Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy

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

“…Rapach et al (2010) adopt a different pooling strategy based on forecast combinations across individual models. Forecast combinations can be seen as a diversification strategy (Timmermann, 2006) that reduces the uncertainty surrounding the constantly evolving data-generating process for stock returns.…”

“…However, improvements are predominantly located in the distant past (see Li & Tsiakas, 2016;Pettenuzzo et al, 2014). 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).…”

“…First and foremost, we refer to studies predicting stock market risk premia with macroeconomic information by exploiting of a large number of potential predictor variables. Empirical studies commonly make use of a strong prior on predictability by examining the forecast performance of individual predictors (e.g., Goyal & Welch, 2008) or forecasting strategies based on a small set of variables (e.g., Rapach & Zhou, 2013;Rapach et al, 2010). However, reported evidence shows that the relation between the macroeconomic situation and the stock market is difficult to grasp by relying on low-dimensional information sets (Timmermann, 2008).…”

“…These are able to consider many potentially important predictor variables simultaneously and, therefore, are able to limit the impact of model uncertainty (see Rapach & Zhou, 2013). Additionally, if the degree of potential model instability is not too dependent across series, pooling strategies might further enhance the overall forecast performance (Rapach et al, 2010;Stock & Watson, 2002a). Explicitly, this study evaluates the predictive performance of forecasting strategies combining information and strategies that pool individual forecasts (Huang & Lee, 2010;Rapach & Zhou, 2013).…”

“…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). Regarding the second set of forecasting strategies, we follow Rapach et al (2010) by using forecast combination strategies.…”