Warning Model of Credit Default Risk Based on Hierarchical Clustering-Neural Network

Abstract

The early warning of default risk is an important issue closely concerned by all stakeholders of family farms and ranches (FF&R). However, due to the lack of current research on credit risk evaluation of FF&R, the existing credit default risk early warning models of FF&Rs mostly follow the credit default risk early warning model of farmers and small and micro enterprises. Therefore, the construction of credit default risk early warning model of FF&R is both a theoretical problem to be solved and of great practical significance. This paper classifies FF&Rs into three types of nondefault, low default and high default by using hierarchical clustering, so as to obtain a more realistic description of default conditions of FF&Rs. At the same time, the classification of three default states is taken as the input layer of the neural network, and the number of selected indicators of FF&Rs is taken as the output layer to construct the default risk warning model of FF&R neural network, thus improving the fitting efficiency of the neural network and the accuracy of default prediction state, timely warning and preventing the further deterioration of the default situation of FF&R. Empirical evidence shows that the hierarchical clustering-neural network model constructed in this paper achieves a default prediction accuracy of 97.9% on the FF&Rs dataset. Even after K-fold cross-validation, the model’s accuracy remains at 90.66%. Furthermore, compared with five traditional evaluation models, the model in this paper still maintains a high level of accuracy.