报告题目：Deep learning for stock market prediction
报告日期及时间：2015年11月2日周一14：30
报告地点： B403
报告人： Yue Zhang 助理教授
报告人单位：Singapore University of Technology and Design
报告人简介：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 CLING 2014, NAACL 2015 and EMNLP 2015.
报告摘要： It has been shown that news events influ- ence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of struc- tured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement pre- diction, extracting structured events from large-scale public news without manual efforts.
Both linear and nonlinear mod- els are employed to empirically investigate the hidden and complex relationships be- tween events and the stock market. Large- scale experiments show that our event-based system out- performs bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data. We further use deep learning to improve the representation of event for enhanced generalization, and to capture the influence of long-term and short-term historical events simultaneously. These techniques boost the accuracy of index prediction from 60% to 66%, and significantly enhance the accuracy of individual stock prediction.
邀请人： 姬东鸿 教授
