Abstract
Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. In contrast to fiat currencies used worldwide, the Bitcoin distributed ledger is publicly available by design. This facilitates observing all financial interactions on the network, and analyzing how the network evolves in time. We introduce a novel concept of chainlets, or Bitcoin subgraphs, which allows us to evaluate the local topological structure of the Bitcoin graph over time. Furthermore, we assess the role of chainlets on Bitcoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on Bitcoin price and investment risk.
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Some representative chainlets from daily clusters 7, 8, 16 and 35 are \(\mathbb {C}_{9 \rightarrow 11}\), \(\mathbb {C}_{3 \rightarrow 17}\), \(\mathbb {C}_{8 \rightarrow 14}\) and \(\mathbb {C}_{1 \rightarrow 1}\), respectively.
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Acknowledgments
This research was supported in part by NIH 1R01HG006844, NSF CNS-1111529, CICI-1547324, IIS-1633331, DMS-1736368 and ARO W911NF-17-1-0356.
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Akcora, C.G., Dey, A.K., Gel, Y.R., Kantarcioglu, M. (2018). Forecasting Bitcoin Price with Graph Chainlets. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_60
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