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Latent Variable Estimation in Bayesian Black-Litterman Models

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  • Thomas Y. L. Lin
  • Jerry Yao-Chieh Hu
  • Paul W. Chiou
  • Peter Lin

Abstract

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 $\Omega$ that describe how much a chosen portfolio should outperform the market. Our key idea is to treat $(q,\Omega)$ 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.

Suggested Citation

  • Thomas Y. L. Lin & Jerry Yao-Chieh Hu & Paul W. Chiou & Peter Lin, 2025. "Latent Variable Estimation in Bayesian Black-Litterman Models," Papers 2505.02185, arXiv.org.
    • Handle: RePEc:arx:papers:2505.02185
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    References listed on IDEAS

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