基于多因素多变量判别分析的中小石化企业信用评价研究
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摘要
近50年来,世界石化企业发展方兴未艾,中小石化企业成为中国经济中不可忽视的一部分。为解决中小石化企业融资问题,规范经营行为,防范信贷风险,建立严格的信用评价体系势在必行。信用评价理论自19世纪中后叶起步以来,已建立了较为规范的指标评价体系和信用等级体系,也随着经济的发展进一步推进。信用评价方法也经历了从传统评价到现代统计技术评价,从定性评价到定量评价、定性与定量科学结合评价的发展历程,并逐步推进和完善。本文在系统总结传统评价方法、多变量评价方法、资本市场方法、衍生工具方法、信用风险集中评价模型的基础上,归纳了多因素多变量判别分析理论,并定义了基本概念,给出了基本数理解释,提出了多因素多变量判别分析的一般评价步骤。结合石化行业垄断加速、行业壁垒、资金密集等特点,定义了中小石化企业标准,根据信用风险要点分析了中小石化企业信用评价的要素指标,在创新传统5C评价方法的基础上对信用评价指标按基本素质、资本实力、营运能力、发展能力、形势环境进行了分类,运用专家调研法、变异系数法和相关系数法相结合的方法,确定了中小石化企业信用评价指标体系,建立了等级标准,为规范行业评级体系开辟了思路。针对企业信用综合评价和即期试点评价两种决策情景,提出了运用模糊评价法、层次分析法构造模型解决多因素多层次项下的中小石化企业信用综合评价,给出了构建步骤、计算和检验方法;将logit二项违约概率模型拓展为多元累积信用等级概率模型,并运用主成分分析法进行多变量降维处理,在最大限度保留基础数据信息含量的基础上提高了运算效率,奠定了科学定量计算信用等级的基础。运用实例验证了多因素多层次模糊综合评价方法和多变量logit累积概率模型,并对其一致性进行了检验比较;充分说明了两种模型应用的可行性和科学性。在信用评价的基础上,结合商业银行信贷风险评估的需求,通过样本测试和训练给出了商业银行对中小石化企业信贷风险评估的Logit二项模型;设计了BP神经网络算法,通过测试和训练,与Logit二项模型运算法进行了比较;实证表明运用两种模型进行信贷风险评估切实可行、科学有效。
In the recent 50 years, the development of petrochemical enterprises all around the world have been in the ascendant, and small and medium-sized petrochemical enterprises in China are playing an important role in China’s economy. It is imperative to establish the strict credit evaluation system to settle the financing problems, to normalize business behaviors and to prevent credit risks in the small and medium-sized petrochemical enterprises. Since the credit evaluation theory started to be used in the late middle ages of 19th century, more standard index evaluation system and credit grading system have been set up, which has been carried forward along with the expanding economy. The method of credit evaluation has experienced the development changes from the traditional to modern statistic techniques, from qualitative to quantitative and in the scientific combination of qualitative and quantitative evaluation, and has been promoted and improved step by step. This essay put forward the multifactor and multivariate discriminant analysis theory according to the systematic review of the traditional, multivariate, capital market, financial derivative instrument, the model of centralized credit risk evaluation methods, defined the basic concept, explained the general logistics and demonstrated the general procedures for performing multifactor and multivariate discriminant analysis. In consideration of the feature of the rapid trade monopoly, trade barrier and capital-intensive in petrochemical industry, the standards for the small and medium-sized petrochemical enterprises are defined. The factors index of the credit evaluation on the small and medium-sized petrochemical enterprises are analyzed according to the essential points of credit risk. The credit evaluation index is classified into basic quality, capital strength, business management ability, development ability and the environment of the situation on the basis of innovation of the traditional 5C evaluation methods. The credit evaluation index system used by the small and medium-sized petrochemical enterprises is finalized, and the grading standards are constituted in combination with specialist research ,variation coefficient and correlation coefficient methods so that the thought of normalizing trade grading system is opened up. Under two decision-making circumstances of overall evaluation on the enterprise credit and immediate pilot evaluation, the apply of fuzzy evaluation and hierarchy evaluation methods are raised to build the model to deal with the overall credit evaluation on the small and medium-sized petrochemical enterprises in terms of multifactor and multilayer conditions as well as the design process and calculating and testing methods are given. The Logit PD (probability of default)model is developed into multivariate accumulation credit grade probability model, the application of principle component analysis method processed the multivariate dimensionality deduction, the efficiency of operation is enhanced as the utmost basic data information is kept, which establishes the basis to measure the credit grade by scientific quantitative calculation. The application of example has verified the multifactor and multilayer fuzzy overall evaluation method and multivariate logit accumulation probability model, and their consistency has been compared with and inspected, which the feasibility and scientificity of applying the two models is also fully illustrated. Based on the credit evaluation and in accordance with credit risk evaluation requirement of the commercial bank, the logit model is provided to the commercial bank for their credit risk evaluation on the small and medium-sized petrochemical enterprises after practice and testing of the sample model. BP Neural Network in the calculation is designed , through the test and practice and in comparison with logit fuzzy operational method, it is practical, scientific and effective to use these two models to implement credit risk evaluation.
