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Stability focused end to end frameworks for risk budgeting portfolios

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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.

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  • Castro-Iragorri, Carlos & Parra-Diaz, Manuel, 2025. "Stability focused end to end frameworks for risk budgeting portfolios," Documentos de Trabajo 21367, Universidad del Rosario.
    • Handle: RePEc:col:000092:021367
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    File URL: https://repository.urosario.edu.co/handle/10336/45088
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    More about this item

    Keywords

    end-to-end framework; neural networks; risk budgeting; stability;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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