Financial interaction networks inferred from traded volumes
In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, by 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, which use however the presumably richer price information: collective behaviour ("market mode") and strong interactions within industry sectors. Synchronous and asynchronous Ising inference methods give results which are coherent with equilibrium ones, and more detailed since the obtained interaction networks are directed.
References listed on IDEAS
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- Thomas Bury, 2012. "Statistical pairwise interaction model of stock market," Papers 1206.4420, arXiv.org, revised Jan 2014.
- Christoly Biely & Stefan Thurner, 2008. "Random matrix ensembles of time-lagged correlation matrices: derivation of eigenvalue spectra and analysis of financial time-series," Quantitative Finance, Taylor & Francis Journals, vol. 8(7), pages 705-722.
- Iacopo Mastromatteo & Matteo Marsili, 2011. "On the criticality of inferred models," Papers 1102.1624, arXiv.org, revised Sep 2011.
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