Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

Abstract

Recently, ability to handle tremendous amounts of information using increased computational capabilities has improved prediction of stock market behavior. Complex machine learning algorithms such as deep learning methods can analyze and detect complex data patterns. The recent prediction models use two types of inputs as (i) numerical information such as historical prices and technical indicators, and (ii) textual information including news contents or headlines. However, the use of textual data involves text representation construction. Traditional methods like word embedding may not be suitable for representing the semantics of financial news due to problems of word sparsity in datasets. In this paper, we aim to improve stock market predictions using a deep learning approach with event embedding vectors extracted from news headlines, historical price data, and a set of technical indicators as input. Our prediction model consists of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) architectures. We use accuracy and annualized return based on trading simulation as performance metrics, and then perform experiments on three datasets obtained from different news sources namely Reuters, Reddit, and Intrinio. Results show that enhancing text representation vectors and considering both numerical and textual information as input to a deep neural network can improve prediction performance.