等距特征映射的支持向量机模型在上市公司信用风险评估中的应用
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摘要
为了实现上市公司信用风险的科学定量管理,提出了一种基于支持向量机(SVM)的信用风险评估方法。考虑到财务数据特征的非线性和高维性,采用等距特征映射(Isomap)算法对财务指标进行特征提取,以减少数据的冗余,针对人为选择SVM参数的盲目性,应用遗传算法优化其参数。通过以中国上市公司财务数据为基础的实证分析表明:基于Isomap的SVM模型比BPNN(BP神经网络)、PCA-SVM(主成分-支持向量机)模型具有更强的信用风险评估能力,小样本评估准确率达到91%。
In order to realize scientific quantitative management of credit risk in the listed companies,we put forward a kind of credit risk assessment based on support vector machine(SVM).Considering the nonlinearity and high dimensionality of the financial data,we use isometric feature mapping(Isomap) to conduct feature extraction towards financial index to reduce data redundancy.In view of the selective blindness of SVM parameters,we use genetic algorithm(GA) to optimize the parameters.The empirical analysis on the basis of financial data of the listed companies in China shows that the SVM model of Isomap has a stronger credit risk assessment ability than the BP neural network and PCA-SVM model.Its small sample evaluation accuracy reaches 91%.
In order to realize scientific quantitative management of credit risk in the listed companies,we put forward a kind of credit risk assessment based on support vector machine(SVM).Considering the nonlinearity and high dimensionality of the financial data,we use isometric feature mapping(Isomap) to conduct feature extraction towards financial index to reduce data redundancy.In view of the selective blindness of SVM parameters,we use genetic algorithm(GA) to optimize the parameters.The empirical analysis on the basis of financial data of the listed companies in China shows that the SVM model of Isomap has a stronger credit risk assessment ability than the BP neural network and PCA-SVM model.Its small sample evaluation accuracy reaches 91%.
引文
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[19]何沛俐,章早立.立体空间下的全新财务危机远期预警模型[C].深圳证券交易所第五界会员单位管理公司研究成果,2002.
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[2]ALTMAN.Financial Ratio,Discriminant Analysis andthe Prediction of Corporate Bankruptcy[J].Journal ofFinance,1968(12):589-609.
[3]AIRMAN R,HALDEMAN,P NARAYANAN.ZetaAnalysis:a New Model to Identify Bankruptcy Risk ofCorporations,Journal of Banking and Finance,1977(4):29-54.
[4]鲁炜,赵恒珩,刘冀云.KMV模型关系函数推测及其在中国股市的验证[J].运筹与管理,2003(3):43-48.
[5]COATS P,FANT L.Recognizing Financial DistressPatterns Using a Neural Network Tool[J].FinancialManagement,1993(3):142-155.
[6]PIRAMUTHU S.Financial,Credit-risk Evaluationwith Neural and Neurofuzzy Systems[J].EuropeanJournal of Operational Research,1999,112(2):310-321.
[7]王春峰,万海晖.商业银行信用风险评估及其实证研究[J].管理科学学报,1998(1):68-72.
[8]章忠志,符林,唐焕文.基于人工神经网络的商业银行信用风险模型[J].经济数学,2003(3):42-47.
[9]韩东平,田艳丽,王悦鑫.基于现金流测量标准的财务预警实证研究[J].财会通讯,2006(12):48-50.
[10]姜秀华,孙铮.治理弱化与财务危机:一个预警模型[J].南开管理评论,2001(5):19-25.
[11]杨淑娥,黄礼.基于神经网络的上市公司财务预警模型[J].系统工程理论与实践,2005(1):12-18.
[12]李杏,基于基本面的上市公司投资风险——判别分析评价[J].企业技术开发(下半月),2009(5):36-37.
[13]刘淑莲,王真,赵建卫.基于因子分析的上市公司信用评级应用研究[J].财经问题研究,2008(7):53-60.
[14]刘彦文,戴红军.基于粗糙集-神经网络的财务危机预警模型实证研究[J].科研管理,2007(11):138-142.
[15]TENENBAUM J B,SILVA V D,LANGFORD J C.AGlobal Geometric Framework for Nonlinear DimensionalityReduction[J].Science,2000,290(5500):2319-2323.
[16]DIJKSTRA E W.A Note on two Problems in Connexionwith Graphs[J].Numerische Mathematik,1959(1):269-271.
[17]CRISTIANINI N.支持向量机导论[M].李国正,译.北京:电子工业出版社,2004.
[18]刘彦文.上市公司财务危机预警模型研究[D].大连:大连理工大学,2008.
[19]何沛俐,章早立.立体空间下的全新财务危机远期预警模型[C].深圳证券交易所第五界会员单位管理公司研究成果,2002.
[20]MARTENS H A,DARDENNE P.Validation and Ver-ification of Regression in Small Data Sets[J].Chemo-metrics and Intelligent Laboratory Systems,1998(44):99-121.
[21]朱仁峰.精通Matlab7[M].北京:清华大学出版社,2006.