Inferring the interplay of network structure and market effects in Bitcoin

TL;DR

This paper analyzes Bitcoin's transaction network over time, revealing how structural changes in user interactions correlate with significant fluctuations in Bitcoin's price, providing insights into market dynamics beyond traditional time series models.

Contribution

It introduces a network-based approach using PCA on transaction data to link structural network changes with market price variations in Bitcoin.

Findings

Network structure changes align with price fluctuations

Unsupervised feature extraction reveals key dynamics

Transaction network analysis enhances understanding of market effects

Abstract

A main focus in economics research is understanding the time series of prices of goods and assets. While statistical models using only the properties of the time series itself have been successful in many aspects, we expect to gain a better understanding of the phenomena involved if we can model the underlying system of interacting agents. In this article, we consider the history of Bitcoin, a novel digital currency system, for which the complete list of transactions is available for analysis. Using this dataset, we reconstruct the transaction network between users and analyze changes in the structure of the subgraph induced by the most active users. Our approach is based on the unsupervised identification of important features of the time variation of the network. Applying the widely used method of Principal Component Analysis to the matrix constructed from snapshots of the network at different times, we are able to show how structural changes in the network accompany significant changes in the exchange price of bitcoins.