發(fā)布時間：2018-04-02 10:08

  本文選題：農(nóng)戶信用評分　切入點(diǎn)：農(nóng)戶信用風(fēng)險　出處：《湖南大學(xué)》2012年碩士論文

【摘要】：本文的研究目的就是通過對申貸農(nóng)戶的各項指標(biāo)的分析，找到能夠顯著區(qū)分信用等級較高的農(nóng)戶(好客戶)和信用等級較低的農(nóng)戶(壞客戶)的指標(biāo)，通過構(gòu)建模型計算農(nóng)戶為好客戶的概率，進(jìn)而得到信用得分，最終得到農(nóng)戶信用等級評判標(biāo)準(zhǔn)，為有關(guān)各方提供決策依據(jù)和參考意見。 本文首先給出了目前湖南省農(nóng)信社普遍采用的農(nóng)戶信用評級方法，然后對當(dāng)前評定過程中存在的問題進(jìn)行了分析，在借鑒國內(nèi)外研究成果的基礎(chǔ)上，通過比較信用評價的各種方法，同時結(jié)合我國農(nóng)戶的信用特征，最終選擇基于BP神經(jīng)網(wǎng)絡(luò)和Logistic回歸兩種方法，并且綜合考慮兩種評級方法的優(yōu)缺點(diǎn)，構(gòu)建基于兩種方法的混合模型，提高預(yù)測精度和穩(wěn)定性。 接下來，在分析農(nóng)戶信用風(fēng)險的產(chǎn)生及其風(fēng)險獨(dú)特性的基礎(chǔ)上，得出了我國農(nóng)戶信用特征是個人和中小企業(yè)的結(jié)合體。因此，在指標(biāo)體系的構(gòu)建上可以參考國內(nèi)外的關(guān)于個人和中小企業(yè)的信用評價指標(biāo)體系。以我國湖南部分地區(qū)申請貸款的農(nóng)戶為研究對象，分別從農(nóng)戶戶主及家庭成員情況、資產(chǎn)情況、負(fù)債情況、經(jīng)營情況、家庭開支五個方面構(gòu)建初始評價指標(biāo)，總共選取22個指標(biāo)，借助SPSS統(tǒng)計分析軟件，利用因子分析法進(jìn)行分析提取了12個主要指標(biāo)，總共選取了646戶農(nóng)戶家庭作為樣本，在此基礎(chǔ)上分別比較基于BP神經(jīng)網(wǎng)絡(luò)的農(nóng)戶信用評價模型和基于Logistic回歸的農(nóng)戶信用評價模型的應(yīng)用效果，然后構(gòu)建基于二者的組合模型，實證結(jié)果顯示：該模型的總體準(zhǔn)確率為97.1%，其中將好客戶判斷為好客戶的準(zhǔn)確率為98.8%，將壞客戶判斷為壞客戶的準(zhǔn)確率為81.8%，可解釋性及穩(wěn)健性都是比較理想的�？梢�，，此模型取得了較好的預(yù)測效果，具有一定的應(yīng)用價值。
[Abstract]:The purpose of this paper is to find out the index that can distinguish the high credit grade farmer (good customer) and the low credit grade farmer (bad customer) through the analysis of the indexes of the farmers applying for loans. The probability of farmers being a good customer is calculated by constructing a model, and then the credit score is obtained. Finally, the evaluation standard of the farmer's credit grade is obtained, and the decision basis and reference advice are provided for the parties concerned. At first, this paper gives the credit rating method of farmers, which is widely used in Hunan Rural Credit Cooperative, then analyzes the problems in the current evaluation process, and draws lessons from the domestic and foreign research results. By comparing various methods of credit evaluation and combining the credit characteristics of farmers in China, two methods based on BP neural network and Logistic regression are selected, and the advantages and disadvantages of the two rating methods are considered synthetically. A hybrid model based on two methods is constructed to improve prediction accuracy and stability. Then, on the basis of analyzing the emergence and uniqueness of peasant household credit risk, it is concluded that the characteristics of peasant household credit in China are the combination of individuals and small and medium-sized enterprises. In the construction of the index system, we can refer to the credit evaluation index system of individuals and small and medium-sized enterprises at home and abroad. This paper constructs the initial evaluation index in five aspects of assets, liabilities, operation and household expenditure. In total, 22 indexes are selected, and 12 main indexes are extracted by factor analysis with the help of SPSS statistical analysis software. A total of 646 households were selected as samples, and then the application effects of the model based on BP neural network and the model based on Logistic regression were compared, and then the combination model based on the two models was constructed. The results show that the overall accuracy of this model is 97.1g, in which the accuracy of judging good customers as good customers is 98.8, and that of bad customers is 81.8. This model has achieved good prediction effect and has certain application value.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F832.43;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張玲,楊貞柿;信用風(fēng)險度量方法綜述[J];財經(jīng)科學(xué);2004年S1期

2 王樹娟,霍學(xué)喜,何學(xué)松;農(nóng)村信用社農(nóng)戶信用綜合評價模型[J];財貿(mào)研究;2005年05期

3 郭N

本文編號：1699897