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European Conference on Genetic Programming

EuroGP 2001: Genetic Programming pp 361–370Cite as

Genetic Programming for Financial Time Series Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

Abstract

This paper describes an application of genetic programming to forecasting financial markets that allowed the authors to rank first in a competition organized within the CEC2000 on “Dow Jones Prediction”. The approach is substantially driven by the rules of that competition, and is characterized by individuals being made up of multiple GP expressions and specific genetic operators.

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Author information

Authors and Affiliations

  1. Polo Didattico e di Ricerca di Crema, Via Bramante, 65 - 26013, Crema, CR

    Massimo Santini & Andrea Tettamanzi

Authors
  1. Massimo Santini
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  2. Andrea Tettamanzi
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Cite this paper

Santini, M., Tettamanzi, A. (2001). Genetic Programming for Financial Time Series Prediction. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_29

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  • DOI: https://doi.org/10.1007/3-540-45355-5_29

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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