Deep Declarative Risk Budgeting Portfolios
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915263723601920 |
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| author | Parra-Diaz, Manuel Castro-Iragorri, Carlos |
| author_facet | Parra-Diaz, Manuel Castro-Iragorri, Carlos |
| contents | Recent advances in deep learning have spurred the development of end-to-end frameworks for portfolio optimization that utilize implicit layers. However, many such implementations are highly sensitive to neural network initialization, undermining performance consistency. This research introduces a robust end-to-end framework tailored for risk budgeting portfolios that effectively reduces sensitivity to initialization. Importantly, this enhanced stability does not compromise portfolio performance, as our framework consistently outperforms the risk parity benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Declarative Risk Budgeting Portfolios Parra-Diaz, Manuel Castro-Iragorri, Carlos Portfolio Management Computational Finance Recent advances in deep learning have spurred the development of end-to-end frameworks for portfolio optimization that utilize implicit layers. However, many such implementations are highly sensitive to neural network initialization, undermining performance consistency. This research introduces a robust end-to-end framework tailored for risk budgeting portfolios that effectively reduces sensitivity to initialization. Importantly, this enhanced stability does not compromise portfolio performance, as our framework consistently outperforms the risk parity benchmark. |
| title | Deep Declarative Risk Budgeting Portfolios |
| topic | Portfolio Management Computational Finance |
| url | https://arxiv.org/abs/2504.19980 |