Forecasting cryptocurrency returns with machine learning

Yujun Liu, Zhongfei Li, Ramzi Nekhili and Jahangir Sultan

Research in International Business and Finance, 2023, vol. 64, issue C

Abstract: 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, cryptocurrencies 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.

Keywords: Cryptocurrency; Machine learning; eXtreme Gradient Boostine; SHapley Additive exPlanations (search for similar items in EconPapers)
JEL-codes: C53 G11 G12 G15 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000314

DOI: 10.1016/j.ribaf.2023.101905

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