Stock Return Predictability: A Bayesian Model Selection Perspective
Top Cited Papers
- 1 July 2002
- journal article
- Published by Oxford University Press (OUP) in The Review of Financial Studies
- Vol. 15 (4) , 1223-1249
- https://doi.org/10.1093/rfs/15.4.1223
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.Keywords
This publication has 41 references indexed in Scilit:
- Stock returns and the term structurePublished by Elsevier ,2002
- Benchmark priors for Bayesian model averagingJournal of Econometrics, 2001
- Portfolio Selection and Asset Pricing ModelsThe Journal of Finance, 2000
- Conditioning Variables and the Cross Section of Stock ReturnsThe Journal of Finance, 1999
- Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?The Review of Financial Studies, 1999
- An Asymtotic Theory of Bayesian Inference for Time SeriesEconometrica, 1996
- Bayes FactorsJournal of the American Statistical Association, 1995
- Variable Selection Via Gibbs SamplingJournal of the American Statistical Association, 1993
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974