商业银行个人信用评估组合预测方法研究
|
摘要
随着中国经济的快速发展,信用消费增长迅速,住房按揭、汽车贷款、教育贷款、信用卡等各种个人消费贷款的规模在迅速扩大。在消费信贷热不断升温的形势下,各商业银行均把发展消费贷款作为未来发展战略的重要组成部分。但是目前国内商业银行对消费贷款的风险管理水平较低,管理手段与方法均较落后,在消费信贷的发放过程中,仍然采用传统的信用分析方法来评价消费信贷申请者的信用状况及还款能力,在个人信用评估方法上仍然没有形成稳健可靠的模型。而目前学术界虽然提出了包括数理统计、人工智能等多种信用评估模型,但是这些方法都无法同时达到精确性和稳健性的最优状态。
本文在分析国内外个人信用评估发展历史及其方法应用现状的基础上,指出目前个人信用评估方法指标体系混乱、非系统性、精确度与稳健性无法兼顾等问题。针对指标体系混乱问题,利用货币效用曲线分析影响个人消费贷款履约行为的关键性因素,参考国内外已有的指标体系,构建适合中国国情的商业银行个人信用评估指标体系。针对非系统性问题,结合目前我国商业银行业务系统中所获取的实际数据情况,确定评估中所使用的指标,并就指标赋值、标准化、数据缺失、违约标准确定等数据处理过程提出了相应方法并加以应用,在目前常用的抽样方法中选择合理的方法加以应用,确定样本容量并进行分组,从而保证数据样本的可靠性,从而建立系统性的评估思路。针对精确度与稳健性无法兼顾问题,利用组合预测的思想,综合考虑权重非负约束、目标函数的最优解准则、可变权重、计算的复杂性、预测精确度等因素,建立了个人信用评估的线性组合预测模型、变权组合预测模型和非线性组合预测模型,提出了应用各模型进行预测的求解方法。分别就个人信用评估的统计模型和神经网络模型进行筛选、应用和比较,选择有效的单一模型对基于统计方法的组合预测模型、基于统计方法和神经网络的组合预测模型、基于神经网络的组合预测模型进行应用,对应用的结果进行比较分析。
通过研究发现,统计模型在分类精确度方面不如神经网络模型,但神经网络模型在稳健型性方面较统计模型差。组合预测模型比单一模型在分类精确度以及模型稳健性方面均具有优势。而组合模型之间比较的结果,综合考
With China's rapid economy development and rapid growth of consumer credit, mortgage and car loans, education loans, credit cards and other consumer loans are increasing rapidly. In condition of continuously heated consumer credit, commercial banks take consumer loans as an important component of their future development strategy. However currently domestic commercial banks' level of risk management on consumer loans is low and their management methods were backward. In consumer credit payment process, they still use traditional methods of credit analysis to evaluate the consumer credit applicants' credit standing and their ability to repay. There has been no reliable model in personal credit scoring methods. Although in current academic community, kinds of credit scoring model are presented including mathematical statistics, artificial intelligence, these are impossible to achieve optimum state of both accuracy and stability.
Based on the analysis about the development history of personal credit scoring history and the application status of personal credit scoring models, this dissertation points out the status of personal credit scoring in China, such as the system chaos and inefficiency as well as non-compatibility of accuracy and stability in personal credit index system. Concerning on current chaos personal credit index, a personal credit index of commercial banks has been established which references existed index systems in and abroad and applies money effect curve to analyze crucial factors effecting personal consuming loan default behavior. Focusing on non-systemic problems, we combine real date in current commercial banks, establish evaluation index, bring forward and applying methods corresponding to data processing such as index evaluation, initialization, data absence and identification of default standards. Choosing sensible methods in current common-used sampling methods, identifying sample capacity and then grouping samples we ensure the credibility of data samples and establish systemic evaluation methods as well. As for the non-capability of accuracy and stability, we employ the combination theory and make a comprehensive consideration on non-negative weights, optimized solution of objective functions,
本文在分析国内外个人信用评估发展历史及其方法应用现状的基础上,指出目前个人信用评估方法指标体系混乱、非系统性、精确度与稳健性无法兼顾等问题。针对指标体系混乱问题,利用货币效用曲线分析影响个人消费贷款履约行为的关键性因素,参考国内外已有的指标体系,构建适合中国国情的商业银行个人信用评估指标体系。针对非系统性问题,结合目前我国商业银行业务系统中所获取的实际数据情况,确定评估中所使用的指标,并就指标赋值、标准化、数据缺失、违约标准确定等数据处理过程提出了相应方法并加以应用,在目前常用的抽样方法中选择合理的方法加以应用,确定样本容量并进行分组,从而保证数据样本的可靠性,从而建立系统性的评估思路。针对精确度与稳健性无法兼顾问题,利用组合预测的思想,综合考虑权重非负约束、目标函数的最优解准则、可变权重、计算的复杂性、预测精确度等因素,建立了个人信用评估的线性组合预测模型、变权组合预测模型和非线性组合预测模型,提出了应用各模型进行预测的求解方法。分别就个人信用评估的统计模型和神经网络模型进行筛选、应用和比较,选择有效的单一模型对基于统计方法的组合预测模型、基于统计方法和神经网络的组合预测模型、基于神经网络的组合预测模型进行应用,对应用的结果进行比较分析。
通过研究发现,统计模型在分类精确度方面不如神经网络模型,但神经网络模型在稳健型性方面较统计模型差。组合预测模型比单一模型在分类精确度以及模型稳健性方面均具有优势。而组合模型之间比较的结果,综合考
With China's rapid economy development and rapid growth of consumer credit, mortgage and car loans, education loans, credit cards and other consumer loans are increasing rapidly. In condition of continuously heated consumer credit, commercial banks take consumer loans as an important component of their future development strategy. However currently domestic commercial banks' level of risk management on consumer loans is low and their management methods were backward. In consumer credit payment process, they still use traditional methods of credit analysis to evaluate the consumer credit applicants' credit standing and their ability to repay. There has been no reliable model in personal credit scoring methods. Although in current academic community, kinds of credit scoring model are presented including mathematical statistics, artificial intelligence, these are impossible to achieve optimum state of both accuracy and stability.
