Research Of Financial Performance Evaluation Of New Energy Listed Companies Based On The BP Neural Network

Under the background of increasing traditional energy consumption and increasing environmental pollution,new energy has become an important energy strategy in China.During the 13 th Five-Year Plan period,the country continued to “mainly” low-carbon green,and implemented relevant documents to encourage the development of new energy sources,and vigorously promoted the development of the new energy industry.The number of new energy companies is increasing and the competition is becoming increasingly fierce,making people pay more attention to the quality of enterprises.Therefore,the evaluation of the financial performance of China's new energy listed companies has important theoretical and practical significance for improving the development efficiency of new energy,reducing environmental pollution,building an environment-friendly society,and realizing the sustainable development of human society.In order to scientifically evaluate the financial performance of new energy listed companies,this paper selects 61 new energy listed companies in Shanghai and Shenzhen as the research object.In the process of performance evaluation,i n order to eliminate the subjective influence,this paper adopts a more objective and scientific evaluation method.Firstly,the selected indicators are selected by cluster analysis and correlation analysis,and used as input units of BP neural network;secondly,based on the characteristics of the sample,the gray correlation method is selected to determine the index weight,and the comprehensive performance value is obtained by the comprehensive weighting method,and is used as the output unit expectation value of the BP neural network;finally,the BP neural network is used to establish a performance evaluation model,and the applicability analysis are done.According to the results of the financial performance evaluation of the sample listed companies,firstly,according to the weight value of the five aspects of the evaluation indicators that reflect the financial performance determined by the grey correlation method,it can be known that the degree of influence of the solvency ability on its performance evaluation has obvious advantages;the proportion of capacity and growth capacity is not much different,and the degree of influence is second;the two aspects of operational capacity and cash flow need to be further improved.Secondly,after constructing the model by training the sample data,the test sample is simulated to observe the applicability of the model.The absolute error is 0.001244,the maximum is 0.014393,and the average error is 0.0056,which indicates that the BP neural network constructed in this paper is scientifically feasible and applicable to it.Third,according to the results of the listed company's comprehensive performance value,it is necessary to pay attention to balanced development and comprehensively improve individual abilit y when improving overall performance.