Volume 133, Number 4, February 2021
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||31 March 2021|
Non-parametric sign prediction of high-dimensional correlation matrix coefficients
Laboratoire de Mathématiques et Informatique pour les Systèmes Complexes, CentraleSupélec, Université Paris Saclay - 3 rue Joliot-Curie, 91192, Gif-sur-Yvette, France
Received: 26 October 2020
Accepted: 15 January 2021
We introduce a method to predict which correlation matrix coefficients are likely 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 to depend on three-by-three relationships inspired by Heider social cohesion theory in this regime. We apply our method to US and Hong Kong equities historical data to illustrate how the structure of correlation matrices influences the stability of the sign of its coefficients.
PACS: 89.65.Gh – Economics; econophysics, financial markets, business and management / 89.90.+n – Other topics in areas of applied and interdisciplinary physics (restricted to new topics in section 89) / 64.60.aq – Networks
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