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Forecasting cryptocurrency returns with machine learning
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DOI：10.1016/j.ribaf.2023.101905.png)
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This article employs machine learning models to predict returns for 3703 cryptocurrencies for the 2013 - 2021 period. Based on daily data, we build an equal (capital)-weighted portfolio that generates 7.1 % (2.4 %) daily return with a 1.95 (0.27) Sharpe ratio. We obtain an out-of-sample R2 of 4.855 %. Our results suggest that cryptocurrencies behave like conventional assets than fiat currencies since variables, including lagged returns, can predict future returns. As assets, cryp-tocurrencies are not weakly efficient, and production costs do not determine their prices. Returns for small cryptocurrencies are more predictable than larger ones. The predictive power of the 1 -day lagged return is stronger than all other features (predictors) combined. The results offer new insights for crypto investors, traders, and financial analysts.
Keyword:
Cryptocurrency
Machine learning
eXtreme Gradient Boostine
SHapley Additive exPlanations
期刊
IF:
6.9
论文数:
2.9K
被引数:
1.0W
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