European Business Schools Librarian's Group
Working Papers,
Copenhagen Business School, Department of Economics
No 20-2020: Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning
Erdinc Akyildirim (), Oguzhan Cepni (), Shaen Corbet () and Gazi Salah Uddin ()
Additional contact information
Erdinc Akyildirim: Department of Mathematics, ETH, Zurich, Switzerland and University of Zurich, Department of Banking and Finance, Zurich, Switzerland and Department of Banking and Finance, Burdur Mehmet Akif Ersoy University, Burdur, Turkey
Oguzhan Cepni: Department of Economics, Copenhagen Business School, Postal: Copenhagen Business School, Department of Economics, Porcelaenshaven 16 A. 1. floor, DK-2000 Frederiksberg, Denmark, , And Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey
Shaen Corbet: DCU Business School, Dublin City University, Dublin 9, Ireland and School of Accounting, Finance and Economics, University of Waikato, New Zealand
Gazi Salah Uddin: Department of Management and Engineering, Linköping University, 581 83 Linköping, Sweden
Abstract: In the aftermath of the global financial crisis and on-going COVID-19, investors face challenges in understanding price dynamics across assets. In this paper, we explore the applicability of a large scale comparison of machine learning algorithms (MLA) to predict mid-price movement for bitcoin futures prices. We use high-frequency intra-day data to evaluate the relative forecasting performances across various time-frequencies, ranging between 5-minutes and 60-minutes. The empirical analysis is based on six different specifications of MLA methods during periods of pandemic. The empirical results show that MLA outperforms the random walk and ARIMA forecasts in Bitcoin futures markets, which may have important implications in the decision-making process of predictability.
Keywords: Cryptocurrency; Bitcoin futures; Machine learning; Covid-19; k-Nearest neighbors; Logistic regression; Naive bayes; Random forest; Support vector machine; Extreme gradient; Boosting
28 pages, December 22, 2020
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