Based on the analysis about the development history of personal credit scoring history and the application status of personal credit scoring models, this dissertation points out the status of personal credit scoring in China, such as the system chaos and inefficiency as well as non-compatibility of accuracy and stability in personal credit index system. Concerning on current chaos personal credit index, a personal credit index of commercial banks has been established which references existed index systems in and abroad and applies money effect curve to analyze crucial factors effecting personal consuming loan default behavior. Focusing on non-systemic problems, we combine real date in current commercial banks, establish evaluation index, bring forward and applying methods corresponding to data processing such as index evaluation, initialization, data absence and identification of default standards. Choosing sensible methods in current common-used sampling methods, identifying sample capacity and then grouping samples we ensure the credibility of data samples and establish systemic evaluation methods as well. As for the non-capability of accuracy and stability, we employ the combination theory and make a comprehensive consideration on non-negative weights, optimized solution of objective functions,
引文
1 Durand, D. Risk elements in consumer installment financing, National Bureau of Economic Research, New York. 1941:189-201
2 Lyn C.Thomas. A Survey of Credit and Behavioral Scoring:Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000, (16):149-172
3 Capon, N. Credit scoring systems:a critical analysis. Journal of marketing. 1982,(46):82-91
4 Elizabeth Mays. Credit Risk Modeling:design and application. Fitzroy Dearborn Publishers. 1998:190-200
5 Eisenbeis, R.A. Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance.1977,(32):875-900
6 Eisenbeis, R.A. Problems in applying discriminant analysis in credit scoring models. Journal of Banking and Finance.1978, (2): 205-219
7 Capon, N. Credit scoring systems: a critical analysis. Journal of Marketing. 1982, 46: 82-91
8 Rosenberg, E. and Gleit, A. Quantitative methods in credit management: a survey. Operations Research. 1994,42: 589-613
9 Steenackers. A., M.J. Govaerts. A credit scoring model for personal loans. Insurance Mathematics and Economics. 1989,98:31-34
10 Freed, N., & Glover, F. A linear programming approach to the discriminant problem. Decision Sciences 1981,(12):68-74
11 Ziari, H.A., Leatham, D.J., Ellinger, P.N. Development of Statistical Discriminant Mathematical Programming Model via Resampling Estimation Techniques.American Journal of Agricultural Economic.1997, 79(4):1352-1362
12 Henley, W.E., and D.J. Hand. A k-nearest-neighbor classification for assessing consumer credit risk. The Statistician. 1996,45 (1):77-95
13 Coffman, J. Y. The proper role of tree analysis in forecasting the risk behavior of borrowers. Management Decision Systems. Atlanta, MDSReports. 1986,(3):47-59
14 David West. Neural Network Credit Scoring Models. Computers & Operations Research. 2000, (27):1131-1152
15 Fogarty, T. C. and Ireson, N. I.Evolving Bayesian classifiers for credit control: a comparison with other machine learning methods.Presented to the 3rd IMA Conference on Credit Scoring and Credit Control.1993,(5)63-76
16 Myers, J.H. and Forgy, E.W. The development of numerical credit evaluation systems. Journal of the American Statistical Association.1963, (58): 799-806
17 Wiginton, J.C. A note on the comparison of logit and discriminant models of consumer credit behaviour. Journal of Financial and Quantitative Analysis.
