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Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain

  • Nino Antulov-Fantulin
  • Dijana Tolic
  • Matija Piskorec
  • Zhang Ce
  • Irena Vodenska
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.

Keywords

Financial networks Machine learning Bitcoin Blockchain 

Notes

Acknowledgement and Contribution

Thanks to students Grüner Maximilian, Weingart Nino, Riesenkampf Heiki for help in processing blockchain data. The work of N.A.F. has been funded by the EU Horizon 2020 SoBigData project under grant agreement No. 654024. All authors contributed to the writing and editing of the manuscript. N.A.F. performed computational modeling and experiments. D.T. performed computational modeling and design of research. M.P. and Z.C. were involved in data processing and analysis. I.V. was involved in financial analysis and interpretation of results.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nino Antulov-Fantulin
    • 1
  • Dijana Tolic
    • 2
  • Matija Piskorec
    • 2
  • Zhang Ce
    • 3
  • Irena Vodenska
    • 4
  1. 1.Computational Social Science, ETH ZurichZurichSwitzerland
  2. 2.Laboratory for Machine Learning and Knowledge RepresentationsInstitute Ruder BoškovićZagrebCroatia
  3. 3.Systems Group, ETHZurichSwitzerland
  4. 4.Metropolitan College, Boston UniversityBrooklineUSA

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