基于模糊综合评价法的上市公司信用风险研究
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摘要
在我国,上市公司已经经过十余年的发展,取得了令人瞩目的成绩。但同时随着上市公司数量的增加,越来越多的上市公司由于财务异常而面临巨大的信用风险。前几年的“银广厦事件“以及去年的“科龙事件”就是很好的例证。这些信用问题,使得投资者特别是中小股东伤透了心,严重地影响了上市公司的整体信用形象,成为制约市场发展的突出问题。因此,对上市公司的信用风险进行研究和评价已显得迫在眉睫。
本文分为五章。第一章绪论,主要介绍了国内外对信用风险的研究现状及本文的研究思路。第二章现代信用风险评估模型及评价,对比分析了现代被广泛使用的几种信用风险度量模型。第三章模糊综合评价模型的构建,详细阐述了模型的构建过程。第四章模糊综合评价模型实证分析。第五章结论。
在评价指标体系的构建上,本文鉴于企业或个人投资者获取信息的局限性,只选择了财务指标。并通过相关性分析和鉴别力分析,对指标进行了遴选,剔除了相关性强、对风险评估作用不大的指标。最后建立了以偿债能力、经营效益、经营能力、经济实力四个因素集15个指标的信用风险评估指标体系。
在权重的确定上,考虑到一般的赋权方法如:AHP法,主观性太强的缺陷,本文选用熵权法来为指标赋权,以客观地反映指标的重要程度。
在模型的建立上,本文对传统的模糊综合评价模型进行了改进,将F函数与多目标多元线性回归相结合构造了多目标多元模糊隶属函数。只需要代入待评价对象的各因素数据,再根据最大隶属度原则,即可测定待评价对象的风险类型,简化了信用风险的评价过程。
为了对模型进行检验,本文选择了沪市工业类上市公司作为测试样本,评出了他们的信用风险类型,并与联合资信评估有限公司的评估结果进行对比。结果显示,该模型与联合资信评估有限公司的评定结果大致吻合,拟合度达到74.7%,特别是正常类和高风险类上市公司,两种评定结果完全一致。这说明本文所建立的模糊综合评价模型能够有效地评价上市公司的信用风险,可以为企业或个人投资者的投资或授信决策提供决策支持。
In our country, the listed companies have developed for more than ten years and get conspicuous success. But with the increase in the number of listed companies, more and more listed companies face rough credit risk because of financial changes. "Yinguangxia incident" and "Kelong incident" are good examples. These credit problems stabbed investors especially the small and medium shareholders to the hearts, seriously affect the wholly credit image of listed companies and become the prominent problems which hold back the development in the market. So it is urgent to research and evaluate the credit risk of listed companies.There are five chapters in this paper. The fist chapter is introduction and introduces the research on credit risk in and out of our country, and the research route. The second chapter is modern credit risk evaluation models and comparison on some credit risk evaluation models, which have come into wide use. The third chapter is the construction of the fuzzy comprehensive evaluation model, expatiating on the process of the construction of the model. The Empirical research of the model and conclusion are respectively in Chapter Four and Chapter Five.On the construction of evaluation index system, considering the limitation of information, which enterprises or personal investors can get, only financial indexes are chosen. And according to correlation analysis and discriminability analysis, some indexes that have strong correlation with other indexes or have only little function to evaluate credit risk are eliminated. At last, the credit risk evaluation index system is set up from four aspects: credit capacity, operation performance, operation capacity and economic strength, with fifteen indexes.On the weighted coefficient, Entropy Weigh Calculation Method is used, as some ordinary methods such as AHP, have strong subjectivity.On the construction of the model, the traditional fuzzy comprehensive evaluation model is improved, and F function and the multidimensional linear regression model of several targets are combined to set up four multidimensional membership function. Just imputing the data of samples and using the principle of biggest degree of dependence, the sample's risk state can be evaluated. It simplifies the evaluation process of credit risk.In order to test the model, this article chooses some industrial listed companies in Shanghai Stock Exchange as testing samples evaluates their credit risk state and compares this result with the result of Lianhe Credit Rating Co., Ltd. The result shows that the two
results are about and about, and the degree of fitting is 74.7%. Especially for natural and high-risk listed companies, the two results are the same. This shows that the fuzzy comprehensive evaluation model in this paper can effectively evaluate the credit risk of listed companies and provide decision support for enterprises or personal investors' investment or credit extension.