In the recent 50 years, the development of petrochemical enterprises all around the world have been in the ascendant, and small and medium-sized petrochemical enterprises in China are playing an important role in China’s economy. It is imperative to establish the strict credit evaluation system to settle the financing problems, to normalize business behaviors and to prevent credit risks in the small and medium-sized petrochemical enterprises. Since the credit evaluation theory started to be used in the late middle ages of 19th century, more standard index evaluation system and credit grading system have been set up, which has been carried forward along with the expanding economy. The method of credit evaluation has experienced the development changes from the traditional to modern statistic techniques, from qualitative to quantitative and in the scientific combination of qualitative and quantitative evaluation, and has been promoted and improved step by step. This essay put forward the multifactor and multivariate discriminant analysis theory according to the systematic review of the traditional, multivariate, capital market, financial derivative instrument, the model of centralized credit risk evaluation methods, defined the basic concept, explained the general logistics and demonstrated the general procedures for performing multifactor and multivariate discriminant analysis. In consideration of the feature of the rapid trade monopoly, trade barrier and capital-intensive in petrochemical industry, the standards for the small and medium-sized petrochemical enterprises are defined. The factors index of the credit evaluation on the small and medium-sized petrochemical enterprises are analyzed according to the essential points of credit risk. The credit evaluation index is classified into basic quality, capital strength, business management ability, development ability and the environment of the situation on the basis of innovation of the traditional 5C evaluation methods. The credit evaluation index system used by the small and medium-sized petrochemical enterprises is finalized, and the grading standards are constituted in combination with specialist research ,variation coefficient and correlation coefficient methods so that the thought of normalizing trade grading system is opened up. Under two decision-making circumstances of overall evaluation on the enterprise credit and immediate pilot evaluation, the apply of fuzzy evaluation and hierarchy evaluation methods are raised to build the model to deal with the overall credit evaluation on the small and medium-sized petrochemical enterprises in terms of multifactor and multilayer conditions as well as the design process and calculating and testing methods are given. The Logit PD (probability of default)model is developed into multivariate accumulation credit grade probability model, the application of principle component analysis method processed the multivariate dimensionality deduction, the efficiency of operation is enhanced as the utmost basic data information is kept, which establishes the basis to measure the credit grade by scientific quantitative calculation. The application of example has verified the multifactor and multilayer fuzzy overall evaluation method and multivariate logit accumulation probability model, and their consistency has been compared with and inspected, which the feasibility and scientificity of applying the two models is also fully illustrated. Based on the credit evaluation and in accordance with credit risk evaluation requirement of the commercial bank, the logit model is provided to the commercial bank for their credit risk evaluation on the small and medium-sized petrochemical enterprises after practice and testing of the sample model. BP Neural Network in the calculation is designed , through the test and practice and in comparison with logit fuzzy operational method, it is practical, scientific and effective to use these two models to implement credit risk evaluation.
