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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1805.01104 |
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| _version_ | 1866909422382481408 |
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| author | Feng, Guanhao He, Jingyu Polson, Nicholas G. Xu, Jianeng |
| author_facet | Feng, Guanhao He, Jingyu Polson, Nicholas G. Xu, Jianeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1805_01104 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Deep Learning in Characteristics-Sorted Factor Models Feng, Guanhao He, Jingyu Polson, Nicholas G. Xu, Jianeng Methodology 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. |
| title | Deep Learning in Characteristics-Sorted Factor Models |
| topic | Methodology |
| url | https://arxiv.org/abs/1805.01104 |