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Stock Return Predictability: A Bayesian Model Selection Perspective

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Author Info
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.

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Publisher Info
Article provided by Oxford University Press for 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. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria," Cahiers du Département d'Econométrie 2004.05, Département d'Econométrie, Université de Genève. [Downloadable!]
  2. Pedro N. Rodríguez, & Simón Sosvilla-Rivero, 2006. "Forecasting Stock Price Changes: Is it Possible?," Working Papers 2006-22, FEDEA. [Downloadable!]
  3. Laurence Fung & Ip-wing Yu, 2008. "Predicting Stock Market Returns by Combining Forecasts," Working Papers 0801, Hong Kong Monetary Authority. [Downloadable!]
  4. Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer, vol. 34(2), pages 173-193, September. [Downloadable!] (restricted)
  5. Jonathan H. Wright, 2003. "Bayesian Model Averaging and exchange rate forecasts," International Finance Discussion Papers 779, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  6. Lillie Lam & Laurence Fung & Ip-wing Yu, 2008. "Comparing Forecast Performance of Exchange Rate Models," Working Papers 0808, Hong Kong Monetary Authority. [Downloadable!]
  7. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  8. Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  9. Jonathan H. Wright, 2003. "Forecasting U.S. inflation by Bayesian Model Averaging," International Finance Discussion Papers 780, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  10. Inoue, Atsushi & Kilian, Lutz, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  11. Lubos Pastor & Robert F. Stambaugh, 2007. "Predictive Systems: Living with Imperfect Predictors," NBER Working Papers 12814, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  12. John M Maheu & Thomas H McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Papers tecipa-293, University of Toronto, Department of Economics. [Downloadable!]
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  13. Francisco Peñaranda, 2004. "Are Vector Autoregressions And Accurate Model For Dynamic Asset Allocation?," Working Papers wp2004_0419, CEMFI. [Downloadable!]
  14. Jacob Boudoukh & Matthew Richardson & Robert Whitelaw, 2005. "The Myth of Long-Horizon Predictability," NBER Working Papers 11841, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  15. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics. [Downloadable!]
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