【摘要】：從2004年起，中共中央連續(xù)多年發(fā)布關(guān)于農(nóng)業(yè)、農(nóng)村問(wèn)題的1號(hào)文件，表明黨和國(guó)家解決“三農(nóng)”問(wèn)題的決心。金融機(jī)構(gòu)在農(nóng)村金融市場(chǎng)的投入偏低，農(nóng)戶開(kāi)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(shè)、農(nóng)業(yè)發(fā)展、農(nóng)民增收的突出問(wèn)題。要解決這一突出問(wèn)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(chǎng)面臨著許多突出問(wèn)題：農(nóng)戶信用體系尚未建立起來(lái)，農(nóng)戶在信貸業(yè)務(wù)中由于擁有較多的信息而處于有利地位，導(dǎo)致農(nóng)戶道德風(fēng)險(xiǎn)和逆向選擇行為時(shí)有發(fā)生，金融機(jī)構(gòu)開(kāi)展農(nóng)戶信貸業(yè)務(wù)面臨較大的信用風(fēng)險(xiǎn)，不良貸款率居高不下，金融機(jī)構(gòu)不愿進(jìn)入農(nóng)村金融市場(chǎng)造成市場(chǎng)上供給主體偏低。因此，識(shí)別和管理農(nóng)戶信用風(fēng)險(xiǎn)就有十分重要的意義。但是目前我國(guó)金融機(jī)構(gòu)對(duì)農(nóng)戶信用風(fēng)險(xiǎn)的度量和評(píng)價(jià)仍處于主觀性很強(qiáng)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(yàn)，金融機(jī)構(gòu)對(duì)農(nóng)戶信貸業(yè)務(wù)缺乏有效的信用風(fēng)險(xiǎn)控制手段。針對(duì)上述問(wèn)題，本文以農(nóng)戶正規(guī)融資的信用風(fēng)險(xiǎn)為研究對(duì)象，，探索適合度量我國(guó)農(nóng)戶信用風(fēng)險(xiǎn)的模型或方法，降低農(nóng)戶違約風(fēng)險(xiǎn)，提高金融機(jī)構(gòu)進(jìn)入農(nóng)村市場(chǎng)、開(kāi)展以農(nóng)戶為服務(wù)對(duì)象信貸業(yè)務(wù)的積極性。 本文首先通過(guò)回顧信用風(fēng)險(xiǎn)度量模型的發(fā)展歷程，重點(diǎn)介紹了四個(gè)信用風(fēng)險(xiǎn)度量模型，為度量農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)度量提供了可以選擇的模型類(lèi)型。其次對(duì)農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)度量的起因進(jìn)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(jī)構(gòu)開(kāi)展農(nóng)戶信貸業(yè)務(wù)的運(yùn)營(yíng)成本較高，對(duì)農(nóng)戶信貸風(fēng)險(xiǎn)缺乏有效的控制手段，造成針對(duì)農(nóng)戶開(kāi)展的信貸業(yè)務(wù)供給偏低，無(wú)法滿足農(nóng)戶日益增長(zhǎng)的資金需求。再次，介紹了農(nóng)戶的信用風(fēng)險(xiǎn)有別于普通貸款的信用風(fēng)險(xiǎn)較高的原因，對(duì)農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)的獨(dú)特性進(jìn)行了具體的分析，指出我國(guó)農(nóng)戶信用風(fēng)險(xiǎn)度量現(xiàn)階段最可行的方法是多元統(tǒng)計(jì)分析方法。然后，在實(shí)地調(diào)研數(shù)據(jù)的基礎(chǔ)上對(duì)農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)度量進(jìn)行實(shí)證分析：將影響農(nóng)戶信用風(fēng)險(xiǎn)的指標(biāo)設(shè)計(jì)為家庭人口特征、家庭財(cái)富擁有量、借貸因素三類(lèi)25個(gè)指標(biāo)，將這些指標(biāo)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(jié)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(duì)識(shí)別農(nóng)戶信用風(fēng)險(xiǎn)貢獻(xiàn)的大小，有欠款總額、土地質(zhì)量、貸款年利率、過(guò)去12個(gè)月的農(nóng)業(yè)總支出、耕地面積、外出務(wù)工人數(shù)、農(nóng)業(yè)勞動(dòng)力人數(shù)、資產(chǎn)價(jià)值、家庭規(guī)模、是否是小組擔(dān)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(guò)去12個(gè)月的消費(fèi)總支出、12歲以下兒童人數(shù)、戶主受教育程度、信譽(yù)評(píng)價(jià)、過(guò)去12個(gè)月的外出務(wù)工收入18個(gè)變量依次進(jìn)入模型，模型對(duì)農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)的判斷的準(zhǔn)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(dāng)Logistic回歸分析到第12步時(shí)對(duì)農(nóng)戶正規(guī)融資信用風(fēng)險(xiǎn)綜合識(shí)別的正確率為84.3%。通過(guò)實(shí)證分析可以看出，逐步判別分析模型在對(duì)農(nóng)戶信用風(fēng)險(xiǎn)識(shí)別和評(píng)價(jià)的準(zhǔn)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(jī)構(gòu)控制農(nóng)戶信用風(fēng)險(xiǎn)的有效手段。最后，為了緩解農(nóng)村金融市場(chǎng)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(fēng)險(xiǎn)控制的有效手段得以推廣，給出相應(yīng)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(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F832.35;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張德棟,張強(qiáng);基于神經(jīng)網(wǎng)絡(luò)的企業(yè)信用評(píng)估模型[J];北京理工大學(xué)學(xué)報(bào);2004年11期

