Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain (CROSBI ID 730128)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Antulov-Fantulin, Nino ; Tolic, Dijana ; Piskorec, Matija ; Ce, Zhang ; Vodenska, Irena
engleski
Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain
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.
Financial networks ; Machine learning ; Bitcoin ; Blockchain
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Podaci o prilogu
508-520.
2018.
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objavljeno
10.1007/978-3-030-05414-4_41
Podaci o matičnoj publikaciji
Studies in computational intelligence
Springer
1860-949X
Podaci o skupu
Complex Networks 2018
predavanje
11.12.2018-13.12.2018
Cambridge, Ujedinjeno Kraljevstvo
Povezanost rada
Interdisciplinarne prirodne znanosti, Interdisciplinarne tehničke znanosti, Računarstvo