基于改进动态组合评价方法的小微企业信用评价研究
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摘要
针对当前小微企业信用评价研究中存在多方法评价结论非一致性现象与静态评价的不足,提出一种基于改进动态组合评价方法的小微企业信用评价模型。通过模糊聚类分析与SOM-K算法确定初始方法的最佳分组,依据同质性、异质性与规模性原则,对组内与组间评价信息进行二次组合,并在定义成长因子的基础上,通过对组合评价值变化趋势的量化处理,求解最终的动态组合评价结果。对11家制造业小微企业的信用评价进行了实证研究,研究表明:该方法可有效融合多种方法的评价信息,同时综合考虑了各期信用状况,所得结果与经客户经理交叉检验的结论相符,证明了该方法的可行性与有效性。
Considering the inconsistency among the evaluation outcomes derived by different methods and the shortcomings of static evaluation in current small and micro businesses credit assessment researches,an improved dynamic combined evaluation method is proposed.The initial methods are grouped using fuzzy clustering analysis and SOM-K-means algorithm,and information inside and outside the groups are combined twice according to the principles of homogeneity,heterogeneity and scale.Then,based on the definition of growth factor,the trends of combined evaluation values are quantified to obtain dynamic combined evaluation results.The paper conducted an empirical study on 11 small and micro manufacturing businesses,whose results verify that this method not only effectively combines the evaluation information obtained using different methods,but also takes into account the evaluation values at different times,and its outcomes are consistent with the conclusions cross-validated by the customer managers,which proves the feasibility and effectiveness of the proposed method.
Considering the inconsistency among the evaluation outcomes derived by different methods and the shortcomings of static evaluation in current small and micro businesses credit assessment researches,an improved dynamic combined evaluation method is proposed.The initial methods are grouped using fuzzy clustering analysis and SOM-K-means algorithm,and information inside and outside the groups are combined twice according to the principles of homogeneity,heterogeneity and scale.Then,based on the definition of growth factor,the trends of combined evaluation values are quantified to obtain dynamic combined evaluation results.The paper conducted an empirical study on 11 small and micro manufacturing businesses,whose results verify that this method not only effectively combines the evaluation information obtained using different methods,but also takes into account the evaluation values at different times,and its outcomes are consistent with the conclusions cross-validated by the customer managers,which proves the feasibility and effectiveness of the proposed method.
引文
[1]ZHANG F,TADIKAMALLA P R,SHANG A J.Corporate Credit-Risk Evaluation System:Integrating Explicit and Implicit Financial Performances[J].International Journal of Production Economics,2016,177:77-100.
[2]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.
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[4]BLANCO-OLIVER A,IRIMIA-DIEGUEZ A,OLI-VER-ALFONSO M,et al.Improving Bankruptcy Prediction in Micro-Entities by Using Nonlinear Effects and Non-Financial Variables[J].Finance a Uver-Czech Journal of Economics and Finance,2015,65(2):144-166.
[5]李战江.基于分层逻辑回归的小企业信用评价模型[J].统计与决策,2016(7):178-182.
[6]FANTAZZINI D,FIGINI A S.Random Survival Forests Models for SME Credit Risk Measurement[J].Methodology&Computing in Applied Probability,2009,11(1):29-45.
[7]ZEKIC-SUSAC M,SARLIJA N,BENSIC A M.Small Business Credit Scoring:A Comparison of Logistic Regression,Neural Network,and Decision Tree Models[C]//26th International Conference on Data of Conference.Cavtat:IEEE,2004:265-270.
[8]王磊,范超,解明明.数据挖掘模型在小企业主信用评分领域的应用[J].统计研究,2014,31(10):89-98.
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[10]胡贤德,曹蓉,李敬明,等.小微企业信用风险评估的IDGSO-BP集成模型构建研究[J].运筹与管理,2017,26(4):132-139.
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[14]SOHN S,JEON A H.Competing Risk Model for Technology Credit Fund for Small and Medium-Sized Enterprises[J].Journal of Small Business Management,2010,48(3):378-394.
