Latent Variable Estimation in Bayesian Black-Litterman Models
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916719559180288 |
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| author | Lin, Thomas Y. L. Hu, Jerry Yao-Chieh Chiou, Paul W. Lin, Peter |
| author_facet | Lin, Thomas Y. L. Hu, Jerry Yao-Chieh Chiou, Paul W. Lin, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Variable Estimation in Bayesian Black-Litterman Models Lin, Thomas Y. L. Hu, Jerry Yao-Chieh Chiou, Paul W. Lin, Peter Portfolio Management Machine Learning Econometrics Methodology 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. |
| title | Latent Variable Estimation in Bayesian Black-Litterman Models |
| topic | Portfolio Management Machine Learning Econometrics Methodology |
| url | https://arxiv.org/abs/2505.02185 |