Meta-Learning Reinforcement Learning for Crypto-Return Prediction
Wang and co-authors (2026) study machine learning return prediction in crypto using a large language model. The abstract mentions excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Experiments across diverse market regimes demonstrate that Meta-RL-Crypto shows good performance on the technical indicators of the real market and outperforming other LLM-based baselines.”
AssetCryptospot crypto
StrategyMachine learningnews sentiment
TechniqueLarge language modeltransformer
ClaimExcess return
Also stated: Price and volume, Text and news, On-chain
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
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