题 目：Asset price prediction by CNN+LSTM
主讲人：Prof. Yongzeng Lai, Wilfrid Laurier University, Canada
Yongzeng Lai教授，现任Department of Mathematics, Wilfrid Laurier University教授、博士生导师。2000年美国加州大学数学博士毕业，2000-2002年在加拿大滑铁卢大学做博士后。主要从事大数据分析，金融定量分析。在Applied Mathematics and Computation，Insurance, Mathematics and Economics，Computers & Operations Research，Computational Statistics & Data Analysis等期刊发表论文20余篇。
Prediction of asset prices is difficult due to the nature of asset prices. Traditional statistical models and some basic machine learning as well as deep learning techniques were used in forecasting stock prices in the literature. In this talk, we will introduce our recent work on asset price prediction using some deep learning based techniques. Various asset prices from different industries in both mature and emerging markets are selected to test the algorithms. Our test results show that the convolutional neural network (CNN) and the long short-term memory (LSTM) based algorithm outperforms other selected neural network based algorithms and ARIMA type time series model.