我國中小企業(yè)信用風險度量研究
發(fā)布時間：2018-09-09 20:44
【摘要】：中小企業(yè)的蓬勃發(fā)展具有較強的經(jīng)濟外部性,不僅是推動國民經(jīng)濟持續(xù)穩(wěn)定高速增長的重要力量,而且在調整經(jīng)濟結構、擴大社會就業(yè)等方面都發(fā)揮著積極的作用。然而,我國中小企業(yè)融資一直面臨著“麥克米蘭缺口”。不管是我國中小企業(yè)的融資趨勢還是國外中小企業(yè)的融資結構都表明:在現(xiàn)實環(huán)境的約束下,解決中小企業(yè)融資困境只有一個方向——擴大間接融資體系,增加來自銀行的債務融資,而加強對中小企業(yè)的信用風險評估是其中的關鍵環(huán)節(jié)。隨著優(yōu)秀大企業(yè)客戶資源的逐漸減少和信貸市場細分,針對中小企業(yè)的信用風險度量必然成為我國銀行未來需要重點關注的一大課題。 本文圍繞如何建立適合我國國情的中小企業(yè)信用風險評估模型這一主題,針對中小企業(yè)這一特殊的企業(yè)群體,在對信用風險度量的相關方法、模型回顧和比較的基礎上,首先指出了現(xiàn)階段我國中小企業(yè)信用風險度量最可行的方法是多元統(tǒng)計分析。然后,依據(jù)113家中小企業(yè)樣本2002年和2003年的財務數(shù)據(jù)和非財務數(shù)據(jù),對20個最初輸入指標進行獨立樣本T檢驗和主成分分析,得到一個僅含10個指標的簡化指標體系。接著,輸入指標數(shù)據(jù)詳細實證檢驗了多元線性判別模型和Logit模型,得到的結論顯示:多元線性判別模型傾向于將簡化后的所有10個指標全部進入判別方程,綜合預測正確率在違約前2年達到76.1%,違約前1年達到81.4%;Logit模型傾向于選擇使用“Backword:Conditional”方法,當Logit分析進行到第4步時的模型,綜合預測正確率在違約前2年達到78.8%,違約前1年達到85.8%;Logit模型的綜合預測率雖然高于多元線性判別模型,但其在關鍵的第二類誤判率上卻不如后者,容易將違約類企業(yè)判斷為正常企業(yè),忽視其潛在的信用風險。最后,在模型結果的基礎上,分析了我國中小企業(yè)的信用風險特征,同時與穆迪公司用RiskCalc~(TM)違約模型對美國、澳大利亞等十個較發(fā)達國家私營企業(yè)信用風險研究的結果相比較,表明:除去非財務因素外,顯著性較高且較為穩(wěn)定,能體現(xiàn)我國中小企信用風險特征的前兩個因素依次為資本結構指標、收益性指標,與十國的平均結果基本類似。
[Abstract]:The vigorous development of small and medium-sized enterprises has a strong economic externality, which is not only an important force to promote the sustained, stable and high speed growth of the national economy, but also plays an active role in adjusting the economic structure and expanding social employment. However, the financing of small and medium enterprises in China has been facing the "Macmillan gap." Both the financing trend of small and medium-sized enterprises in China and the financing structure of foreign small and medium-sized enterprises show that under the constraints of the real environment, there is only one direction to solve the financing dilemma of small and medium-sized enterprises-to expand the indirect financing system. Increasing debt financing from banks and strengthening credit risk assessment of small and medium-sized enterprises is the key link. With the decreasing of customer resources and the subdivision of credit market, the measurement of credit risk for small and medium-sized enterprises will inevitably become a major issue that Chinese banks should pay more attention to in the future. This paper focuses on the topic of how to establish a credit risk assessment model suitable for China's national conditions, aiming at SMEs as a special enterprise group, on the basis of reviewing and comparing the relevant methods and models of credit risk measurement. Firstly, it points out that multivariate statistical analysis is the most feasible method to measure the credit risk of SMEs in China at present. Then, based on the financial data and non-financial data of 113 small and medium-sized enterprises in 2002 and 2003, the independent sample T test and principal component analysis are carried out on 20 initial input indicators, and a simplified index system with only 10 indicators is obtained. Then, the multiple linear discriminant model and the Logit model are tested in detail by the input index data. The conclusion is that the multivariate linear discriminant model tends to enter the discriminant equation with all the 10 simplified indexes into the discriminant equation. The accuracy rate of comprehensive prediction reached 76.1g 2 years before default, 81.4 logit model one year before default preferred to use "Backword:Conditional" method, and the model when Logit analysis reached step 4, The accuracy rate of comprehensive prediction is 78.8% two years before default, and 85.8% of logit model is higher than that of multivariate linear discriminant model one year before default, but it is not as good as the latter in the second kind of critical misjudgment rate. It is easy to judge the defaulting enterprise as a normal enterprise and ignore its potential credit risk. Finally, on the basis of the results of the model, this paper analyzes the credit risk characteristics of small and medium-sized enterprises in China, and compares the credit risk characteristics of small and medium-sized enterprises in China with the credit risk study of private enterprises in ten more developed countries, such as the United States, Australia and other more developed countries, using Moody's default model. The results show that, except for non-financial factors, it is significant and stable. The first two factors which can reflect the characteristics of credit risk of SMEs in China are capital structure index and profitability index, which are basically similar to the average results of the ten countries.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2006
