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Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
Author
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-05-05
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Su, Liyun. 2011. Multivariate Local Polynomial Regression with Application to Shenzhen Component Index. Discrete Dynamics in Nature and Society،Vol. 2011, no. 2011, pp.1-11.
https://search.emarefa.net/detail/BIM-509119
Modern Language Association (MLA)
Su, Liyun. Multivariate Local Polynomial Regression with Application to Shenzhen Component Index. Discrete Dynamics in Nature and Society No. 2011 (2011), pp.1-11.
https://search.emarefa.net/detail/BIM-509119
American Medical Association (AMA)
Su, Liyun. Multivariate Local Polynomial Regression with Application to Shenzhen Component Index. Discrete Dynamics in Nature and Society. 2011. Vol. 2011, no. 2011, pp.1-11.
https://search.emarefa.net/detail/BIM-509119
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-509119