Machine Learning and the Implementable Efficient Frontier

• 成果类型: Article; Early Access
• 署名作者: Jensen, Theis Ingerslev; Kelly, Bryan; Malamud, Semyon; Pedersen, Lasse Heje
• 署名单位: Yale University; Yale University; National Bureau of Economic Research; Swiss Finance Institute (SFI); Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research - UK; Copenhagen Business School
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454; 1465-7368
• DOI: 10.1093/rfs/hhag022
• 发表日期: 2026-04-06
• 关键词: C5; C61; G00; G11; G12; transaction costs; predictability; returns

摘要

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.