引文
[1]近年石化工业发展特点及2010年趋势. http://publishblog.blogchina.com/blog,2005.8.14
[2]张玲,张佳林.信用风险评估方法发展趋势.预测,2000,4:72-75
[3]梁晓娟.中小企业信用评价方法及其应用.天海财经资料数据库,2004,5:6-7
[4]李振宇,李信宏,邵立强等.资信评级原理[M].中国方正出版社.2003
[5]安立伟.商业银行对贷款企业的信用评价研究[D],2004.2:13-15
[6]卢世春,欧阳植.商业银行信用风险跟踪预警检测模型. 1999,1:9-6
[7]潘爱香.财务报表分析[M].北京经济科学出版社,1999
[8]Beaver W.H. Financial ratios as Predictors of failure IJ].Journal of Accounting Research,1966,1(Supplement):71-111
[9] Altman E I. Corporate financial distress: a complete guide to predicting, Avoiding, and dealing with bankruptcy[M]. New York: john wiley&sons,1983
[10]Altman E I. Financial rations, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance,Vol.23, 1968
[11] Edward1.Altman.Finaneialratios,Discriminant analysis and the Prediction of corporate bankruptcy [J].Journal of Finance,1968,4:589-609
[12] E. I. Altman. Predicting Financial Distress of companies: Revisiting the Z-Score and ZETA Models, SSRN Working Paper Series, 1977
[13] Edmister R,An empirical test of financial ratio analysis for small business failure Prediction [J].Journal of Financial and Quantitative Analysis,1972,7(2):147-193
[14]Sueyoushi T. Extended DEA-Discriminant Analysis, European Journal of Operational Operational Research [J], 2001, 131:324-351
[15] Greene DP and Smith SF. A genetic system for learning models of Consumer Choice [A].Proc, 2nd Inter Conf on GA[C], Hillsdale, 1987:217-223
[16] Martin D. Early warning of bank failure: a logit regression approach [J].Journal of Banking and Finance, 1977, 249-276
[17]Ohlson J. Financial ratios and the Probabilistic Prediction of bankruptcy, Journal of Accounting Research, Vol.18, 1980
[18]倪晓华.我国中小企业信用评价指标体系标准化研究,2007,6:10-11
[19]Lundy M, Cluster analysis in credit scoring, Credit scoring and credit Control, oxford University press, 1993
[20]Tam K Y, Kiang M, Managerial applications of neural networks: The case of bank Fai1ure predictions Management Science, Vol. 38, 1992
[21]Quinlan J R. Programs for machine learning [J]. San Mate, CA: Morgan Kaufmannpublisher,1993
[22] Black F, Scholes M. The pricing of options and corporate liabilities[J]. Journal of Political Economy,1973, 637-659
[23] Altman E I. Measuring corporate bond mortality and performance[J]. Journal of Finance, Sept1989, 909-922
[24] Asquith P, et al. Original issue high yield bonds: aging analysis of defaults, exchanges and calls [J]. Journal of Finance. 1989, 923-953
