Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market
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 met...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2008-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2008/526734 |
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| _version_ | 1832557085616766976 |
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| author | Junhai Ma Lixia Liu |
| author_facet | Junhai Ma Lixia Liu |
| author_sort | Junhai Ma |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3b646f74426544f3bab85717f9059458 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-3b646f74426544f3bab85717f90594582025-02-03T05:43:42ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2008-01-01200810.1155/2008/526734526734Multivariate Nonlinear Analysis and Prediction of Shanghai Stock MarketJunhai Ma0Lixia Liu1School of Management, Tianjin University, Tianjin 300072, ChinaSchool of Management, Tianjin University, Tianjin 300072, ChinaThis 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.http://dx.doi.org/10.1155/2008/526734 |
| spellingShingle | Junhai Ma Lixia Liu Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market Discrete Dynamics in Nature and Society |
| title | Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market |
| title_full | Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market |
| title_fullStr | Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market |
| title_full_unstemmed | Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market |
| title_short | Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market |
| title_sort | multivariate nonlinear analysis and prediction of shanghai stock market |
| url | http://dx.doi.org/10.1155/2008/526734 |
| work_keys_str_mv | AT junhaima multivariatenonlinearanalysisandpredictionofshanghaistockmarket AT lixialiu multivariatenonlinearanalysisandpredictionofshanghaistockmarket |