In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

中文翻译：

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使用机器学习预测比特币期货的中间价格变动。


在全球金融危机和持续的 COVID-19 大流行之后，投资者在了解各种资产的价格动态方面面临着挑战。本文探讨了各种类型的机器学习算法（MLA）的性能，以预测比特币期货价格的中间价格变动。我们使用高频日内数据来评估不同时间频率（5 到 60 分钟之间）的相对预测性能。我们的研究结果表明，六个 MLA 中的五个的平均分类准确度始终高于 50% 的阈值，这表明 MLA 在预测比特币期货价格方面优于 ARIMA 和随机游走等基准模型。这凸显了 MLA 在 COVID-19 动荡期间对比特币期货价格进行准确预测的重要性和相关性。