[25]Messier W F, Hansen J V. Inducing rules for expert system development an example using default and Bankruptcy data. Management science, 1988, 34:12
[26]Raymond Beshinske S.R. Spence etal. Margin Credit Evaluation system. International Conference on Artificial Intelligence Applications on Wall Street. IEEE Computer society Press, 1991
[27]Altman E I, Marco G, Varetto F, Corporate distress diagnosis : comparison using linear discriminant analysis and neural networks, Journal of Banking and Finance, Vol.18,1994
[28]马超群,高仁祥.现代预测理论与方法[M].长沙:湖南大学出版社,1999
[29]Coats P, Pant L. Recoganizing financial distress patterns using a neural network tool [M]. Financial Management. 1993.142-155
[30]柯孔林,周春喜.商业银行信用风险评估方法研究述评.商业经济与管理,2005,164(6):57-58
[31]Spinner K. Measuring credit exposures the JP Morgan way [M]. Wall Street& Technology(Product Digest Supplement), 1998. 16-20
[32]任永平,梅强.中小企业信用评价指标体系探讨[J].现代经济探讨,2001,4:60-62
[33]中国企业信用评价体系与方法获得突破性成果. http://business.sohu.com/国内财经,2002.2.14
[34]王春峰,万海晖,张维.组合预测在商业银行信用风险评估中的应用[J].管理工程学报,1999,13(1):5-30
[35]王春峰,万海晖,张维.商业银行信用风险评估及其实证研究.管理科学学报[J],1998
[36]卢世春,欧阳植.商业银行信用风险跟踪预警检测模型. 1999(1):59-62
[37]张贵清,刘树林.我国商业银行信用风险评级实证分析.河北经贸大学学报,2005
[38]王春峰,李汶华.商业银行信用风险评估:投影寻踪判别分析模型.管理工程学报[J],2000
[39]王洪礼,冯详立.基于投影寻踪回归理论的UPR消费量预测研究.中国科技论文在线. http://www.paper.edu.cn,2006
[40]柯孔林,薛锋.基于扩展数据包络判别法的商业银行信用风险评估.系统工程理论与实践[J],2004(4):ll7-122
[41]王春峰,康莉.基于遗传规划方法的商业银行信用风险评估模型.系统工程理论与实践[J],2001(2):73-79
[42]周春喜.企业信用等级综合评价指标体系及其评价IJ].科技进步与对策,2003,4:124-126
[43]张维,李玉霜.商业银行信用风险分析综述[J].管理科学学报,1998,(9)
[44]张艳军,陈友,郭莉,程学旗.基于决策树的递归包分类算法.北京:北京邮电大学学报,2006,Vol.29
[45]张维,李玉霜,王春峰.递归分类树在信用风险分析中的应用.系统工程理论与系统,2003(3)
[46]张剑飞.数据挖掘中决策树分类方法研究[J].长春:长春师范学院学报,2005(1)
[47]吴世农,卢贤义.我国上市会司财务困境的预测模型研究[J].经济研究,2001
[48]李萌. logit模型在商业银行信用风险评估中的应用研究[J].北京:管理科学,2005
[49]方洪全,曾勇.对银行信用风险评价体系的比较[J].系统工程理论方法,2004
[50]钟田丽,贾立恒.中小企业信用评价的神经网络法[J].技术经济与管理研究, 2005,5:30-32
[51]王春峰,万海晖,张维.基于神经网络技术的商业银行信用风险评估[J].北京:系统工程理论与实践,1999,(9)
[52]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.北京:经济数学[J] ,2003
[53]于立勇.商业银行信用风险评估预测模型研究[J].北京:管理科学学,2003,(1)
[54]王莉,郑兆瑞,郝记秀. BP神经网络在信用风险评估中的应用[J].山西:太原理工大学学报,2005,36(2):216-219
[55]杨保安,朱明.基于神经网络与专家系统结合的银行贷款风险管理[J].北京:系统工程理论方法应用,1999,8(1)
[56]王春峰,万海晖,张维.组合预测在商业银行信用风险评估中的应用[J].北京:管理工程学报,1999,13(1):5-9
[57]李云杰等.人工神经网络在经济预测中的应用[J].天津:商学院学报,1996
[58]王嘉诚等.神经网络方法在评价金融企业风险中的应用[J].中国软科学,1998
[59]杨保安、季海.基于人工神经网络的商业银行货款风险预警研究.系统工程理论与实践[J],2001
