Review Articles

The study on systemic risk of rural finance based on macro–micro big data and machine learning

Wanling Zhou ,

Institute of Finance Engineering in School of Management, Jinan University, Guangzhou, People’s Republic of China

Sulin Pang ,

Institute of Finance Engineering in School of Management, Jinan University, Guangzhou, People’s Republic of China; School of Public Administration and Emergency Management, Jinan University, Guangzhou, People’s Republic of China

jrgcyjs@gmail.com

Zhiliang He

School of Management, Jinan University, Guangzhou, People’s Republic of China

Pages | Received 11 Nov. 2022, Accepted 14 Jul. 2023, Published online: 06 Aug. 2023,
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It’s the basic premise of promoting the healthy development of rural finance and strengthening macro-prudential supervision to measure the systemic risk of rural finance accurately. We establish the dynamic factor CAPM and make an all-round and multi-angle quantitative study on the systemic risk of rural finance in China by constructing Macro–micro index system and using machine learning to reduce the dimension of high-dimensional data. Our results show that the dynamic factor CAPM of using Macro–micro big data can evaluate systemic risk of rural finance more comprehensively and systematically, and machine learning performs well in processing high-dimensional data. In addition, China's rural financial systemic risk is stable compared with the Shanghai and Shenzhen main markets, but it is also susceptible to macro and micro influenced factors. Finally, it is pointed out that the early warning system of rural financial systemic risk could be constructed at macro and micro level, respectively.