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Testing moving average trading strategies on ETFs

Author

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  • Huang, Jing-Zhi
  • Huang, Zhijian (James)

Abstract

The evidence for the profitability of MA strategies documented in the literature is usually based on non-tradable indices or portfolios/factors and the use of the zero return or risk-free rate as the benchmark. In this paper we implement MA strategies using ETFs and examine the performance of such strategies using a variety of risk-adjusted performance measures. We find that relative to the buy-and-hold strategy, MA strategies have lower average returns and Sharpe ratios, but fare better under factor-adjusted performance measures such as the CAPM alpha. We also find that MA strategies become less profitable when they are implemented using ETFs than using their underlying indices. In addition, we propose a quasi-intraday version of the standard MA strategy (QUIMA) that allows investors to trade immediately upon observing MA crossover signals. The QUIMA strategy outperforms the standard one that only trades at the close of a trading day, when the long-term MA lag length is no more than 50 days.

Suggested Citation

  • Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
  • Handle: RePEc:eee:empfin:v:57:y:2020:i:c:p:16-32
    DOI: 10.1016/j.jempfin.2019.10.002
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    References listed on IDEAS

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    Cited by:

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    3. Ergun, Lerby & Molchanov, Alexander & Stork, Philip, 2023. "Technical trading rules, loss avoidance, and the business cycle," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    4. Chan Kyu Paik & Jinhee Choi & Ivan Ureta Vaquero, 2024. "Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices," JRFM, MDPI, vol. 17(3), pages 1-18, February.
    5. Jin, Xiaoye, 2022. "Testing technical trading strategies on China's equity ETFs: A skewness perspective," Emerging Markets Review, Elsevier, vol. 51(PA).
    6. 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.
    7. Agosto, Arianna & Cerchiello, Paola & Pagnottoni, Paolo, 2022. "Sentiment, Google queries and explosivity in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    8. Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    Moving average; Technical trading rules; Data-snooping bias; Exchange traded funds;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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