Harnessing Machine Learning for Predicting Cryptocurrency Returns

Abstract

The study investigates the predictability of both the individual and basket of 10 major cryptocurrencies’ daily price changes between 2017 and 2023 by employing various machine learning classification algorithms such as random forests, k-nearest neighbour, decision trees, logistic regression, and Bernoulli naïve Bayes. These models utilize 15 different features based on historical price data and technical indicators as input features. The study estimates find logistic regression as superior over other models under consideration in predicting cryptocurrency daily returns. Overall, the study finds that on an average machine learning classification algorithms predictive accuracies have surpassed 50% when applied to daily frequencies on the basket of 10 major cryptocurrencies.