On the Predictability of Stock Returns: An Asset-Allocation Perspective




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    • Recanati Graduate School of Business Administration, Tel-Aviv University and The Wharton School, University of Pennsylvania (Kandel) and The Wharton School, University of Pennsylvania and National Bureau of Economic Research (Stambaugh). The authors are grateful for comments by René Stulz, three anonymous referees, and workshop participants at Baruch College (CUNY), Duke University, Northwestern University, Ohio State University, Rutgers University, the University of North Carolina, the University of Pennsylvania, the University of Rochester, the University of Washington, and Washington University in St. Louis. We also wish to thank participants in the NBER 1995 Summer Institute, especially Bill Schwert, the discussant for the paper.


Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk-averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures, but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability.