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Further Mining the Predictability of Moving Averages: Evidence from the US Stock Market

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  • Chaoqun Ma
  • Danyan Wen
  • Gang‐Jin Wang
  • Yong Jiang

Abstract

Most studies on the predictability of moving average (MA) technical analysis use the discrete (buy/sell) trading recommendations. However, it is possibly incomplete or unreliable to explore the predictability of MA by only employing its generated trading signals. To further explore the forecastability of MA, we study its measurable impact on the stock market returns by using a conventional predictive regression framework. Our empirical study on the US stock market with respect to more detailed price information finds, (i) that the proposed predictor, MADP (MA based on daily prices) shows significant predictability in‐ and out‐of‐sample, and significantly outperforms the historical average (HA) benchmark as well as the MA based on monthly prices, (ii) that the predictability of MADP centers on the short‐term lags (within the most recent 10 days) and disappears when lags are beyond 20 days, and (iii) that the economic evaluation of the portfolios based on trading strategies confirms the superior performance of MADP with short‐term lags against the benchmark even though considering transaction costs.

Suggested Citation

  • Chaoqun Ma & Danyan Wen & Gang‐Jin Wang & Yong Jiang, 2019. "Further Mining the Predictability of Moving Averages: Evidence from the US Stock Market," International Review of Finance, International Review of Finance Ltd., vol. 19(2), pages 413-433, June.
    • Handle: RePEc:bla:irvfin:v:19:y:2019:i:2:p:413-433
    DOI: 10.1111/irfi.12166
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    Cited by:

    1. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    2. Anwen Yin, 2022. "Does the kitchen‐sink model work forecasting the equity premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 223-247, March.
    3. Yufeng Lin & Xiaogang Wang & Yuehua Wu, 2023. "An Adaptive Multiple-Asset Portfolio Strategy with User-Specified Risk Tolerance," Mathematics, MDPI, vol. 11(7), pages 1-35, March.

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