Incorporating Different Sources of Information for Bayesian Optimal Portfolio Selection
提出一种新的贝叶斯共轭先验用于切线投资组合权重推断，能自动融入高频收益率和市场条件指标（如VIX和EPU），实证表明该方法在多数情况下优于现有交易策略。
This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy Uncertainty Index (EPU), significantly enhancing the decision-making process for optimal portfolio construction. While the Jeffreys’ prior is also acknowledged, emphasis is placed on the advantages and practical applications of the conjugate prior. An extensive empirical study reveals that our method, leveraging this conjugate prior, consistently outperforms existing trading strategies in the majority of examined cases.