Inferring the interplay between network structure and market effects in Bitcoin

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.

Inferring Short-Term Volatility Indicators from the Bitcoin Blockchain

“…The user transaction network can be extracted from the blockchain by exploiting the fact that initiating a transaction with multiple inputs requires signing it with the private keys of all input addresses. This implies that all of these addresses are controlled by the same entity [1, 21,28] that we simply call a user. Similar as in literature [21,28], we process hundreds of gigabytes of Bitcoin blockchain data by merging all addresses that belong to the same user.…”

“…al. [21] demonstrated that there exists a certain correlation between the Bitcoin network structure and the market effects i.e. Bitcoin price change, up to early 2014.…”

“…Bitcoin price change, up to early 2014. However, the authors of [21] did not test the predictive power on hold-out data. This has motivated us to analyze patterns in the transaction graphs from the Bitcoin blockchain using unsupervised and supervised machine learning.…”

Sampling Methods for Counting Temporal Motifs

“…Table 1 lists summary statistics of the datasets, and we briefly describe them below. Each dataset is a collection of timestamped directed [26]. Timestamps are the creation time of the block on the blockchain containing the transaction.…”

Focus on networks, energy and the economy

“…Methods from statistical physics are also valuable to analyze the dynamics of general economic networks. Kondor et al show analyze the network of transactions of the digital currency system Bitcoin [31]. They show that the evolution of a macroscopic quantity, the market price, can be recovered from rather little information about the microscopic structure of the trading network.…”