A Deep Learning Model for Stock Return Prediction Using Social Media Data
PI: Dr. Michael Chau, HKU
Co-I: Dr. Wenwen Li, Fudan University
The predictability of stock market has been studied for a long time in financial economics. To some degree, stock market is predictable based on its historical behavior and other market information. Studies have shown that information from social media can be used to predict stock price movement to some extent. In this project, we propose a novel deep learning model that aims to improve of prediction of stock price movement using social media data compared with existing models. Our proposed model has three main components, namely (1) bidirectional long short-term memory network, (2) attention mechanism, and (3) sample weight adjustment based on social media engagement data. To our best knowledge, we are the first to propose using social media engagement data to adjust sample weights in deep learning models. We will evaluate the performance of the proposed model using real world social media and financial data in the U.S. stock market. We will also propose and evaluate trading strategies based on our model. The results of our findings and applications will be useful for both researchers and practitioners.