Dimitriadou Athanasia, Gregoriou Andros
College of Business, Law and Social Sciences, University of Derby, Lonsdale House, Quaker Way, Derby DE1 3HD, UK.
School of Business and Law, University of Brighton, Elm House, Lewes Road, Brighton BN2 4AT, UK.
Entropy (Basel). 2023 May 10;25(5):777. doi: 10.3390/e25050777.
In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market.
在本文中，我们利用机器学习框架预测比特币的走势。我们编制了一个包含24个潜在解释变量的数据集，这些变量常用于金融文献中。利用2014年12月2日至2019年7月8日的每日数据，我们构建了预测模型，该模型利用比特币的过往价值、其他加密货币、汇率及其他宏观经济变量。我们的实证结果表明，传统逻辑回归模型优于线性支持向量机和随机森林算法，准确率达到66%。此外，基于这些结果，我们提供了证据，表明比特币市场弱式有效被拒绝。
