The evolving liaisons between the transaction networks of Bitcoin and its price dynamics

Abstract

Cryptocurrencies are distributed systems that allow exchanges of native tokens among participants, or the exchange of such tokens for fiat currencies in markets external to these public ledgers. The availability of their complete historical bookkeeping opens up the possibility of understanding the relationship between aggregated users' behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider four different representations of it, over a period of nine years since the Bitcoin creation and involving 16 million users and 283 million transactions. By analysing these networks, we show the existence of causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of nodes activity.

MicroVelocity: rethinking the Velocity of Money for digital currencies

“…We conclude our analysis by considering the role of MicroVelocity in the structure of transaction patterns, introducing transaction networks. A network is an ordered pair of sets G = (V, E), called the nodes V and edges (or links) E, where the elements of E ∈ V × V represent connections between elements of V. Identifying agents as nodes and transactions as directed connections from the transaction sender to the receiver, as done for instance in [27], it is possible to obtain a topological description of the economy, highlighting with whom agents exchange tokens and how central they are in the flow of value in the economy. We construct transaction networks on weekly aggregation, which is the minimum timescale that allows to mitigate the effect of day-of-the-week seasonalities that are common in economic and financial data (e.g.…”

Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints

“…a ij counts the number of nodes i 'points' to. The benchmark defined by this set of constraints is known as Directed Binary Configuration Model (DBCM) whose Hamiltonian reads (34) as in the undirected case, entropy maximization [15,16] leads to a factorized probability distribution, i.e.…”

“…The latter ones span economic, financial and social networks. In particular, we have considered the World Trade Web (WTW) during the decade 1992-2002 [33], a pair of snapshots of the Bitcoin User Network at the weekly time scale (the first day of those weeks being 13-02-12 and 27-04-15, respectively) [34] and of the corresponding largest weakly connected component (whose size is, respectively, ≃ 65% and ≃ 90% of the full network size) and a snapshot of the semantic network concerning the Twitter discussion about the Covid-19 pandemics 4 [35]. Before commenting on the results of our numerical exercises, let us, first, describe how the latter ones have been carried out.…”

Bitcoin Transaction Networks: an overview of recent results

“…With the present work, we aim at summing up the results of three papers [16][17][18]. In [16], the authors analyse the local properties of two different Bitcoin representations, i.e.…”

“…With the present work, we aim at summing up the results of three papers [16][17][18]. In [16], the authors analyse the local properties of two different Bitcoin representations, i.e. the Bitcoin Address Network (BAN) and the Bitcoin User Network (BUN) and inspect the presence of correlations between (exogenous) price movements and (endogenous) changes in the topological structure of the aforementioned networks.…”