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Irish Conference on Artificial Intelligence and Cognitive Science

AICS 2002: Artificial Intelligence and Cognitive Science pp 165–170Cite as

Investigations into Market Index Trading Models Using Evolutionary Automatic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2464))

Abstract

This study examines the potential of an evolutionary automatic programming methodology to uncover a series of useful technical tradingrules for the US S&P stock index. Index values for the period 01/01/1991 to 01/10/1997 are used to train and test the evolved rules. A number of replacement strategies, and a novel approach to constant evolution are investigated. The findings indicate that the automatic programming methodology has much potential with the evolved rules makingg ains of approximately 13% over a 6 year test period.

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Author information

Authors and Affiliations

  1. Dept. Of Computer Science And Information Systems, University of Limerick, Ireland

    Ian Dempsey & Michael O’Neill

  2. Dept. Of Accountancy, University College Dublin, Ireland

    Anthony Brabazon

Authors
  1. Ian Dempsey
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  2. Michael O’Neill
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  3. Anthony Brabazon
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Ireland

    Michael O’Neill , Richard F. E. Sutcliffe , Conor Ryan , Malachy Eaton  & Niall J. L. Griffith , , ,  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Dempsey, I., O’Neill, M., Brabazon, A. (2002). Investigations into Market Index Trading Models Using Evolutionary Automatic Programming. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_21

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  • DOI: https://doi.org/10.1007/3-540-45750-X_21

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44184-7

  • Online ISBN: 978-3-540-45750-3

  • eBook Packages: Springer Book Archive

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