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Sampling frequency and the performance of different types of technical trading rules

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  • Hudson, Robert
  • McGroarty, Frank
  • Urquhart, Andrew

Abstract

The predictive ability of technical trading rules has been studied in great detail however many papers group all technical trading rules together into one basket. We argue that there are two main types of technical trading rules, namely rules based on trend-following and mean reversion. Utilising high-frequency commodity ETF data, we show that mean-reversion based rules perform increasingly better as sampling frequencies increase and that conversely the performance of trend-following rules deteriorate at higher-frequencies. These findings are possibly related to noise created by high-frequency traders.

Suggested Citation

  • Hudson, Robert & McGroarty, Frank & Urquhart, Andrew, 2017. "Sampling frequency and the performance of different types of technical trading rules," Finance Research Letters, Elsevier, vol. 22(C), pages 136-139.
    • Handle: RePEc:eee:finlet:v:22:y:2017:i:c:p:136-139
    DOI: 10.1016/j.frl.2016.12.015
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    References listed on IDEAS

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    Cited by:

    1. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    2. Condorelli, Stefano, 2018. "Price momentum and the 1719-20 bubbles: A method to compare and interpret booms and crashes in asset markets," MPRA Paper 89888, University Library of Munich, Germany.
    3. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Market Timing with Moving Averages for Fossil Fuel and Renewable Energy Stocks," Documentos de Trabajo del ICAE 2018-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. Xiaoye Jin, 2022. "Evaluating the predictive power of intraday technical trading in China's crude oil market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1416-1432, November.
    5. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    6. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    7. 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.
    8. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Moving Average Market Timing in European Energy Markets: Production Versus Emissions," Energies, MDPI, vol. 11(12), pages 1-24, November.

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    More about this item

    Keywords

    Technical analysis; High-frequency trading; Commodity ETFs; Market efficiency;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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