Research on Regional Financial Risk Identification and Contribution Analysis Based on ANN-RBF Neural Network

Abstract

Regional financial risk is an issue that cannot be ignored around the world today. Studying financial risk and analyzing the deep-seated influential factors is conducive to reducing the generation of financial crises. This paper aims to study the influencing factors of regional financial risk and their contribution. Firstly, we identify influential factors of regional risk in the three northeastern provinces of China, then based on the artificial neural network-radial basis function (ANN-RBF) algorithm, we collect data from the region for the past 20 years for analysis and calculate the contribution of different risk influential factors to regional risk. The results show that: (1) CPI dominates in influencing the regional financial risk in the three northeastern provinces of China, followed by the growth rate of import and export and the local public finance surplus. (2) When the neuron is 29, the accuracy of the algorithm converges at this point and the model reaches optimum. The research in this paper is conducive to uncovering the influential factors of regional financial risk and reducing the generation of financial crises.