Financial Time Series Stock Price Prediction using Deep Learning

Abstract

Several research studies have been devoted for the last two decades to make estimates on or to forecast stock prices. Accurate stock prediction movement is still an open question for many companies and financial organizations. This article analyses the stock market prediction using deep learning model. The empirical results reveal the superiority of the LSTM recurrent deep learning model over Feed forward neural network and time series ARIMA model in terms four prediction metrics i.e. mean square error, root mean square error, mean average error and mean average percent error.