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In order to use the advanced inference techniques available for Ising models,we transform complex data (real vectors) into binary strings, by localaveraging and thresholding. This transformation introduces parameters, whichmust be varied to characterize the behaviour of the system. The approach isillustrated on financial data, using three inference methods -- equilibrium,synchronous and asynchronous inference -- to construct functional connectionsbetween stocks. We show that the traded volume information is enough to obtainwell known results about financial markets, which use however the presumablyricher price information: collective behaviour ("market mode") and stronginteractions within industry sectors. Synchronous and asynchronous Isinginference methods give results which are coherent with equilibrium ones, andmore detailed since the obtained interaction networks are directed.

Financial interaction networks inferred from traded volumes