We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), a framework that reconciles the predictive power of deep learning with the structural transparency of traditional finance. By embedding aggregate analyst consensus as a structural "bottleneck", the model treats professional beliefs as a sufficient statistic for the market's high-dimensional information set. We document a striking "interpretability-accuracy amplification effect" for annual horizons, the structural constraint acts as an endogenous regularizer that significantly improves out-of-sample R2 over unconstrained benchmarks. Portfolios sorted on CB-APM forecasts exhibit a strong monotonic return gradient, delivering an annualized Sharpe ratio of 1.44 and robust performance across macroeconomic regimes. Furthermore, pricing diagnostics reveal that the learned consensus captures priced variation only partially spanned by canonical factor models, identifying structured risk heterogeneity that standard linear models systematically miss. Our results suggest that anchoring machine intelligence to human-expert belief formation is not merely a tool for transparency, but a catalyst for uncovering new dimensions of belief-driven risk premiums.

翻译：本文提出共识瓶颈资产定价模型（CB-APM），该框架将深度学习的预测能力与传统金融的结构透明度相融合。通过将分析师集体共识作为结构性“瓶颈”嵌入模型，该框架将专业信念视为市场高维信息集的充分统计量。我们发现在年度预测周期中存在显著的“可解释性-准确性放大效应”：结构性约束作为内生正则化器，其样本外R2显著优于无约束基准模型。基于CB-APM预测构建的投资组合呈现明显的单调收益梯度，年化夏普比率达1.44，并在不同宏观经济体制下保持稳健表现。进一步定价诊断表明，习得的共识捕捉了经典因子模型仅部分覆盖的定价变异，识别出标准线性模型系统性忽略的结构化风险异质性。研究结果表明，将机器智能锚定于人类专家信念形成过程，不仅是实现透明化的工具，更是发现信念驱动风险溢价新维度的催化剂。