Machine Learning and the Implementable Efficient Frontier
Abstract
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.”
Fields of Study & Subjects
Financial Markets and Investment Strategies
Advanced Bandit Algorithms Research
Stock Market Forecasting Methods
Publication details
- Journal / source
- Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Volume
- 39
- Issue
- 10
- Pages
- 3035-3078
- Publication date
- 12 March 2026
- Language
- EN