Machine Learning and the Implementable Efficient Frontier
A 2022 paper by Jensen and co-authors on machine learning return prediction: the abstract mentions transaction costs and leaves out a benchmark, an out-of-sample test, multiple testing and the size of the universe.
“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.”
StrategyMachine learningportfolio optimization
Also stated: costs
Missing from abstract: benchmark, out-of-sample test, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
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