本文選題：信用評級　切入點(diǎn)：違約風(fēng)險(xiǎn)　出處：《大連理工大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：信用評級的本質(zhì)是揭示貸款數(shù)據(jù)與違約風(fēng)險(xiǎn)的關(guān)系與規(guī)律,確定一筆貸款或債務(wù)違約的可能性。由于小型工業(yè)企業(yè)規(guī)模小,財(cái)務(wù)信息不完善,很難找到經(jīng)典的指標(biāo)和信用評價(jià)理論進(jìn)行評價(jià),因此建立一套適用于小型工業(yè)企業(yè)的信用風(fēng)險(xiǎn)評級體系是金融機(jī)構(gòu)亟需解決的問題�；谶`約風(fēng)險(xiǎn)判別的小型工業(yè)企業(yè)信用風(fēng)險(xiǎn)評級研究包括小型工業(yè)企業(yè)信用評級指標(biāo)體系的構(gòu)建、小型工業(yè)企業(yè)信用評分模型的構(gòu)建、以及小型工業(yè)企業(yè)信用等級劃分模型的構(gòu)建三部分內(nèi)容。一是小型工業(yè)企業(yè)信用評級指標(biāo)體系的構(gòu)建是指根據(jù)指標(biāo)對違約狀態(tài)鑒別精度的影響程度對指標(biāo)進(jìn)行遴選,構(gòu)建既能顯著區(qū)分小型工業(yè)企業(yè)違約狀態(tài)、又反映小型工業(yè)企業(yè)客戶清償能力的信用風(fēng)險(xiǎn)評級指標(biāo)體系。二是小型工業(yè)企業(yè)信用評分模型的構(gòu)建是指根據(jù)非違約企業(yè)的評價(jià)得分越高、違約企業(yè)的評價(jià)得分越低的基本評價(jià)思路,建立多目標(biāo)非線性規(guī)劃模型對遴選出的評價(jià)指標(biāo)進(jìn)行組合賦權(quán),建立信用評分模型,求解不同小型工業(yè)企業(yè)的信用評分。三是小型工業(yè)企業(yè)信用等級劃分模型的構(gòu)建是指以信用差異度和違約金字塔為標(biāo)準(zhǔn),構(gòu)建非線性規(guī)劃模型劃分信用等級,使信用等級劃分結(jié)果不僅能滿足信用等級越高、違約損失率越低的違約金字塔標(biāo)準(zhǔn),還能保證信用狀況差異大的客戶劃分為不同信用等級。本論文共分為六章。第一章是緒論；第二章是基于違約風(fēng)險(xiǎn)判別的信用評級理論基礎(chǔ)；第三章是基于Fisher判別的小型工業(yè)企業(yè)信用評級指標(biāo)體系的構(gòu)建；第四章是基于最大違約鑒別能力組合賦權(quán)的信用評分模型的構(gòu)建；第五章是基于信用差異度最大的信用等級劃分模型研究；第六章是結(jié)論及展望。本論文的主要工作及創(chuàng)新如下：(1)信用等級劃分方面的工作及創(chuàng)新：一是根據(jù)第七個(gè)信用等級中最后一個(gè)樣本的信用評分P朋,與第n1個(gè)信用等級中第一個(gè)樣本的信用評分P1k+1確定相鄰兩個(gè)等級的信用評分差值,以所有信用等級的評分差值之和∑(Pmkk-P1k+1)最大為目標(biāo)函數(shù),保證信用狀況差異大的客戶劃分為不同信用等級。二是以信用等級由高到低的違約損失率嚴(yán)格遞增為約束條件建立信用等級劃分模型,保證信用等級劃分結(jié)果滿足信用等級越高、違約損失率越低的違約金字塔標(biāo)準(zhǔn),避免出現(xiàn)信用等級很高、違約損失率反而不低的不合理現(xiàn)象。(2)指標(biāo)組合賦權(quán)方面的工作及創(chuàng)新：通過以非違約企業(yè)的指標(biāo)加權(quán)數(shù)據(jù)到正理想點(diǎn)的距離代數(shù)和最小為第一個(gè)目標(biāo)函數(shù),以違約企業(yè)的指標(biāo)加權(quán)數(shù)據(jù)到負(fù)理想點(diǎn)的距離代數(shù)和最小為第二個(gè)目標(biāo)函數(shù),構(gòu)建多目標(biāo)非線性規(guī)劃模型進(jìn)行組合賦權(quán),在滿足了“非違約企業(yè)的評價(jià)得分越高、違約企業(yè)的評價(jià)得分越低”要求的目標(biāo)下得到最優(yōu)的組合賦權(quán)的權(quán)重系數(shù),使賦權(quán)結(jié)果保證了評級模型能夠?qū)⑦`約企業(yè)與非違約企業(yè)最大地區(qū)分開。改變了現(xiàn)有研究的組合賦權(quán)脫離評價(jià)目的的弊端,改變了現(xiàn)有研究中違約與非違約企業(yè)的評價(jià)得分存在大量重疊、對兩類企業(yè)的區(qū)分能力低的弊端。(3)指標(biāo)遴選方面的工作及創(chuàng)新：根據(jù)有、無特定指標(biāo)兩種狀態(tài)下、Fisher判別對違約狀態(tài)鑒別精度的提高或降低,反映特定指標(biāo)對違約狀態(tài)的影響程度,剔除對違約狀態(tài)的判別精度沒有影響或有降低影響的指標(biāo),保留可以顯著提高違約狀態(tài)判別精度的指標(biāo),完善了現(xiàn)有研究遴選指標(biāo)的標(biāo)準(zhǔn)與違約狀態(tài)無關(guān)的不足。
[Abstract]:The nature of credit rating is to reveal the loan default risk and the relationship between data and rules, to determine the possibility of a loan or debt default. Because the scale of small and medium sized industrial enterprises, financial information is not perfect, it is difficult to find the index and credit evaluation of the classical theory of evaluation, therefore to establish a suitable for small and medium-sized industrial enterprises credit rating the system of financial institutions is an urgent problem to be solved. The construction of the credit risk rating of small and medium sized industrial enterprises default risk identification including small and medium sized industrial enterprises credit rating index system based on the construction of evaluation model for small and medium sized industrial enterprises, and small industrial enterprises credit rating classification model is constructed of three parts. One is the construction of small and medium sized industrial enterprises credit rating the index system is defined according to the indicators of the index of the influence degree of default identification accuracy of selection, which can not only significantly The distinction between small and medium sized industrial enterprises default, credit risk rating index system to reflect the small industrial enterprises customer solvency. The two is to build a small industrial enterprises credit scoring model refers to the non default enterprise evaluation scores higher default evaluation score lower basic