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

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

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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.

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Changeun Kim & Younwoo Jeong & Bong-Gyu Jang, 2025.
"
Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model
,"

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2512.16251, arXiv.org, revised Apr 2026.

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</reference>

<statements>
1. It is possible to systematically combine these model families: use deep/ML models to generate data‑driven views and risk diagnostics, plug them into a Black‑Litterman/mean‑risk optimizer with more robust risk measures (e.g. CVaR, drawdown), and implement dynamic policies via reinforcement learning, with interpretability enforced by XAI or interpretable architectures.
2. Interpretable architectures: Concept-bottleneck or partially interpretable neural networks (e.g. CB-APM) embed economically meaningful bottlenecks (analyst consensus, factors) that preserve structure while delivering state-of-the-art performance.
3. This layer is agnostic about the higher-level model but is designed to support both interpretable factors (value, momentum, quality) and high-dimensional signals used by deep models.
4. Use interpretable or semi-interpretable ML architectures (e.g. concept-bottleneck models like CB-APM, or structured deep nets with economic constraints) to predict conditional expected returns or risk premia.
5. Apply BL’s mixed-estimation formula to blend equilibrium prior and ML views into posterior expected returns that are statistically regularized and economically anchored.
6. Recent work on interpretable deep asset pricing (e.g. CB‑APM) shows that interpretability constraints can sometimes **improve** performance, not just preserve it, by acting as regularizers aligned with economic structure.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.