用神经谓词自动生成投资观点，让黑-利特曼模型更可解释、可学习。
Grounding Investor Views: Neural Predicates in the Black-Litterman Model
- 用神经谓词层级结构生成金融观点的概率分布
- 观点置信度由模型输出决定，替代主观不确定性评估
- 结果可追溯、支持端到端训练，适合量化投资研究
黑-利特曼模型进行组合构建时，需投资者提供资产收益观点及明确的不确定性估计，该过程仍高度主观且难以扩展。本文提出一种形式化方法：利用神经谓词作为结构化、概率化的观点生成机制。在该框架中，结构化金融分析数据通过神经谓词的组合层次处理，其输出——市场立场的概率分布——被映射为黑-利特曼模型中的选取矩阵 $\mathbf{P}$、观点收益向量 $\mathbf{q}$ 以及观点不确定性矩阵 $\boldsymbolΩ$。观点置信度由谓词输出分布推导得出，提供了一种数据驱动的不确定性估算替代方案。该方法具有可解释性，任何组合权重均可通过谓词逻辑链回溯至原始数据；同时具备完全可微特性，支持端到端学习。
原文摘要 · Abstract (English)
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 confidence is derived from predicate output distributions, providing a data-driven alternative to subjective uncertainty elicitation. The resulting approach is interpretable, in the sense that any portfolio weight can be traced back through the predicate's logical chain to the underlying data, and fully differentiable, enabling end-to-end learning.
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