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| Author: | Cremers, K. J. M. |
| Title: | Stock Return Predictability: A Bayesian Model Selection Perspective |
| Journal: | Review of Financial Studies
2002 : FALL, VOL. 15:4, p. 1223-1250 |
| Index terms: | STOCKS STOCK RETURNS MODELS |
| Language: | eng |
| 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. The authors 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. The authors' approach minimizes data snooping given the information set and the priors. The authors 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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