Beyond the Hype: A Multi-Layer Machine Learning Framework for Cryptocurrency Return Forecasting
Khoso (2026) studies machine learning return prediction in crypto using tree ensembles. The abstract mentions an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“A mean-variance investor would earn a certainty equivalent gain of 2.4% annually by exploiting these forecasts.”
AssetCryptospot crypto
StrategyMachine learningliquidity premium
TechniqueTree ensembleclassical time series
GeographyGlobal
Also stated: Intraday, Price and volume, Fundamentals, Text and news, out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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