Combining News Content with Deep Learning Models for Stock Trend Prediction

抄録

Stock markets are usually affected by many factors which makes it very challenging to predict. Since there’s more information available, such as stock prices, company revenues, news reports, and technical indicators, it is common to predict stock trends using machine learning and deep learning models. In this paper, we combine news content with stock prices using fusion models for stock trend prediction. First, we utilize Long Short-Term Memory (LSTM) to learn sequential information from stock prices. Then, we combine Hybrid Attention Networks (HAN) to discover the relative importance of words from news reports to improve stock trend prediction. The experimental results show that the best macro-F1 score of 79.0 % can be achieved when we combine news content and stock prices. As compared to individual models, the performance improvement of up to 40% can be obtained. This shows the potential of our proposed approach.