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FX technical trading rules can be profitable sometimes!

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  • Zarrabi, Nima
  • Snaith, Stuart
  • Coakley, Jerry

Abstract

This paper investigates the profitability of technical trading rules in the foreign exchange market taking into account data snooping bias and transaction costs. A universe of 7650 trading rules is applied to six currencies quoted in U.S. dollars over the 1994:3–2014:12 period. The Barras, Scaillet, and Wermers (2010) false discovery rate method is employed to deal with data snooping and it detects almost all outperforming trading rules while keeping the proportion of false discoveries to a pre-specified level. The out-of-sample results reveal a large number of outperforming rules that are profitable over short periods based on the Sharpe ratio. However, they are not consistently profitable and so the overall results are more consistent with the adaptive markets hypothesis.

Suggested Citation

  • Zarrabi, Nima & Snaith, Stuart & Coakley, Jerry, 2017. "FX technical trading rules can be profitable sometimes!," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 113-127.
    • Handle: RePEc:eee:finana:v:49:y:2017:i:c:p:113-127
    DOI: 10.1016/j.irfa.2016.12.010
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    Cited by:

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    2. Lorenzo Reus & Guillermo Alexander Sepúlveda-Hurtado, 2023. "Foreign exchange trading and management with the stochastic dual dynamic programming method," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    3. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).
    4. Amer M. Bakhach & Edward P.K. Tsang & V.L. Raju Chinthalapati, 2018. "TSFDC: A trading strategy based on forecasting directional change," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(3), pages 105-123, July.
    5. J. C. Garza Sepúlveda & F. Lopez-Irarragorri & S. E. Schaeffer, 2023. "Forecasting Forex Trend Indicators with Fuzzy Rough Sets," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 229-287, June.
    6. Yamani, Ehab, 2021. "Foreign exchange market efficiency and the global financial crisis: Fundamental versus technical information," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 74-89.
    7. Yamani, Ehab, 2021. "Can technical trading beat the foreign exchange market in times of crisis?," Global Finance Journal, Elsevier, vol. 48(C).
    8. Peng, Yaohao & de Moraes Souza, João Gabriel, 2024. "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    9. Urquhart, Andrew & Zhang, Hanxiong, 2019. "The performance of technical trading rules in Socially Responsible Investments," International Review of Economics & Finance, Elsevier, vol. 63(C), pages 397-411.
    10. Azzam, Islam & El-Masry, Ahmed A. & Yamani, Ehab, 2023. "Foreign exchange market efficiency during COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 717-730.
    11. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
    12. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.
    13. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    14. Batten, Jonathan A. & Lucey, Brian M. & McGroarty, Frank & Peat, Maurice & Urquhart, Andrew, 2018. "Does intraday technical trading have predictive power in precious metal markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 102-113.
    15. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    16. Dockery, Everton & Todorov, Ivan, 2023. "Further evidence on the returns to technical trading rules: Insights from fourteen currencies," Journal of Multinational Financial Management, Elsevier, vol. 69(C).

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    More about this item

    Keywords

    Technical trading; False discovery rate; Persistence analysis; Exchange rate;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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