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Article (Scientific journals)
Financial interaction networks inferred from traded volumes
Zeng, Hong-Li; Lemoy, Rémi; Alava, Mikko
2014In Journal of Statistical Mechanics: Theory and Experiment, p. 07008-17
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Keywords :
models of financial markets; network reconstruction; statistical inference; kinetic Ising models
Abstract :
[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.
Disciplines :
Physics
Mechanical engineering
Author, co-author :
Zeng, Hong-Li;  Aalto Univ, Dept Appl Phys, Espoo, Finland.
Lemoy, Rémi ;  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.
External co-authors :
yes
Title :
Financial interaction networks inferred from traded volumes
Publication date :
2014
Journal title :
Journal of Statistical Mechanics: Theory and Experiment
ISSN :
1742-5468
Publisher :
Iop Publishing Ltd, Bristol, Unknown/unspecified
Pages :
P07008-17
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
Finnish graduate school for Computational Science (FICS)
Centre of Excellence program of the Academy of Finland
Commentary :
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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