Research And Application Of Financial Early Warning Model Of Listed Companies Based On Industry Differences And Convolutional Neural Networks

In the past two years,due to the continued impact of the epidemic and the escalating economic and trade friction between China and the United States,the market environment in which Chinese enterprises operate has become more and more competitive,and the requirements for enterprise management,especially financial crisis management,have become higher and higher.In the face of the rapidly changing market environment,there are many different factors that can trigger a financial crisis,so it is especially important to build a more scientific and effective financial crisis early warning model with high accuracy for enterprises to achieve higher quality financial management goals.The existing financial crisis early warning models are mostly based on simple Z-score model,support vector machine and BP neural network model due to the constraints of the development of data analysis technology,and there are shortcomings in the construction of the model such as the sample data selection is not wide enough,the selection of index system is not comprehensive,and the modeling process is influenced by human factors,etc.Therefore,in the background of the big data era,this paper uses the deep learning in the field of artificial intelligence to address the above problems.Therefore,in the context of the big data era,this paper uses the deep learning technology in the field of artificial intelligence to build a financial crisis early warning model with higher prediction performance.Firstly,on the basis of reviewing the relevant research results conducted by domestic and foreign researchers,the financial indicators reflecting the financial status of listed companies were selected from the WAND database based on the following five dimensions:solvency,operational capacity,profitability,enterprise growth capacity and cash flow capacity,while other non-financial indicators reflecting the changes in the financial status of enterprises were also selected to build a system of 48 indicators including the dummy variable "industry".On this basis,the financial data of 749 A-share listed companies from1999 to 2020 in Shanghai and Shenzhen were selected as sample data for the years T-2 and T-3,and then the financial indicators were verified to be significantly different among industries.Then,based on the verification of the significant differences of financial indicators between industries,the financial early warning model based on industry differences and convolutional neural network,the financial early warning model based on convolutional neural network without considering industry differences,and the financial early warning model based on industry differences with BP neural network were established respectively.The results of the empirical study show that the prediction accuracy of the financial early warning model based on industry variability and convolutional neural network is95.11%,which is a significant improvement over the prediction accuracy of 91.56% of the convolutional neural network early warning model without considering industry variability and the prediction accuracy of 87.33% of the BP neural network financial early warning model based on industry variability.It verifies that the new model has more accurate early warning evaluation performance and further improves the stability and applicability of the financial crisis early warning model.