18 Grablowsky, B.J. and Talley, W.K. Probit and discriminant functions for classifying credit applicants: a comparison. Journal of Economics and Business.1981,(33): 254-261
19 Hardy,W.E.,and J.L.Adrian. A Linear Programming Alternative to Discriminant Analysis in Credit Scoring. Abribus. 1985, (1):285-92
20 Nath, R., Jackson, W. M., & Jones, T. W. A comparison of the classification problem in discriminant analysis. Journal of Statistical Computation and Simulation. 1992,41:73-93
21 Boyle, M., Crook, J.N., Hamilton, R. and Thomas, L.C. Methods for credit scoring applied to slow payers. Credit Scoring and Credit Control.Oxford: Clarendon Press. 1992 :75-90
22 Davis, R.H., Edelman, D.B., and Gammerman, A.J. Machine-learning algorithms for credit-card applications. IMA Journal of Mathematics Applied in Business and Industry.1992, (4):43-51.
23 Desai, V. S., Convay, D. G., Crook, J. N., & Overstreet, G. A. Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA Journal of Mathematics Applied in Business and Industry.1997,(8):323-346
24 Arnminger, G., D.Enache and T.Bonne. Analyzing credit risk data:a comparison of logistic discrimination, classification tree analysis and feed-forward networks, Computational Statistics. 1997,(12):293-310
25 Altman, E. I., Marco, G., & Varetto, F. Corporate distress diagnosis; Comparisons using linear discriminant analysis and neural networks. Journalof Banking and Finance. 1996,(18):505-529
26 Piramuthu, S.Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research. 1999,112 (2):310-321
27 V. S. Desai, J. N. Crook, G. A. Overstreet. A Comparison of Neural Networks and Linear Scoring Models in the Credit Environment. European Journal of Operational Research. 1996, 95:24-37
28 黄 大 玉 , 王 玉 东 . 论 建 立 中 国 的 个 人 信 用 制 度 . 城 市 金 融 论 坛 . 2000,(3):27-33
29 康世瀛.个人信用评估及贷款决策研究. 经济问题探索. 2002,(9):108-113
30 梁世栋等.信用风险模型比较分析.中国管理科学.2002,20(1):17-22
31 石庆焱,靳云汇.多种个人信用评分模型在中国应用的比较研究.统计研究.2004,(6):43-47
32 李 曙 光 , 神 经 网 络 在 消 费 者 信 用 评 分 中 的 应 用 探 讨 , 技 术 经济,2003,(3):23-26。
33 朱兴德,冯铁军.基于 GA 神经网络的个人信用评估,系统工程理论与实践,2003,(16):34-38。
34 孟晓俊, 王忠郴. 个人消费贷款信用等级评定的模糊间接识别方法 . 南昌大学学报(理科版). 2000,(1):89-94
35 Bates J M, Granger C W J. Combination of Forecasts. Operations Research Quarterly. 1969,20(4): 451-468
36 Clemen, R. T. Combining Forecast.International Journal of Forecasting.1989,(5):559-583
37 Bunn, D. W. A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly.1975,26:325-329
38 Bunn, D. W. Statistical efficiency in the linear combination of forecasts. International Journal of Forecasting. 1985,(1):151-163
39 Granger C, Ramanathan R. Improved Methods of Combining Forecasting," with R. Ramanathan. Journal of Forecasting. 1984, (3):197-204
40 R. Glen Donaldson , Mark Kamstra. Forecast combining with neural networks. Research Article. 1995,( 7):321-329
41 Neural Network Linear Forcasts For Stock Returns Angelos Kanas. International Journal of Finance and Economics. 2001,(6): 245-254
42 Victor M. Guerrero, Daniel Pena A. Linear Combination of Restrictions andForecasts in Time Series Analysis. Journal of Forecasting. 2000, (19):103-122
43 James H. Stock, Mark W. Watson. Combination Forecasts of Output Growth in a Seven-Country Data Set. Journal of Forecasting. 2004, (23):405-430
44 Christian L. Dunis, Xuehuan HUANG. Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination . Journal of Forecasting. 2002, (21): 317-354
45 James R. Coakley, Carol E. Brown. Artificial Neural Networks in Accounting and Finance: Modeling Issues. International Journal of Intelligent Systems in Accounting, Finance & Management. 2000,(9):119-144
46 Nigel Meade. Evidence for the Selection of Forecasting Method. Journal of Forecasting. 2000, (19): 515-535
47 Norman R, Tian Zeng. Choosing Among Competing Econometric Forecasts: Regression-based Forecast Combination Using Model Selection. Journal of Forecasting. 2001, (20): 425-440
