Improved prediction of financial market cycles with artificial neural network and markov regime switching

David Liu; Lei Zhang

doi:10.1007/978-94-007-1192-1_33

Improved prediction of financial market cycles with artificial neural network and markov regime switching

David Liu^*, Lei Zhang

^*Corresponding author for this work

Department of Financial and Actuarial Mathematics

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

Abstract

This paper provides an analysis of the Shanghai Stock Exchange Composite Index Movement Forecasting for the period 1999-2009 using two competing non-linear models, univariate Markov Regime Switching model and Artificial Neural Network Model (RBF). The experiment shows that RBF is a useful method for forecasting the regime duration of the Moving Trends of Stock Composite Index. The framework employed also proves useful for forecasting Stock Composite Index turning points. The empirical results in this paper show that ANN method is preferable to Markov-Switching model to some extent.

Original language	English
Title of host publication	Electrical Engineering and Applied Computing
Editors	Sio Iong Ao, Len Gelman
Pages	405-417
Number of pages	13
DOIs	https://doi.org/10.1007/978-94-007-1192-1_33
Publication status	Published - 2011

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	90 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Keywords

Artificial neural networks
Nonparametric estimation
RBF
Regime switching

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10.1007/978-94-007-1192-1_33

Cite this

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title = "Improved prediction of financial market cycles with artificial neural network and markov regime switching",

abstract = "This paper provides an analysis of the Shanghai Stock Exchange Composite Index Movement Forecasting for the period 1999-2009 using two competing non-linear models, univariate Markov Regime Switching model and Artificial Neural Network Model (RBF). The experiment shows that RBF is a useful method for forecasting the regime duration of the Moving Trends of Stock Composite Index. The framework employed also proves useful for forecasting Stock Composite Index turning points. The empirical results in this paper show that ANN method is preferable to Markov-Switching model to some extent.",

keywords = "Artificial neural networks, Nonparametric estimation, RBF, Regime switching",

author = "David Liu and Lei Zhang",

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Improved prediction of financial market cycles with artificial neural network and markov regime switching. / Liu, David; Zhang, Lei.
Electrical Engineering and Applied Computing. ed. / Sio Iong Ao; Len Gelman. 2011. p. 405-417 (Lecture Notes in Electrical Engineering; Vol. 90 LNEE).

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

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AB - This paper provides an analysis of the Shanghai Stock Exchange Composite Index Movement Forecasting for the period 1999-2009 using two competing non-linear models, univariate Markov Regime Switching model and Artificial Neural Network Model (RBF). The experiment shows that RBF is a useful method for forecasting the regime duration of the Moving Trends of Stock Composite Index. The framework employed also proves useful for forecasting Stock Composite Index turning points. The empirical results in this paper show that ANN method is preferable to Markov-Switching model to some extent.

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Improved prediction of financial market cycles with artificial neural network and markov regime switching

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