Robust Bayesian Portfolio Choices

• 成果类型: Article
• 署名作者: Anderson, Evan W.; Cheng, Ai-Ru (Meg)
• 署名单位: Northern Illinois University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhw001
• 发表日期: 2016
• 页码: 1330
• 关键词: stock return predictability; NAIVE DIVERSIFICATION; MODEL; uncertainty; parameter; selection; MARKOWITZ

摘要

We propose a Bayesian-averaging portfolio choice strategy with excellent out-of-sample performance. Every period a new model is born that assumes means and covariances are constant over time. Each period we estimate model parameters, update model probabilities, and compute robust portfolio choices by taking into account model uncertainty, parameter uncertainty, and non-stationarity. The portfolio choices achieve higher out-of-sample Sharpe ratios and certainty equivalents than rolling window schemes, the 1/N approach, and other leading strategies do on a majority of 24 datasets.