Forecasting Digital Asset Return: An Application of Machine Learning Model
研究了三种机器学习模型（双深度Q学习、XGBoost、ARFIMA-GARCH）在预测比特币价格上的表现，发现双深度Q学习在回报和Sortino比率上优于其他模型，能一步预测回报方向，对从业者和监管者有参考价值。
ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q‐learning, XGBoost and ARFIMA‐GARCH. The findings show that the double deep Q‐learning model outperforms the others in terms of returns and Sortino ratio and is capable of one‐step‐ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model.