Deep Learning in Characteristics-Sorted Factor Models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 3001-3036 |
Journal / Publication | Journal of Financial and Quantitative Analysis |
Volume | 59 |
Issue number | 7 |
Online published | 24 Jul 2023 |
Publication status | Published - Nov 2024 |
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Abstract
This paper 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. © The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
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Research Unit(s) information for this publication is provided by the author(s) concerned.
Citation Format(s)
Deep Learning in Characteristics-Sorted Factor Models. / Feng, Guanhao; He, Jingyu; Polson, Nick G. et al.
In: Journal of Financial and Quantitative Analysis, Vol. 59, No. 7, 11.2024, p. 3001-3036.
In: Journal of Financial and Quantitative Analysis, Vol. 59, No. 7, 11.2024, p. 3001-3036.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review