Forecasting the Equity Risk Premium: The Role of Technical Indicators

Abstract

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 . This paper was accepted by Wei Jiang, finance.

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

“…Ludvigson and Ng (2009) successfully used factor decomposition for risk premium prediction on the bond market, and Moench (2008) considered factor-augmented forecasts for the yield curve. Next to Ludvigson and Ng (2009), our approach bears a strong resemblance to Ludvigson and Ng (2007), Cakmakli and van Dijk (2016), and Neely et al (2014), who analyze the forecasting performance of latent common components for the equity premium and its volatility. Timmermann (2008) also used factor models to predict short-term size returns.…”

“…So far, presented results are based on "smoothed" factor estimates (covering the full sample information), which h = 1 h = 3 h = 12 h = 24 h = 1 h = 3 h = 12 h = 24 h = 1 h = 3 h = 12 h = 24 h = 1 h = 3 h = 12 h = 24 Neely et al (2014) for the market excess return. For this purpose, we evaluate the forecasting power of all previously mentioned pooling strategies against simple historical average forecasts.…”

“…The restricted to more recent return movements. In detail, we follow Neely et al (2014) in this respect and assume an initial estimation period of 180 months, yielding 456 OOS observations in total. Thus our evaluation period starts in 1975:02 and is closely linked to the sample period over which Goyal and Welch (2008) mention that no commonly used predictor variable has superior OOS performance.…”

“…Further forecasting advances incorporate the usage of pooling strategies like principal component analysis (proposed by Stock & Watson, 2002b) or forecast combination methods (proposed by Rapach, Strauss, & Zhou, 2010). Although Rapach and Zhou (2013) report evidence in favor of principal component predictive regressions, the same set of predictors performs considerably worse than the historical average benchmark by considering a more recent data sample (see, e.g., Neely, Rapach, Tu, & Zhou, 2014). This behavior also seems to be evident for alternative pooling strategies (Baetje & Menkhoff, 2016).…”

“…Explicitly, this study evaluates the predictive performance of forecasting strategies combining information and strategies that pool individual forecasts (Huang & Lee, 2010;Rapach & Zhou, 2013). For this purpose, we first apply principal component predictive regressions considering a small number of latent common components as potential predictor variables (Cakmakli & van Dijk, 2016;Ludvigson & Ng, 2007;Neely et al, 2014). Furthermore, we follow Pruitt (2013, 2015) and make use of a three-pass regression filter estimating target relevant factors by particularly taking into account the relationship between the estimated factors and the target relevant variables (i.e., stock returns).…”