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

Chaoqun Ma, Danyan Wen, Gang-Jin Wang and Yong Jiang

International Review of Finance, 2019, vol. 19, issue 2, 413-433

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.

Date: 2019
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International Review of Finance is currently edited by Bruce D. Grundy, Naifu Chen, Ming Huang, Takao Kobayashi and Sheridan Titman

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