Reference : Financial interaction networks inferred from traded volumes
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/10993/31615
Financial interaction networks inferred from traded volumes
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Zeng, Hong-Li [Aalto Univ, Dept Appl Phys, Espoo, Finland.]
Lemoy, Rémi mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Identités, Politiques, Sociétés, Espaces (IPSE)]
Alava, Mikko [Aalto Univ, Dept Appl Phys, Espoo, Finland.]
2014
Journal of Statistical Mechanics: Theory and Experiment
Iop Publishing Ltd
P07008-17
Yes (verified by ORBilu)
International
1742-5468
Bristol
[en] models of financial markets ; network reconstruction ; statistical inference ; kinetic Ising models
[en] In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, using local averaging and thresholding. This transformation introduces parameters, which must be varied to characterize the behaviour of the system. The approach is illustrated on financial data, using three inference methods-equilibrium, synchronous and asynchronous inference-to construct functional connections between stocks. We show that the traded volume information is enough to obtain well-known results about financial markets that use, however, presumably richer price information: collective behaviour ('market mode') and strong interactions within industry sectors. Synchronous and asynchronous Ising inference methods give results that are coherent with equilibrium ones and are more detailed since the obtained interaction networks are directed.
Finnish graduate school for Computational Science (FICS) ; Centre of Excellence program of the Academy of Finland
http://hdl.handle.net/10993/31615
10.1088/1742-5468/2014/07/P07008
We thank Matteo Marsili for providing the data, and acknowledge interesting discussions with Erik Aurell, Matteo Marsili, Iacopo Mastromatteo, Alexander Mozeika and Onur Dikmen, as well as helpful suggestions by the editor and two anonymous referees. This work was supported by funding from the Finnish graduate school for Computational Science (FICS) and the Centre of Excellence program of the Academy of Finland, for the COMP and COIN Centres. We acknowledge the computational resources provided by the Aalto Science-IT project.

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