Research On Energy Companies Financial Early Warning Based On Logistic Model And BP Neural Network Model

China has gradually become a world leader in energy production and consumption.The rapid economic development in China can be attributed in part to the rapid development of the energy industry.It can be said that the energy industry has occupied an important position in China's economic development.Energy issues have long been a key issue in the economic development of various countries.In the face of such fierce competition,energy companies have also experienced various financial problems that have led to financial crisis.The characteristics of Chinese energy companies with high growth and high risk,which determines the energy market risk exists in the process of operation and most of the risk in the early stage can be reflected by the enterprise's financial statements.As a result,the concept of financial risk early warning has emerged,and models have been established based on financial indicator data using various materials that reflect business operations,such as financial reports,annual audit reports and company information disclosure,to predict the financial risk and inform the business operators in time.Although there are many researches on financial early warning,the research on traditional energy companies is not perfect.I hope that the research results of this paper can provide some reference for the early warning of energy companies.First of all,the article expounds the research background and domestic and foreign research results,combines the status and characteristics of China's energy industry,and analyzes the causes of financial risks in the energy industry.Secondly,the basic design part of the model.According to the conditions of the early warning of the enterprise,three indicators are selected as the basis for the classification of the financial early warning interval of this article.Two-year data of 50 energy companies from 2014 to 2016 years were selected as training samples and 20 energy companies' data were used as forecast samples.Through the normal test and significant test,12 indicator variables that can reflect the financial problems of energy companies were selected as the basis for modeling.Then,the Logistic model and the BP neural network model are set up respectively,and the financial risk of one year after 2014 to 2015 is predicted and compared.Compared with the two models,it is concluded that the financial early warning model should be added to the non financial indicators to eliminate the limitations of the financial early warning model,and to improve the accuracy of the forecast,which is more suitable for the application of energy enterprises in practice.In the two models,the BP neural network model is superior to the Logistic model in terms of fitting degree or prediction accuracy for energy enterprise financial distress prediction.This paper analyzes the problems and limitations of the model,and proves that the BP neural network financial early warning model is more suitable for the practical application of the Chinese energy enterprises through empirical research.Finally,a summary of this paper combining the characteristics and modeling of energy enterprises by analyzing the results of the proposed energy companies to deal with financial risk strategy,and put forward the problems and Prospect of this paper.