Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain

Simon Trimborn, Hanqiu Peng and Ying Chen

Journal of Empirical Finance, 2024, vol. 78, issue C

Abstract: Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.

Keywords: Bitcoin blockchain; Network dynamics; Two-layer sparsity (search for similar items in EconPapers)
JEL-codes: C55 C58 C60 G17 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000641

DOI: 10.1016/j.jempfin.2024.101529

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