Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2011/930958 |
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| _version_ | 1832558169844350976 |
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| author | Liyun Su |
| author_facet | Liyun Su |
| author_sort | Liyun Su |
| collection | DOAJ |
| description | This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction. |
| format | Article |
| id | doaj-art-41b983c86aee4e68879d13d15698fcb1 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-41b983c86aee4e68879d13d15698fcb12025-02-03T01:33:06ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2011-01-01201110.1155/2011/930958930958Multivariate Local Polynomial Regression with Application to Shenzhen Component IndexLiyun Su0School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, ChinaThis study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.http://dx.doi.org/10.1155/2011/930958 |
| spellingShingle | Liyun Su Multivariate Local Polynomial Regression with Application to Shenzhen Component Index Discrete Dynamics in Nature and Society |
| title | Multivariate Local Polynomial Regression with Application to Shenzhen Component Index |
| title_full | Multivariate Local Polynomial Regression with Application to Shenzhen Component Index |
| title_fullStr | Multivariate Local Polynomial Regression with Application to Shenzhen Component Index |
| title_full_unstemmed | Multivariate Local Polynomial Regression with Application to Shenzhen Component Index |
| title_short | Multivariate Local Polynomial Regression with Application to Shenzhen Component Index |
| title_sort | multivariate local polynomial regression with application to shenzhen component index |
| url | http://dx.doi.org/10.1155/2011/930958 |
| work_keys_str_mv | AT liyunsu multivariatelocalpolynomialregressionwithapplicationtoshenzhencomponentindex |