Influencing factors of the risk correlation of financial institutions: Evidence from random forest fusion

LI Jingyu, GUO Xiangyuan, XIE Qiwei, ZHENG Xiaolong

Systems Engineering - Theory & Practice ›› 2024, Vol. 44 ›› Issue (1) : 296-315.

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Systems Engineering - Theory & Practice ›› 2024, Vol. 44 ›› Issue (1) : 296-315. DOI: 10.12011/SETP2023-1782

Influencing factors of the risk correlation of financial institutions: Evidence from random forest fusion

  • LI Jingyu1, GUO Xiangyuan1, XIE Qiwei1, ZHENG Xiaolong2,3
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Abstract

Being "too interconnected to fail" has made the risk correlation of financial institutions and its influencing factors a crucial issue in maintaining financial stability. Drawing inspiration from gene regulatory research using random forests, this paper proposes a method to construct a network that captures the relations between different indicators, for the purpose of exploring the influences between risk correlation and its related factors. It is achieved by integrating forest fusion and random permutation. The proposed method overcomes the limitations of traditional regression analysis, Granger causality test, and Bayesian networks, while the introduction of random permutation enhances the model's capability to handle variable heterogeneity. Empirical results based on 46 listed financial institutions in China from 2012 to 2022 demonstrate that the constructed network can identify the direct or indirect impact of different factors on risk correlation and reveal the influence paths of factors. This provides more comprehensive empirical evidence of complex relationships, highlighting the applicability of the proposed approach in addressing this issue and potentially offering a useful tool for financial regulation and risk management.

Key words

financial institutions / risk correlation / random forest / random permutation

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LI Jingyu , GUO Xiangyuan , XIE Qiwei , ZHENG Xiaolong. Influencing factors of the risk correlation of financial institutions: Evidence from random forest fusion. Systems Engineering - Theory & Practice, 2024, 44(1): 296-315 https://doi.org/10.12011/SETP2023-1782

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Funding

Scientific and Technological Innovation 2030——"New Generation Artificial Intelligence" Major Project (2020AAA0108401); National Natural Science Foundation of China (72201012, 72225011); The Key Programs of Social Science of Beijing Municipal Education Commission (SZ202210005004); Beijing Municipal Social Science Foundation (22JCC068)
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