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Stock Index Modeling Using Hierarchical Radial Basis Function Networks

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4253))

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Abstract

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a Hierarchical Radial Basis Function Network (HiRBF) model for forecasting three major international currency exchange rates. Based on the pre-defined instruction sets, HRBF model can be created and evolved. The HRBF structure is developed using the Extended Compact Genetic Programming (ECGP) and the free parameters embedded in the tree are optimized by the Degraded Ceiling Algorithm (DCA). Empirical results indicate that the proposed method is better than the conventional neural network and RBF networks forecasting models.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Jinan University, Jinan, 250022, P.R. China

    Yuehui Chen, Lizhi Peng & Ajith Abraham

  2. IITA Professorship Program, School of Computer Science and Engg., Chung-Ang University, Seoul, Republic of Korea

    Ajith Abraham

Authors
  1. Yuehui Chen
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  2. Lizhi Peng
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  3. Ajith Abraham
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Editor information

Editors and Affiliations

  1. School of Design, Engineering and Computing, Bournemouth University, UK

    Bogdan Gabrys

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, Y., Peng, L., Abraham, A. (2006). Stock Index Modeling Using Hierarchical Radial Basis Function Networks. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_51

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  • DOI: https://doi.org/10.1007/11893011_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46542-3

  • Online ISBN: 978-3-540-46544-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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