Nonparametric sign prediction of high-dimensional correlation matrix coefficients

Christian Bongiorno () and Damien Challet
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Christian Bongiorno: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay

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Abstract: 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 .

Date: 2021-03-31
Note: View the original document on HAL open archive server: https://hal.science/hal-02335586
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Published in EPL - Europhysics Letters, 2021, 133 (4), pp.48001. ⟨10.1209/0295-5075/133/48001⟩

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Working Paper: Nonparametric sign prediction of high-dimensional correlation matrix coefficients (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02335586

DOI: 10.1209/0295-5075/133/48001

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