【摘要】：信用在市場(chǎng)經(jīng)濟(jì)條件下具有非常重要的作用,信用是市場(chǎng)經(jīng)濟(jì)運(yùn)行的前提和基礎(chǔ),它幫助資金和其他生產(chǎn)要素在經(jīng)濟(jì)體系內(nèi)部流動(dòng),是整個(gè)經(jīng)濟(jì)的潤(rùn)滑劑。商業(yè)銀行作為我國(guó)金融體系的重要組成部分,面臨著各種風(fēng)險(xiǎn),而信用風(fēng)險(xiǎn)是我國(guó)商業(yè)銀行中最主要的風(fēng)險(xiǎn)之一。在經(jīng)濟(jì)全球化的背景下,行業(yè)內(nèi)的競(jìng)爭(zhēng)日益激烈,因此提高我國(guó)商業(yè)銀行的信用風(fēng)險(xiǎn)管理能力至關(guān)重要。但是,由于信用風(fēng)險(xiǎn)不確定性及違約數(shù)據(jù)難獲得的特點(diǎn),我國(guó)長(zhǎng)期以來(lái)對(duì)信用風(fēng)險(xiǎn)的分析停留在傳統(tǒng)的歷史財(cái)務(wù)比率分析和信用分析上,因此,找到一個(gè)準(zhǔn)確度量、控制并管理信用風(fēng)險(xiǎn)是當(dāng)今金融業(yè)的一個(gè)重點(diǎn)和挑戰(zhàn)。 本文首先介紹了信用風(fēng)險(xiǎn)和信用風(fēng)險(xiǎn)管理的概念、研究背景和發(fā)展歷程,然后介紹了目前信用風(fēng)險(xiǎn)管理的幾種方法,并對(duì)其優(yōu)缺點(diǎn)進(jìn)行簡(jiǎn)單分析。第二部分重點(diǎn)介紹了支持向量機(jī)方法,介紹了理論基礎(chǔ)和SVM方法的應(yīng)用。第三部分,本文對(duì)模型進(jìn)行了一些改進(jìn),從模型樣本數(shù)據(jù)變量的選擇、最優(yōu)參數(shù)尋優(yōu)等方面進(jìn)行了改進(jìn),提高了模型的預(yù)測(cè)正確率。最后,通過(guò)某商業(yè)銀行企業(yè)客戶數(shù)據(jù)的測(cè)試表明,改進(jìn)的支持向量機(jī)方法對(duì)于信用風(fēng)險(xiǎn)違約情況的預(yù)測(cè)正確率要高于傳統(tǒng)的SVM算法。
[Abstract]:Credit plays a very important role in the market economy. Credit is the premise and foundation of the market economy. It helps the capital and other factors of production to flow through the economic system and is the lubricant of the whole economy. As an important part of our country's financial system, commercial banks are faced with various risks, and credit risk is one of the most important risks in our country's commercial banks. Under the background of economic globalization, the competition in the industry is becoming increasingly fierce, so it is very important to improve the credit risk management ability of commercial banks in China. However, because of the uncertainty of credit risk and the difficulty of obtaining default data, the analysis of credit risk in our country for a long time has stayed in the traditional historical financial ratio analysis and credit analysis. Controlling and managing credit risk is a key point and challenge of financial industry today. This paper first introduces the concept of credit risk and credit risk management, research background and development process, then introduces several methods of credit risk management, and analyzes their advantages and disadvantages. In the second part, the support vector machine (SVM) method is introduced, the theoretical basis and the application of SVM method are introduced. In the third part, some improvements are made to the model, such as the selection of sample data variables, the optimization of optimal parameters, and so on, so as to improve the prediction accuracy of the model. Finally, a commercial bank enterprise customer data test shows that the improved support vector machine method for credit risk default prediction accuracy is higher than the traditional SVM algorithm.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.33;TP18

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本文編號(hào)：2263776