Abstract
In the context of COVID-19, many companies have been affected by the financial crisis. In order to carry out a comparative study on the accuracy of the company’s financial crisis early warning method, this study used RPROP artificial neural network and support vector machine, with 162 listed companies’ two-year panel financial indicator data as a model sample, and the test sample established a financial crisis early warning model. The theory of comprehensive evaluation combining two kinds of neural network methods is put forward innovatively. The predicted results can strengthen the supervision of the listed companies with risks by themselves and others and have important economic and social significance to ensure the stable operation of the listed companies, the securities market, and the national economy.
Highlights
Due to the impact of COVID-19 epidemic, many companies are facing financial crisis. e number of companies that run into difficulties or even go bankrupt due to financial risks has greatly increased. e financial crisis of company operation is usually caused by the accumulation of various problems in the company’s long-term business activities, and these problems can be discovered and solved in advance
This study adopts the method of data mining, using RPROP neural network and support vector machine (SVM), two years in 162 listed companies panel financial index data as the modeling sample, test sample, and a financial crisis warning model, in order to manage the financial risk of listed companies and guarantee smooth running of listed companies, securities market, and the national economy
Conclusions is study compares RPROP neural network and support vector machine algorithm to analyze the financial crisis of listed companies. e research results show that the RPROP artificial neural network method based on financial indicator information combined with support vector machine is a more effective method to predict whether financial crisis will occur in a company’s finance
Summary
Due to the impact of COVID-19 epidemic, many companies are facing financial crisis. e number of companies that run into difficulties or even go bankrupt due to financial risks has greatly increased. e financial crisis of company operation is usually caused by the accumulation of various problems in the company’s long-term business activities, and these problems can be discovered and solved in advance. This study adopts the method of data mining, using RPROP neural network and support vector machine (SVM), two years in 162 listed companies panel financial index data as the modeling sample, test sample, and a financial crisis warning model, in order to manage the financial risk of listed companies and guarantee smooth running of listed companies, securities market, and the national economy. Is study uses a comparative method and uses two neural network models to establish financial crisis early warning systems. This paper will discuss two kinds of neural network models, including RPROP neural network and support vector machine, use these two methods to conduct financial crisis prediction analysis on two years’ data of 162 listed companies, and use Mathematical Problems in Engineering modeling and cross-validation to verify the accuracy of the models. Conclusions and suggestions will be made for the overall study
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