Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms

Abstract

Existing evidence shows that daily cryptocurrency returns are predictable by publicly available variables. However, a majority of evidence relies on potentially over-fitted in-sample estimation. This paper provides a comprehensive comparison of predictors and forecasting methods in the literature for out-of-sample return predictions of Bitcoin, Ethereum, and Ripple. We find that (1) well-known in-sample predictors such as investor attention and trading volume fail to produce statistically significant out-of-sample predictability, (2) a change in stochastic correlation with stock markets is the only meaningful predictor with out-of-sample R2 up to 2.69%, 1.71%, and 2.12% for Bitcoin, Ethereum, and Ripple, respectively, and (3) forecasting methods greatly differ in their performances; methods that are inspired by economic mechanism outperform universal forecasting methods such as shrinkage estimators, combination forecasts, monitoring forecasts, and various machine learning algorithms that are commonly used in practice.