Stock return predictability and model uncertainty

• 成果类型: Article
• 署名作者: Avramov, D
• 署名单位: University System of Maryland; University of Maryland College Park
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/S0304-405X(02)00131-9
• 发表日期: 2002
• 页码: 423-458
• 关键词: stock return predictability; model uncertainty; Bayesian model averaging; PORTFOLIO SELECTION; variance decomposition

摘要

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictor.;. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. Vie show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses. (C) 2002 Elsevier Science B.V. All rights reserved.