Predicting the price of Bitcoin by the most frequent edges of its transaction network

Marcell Tamás Kurbucz

Economics Letters, 2019, vol. 184, issue C

Abstract: Research on the Bitcoin transaction network has increased rapidly in recent years, but still, little is known about the network’s influence on Bitcoin prices. The goals of this paper are twofold: to determine the predictive power of the transaction network’s most frequent edges on the future price of Bitcoin and to provide an efficient technique for applying this untapped dataset in day trading. To accomplish these goals, a complex method consisting of single-hidden layer feedforward neural networks (SLFNs) is used. Based on the results, the presented method achieved an accuracy of approximately 60.05% during daily price movement classifications, despite only considering a small subset of edges.

Keywords: Bitcoin; Transaction network; Price prediction; Artificial neural network (search for similar items in EconPapers)
JEL-codes: C12 C6 G1 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:184:y:2019:i:c:s0165176519303271

DOI: 10.1016/j.econlet.2019.108655

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