Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model

Changeun Kim, Younwoo Jeong and Bong-Gyu Jang

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Abstract: 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.

Date: 2025-12, Revised 2026-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-rmg
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