Cryptocurrency Return Predictability: A Machine-Learning Analysis

Abstract

We investigate the out-of-sample predictability of daily cryptocurrency returns using modern machine-learning methods. We consider a large number of cryptocurrencies (41) and a rich set of predictors relating to a cryptocurrency’s network value and activity, time-series momentum, technical signals, and investor attention and sentiment. Our results indicate that return predictability is an important feature of the cryptocurrency market: machine-learning methods significantly improve the statistical accuracy of cryptocurrency return forecasts and provide substantial economic value to an investor. We find that a diverse set of predictors contribute to cryptocurrency return predictability and that nonlinearities play a prominent role.

Keywords: Out-of-sample return prediction, Random forest, XGBoost, Deep neural network, Network value, Network activity, Time-series momentum, Investor attention, Shapley values

JEL Classification: C52, C53, G11, G12, G17