Short term stock price prediction using deep learning

Abstract

Short — term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short — period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. This paper plans to forecast these short — term prices of stocks. 10 unique stocks recorded on New York Stock Exchange are considered for this review. The review essentially focuses on the prediction of these short — term prices leveraging the power of technical analysis. Technical Analysis guides the framework to understand the patterns from the historical prices fed into it, and attempts to probabilistically forecast the fleeting future prices of the stock under review. The paper discusses about two distinct sorts of Artificial Neural Networks, Feed Forward Neural Networks and Recurrent Neural Networks. The review uncovers that Feed Forwards Multilayer Perceptron perform superior to Long Short-Term Memory, at predicting the short — term prices of a stock.