Thomas Y. L. Lin, Jerry Yao-Chieh Hu, Paul W. Chiou, Peter Lin
arXiv 4 May 2025 · Finance — Portfolio Management
arXiv:2505.02185 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Petter Kolm and Gordon Ritter (2017) On the bayesian interpretation of black–litterman | 0.894 | 7 | 3 | 71% |
| 2 | Fischer Black and Robert Litterman (1992) Global portfolio optimization | 0.843 | 10 | 6 | 60% |
| 3 | Petter N Kolm and Gordon Ritter (2021) Factor investing with black–litterman–bayes: incorporating factor views and priors in portfolio construction | 0.737 | 5 | 3 | 40% |
| 4 | Thomas Idzorek (2007) A step-by-step guide to the black-litterman model: Incorporating user-specified confidence levels | 0.737 | 4 | 4 | 50% |
| 5 | Anisa Salomons (2007) The black-litterman model hype or improvement? | 0.737 | 4 | 3 | 50% |
| 6 | Wai Lee (2000) Theory and methodology of tactical asset allocation, volume 65 | 0.737 | 3 | 3 | 67% |
| 7 | Harry Markowitz (1952) Portfolio selection | 0.644 | 3 | 2 | 67% |
| 8 | Steven L Beach and Alexei G Orlov (2007) An application of the black–litterman model with egarch-m-derived views for international portfolio management | 0.511 | 3 | 2 | 33% |
| 9 | Andi Duqi, Leonardo Franci, and Giuseppe Torluccio (2014) The black–litterman model: the definition of views based on volatility forecasts | 0.511 | 3 | 2 | 33% |
| 10 | Mahmut Kara, Aydin Ulucan, and Kazim Baris Atici (2019) A hybrid approach for generating investor views in black–litterman model | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 61 scored citations.