[60]张立明.人工神经网络的模型及其运用.复旦大学出版社,1993
[61]沈利生,王恒.授信风险限额的人工神经网络模型检验.数量经济技术经济研究.2007(3)
[62]周晶晗,马兰萍,邱长溶.基于多因素层次模糊评价的上市公司财务质量评价.太原理工大学学报(社会科学版),2003,Vol.21,No.3:27-30
[63]杨新娟,庄宇.船舶企业信用的多级模糊综合评价.船舶工程,2006,Vol.28,No.4
[64]谢季坚,刘承平.模糊数学方法及其应用(第2版)[M].河北:华中科技大学出版社,2001
[65]王煦逸.商业银行客户资信评价模糊综合判别模型[J].金融研究,2002(7)
[66]吴冲,吕静杰,潘启树,刘云煮.基于模糊神经网络的商业银行信用风险评估模型研究.系统工程理论与实践,2004(11)
[67]朱顺泉.基于层次分析模糊综合评判法的商业银行信用评价[J].统计与信息论坛,2002,17(1):29-33
[68]吴丽民.多因素层次模糊综合评价法在上市公司盈利能力评价中的应用[J].技术经济与管理研究,2001,(5):65-67
[69]周海涛.风险企业信用等级指标体系的构建及模糊评判[J].系统工程理论方法应用,2002,11(3):202-206
[70]常大勇.经济管理中的模糊数学方法[M].北京:北京经济学院出版社,1995
[71]高凌云,程敏,徐海俊.我国中小企业信用评价体系构建研究[J].华东交通大学学报,2004,6:90-93
[72]朱荣恩,徐建新.资信评级.上海:三联出版社,1996
[73]张琳,陈优生,刘莉,严凌.企业信用等级评价指标体系综述[D].北京:中国农业大学经济管理学院,2005.12.13
[74]王玉娥,叶莉,郭继鸣.工业企业信用评价方法研究[J].河北:河北工业大学学报,2004,(1):90
[75]范博乃,朱文斌.中小企业信用评价指标的理论遴选与实证分析[J].北京:科研管理,2003,Vol.24(6):83-88
[76]王恒,沈利生.客户信用评级系统的经济计量模型检验[J].北京:数量经济技术经济研究,2006(6)
[77]“3+1”体系:评价企业诚信试金石[N].金羊网--民营经济报,2005.12.13
[78]蒋超良.商业银行与西方金融运作.北京:中国发展出版社,1994.6
[79]Altman E. I. Predicting Financial Distress of Companies: Revisiting the Z - Score and Zeta Models [C]. Working Paper,NYU Salomon Center,June 1995
[80]梅强.中小企业信用担保理论模式及政策[M].北京:经济管理出版社,2001
[81]安乐尼·桑德斯.信用风险度量[M].北京:机械工业出版社,2001.56-67
[82]宋智勇.客户资信调研[M].广州:广东经济出版社,2002. 15-17
[83] Friedman J. A Recursive Partitioning Decision Rule for Nonparametric Classification. IEEE Trans on Computers, 1977, 26 (4):404-408
[84] Colorni A, Dorigo M, Maniezzo. Distributed optimization by ant colonies [A]. In: In proceeding of the first European conference on artificial life[C]. Paris: 1992:134-142
[85]王春峰,赵欣,韩冬.基于改进蚁群算法的商业银行信用风险评估方法[N].天津:天津大学学报(社会科学版),Mar.2005,Vol.7 No2
[86]程涛.财务预警模型综述[J].山西:山西财经大学报,2003,Vol25,No5:105-106
[87]陈静.一种基于神经网络的信用评价模型与算法研究[D],2007.4:11-17
[88]Black-Scholes期权定价模型(Black-Scholes Option Pricing Model).MBA智库百科(http://wiki.mbalib.com/)
[89]王保华.基于神经网络和模糊逻辑的信用评价模型.北京:北京工商大学学报(社会科学版) [N],Sep.2003,Vol.18,No.5:39
[90]张润楚.多元统计分析[M],北京科学出版社,2007:144-165
[91]谢季坚,刘承平.模糊数学方法应用[M],武昌,华中科技大学出版社,2000:19-52
[92]张尧庭,方开泰.多元统计分析引论[M],北京科学出版社,2003
[93]余雪丽.神经网络实例与学习[M],北京,中国铁道出版社,1996
[94]邵万宏.中国中小企业定义对比分析[Z].中小企业IT采购网,2007,5,8
[95]《节能减排授信工作指导意见》第十三条[Z].关于印发〈节能减排授信工作指导意见〉的通知)(银监发〔2007〕83号)文件,2007. 6-7
[96]郭璐云,刘蓓蕾.基于变异系数法的上市公司经营业绩灰色关联评价.统计与决策2005年第3期[J],2005(3):18-19
[97]王琦.实用模糊数学[M].第2版.北京:科学技术文献出版社,1992
[98]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].北京:经济研究,2001,(6):46-55
