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Machine Learning and the Implementable Efficient Frontier

Theis Ingerslev Jensen et al.

The Review of Financial Studies2026https://doi.org/10.1093/rfs/hhag022article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

What the paper says

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.”

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https://doi.org/https://doi.org/10.1093/rfs/hhag022

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@article{theis2026,
  title        = {{Machine Learning and the Implementable Efficient Frontier}},
  author       = {Theis Ingerslev Jensen et al.},
  journal      = {The Review of Financial Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/rfs/hhag022},
}

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Machine Learning and the Implementable Efficient Frontier

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.