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The mesoscopic organization of complex systems, from financial markets to thebrain, is an intermediate between the microscopic dynamics of individual units(stocks or neurons, in the mentioned cases), and the macroscopic dynamics ofthe system as a whole. The organization is determined by "communities" of unitswhose dynamics, represented by time series of activity, is more stronglycorrelated internally than with the rest of the system. Recent studies haveshown that the binary projections of various financial and neural time seriesexhibit nontrivial dynamical features that resemble those of the original data.This implies that a significant piece of information is encoded into the binaryprojection (i.e. the sign) of such increments. Here, we explore whether thebinary signatures of multiple time series can replicate the same complexcommunity organization of the financial market, as the original weighted timeseries. We adopt a method that has been specifically designed to detectcommunities from cross-correlation matrices of time series data. Our analysisshows that the simpler binary representation leads to a community structurethat is almost identical with that obtained using the full weightedrepresentation. These results confirm that binary projections of financial timeseries contain significant structural information.

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations