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  本文關(guān)鍵詞：基于非線性插值的小企業(yè)信用評級研究　出處：《大連理工大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 信用評級 違約風(fēng)險(xiǎn) 指標(biāo)體系 最優(yōu)權(quán)重 小企業(yè)

【摘要】：小企業(yè)提供了超過80%的城鎮(zhèn)就業(yè)崗位,創(chuàng)造了52.2%的稅收以及58.5%的國內(nèi)生產(chǎn)總值,其貸款需求也分別高出大型和中型企業(yè)5.9個和4.3個百分點(diǎn)。由于小企業(yè)自身財(cái)務(wù)信息不夠公開透明,制度不夠具體規(guī)范,銀行較難把握其真實(shí)發(fā)展?fàn)顩r,導(dǎo)致銀行不愿為小企業(yè)進(jìn)行融資等原因外,商業(yè)銀行缺少一套專門針對小企業(yè)貸款的信用評級體系也是其中一個關(guān)鍵的原因,因此亟需構(gòu)建一套適用于小企業(yè)的信用評級體系。信用評級的本質(zhì)是挖掘評級數(shù)據(jù)與違約風(fēng)險(xiǎn)之間的規(guī)律性聯(lián)系,揭示一個客戶或一筆債務(wù)的違約風(fēng)險(xiǎn)大小,評估其償還的可能性及違約損失率大小。信用評級對違約風(fēng)險(xiǎn)識別能力的強(qiáng)弱直接關(guān)乎金融市場的穩(wěn)定性。2008年次貸危機(jī)的發(fā)生正是源起違約風(fēng)險(xiǎn)識別錯誤,在此之后穆迪等權(quán)威機(jī)構(gòu)的“黑箱”評級過程也備受質(zhì)疑,從沉重的金融危機(jī)中可知,信用評級中指標(biāo)篩選、指標(biāo)賦權(quán)、信用等級確定等關(guān)鍵環(huán)節(jié)均要以識別違約風(fēng)險(xiǎn)為標(biāo)準(zhǔn),否則無論多么流行、權(quán)威的信用評級體系都是不合理的。因此構(gòu)建一套能夠有效識別違約風(fēng)險(xiǎn)的信用評級體系是至關(guān)重要的�；诜蔷�性插值的小企業(yè)信用評級研究,研究內(nèi)容主要包括以下三部分：小企業(yè)信用評級指標(biāo)體系的構(gòu)建、小企業(yè)信用評分模型的建立,以及小企業(yè)信用評級模型的建立。其中,小企業(yè)信用評級體系的構(gòu)建系指根據(jù)指標(biāo)對違約狀態(tài)的鑒別能力越大、越應(yīng)保留的思路,構(gòu)建一套既能反映企業(yè)客戶償還能力,又能顯著區(qū)分違約狀態(tài)的信用評價指標(biāo)體系。小企業(yè)信用評價模型是指根據(jù)違約與違約樣本的距離最大為目標(biāo)函數(shù),反推出最優(yōu)信用評價方程的權(quán)重,從而建立小企業(yè)信用評分模型、進(jìn)而得出小企業(yè)的信用得分。小企業(yè)信用評級模型的建立是通過非線性插值方法對舊數(shù)據(jù)進(jìn)行“加權(quán)平移”,取得與“通過新、舊全部樣本的統(tǒng)計(jì)規(guī)律挖掘出的另一套指標(biāo)體系”一致的評級結(jié)果,確定出小企業(yè)的信用等級。本論文共分五章。第一章是緒論,對研究背景及意義進(jìn)行了介紹,并對國內(nèi)外相關(guān)研究進(jìn)行了梳理；第二章是基于邏輯回歸顯著性判別的小企業(yè)信用評級指標(biāo)體系的構(gòu)建；第三章是基于“違約與非違約樣本距離”最大的信用評分模型的構(gòu)建；第四章是基于信用得分非線性插值的信用評級模型研究；第五章是結(jié)論及展望。本論文的主要工作及創(chuàng)新如下：(1)信用評級方面的工作及創(chuàng)新：通過舊樣本的指標(biāo)數(shù)據(jù)的“加權(quán)平移變換”構(gòu)建信用評級模型,保證了當(dāng)采用與“通過舊樣本的統(tǒng)計(jì)規(guī)律遴選或挖掘出的一套指標(biāo)體系”一模一樣的指標(biāo)體系進(jìn)行新樣本的評級時,也能得到與“通過新、舊全部樣本的統(tǒng)計(jì)規(guī)律挖掘出的另一套指標(biāo)體系”同樣的評級結(jié)果.通過對舊樣本數(shù)據(jù)進(jìn)行加權(quán)平移變換,構(gòu)建非線性插值信用評級模型,在樣本增加的情況下無需重新進(jìn)行“指標(biāo)遴選、指標(biāo)賦權(quán)、評級方程”等繁瑣過程,僅僅需要把新樣本的指標(biāo)數(shù)據(jù)直接輸入到評級方程中,便可得到與“通過新、舊全部樣本的統(tǒng)計(jì)規(guī)律挖掘出的另一套指標(biāo)體系”一致的評級結(jié)果,保證了評級指標(biāo)體系的不變,事實(shí)上,任何一家評級公司的指標(biāo)體系在相當(dāng)長一段時期內(nèi)都是不變的,而不是頻繁地變動評級指標(biāo)體系。并彌補(bǔ)了現(xiàn)有研究中直接根據(jù)過去樣本挖掘的指標(biāo)體系確定新客戶信用等級,忽視加入一個或多個樣本后樣本的統(tǒng)計(jì)規(guī)律已經(jīng)發(fā)生變化、舊樣本挖掘的指標(biāo)體系已經(jīng)不適用于確定新樣本的評級結(jié)果的問題。(2)指標(biāo)賦權(quán)方面的工作及創(chuàng)新：根據(jù)“違約客戶與非違約客戶信用得分的組間距離越大、組內(nèi)平均距離越小,則評價方程鑒別違約能力越強(qiáng)”的思路構(gòu)建多目標(biāo)規(guī)劃模型,求解最優(yōu)的權(quán)重系數(shù),保證賦權(quán)后的評價得分違約鑒別能力最大。通過違約和非違約客戶信用得分0的組間距離越大、組內(nèi)平均距離越小,則評價得分Sj區(qū)分違約狀態(tài)的能力越強(qiáng)的思路,設(shè)定違約非違約兩類樣本的組間距離與組內(nèi)平均距離的比值最大為目標(biāo)函數(shù),以單一賦權(quán)的最大值和最小值為約束條件,構(gòu)建線性規(guī)劃模型。由于目標(biāo)函數(shù)是關(guān)于信用得分Sj的函數(shù),而信用得分Sj是關(guān)于權(quán)重wi的函數(shù),因此目標(biāo)函數(shù)是關(guān)于權(quán)重w,的函數(shù),也就是通過求解目標(biāo)規(guī)劃的最優(yōu)解即可得到評價指標(biāo)權(quán)重wi的最優(yōu)解。保證了求解的指標(biāo)權(quán)重w,能夠最大程度的區(qū)分違約與非違約客戶的信用得分Sj,改變了現(xiàn)有研究中計(jì)算指標(biāo)權(quán)重的過程主觀性較強(qiáng)、無法讓評價模型達(dá)到最強(qiáng)的違約狀態(tài)鑒別能力的弊端。(3)指標(biāo)遴選方面的工作及創(chuàng)新：通過構(gòu)建評級指標(biāo)與違約狀態(tài)之間的邏輯回歸模型,求解每個指標(biāo)判別違約狀態(tài)的顯著性水平,遴選其中對違約狀態(tài)影響顯著的指標(biāo),彌補(bǔ)現(xiàn)有小企業(yè)信用評級指標(biāo)體系沒有根據(jù)違約狀態(tài)遴選指標(biāo)、無法反映指標(biāo)對違約狀態(tài)影響大小的不足。以違約狀態(tài)yi為因變量,以評價指標(biāo)xij為自變量構(gòu)建邏輯回歸模型,求解每個指標(biāo)對違約狀態(tài)判別的顯著性水平,即W統(tǒng)計(jì)量檢驗(yàn)概率值sigj。將概率值sigj與預(yù)先給定的顯著水平α進(jìn)行對比,若sigj≤α,表明第j個評價指標(biāo)xij對小企業(yè)的違約狀況顯著影響,該指標(biāo)應(yīng)予以保留；反之,若sigjα,則表示第j個指標(biāo)xij對小企業(yè)違約狀況顯著不影響,可以被剔除。保證了篩選后保留的指標(biāo)能顯著區(qū)分小企業(yè)的違約狀態(tài),彌補(bǔ)現(xiàn)有小企業(yè)信用評級指標(biāo)體系沒有根據(jù)違約狀態(tài)遴選指標(biāo)、無法反映指標(biāo)對違約狀態(tài)影響大小的不足。
