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

• 成果类型: Article
• 署名作者: Rapach, David E.; Strauss, Jack K.; Zhou, Guofu
• 署名单位: Washington University (WUSTL); Saint Louis University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhp063
• 发表日期: 2010
• 页码: 821
• 关键词: BOOK-TO-MARKET; STOCK RETURN PREDICTABILITY; risk-factors; tests; inflation; yield; consumption; accuracy; models; ratios

摘要

Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy. (JEL C22, C53, G11, G12)