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

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.

Forecasting the Market Equity Premium: Does Nonlinearity Matter?

“…Jiang et al (2019) build a new composite index measuring market sentiment, the manager sentiment index, and show that it contains genuine predictive content for the market equity premium beyond those embedded in typical sentiment indexes in behavior finance. Ma et al (2019) propose a new predictor, MADP, a moving-average momentum strategy based on daily prices, and show that it outperforms the historical mean benchmark as well as various standard moving-average momentum strategies based on monthly prices.…”

Does the kitchen‐sink model work forecasting the equity premium?

“…Accurate forecasts of the equity premium play a critical role in diverse areas of empirical finance such as optimal portfolio decisions, capital budgeting, and performance evaluation of investment funds managers (see, for example, Ait‐Sahali & Brandt, 2001; Avramov & Wermers, 2006; Barberis, 2000; Xia, 2001). Consequently, numerous studies have provided empirical evidences of both the in‐sample and out‐of‐sample predictability for a multitude of financial and economic variables forecasting the equity premium (see, for instance, Campbell, 1987; Campbell & Shiller, 1988; Fama & French, 1988; Fama & French, 1989; Faria & Verona, 2018; Ferreira & Santa‐Clara, 2011; Jiang, Lee, Martin, & Zhou, 2019; Li, Ng, & Swaminathan, 2013; Ma, Wen, Wang, & Jiang, 2019; Rapach, Ringgenberg, & Zhou, 2016).…”

The profitability of Ichimoku Kinkohyo based trading rules in stock markets and FX markets

“…Nonetheless, EMH has been challenged by numerous researchers in academic, who argued that the linear and nonlinear stock return predictability does vary over time and market efficiency is not an all-or-nothing condition (Urquhart & Mcgroarty, 2016). Indeed, technical analysis methods are still widely used by investors and analysts in financial markets all over the world, particularly in foreign exchange (FX) markets (Hsu, Taylor, & Wang, 2016;Oberlechner, 2010) and stock markets (Ma, Wen, Wang, & Yong, 2017;Rousis & Papathanasiou, 2018). The abnormal returns generated by using technical analysis that reported by those studies present a serious challenge to EMH.…”