用深度集成学习预测最优投资组合，提升收益稳定性和抗风险能力。
Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization
- 将投资组合构建转为监督学习问题，用熵损失训练模型预测权重。
- 集成规模越大，平均收益越高，夏普与索提诺比率更稳定。
- 适合量化投资、金融工程领域研究者和从业者参考使用。
我们提出决策监督学习（DSL），一种用于稳健投资组合优化的实用框架。DSL将投资组合构建重新定义为监督学习任务：模型通过交叉熵损失训练，以预测最大化夏普比率或索提诺比率的投资组合权重。为增强稳定性与可靠性，DSL采用深度集成方法，显著降低投资组合配置的方差。在多种市场组合与神经网络架构下进行的全面回测表明，其表现优于传统策略及领先的机器学习方法，包括预测导向学习与端到端学习。我们发现，增加集成规模可带来更高中位数收益与更稳定的风险调整后表现。代码已开源：https://github.com/DSLwDE/DSLwDE。
原文摘要 · Abstract (English)
We propose Decision by Supervised Learning (DSL), a practical framework for robust portfolio optimization. DSL reframes portfolio construction as a supervised learning problem: models are trained to predict optimal portfolio weights, using cross-entropy loss and portfolios constructed by maximizing the Sharpe or Sortino ratio. To further enhance stability and reliability, DSL employs Deep Ensemble methods, substantially reducing variance in portfolio allocations. Through comprehensive backtesting across diverse market universes and neural architectures, shows superior performance compared to both traditional strategies and leading machine learning-based methods, including Prediction-Focused Learning and End-to-End Learning. We show that increasing the ensemble size leads to higher median returns and more stable risk-adjusted performance. The code is available at https://github.com/DSLwDE/DSLwDE.
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