Boosting Cryptocurrency Return Prediction

40 Pages Posted: 1 Sep 2021

See all articles by Ilias Filippou

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School

David Rapach

Washington University in St. Louis; Saint Louis University

Christoffer Thimsen

Aarhus University - Department of Economics and Business Economics

Date Written: August 30, 2021

Abstract

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals, technical indicators, Google Trends searches, Reddit comments, and articles from Factiva. We use the XGBoost algorithm to boost trees and find that excess return forecasts based on boosted trees produce statistically and economically significant out-of-sample gains. We explore the importance of individual predictors and nonlinearities in the fitted boosted trees. We find that a broad array of predictors are relevant for forecasting daily cryptocurrency returns and that strong nonlinearities characterize the predictive relationships.

Keywords: Bitcoin, Ethereum, Out-of-sample return prediction, Machine learning, XGBoost, SHAP values

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

Suggested Citation

Filippou, Ilias and Rapach, David and Rapach, David and Thimsen, Christoffer, Boosting Cryptocurrency Return Prediction (August 30, 2021). Available at SSRN: https://ssrn.com/abstract=3914414 or http://dx.doi.org/10.2139/ssrn.3914414

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

David Rapach (Contact Author)

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Christoffer Thimsen

Aarhus University - Department of Economics and Business Economics ( email )

Nordre Ringgade 1
Aarhus, 8000
Denmark

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