Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning

Erdinc Akyildirim*, Oguzhan Cepni, Shaen Corbet, Gazi Salah Uddin

*Corresponding author for this work

Research output: Working paperResearchpeer-review

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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.
Original languageEnglish
Place of PublicationFrederiksberg
PublisherCopenhagen Business School [wp]
Number of pages28
Publication statusPublished - 2020
SeriesWorking Paper / Department of Economics. Copenhagen Business School
Number20-2020

Keywords

  • Cryptocurrency
  • Bitcoin futures
  • Machine learning
  • Covid-19
  • K-nearest neighbors
  • Logistic regression
  • Naive bayes
  • Random forest
  • Support vector machine
  • Extreme gradient
  • Boosting

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