Stock Price Forecasting Using A Hybrid ARMA and BP Neural Network and Markov Model

摘要：

　　Stock price forecasting is a very important financial topic and it is of great importance to both market economy and investors.Stock price series is complex,nonlinear and dynamic that its difficult to predict it effectively by a single method.This paper proposes a hybrid method combining autoregressive and moving average (ARMA),back propagation neural network (BPNN) and Markov model to forecast the stock price.ARMA and BPNN solve the linear and nonlinear component of the stock price series respectively and Markov model can modify the result to be better.The experimental result shows that the proposed method can improve forecasting accuracy.

关键词： stock price forecasting ARMA BPNN Markov model

作者: Shuzhen Shi Wenlong Liu Minglu Jin

作者单位: School of Information and Communication Engineering Dalian University of Technology Dalian, China

会议类型: 国际会议

会议名称: 2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))

会议地点: 成都

会议语种:英文

页码: 1060-1064

在线出版日期: 2012-11-09（万方平台首次上网日期，不代表论文的发表时间）