本文選題：層次分析法 + BP神經(jīng)網(wǎng)絡(luò)　； 參考：《延邊大學(xué)》2017年碩士論文

【摘要】：隨著我國信用風險研究的發(fā)展,人類在享受成果的同時,綠色信貸風險問題也日漸突出。本文依托大數(shù)據(jù)分析理論對綠色信貸這一熱點問題,結(jié)合所學(xué)知識,運用統(tǒng)計分析中的層次聚類分析、主成分分析等特征工程方法進行研究,同時利用機器學(xué)習(xí)中的監(jiān)督學(xué)習(xí)式BP神經(jīng)網(wǎng)絡(luò)模型進行仿真分析,進而得到更加科學(xué)的結(jié)果。銀行機構(gòu)在企業(yè)貸款中最重要的風險之一就是信貸風險,實行風險防控對機構(gòu)的長遠發(fā)展有至關(guān)重要的作用。本文所闡述的綠色信貸是指銀行等金融機構(gòu)在進行企業(yè)信貸時結(jié)合生態(tài)保護建設(shè)與開發(fā)等方面,在原有信用風險基礎(chǔ)之上,重新評定企業(yè)的信貸能力�？v觀文獻可了解到國內(nèi)外諸多學(xué)者對信用風險有著大量研究,但針對綠色信貸這一剛剛興起的熱點問題,還處在探索階段,發(fā)展還不成熟。本文探索性構(gòu)建了兩個數(shù)據(jù)挖掘模型,分別是基于AHP的綠色環(huán)保評級模型以及基于PCA的BP神經(jīng)網(wǎng)路綠色信貸信用評估風險模型。首先,針對綠色信貸在我國處于起步階段,缺乏相關(guān)數(shù)據(jù),本文查閱相關(guān)文獻并進行研究,結(jié)合銀行現(xiàn)有信用風險體系,利用AHP構(gòu)建綠色信貸信用風險評估指標層次體系。接著,通過東北證券同花順提取2000多家上市公司基礎(chǔ)數(shù)據(jù),選取企業(yè)類型為綜合型企業(yè)作為樣本數(shù)據(jù),應(yīng)用系統(tǒng)聚類分析針對原有風險評價研究的10個指標進行分析,剔除高危風險企業(yè)。針對篩選后的企業(yè)增加4個綠色風險指標,以經(jīng)濟以及綠色風險指標作為一級指標,下設(shè)14個二級指標,其中10個為基礎(chǔ)財務(wù)指標,4個為層次分析法準則層指標,利用主成分分析方法來構(gòu)建模型與處理數(shù)據(jù),通過PCA對14個指標進行處理,得出6個公共因子。最后利用BP神經(jīng)網(wǎng)絡(luò)進行仿真分析(matlab2012a),驗證其可行性。以定量與定性分析的方式為綠色信貸風險評估的方法選擇與運用提供參考。
[Abstract]:With the development of credit risk research in China, the problem of green credit risk is becoming more and more prominent. Based on the theory of big data analysis, this paper studies the hot issue of green credit, combining with the knowledge learned, using hierarchical cluster analysis, principal component analysis and other characteristic engineering methods in statistical analysis. At the same time, the supervised learning BP neural network model in machine learning is used for simulation and analysis, and more scientific results are obtained. The credit risk is one of the most important risks in the enterprise loan. The risk prevention and control plays an important role in the long-term development of the institution. The green credit described in this paper means that banks and other financial institutions reassess the credit ability of enterprises on the basis of the original credit risk in combination with the ecological protection construction and development of enterprise credit. Throughout the literature, we can find that many scholars at home and abroad have a lot of research on credit risk, but the green credit is still in the exploration stage, and the development is not mature. In this paper, two data mining models are constructed, one is based on AHP and the other is the risk model of credit assessment based on BP neural network based on PCA. First of all, in view of the green credit in our country is in the initial stage, the lack of relevant data, this paper refer to the relevant literature and research, combined with the existing credit risk system of banks, using AHP to construct the evaluation index system of green credit risk. Then, the basic data of more than 2000 listed companies are extracted through Tonghuashun of Northeast Securities, and the comprehensive enterprise is selected as the sample data. The system cluster analysis is applied to analyze the 10 indexes of the original risk evaluation research. Eliminate high-risk enterprises. In view of the increase of 4 green risk indicators in the selected enterprises, taking the economic and green risk indicators as the first class index, there are 14 secondary indicators, of which 10 are the basic financial indicators, 4 are the Analytic hierarchy process (AHP) criteria. The principal component analysis (PCA) method was used to construct the model and process the data. The 14 indexes were processed by PCA and 6 common factors were obtained. Finally, BP neural network is used for simulation analysis (matlab2012a) to verify its feasibility. It provides a reference for the selection and application of green credit risk assessment methods by quantitative and qualitative analysis.
【學(xué)位授予單位】：延邊大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O211.67

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