Supervised portfolios
提出一种资产配置策略，先计算最优权重再输入监督学习算法，让机器学习风险度量、偏好和约束，实证表明直接预测最优权重比传统两步法更稳定，风险调整后表现更好。
We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences, and constraints beyond simple expected returns, within a flexible, forward-looking, and non-linear framework. Our empirical analysis illustrates that predicting the optimal weights directly instead of the traditional two-step approach leads to more stable portfolios with statistically better risk-adjusted performance measures.