Stability focused end to end frameworks for risk budgeting portfolios

Carlos Castro-Iragorri () and Manuel Parra-Diaz ()
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Carlos Castro-Iragorri: Universidad del Rosario, Postal: Calle 12c #4-69, https://ccastroiragorri.github.io/
Manuel Parra-Diaz: Universidad del Rosario
Authors registered in the RePEc Author Service: Carlos Castro Iragorri

No 21367, Documentos de Trabajo from Universidad del Rosario

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.

Keywords: end-to-end framework; neural networks; risk budgeting; stability (search for similar items in EconPapers)
JEL-codes: C13 C45 G11 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2025-03-05
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:col:000092:021367

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