arXiv
Open Access
2025
Deep Declarative Risk Budgeting Portfolios
Manuel Parra-Diaz
Carlos Castro-Iragorri
Abstrak
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.
Penulis (2)
M
Manuel Parra-Diaz
C
Carlos Castro-Iragorri
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2025
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- en
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- arXiv
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