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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.
Author(s): Emary C, Fort H
Publication type: Article
Publication status: Published
Journal: Journal of Complex Networks
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
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