[Abstract]:Small enterprises provide more than 80% of urban jobs, creating a 52.2% tax and 58.5% of GDP, the demand for loans were also higher than large and medium-sized enterprises of 5.9 and 4.3 percentage points. Due to the small enterprises financial information is not transparent, the system is not practical regulation, the bank is difficult to grasp the real development the status, cause banks' reluctance to small business financing and other reasons, commercial banks lack a specific small business loan credit rating system is one of the key reasons, the credit rating system so it is necessary to construct a set of suitable for small enterprises. The essence of credit rating and default rating data mining is the relationship of risk between, revealing a customer or a debt default risk, assess the likelihood of repayment and default loss rate. The credit rating of the default risk identification The strength of the force is directly related to the stability of financial markets.2008 the subprime mortgage crisis is the origin of the risk of default false recognition, after Moodie, the authority of the "black box" rating process has been questioned, as can be seen from the heavy financial crisis, credit rating index selection, index weight, credit rating to determine the key link to identify the risk of default as the standard, otherwise no matter how popular, the credit rating system of authority are not reasonable. So build a credit rating system can effectively identify the risk of default is very important. The research on credit rating of small enterprises based on nonlinear interpolation, the research content mainly includes the following three parts: the construction of the credit rating index system small enterprises, establish the credit scoring model for small businesses, and to establish a credit rating model for small businesses. Among them, the credit rating system for small enterprises Construction refers to the ability to identify the default state according to the index is bigger, more should be reserved for the construction of a set of ideas, which can reflect the enterprise customers the ability to repay, and can significantly distinguish default credit evaluation index system. The small enterprise credit evaluation model is based on breach of contract and breach of the sample distance as the objective function, anti the launch weight optimal credit evaluation equation, so as to establish a credit scoring model, and then draw the small business credit score. To establish a credit rating model of small enterprises is "weighted translation" of old data by nonlinear interpolation method, and the adoption of new, another set of index system of "statistical law of the whole sample of old mining the same rating results, determine the small enterprise credit rating. This paper consists of five chapters. The first chapter is the introduction, the research background and significance are introduced, and the domestic and foreign. Research carried out; the second chapter is the construction of logic regression discriminant small enterprise credit rating index system based on; the third chapter is based on the "default and non default sample from" the biggest credit scoring model construction; the fourth chapter is the research on the model of credit rating credit score based on the nonlinear interpolation; the fifth chapter is the conclusion and prospect. The main work and innovation are as follows: (1) the work and innovation of credit ratings: the old sample index data of "weighted translation" to construct a credit rating model, guaranteed when using and selection or dig out the statistics law old samples a set of index system index system the new sample rating as like as two peas, and also can get through the new, another set of index system of "statistical law of the whole sample old mined the same rating results. Weighted by the translation of the old data, construct the nonlinear interpolation model of