2 李俊麗;;基于層次分析法的農(nóng)戶信用評(píng)估[J];商業(yè)研究;2009年10期

3 李正波;高杰;;農(nóng)戶信用社貸款的信用風(fēng)險(xiǎn)判別分析[J];山東工商學(xué)院學(xué)報(bào);2007年01期

4 秦建國(guó);呂忠偉;秦建群;;我國(guó)西部地區(qū)農(nóng)戶借貸行為影響因素的實(shí)證研究——基于804戶農(nóng)戶調(diào)查數(shù)據(jù)分析[J];財(cái)經(jīng)論叢;2011年03期

5 施錫銓,鄒新月;典型判別分析在企業(yè)信用風(fēng)險(xiǎn)評(píng)估中的應(yīng)用[J];財(cái)經(jīng)研究;2001年10期

6 張玲,曾維火;基于Z值模型的我國(guó)上市公司信用評(píng)級(jí)研究[J];財(cái)經(jīng)研究;2004年06期

7 胡愈;許紅蓮;王雄;;農(nóng)戶小額信用貸款信用評(píng)級(jí)探究[J];財(cái)經(jīng)理論與實(shí)踐;2007年01期

8 王樹(shù)娟,霍學(xué)喜,何學(xué)松;農(nóng)村信用社農(nóng)戶信用綜合評(píng)價(jià)模型[J];財(cái)貿(mào)研究;2005年05期

9 周成;;運(yùn)用CreditMetrics模型進(jìn)行銀行貸款信用風(fēng)險(xiǎn)管理[J];當(dāng)代經(jīng)濟(jì)(下半月);2007年09期

10 何樹(shù)紅;王善民;;基于變量雙重檢驗(yàn)的Fisher信用風(fēng)險(xiǎn)度量模型[J];系統(tǒng)工程;2007年08期

相關(guān)碩士學(xué)位論文 前2條

1 周陽(yáng);我國(guó)中小企業(yè)信用風(fēng)險(xiǎn)度量研究[D];浙江大學(xué);2006年

2 馬文勤;基于BP神經(jīng)網(wǎng)絡(luò)的農(nóng)戶小額信貸信用風(fēng)險(xiǎn)評(píng)估研究[D];西北農(nóng)林科技大學(xué);2010年

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