Abstract

http://ssrn.com/abstract=251279
 
 

References (54)



 
 

Citations (86)



 


 



Stock Return Predictability: A Bayesian Model Selection Perspective


Martijn Cremers


University of Notre Dame

June 19, 2000

EFA 0524; EFMA 2000 Athens

Abstract:     
Attempts to characterize stock return predictability have generated a plethora of papers documenting the ability of various variables to explain conditional expected returns. However, there is little consensus on what the important conditioning variables are, giving rise to a great deal of model uncertainty and data snooping fears. In this paper, we introduce a new methodology that explicitly takes the model uncertainty into account by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful prior information. Therefore, 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, over a wide range of prior views. Furthermore, the stalwarts such as dividends and past returns do not perform well. The out-of- sample results for the Bayesian average models show improved forecasts relative to the classical statistical model selection methods, are consistent with the in-sample results and show some, albeit small, evidence of predictability.

Number of Pages in PDF File: 36

JEL Classification: G12

working papers series


Download This Paper

Date posted: January 15, 2001  

Suggested Citation

Cremers, Martijn, Stock Return Predictability: A Bayesian Model Selection Perspective (June 19, 2000). EFA 0524; EFMA 2000 Athens. Available at SSRN: http://ssrn.com/abstract=251279 or http://dx.doi.org/10.2139/ssrn.251279

Contact Information

K. J. Martijn Cremers (Contact Author)
University of Notre Dame ( email )
P.O. Box 399
Notre Dame, IN 46556-0399
United States
Feedback to SSRN


Paper statistics
Abstract Views: 3,444
Downloads: 1,014
Download Rank: 11,003
References:  54
Citations:  86

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo6 in 0.375 seconds