Market structure explained by pairwise interactions

TL;DR

This paper demonstrates that a pairwise maximum entropy model effectively captures the dynamics and structure of financial markets, revealing insights into market states, topology, and potential applications in regulation and crisis detection.

Contribution

It introduces a pairwise maximum entropy approach to model market interactions, linking it with graph theory to analyze market structure and dynamics.

Findings

Model captures transitions between market states.

Reproduces scale-free topology and clustering features.

Useful for detecting crises and informing regulation.

Abstract

Financial markets are a typical example of complex systems where interactions between constituents lead to many remarkable features. Here, we show that a pairwise maximum entropy model (or auto-logistic model) is able to describe switches between ordered (strongly correlated) and disordered market states. In this framework, the influence matrix may be thought as a dissimilarity measure and we explain how it can be used to study market structure. We make the link with the graph-theoretic description of stock markets reproducing the non-random and scale-free topology, shrinking length during crashes and meaningful clustering features as expected. The pairwise model provides an alternative method to study financial networks which may be useful for characterization of abnormal market states (crises and bubbles), in capital allocation or for the design of regulation rules.