credit rating, in the sample increases without re "index selection, index weight, rating equation and other complicated process, only need to index data of the new sample directly into the rating equation, we can get through the new, and" another set of index system of "statistical law old total samples excavated consistent rating results, the rating index system unchanged, in fact, the index system of any Rating firm are unchanged in quite a long period of time, rather than the frequent change of rating index system and make up the existing research. In the past the sample directly according to the mining index system to identify new customer credit rating, ignore to one or more of the sample after sample statistics has changed, the old mining refers to the sample Standard system is not suitable to determine the new sample rating results. (2) determine the work and innovation aspects: according to the "default and non default customer customer credit score between the groups is bigger, in the group average distance is small, the multi-objective programming model to construct the evaluation equation of differential default stronger" the idea of solving the optimal weight coefficient, ensure that the evaluation score after weighting the greatest discriminating power. By default default and non default credit score of 0 groups within the group average distance is, the smaller the distance, the evaluation score of Sj between default state more ideas, set the default non default two the ratio of the average distance between the sample group and the distance within the group as the objective function, the maximum value and the minimum value of the weighted single constraint conditions of linear programming model. The objective function is a function of the credit score of Sj However, the credit score Sj is a function of the weight of the WI, so the objective function is the weight of about W, the function is through the optimal solution can be obtained by the optimal target planning evaluation index weight solution of wi. To ensure that the w index weights, can distinguish the maximum default and non default client's credit score Sj the process of change, the subjective index weight calculation of strong, can let the evaluation model get the strongest default ability to identify defects. (3) the index selection work and Innovation: by and against the construction of evaluation index about the state of the logic regression model, the significant level of each index for default judgment. The selection of the indicators of the impact of default status significantly, make up the credit rating index system according to the existing small businesses do not have the default selection index can not reflect the index of the default state. The size of the ring. The dependent variable Yi as the default state, the evaluation index Xij as independent variables to construct a logistic regression model, for each index of the default level state identification, namely W statistic probability probability value sigj and value sigj. will be given a significant contrast, if sigj is less than or equal to alpha, that article the j index of Xij significant effect on the default status of small enterprises, the index should be retained; on the other hand, if the sigj alpha, said the j Xij index for small businesses do not affect significantly the default status, can be removed. The screen is retained after the index can significantly distinguish the default state of small enterprises, make up for the credit rating index system according to the existing small businesses do not have the default selection index can not reflect the index of the size of the state. The default problem

【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】：F276.3;F832.4

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