分析我国中小企业目前的融资困境及其根源,提出改进模糊综合评价模型—AFF模型(Analytic hierarchy process- Factor Analysis- Fuzzy Comprehensive Evaluation)对中小企业进行信用评估。该模型在进行指标权重决策问题时,不仅考虑了复杂大群体决策的不确定性,更是将主观赋权法和客观赋权法思想相结合,提出以基于群决策的AHP方法确定主观权重,基于因子分析法(FA)确定客观权重,最后将主客观权重集结得到指标的综合权重。模型既克服了传统主观因素赋权的不足,又充分考虑客观因素,而且引入了群体决策的思想,拓展了模糊综合评价法的应用,更具理论实际意义。选取信息技术服务业53家公司为样本,利用该模型进行了实证分析,验证了该模型的适用性、稳定性和客观性。分析结果表明,采用AFF模型能准确地得到公司的信用等级,并能发现导致公司信用状况不佳的相关因素,通过对相关因素的剖析,帮助领导者和决策者改善公司信用状况,具有较强的实践意义。AFF模型在各类理论与实践的综合评价中均具有应用和推广价值。
With the analysis of the current financing difficulties and its causes of SMEs in China, the improved fuzzy comprehensive evaluation model—AFF model(Analytic hierarchy process- Factor Analysis- Fuzzy Comprehensive Evaluation) is proposed in this paper, in order to deal with the credit assessment on SMEs. During the index weight in decision-making problems, the proposed model considered the uncertainty of a complex large group decision-making, and the problem of index weights in the fuzzy comprehensive evaluation method is discussed, combined subjective weighting method with the objective weighting method. Based on that group decision making AHP determined the subjective weights and factor analysis determined the objective weights, there goes the comprehensive weight determined by the multiplication normalization formula. The model not only overcomes the shortcomings of traditional subjective factors empowerment method, but also takes full account of objective factors, and introduced the idea of group decision-making, expanding the application of fuzzy comprehensive evaluation method, with more theoretical practical significance. 53 companies of IT service industry are selected as the sample, the model is used to do empirical analysis, which verifies the model's applicability, stability and objectivity. The results show that the AFF model can accurately get the company's credit rating, and can find some relevant factors that will lead to bad credit. With the analysis of the relevant factors, it can help leaders and decision-makers to improve the company's credit situation, thus has a strong practical significance. AFF model has both application and popularization in the comprehensive evaluation area.
[1] 陈晓红,曹裕,马跃如. 基于外部环境视角下的我国中小企业生命周期——以深圳等五城市为样本的实证研究[J]. 系统工程理论与实践,2009,29(1):64-72.
[2] 毕克新,孙金花,张铁柱,等.基于模糊积分的区域中小企业技术创新测度与评价[J]. 系统工程理论与实践,2005,25(2):40-46.
[3] 胡援成. 中小企业融资的调查与思考[J]. 管理世界(月刊),2004,(4):148-149.
[4] 田厚平,刘长贤.企业资产规模、信贷市场结构与中小企业融资[J].管理科学学报,2010,13(5):51-61.
[5] 陈晓红. 中小企业融资创新与信用担保[M].北京:中国人民大学出版社,2003.
[6] Crook J N, Edelman D B, Thomas L C. Recent developments in consumer credit risk assessment[J]. European Journal of Operational Research,2007,183(3):1447-1465.
[7] Finlay S,Multiple classifier architectures and their application to credit risk assessment[J]. European Journal of Operational Research,2011,210(2): 368-378.
[8] 吴冲,夏晗. 基于支持向量机集成的电子商务环境下客户信用评估模型研究[J]. 中国管理科学,2008,16(10):362-367.
[9] 王春峰,李汶华. 小样本数据信用风险评估研究[J]. 管理科学学报,2001,4(1):28-32.
[10] 孙庆文,张琼琼,仇静莉,等. 基于不同信息获取量的赊销决策风险度判别模型[J]. 系统工程理论与实践, 2012, 32(1):41-48.
[11] 梁樑,吴德胜,王志强,等. 基于ANFIS和Elman网络的信用评价研究[J]. 管理工程学报,2005,19(1):69-73.
