Predicting excess stock returns out of sample: Can anything beat the historical average?

• 成果类型: Article
• 署名作者: Campbell, John Y.; Thompson, Samuel B.
• 署名单位: Harvard University; National Bureau of Economic Research
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhm055
• 发表日期: 2008
• 页码: 1509
• 关键词: BOOK-TO-MARKET; equity premium; exchange-rates; INTEGRATED REGRESSORS; Expected returns; dividend yields; ratios; predictability; models; fundamentals

摘要

Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns.