in deep learning. The second technique is to predict the stock price of an SBI company using historical data of the target company followed by the auto-regressive integrated moving average—gated recurrent unit (ARIMA-GRU) model. ARIMA-GRU model is a combined model which gives better accuracy for predicting stock price data. In the hybrid model, we take the result of both the models as an input. This paper aims to compare the proposed hybrid model with other two single-aspect models on which stock price depends and proves in terms of accuracy that the hybrid model of all aspects gives better results in comparison to single-aspect models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.