48 R. Venkatachalam, Jeffrey E. Sohl. An Intelligent Model Selection and Forecasting System. Journal of Forecasting. 1999, (18): 167-180
49 陈华友, 许义生. 组合预测的权估计及其显著性检验. 运筹与管理. 2000,(2): 75-78
50 谢开贵, 高翔, 周家启. 应用遗传算法确定组合预测权系数. 重庆邮电学院学报. 1999,(4): 24-27
51 钟波,肖智,周家启. 组合预测中基于粗糙集理论的权值确定方法. 重庆大学学报. 2002,(7): 127-130
52 陈华友. 组合预测权系数确定的一种合作对策方法. 预测. 2003,(1): 75-78
53 曹长修, 王景, 唐小我. 一种模糊变权重组合预测方法——FVW 法的研究. 预测. 1996,(5):96-101
54 唐小我, 王景, 曹长修. 一种新的模糊自适应变权重组合预测方法. 电子科技大学学报. 1997,(3):47-53
55 王应明,罗英. 广义加权对数比例平均组合预测方法研究. 数理统计与管理. 1998,(5): 34-38
56 王应明. 广义加权几何平均组合预测方法研究. 厦门大学学报(自然科学版). 1998,(1): 6-10
57 王应明, 罗英. 广义加权算术平均组合预测方法研究. 预测. 1998,(1): 51-54
58 董景荣, 杨秀苔. 基于模糊逻辑系统的非线性组合预测方法研究. 管理科学学报. 1999,(3): 28-33
59 董景荣. 基于小波网络的非线性组合预测方法研究. 系统工程学报. 2000,(4): 383-388
60 曹永强, 张道军. 非线性智能组合预测模型及其应用. 中国矿业大学学报. 2004,(4): 428-432
61 董景荣, 杨秀苔. 汇率的非线性组合预测方法研究. 中国管理科学. 2001,(10):1-7
62 韩平, 席酉民. 基于模糊神经网络的信贷风险组合预测. 数量经济技术经济研究. 2001,(5):107-110
63 魏巍贤. 非平稳时间序列的组合预测建模条件及应用. 系统工程与电子技术. 1998: 30-34
64 安德洪, 韩文秀, 岳毅宏. 组合预测法的改进及其在负荷预测中的应用. 系统工程与电子技术. 2004,(6): 842-844
65 吴 祈 宗 , 宋 颖 . 一 种 模 糊 组 合 预 测 方 法 . 北 京 理 工 大 学 学 报 . 2004,(4):373-37
66 李秉祥. 基于模糊神经网络的企业财务危机非线性组合预测方法研究. 管理工程学报. 2005,(1): 19-23
67 凡勃伦.有闲阶级论——关于制度的经济研究.商务印书馆.1964:109
68 Michel Crouhya, Dan Galaib, Robert Marka. comparative analysis of current credit risk modelsJournalofBanking&Finance.2000, (24):59-117
69 Lyn C. Thomas. A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000, (16): 149-172
70 张翠荣. 消费信贷沦陷个人信用困局. 金融论坛. 2004,(1): 84
71 Kasper Roszbach,.Bank Lending Policy, Credit Scoring, and the Survival of Loans,The Review of Economics and Statistics, MIT Press, 2004,86(4):946-958
72 Kenneth Carling, Tor Jacobson, Kasper Roszbach. Dormancy risk and expected profits of consumer loans. Journal of Banking & Finance 2001,25:717-739
73 陈元燮. 建立信用评级指标体系的几个理论问题.财经问题研究.2000, (8):3-8
74 Krzanowski, W. J. Discriminant and classification using both binary and continuous variables. Journal of the American Statistical Association. 1975, (70): 782-790
75 Vlachonikolis, I. G. On the estimation of the expected probability of misclassification in discriminant analysis with mixed binary and continuous variables. Computers and Maths with Applications. 1986,(12A):187-195
76 Aggarawal, A. Categorical data analysis. Wiley, New York. 1990
77 Boyle, M., Crook, J. H., Olshen, R. A., & Thomas, L. C. Methods for credit scoring applied to slow payers. In: Thomas, L. C., Crook, J. N., & Edelman, D. B. (Eds.), Credit scoring and credit control, Oxford University Press, Oxford, 1992: 75-90
78 Showers, J. L., & Chakrin, L. M. Reducing revenue from residential telephone customers. Interfaces. 1981,(11):21-31
79 沈翠华.基于支持向量机的消费信贷中个人信用评估方法研究.博士学位论文.中国农业大学. 2004,11
80 Georges Dionne, Manuel Artis, Montserrat Guillen. Count Data Models for a Credit Scoring System. Journal of Empirical Finance. 1996, (3): 322
81 William Greene. Sample Selection in Credit-scoring Models. Japan and World Economy. 1998, (10): 300 –301
82 金勇进, 蒋妍, 李序颖. 抽样技术.北京:中国人民大学出版社, 2002: 163
83 J. Scoot Long. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, California, 1997: 315
84 王应明,傅国伟.基于不同误差准则和范数的组合预测方法研究.控制与决策.1994,9(1):20-28
85 Granger, C.W.J. and Ramanathan, R. Improved Methods of Combining Forecasts. Journal of Forecasting,1984, 3(2): 197-204
86 曾勇,唐小我.线性规划在非负权重最优组合预测计算中的应用.预测.1994,(3):55-56
87 唐小我,曾勇,曹长修.非负权重最优组合预测的迭代算法研究.系统工程理论方法应用.1994,3(4):48-52
88 马 永 开 , 唐 小 我 , 杨 桂 元 . 优 性 组 合 预 测 方 法 存 在 判 别 定 理 . 预测.1995,(2):57-59
89 杨桂元等.关于非负权重组合预测的若干问题的探讨.电子科技大学学报 1996,25(2):210-215
90 陈华友,侯定丕.基于预测有效度的组合预测方法冗余信息的判定.数学的实践与认识.2004,34(1):56-64
91 马永开,唐小我,杨桂元.非负权重最优组合预测方法的基本理论研究.运筹与管理.1997,6(2):1-7
92 马永开等.非负权重组合预测的冗余定理.系统工程理论方法应用.1995,4(4):33-39
93 曾勇等.组合预测冗余方法判定的一个补充规则.电子科技大学学报.1998,27(2):190-193
