Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming

  • Garnett Wilson
  • Wolfgang Banzhaf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

A developmental co-evolutionary genetic programming approach (PAM DGP) is compared to a standard linear genetic programming (LGP) implementation for trading of stocks across market sectors. Both implementations were found to be impressively robust to market fluctuations while reacting efficiently to opportunities for profit, where PAM DGP proved slightly more reactive to market changes than LGP. PAM DGP outperformed, or was competitive with, LGP for all stocks tested. Both implementations had very impressive accuracy in choosing both profitable buy trades and sells that prevented losses, where this occurred in the context of moderately active trading for all stocks. The algorithms also appropriately maintained maximal investment in order to profit from sustained market upswings.

Keywords

Developmental Genetic Programming Linear Genetic Programming Computational Finance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)MATHGoogle Scholar
  2. 2.
    Yan, W., Sewell, M., Clack, C.D.: Learning to Optimize Profits Beats Predicting Returns —Comparing Techniques for Financial Portfolio Optimisation. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2008, pp. 1681–1688. ACM Press, New York (2008)Google Scholar
  3. 3.
    Grosan, C., Abraham, A.: Stock Market Modeling Using Genetic Programming Ensembles. Studies in Computational Intelligence 13, 131–146 (2006)Google Scholar
  4. 4.
    Drezewski, R., Sepielak, J.: Evolutionary System for Generating Investment Strategies. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 83–92. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Wilson, G., Heywood, M.: Introducing Probabilistic Adaptive Mapping Developmental Genetic Programming with Redundant Mappings. Genetic Programming and Evolvable Machines 8, 187–220 (2007)CrossRefGoogle Scholar
  6. 6.
    Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer, New York (2007)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Garnett Wilson
    • 1
  • Wolfgang Banzhaf
    • 1
  1. 1.Memorial University of NewfoundlandSt. John‘sCanada

Personalised recommendations