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

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 atthis https URL

@article{kim2025_2503.13544,
  title={ Semi-Decision-Focused Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization },
  author={ Juhyeong Kim },
  journal={arXiv preprint arXiv:2503.13544},
  year={ 2025 }
}