Modelling Equity Transaction Networks as Bursty Processes

TL;DR

This paper models the bursty nature of stock transaction networks using Hawkes processes, revealing endogenous excitations and mutual influences between buys and sells, with implications for understanding market dynamics.

Contribution

It introduces a parametric Hawkes process approach to model and analyze the bursty, hub-dominated transaction networks in financial markets, highlighting mutual excitation effects.

Findings

Hawkes processes effectively reproduce bursty transaction patterns.

Mutual excitation between buys and sells is significant in market modeling.

Univariate models are less effective than multivariate Hawkes models for these data.

Abstract

Trade executions for major stocks come in bursts of activity, which can be partly attributed to the presence of self- and mutual excitations endogenous to the system. In this paper, we study transaction reports for five FTSE 100 stocks. We model the dynamic of transactions between counterparties using both univariate and multivariate Hawkes processes, which we fit to the data using a parametric approach. We find that the frequency of transactions between counterparties increases the likelihood of them to transact in the future, and that univariate and multivariate Hawkes processes show promise as generative models able to reproduce the bursty, hub dominated systems that we observe in the real world. We further show that Hawkes processes perform well when used to model buys and sells through a central clearing counterparty when considered as a bivariate process, but not when these are modelled as individual univariate processes, indicating that mutual excitation between buys and sells is present in these markets.