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Technical Trading Versus Market Efficiency-A Genetic Programming Approach

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Listed:
  • J.P. Marney

    (University of Paisley)

  • H. Tarbert

    (University of Strathclyde)

  • C. Fyfe

Abstract

In this paper genetic programming is used to investigate a number of long time series of price data for a stock exchange quoted share, in order to discern whether there are any patterns in the data which could be used for technical trading purposes. This extends the work done by the authors in a previous paper (Fyfe et al. 1999) which suggested that, although it was possible to find a rule which did outperform simple buy and hold, there were insufficient grounds for the rejection of the efficient market hypothesis. The purpose of the present paper is to investigate the robustness and generalisability of the conclusion reached by Fyfe et. al.

Suggested Citation

  • J.P. Marney & H. Tarbert & C. Fyfe, 2000. "Technical Trading Versus Market Efficiency-A Genetic Programming Approach," Computing in Economics and Finance 2000 169, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:169
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    Cited by:

    1. Colin Fyfe & John Paul Marney & Heather Tarbert, 2005. "Risk adjusted returns from technical trading: a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 15(15), pages 1073-1077.
    2. J. P. Marney & Heather Tarbert & Jos Koetsier & Marco Guidi, 2008. "The application of the self-organizing map, the k-means algorithm and the multi-layer perceptron to the detection of technical trading patterns," Applied Financial Economics, Taylor & Francis Journals, vol. 18(12), pages 1009-1019.

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