Research On Financial Crisis Early Warning Of Poten Environmental Company Based On BP Neural Network

The report of the 20 th National Congress of the Communist Party of China clearly points out the importance of strengthening resilience to risks,stressing that China’s development has entered a period of coexistence of strategic opportunities,risks and challenges,and increasing uncertainties and uncertainties.At the same time,in the context of green development concept,environmental protection has become a key topic in today’s society,and environmental protection listed companies are facing opportunities and challenges in the process of development.Therefore,in today’s environment,risk prevention is particularly important,which can help enterprises to effectively identify and reduce potential financial risks,take corresponding measures in time to implement,avoid enterprises falling into financial crisis,so as to ensure the healthy and stable development of enterprises.Based on the concept of financial crisis and related theories,this paper sorted out the selection of financial crisis indicators and models,and took Poten Environmental Company,a representative environmental protection company,as the research object,analyzed its internal environment and financial situation,selected listed environmental protection companies as research samples,and initially selected representative financial indicators and non-financial indicators on the basis of reading literature.Furthermore,indicators with significance were further screened out through correlation tests,and then principal component extraction was carried out on the significance indicators to eliminate the multicollinearity between data,so as to make the warning indicators more reasonable.Secondly,the BP neural network is used to construct an early warning model for the financial crisis of environmental listed companies by using the extracted principal components and non-financial indicators as input variables,which has many advantages,such as strong learning and adaptive ability,high computational ability and good information processing.In addition,it is known from the literature that BP neural network has a high accuracy in predicting the financial crisis.After the study,the following conclusions are drawn: First,the BP neural network model has a good effect on the early warning of the financial crisis of environmental protection listed companies.In this paper,SPSS software was used to conduct normality test and Mann-Whitney U test for relevant indicator data,and 16 significant indicators were obtained from 24 indicators.Principal components were extracted to optimize the financial crisis warning index system of listed environmental protection companies,and the optimized index system was used to build the BP neural network model.Results show that the overall prediction accuracy of this model is high,reaching 93.3%.Therefore,it is of certain value to apply BP neural network model to the financial crisis warning of environmental listed companies.Second,through the financial crisis warning model built for listed environmental protection companies,the case company in this paper--Poten Environmental Protection Company is indeed predicted to suffer financial crisis in 2022.In the future,Poten Environmental Company should strengthen accounts receivable management,pay attention to cash flow management,improve profitability,pay attention to the establishment and improvement of financial crisis early warning mechanism,strengthen personnel training to prevent the recurrence of financial crisis.Exhibition.