Prediction of systemic risk of China's financial industry based on phase space reconstruction and machine learning

Yulian An; Zhaojun Liu; Zhenkuan Sun; Boan Li; Yulian An; Zhaojun Liu; Zhenkuan Sun; Boan Li

doi:10.3934/nhm.2026033

Networks and Heterogeneous Media

2026, Volume 21, Issue 3: 773-800. doi: 10.3934/nhm.2026033

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Prediction of systemic risk of China's financial industry based on phase space reconstruction and machine learning

1.
School of Economics and Finance, Shanghai International Studies University, Shanghai 201620, China
2.
NewSchool Of Architecture And Design, San Diego, CA 92101, USA

Received: 31 December 2025 Revised: 07 April 2026 Accepted: 13 April 2026 Published: 28 April 2026

Forecasting systemic financial risks is crucial for effectively preventing and mitigating such risks. This paper introduces an integrated framework for systemic financial risk forecasting, with its core innovation being the novel combination of phase space reconstruction with delay parameterization (RDPM) and a radial basis function neural network (RBFNN). Based on stock trading data from 41 listed companies in the China A-share stock market, a complex financial network is constructed.
- systemic financial risk,
- forecast,
- phase space reconstruction,
- delay parameterization method,
- machine learning
Citation: Yulian An, Zhaojun Liu, Zhenkuan Sun, Boan Li. Prediction of systemic risk of China's financial industry based on phase space reconstruction and machine learning[J]. Networks and Heterogeneous Media, 2026, 21(3): 773-800. doi: 10.3934/nhm.2026033

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Abstract

Forecasting systemic financial risks is crucial for effectively preventing and mitigating such risks. This paper introduces an integrated framework for systemic financial risk forecasting, with its core innovation being the novel combination of phase space reconstruction with delay parameterization (RDPM) and a radial basis function neural network (RBFNN). Based on stock trading data from 41 listed companies in the China A-share stock market, a complex financial network is constructed.

References

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