Discrete Dynamics in Nature and Society
Volume 2011 (2011), Article ID 930958, 11 pages
doi:10.1155/2011/930958
Research Article

Multivariate Local Polynomial Regression with Application to Shenzhen Component Index

School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, China

Received 24 November 2010; Revised 28 January 2011; Accepted 9 March 2011

Academic Editor: Carlo Piccardi

Copyright © 2011 Liyun Su. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.