Stock Index Modeling Using Hierarchical Radial Basis Function Networks

  • Yuehui Chen
  • Lizhi Peng
  • Ajith Abraham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)


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.


Exchange Rate Normalize Mean Square Error Gaussian Radial Basis Function Currency Exchange Rate Conventional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Lizhi Peng
    • 1
  • Ajith Abraham
    • 1
    • 2
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China
  2. 2.IITA Professorship Program, School of Computer Science and Engg.Chung-Ang UniversitySeoulRepublic of Korea

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