Multivariate nonlinear analysis and prediction of Shanghai stock market

作者：Ma Junhai^*; Liu Lixia

来源：Discrete Dynamics in Nature and Society, 2008, 526734.

DOI：10.1155/2008/526734

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.

• 出版日期2008
• 单位天津大学