Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?
John Campbell () and
Samuel P. Thompson
Scholarly Articles from Harvard University Department of Economics
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.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (355) Track citations by RSS feed
Published in The Review of Financial Studies
Downloads: (external link)
Journal Article: Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:hrv:faseco:2622619
Access Statistics for this paper
More papers in Scholarly Articles from Harvard University Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Office for Scholarly Communication ().