94 唐小我等.现代组合预测和组合投资决策方法及应用.北京:科学出版社.2003,1:178-201
95 谢如贤 , 成盛超 , 吴健中.变权重组合预测模型的建立与应用.预测.1992,(4):62-65
96 唐小我,曾勇,曹长修.变权组合预测模型研究.预测.1993,(3):46-48
97 文新辉,牛明洁.一种新的基于神经网络的非线性组合预测方法.系统工程理论与实践.1994,(12):66-72
98 Lyn C. Thomas. A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000,(16):149-172
99 D.J.Hand;W.E.Henley. Statistical Classification Methods in Consumer Credit Scoring:A Review. Journal of the Royal Statistical Society. Series A ( Statistics in Society),1997,160(3):523-541
100 Steenackers. A., M.J. Govaerts. A credit scoring model for personal loans. Insurance Mathematics and Economics. 1989,(98): 31-34
101 Anderson, J. A. Regression and ordered categorical variables (With discussion). J. R. Statist. Soc. B. 1984,(46): 1-30
102 E. Malinvaud. Statistical Method of Econometrics. Rand McNally& Company, Chicago, 1966: 195-197
103 John Aldrich, Forrest D. Nelson. Linear Probability, Logit, and Probit Models. Newbury Park, CA, 1984: 26
104 David West, Scott Dellana, Jingxia Qian,Neural Network Ensemble Strategies for Financial Decision Applications. Computers & Operations Research, 2005,(32): 2544
105 Kasper Roszbach. Bank Lending Policy, Credit Scoring, and the Survival of Loans. The Review of Economics and Statistics. 2004,86(4): 947-948
106 闻新等. MATLAB 神经网络应用设计. 北京:科学出版社. 2001:244-260
107 飞思科技产品研发中心.神经网络理论与 MATLAB7 实现.电子工业出版社.2005:116-126
108 Jeffrey L. Elman,Finding Structure in Time,Cognitive Science, 1990,14, 179-211
109 Kohonen T. Self-organizing and associative memory. Heidelberg:Springer, 1984:290-299
110 杨辉煌. 两预测方法的相关性对它们最优组合预测有效性的影响程度及实例分析. 浙江财经学院学报. 1996,(4):77-80
111 周传世,刘永清. 变权重组合预测模型的研究. 预测. 1995,(4):47-48
2 Lyn C.Thomas. A Survey of Credit and Behavioral Scoring:Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000, (16):149-172
3 Capon, N. Credit scoring systems:a critical analysis. Journal of marketing. 1982,(46):82-91
4 Elizabeth Mays. Credit Risk Modeling:design and application. Fitzroy Dearborn Publishers. 1998:190-200
5 Eisenbeis, R.A. Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance.1977,(32):875-900
6 Eisenbeis, R.A. Problems in applying discriminant analysis in credit scoring models. Journal of Banking and Finance.1978, (2): 205-219
7 Capon, N. Credit scoring systems: a critical analysis. Journal of Marketing. 1982, 46: 82-91
8 Rosenberg, E. and Gleit, A. Quantitative methods in credit management: a survey. Operations Research. 1994,42: 589-613
9 Steenackers. A., M.J. Govaerts. A credit scoring model for personal loans. Insurance Mathematics and Economics. 1989,98:31-34
10 Freed, N., & Glover, F. A linear programming approach to the discriminant problem. Decision Sciences 1981,(12):68-74
11 Ziari, H.A., Leatham, D.J., Ellinger, P.N. Development of Statistical Discriminant Mathematical Programming Model via Resampling Estimation Techniques.American Journal of Agricultural Economic.1997, 79(4):1352-1362
12 Henley, W.E., and D.J. Hand. A k-nearest-neighbor classification for assessing consumer credit risk. The Statistician. 1996,45 (1):77-95
13 Coffman, J. Y. The proper role of tree analysis in forecasting the risk behavior of borrowers. Management Decision Systems. Atlanta, MDSReports. 1986,(3):47-59
14 David West. Neural Network Credit Scoring Models. Computers & Operations Research. 2000, (27):1131-1152
15 Fogarty, T. C. and Ireson, N. I.Evolving Bayesian classifiers for credit control: a comparison with other machine learning methods.Presented to the 3rd IMA Conference on Credit Scoring and Credit Control.1993,(5)63-76
16 Myers, J.H. and Forgy, E.W. The development of numerical credit evaluation systems. Journal of the American Statistical Association.1963, (58): 799-806
17 Wiginton, J.C. A note on the comparison of logit and discriminant models of consumer credit behaviour. Journal of Financial and Quantitative Analysis.
