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Deep Learning in Characteristics-Sorted Factor Models

Author

Listed:
  • Feng, Guanhao
  • He, Jingyu
  • Polson, Nicholas G.
  • Xu, Jianeng

Abstract

This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

Suggested Citation

  • Feng, Guanhao & He, Jingyu & Polson, Nicholas G. & Xu, Jianeng, 2024. "Deep Learning in Characteristics-Sorted Factor Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 59(7), pages 3001-3036, November.
    • Handle: RePEc:cup:jfinqa:v:59:y:2024:i:7:p:3001-3036_1
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    Citations

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    Cited by:

    1. Jungjun Choi & Ming Yuan, 2025. "Inferential Theory for Pricing Errors with Latent Factors and Firm Characteristics," Papers 2511.03076, arXiv.org.
    2. Doron Avramov & Xin He, 2026. "Stochastic Discount Factors with Cross-Asset Spillovers," Papers 2602.20856, arXiv.org.
    3. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    4. de la Barra, Joaquín & Salo, Ahti & Olander, Leevi & Barker, Kash & Kangaspunta, Jussi, 2026. "Fortifying critical infrastructure networks with multicriteria portfolio decision analysis: An application to railway stations in Finland," Reliability Engineering and System Safety, Elsevier, vol. 268(C).
    5. Duo Zhang & Jiayu Li & Junyi Mo & Elynn Chen, 2025. "Time-Varying Factor-Augmented Models for Volatility Forecasting," Papers 2508.01880, arXiv.org, revised Oct 2025.
    6. Zhangyuhua Weng & Shengli Zhang & Taotao Wang & Yihan Xia, 2026. "AlphaLogics: A Market Logic-Driven Multi-Agent System for Scalable and Interpretable Alpha Factor Generation," Papers 2603.20247, arXiv.org.
    7. Fan, Yinghua & Feng, Guanhao & Qiao, Xiao & Baronyan, Sayad, 2025. "Institutional granular impact is benign on asset sales and price efficiency," Journal of Financial Markets, Elsevier, vol. 75(C).
    8. Wu, Hongxu & Wang, Qiao & Li, Jianping & Deng, Zhibin, 2025. "Enhancing stock return prediction in the Chinese market: A GAN-based approach," Research in International Business and Finance, Elsevier, vol. 75(C).
    9. Yeonchan Kang & Doojin Ryu & Robert I. Webb, 2025. "How well do machine learning models in finance work?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-30, December.

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