Research on the Small Industrial Business Credit Rating Model and Empirical Analysis Based on Wilcoxon Test

Abstract

Abstract:

The essence of credit rating is to measure the default probability of a loan, specifically, credit rating decreases with the increase of the default probability. Small industrial enterprises with less information available are not fully covered by financial analysts at home and abroad and that's why the credit rating model proposed in the existing researches for small industrial enterprises is defective. In this paper we use Wilcoxon test to establish a credit rating model for small industrial enterprises. There are three aspects of innovations and features in this paper. Firstly, the Wilcoxon method is applied to select features to establish a credit rating index system, which ensures that the index system can distinctly discriminate the default status of an enterprise; secondly, the run test is applied to evaluate the performance of the index system, which can do the validation of the credit rating model. Thirdly, index weight is determined on the basis of the regular pattern that the stronger ability an index has to discriminate the default status, the greater the weight is. The index system established this way successfully solves the problem in existing researches that the weight of an indicator does not match its ability of distinguishing the default state.

Key words: credit rating, small business credit rating, wilcoxon test, run test, default discrimination

Li Gang, Zhang Yajing, Wang Jifan, Chi Guotai. Research on the Small Industrial Business Credit Rating Model and Empirical Analysis Based on Wilcoxon Test[J]. , 2019, 31(11): 3-19.

References

[1] 国务院发展研究中心.中小企业发展现状、问题和政策[EB/OL]. http://finance.sina.com.cn/hy/20120426/100211929864.shtml, 2012-04-26
[2] 工业和信息化部,国家统计局,发展改革委,财政部.中小企业划型标准规定[EB/OL]. http://www.gov.cn/zwgk/2011-07/04/content_1898747.htm, 2011-06-18
[3] Chi G., Zhang Z. Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method[J]. Sustainability, 2017,9(10):1834-1857
[4] Geng R., Bose I., Chen X. Prediction of Financial Distress:An Empirical Study of Listed Chinese Companies Using Data Mining[J]. European Journal of Operational Research, 2015,241(1):236-247
[5] 迟国泰,潘明道,程砚秋.基于综合判别能力的农户小额贷款信用评价模型[J].管理评论, 2015,27(6):42-57
[6] 刘巧莉,温浩宇,Hong Q. P2P网络信贷中投资行为影响因素研究——基于拍拍贷平台交易的证据[J].管理评论, 2017,29(6):13-22
[7] Zhang Q., Wang J., Lu A., et al. An Improved SMO Algorithm for Financial Credit Risk Assessment——Evidence from China's Banking[J]. Neurocomputing, 2018,272(1):314-325
[8] 胡海青,崔杰,张道宏,等.中小企业商业信用融资影响因素研究——基于陕西制造类非上市企业的证据[J].管理评论, 2014,26(2):36-48
[9] 舒扬,杨秋怡.基于大样本数据模型的汽车贷款违约预测研究[J].管理评论, 2017,29(9):59-71
[10] Dierkes M., Erner C., Langer T., et al. Business Credit Information Sharing and Default Risk of Private Firms[J]. Journal of Banking&Finance, 2013,37(8):2867-2878
[11] Lin F., Liang D., Yeh C. C., et al. Novel Feature Selection Methods to Financial Distress Prediction[J]. Expert Systems with Applications, 2014,41(5):2472-2483
[12] Shi B. F., Chi G. T. A Credit Risk Evaluation Index Screening Model of Petty Loans for Small Private Business and Its Application[J]. Advances in Information Sciences and Service Sciences, 2013,5(7):1116-1124
[13] Liang D., Tsai C. F., Wu H. T. The Effect of Feature Selection on Financial Distress Prediction[J]. Knowledge-Based Systems, 2015,73(1):289-297
[14] Oreski S., Oreski G. Genetic Algorithm-Based Heuristic for Feature Selection in Credit Risk Assessment[J]. Expert Systems with Applications, 2014,41(4):2052-2064
[15] 马晓君.基于数据挖掘的新标准客户信用风险管理规则的构建——以央企中航国际钢铁贸易公司为例[J].管理世界, 2015,(3):184-185
[16] 方匡南,章贵军,张惠颖.基于Lasso-logistic模型的个人信用风险预警方法[J].数量经济技术经济研究, 2014,31(2):125-136
[17] 陈晓红,杨志慧.基于改进模糊综合评价法的信用评估体系研究——以我国中小上市公司为样本的实证研究[J].中国管理科学, 2015,23(1):146-153
[18] 张奇,胡蓝艺,王珏.基于Logit与SVM的银行业信用风险预警模型研究[J].系统工程理论与实践, 2015,35(7):1784-1790
[19] Gonçalves T. S. H., Ferreira F. A. F., Jalali M. S., et al. An Idiosyncratic Decision Support System for Credit Risk Analysis of Small and Medium-Sized Enterprises[J]. Technological&Economic Development of Economy, 2016,22(4):598-616
[20] Zhang Z., Gao G., Shi Y. Credit Risk Evaluation Using Multi-criteria Optimization Classifier with Kernel, Fuzzification and Penalty Factors[J]. European Journal of Operational Research, 2014,237(1):335-348
[21] 迟国泰,石宝峰.基于信用等级与违约损失率匹配的信用评级系统与方法[P].中国:CN102779317A, 2012-11-14
[22] 王星,褚挺进.非参数统计(第二版)[M].北京:清华大学出版社, 2014
[23] 贾俊平,何晓群,金勇进.统计学[M].北京:中国人民大学出版社, 2012
[24] 迟国泰,王卫.基于科学发展的综合评价理论、方法与应用[M].北京:科学出版社, 2009
[25] 刘顺忠,荣丽敏,景丽芳.非参数统计和SPSS软件应用[M].武汉:武汉大学出版社, 2008
[26] 大连银行.大连银行小企业信贷系统[R].大连银行, 2013
[27] 李战江.租赁和商务服务业小企业的信用评价研究[M].北京:经济科学出版社, 2014
[28] 迟国泰,张亚京,石宝峰.基于Probit回归的小企业债信评级模型及实证[J].管理科学学报, 2016,19(6):136-156