Stock Prices Prediction with Recurrent Neural Networks

Abstract

Data and Information is the base for making investment choices. Stock market is typically a place where shares of certain companies trying to raise their capital, are traded. With the availability of large amount of data and refinement methods, investors nowadays, are able to make rational investment decisions. Advancement in computational intelligence, use of AI in the form of Neural Networks has created a new basis for predicting stock prices. In this work, we have employed Recurrent Neural Networks to implement time series prediction. The Long Term Short Memory Architecture has been used as the network architecture to perform prediction on Apple Stock Prices. The implementation is done on Keras platform.