用分析师共识构建可解释的股票收益预测模型，提升准确性并揭示隐藏风险。
Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model
- 将分析师共识作为信息瓶颈，直接嵌入模型设计实现可解释性
- 按模型预测分组的投资组合在不同经济环境下均呈现显著收益梯度
- 发现共识中包含传统因子模型未捕捉的信念驱动风险
我们提出共识-瓶颈资产定价模型（CB-APM），将分析师集体意见作为结构化瓶颈，将其视为市场高维信息集的充分统计量。与事后解释方法不同，CB-APM通过设计实现可解释性：瓶颈约束充当内生正则化器，同时提升外样本预测精度，并使推断锚定于经济可解释的驱动因素。按CB-APM预测分组的投资组合表现出强单调收益梯度，在宏观经济周期中均稳健。定价诊断进一步表明，学习到的共识编码了经典因子模型未覆盖的定价变异，揭示出标准线性框架系统性遗漏的信念驱动风险异质性。
原文摘要 · Abstract (English)
We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), which embeds aggregate analyst consensus as a structural bottleneck, treating professional beliefs as a sufficient statistic for the market's high-dimensional information set. Unlike post-hoc explainability approaches, CB-APM achieves interpretability-by-design: the bottleneck constraint functions as an endogenous regularizer that simultaneously improves out-of-sample predictive accuracy and anchors inference to economically interpretable drivers. Portfolios sorted on CB-APM forecasts exhibit a strong monotonic return gradient, robust across macroeconomic regimes. Pricing diagnostics further reveal that the learned consensus encodes priced variation not spanned by canonical factor models, identifying belief-driven risk heterogeneity that standard linear frameworks systematically miss.
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