Grounding Investor Views: Neural Predicates in the Black-Litterman Model
Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates -- a process that remains largely subjective and difficult to scale. We propose a formal approach in which neural predicates serve as a structured, probabilistic mechanism for view generation. In our formulation, structured financial analysis data is processed through a compositional hierarchy of neural predicates whose outputs -- probability distributions over market stances -- are mapped to the pick matrix $\mathbf{P}$, the view return vector $\mathbf{q}$, and the view uncertainty matrix $\boldsymbolΩ$ of the Black-Litterman model. View con...
| Authors | Marcos Florencio |
|---|---|
| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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