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Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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Abstract

Over the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clear-cut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends.

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References

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Author information

Authors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, 11623, Taiwan

    Shu-Heng Chen & Nicolas Navet

  2. LORIA-INRIA, Campus-Scientifique, BP239, F-54506, Vandoeuvre, France

    Nicolas Navet

Authors
  1. Shu-Heng Chen
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  2. Nicolas Navet
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, SH., Navet, N. (2006). Pretests for Genetic-Programming Evolved Trading Programs: “zero-intelligence” Strategies and Lottery Trading. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_50

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  • DOI: https://doi.org/10.1007/11893295_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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