Modeling financial transactions via random walks on temporal networks

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.