Deep learning for stock market prediction
In this talk I share with you two of our recent research projects leveraging unstructured data for predicting stock market movements. In the first project, we use open information extraction techniques to extract event structure out of news titles, representing the results using deep learning, and building neural network models to match them with S&P 500 company returns. In the second project, we extend the efforts further, by using deep learning models to directly learn representations of news abstracts, from which we predict cumulative abnormal returns of S&P companies over 3 days. The results are promising, beating sentiment baselines significantly without using time series data.
Yue Zhang is currently an assistant professor at Singapore University of Technology and Design. Before joining SUTD in July 2012, he worked as a postdoctoral research associate in University of Cambridge, UK. Yue Zhang received his DPhil and MSc degrees from University of Oxford, UK, and his BEng degree from Tsinghua University, China. His research interests include natural language processing, machine learning and artificial Intelligence. He has been working on statistical parsing, parsing, text synthesis, machine translation, sentiment analysis and stock market analysis intensively. Yue Zhang serves as the reviewer for top journals such as Computational Linguistics, Transaction of Association of Computational Linguistics and Journal of Artificial Intelligence Research. He is also PC member for conferences such as ACL, COLING, EMNLP, NAACL, EACL, AAAI and IJCAI. Recently, he was the area chairs of COLING 2014, NAACL 2015, EMNLP 2015, ACL 2017 and EMNLP2017.