[15]ONO A,HASUMI R,HIRATA H.Differentiated Use of Small Business Credit Scoring by Relationship Lenders and Transactional Lenders:Evidence From Firm-Bank Matched Data in Japan[J].Journal of Banking&Finance,2014,42:371-380.
[16]董汝杰.基于logit模型的微型企业信用评价研究[J].新金融,2011(8):54-58.
[17]马晓青,刘莉亚,胡乃红,等.小企业信用评估的模型构建与实证分析[J].财经研究,2012,38(5):28-37.
[18]何光辉,杨咸月.中国小微企业信用违约影响因素的实证检验---来自某国有银行地区分行的证据[J].上海财经大学学报,2015,17(6):67-79.
[19]肖斌卿,柏巍,姚瑶,等.基于LS-SVM的小微企业信用评估研究[J].审计与经济研究,2016,31(6):102-111.
[20]肖斌卿,杨旸,李心丹,等.基于模糊神经网络的小微企业信用评级研究[J].管理科学学报,2016,19(11):114-126.
[21]李敬明,阮素梅,刘奎户.基于改进模糊综合评价法的小微企业金融风险分析模型研究[J].运筹与管理,2015,24(6):217-224.
[22]张发明,刘志平.基于CW算子的组合评价信息集结方法及应用[J].系统管理学报,2017,26(2):268-276.
[23]李美娟,陈国宏,陈勃,等.基于方法集化的动态组合评价方法研究[J].中国管理科学,2013,21(2):132-137.
[24]张发明.一种融合SOM与K-means算法的动态信用评价方法及应用[J].运筹与管理,2014,23(6):186-192.
[25]张发明.一种融合相似与差异特征的组合评价方法及应用[J].系统管理学报,2013,22(4):498-504.
[26]刘微微,石春生,吴际.具有速度特征的动态评价方法及应用[J].中国软科学,2010(10):180-185.
[27]ALTMAN E I,SABATO A G.Modelling Credit Risk for SMEs:Evidence from the U.S.Market[J].Abacus,2014,43(3):332-357.
[28]DOUMPOS M,NIKLIS D,ZOPOUNIDIS C,et al.Combining Accounting Data and a Structural Model for Predicting Credit Ratings:Empirical Evidence From European Listed Firms[J].Journal of Banking&Finance,2015,50:599-607.
[29]陈影,任永平.基于支持向量机的中小企业信用准入评价的简化变量分析[J].上海金融,2016(11):91-95.
[30]李新春,潮海晨,叶文平.创业融资担保的社会支持机制[J].管理学报,2017,14(1):55-62.
[31]中华人民共和国国家统计局.2016年中国中小企业运行报告[EB/OL].(2017-05-24)[2018-04-20].http://www.lwzb.gov.cn/pub/gjtjlwzb/sjyfx/201705/t20170524_3750.html.
[32]翁春颖,韩明华.全球价值链驱动、知识转移与我国制造业升级[J].管理学报,2015,12(4):517-521.
[2]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.
[3]CIAMPI F.Small Enterprise Default Prediction Modeling through Artificial Neural Networks:An Empirical Analysis of Italian Small Enterprises[J].Journal of Small Business Management,2013,51(1):23-45.
[4]BLANCO-OLIVER A,IRIMIA-DIEGUEZ A,OLI-VER-ALFONSO M,et al.Improving Bankruptcy Prediction in Micro-Entities by Using Nonlinear Effects and Non-Financial Variables[J].Finance a Uver-Czech Journal of Economics and Finance,2015,65(2):144-166.
[5]李战江.基于分层逻辑回归的小企业信用评价模型[J].统计与决策,2016(7):178-182.
[6]FANTAZZINI D,FIGINI A S.Random Survival Forests Models for SME Credit Risk Measurement[J].Methodology&Computing in Applied Probability,2009,11(1):29-45.
[7]ZEKIC-SUSAC M,SARLIJA N,BENSIC A M.Small Business Credit Scoring:A Comparison of Logistic Regression,Neural Network,and Decision Tree Models[C]//26th International Conference on Data of Conference.Cavtat:IEEE,2004:265-270.
[8]王磊,范超,解明明.数据挖掘模型在小企业主信用评分领域的应用[J].统计研究,2014,31(10):89-98.
