Boosting Cryptocurrency Return Prediction
40 Pages Posted: 1 Sep 2021
Date Written: August 30, 2021
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
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