标题: 建构台湾中小企业两阶段风险评估模型
Constructing a Two-Stage Risk Assessment Model for Small and Medium Enterprises in Taiwan
作者: 吴佩珊
Pei-Shan Wu
唐丽英
张永佳
Lee-Ing Tong
Yung-Chia Chang
工业工程与管理学系
关键字: 风险评估;中小企业;逻辑斯回归;支持向量机;risk assessment;small and medium enterprise;logistic regression;support vector machine
公开日期: 2007
摘要: 由于全球性的经济不景气,导致银行与金融机构承受相当大的财务风险,因此国际清算银行于2004年公布之新巴塞尔资本协定(Basel II),允许银行及金融机构可以自行利用内部评等方法建立风险评估模型来衡量借款客户之风险。目前中、外文献虽已发展出许多风险评估模型,但大多是针对上市、上柜公司,少有以中小企业为研究对象,由于中小企业占台湾企业数九成以上,为国内金融机构主要放款对象,现有文献所建议之风险评估模型若直接应用到台湾中小企业上,预测可能不准确。现有的风险评估模型多是建构一个分类判别模型(如区别分析模型、逻辑斯回归模型等),将借款客户分成违约(default)及不违约(non-default)两类,然而利用这些判别模型评估借款企业之风险时,虽然有不错的整体准确率,但常会出现某类借款客户(如违约客户)准确率高,而另一类客户(如不违约客户)之准确率却偏低的情况,此种判定模型即使整体判别准确率不错,但对于金融机构而言,其实用性不高。因此,本研究针对中小企业之特性,发展出一套两阶段的风险评估模型,以改善这种准确率偏向某一类客户的问题,并提升传统风险判别模型之准确率。本研究利用逻辑斯回归(logistic regression)与支持向量机(support vector machine, SVM)建构此两阶段风险评估模型,然后依照判定模式给予中小企业一个信用风险等级,以供银行或金融机构能够制订出最佳之放款策略。最后,本研究利用国内某金融机构所提供之中小企业借款历史资料,验证了本研究之两阶段风险评估模型确实有效可行。
Due to the global economic recession, enterprises are facing strong financial stress. For this reason, banks or financial institutions are suffered from serious financial risk. In order to reduce the global financial risk, banks or financial institutions need to develop to their own internal measures for assessing the borrower’s credit according to the New Basel Capital Accord (BASEL II). Most risk assessment models found in literature were constructed for the publicly traded companies. However, 90% of enterprises in Taiwan are small and medium enterprises. It is not quite appropriate to apply the risk assessment models for publicly traded companies directly to those banks or financial institutions whose borrowers are mainly small and medium enterprises. Furthermore, most available risk assessment models use classification methods (such as the discriminant analysis model and logistic regression model) to construct the models and classify the loan borrowers into default and non-default groups. Although the total accuracy rate of classification may be good, but the accuracy rate for a particular group (such as the default group) is significantly higher than the other. It is often found that the accuracy rates for both groups are not balanced. It causes serious problem in practice use for the financial institutions. Therefore, the objective of this study is to develop a two-stage risk assessment model to improve the unbalanced accuracy rate for different group, furthermore, to increase the total accuracy rate. This study utilizes logistic regression at the first stage and support vector machine (SVM) at the second stage to construct this two-stage risk assessment model. Finally, a real case from a Taiwanese financial institution is utilized to demonstrate the effectiveness of the proposed procedure.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009533512
http://hdl.handle.net/11536/39145
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