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Can channel pattern trading be profitably automated?

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  • M. A. H. Dempster
  • C. M. Jones

Abstract

Financial markets, such as the global foreign exchange (FX) market, often exhibit trending behaviour. Within such trends, the market level oscillates with changes in market consensus. Continued oscillations of this type result in the formation of wave patterns within the underlying trend known as channels, which are used by technical analysts as trade entry signals. A sample space of such channels has been constructed from a set of US dollar/British pound Spot FX tick data from 1989-1997 using pattern recognition algorithms and the profitability of trading using such patterns has been estimated. A number of attributes of the resulting collection of channels has been subjected to statistical analysis with the aim of classifying patterns that can be traded profitably using a number of simple trading rules. Results of this analysis show that there exist statistically significant links between the channels' attributes and profitability.

Suggested Citation

  • M. A. H. Dempster & C. M. Jones, 2002. "Can channel pattern trading be profitably automated?," The European Journal of Finance, Taylor & Francis Journals, vol. 8(3), pages 275-301.
    • Handle: RePEc:taf:eurjfi:v:8:y:2002:i:3:p:275-301
    DOI: 10.1080/13518470110052831
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    References listed on IDEAS

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    Cited by:

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    2. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    3. Mark Austin & Graham Bates & Michael Dempster & Vasco Leemans & Stacy Williams, 2004. "Adaptive systems for foreign exchange trading," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 37-45.
    4. Stephan Schulmeister, 2009. "Technical Trading and Trends in the Dollar-Euro Exchange Rate," WIFO Studies, WIFO, number 37582.
    5. Shangkun Deng & Kazuki Yoshiyama & Takashi Mitsubuchi & Akito Sakurai, 2015. "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 49-89, January.

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