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Rev Fin 2002; 15:1223-1249
© 2002 the Society for Financial Studies

Stock Return Predictability: A Bayesian Model Selection Perspective

K. J. Martijn Cremers
Yale University

Address correspondence to K. J. Martijn Cremers, Yale School of Management, International Center for Finance, Box 20820, 135 Prospect Street, New Haven, CT 06520, or e-mail: kcremers{at}stern.nyu.edu.

Abstract

Attempts to characterize stock return predictability have resulted in little consensus on the important conditioning variables, giving rise to model uncertainty and data snooping fears. We introduce a new methodology that explicitly incorporates model uncertainty by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful information. Our approach minimizes data snooping given the information set and the priors. We compare the prior views of a skeptic and a confident investor. The data imply posterior probabilities that are in general more supportive of stock return predictability than the priors for both types of investors.


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