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Stock Return Predictability: A Bayesian Model Selection Perspective

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  • K. J. Martijn Cremers

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. Copyright 2002, Oxford University Press.

Suggested Citation

  • K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
    • Handle: RePEc:oup:rfinst:v:15:y:2002:i:4:p:1223-1249
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    References listed on IDEAS

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    1. Wayne E. Ferson & Campbell R. Harvey, 1999. "Conditioning Variables and the Cross-Section of Stock Returns," NBER Working Papers 7009, National Bureau of Economic Research, Inc.
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