Research On Financial Risk Early Warning Based On PSO-ELM

With the development of the Internet economy,the number of listed companies has gradually increased,the scale has gradually expanded,the opportunities for enterprises have become more and more,and the competition has become more intense.There are hidden risks and crises in the market.For listed companies,because of the occurrence Events in which financial risks are forced to delist are frequent.Listed companies are a representative type of industry in China.They have an important position and influence in the Chinese market.As the industry continues to deepen,listed companies in the manufacturing industry face a series of challenges in the development process,such as the deterioration of the external environment.Internal management chaos,serious asset loss,problems in the capital chain,etc.,these situations may cause the company's financial situation to be in crisis.If the company faces financial risks and fails to take effective measures in time,it will lead to problems in the funds,and may even go bankrupt.How to detect and predict financial risks and realize the healthy development of enterprises as soon as possible is not only the focus of listed companies,but also the urgent needs of various stakeholders.Therefore,building an effective and applicable financial risk early warning model is of great significance to both listed companies and stakeholders.The purpose of this paper is to analyze the various early-warning indicators of listed companies in China,extract financial indicators that can reflect the normal financial performance,and establish a corresponding index evaluation system to build a financial risk warning model for various interests.Relevant parties provide reference and decision basis.Based on the financial risk early warning of listed companies,this paper studies 90 domestic enterprises based on the previous research on financial risk literature,and uses the literature statistics method to obtain 25 indicators that affect financial risks.The gray correlation method is used to screen the indicators,and the corresponding index system is established to optimize the input data of the extreme learning machine.Then the particle swarm optimization algorithm is introduced to optimize the input weight and hidden layer threshold of the extreme learning machine to establish an optimized PSO-ELM early warning model.Using the financial indicator data of 90 companies,the model is empirically analyzed.The analysis results show that the optimized model has better stability and accuracy than the standard extreme learning machine model.This paper examines the model with 10 new companies as examples.The results show that the model can more accurately assess the financial risk of the enterprise and has practicality.