A growing empirical literature suggests that equity-premium predictability is state dependent, with much of the forecasting power concentrated around recessionary periods (Henkel et al., 2011; Dangl and Halling, 2012; Devpura et al., 2018). I study U.S. stock return predictability across economic regimes and document strong evidence of time-varying expected returns across both expansionary and contractionary states. I contribute in two ways. First, I introduce a state-switching predictive regression in which the market state is defined in real time using the slope of the yield curve. Relative to the standard one-state predictive regression, the state-switching specification increases both in-sample and out-of-sample performance for the set of popular predictors considered by Welch and Goyal (2008), improving the out-of-sample performance of most predictors in economically meaningful ways. Second, I propose a new aggregate predictor, the Aligned Economic Index, constructed via partial least squares (PLS). Under the state-switching model, the Aligned Economic Index exhibits statistically and economically significant predictive power in sample and out of sample, and it outperforms widely used benchmark predictors and alternative predictor-combination methods.
翻译:越来越多的实证研究表明,股权溢价的可预测性具有状态依赖性,其预测能力主要集中在经济衰退期(Henkel等,2011;Dangl和Halling,2012;Devpura等,2018)。本文研究了美国股票回报在不同经济体制下的可预测性,并记录了在扩张期和收缩期均存在时变预期回报的有力证据。本文在两方面做出贡献。首先,我引入了一种状态转换预测回归模型,其中市场状态通过收益率曲线的斜率实时定义。相较于标准单状态预测回归,状态转换设定显著提升了Welch和Goyal(2008)所考察的常用预测因子的样本内及样本外表现,并以具有经济意义的方式改进了多数预测因子的样本外预测性能。其次,我提出了一种新的综合预测因子——对齐经济指数,该指数通过偏最小二乘法构建。在状态转换模型框架下,对齐经济指数在样本内和样本外均表现出统计显著且经济意义显著的预测能力,其表现优于广泛使用的基准预测因子及其他预测因子组合方法。