Forecasting mid-price movement of Bitcoin futures using machine learning

【Author】 Akyildirim, Erdinc; Cepni, Oguzhan; Corbet, Shaen; Uddin, Gazi Salah

【Source】ANNALS OF OPERATIONS RESEARCH

【影响因子】4.820

【Abstract】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.

【Keywords】Cryptocurrency; Bitcoin futures; Machine learning; Covid-19; k-Nearest neighbours; Logistic regression; Naive Bayes; Random forest; Support vector machine; Extreme gradient boosting

【发表时间】2021

【收录时间】2022-01-02

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