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Technical analysis versus market efficiency - a genetic programming approach

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  • Colin Fyfe
  • John Paul Marney
  • Heather Tarbert

Abstract

In the paper the authors maintain that the prevalence of technical analysis in professional investment argues that such techniques should perhaps be taken more seriously by academics. The new technique of genetic programming is used to investigate a long time series of price data for a quoted property investment company, to discern whether there are any patterns in the data which could be used for technical trading purposes. A successful buy rule is found which generates returns in excess of what would be expected from the best-fitting null time-series model. Nevertheless, this turns out to be a more sophisticated variant of the buy and hold rule, which the authors term timing specific buy and hold. Although the rule does outperform simple buy and hold, it really does not provide sufficient grounds for the rejection of the efficient market hypothesis, though it does suggest that further investigation of the specific conditions of applicability of the EMH may be appropriate.

Suggested Citation

  • Colin Fyfe & John Paul Marney & Heather Tarbert, 1999. "Technical analysis versus market efficiency - a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 183-191.
    • Handle: RePEc:taf:apfiec:v:9:y:1999:i:2:p:183-191
    DOI: 10.1080/096031099332447
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    References listed on IDEAS

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    2. 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.
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    Cited by:

    1. Marcos Álvarez-Díaz & Alberto Álvarez, 2002. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0205, Universidade de Vigo, Departamento de Economía Aplicada.
    2. Marcos Álvarez-Díaz & Alberto Álvarez, 2003. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0301, Universidade de Vigo, Departamento de Economía Aplicada.
    3. Yujie Zhu & Tieqi Wang, 2017. "Deriving momentum strategies in Chinese stock Market: Using Gene Expression Programming," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(6), pages 1-4.
    4. 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.
    5. Marcos Alvarez Díaz & Lucy Amigo Dobano & Francisco Rodríguez de Prado, "undated". "Taxing on Housing: A Welfare Evaluation of the Spanish Personal Income Tax," Studies on the Spanish Economy 142, FEDEA.
    6. Marcos Álvarez-Díaz & Lucy Amigo Dobaño, 2003. "Métodos No-Lineales De Predicción En El Mercado De Valores Tecnológicos En España. Una Verificación De La Hipótesis Débil De Eficiencia," Working Papers 0303, Universidade de Vigo, Departamento de Economía Aplicada.
    7. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.

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