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  本文選題：德爾菲法　切入點：粗糙集理論　出處：《華南理工大學》2015年碩士論文

【摘要】：作為擁有巨額資本、海量客戶的銀行業(yè)來說,為社會上主要資金的流通起著重要作用,是現代金融體系中重要的組成部分之一。對我國銀行現狀來說,貸款業(yè)務是其最重要的業(yè)務之一,銀行的利潤大部分來自貸款業(yè)務。近幾年在政策大力支持和市場巨大需求下銀行信貸投放量不斷上升,信貸風險也不斷攀高。實際上,對信貸風險處理如稍有不慎,便有可能造成銀行經營危機甚至破產,多米諾骨牌效應的影響下信貸風險可能越演越烈,從而造成整個金融體系的癱瘓。因此,在貸款過程中如何加強對信貸風險進行管控不僅成為競爭日趨激烈下銀行經營管理的重要組成部分,而且也關系到我國整個金融市場的發(fā)展。信貸風險評估屬于銀行信貸風險管控體系中的一部分,如果信貸風險不能得到科學合理的評估,那么就談不上隨之的風險應對、監(jiān)控等。通常銀行在過去實踐當中,對信貸風險評估過多的依據借款人的財務因素,而忽視了非財務因素的影響。本文首先在前人研究的基礎上歸納了信貸風險評估指標,通過德爾菲法對初選指標進行統(tǒng)計分析,從而構建了較為完善的指標體系,為后面的評估研究作了良好的鋪墊。接下來運用粗糙集理論建立了信貸風險評估模型,在分析比較每個步驟通用方法的基礎上選用了適合信貸風險評估的方法,針對屬性約簡,介紹了常用的約簡算法,鑒于信貸風險評估對象數據量較大且指標較多的情況,引入了遺傳算法進行屬性約簡,在結合粗糙集理論基礎上利用改進的遺傳算法進行研究,通過實例分析結果顯示改進啟發(fā)式遺傳算法能夠加快收斂速度并且相對于一般屬性約簡遺傳算法能夠節(jié)省運行時間,能夠較好地應用于信貸風險評估時的屬性約簡。最后本文選取了我國滬深市場的上市公司數據進行實證分析,實證結果顯示基于粗糙集理論的信貸風險評估準確度較高,能夠在一定程度上為銀行信貸人員在進行信貸決策過程中提供參考。
[Abstract]:As a banking industry with huge capital and huge customers, it plays an important role in the circulation of major funds in the society and is one of the important parts of the modern financial system. Loan business is one of its most important business, and the profits of banks come mostly from loan business. In recent years, under the strong policy support and the huge market demand, the amount of bank credit has been increasing, and the credit risk is also rising. In fact, If there is a slight carelessness in the handling of credit risk, it may result in a banking crisis or even bankruptcy. Under the influence of the domino effect, the credit risk may become more and more severe, resulting in the paralysis of the entire financial system. How to strengthen the credit risk control in the process of loan is not only an important part of the bank management under the increasingly fierce competition. It is also related to the development of the whole financial market in China. Credit risk assessment is a part of the credit risk control system of banks. If the credit risk cannot be scientifically and reasonably assessed, then there is no corresponding risk response. In the past, banks usually overevaluate the credit risk based on the borrower's financial factors, but ignore the influence of non-financial factors. Firstly, this paper summarizes the credit risk assessment indicators based on the previous studies. Through the statistical analysis of the primary index by Delphi method, a perfect index system is constructed, which makes a good foreshadowing for the later evaluation research. Then, the credit risk assessment model is established by using rough set theory. On the basis of analyzing and comparing the general methods of each step, the suitable method for credit risk assessment is selected. Aiming at attribute reduction, the commonly used reduction algorithms are introduced. In view of the large amount of data and the large index of credit risk assessment object, In this paper, genetic algorithm is introduced to reduce attributes. Based on rough set theory, improved genetic algorithm is used to study. The results of example analysis show that the improved heuristic genetic algorithm can accelerate the convergence speed and save the running time compared with the general attribute reduction genetic algorithm. Finally, this paper selects the data of listed companies in Shanghai and Shenzhen market for empirical analysis. The empirical results show that the accuracy of credit risk assessment based on rough set theory is high. To a certain extent, can provide a reference for bank credit personnel in the credit decision-making process.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F832.4

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本文編號：1667994