Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models

Abstract

Prediction of stock price and stock price movement patterns has always been a crucial task for researchers. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems can enable us to predict stock prices with a very high level of accuracy. This paper presents a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, this research work has utilized the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes time interval during each working day in a week. Using these extremely granular stock price data, proposed system has built four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. Furthermore, this research work provides the detail results on forecasting accuracies of all the proposed models based on their execution time and their root mean square error (RMSE) values.