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Geometric Semantic Genetic Programming for Financial Data

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Applications of Evolutionary Computation (EvoApplications 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

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Abstract

We cast financial trading as a symbolic regression problem on the lagged time series, and test a state of the art symbolic regression method on it. The system is geometric semantic genetic programming, which achieves good performance by converting the fitness landscape to a cone landscape which can be searched by hill-climbing. Two novel variants are introduced and tested also, as well as a standard hill-climbing genetic programming method. Baselines are provided by buy-and-hold and ARIMA. Results are promising for the novel methods, which produce smaller trees than the existing geometric semantic method. Results are also surprisingly good for standard genetic programming. New insights into the behaviour of geometric semantic genetic programming are also generated.

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Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, Complex and Adaptive Systems Lab, University College Dublin, Dublin, Ireland

    James McDermott, Alexandros Agapitos, Anthony Brabazon & Michael O’Neill

  2. Management Information Systems, Lochlann Quinn School of Business, University College Dublin, Dublin, Ireland

    James McDermott

  3. Accountancy, Lochlann Quinn School of Business, University College Dublin, Dublin, Ireland

    Anthony Brabazon

Authors
  1. James McDermott
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  2. Alexandros Agapitos
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  3. Anthony Brabazon
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  4. Michael O’Neill
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Corresponding author

Correspondence to James McDermott .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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McDermott, J., Agapitos, A., Brabazon, A., O’Neill, M. (2014). Geometric Semantic Genetic Programming for Financial Data. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_18

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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