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Genetic Programming of Polynomial Models for Financial Forecasting

  • Nikolay Y. Nikolaev
  • Hitoshi Iba
Chapter

Abstract

This paper addresses the problem of finding trends in financial data series using genetic programming (GP). A GP system STROGANOFF that searches for polynomial autoregressive models is presented. The system is specialized for time series processing with elaborations in two aspects: 1) preprocessing the given series using data transformations and embedding; and, 2) design of a fitness function for efficient search control that favours accurate, parsimonious, and predictive models. STROGANOFF is related to a traditional GP system which manipulates functional expressions. Both GP systems are examined on a Nikkei225 series from the Tokyo Stock Exchange. Using statistical and economical measures we show that STROGANOFF outperforms traditional GP, and it can evolve profitable polynomials.

Keywords

Genetic Programming Polynomial Models Overfitting Avoidance 

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Nikolay Y. Nikolaev
    • 1
  • Hitoshi Iba
    • 2
  1. 1.Dept. of Math. and Computing Sciences Goldsmiths CollegeUniversity of LondonLondonUK
  2. 2.Dept. of Inf. and Comm. Engineering School of Engineering The University of TokyoJapan

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