Modeling financial transactions via random walks on temporal networks

TL;DR

This paper presents a model of financial transactions as random walks on temporal networks, capturing key statistical properties and correlations observed in empirical data, thus enhancing understanding of money circulation dynamics.

Contribution

It introduces a novel framework that analytically derives heavy-tailed distributions and correlations in financial transaction data using activity-driven temporal networks.

Findings

Reproduces heavy-tailed distributions of balances and transaction sizes.

Captures correlations between inflows and outflows.

Aligns well with empirical data from digital currency communities.

Abstract

We model financial transactions as random walks on activity-driven temporal networks. By enforcing fund conservation, our framework analytically derives heavy-tailed distributions for the stationary balances and transaction sizes. Crucially, the latter is driven by variance in the spending propensity of individuals. Calibrated with empirical data from a closed, digital currency community, the model also reproduces observed correlations between inflows and outflows. Our findings provide a path for understanding emergent properties of the circulation of money.