DYNAMIC ORDINARY DIFFERENTIAL EQUATION MODELING OF STOCK MARKET PREDICTION WITH GENE EXPRESSION PROGRAMMING

摘要：

Because stock market is a complicated, nonlinear and changeable system, prediction of stock price is far more challenging than ordinary time series problems. This paper proposes a novel approach called Dynamic Ordinary Differential Equation (DyODE) modeling of stock market prediction with Gene Expression Programming (GEP). DyODE selects suitable data as training set to build prediction model and hence avoids prediction error caused by using obsolete data. Prediction accuracy of DyODE is tested on the stock prices series. Results show that the accuracy is much higher than that of traditional approachs.

关键词： Dynamic Ordinary Differential Equation Model Stock Market Prediction Gene Expression Programming

作者: REN CHENG LI QU CHEN CHAO

作者单位: College of Computer Science Zhejiang University of Technology Hangzhou, China

会议类型: 国际会议

会议名称: 3rd International Conference on Mechanical and Electrical Technology(ICMET2011) (2011第三届机械与电气技术国际会议)

会议地点: 大连

会议语种:英文

页码: 203-207

在线出版日期: 2011-08-26（万方平台首次上网日期，不代表论文的发表时间）