Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911620690608128 |
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| author | Kim, Changeun Jeong, Younwoo Jang, Bong-Gyu |
| author_facet | Kim, Changeun Jeong, Younwoo Jang, Bong-Gyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model Kim, Changeun Jeong, Younwoo Jang, Bong-Gyu Pricing of Securities Artificial Intelligence Machine Learning 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. |
| title | Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model |
| topic | Pricing of Securities Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.16251 |