  Prediction of stock price and stock price movement patterns has always been a
critical area of research. 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 that can enable us to predict stock prices with a very high
level of accuracy. In this paper, we present 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, we use 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 intervals of time during each working day in a
week. Using these extremely granular stock price data, we build 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. We provide detailed results on the forecasting accuracies of all our
proposed models based on their execution time and their root mean square error
(RMSE) values.