evaluation idea, evaluation index system of multiobjective nonlinear programming the model of selected combination weighting, establish credit scoring model, credit for small industrial enterprises score. Three is to build a classification model of credit rating for small industrial enterprises refers to the letter of the difference and default of Pyramid as a standard nonlinear programming model is constructed based on division of credit rating, the credit rating classification results can not only meet the credit the higher the level, the default loss rate lower default Pyramid standards, but also ensure the credit status of customers is not the division of major differences with the letter Use level. This paper is divided into six chapters. The first chapter is the introduction; the second chapter is the default risk discrimination of credit rating based on the theoretical basis of; the third chapter is the construction of credit rating index system of small and medium sized industrial enterprises based on Fisher discriminant; the fourth chapter is based on the maximum default identification capability of combination weighting of the credit scoring model; the fifth chapter is the research on credit rating classification model based on the maximum credit difference; the sixth chapter is the conclusion and prospect. The main work and innovation are as follows: (1) the work and innovation of credit rating Division: one is according to a sample of seventh credit rating in credit scoring P friends, with the first sample N1 credit rating in credit scoring to determine the difference of P1k+1 credit score of two adjacent level, with all the credit rating score and sigma (Pmkk-P1k+1) as the objective function, Ensure the credit status of customers for different division of major differences in credit rating. Two is a credit rating from high to low LGD strictly increasing established credit rating classification model as constraint conditions, guarantee the credit rating classification results meet the higher the credit rating, default loss rate is lower in Pyramid to avoid the default standard, credit rating is high default, unreasonable loss rate but not low. (2) index combination weighting work and Innovation: the distance algebra positive ideal point to non default weighted index data of the enterprise to minimum as the first objective function, the default weighted index data of the enterprise to the distance from the negative ideal point and minimum algebra for the second objective functions, construct the combination weighting multi-objective nonlinear programming model, to meet the "non default enterprise evaluation score higher, lower default enterprise evaluation score "To get the optimal weighting coefficient of combination weighting requirements under the target of the weighted results ensure that the rating model can separate default and non default maximum enterprise enterprise area. Changing the combination of existing research from the drawbacks of weighting the evaluation objective, change the existing research in default and non default valuation there is a lot of overlap score. The ability to distinguish defects on two types of enterprises is low. (3) index selection work and innovation aspects: according to the index of two, no specific condition, Fisher discriminant of default state identification accuracy increase or decrease, reflecting the specific impact on the default index, excluding the default state without discrimination accuracy influence or reduce the effect of retention index, can significantly improve the classification accuracy of default index, improve the existing research on the selection index of the standard has nothing to do with the default is not enough.

【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：F832.4;F270;F425

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