【摘要】：我國(guó)的租賃和商務(wù)服務(wù)業(yè)小企業(yè)發(fā)展快速且數(shù)量眾多,租賃和商務(wù)服務(wù)業(yè)小企業(yè)貸款難的問(wèn)題一直是困擾這類(lèi)企業(yè)發(fā)展的難題。由于現(xiàn)有的信用評(píng)價(jià)體系無(wú)法反映租賃和商務(wù)服務(wù)業(yè)小企業(yè)的信用評(píng)價(jià)特征,甚至絕大多數(shù)銀行都沒(méi)有建立租賃和商務(wù)服務(wù)業(yè)小企業(yè)的信用評(píng)價(jià)體系,因此我國(guó)租賃和商務(wù)服務(wù)業(yè)小企業(yè)的信用評(píng)價(jià)問(wèn)題急需解決。 本論文由五章組成。第一章是緒論。第二章是基于顯著性檢驗(yàn)的租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用評(píng)價(jià)指標(biāo)體系構(gòu)建。第三章是基于投影尋蹤判別的租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用評(píng)價(jià)模型。第四章是基于Copula-等分法的租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用等級(jí)劃分。第五章是結(jié)論與展望。 本論文的主要工作有三： (1)構(gòu)建了租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用評(píng)價(jià)指標(biāo)體系。通過(guò)秩和檢驗(yàn)、方差檢驗(yàn)以及秩相關(guān)分析的三次組合方法篩選出能顯著區(qū)別違約狀態(tài)的信用評(píng)價(jià)指標(biāo)體系。 通過(guò)秩和檢驗(yàn)保留顯著區(qū)分違約狀態(tài)的信用評(píng)價(jià)指標(biāo)；通過(guò)方差檢驗(yàn)保留顯著區(qū)分違約狀態(tài)的信用評(píng)價(jià)指標(biāo)；通過(guò)秩相關(guān)分析進(jìn)一步刪除區(qū)分違約狀態(tài)能力弱的評(píng)價(jià)指標(biāo),建立了能夠顯著區(qū)分違約狀態(tài)的租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用評(píng)價(jià)指標(biāo)體系。 (2)測(cè)算了租賃和商務(wù)服務(wù)業(yè)小企業(yè)的信用得分。通過(guò)違約企業(yè)與不違約企業(yè)最大分離的投影尋蹤判別模型測(cè)算評(píng)價(jià)指標(biāo)的權(quán)重,通過(guò)評(píng)價(jià)指標(biāo)與權(quán)重的加權(quán)線(xiàn)性組合建立信用得分測(cè)算模型。 通過(guò)構(gòu)造違約類(lèi)樣本投影點(diǎn)逼近負(fù)理想值、不違約類(lèi)樣本投影點(diǎn)逼近正理想值的投影尋蹤判別模型,反映了違約企業(yè)樣本與不違約企業(yè)樣本差距越大則評(píng)價(jià)指標(biāo)越重要的權(quán)重測(cè)算思路,解決了租賃和商務(wù)服務(wù)業(yè)小企業(yè)信用評(píng)價(jià)指標(biāo)的權(quán)重測(cè)算問(wèn)題。 (3)合理劃分了租賃和商務(wù)服務(wù)業(yè)小企業(yè)的信用等級(jí)。通過(guò)Copula方法產(chǎn)生評(píng)級(jí)大樣本數(shù)據(jù),通過(guò)等分-動(dòng)態(tài)調(diào)整法建立租賃和商務(wù)服務(wù)業(yè)小企業(yè)的合理評(píng)級(jí)模型。 通過(guò)信用得分、應(yīng)還本息以及未還本息這三個(gè)變量的Copula聯(lián)合分布函數(shù)模擬得到評(píng)級(jí)大樣本,改變了小違約樣本不能分級(jí)的現(xiàn)狀,解決了小違約樣本下如何建立信用等級(jí)越高而對(duì)應(yīng)等級(jí)違約損失率越低的合理評(píng)級(jí)體系問(wèn)題。實(shí)踐中租賃和商務(wù)服務(wù)業(yè)小企業(yè)共113個(gè)樣本,其中違約樣本26個(gè),現(xiàn)有小違約樣本由于數(shù)量不足無(wú)法驗(yàn)證信用等級(jí)越高而對(duì)應(yīng)等級(jí)違約損失率越低的評(píng)級(jí)體系。
[Abstract]:The small enterprises in leasing and business service industry are developing rapidly and in large quantities in China. The problem of loan difficulty for small enterprises in leasing and business service industry has always been a difficult problem for the development of this kind of enterprises. Because the existing credit evaluation system can not reflect the credit evaluation characteristics of small enterprises in leasing and business service industries, even most banks have not established credit evaluation systems for small enterprises in leasing and business service industries. Therefore, the credit evaluation of small enterprises in leasing and business service industry needs to be solved urgently. This thesis consists of five chapters. The first chapter is the introduction. The second chapter is the construction of credit evaluation index system of small enterprises in leasing and business service industry based on significance test. Chapter three is the credit evaluation model of small enterprises in leasing and business service industry based on projection pursuit. The fourth chapter is the classification of credit grade of small enterprises in leasing and business service industry based on Copula- and other methods. The fifth chapter is the conclusion and prospect. The main work of this paper is as follows: (1) the credit evaluation index system of small enterprises in leasing and business service industry is constructed. The credit evaluation index system which can distinguish the default state is selected by the three combination methods of rank sum test variance test and rank correlation analysis. The credit evaluation index which distinguishes the default state by rank sum test, the credit evaluation index by variance test, and the credit evaluation index which distinguishes the default state by the variance test. Based on rank correlation analysis, the index system of credit evaluation for small enterprises in leasing and business service industries, which can distinguish the state of default between leasing and business service enterprises, is further deleted. (2) the credit score of small enterprises in leasing and business service industry is calculated. The weight of the evaluation index is calculated by the projection pursuit discriminant model of the maximum separation between the defaulting enterprise and the non-defaulting enterprise, and the credit score calculation model is established by the weighted linear combination of the evaluation index and the weight. In this paper, a projection pursuit discriminant model is constructed, in which the projection point approximates the negative ideal value and the non-default sample projection point approximates the positive ideal value. It reflects that the bigger the gap between the sample of defaulting enterprise and the non-defaulting enterprise, the more important the weight of evaluation index is, and the problem of weight calculation of credit evaluation index of small enterprise in leasing and business service industry is solved. (3) the credit grade of small enterprises in leasing and business service industry is divided reasonably. The Copula method is used to generate large sample data, and the reasonable rating model for small enterprises in leasing and business service industries is established by the method of equipartition and dynamic adjustment. Through the credit score, the Copula joint distribution function simulation of the three variables, should pay back principal and interest and not repay principal and interest, obtained the large sample rating, which changed the current situation that small default sample can not be classified. It solves the problem of how to establish a reasonable rating system with higher credit grade and lower default loss rate of corresponding grade under small default sample. In practice, there are 113 samples of small enterprises in leasing and business service industry, of which 26 are default samples. Due to the insufficient number of small default samples, the higher the credit rating is, the lower the default loss rate of the corresponding grade is.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F276.3;F832.4

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