Forecasting the Equity Risk Premium: The Role of Technical Indicators

Neely, Christopher J.; Rapach, David E.; Tu, Jun; Zhou, Guofu

doi:10.2139/ssrn.1787554

Cited by 159 publications

(444 citation statements)

References 73 publications

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“…In contrast, the slope coefficients of MAs based on longer lags, from MADP (20) os values of all economic variables are negative, suggesting that the economic variables are weak at forecasting the stock market returns. Note that our results on economic variables are consistent with those of Neely et al (2014).…”

Section: A Bivariate Predictive Regressionssupporting

confidence: 89%

“…to 2.75%. According to Campbell and Thompson (2008) and Neely et al (2014), a predictor can be considered as having the economically significant risk premium forecasting ability if its monthly in-sample R 2 closes to 0.5%. More importantly, the values of out-of-sample R 2 os for MADP(2) to MADP (10) are positive and significant at least at the 10% level in terms of the adjusted MSFE exhibiting the strongly significant predictability relative to the HA forecast.…”

Section: A Bivariate Predictive Regressionsmentioning

confidence: 99%

“…Stock return forecastability has been considered as a "stylized fact." While some researchers employ economic variables to predict the stock market (e.g., Cochrane 2006), more and more studies provide the evidence that technical indicators, especially the moving average (MA), 1 have strongly predictive power in the stock market (e.g., Brock et al 1992;Gençay and Stengos 1998;Gençay 1998b;Lento and Gradojevic 2007;Neely et al 2014;Glabadanidis 2015;Marshall et al 2017;Mitra et al 2017;Zakamulin 2017). Brock et al (1992) seems the first major study to provide strong support of the predictability of MA trading indicators.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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Section: A Bivariate Predictive Regressionssupporting

confidence: 89%

Section: A Bivariate Predictive Regressionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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“…Zhu and Zhou (2009) provide a solid theoretical reason why technical indicators could be a potentially useful state variable in an environment where investors need to learn over time the fundamental value of the risky asset they invest in. More recently, Neely et al (2010Neely et al ( , 2011 find that technical analysis has as much forecasting power over the equity risk premium as the information provided by economic fundamentals. The practitioners literature also includes Faber (2007) and Kilgallen (2012) who thoroughly document the risk-adjusted returns to the MA strategy using various portfolios, commodities, and currencies.…”

Section: Introductionmentioning

confidence: 99%

The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes

Glabadanidis

2013

Int Rev Finance

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I present evidence that a moving average (MA) trading strategy dominates buying and holding the underlying asset in a mean‐variance sense using monthly returns of value‐weighted and equal‐weighted US REIT indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart factors and produce economically and statistically significant alphas of between 10 and 15% per year after transaction costs. This performance is robust to different lags of the MA and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at‐the‐money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over the subsequent return of the REIT index. The MA strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy‐and‐hold strategy using all of the 20 REIT indexes. The results from applying the MA strategy with 274 individual REITs largely corroborate the findings for the REIT indexes.

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“…Exploring the predictability of bear stock market is essential for financial applications and other reasons. Predicting bear stock market is related to, but different from, predicting equity premium; see, for example, Chauvet and Potter (2000), Resnick and Shoesmith (2002), Chen (2009) and Nyberg (2013) for the former, and Goyal and Welch (2008), Rapach et al (2010) and Neely et al (2014) for recent examples of the latter. Since stock market states and the equity premium both contain price information, these two tasks are equally important for evaluating the market efficiency hypothesis.…”

Section: Introductionmentioning

confidence: 99%

The Role of Momentum, Sentiment, and Economic Fundamentals in Forecasting Bear Stock Market

Chen

Vincent

2016

Journal of Forecasting

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This article examines the role of market momentum, investor sentiment, and economic fundamentals in forecasting bear stock market. We find strong evidence that bear stock market is predictable by market momentum and investor sentiment in full‐sample and out‐of‐sample analyses. Most economic fundamental variables lose their out‐of‐sample significance once we control for market momentum and investor sentiment. However, the inclusion of economic fundamentals can improve the economic value of the forecasting model in our trading experiments. Copyright © 2016 John Wiley & Sons, Ltd.

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Forecasting the Equity Risk Premium: The Role of Technical Indicators

Cited by 159 publications

References 73 publications

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

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

The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes

The Role of Momentum, Sentiment, and Economic Fundamentals in Forecasting Bear Stock Market

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