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

James Yae and George Zhe Tian

Physica A: Statistical Mechanics and its Applications, 2022, vol. 598, issue C

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.

Keywords: Cryptocurrency; Return predictability; Out-of-sample tests; Machine learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002928

DOI: 10.1016/j.physa.2022.127379

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