AI 中文总结
研究在布莱克-利特曼模型下投资组合构建问题，提出用神经谓词作为观点生成机制，将结构化金融分析数据经其处理后映射到模型相关矩阵，方法可解释且完全可微，实现端到端学习。
AI 中文摘要
在布莱克-利特曼模型下进行投资组合构建时，投资者需指定资产回报观点并给出明确的不确定性估计，此过程主观且难以扩展。我们提出一种形式化方法，其中神经谓词作为观点生成的结构化概率机制。结构化金融分析数据通过神经谓词的组合层次结构进行处理，其输出映射到布莱克-利特曼模型的挑选矩阵\(\mathbf{P}\)、观点回报向量\(\mathbf{q}\)和观点不确定性矩阵\(\boldsymbol{\Omega}\)。观点置信度从谓词输出分布得出，提供了主观不确定性引出的数据驱动替代方案。该方法可解释，任何投资组合权重都可追溯到基础数据，且完全可微，实现端到端学习。
英文摘要
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.
