Sparse-patterned wavelet neural networks and their applications to stock market forecasting

Jack Penm; R. D. Terrell

doi:10.1057/9780230295216_8

Sparse-patterned wavelet neural networks and their applications to stock market forecasting

Jack Penm, R. D. Terrell

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

Abstract

Wavelet neural networks combine the theories of wavelet analysis and neural networks. This Chapter proposes construction approaches to develop sparse-patterned wavelet neural networks, which demonstrate the ‘presence and absence’ restrictions on the coefficients of a subset time-series system. To demonstrate the effectiveness of the proposed nonlinear approaches, the developed sparse-patterned wavelet neural networks are applied to stock market forecasting.

Original language	English
Title of host publication	Nonlinear Financial Econometrics
Subtitle of host publication	Markov Switching Models, Persistence and Nonlinear Cointegration
Publisher	Palgrave Macmillan
Pages	161-170
Number of pages	10
ISBN (Electronic)	9780230295216
ISBN (Print)	9780230283640
DOIs	https://doi.org/10.1057/9780230295216_8
Publication status	Published - 1 Jan 2010

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AB - Wavelet neural networks combine the theories of wavelet analysis and neural networks. This Chapter proposes construction approaches to develop sparse-patterned wavelet neural networks, which demonstrate the ‘presence and absence’ restrictions on the coefficients of a subset time-series system. To demonstrate the effectiveness of the proposed nonlinear approaches, the developed sparse-patterned wavelet neural networks are applied to stock market forecasting.

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Sparse-patterned wavelet neural networks and their applications to stock market forecasting

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