Financial Markets as Complex Networks

Prices and returns in financial markets exhibit robust statistical regularities across space and time. We argue that the network structure among financial market participants (investors, traders, analysts, service providers, institutional platforms, etc.) is of crucial importance for the generation of these regularities, but has been neglected to a large extent until now. As a starting point, we illustrate how a simple model with hierarchical "superstructures" in complex networks could explain these statistical regularities. Our project focuses (i) on an inverse identification of relevant network structures, given the statistical regularities of financial returns; (ii) on the calibration of behavioural parameters in these complex hierarchical networks; and (iii) on economically motivated processes that are capable of generating such complex hierarchical networks.

Contact: Thomas Lux 
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