本文分为五章。第一章绪论,主要介绍了国内外对信用风险的研究现状及本文的研究思路。第二章现代信用风险评估模型及评价,对比分析了现代被广泛使用的几种信用风险度量模型。第三章模糊综合评价模型的构建,详细阐述了模型的构建过程。第四章模糊综合评价模型实证分析。第五章结论。
在评价指标体系的构建上,本文鉴于企业或个人投资者获取信息的局限性,只选择了财务指标。并通过相关性分析和鉴别力分析,对指标进行了遴选,剔除了相关性强、对风险评估作用不大的指标。最后建立了以偿债能力、经营效益、经营能力、经济实力四个因素集15个指标的信用风险评估指标体系。
在权重的确定上,考虑到一般的赋权方法如:AHP法,主观性太强的缺陷,本文选用熵权法来为指标赋权,以客观地反映指标的重要程度。
在模型的建立上,本文对传统的模糊综合评价模型进行了改进,将F函数与多目标多元线性回归相结合构造了多目标多元模糊隶属函数。只需要代入待评价对象的各因素数据,再根据最大隶属度原则,即可测定待评价对象的风险类型,简化了信用风险的评价过程。
为了对模型进行检验,本文选择了沪市工业类上市公司作为测试样本,评出了他们的信用风险类型,并与联合资信评估有限公司的评估结果进行对比。结果显示,该模型与联合资信评估有限公司的评定结果大致吻合,拟合度达到74.7%,特别是正常类和高风险类上市公司,两种评定结果完全一致。这说明本文所建立的模糊综合评价模型能够有效地评价上市公司的信用风险,可以为企业或个人投资者的投资或授信决策提供决策支持。
In our country, the listed companies have developed for more than ten years and get conspicuous success. But with the increase in the number of listed companies, more and more listed companies face rough credit risk because of financial changes. "Yinguangxia incident" and "Kelong incident" are good examples. These credit problems stabbed investors especially the small and medium shareholders to the hearts, seriously affect the wholly credit image of listed companies and become the prominent problems which hold back the development in the market. So it is urgent to research and evaluate the credit risk of listed companies.There are five chapters in this paper. The fist chapter is introduction and introduces the research on credit risk in and out of our country, and the research route. The second chapter is modern credit risk evaluation models and comparison on some credit risk evaluation models, which have come into wide use. The third chapter is the construction of the fuzzy comprehensive evaluation model, expatiating on the process of the construction of the model. The Empirical research of the model and conclusion are respectively in Chapter Four and Chapter Five.On the construction of evaluation index system, considering the limitation of information, which enterprises or personal investors can get, only financial indexes are chosen. And according to correlation analysis and discriminability analysis, some indexes that have strong correlation with other indexes or have only little function to evaluate credit risk are eliminated. At last, the credit risk evaluation index system is set up from four aspects: credit capacity, operation performance, operation capacity and economic strength, with fifteen indexes.On the weighted coefficient, Entropy Weigh Calculation Method is used, as some ordinary methods such as AHP, have strong subjectivity.On the construction of the model, the traditional fuzzy comprehensive evaluation model is improved, and F function and the multidimensional linear regression model of several targets are combined to set up four multidimensional membership function. Just imputing the data of samples and using the principle of biggest degree of dependence, the sample's risk state can be evaluated. It simplifies the evaluation process of credit risk.In order to test the model, this article chooses some industrial listed companies in Shanghai Stock Exchange as testing samples evaluates their credit risk state and compares this result with the result of Lianhe Credit Rating Co., Ltd. The result shows that the two
results are about and about, and the degree of fitting is 74.7%. Especially for natural and high-risk listed companies, the two results are the same. This shows that the fuzzy comprehensive evaluation model in this paper can effectively evaluate the credit risk of listed companies and provide decision support for enterprises or personal investors' investment or credit extension.
