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We introduce a method to predict which correlation matrix coefficients arelikely to change their signs in the future in the high-dimensional regime, i.e.when the number of features is larger than the number of samples per feature.The stability of correlation signs, two-by-two relationships, is found todepend on three-by-three relationships inspired by Heider social cohesiontheory in this regime. We apply our method to US and Hong Kong equitieshistorical data to illustrate how the structure of correlation matricesinfluences the stability of the sign of its coefficients.

Nonparametric sign prediction of high-dimensional correlation matrix coefficients