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The application of the self-organizing map, the k-means algorithm and the multi-layer perceptron to the detection of technical trading patterns

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  • J. P. Marney
  • Heather Tarbert
  • Jos Koetsier
  • Marco Guidi

Abstract

A number of neural network techniques, namely multi-layer perceptron, k-means algorithm and the self-organizing map are applied to the detection of technical trading patterns within stock markets. We do not find exploitable information content and it is concluded that there are no significant patterns in any of the data analysed.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apfiec:v:18:y:2008:i:12:p:1009-1019
    DOI: 10.1080/09603100701367385
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    References listed on IDEAS

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    1. 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.
    2. Meredith Beechey & David Gruen & James Vickery, 2000. "The Efficient Market Hypothesis: A Survey," RBA Research Discussion Papers rdp2000-01, Reserve Bank of Australia.
    3. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(4), pages 405-426, December.
    4. P.H. Kevin Chang & Carol L. Osler, 1995. "Head and shoulders: not just a flaky pattern," Staff Reports 4, Federal Reserve Bank of New York.
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