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Stock Return Predictability: A Bayesian Model Selection Perspective

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  • K. J. Martijn Cremers
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    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.

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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal The Review of Financial Studies.

    Volume (Year): 15 (2002)
    Issue (Month): 4 ()
    Pages: 1223-1249

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    Handle: RePEc:oup:rfinst:v:15:y:2002:i:4:p:1223-1249

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    1. A. Craig MacKinlay & Lubos Pastor, . "Asset Pricing Models: Implications for Expected Returns and Portfolio Selection," Rodney L. White Center for Financial Research Working Papers 13-99, Wharton School Rodney L. White Center for Financial Research.
    2. Wayne E. Ferson & Campbell R. Harvey, 1999. "Conditioning Variables and the Cross Section of Stock Returns," Journal of Finance, American Finance Association, vol. 54(4), pages 1325-1360, 08.
    3. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, December.
    4. John Y. Campbell, 1985. "Stock Returns and the Term Structure," NBER Working Papers 1626, National Bureau of Economic Research, Inc.
    5. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
    6. Lubos Pastor & Robert F. Stambaugh, . "Comparing Asset Pricing Models: An Investment Perspective," Rodney L. White Center for Financial Research Working Papers 16-99, Wharton School Rodney L. White Center for Financial Research.
    7. Lubo Pástor, . "Portfolio Selection and Asset Pricing Models," CRSP working papers 498, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
    8. Harvey, Campbell R., 1989. "Time-varying conditional covariances in tests of asset pricing models," Journal of Financial Economics, Elsevier, vol. 24(2), pages 289-317.
    9. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    10. Foster, F Douglas & Smith, Tom & Whaley, Robert E, 1997. " Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R-Squared," Journal of Finance, American Finance Association, vol. 52(2), pages 591-607, June.
    11. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    12. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
    13. Ferson, Wayne E, 1990. " Are the Latent Variables in Time-Varying Expected Returns Compensation for Consumption Risk?," Journal of Finance, American Finance Association, vol. 45(2), pages 397-429, June.
    14. Richard, J. F. & Steel, M. F. J., 1988. "Bayesian analysis of systems of seemingly unrelated regression equations under a recursive extended natural conjugate prior density," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 7-37.
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