Multivariate nonlinear analysis and prediction of Shanghai stock market
type: Item
instance of: scholarly article
title: Multivariate nonlinear analysis and prediction of Shanghai stock market
zbMATH Open document ID: 1141.91650
DOI: 10.1155/2008/526734
author: Junhai Ma
author: Lixia Liu
published in: Discrete Dynamics in Nature and Society
publication date: 2008-08-20T00:00:00Z
full work available at URL: https://eudml.org/doc/129113
review text: Summary: 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.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 91B28
Mathematics Subject Classification ID: 62P05
zbMATH DE Number: 5314256
Wikidata QID: Q58644501
MaRDI profile type: Publication
OpenAlex ID: W2020873036
cites work: Testing for nonlinearity in time series: the method of surrogate data
cites work: Detecting nonlinearity in multivariate time series
cites work: Dynamics from multivariate time series
description: scientific article; zbMATH DE number 5314256