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.
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