Machine Learning and the Implementable Efficient Frontier
提出用扣除交易成本后的净收益评估投资策略，构建“可执行的有效前沿”，通过将交易成本感知的优化与机器学习结合，直接学习投资组合权重，提升净收益表现。
Abstract We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the “implementable efficient frontier.” While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”