Ever since the introduction of Bitcoin, cryptocurrencies have attracted interest from many due to their potential for appreciation. They have also been stigmatized for their volatility, low correlation with traditional assets, and uncertain regulatory conditions. Nevertheless, many cryptocurrencies still attract the attention of major players in finance. As in the management of a portfolio of other assets, there is a need to regularly rebalance the portfolio to optimize returns across different time horizons. This study evaluates four machine learning models, Logistic Regression, Decision Tree, K-Nearest Neighbors, and Gradient Boosting, for identifying optimal cryptocurrency rebalancing decisions. Using a momentum-based approach with a 30-day forward window, the models are trained to classify assets as hold or rebalance based on future price movement. Our approach is based on feature engineering and hyperparameter tuning. Results show that tree-based models, such as Decision Tree and Gradient Boosting, demonstrate superior classification performance in identifying optimal rebalancing moments. However, the study also highlights the limitations of using historical data exclusively without referring to other external factors such as market sentiment and regulatory changes. Overall, the study makes a contribution to the field of cryptocurrency portfolio management by providing one of the first comparative evaluations of multiple ML architectures for cryptocurrency rebalancing decisions, demonstrating the potential of machine learning to improve portfolio management in highly volatile markets.
A Mon, study studied this question.