基于SVM的我國商業(yè)銀行信用風險管理模型研究
發(fā)布時間：2018-10-11 09:57
【摘要】：信用在市場經濟條件下具有非常重要的作用,信用是市場經濟運行的前提和基礎,它幫助資金和其他生產要素在經濟體系內部流動,是整個經濟的潤滑劑。商業(yè)銀行作為我國金融體系的重要組成部分,面臨著各種風險,而信用風險是我國商業(yè)銀行中最主要的風險之一。在經濟全球化的背景下,行業(yè)內的競爭日益激烈,因此提高我國商業(yè)銀行的信用風險管理能力至關重要。但是,由于信用風險不確定性及違約數(shù)據(jù)難獲得的特點,我國長期以來對信用風險的分析停留在傳統(tǒng)的歷史財務比率分析和信用分析上,因此,找到一個準確度量、控制并管理信用風險是當今金融業(yè)的一個重點和挑戰(zhàn)。 本文首先介紹了信用風險和信用風險管理的概念、研究背景和發(fā)展歷程,然后介紹了目前信用風險管理的幾種方法,并對其優(yōu)缺點進行簡單分析。第二部分重點介紹了支持向量機方法,介紹了理論基礎和SVM方法的應用。第三部分,本文對模型進行了一些改進,從模型樣本數(shù)據(jù)變量的選擇、最優(yōu)參數(shù)尋優(yōu)等方面進行了改進,提高了模型的預測正確率。最后,通過某商業(yè)銀行企業(yè)客戶數(shù)據(jù)的測試表明,改進的支持向量機方法對于信用風險違約情況的預測正確率要高于傳統(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.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.33;TP18
本文編號：2263776
[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.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.33;TP18
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