[12] 刘京礼,李建平,徐伟宣,等. 信用评估中的鲁棒赋权自适应Lp最小二乘支持向量机方法[J]. 中国管理科学, 2010,18(5):28-33.
[13] 王刚,黄丽华,高阳. 基于方法集的农业产业化综合评价模型[J]. 系统工程理论与实践, 2009,29(4):161-168.
[14] 何湘桂.KMV模型的实现与应用[D].武汉:华中科技大学,2008.
[15] 张婷.企业信用评估体系研究与实证分析[D].西安:西安建筑科技大学,2010.
[16] 罗新华.企业家人力资本价值计量模型的设计[J].管理世界(月刊),2008,(7):184-185.
[17] 裴菁,汪定伟.虚拟企业协作中的竞标策略研究[J].管理科学学报,2002,5(1):35-39.
[18] 桂宏新,杨昌昊,程飞. 基于客户感知的移动业务综合评价[J]. 中国管理科学,2008,16(S1):113-116.
[19] 韩利,梅强,陆玉梅,等.AHP-模糊综合评价方法的分析与研究[J].中国安全科学学报,2004,14(7):86-89.
[20] 李贵春,李从东,李龙洙. 供应链绩效评价指标体系与评价方法研究[J]. 管理工程学报, 2004,18(1):104-106.
[21] 肖凤. 基于模糊综合评价法的高校网络德育绩效评价研究[D]. 长沙:中南大学,2012.
[22] 孙洪武,齐博,刘志民. 中试熟化型农业科技项目实施效果评估研究-基于农业科技跨越计划的实证分析[J]. 科研管理,2012,33(11):122-128.
[23] 吴殿廷,李东方. 层次分析法的不足及其改进的途径[J]. 北京师范大学学报(自然科学版),2004,40(2):264-268.
[24] 兵光富,李学军,Dhillon B S,等. 基于模糊层次分析法的设备状态系统量化评价新方法[J]. 系统工程理论与实践, 2010,30(4):744-780.
[25] 赵晓冬,郑涛. 基于FUZZY-AHP评价方法的个人信用等级评价模型指标体系[J]. 数量经济技术经济研究,2003,(6):97-100.
[26] 海热提·涂尔逊,王华东,王力红,等. 城市可持续发展的综合评价[J]. 中国人口、资源与环境, 1997,7(2):46-50.
[27] 蓝华,刘微微,于渤.基于影响要素的科技学术期刊质量模糊综合评价[J]. 中国软科学,2009,(1):144-151.
[28] 黄良文.统计学原理[M].北京:中国统计出版社,2008.
[29] 于秀林,任学松.多元统计分析[M].北京:中国统计出版社,1999.
[30] http://www.stockstar.com/.
[31] 荆新,王化成,刘俊彦.财务管理学[M].北京:中国人民大学出版社,2006.
[32] 国务院国资委统计评价局.企业绩效标准评价值2009[M].北京:经济科学出版社,2009.
[33] Huang Y N, Hung C M, Jiau H C. Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem[J]. Nonlinear Analysis: Real World Applications, 2006,7(4):720-747.
[34] Chijoriga M M. Application of multiple discriminant analysis (MDA) as a credit scoring and risk assessment model[J]. International Journal of Emerging Markets, 2011,6(2):132-147.
[35] Ohlson J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of Accounting Research,1980,18(1):109-130.
[36] 唐亮,张北阳,陈守东. 我国上市公司信用风险评价和度量——基于面板数据Logit模型的实证分析[J]. 工业技术经济, 2011,(2):140-147.
[37] Lu Yangcheng,Shen C H, Wei Yuchen. Revisiting early warning signals of corporate credit default using linguistic analysis[J]. Pacific-Basin Finance Journal,2013,24:1-21.
[38] Wang H Y, Liao Chechen, Kao C H.A credit assessment mechanism for wireless telecommunication debt collection: An empirical study[J]. Information Systems and e-Business Management,2012,11(3):357-375.
[39] Mittal S, Gupta P, Jain K. Neural network credit scoring model for micro enterprise financing in India[J]. Qualitative Research in Financial Markets,2011,3(3):224-242.
[40] Oreski S, Oreski D, Oreski G. Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment[J]. Expert Systems with Applications,2012,39(16):12605-12617.