18 Grablowsky, B.J. and Talley, W.K. Probit and discriminant functions for classifying credit applicants: a comparison. Journal of Economics and Business.1981,(33): 254-261
19 Hardy,W.E.,and J.L.Adrian. A Linear Programming Alternative to Discriminant Analysis in Credit Scoring. Abribus. 1985, (1):285-92
20 Nath, R., Jackson, W. M., & Jones, T. W. A comparison of the classification problem in discriminant analysis. Journal of Statistical Computation and Simulation. 1992,41:73-93
21 Boyle, M., Crook, J.N., Hamilton, R. and Thomas, L.C. Methods for credit scoring applied to slow payers. Credit Scoring and Credit Control.Oxford: Clarendon Press. 1992 :75-90
22 Davis, R.H., Edelman, D.B., and Gammerman, A.J. Machine-learning algorithms for credit-card applications. IMA Journal of Mathematics Applied in Business and Industry.1992, (4):43-51.
23 Desai, V. S., Convay, D. G., Crook, J. N., & Overstreet, G. A. Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA Journal of Mathematics Applied in Business and Industry.1997,(8):323-346
24 Arnminger, G., D.Enache and T.Bonne. Analyzing credit risk data:a comparison of logistic discrimination, classification tree analysis and feed-forward networks, Computational Statistics. 1997,(12):293-310
25 Altman, E. I., Marco, G., & Varetto, F. Corporate distress diagnosis; Comparisons using linear discriminant analysis and neural networks. Journalof Banking and Finance. 1996,(18):505-529
26 Piramuthu, S.Financial credit-risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research. 1999,112 (2):310-321
27 V. S. Desai, J. N. Crook, G. A. Overstreet. A Comparison of Neural Networks and Linear Scoring Models in the Credit Environment. European Journal of Operational Research. 1996, 95:24-37
28 黄 大 玉 , 王 玉 东 . 论 建 立 中 国 的 个 人 信 用 制 度 . 城 市 金 融 论 坛 . 2000,(3):27-33
29 康世瀛.个人信用评估及贷款决策研究. 经济问题探索. 2002,(9):108-113
30 梁世栋等.信用风险模型比较分析.中国管理科学.2002,20(1):17-22
31 石庆焱,靳云汇.多种个人信用评分模型在中国应用的比较研究.统计研究.2004,(6):43-47
32 李 曙 光 , 神 经 网 络 在 消 费 者 信 用 评 分 中 的 应 用 探 讨 , 技 术 经济,2003,(3):23-26。
33 朱兴德,冯铁军.基于 GA 神经网络的个人信用评估,系统工程理论与实践,2003,(16):34-38。
34 孟晓俊, 王忠郴. 个人消费贷款信用等级评定的模糊间接识别方法 . 南昌大学学报(理科版). 2000,(1):89-94
35 Bates J M, Granger C W J. Combination of Forecasts. Operations Research Quarterly. 1969,20(4): 451-468
36 Clemen, R. T. Combining Forecast.International Journal of Forecasting.1989,(5):559-583
37 Bunn, D. W. A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly.1975,26:325-329
38 Bunn, D. W. Statistical efficiency in the linear combination of forecasts. International Journal of Forecasting. 1985,(1):151-163
39 Granger C, Ramanathan R. Improved Methods of Combining Forecasting," with R. Ramanathan. Journal of Forecasting. 1984, (3):197-204
40 R. Glen Donaldson , Mark Kamstra. Forecast combining with neural networks. Research Article. 1995,( 7):321-329
41 Neural Network Linear Forcasts For Stock Returns Angelos Kanas. International Journal of Finance and Economics. 2001,(6): 245-254
42 Victor M. Guerrero, Daniel Pena A. Linear Combination of Restrictions andForecasts in Time Series Analysis. Journal of Forecasting. 2000, (19):103-122