引文
[1] 约翰.B.考埃特,爱德华.I.爱特曼,石晓军,张振霞译.演进着的信用风险管理[M].北京:机械工业出版社,2001
[2] 布赖恩.科伊尔,周道许,关伟译.信用风险管理[M].北京:中信出版社,2003
[3] 张玲,曾维火.基于Z值模型的我国上市公司信用评级研究[J].财经研究,2004,(6)P:5-13
[4] Beaver W. H. Financial ratios as predictors of failure. Empirical Research in Accounting;Selected Studies[J]. Supplement to Journal of Accounting Research, 1966, 4, P: 71-111.
[5] Altman E I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 1968, (9) Vol. ⅩⅩⅢ
[6] Altman E L. Corporate Financial Distress. New York: John Wiley & Sons, 1983
[7] Altman E. I.. Predicting, Finacial Distress of Companies: Revisiting the Z-Score and Zata Models[C]. Working Paper. NYU Salomon Center, June 1995.
[8] 范柏乃,朱文斌.中小企业信用评价指标的理论遴选与实证分析[J].科研管理,2003,(6)P:83-87
[9] 吴世农.我国上市公司财务困境的预测模型研究[J].经济研究,2001,(6)P:46-55
[10] Aziz. A. D, Emanuel. G. Lawson. Bank Prediction: An investigation of cash flow based models[J]Journal of Management Studies, 25, P: 419-437
[11] Martin D. Earky warning of bank failure: a logit regression approach[J]. Journal of Banking and Finance, 1977, P: 249—276.
[12] Olson J. Financial rations and the probabilistic prediction of bankruptcy[J]. Accounting Research, 1980, Sprong, P: 109-130.
[13] Madalla G. S. Limited-Dependent and qualitative variables in Econometrics. Cambridge: Cambridge University Press, 1983
[14] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24, 26-49
[15] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance,, 2000, 24, 4-26
[16] Altman. Eward I, Suggitt. Heather J. Default rates in the syndicated bank loan market: A Mortality analysis. Journal of Banking and Finance, 2000, 24, 229-253
[17] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24, 49-55
[18] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24. 55-58
[19] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24.
[20] Crouhy. Michel. Comments on information about bank risk in options prices[J]. Journal of Banking and Finance, 2002, 26: 1059-1064
[21] 陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999,(4):31-38
[22] 高培业,张道奎.企业失败判别模型实证研究[J].统计研究,2000,(10):46-51
[23] 吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001,(6):46-53
[24] 张玲.财务危机预警分析判别模型[J].数量经济技术经济研究,2000,(3):49-51
[25] 梁琪.企业信用风险的主成分判别模型及其实证研究[J].财经研究,2003,(5):52-58
[26] 于立勇,詹捷辉.基于Logistic回归分析的违规概率预测研究[J],财经研究,2004,(9):15-23
[27] 周春喜,任佳慧.商业银行信用风险综合评价研究[J].科研管理,2004,(2):53-58
[28] 唐小岸,孟庆福.上市公司信用评价模型——二元因变量模型的运用[J].预测,2002,(2)
[29] 王小华,邵斌.基于Leland Toft模型的我国上市公司信用风险研究[J].财经研究,2005(8):38-58
[30] Basle. Committee on Banking Supervision Credit risk modeling: Current practice and applications[R]. Consultative Paper, 1999
[31] 朱小宗,张宗益,耿华丹.现代信用风险度量模型剖析与综合比较分析[J].财经研究,2004(09):33-46
[32] Dan. MIC, Mark. G. R. A comparative analysis of current credit risk models[J]. Journal of Banking & Finance 24, 2000: 59-1171
[33] Michael B. Gordy. A comparative anatomy of credit Risk models[J]. Journal of Banking & Financial 24, 2000: 119-1491
[34] Odom M D, Sharda R. A Neural Network Model for Bankruptcy Prediction[A]. Proceedings of the IEEE international Joint Conference on Neural Networks[C]. 1990, 2. 163-168.
