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Trend following with momentum versus moving averages: a tale of differences

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  • Valeriy Zakamulin
  • Javier Giner

Abstract

Despite the ever-growing interest in trend following and a series of publications in academic journals, there is a dearth of theoretical results on the properties of trend-following rules. Our paper fills this gap by comparing and contrasting the two most popular trend-following rules, the momentum (MOM) and moving average (MA) rules, from a theoretical perspective. We provide theoretical results on the similarity between different trend-following rules and the forecast accuracy of trading rules. Our results show that the similarity between the MOM and MA rules is high and increases with the strength of the trend. However, compared to the MOM rule, the MA rules exhibit more robust forecast accuracy for the future direction of price trends. In this paper, we also develop a hypothesis about uncertain market dynamics. We show that this hypothesis, coupled with our analytical results, has far-reaching practical implications and can explain a number of empirical observations. Among other things, our hypothesis explains why the empirical performance of the MA rules is better than that of the MOM rule. We broaden the appeal and practical importance of our theoretical results by offering various illustrations and real-world examples.

Suggested Citation

  • Valeriy Zakamulin & Javier Giner, 2020. "Trend following with momentum versus moving averages: a tale of differences," Quantitative Finance, Taylor & Francis Journals, vol. 20(6), pages 985-1007, June.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:6:p:985-1007
    DOI: 10.1080/14697688.2020.1716057
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    Cited by:

    1. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    2. Marco Corazza & Claudio Pizzi & Andrea Marchioni, 2024. "A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization," Computational Management Science, Springer, vol. 21(1), pages 1-29, June.
    3. Zakamulin, Valeriy & Giner, Javier, 2022. "Time series momentum in the US stock market: Empirical evidence and theoretical analysis," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Valeriy Zakamulin & Javier Giner, 2024. "Optimal trend-following rules in two-state regime-switching models," Journal of Asset Management, Palgrave Macmillan, vol. 25(4), pages 327-348, July.
    5. Zakamulin, Valeriy & Giner, Javier, 2023. "Optimal trend-following with transaction costs," International Review of Financial Analysis, Elsevier, vol. 90(C).

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