Forecasting mid-price movement of Bitcoin futures using machine learning.

Autor: Akyildirim E; Department of Banking and Finance, Burdur Mehmet Akif Ersoy University, Burdur, Turkey.; Department of Banking and Finance, University of Zurich, Zurich, Switzerland., Cepni O; Department of Economics, Copenhagen Business School, Porcelænshaven 16A, 2000 Frederiksberg, Denmark.; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10, 06050 Ankara, Turkey., Corbet S; DCU Business School, Dublin City University, Dublin 9, Ireland.; School of Accounting, Finance and Economics, University of Waikato, Hamilton, New Zealand., Uddin GS; Department of Management and Engineering, Linköping University, 581 83 Linköping, Sweden.
Jazyk: angličtina
Zdroj: Annals of operations research [Ann Oper Res] 2021 Jul 22, pp. 1-32. Date of Electronic Publication: 2021 Jul 22.
DOI: 10.1007/s10479-021-04205-x
Abstrakt: 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.
(© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)
Databáze: MEDLINE
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