[35] Altman E, Marco G, Varetrto F. Corporate. Distress Diagnosis Comparisons Using Linear Discriminate Analysis and Neural Networks[J]. Banking and Finance.1994, 18: 505-529.
[36] Tam K, Kiang M. Managerial Applications of the Neural Networks. The Case of Bank Failure Predictions[J]. Management Science, 1992: 38 416-430.
[37] 王春峰,万海晖,张维.基于神经网络技术的商业银行的信用风险评估[J].系统工程理论与实践,1998,19(9):24-32
[38] 吴得胜,梁樑.基于V-fold Cross-calidation和Elman神经网络的信用评价研究[J].系统工程理论与实践,2004,92(4):24-32
[39] 施鸿宝.神经网络及其应用[M].西安:西安交通大学出版社,1993
[40] 石晓军,陈殿左.信用治理:文化、流程与工具[M].北京:机械工业出版社,2003
[41] Liu You-cai, Liu Zeng-liang. Principle and Design of Fuzzy Expert System[M]. Beijing: Publishing House of Beijing University of Aeronautics and Astronautics, 1996: 237-242
[42] 刘智,端木京顺,王强.基于熵权多目标决策的方案评估方法研究[J].数学的实践与认识,2005,35(10):114-119
[43] 中国证券监督管理委员会贵阳特派办.上市公司诚信评价系统研究[N].中国证券报,2003-08-08(T00)
[44] 姜静.金融风险预测的模糊综合评价模型:(硕士学位论文).长春:吉林大学,2005
[45] 林汉川,夏敏仁.企业信用评级理论与实物[M].北京:对外经济贸易大学出版社,2003
[2] 布赖恩.科伊尔,周道许,关伟译.信用风险管理[M].北京:中信出版社,2003
[3] 张玲,曾维火.基于Z值模型的我国上市公司信用评级研究[J].财经研究,2004,(6)P:5-13
[4] Beaver W. H. Financial ratios as predictors of failure. Empirical Research in Accounting;Selected Studies[J]. Supplement to Journal of Accounting Research, 1966, 4, P: 71-111.
[5] Altman E I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 1968, (9) Vol. ⅩⅩⅢ
[6] Altman E L. Corporate Financial Distress. New York: John Wiley & Sons, 1983
[7] Altman E. I.. Predicting, Finacial Distress of Companies: Revisiting the Z-Score and Zata Models[C]. Working Paper. NYU Salomon Center, June 1995.
[8] 范柏乃,朱文斌.中小企业信用评价指标的理论遴选与实证分析[J].科研管理,2003,(6)P:83-87
[9] 吴世农.我国上市公司财务困境的预测模型研究[J].经济研究,2001,(6)P:46-55
[10] Aziz. A. D, Emanuel. G. Lawson. Bank Prediction: An investigation of cash flow based models[J]Journal of Management Studies, 25, P: 419-437
[11] Martin D. Earky warning of bank failure: a logit regression approach[J]. Journal of Banking and Finance, 1977, P: 249—276.
[12] Olson J. Financial rations and the probabilistic prediction of bankruptcy[J]. Accounting Research, 1980, Sprong, P: 109-130.
