基于BP神经网络的财务危机预警模型研究——以宁德时代为例  

Research on financial crisis warning model based on BP neural network:taking the CATL as an example

作　　者：曾若辰 颜剩勇[1] ZENG Ruochen;YAN Shengyong(School of Business,Hunan University of Science and Technology,Xiangtan,Hunan 411100,China)

机构地区：[1]湖南科技大学商学院,湖南湘潭411100

出　　处：《内江师范学院学报》2024年第10期94-100,共7页Journal of Neijiang Normal University

基　　金：2020年湖南省社会科学基金项目(20JD031)。

摘　　要：随着新能源产业的发展,近年来锂电池企业成为关注重点.为保障锂电池企业更好地发展,本文以28家锂电池上市企业2018—2022年财务数据为研究对象,利用因子分析法选取其中16个具有代表性的预警指标,构建了BP神经网络模型进行财务危机预警;并以锂电池行业巨头企业宁德时代为例,采用功效系数法结合实际情况进行了案例分析.结果表明:本文所构建的锂电池行业BP神经网络财务危机预警模型预测的准确率达82.6%,准确度相对较高,且与具体案例实际情况分析的结果较为吻合,具有较强实用性,可以准确地识别锂电池企业财务危机情况并进行预警.With the development of the new energy industry,lithium battery enterprises have become a focus of attention in recent years.To ensure the better development of lithium battery enterprises,this article takes the financial data of 28 listed lithium battery companies from 2018 to 2022 as the research object,uses factor analysis to select 16 representative warning indicators,and constructs a BP neural network model for financial crisis warning;Taking CATL,a giant enterprise in the lithium battery industry,as an example,a case study was conducted using the efficiency coefficient method combined with actual situations.The results show that the BP neural network financial crisis warning model for the lithium battery industry constructed in this article has an accuracy rate of 82.6%,which is relatively high and consistent with the actual situation analysis of specific cases.It has strong practicality and can accurately identify the financial crisis situation of lithium battery enterprises and provide early warning.

关 键 词：BP神经网络 宁德时代 财务危机预警 

分 类 号：F275[经济管理—企业管理]