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Fuzzy logic, trading uncertainty and technical trading

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  • Gradojevic, Nikola
  • Gençay, Ramazan

Abstract

From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.

Suggested Citation

  • Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
    • Handle: RePEc:eee:jbfina:v:37:y:2013:i:2:p:578-586
    DOI: 10.1016/j.jbankfin.2012.09.012
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    7. Muhammad A. Cheema & Gilbert V. Nartea & Yimei Man, 2018. "Cross‐Sectional and Time Series Momentum Returns and Market States," International Review of Finance, International Review of Finance Ltd., vol. 18(4), pages 705-715, December.
    8. Thorsten Hens & Terje Lensberg & Klaus Reiner Schenk‐Hoppé, 2018. "Front‐Running and Market Quality: An Evolutionary Perspective on High Frequency Trading," International Review of Finance, International Review of Finance Ltd., vol. 18(4), pages 727-741, December.
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    13. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
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    15. Ghandar, Adam & Michalewicz, Zbigniew & Zurbruegg, Ralf, 2016. "The relationship between model complexity and forecasting performance for computer intelligence optimization in finance," International Journal of Forecasting, Elsevier, vol. 32(3), pages 598-613.
    16. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    17. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    18. Tzu‐Pu Chang, 2021. "Buy Low and Sell High: The 52‐Week Price Range and Predictability of Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 336-344, March.
    19. Yung-Ho Chang, 2019. "Cross-market information spillover and the performance of technical trading in the foreign exchange market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 211-227, April.

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    More about this item

    Keywords

    Foreign exchange markets; Technical trading; Uncertainty; Fuzzy logic; Filtering;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • F3 - International Economics - - International Finance

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