43 James H. Stock, Mark W. Watson. Combination Forecasts of Output Growth in a Seven-Country Data Set. Journal of Forecasting. 2004, (23):405-430
44 Christian L. Dunis, Xuehuan HUANG. Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination . Journal of Forecasting. 2002, (21): 317-354
45 James R. Coakley, Carol E. Brown. Artificial Neural Networks in Accounting and Finance: Modeling Issues. International Journal of Intelligent Systems in Accounting, Finance & Management. 2000,(9):119-144
46 Nigel Meade. Evidence for the Selection of Forecasting Method. Journal of Forecasting. 2000, (19): 515-535
47 Norman R, Tian Zeng. Choosing Among Competing Econometric Forecasts: Regression-based Forecast Combination Using Model Selection. Journal of Forecasting. 2001, (20): 425-440
48 R. Venkatachalam, Jeffrey E. Sohl. An Intelligent Model Selection and Forecasting System. Journal of Forecasting. 1999, (18): 167-180
49 陈华友, 许义生. 组合预测的权估计及其显著性检验. 运筹与管理. 2000,(2): 75-78
50 谢开贵, 高翔, 周家启. 应用遗传算法确定组合预测权系数. 重庆邮电学院学报. 1999,(4): 24-27
51 钟波,肖智,周家启. 组合预测中基于粗糙集理论的权值确定方法. 重庆大学学报. 2002,(7): 127-130
52 陈华友. 组合预测权系数确定的一种合作对策方法. 预测. 2003,(1): 75-78
53 曹长修, 王景, 唐小我. 一种模糊变权重组合预测方法——FVW 法的研究. 预测. 1996,(5):96-101
54 唐小我, 王景, 曹长修. 一种新的模糊自适应变权重组合预测方法. 电子科技大学学报. 1997,(3):47-53
55 王应明,罗英. 广义加权对数比例平均组合预测方法研究. 数理统计与管理. 1998,(5): 34-38
56 王应明. 广义加权几何平均组合预测方法研究. 厦门大学学报(自然科学版). 1998,(1): 6-10
57 王应明, 罗英. 广义加权算术平均组合预测方法研究. 预测. 1998,(1): 51-54
58 董景荣, 杨秀苔. 基于模糊逻辑系统的非线性组合预测方法研究. 管理科学学报. 1999,(3): 28-33
59 董景荣. 基于小波网络的非线性组合预测方法研究. 系统工程学报. 2000,(4): 383-388
60 曹永强, 张道军. 非线性智能组合预测模型及其应用. 中国矿业大学学报. 2004,(4): 428-432
61 董景荣, 杨秀苔. 汇率的非线性组合预测方法研究. 中国管理科学. 2001,(10):1-7
62 韩平, 席酉民. 基于模糊神经网络的信贷风险组合预测. 数量经济技术经济研究. 2001,(5):107-110
63 魏巍贤. 非平稳时间序列的组合预测建模条件及应用. 系统工程与电子技术. 1998: 30-34
64 安德洪, 韩文秀, 岳毅宏. 组合预测法的改进及其在负荷预测中的应用. 系统工程与电子技术. 2004,(6): 842-844
65 吴 祈 宗 , 宋 颖 . 一 种 模 糊 组 合 预 测 方 法 . 北 京 理 工 大 学 学 报 . 2004,(4):373-37
66 李秉祥. 基于模糊神经网络的企业财务危机非线性组合预测方法研究. 管理工程学报. 2005,(1): 19-23
67 凡勃伦.有闲阶级论——关于制度的经济研究.商务印书馆.1964:109
68 Michel Crouhya, Dan Galaib, Robert Marka. comparative analysis of current credit risk modelsJournalofBanking&Finance.2000, (24):59-117
69 Lyn C. Thomas. A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000, (16): 149-172
70 张翠荣. 消费信贷沦陷个人信用困局. 金融论坛. 2004,(1): 84
71 Kasper Roszbach,.Bank Lending Policy, Credit Scoring, and the Survival of Loans,The Review of Economics and Statistics, MIT Press, 2004,86(4):946-958
72 Kenneth Carling, Tor Jacobson, Kasper Roszbach. Dormancy risk and expected profits of consumer loans. Journal of Banking & Finance 2001,25:717-739
73 陈元燮. 建立信用评级指标体系的几个理论问题.财经问题研究.2000, (8):3-8
74 Krzanowski, W. J. Discriminant and classification using both binary and continuous variables. Journal of the American Statistical Association. 1975, (70): 782-790
75 Vlachonikolis, I. G. On the estimation of the expected probability of misclassification in discriminant analysis with mixed binary and continuous variables. Computers and Maths with Applications. 1986,(12A):187-195
76 Aggarawal, A. Categorical data analysis. Wiley, New York. 1990
