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The application of ridge polynomial neural network to multi-step ahead financial time series prediction

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Abstract

Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. The RPNN is constructed from a number of increasing orders of Pi–Sigma units, which are used to capture the underlying patterns in financial time series signals and to predict future trends in the financial market. In particular, this paper systematically investigates a method of pre-processing the financial signals in order to reduce the influence of their trends. The performance of the networks is benchmarked against the performance of MLPs, functional link neural networks (FLNN), and Pi–Sigma neural networks (PSNN). Simulation results clearly demonstrate that RPNNs generate higher profit returns with fast convergence on various noisy financial signals.

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Acknowledgments

The work of R. Ghazali is supported by Universiti Tun Hussein Onn (UTHM), Malaysia.

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Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK

    R. Ghazali & A. J. Hussain

  2. School of Engineering and Mathematical Sciences, City University, London, UK

    P. Liatsis

  3. Intelligent and Distributed Systems (IDS) Laboratory, Liverpool Hope University, Liverpool, UK

    H. Tawfik

Authors
  1. R. Ghazali
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  2. A. J. Hussain
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  3. P. Liatsis
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  4. H. Tawfik
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Correspondence to R. Ghazali.

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Ghazali, R., Hussain, A.J., Liatsis, P. et al. The application of ridge polynomial neural network to multi-step ahead financial time series prediction. Neural Comput & Applic 17, 311–323 (2008). https://doi.org/10.1007/s00521-007-0132-8

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  • DOI: https://doi.org/10.1007/s00521-007-0132-8

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