用数据自动推断投资观点，让投资组合更稳定高效
Latent Variable Estimation in Bayesian Black-Litterman Models
- 将投资观点视为隐藏变量，从市场数据中学习
- 相比经典方法，夏普比率提升50%，换手率降低55%
- 适合追求数据驱动、无需主观判断的量化投资者
我们重新审视贝叶斯黑-莉特尔曼（Bayesian Black-Litterman, BL）投资组合模型，消除其对主观投资者观点的依赖。传统BL需要投资者提供预测向量 $q$ 及其不确定性矩阵 $Ω$，描述特定组合相对于市场的预期表现。本文核心思想是将 $(q, Ω)$ 视为潜在变量，在单一贝叶斯网络中通过市场数据进行学习。由此得到的后验估计具有闭式表达，支持快速推理与稳定的投资组合权重。在此基础上，我们提出两种机制捕捉特征与收益的交互：共享潜在参数化和特征影响观点，二者均能还原经典BL和马科维茨（Markowitz）投资组合作为特例。实证上，在30年道琼斯和20年行业ETF数据上，相较于马科维茨与指数基准，夏普比率提高50%，换手率降低55%。该工作将BL转化为一个完全数据驱动、无须主观观点、且理论一致的贝叶斯投资组合优化框架。
原文摘要 · Abstract (English)
We revisit the Bayesian Black-Litterman (BL) portfolio model and remove its reliance on subjective investor views. Classical BL requires an investor "view": a forecast vector $q$ and its uncertainty matrix $Ω$ that describe how much a chosen portfolio should outperform the market. Our key idea is to treat $(q,Ω)$ as latent variables and learn them from market data within a single Bayesian network. Consequently, the resulting posterior estimation admits closed-form expression, enabling fast inference and stable portfolio weights. Building on these, we propose two mechanisms to capture how features interact with returns: shared-latent parametrization and feature-influenced views; both recover classical BL and Markowitz portfolios as special cases. Empirically, on 30-year Dow-Jones and 20-year sector-ETF data, we improve Sharpe ratios by 50% and cut turnover by 55% relative to Markowitz and the index baselines. This work turns BL into a fully data-driven, view-free, and coherent Bayesian framework for portfolio optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。