Stock return predictability: A Bayesian model selection perspective

• 成果类型: Article
• 署名作者: Cremers, KJM
• 署名单位: Yale University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/15.4.1223
• 发表日期: 2002
• 页码: 1223
• 关键词: asset pricing-models; variable selection; Expected returns; Graphical Models; inference; tests; RISK

摘要

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.