[99]张文彤. SPSSII统计分析教程[M] .北京:北京希望电子出版社,2002. 190-202
[2]张玲,张佳林.信用风险评估方法发展趋势.预测,2000,4:72-75
[3]梁晓娟.中小企业信用评价方法及其应用.天海财经资料数据库,2004,5:6-7
[4]李振宇,李信宏,邵立强等.资信评级原理[M].中国方正出版社.2003
[5]安立伟.商业银行对贷款企业的信用评价研究[D],2004.2:13-15
[6]卢世春,欧阳植.商业银行信用风险跟踪预警检测模型. 1999,1:9-6
[7]潘爱香.财务报表分析[M].北京经济科学出版社,1999
[8]Beaver W.H. Financial ratios as Predictors of failure IJ].Journal of Accounting Research,1966,1(Supplement):71-111
[9] Altman E I. Corporate financial distress: a complete guide to predicting, Avoiding, and dealing with bankruptcy[M]. New York: john wiley&sons,1983
[10]Altman E I. Financial rations, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance,Vol.23, 1968
[11] Edward1.Altman.Finaneialratios,Discriminant analysis and the Prediction of corporate bankruptcy [J].Journal of Finance,1968,4:589-609
[12] E. I. Altman. Predicting Financial Distress of companies: Revisiting the Z-Score and ZETA Models, SSRN Working Paper Series, 1977
[13] Edmister R,An empirical test of financial ratio analysis for small business failure Prediction [J].Journal of Financial and Quantitative Analysis,1972,7(2):147-193
[14]Sueyoushi T. Extended DEA-Discriminant Analysis, European Journal of Operational Operational Research [J], 2001, 131:324-351
[15] Greene DP and Smith SF. A genetic system for learning models of Consumer Choice [A].Proc, 2nd Inter Conf on GA[C], Hillsdale, 1987:217-223
[16] Martin D. Early warning of bank failure: a logit regression approach [J].Journal of Banking and Finance, 1977, 249-276
[17]Ohlson J. Financial ratios and the Probabilistic Prediction of bankruptcy, Journal of Accounting Research, Vol.18, 1980
[18]倪晓华.我国中小企业信用评价指标体系标准化研究,2007,6:10-11
[19]Lundy M, Cluster analysis in credit scoring, Credit scoring and credit Control, oxford University press, 1993
[20]Tam K Y, Kiang M, Managerial applications of neural networks: The case of bank Fai1ure predictions Management Science, Vol. 38, 1992
[21]Quinlan J R. Programs for machine learning [J]. San Mate, CA: Morgan Kaufmannpublisher,1993
[22] Black F, Scholes M. The pricing of options and corporate liabilities[J]. Journal of Political Economy,1973, 637-659
[23] Altman E I. Measuring corporate bond mortality and performance[J]. Journal of Finance, Sept1989, 909-922
[24] Asquith P, et al. Original issue high yield bonds: aging analysis of defaults, exchanges and calls [J]. Journal of Finance. 1989, 923-953
