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Deep Declarative Risk Budgeting Portfolios

Author

Listed:
  • Manuel Parra-Diaz
  • Carlos Castro-Iragorri

Abstract

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.

Suggested Citation

  • Manuel Parra-Diaz & Carlos Castro-Iragorri, 2025. "Deep Declarative Risk Budgeting Portfolios," Papers 2504.19980, arXiv.org.
    • Handle: RePEc:arx:papers:2504.19980
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    References listed on IDEAS

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    1. Dai, Zhifeng & Wen, Fenghua, 2018. "Some improved sparse and stable portfolio optimization problems," Finance Research Letters, Elsevier, vol. 27(C), pages 46-52.
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