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

TL;DR

This paper demonstrates that binary sign-based representations of financial time series can effectively reveal the same mesoscopic community structures as the original weighted data, highlighting the importance of sign information in financial correlations.

Contribution

It shows that binary sign projections of financial data preserve the complex community organization found in weighted correlations, offering a simpler yet effective analysis method.

Findings

Binary sign representations replicate community structures of weighted data.

Binary projections contain significant structural information.

Community detection results are almost identical between binary and weighted data.

Abstract

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