[25]Messier W F, Hansen J V. Inducing rules for expert system development an example using default and Bankruptcy data. Management science, 1988, 34:12
[26]Raymond Beshinske S.R. Spence etal. Margin Credit Evaluation system. International Conference on Artificial Intelligence Applications on Wall Street. IEEE Computer society Press, 1991
[27]Altman E I, Marco G, Varetto F, Corporate distress diagnosis : comparison using linear discriminant analysis and neural networks, Journal of Banking and Finance, Vol.18,1994
[28]马超群,高仁祥.现代预测理论与方法[M].长沙:湖南大学出版社,1999
[29]Coats P, Pant L. Recoganizing financial distress patterns using a neural network tool [M]. Financial Management. 1993.142-155
[30]柯孔林,周春喜.商业银行信用风险评估方法研究述评.商业经济与管理,2005,164(6):57-58
[31]Spinner K. Measuring credit exposures the JP Morgan way [M]. Wall Street& Technology(Product Digest Supplement), 1998. 16-20
[32]任永平,梅强.中小企业信用评价指标体系探讨[J].现代经济探讨,2001,4:60-62
[33]中国企业信用评价体系与方法获得突破性成果. http://business.sohu.com/国内财经,2002.2.14
[34]王春峰,万海晖,张维.组合预测在商业银行信用风险评估中的应用[J].管理工程学报,1999,13(1):5-30
[35]王春峰,万海晖,张维.商业银行信用风险评估及其实证研究.管理科学学报[J],1998
[36]卢世春,欧阳植.商业银行信用风险跟踪预警检测模型. 1999(1):59-62
[37]张贵清,刘树林.我国商业银行信用风险评级实证分析.河北经贸大学学报,2005
[38]王春峰,李汶华.商业银行信用风险评估:投影寻踪判别分析模型.管理工程学报[J],2000
[39]王洪礼,冯详立.基于投影寻踪回归理论的UPR消费量预测研究.中国科技论文在线. http://www.paper.edu.cn,2006
[40]柯孔林,薛锋.基于扩展数据包络判别法的商业银行信用风险评估.系统工程理论与实践[J],2004(4):ll7-122
[41]王春峰,康莉.基于遗传规划方法的商业银行信用风险评估模型.系统工程理论与实践[J],2001(2):73-79
[42]周春喜.企业信用等级综合评价指标体系及其评价IJ].科技进步与对策,2003,4:124-126
[43]张维,李玉霜.商业银行信用风险分析综述[J].管理科学学报,1998,(9)
[44]张艳军,陈友,郭莉,程学旗.基于决策树的递归包分类算法.北京:北京邮电大学学报,2006,Vol.29
[45]张维,李玉霜,王春峰.递归分类树在信用风险分析中的应用.系统工程理论与系统,2003(3)
[46]张剑飞.数据挖掘中决策树分类方法研究[J].长春:长春师范学院学报,2005(1)
[47]吴世农,卢贤义.我国上市会司财务困境的预测模型研究[J].经济研究,2001
[48]李萌. logit模型在商业银行信用风险评估中的应用研究[J].北京:管理科学,2005
[49]方洪全,曾勇.对银行信用风险评价体系的比较[J].系统工程理论方法,2004
[50]钟田丽,贾立恒.中小企业信用评价的神经网络法[J].技术经济与管理研究, 2005,5:30-32
[51]王春峰,万海晖,张维.基于神经网络技术的商业银行信用风险评估[J].北京:系统工程理论与实践,1999,(9)
[52]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.北京:经济数学[J] ,2003
[53]于立勇.商业银行信用风险评估预测模型研究[J].北京:管理科学学,2003,(1)
[54]王莉,郑兆瑞,郝记秀. BP神经网络在信用风险评估中的应用[J].山西:太原理工大学学报,2005,36(2):216-219
[55]杨保安,朱明.基于神经网络与专家系统结合的银行贷款风险管理[J].北京:系统工程理论方法应用,1999,8(1)
[56]王春峰,万海晖,张维.组合预测在商业银行信用风险评估中的应用[J].北京:管理工程学报,1999,13(1):5-9
[57]李云杰等.人工神经网络在经济预测中的应用[J].天津:商学院学报,1996
[58]王嘉诚等.神经网络方法在评价金融企业风险中的应用[J].中国软科学,1998
[59]杨保安、季海.基于人工神经网络的商业银行货款风险预警研究.系统工程理论与实践[J],2001
[60]张立明.人工神经网络的模型及其运用.复旦大学出版社,1993
