Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning

Erdinc Akyildirim (), Oguzhan Cepni, Shaen Corbet and Gazi 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

No 20-2020, Working Papers from Copenhagen Business School, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C60 E50 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2020-12-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10398/1f7d2c7c-110a-4972-a4c4-47da5119ebd8 Full text (application/pdf)
Full text not avaiable

Related works:
Journal Article: Forecasting mid-price movement of Bitcoin futures using machine learning (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hhs:cbsnow:2020_020

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Working Papers from Copenhagen Business School, Department of Economics Copenhagen Business School, Department of Economics, Porcelaenshaven 16 A. 1.floor, DK-2000 Frederiksberg, Denmark. Contact information at EDIRC.
Bibliographic data for series maintained by CBS Library Research Registration Team ().