报告人:赖永增(加拿大Wilfrid Laurier University)
报告地点:数学院南阶梯教室
报告时间:2019年12月20日15:00
报告摘要:
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.
报告人简介:
赖永增,加拿大Wilfrid Laurier University数学系教授,于1983年和1988年分别在中山大学获得学士学位和硕士学位,于2000年在美国加州克莱蒙研究生院获得博士学位,2000年5月至2002年6月在加拿大滑铁卢大学高级金融研究中心和统计与精算学系做博士后研究员。2002年6月到现在一直在Wilfrid Laurier University数学系做教授。主持加拿大国家自然科学基金多项。主要研究领域包括金融数学(衍生产品的定价与风险管理、金融计算、投资组合优化、随机分析在金融和保险中的应用)、微分方程在金融和经济学中的应用、蒙特卡洛和拟蒙特卡洛仿真方法及应用。在Automatica, Journal of Computational Finance, Computers & Operations Research, Insurance Mathematics and Economics, Economic Modeling, Nonlinear Analysis, Computational Statistics & Data Analysis等国际期刊及会议录上发表50多篇论文。