{"ID":2827425,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16251","arxiv_id":"2512.16251","title":"Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model","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.","short_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-...","url_abs":"https://arxiv.org/abs/2512.16251","url_pdf":"https://arxiv.org/pdf/2512.16251v5","authors":"[\"Changeun Kim\",\"Younwoo Jeong\",\"Bong-Gyu Jang\"]","published":"2025-12-18T07:05:25Z","proceeding":"q-fin.PR","tasks":"[\"q-fin.PR\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}