Forecasting stock market trends using deep learning on financial and textual data

Abstract

Stock market research has increased significantly in recent years. Researchers from both economics and computer science backgrounds are applying novel machine learning techniques to the stock market. In this paper we combine some of the techniques used in both of these fields, namely Technical Analysis and Sentiment Analysis techniques, to show whether or not it is possible to successfully forecast the trend of the stock price and to what extent. Using the four tickers AAPL, GOOG, NVDA and S&P 500 Information Technology, we collected historical financial data and historical textual data and we used each type of data individually and in unison, to display in which case the results were more accurate and more profitable. We describe in detail how we analysed each type of data, how we used it to come up with our results.