[13] Madalla G. S. Limited-Dependent and qualitative variables in Econometrics. Cambridge: Cambridge University Press, 1983
[14] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24, 26-49
[15] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance,, 2000, 24, 4-26
[16] Altman. Eward I, Suggitt. Heather J. Default rates in the syndicated bank loan market: A Mortality analysis. Journal of Banking and Finance, 2000, 24, 229-253
[17] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24, 49-55
[18] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24. 55-58
[19] Michael Crouhy, Dan Galai, Robert Mark. A comparative analysis of current credit risk models[J]. Journal of Banking and Finance, 2000, 24.
[20] Crouhy. Michel. Comments on information about bank risk in options prices[J]. Journal of Banking and Finance, 2002, 26: 1059-1064
[21] 陈静.上市公司财务恶化预测的实证分析[J].会计研究,1999,(4):31-38
[22] 高培业,张道奎.企业失败判别模型实证研究[J].统计研究,2000,(10):46-51
[23] 吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].经济研究,2001,(6):46-53
[24] 张玲.财务危机预警分析判别模型[J].数量经济技术经济研究,2000,(3):49-51
[25] 梁琪.企业信用风险的主成分判别模型及其实证研究[J].财经研究,2003,(5):52-58
[26] 于立勇,詹捷辉.基于Logistic回归分析的违规概率预测研究[J],财经研究,2004,(9):15-23
[27] 周春喜,任佳慧.商业银行信用风险综合评价研究[J].科研管理,2004,(2):53-58
[28] 唐小岸,孟庆福.上市公司信用评价模型——二元因变量模型的运用[J].预测,2002,(2)
[29] 王小华,邵斌.基于Leland Toft模型的我国上市公司信用风险研究[J].财经研究,2005(8):38-58
[30] Basle. Committee on Banking Supervision Credit risk modeling: Current practice and applications[R]. Consultative Paper, 1999
[31] 朱小宗,张宗益,耿华丹.现代信用风险度量模型剖析与综合比较分析[J].财经研究,2004(09):33-46
[32] Dan. MIC, Mark. G. R. A comparative analysis of current credit risk models[J]. Journal of Banking & Finance 24, 2000: 59-1171
[33] Michael B. Gordy. A comparative anatomy of credit Risk models[J]. Journal of Banking & Financial 24, 2000: 119-1491
[34] Odom M D, Sharda R. A Neural Network Model for Bankruptcy Prediction[A]. Proceedings of the IEEE international Joint Conference on Neural Networks[C]. 1990, 2. 163-168.
[35] Altman E, Marco G, Varetrto F. Corporate. Distress Diagnosis Comparisons Using Linear Discriminate Analysis and Neural Networks[J]. Banking and Finance.1994, 18: 505-529.
[36] Tam K, Kiang M. Managerial Applications of the Neural Networks. The Case of Bank Failure Predictions[J]. Management Science, 1992: 38 416-430.
[37] 王春峰,万海晖,张维.基于神经网络技术的商业银行的信用风险评估[J].系统工程理论与实践,1998,19(9):24-32
[38] 吴得胜,梁樑.基于V-fold Cross-calidation和Elman神经网络的信用评价研究[J].系统工程理论与实践,2004,92(4):24-32
[39] 施鸿宝.神经网络及其应用[M].西安:西安交通大学出版社,1993
[40] 石晓军,陈殿左.信用治理:文化、流程与工具[M].北京:机械工业出版社,2003
[41] Liu You-cai, Liu Zeng-liang. Principle and Design of Fuzzy Expert System[M]. Beijing: Publishing House of Beijing University of Aeronautics and Astronautics, 1996: 237-242
[42] 刘智,端木京顺,王强.基于熵权多目标决策的方案评估方法研究[J].数学的实践与认识,2005,35(10):114-119
[43] 中国证券监督管理委员会贵阳特派办.上市公司诚信评价系统研究[N].中国证券报,2003-08-08(T00)
[44] 姜静.金融风险预测的模糊综合评价模型:(硕士学位论文).长春:吉林大学,2005
[45] 林汉川,夏敏仁.企业信用评级理论与实物[M].北京:对外经济贸易大学出版社,2003
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