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The evolution of the bitcoin economy: Extracting and analyzing the network of payment relationships

Paolo Tasca (University College London, London, UK)
Adam Hayes (University of Wisconsin Madison, Madison, University of Wisconsin Madison, Madison, Wisconsin, USA)
Shaowen Liu (Deutsche Bundesbank, Frankfurt, Germany)

Journal of Risk Finance

ISSN: 1526-5943

Article publication date: 19 March 2018

2490

Abstract

Purpose

This paper aims to gather together the minimum units of users’ identity in the Bitcoin network (i.e. the individual Bitcoin addresses) and group them into representations of business entities, what we call “super clusters”. While these clusters can remain largely anonymous, the authors are able to ascribe many of them to particular business categories by analyzing some of their specific transaction patterns (TPs), as observed during the period from 2009 to 2015. The authors are then able to extract and create a map of the network of payment relationships among them, and analyze transaction behavior found in each business category. They conclude by identifying three marked regimes that have evolved as the Bitcoin economy has grown and matured: from an early prototype stage; to a second growth stage populated in large part with “sin” enterprise (i.e. gambling, black markets); to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises.

Design/methodology/approach

Data mining.

Findings

Four primary business categories are identified in the Bitcoin economy: miners, gambling services, black markets and exchanges. Common patterns of transaction behavior between the business categories and their users are a “one-day” holding period for bitcoin transactions is somewhat typical. That is, a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily basis. There seems to be a strong preference to do business within the bitcoin economy in round lot amounts, whether it is more typical of traders exchanging for fiat money, gamblers placing bets or black market goods being bought and sold. Distinct patterns of transaction behavior among the business categories and their users are flows between traders and exchanges average just around 20 BTC, and traders buy or sell on average every 11 days. Meanwhile, gamblers wager just 0.5 BTC on average, but re-bet often within the same day. Three marked regimes have evolved, as the Bitcoin economy has grown and matured: from an early prototype stage, to a second growth stage populated in large part with “sin” enterprises (i.e. gambling, black markets), to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises. This evolution of the Bitcoin economy suggests a trend toward legitimate commerce.

Originality/value

The authors propose a new theoretical framework that allows investigating and exploring the network of payment relationships in the Bitcoin economy. This study starts by gathering together the minimum units of Bitcoin identities (the individual addresses), and it goes forward in grouping them into approximations of business entities, what is called “super clusters”, by using tested techniques from the literature. A super cluster can be thought of as an approximation of a business entity in that it describes a number of individual addresses that are owned or controlled collectively by the same beneficial owner for some special economic purposes. The majority of these important clusters are initially unknown and uncategorized. The novelty of this study is given by the pure user group and the TP analyses, by means of which the authors are able to ascribe the super clusters into specific business categories and outline a map of the network of payment relationships among them.

Keywords

Acknowledgements

This paper forms part of a special section “Digital currencies”, guest edited by Paolo Tasca.

Citation

Tasca, P., Hayes, A. and Liu, S. (2018), "The evolution of the bitcoin economy: Extracting and analyzing the network of payment relationships", Journal of Risk Finance, Vol. 19 No. 2, pp. 94-126. https://doi.org/10.1108/JRF-03-2017-0059

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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