Discrete Dynamics in Nature and Society
Volume 2008 (2008), Article ID 526734, 8 pages
Research Article

Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

Junhai Ma and Lixia Liu

School of Management, Tianjin University, Tianjin 300072, China

Received 8 August 2007; Accepted 15 April 2008

Academic Editor: Masahiro Yabuta

Copyright © 2008 Junhai Ma and Lixia Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.