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RFS Advance Access first published online on November 20, 2007
This version published online on November 22, 2007
Review of Financial Studies, doi:10.1093/rfs/hhm055
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© The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?

John Y. Campbell
Department of Economics, Harvard University and NBER

Samuel B. Thompson
Arrowstreet Capital, LP

Address correspondence to John Y. Campbell, Department of Economics, Littauer Center, Harvard University, Cambridge, MA 02138, telephone: (617) 496-6448, e-mail: john_campbell{at}harvard.edu.

JEL Classification: G10, G11


  Abstract

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.

The authors are grateful to Jan Szilagyi for able research assistance, to Amit Goyal and Ivo Welch for sharing their data, and to Malcolm Baker, Lutz Kilian, Martin Lettau, Sydney Ludvigson, Rossen Valkanov, the editor, and two anonymous referees for helpful comments on an earlier draft entitled "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?" This material is based upon the work supported by the National Science Foundation under Grant No. 0214061 to Campbell.


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Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy
Rev. Financ. Stud., August 11, 2009; (2009) hhp063v1.
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