Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-05-18
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ma, Junhai& Liu, Lixia. 2008. Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market. Discrete Dynamics in Nature and Society،Vol. 2008, no. 2008, pp.1-8.
https://search.emarefa.net/detail/BIM-478726
Modern Language Association (MLA)
Ma, Junhai& Liu, Lixia. Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market. Discrete Dynamics in Nature and Society No. 2008 (2008), pp.1-8.
https://search.emarefa.net/detail/BIM-478726
American Medical Association (AMA)
Ma, Junhai& Liu, Lixia. Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market. Discrete Dynamics in Nature and Society. 2008. Vol. 2008, no. 2008, pp.1-8.
https://search.emarefa.net/detail/BIM-478726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478726