Deep Learning Modelling of Systemic Financial Risk

Received: 17 November 2019 Accepted: 9 January 2020 This paper attempts to improve the ability to prevent systemic financial risk (SFR). Based on the generation mechanism of China’s SFR, this paper presents an evaluation index system for financial risks, and then sets up a deep learning (DL) model for SFR prewarning. The proposed model inherits the merits of the DL in nonlinear approximation and selflearning, and overcomes the defects of conventional neural network (NN) model. Our model can capture the multi-dimensional changes in risk evaluation indices, and make accurate prewarning of the SFR. Our model can capture the multi-dimensional changes in risk evaluation indices, and make accurate prewarning of the SFR. Finally, empirical analysis proves that our model can retain much of the original features, and achieve highly accurate prewarning of the SFR. The research results provide technical support to risk regulation and decision-making of financial authorities.