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Semi-Decision-Focused Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization

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  • Juhyeong Kim

Abstract

I propose Semi-Decision-Focused Learning, a practical adaptation of Decision-Focused Learning for portfolio optimization. Rather than directly optimizing complex financial metrics, I employ simple target portfolios (Max-Sortino or One-Hot) and train models with a convex, cross-entropy loss. I further incorporate Deep Ensemble methods to reduce variance and stabilize performance. Experiments on two universes (one upward-trending and another range-bound) show consistent outperformance over baseline portfolios, demonstrating the effectiveness and robustness of my approach. Code is available at https://github.com/sDFLwDE/sDFLwDE

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  • Juhyeong Kim, 2025. "Semi-Decision-Focused Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization," Papers 2503.13544, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2503.13544
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    References listed on IDEAS

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    1. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
    2. Ayse Sinem Uysal & Xiaoyue Li & John M. Mulvey, 2021. "End-to-End Risk Budgeting Portfolio Optimization with Neural Networks," Papers 2107.04636, arXiv.org.
    3. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
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