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Markets as ecological networks: inferring interactions and identifying communities

Lookup NU author(s): Dr Clive Emary



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Financial markets are paradigmatic examples of complex systems and have been compared to ecologicalnetworks in which different species (firms) interact and co-evolve. A central object governing speciesdynamics in ecology is the community matrix, whose elements are closely related to pairwise interspecificinteraction coefficients. Using this ecological analogy we propose a method, based on the MaximumEntropy (MaxEnt) principle, that allows us to infer candidates for an economic community matrix fromtime series data of market values. To assess the usefulness of this picture, we construct community matricesfor a set of companies belonging to the Fortune 500 list and perform a community analysis on the resultantnetworks. This analysis shows these networks to strongly reflect the known industry groupings of the firms.We conclude therefore that our community matrices capture non-trivial information about the interactionof firms, not immediately apparent from the covariance of market values. We anticipate our approach beinguseful in elucidating further aspects of market structure, as well as forming the basis of forecasting marketdynamics.

Publication metadata

Author(s): Emary C, Fort H

Publication type: Article

Publication status: Published

Journal: Journal of Complex Networks

Year: 2021

Volume: 9

Issue: 2

Online publication date: 24/08/2021

Acceptance date: 15/06/2021

Date deposited: 25/08/2021

ISSN (print): 2051-1310

ISSN (electronic): 2051-1329

Publisher: Oxford University Press


DOI: 10.1093/comnet/cnab022


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Funder referenceFunder name
CHL\R1\180156Royal Society