Dynamic Early Warning Method of Multiclassification Financial Crisis of Listed Companies Based on Particle Swarm Optimization

Abstract

In the Internet plus era, the environment of the world’s economy is changing towards globalization and information. The development of Chinese enterprises is facing hitherto unknown opportunities. Meanwhile, the financial risks facing enterprises are increasing day by day, because the constraints of environmental factors, economic and market factors, laws and regulations, social and cultural factors, policy environmental factors, and other unpredictable environmental factors have brought uncertainty to the financial situation of enterprises. The way a company handles its own financial risk has a big impact on its overall success. Many enterprises fall into economic crisis early because they do not pay enough attention to financial problems in the early stages and do not take effective measures to deal with the crisis situation in a timely manner, resulting in internal management confusion, deterioration of the external environment, capital chain problems, and serious asset losses. It is difficult to recover, and individuals who are experiencing a more significant financial crisis are at risk of becoming bankrupt. As a result, the success or failure of an enterprise’s early development is determined by the success or failure of its subsequent development. As a result, in order to devote more resources to the study of enterprise financial risk, this paper develops a variety of dynamic early warning models of financial crises in publicly traded companies by analyzing relevant theories of financial risk and combining the particle swarm optimization algorithm. This model has great efficiency and scientific early warning capabilities, and it can better forecast business financial crises.