Dual-CLVSA: A Deep Learning Approach to Financial Market Prediction using Trading Data and Sentiment Measurements | |||
报告人（单位） | 术洪炜 Prof. Hongwei ZHU（University of Massachusetts Lowell） | ||
点评人（单位） | 刘晓星 教授 （东南大学） | 点评人（单位） | 尹威 副教授 （东南大学） |
时间地点 | 2021年11月15日星期一19: 30，腾讯会议ID：114 198 343 | ||
报告内容摘要 | |||
It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who cannot stay rational and are often affected by emotions. Extending a hybrid convolutional LSTM-based variational sequence-to- sequence model with attention (CLVSA) model, we develop dual-CLVSA to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data. We further show that sentiment measurements are informative for financial market prediction and using dual-CLVSA we can extract profitable features to boost the performance of our prediction system. | |||
报告人简介 | |||
Hongwei Zhu is a professor of Management Information Systems in the Operations and Information Systems Department at the University of Massachusetts Lowell. His research focuses on data quality and analytics, with applications in finance and accounting. His work has appeared in MIT Sloan Management Review, Journal of Management Information Systems, Information Systems Research, and transactions and journals of ACM and IEEE. He is an associate editor of the ACM Journal of Data and Information Quality. | |||