77 Boyle, M., Crook, J. H., Olshen, R. A., & Thomas, L. C. Methods for credit scoring applied to slow payers. In: Thomas, L. C., Crook, J. N., & Edelman, D. B. (Eds.), Credit scoring and credit control, Oxford University Press, Oxford, 1992: 75-90
78 Showers, J. L., & Chakrin, L. M. Reducing revenue from residential telephone customers. Interfaces. 1981,(11):21-31
79 沈翠华.基于支持向量机的消费信贷中个人信用评估方法研究.博士学位论文.中国农业大学. 2004,11
80 Georges Dionne, Manuel Artis, Montserrat Guillen. Count Data Models for a Credit Scoring System. Journal of Empirical Finance. 1996, (3): 322
81 William Greene. Sample Selection in Credit-scoring Models. Japan and World Economy. 1998, (10): 300 –301
82 金勇进, 蒋妍, 李序颖. 抽样技术.北京:中国人民大学出版社, 2002: 163
83 J. Scoot Long. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, California, 1997: 315
84 王应明,傅国伟.基于不同误差准则和范数的组合预测方法研究.控制与决策.1994,9(1):20-28
85 Granger, C.W.J. and Ramanathan, R. Improved Methods of Combining Forecasts. Journal of Forecasting,1984, 3(2): 197-204
86 曾勇,唐小我.线性规划在非负权重最优组合预测计算中的应用.预测.1994,(3):55-56
87 唐小我,曾勇,曹长修.非负权重最优组合预测的迭代算法研究.系统工程理论方法应用.1994,3(4):48-52
88 马 永 开 , 唐 小 我 , 杨 桂 元 . 优 性 组 合 预 测 方 法 存 在 判 别 定 理 . 预测.1995,(2):57-59
89 杨桂元等.关于非负权重组合预测的若干问题的探讨.电子科技大学学报 1996,25(2):210-215
90 陈华友,侯定丕.基于预测有效度的组合预测方法冗余信息的判定.数学的实践与认识.2004,34(1):56-64
91 马永开,唐小我,杨桂元.非负权重最优组合预测方法的基本理论研究.运筹与管理.1997,6(2):1-7
92 马永开等.非负权重组合预测的冗余定理.系统工程理论方法应用.1995,4(4):33-39
93 曾勇等.组合预测冗余方法判定的一个补充规则.电子科技大学学报.1998,27(2):190-193
94 唐小我等.现代组合预测和组合投资决策方法及应用.北京:科学出版社.2003,1:178-201
95 谢如贤 , 成盛超 , 吴健中.变权重组合预测模型的建立与应用.预测.1992,(4):62-65
96 唐小我,曾勇,曹长修.变权组合预测模型研究.预测.1993,(3):46-48
97 文新辉,牛明洁.一种新的基于神经网络的非线性组合预测方法.系统工程理论与实践.1994,(12):66-72
98 Lyn C. Thomas. A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting. 2000,(16):149-172
99 D.J.Hand;W.E.Henley. Statistical Classification Methods in Consumer Credit Scoring:A Review. Journal of the Royal Statistical Society. Series A ( Statistics in Society),1997,160(3):523-541
100 Steenackers. A., M.J. Govaerts. A credit scoring model for personal loans. Insurance Mathematics and Economics. 1989,(98): 31-34
101 Anderson, J. A. Regression and ordered categorical variables (With discussion). J. R. Statist. Soc. B. 1984,(46): 1-30
102 E. Malinvaud. Statistical Method of Econometrics. Rand McNally& Company, Chicago, 1966: 195-197
103 John Aldrich, Forrest D. Nelson. Linear Probability, Logit, and Probit Models. Newbury Park, CA, 1984: 26
104 David West, Scott Dellana, Jingxia Qian,Neural Network Ensemble Strategies for Financial Decision Applications. Computers & Operations Research, 2005,(32): 2544
105 Kasper Roszbach. Bank Lending Policy, Credit Scoring, and the Survival of Loans. The Review of Economics and Statistics. 2004,86(4): 947-948
106 闻新等. MATLAB 神经网络应用设计. 北京:科学出版社. 2001:244-260
107 飞思科技产品研发中心.神经网络理论与 MATLAB7 实现.电子工业出版社.2005:116-126
108 Jeffrey L. Elman,Finding Structure in Time,Cognitive Science, 1990,14, 179-211
109 Kohonen T. Self-organizing and associative memory. Heidelberg:Springer, 1984:290-299
110 杨辉煌. 两预测方法的相关性对它们最优组合预测有效性的影响程度及实例分析. 浙江财经学院学报. 1996,(4):77-80
111 周传世,刘永清. 变权重组合预测模型的研究. 预测. 1995,(4):47-48