基于SVM的长三角科技型企业财务风险研究  

Research on financial risk of technology-based enterprises in Yangtze River Delta based on SVM

作　　者：王鸿博 徐奕辰 孙皓磊 沈双喆 张立(指导) WANG Hongbo;XU Yichen;SUN Haolei;SHEN Shuangzhe;ZHANG Li(School of Information Management,Shanghai Lixin University of Accounting and Finance,Shanghai 201620,China)

机构地区：[1]上海立信会计金融学院信息管理学院,上海201620

出　　处：《上海电机学院学报》2023年第2期110-116,共7页Journal of Shanghai Dianji University

基　　金：国家社会科学基金项目(18BTJ020)。

摘　　要：为了研究长三角科技型企业所面临的潜在财务风险状况,运用知识图谱筛选出与长三角科技型企业财务风险相关度较高的指标,采用因子分析法对所筛选出指标之间的相关性进行分析,建立财务风险预警指标体系,并选取长三角科技型上市企业11年的样本数据,基于支持向量机(SVM)算法构建企业财务预警模型。结果表明:建立的财务风险五分类预测模型,对长三角科技型上市公司的财务风险预测准确度达到92%,可以有效地感知企业财务风险,该模型可为企业管理决策提供科学有效支撑。In order to study the potential financial risks faced by technology-based enterprises in the Yangtze River Delta,the knowledge map is used to screen out indicators that are highly correlated with the financial risks of technology-based enterprises in the Yangtze River Delta.First,the factor analysis method is applied to analyze the correlation between the screened indicators,and a financial risk early warning indicator system is established.Then the 11 years of sample data of technology-based listed enterprises in the Yangtze River Delta is selected and the enterprise financial early warning model is constructed based on the support vector machine(SVM)algorithm.The results show that the accuracy of the five-classification prediction model for the financial risk of listed companies in the Yangtze River Delta can reach 92%,which can effectively perceive the financial risk of enterprises.The proposed model can provide scientific and effective support for enterprise management decision-making.

关 键 词：财务预警模型 支持向量机(SVM) 因子分析 

分 类 号：F832[经济管理—金融学]