Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis

Abstract

Abstract This article first uses a new method of nonlinear combination forecasting based on neural networks to construct a financial crisis early warning model and conduct an empirical study. The drafting article uses Fisher's second-class linear discriminant analysis and binary logistic regression to establish a three-year early warning model for listed companies before the financial crisis. Empirical research shows that this early warning model applies to various industries. It can play a certain role in predicting and preventing the financial crisis of Chinese companies.