הצגת תפיסות משקיעים: נראטיבים נוירליים בדגם Black-Litterman

תקציר מקורי באנגליתarXiv:2607.20533v1 Announce Type: cross Abstract: 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 $\boldsymbol{\Omega}$ of the Black-Litterman model. View confidence is derived from predicate output distr