[9]SHI B,CHEN N,WANG A J.A Credit Rating Model of Microfinance Based on Fuzzy Cluster Analysis and Fuzzy Pattern Recognition:Empirical Evidence from Chinese 2 157Small Private Businesses[J].Journal of Intelligent&Fuzzy Systems,2016,31(6):3 095-3 102.
[10]胡贤德,曹蓉,李敬明,等.小微企业信用风险评估的IDGSO-BP集成模型构建研究[J].运筹与管理,2017,26(4):132-139.
[11]EDMISTER R O.An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction[J].Journal of Financial&Quantitative Analysis,1972,7(2):1 477-1 493.
[12]MEYER L H.The Present and Future Roles of Banks in Small Business Finance[J].Journal of Banking&Finance,1998,22(6~8):1 109-1 116.
[13]MINNIS M,SUTHERLAND A A.Financial Statements as Monitoring Mechanisms:Evidence from Small Commercial Loans[J].Journal of Accounting Research,2017,55(1):197-233.
[14]SOHN S,JEON A H.Competing Risk Model for Technology Credit Fund for Small and Medium-Sized Enterprises[J].Journal of Small Business Management,2010,48(3):378-394.
[15]ONO A,HASUMI R,HIRATA H.Differentiated Use of Small Business Credit Scoring by Relationship Lenders and Transactional Lenders:Evidence From Firm-Bank Matched Data in Japan[J].Journal of Banking&Finance,2014,42:371-380.
[16]董汝杰.基于logit模型的微型企业信用评价研究[J].新金融,2011(8):54-58.
[17]马晓青,刘莉亚,胡乃红,等.小企业信用评估的模型构建与实证分析[J].财经研究,2012,38(5):28-37.
[18]何光辉,杨咸月.中国小微企业信用违约影响因素的实证检验---来自某国有银行地区分行的证据[J].上海财经大学学报,2015,17(6):67-79.
[19]肖斌卿,柏巍,姚瑶,等.基于LS-SVM的小微企业信用评估研究[J].审计与经济研究,2016,31(6):102-111.
[20]肖斌卿,杨旸,李心丹,等.基于模糊神经网络的小微企业信用评级研究[J].管理科学学报,2016,19(11):114-126.
[21]李敬明,阮素梅,刘奎户.基于改进模糊综合评价法的小微企业金融风险分析模型研究[J].运筹与管理,2015,24(6):217-224.
[22]张发明,刘志平.基于CW算子的组合评价信息集结方法及应用[J].系统管理学报,2017,26(2):268-276.
[23]李美娟,陈国宏,陈勃,等.基于方法集化的动态组合评价方法研究[J].中国管理科学,2013,21(2):132-137.
[24]张发明.一种融合SOM与K-means算法的动态信用评价方法及应用[J].运筹与管理,2014,23(6):186-192.
[25]张发明.一种融合相似与差异特征的组合评价方法及应用[J].系统管理学报,2013,22(4):498-504.
[26]刘微微,石春生,吴际.具有速度特征的动态评价方法及应用[J].中国软科学,2010(10):180-185.
[27]ALTMAN E I,SABATO A G.Modelling Credit Risk for SMEs:Evidence from the U.S.Market[J].Abacus,2014,43(3):332-357.
[28]DOUMPOS M,NIKLIS D,ZOPOUNIDIS C,et al.Combining Accounting Data and a Structural Model for Predicting Credit Ratings:Empirical Evidence From European Listed Firms[J].Journal of Banking&Finance,2015,50:599-607.
[29]陈影,任永平.基于支持向量机的中小企业信用准入评价的简化变量分析[J].上海金融,2016(11):91-95.
[30]李新春,潮海晨,叶文平.创业融资担保的社会支持机制[J].管理学报,2017,14(1):55-62.
[31]中华人民共和国国家统计局.2016年中国中小企业运行报告[EB/OL].(2017-05-24)[2018-04-20].http://www.lwzb.gov.cn/pub/gjtjlwzb/sjyfx/201705/t20170524_3750.html.
[32]翁春颖,韩明华.全球价值链驱动、知识转移与我国制造业升级[J].管理学报,2015,12(4):517-521.