【摘要】：從2004年起，中共中央連續(xù)多年發(fā)布關于農(nóng)業(yè)、農(nóng)村問題的1號文件，表明黨和國家解決“三農(nóng)”問題的決心。金融機構在農(nóng)村金融市場的投入偏低，農(nóng)戶開展農(nóng)業(yè)產(chǎn)業(yè)化、現(xiàn)代化資金不足，是制約農(nóng)村建設、農(nóng)業(yè)發(fā)展、農(nóng)民增收的突出問題。要解決這一突出問題，主要依靠農(nóng)村金融的資金支持。然而，目前農(nóng)村金融市場面臨著許多突出問題：農(nóng)戶信用體系尚未建立起來，農(nóng)戶在信貸業(yè)務中由于擁有較多的信息而處于有利地位，導致農(nóng)戶道德風險和逆向選擇行為時有發(fā)生，金融機構開展農(nóng)戶信貸業(yè)務面臨較大的信用風險，不良貸款率居高不下，金融機構不愿進入農(nóng)村金融市場造成市場上供給主體偏低。因此，識別和管理農(nóng)戶信用風險就有十分重要的意義。但是目前我國金融機構對農(nóng)戶信用風險的度量和評價仍處于主觀性很強的古典信用定性的分析階段，主要依靠信貸員的工作經(jīng)驗，金融機構對農(nóng)戶信貸業(yè)務缺乏有效的信用風險控制手段。針對上述問題，本文以農(nóng)戶正規(guī)融資的信用風險為研究對象，，探索適合度量我國農(nóng)戶信用風險的模型或方法，降低農(nóng)戶違約風險，提高金融機構進入農(nóng)村市場、開展以農(nóng)戶為服務對象信貸業(yè)務的積極性。 本文首先通過回顧信用風險度量模型的發(fā)展歷程，重點介紹了四個信用風險度量模型，為度量農(nóng)戶正規(guī)融資信用風險度量提供了可以選擇的模型類型。其次對農(nóng)戶正規(guī)融資信用風險度量的起因進行了分析，農(nóng)戶有著旺盛且多元化的融資需求，但是金融機構開展農(nóng)戶信貸業(yè)務的運營成本較高，對農(nóng)戶信貸風險缺乏有效的控制手段，造成針對農(nóng)戶開展的信貸業(yè)務供給偏低，無法滿足農(nóng)戶日益增長的資金需求。再次，介紹了農(nóng)戶的信用風險有別于普通貸款的信用風險較高的原因，對農(nóng)戶正規(guī)融資信用風險的獨特性進行了具體的分析，指出我國農(nóng)戶信用風險度量現(xiàn)階段最可行的方法是多元統(tǒng)計分析方法。然后，在實地調(diào)研數(shù)據(jù)的基礎上對農(nóng)戶正規(guī)融資信用風險度量進行實證分析：將影響農(nóng)戶信用風險的指標設計為家庭人口特征、家庭財富擁有量、借貸因素三類25個指標，將這些指標分別輸入到判別分析模型和Logistic回歸分析模型中，得出如下結論：判別分析模型更傾向于選擇逐步判別分析模型，根據(jù)自變量對識別農(nóng)戶信用風險貢獻的大小，有欠款總額、土地質量、貸款年利率、過去12個月的農(nóng)業(yè)總支出、耕地面積、外出務工人數(shù)、農(nóng)業(yè)勞動力人數(shù)、資產(chǎn)價值、家庭規(guī)模、是否是小組擔保、農(nóng)戶聯(lián)保的成員、65歲以上老人數(shù)、是否是信用社成員、存款比例、過去12個月的消費總支出、12歲以下兒童人數(shù)、戶主受教育程度、信譽評價、過去12個月的外出務工收入18個變量依次進入模型，模型對農(nóng)戶正規(guī)融資信用風險的判斷的準確率為88.5%。Logistic回歸分析模型傾向于選擇使用向后逐步法，當Logistic回歸分析到第12步時對農(nóng)戶正規(guī)融資信用風險綜合識別的正確率為84.3%。通過實證分析可以看出，逐步判別分析模型在對農(nóng)戶信用風險識別和評價的準確率上高于Logistic回歸分析模型，逐步判別分析模型能夠成為金融機構控制農(nóng)戶信用風險的有效手段。最后，為了緩解農(nóng)村金融市場上的供需矛盾，為了使逐步判別分析模型作為農(nóng)戶正規(guī)融資信用風險控制的有效手段得以推廣，給出相應的政策建議。
[Abstract]:Since 2004, the Central Committee of the Communist Party of China has issued the No.1 document on agriculture and rural issues for many years, which shows the determination of the Party and the state to solve the "three rural" problem. However, the rural financial market is facing many outstanding problems: the peasant household credit system has not been established, the peasant household is in a favorable position because of having more information in the credit business, leading to the peasant household moral hazard and adverse selection behavior occurring from time to time. Financial institutions are facing great credit risks in developing peasant households'credit business, the rate of non-performing loans is high, and the reluctance of financial institutions to enter the rural financial market results in the low supply subject. Therefore, it is of great significance to identify and manage peasant households' credit risks. The evaluation is still in the stage of classical credit qualitative analysis with strong subjectivity, mainly relying on the work experience of the creditors, and the financial institutions lack effective means to control the credit risk of farmers'credit business. The model or method can reduce the default risk of peasant households, improve the enthusiasm of financial institutions to enter the rural market and develop credit business for peasant households.
Firstly, by reviewing the development of credit risk measurement model, this paper introduces four credit risk measurement models, which provide alternative models for measuring the credit risk of farmers'formal financing. However, the operation cost of farmers'credit business in financial institutions is high, and the credit risk of farmers is lack of effective control means. As a result, the supply of credit business for farmers is too low to meet the growing demand of farmers. Thirdly, the paper introduces the credit risk of farmers is different from that of ordinary loans. This paper analyzes the uniqueness of farmers'credit risk in formal financing and points out that the most feasible method to measure farmers' credit risk is multivariate statistical analysis. The risk indicators are designed as 25 indicators of household demographic characteristics, household wealth ownership, and lending factors. These indicators are input into the discriminant analysis model and the logistic regression analysis model respectively. The conclusion is that the discriminant analysis model is more inclined to choose the stepwise discriminant analysis model and identify the credit risk of farmers according to the independent variable pairs. Contribution size, total amount of arrears, land quality, annual interest rate of loans, total agricultural expenditure over the past 12 months, arable land area, number of migrant workers, number of agricultural labor force, asset value, family size, whether it is group guarantee, members of farmers'joint insurance, number of people over 65 years old, whether it is a member of credit cooperatives, deposit ratio, the past 12 months of consumption The total expenditure, the number of children under 12 years old, the educational level of the household head, and the credit rating of migrant workers in the past 12 months entered the model in turn. The accuracy of the model was 88.5%. Logistic regression analysis model tended to use backward stepwise method when Logistic regression analysis reached 1. Through the empirical analysis, it can be seen that the accuracy of the stepwise discriminant analysis model is higher than that of the logistic regression analysis model in identifying and evaluating farmers'credit risk. The stepwise discriminant analysis model can be an effective tool for financial institutions to control farmers' credit risk. Finally, in order to alleviate the contradiction between supply and demand in rural financial market, the corresponding policy suggestions are put forward in order to popularize the stepwise discriminant analysis model as an effective means to control the credit risk of farmers'formal financing.
【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.35;F224

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