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Title: Topological isomorphisms of human brain and financial market networks
Authors: Vertes, Petra E.
Nicol, Ruth M.
Chapman, Sandra C.
Watkins, Nicholas W.
Robertson, Duncan A.
Bullmore, Edward T.
Keywords: Human brain
Financial markets
Issue Date: 2011
Publisher: Frontiers
Citation: VERTES, P.E. ... et al, 2011. Topological isomorphisms of human brain and financial market networks. Frontiers in Systems Neuroscience, 5, 75, DOI: 10.3389/fnsys.2011.00075.
Abstract: Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets.
Description: This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by/3.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor: This work was supported by a grant from the Engineering and Physical Sciences Research Council and by the Behavioural and Clinical Neuroscience Institute, University of Cambridge, which is funded by the Medical Research Council and the Wellcome Trust. Complexity research at BAS is part of the Polar Science for Planet Earth Programme, funded by the Natural Environment Research Council.
Version: Published
DOI: 10.3389/fnsys.2011.00075
URI: https://dspace.lboro.ac.uk/2134/18128
Publisher Link: http://dx.doi.org/10.3389/fnsys.2011.00075
ISSN: 1662-5137
Appears in Collections:Published Articles (Business School)

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