【分類號】：F224
本文編號：2233520
[Abstract]:The vigorous development of small and medium-sized enterprises has a strong economic externality, which is not only an important force to promote the sustained, stable and high speed growth of the national economy, but also plays an active role in adjusting the economic structure and expanding social employment. However, the financing of small and medium enterprises in China has been facing the "Macmillan gap." Both the financing trend of small and medium-sized enterprises in China and the financing structure of foreign small and medium-sized enterprises show that under the constraints of the real environment, there is only one direction to solve the financing dilemma of small and medium-sized enterprises-to expand the indirect financing system. Increasing debt financing from banks and strengthening credit risk assessment of small and medium-sized enterprises is the key link. With the decreasing of customer resources and the subdivision of credit market, the measurement of credit risk for small and medium-sized enterprises will inevitably become a major issue that Chinese banks should pay more attention to in the future. This paper focuses on the topic of how to establish a credit risk assessment model suitable for China's national conditions, aiming at SMEs as a special enterprise group, on the basis of reviewing and comparing the relevant methods and models of credit risk measurement. Firstly, it points out that multivariate statistical analysis is the most feasible method to measure the credit risk of SMEs in China at present. Then, based on the financial data and non-financial data of 113 small and medium-sized enterprises in 2002 and 2003, the independent sample T test and principal component analysis are carried out on 20 initial input indicators, and a simplified index system with only 10 indicators is obtained. Then, the multiple linear discriminant model and the Logit model are tested in detail by the input index data. The conclusion is that the multivariate linear discriminant model tends to enter the discriminant equation with all the 10 simplified indexes into the discriminant equation. The accuracy rate of comprehensive prediction reached 76.1g 2 years before default, 81.4 logit model one year before default preferred to use "Backword:Conditional" method, and the model when Logit analysis reached step 4, The accuracy rate of comprehensive prediction is 78.8% two years before default, and 85.8% of logit model is higher than that of multivariate linear discriminant model one year before default, but it is not as good as the latter in the second kind of critical misjudgment rate. It is easy to judge the defaulting enterprise as a normal enterprise and ignore its potential credit risk. Finally, on the basis of the results of the model, this paper analyzes the credit risk characteristics of small and medium-sized enterprises in China, and compares the credit risk characteristics of small and medium-sized enterprises in China with the credit risk study of private enterprises in ten more developed countries, such as the United States, Australia and other more developed countries, using Moody's default model. The results show that, except for non-financial factors, it is significant and stable. The first two factors which can reflect the characteristics of credit risk of SMEs in China are capital structure index and profitability index, which are basically similar to the average results of the ten countries.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2006
【分類號】：F224
【引證文獻】
相關碩士學位論文 前4條
1 徐璐;中小房地產(chǎn)企業(yè)貸款信用風險評價研究[D];西南財經(jīng)大學;2011年
2 應千凡;中國非上市公司信用風險度量研究[D];浙江大學;2007年
3 李鑫;物流企業(yè)信貸信用風險度量研究[D];西南交通大學;2009年
4 陳平平;中國商業(yè)銀行信用風險度量實證研究[D];江西財經(jīng)大學;2012年
，本文編號：2233520
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