[61]沈利生,王恒.授信风险限额的人工神经网络模型检验.数量经济技术经济研究.2007(3)
[62]周晶晗,马兰萍,邱长溶.基于多因素层次模糊评价的上市公司财务质量评价.太原理工大学学报(社会科学版),2003,Vol.21,No.3:27-30
[63]杨新娟,庄宇.船舶企业信用的多级模糊综合评价.船舶工程,2006,Vol.28,No.4
[64]谢季坚,刘承平.模糊数学方法及其应用(第2版)[M].河北:华中科技大学出版社,2001
[65]王煦逸.商业银行客户资信评价模糊综合判别模型[J].金融研究,2002(7)
[66]吴冲,吕静杰,潘启树,刘云煮.基于模糊神经网络的商业银行信用风险评估模型研究.系统工程理论与实践,2004(11)
[67]朱顺泉.基于层次分析模糊综合评判法的商业银行信用评价[J].统计与信息论坛,2002,17(1):29-33
[68]吴丽民.多因素层次模糊综合评价法在上市公司盈利能力评价中的应用[J].技术经济与管理研究,2001,(5):65-67
[69]周海涛.风险企业信用等级指标体系的构建及模糊评判[J].系统工程理论方法应用,2002,11(3):202-206
[70]常大勇.经济管理中的模糊数学方法[M].北京:北京经济学院出版社,1995
[71]高凌云,程敏,徐海俊.我国中小企业信用评价体系构建研究[J].华东交通大学学报,2004,6:90-93
[72]朱荣恩,徐建新.资信评级.上海:三联出版社,1996
[73]张琳,陈优生,刘莉,严凌.企业信用等级评价指标体系综述[D].北京:中国农业大学经济管理学院,2005.12.13
[74]王玉娥,叶莉,郭继鸣.工业企业信用评价方法研究[J].河北:河北工业大学学报,2004,(1):90
[75]范博乃,朱文斌.中小企业信用评价指标的理论遴选与实证分析[J].北京:科研管理,2003,Vol.24(6):83-88
[76]王恒,沈利生.客户信用评级系统的经济计量模型检验[J].北京:数量经济技术经济研究,2006(6)
[77]“3+1”体系:评价企业诚信试金石[N].金羊网--民营经济报,2005.12.13
[78]蒋超良.商业银行与西方金融运作.北京:中国发展出版社,1994.6
[79]Altman E. I. Predicting Financial Distress of Companies: Revisiting the Z - Score and Zeta Models [C]. Working Paper,NYU Salomon Center,June 1995
[80]梅强.中小企业信用担保理论模式及政策[M].北京:经济管理出版社,2001
[81]安乐尼·桑德斯.信用风险度量[M].北京:机械工业出版社,2001.56-67
[82]宋智勇.客户资信调研[M].广州:广东经济出版社,2002. 15-17
[83] Friedman J. A Recursive Partitioning Decision Rule for Nonparametric Classification. IEEE Trans on Computers, 1977, 26 (4):404-408
[84] Colorni A, Dorigo M, Maniezzo. Distributed optimization by ant colonies [A]. In: In proceeding of the first European conference on artificial life[C]. Paris: 1992:134-142
[85]王春峰,赵欣,韩冬.基于改进蚁群算法的商业银行信用风险评估方法[N].天津:天津大学学报(社会科学版),Mar.2005,Vol.7 No2
[86]程涛.财务预警模型综述[J].山西:山西财经大学报,2003,Vol25,No5:105-106
[87]陈静.一种基于神经网络的信用评价模型与算法研究[D],2007.4:11-17
[88]Black-Scholes期权定价模型(Black-Scholes Option Pricing Model).MBA智库百科(http://wiki.mbalib.com/)
[89]王保华.基于神经网络和模糊逻辑的信用评价模型.北京:北京工商大学学报(社会科学版) [N],Sep.2003,Vol.18,No.5:39
[90]张润楚.多元统计分析[M],北京科学出版社,2007:144-165
[91]谢季坚,刘承平.模糊数学方法应用[M],武昌,华中科技大学出版社,2000:19-52
[92]张尧庭,方开泰.多元统计分析引论[M],北京科学出版社,2003
[93]余雪丽.神经网络实例与学习[M],北京,中国铁道出版社,1996
[94]邵万宏.中国中小企业定义对比分析[Z].中小企业IT采购网,2007,5,8
[95]《节能减排授信工作指导意见》第十三条[Z].关于印发〈节能减排授信工作指导意见〉的通知)(银监发〔2007〕83号)文件,2007. 6-7
[96]郭璐云,刘蓓蕾.基于变异系数法的上市公司经营业绩灰色关联评价.统计与决策2005年第3期[J],2005(3):18-19
[97]王琦.实用模糊数学[M].第2版.北京:科学技术文献出版社,1992
[98]吴世农,卢贤义.我国上市公司财务困境的预测模型研究[J].北京:经济研究,2001,(6):46-55
[99]张文彤. SPSSII统计分析教程[M] .北京:北京希望电子出版社,2002. 190-202