本文關(guān)鍵詞： P2P 風險識別 多項Logistic回歸模型 BP神經(jīng)網(wǎng)絡(luò)模型　出處：《吉林財經(jīng)大學》2017年碩士論文　論文類型：學位論文

【摘要】：自2007年P(guān)2P網(wǎng)絡(luò)借貸傳入中國以來,經(jīng)歷了短暫的時間便獲得了驚人的發(fā)展。P2P網(wǎng)絡(luò)借貸借助于互聯(lián)網(wǎng)經(jīng)濟的快速發(fā)展,運營平臺的數(shù)量呈現(xiàn)出井噴式增長。2007年至2015年之間,P2P網(wǎng)絡(luò)借貸行業(yè)運營平臺從開始的第一家到現(xiàn)在的第2595家,并且2015年單年增加了1020家,絕對的增加量遠遠超過過去任何一個時期。據(jù)統(tǒng)計,平臺平均每個月的成交額為492.6億元,并且一直處于擴張的狀態(tài)。但是,由于P2P網(wǎng)絡(luò)借貸平臺具有進入門檻低、缺乏行業(yè)標準、監(jiān)管不嚴格等特點,致使P2P網(wǎng)絡(luò)借貸平臺存在嚴重的信息不對稱問題,從而P2P平臺會出現(xiàn)我們經(jīng)常聽到的“跑路”等現(xiàn)象。據(jù)統(tǒng)計,截止到2015年6月,國內(nèi)出現(xiàn)信用問題的P2P網(wǎng)絡(luò)借貸平臺達到550家之多。目前,國外的金融市場給P2P網(wǎng)絡(luò)借貸行業(yè)提供了一個健康的發(fā)展環(huán)境,此外,全面的監(jiān)管機制也促使了國外P2P網(wǎng)絡(luò)借貸行業(yè)的發(fā)展。但是P2P網(wǎng)絡(luò)借貸公司的發(fā)展受到實際的一定限制,由于P2P屬于新興起的行業(yè),風險控制能力和傳統(tǒng)的銀行是無法相比的。一般而言,網(wǎng)絡(luò)借貸主要風險有以下兩種:基本風險和特定風險�；撅L險有法律風險、信用風險和監(jiān)管風險等;特定風險則包括投資風險、信息不對稱風險等。而信用風險危害最為嚴重,很多借貸平臺還是采用的傳統(tǒng)商業(yè)銀行的信用識別模型,并沒有根據(jù)網(wǎng)絡(luò)借貸的特點開發(fā)出更適合網(wǎng)絡(luò)借貸的信用風險識別模型。本文基于“拍拍貸”的數(shù)據(jù),結(jié)合國內(nèi)外現(xiàn)有的信用風險識別方法,建立了信用風險識別的指標體系。在SPSS和MATLAB操作軟件的基礎(chǔ)上,應(yīng)用多項Logistic回歸方法,以魔鏡等級“E”為參照等級,分別建立A-F等級的多項回歸方程,得到每個等級的顯著性影響因素。后續(xù)建立了神經(jīng)網(wǎng)絡(luò)模型,首先將變量進行歸一化處理,然后確定模型中的閾值和權(quán)值,通過不斷的改變權(quán)值,達到了對模型訓練及仿真的目的。最后對兩種方法進行了對比和總結(jié)。根據(jù)本次研究,以上方法都可以對借款人信用風險進行有效的識別,根據(jù)計算結(jié)果可以有效的識別借款人的信用風險等級,并且驗證了兩個模型對網(wǎng)絡(luò)借貸借款人信用風險的識別能力。后續(xù)的研究中,對如何提升借款人信用風險的識別能力提出了建議。為加強網(wǎng)絡(luò)借貸行業(yè)的風險識別能力,應(yīng)豐富多層次的信息認證指標,并加強政府機構(gòu)對P2P網(wǎng)絡(luò)借貸行業(yè)的有效監(jiān)管。
[Abstract]:Since 2007, P2P lending into China, experienced a short period of time will get rapid development of.P2P lending network alarming with the help of the Internet economy, between the number of operating platform showing a growth spurt in.2007 years to 2015, P2P network lending industry operating platform to the present 2595th from the beginning of the first, and 2015 single year increase of 1020, the absolute increase in the amount of far more than ever. According to statistics, the average monthly turnover platform for 49 billion 260 million yuan, and has been in the expansion of the state. However, due to the P2P network lending platform has low barriers to entry, the lack of industry standards, not strict supervision characteristics. The P2P network lending platform serious asymmetric information problems, so that the P2P platform will appear we often hear the "run away" phenomenon. According to statistics, by the end of June 2015, China The P2P network lending platform credit problems reach as much as 550. At present, the international financial market to provide a healthy environment for development, in addition to P2P network lending industry, a comprehensive regulatory mechanism also contributed to the development of foreign P2P network lending industry. But limited the development of P2P network lending company by the actual. Because P2P belongs to a new industry, risk control ability and the traditional bank cannot be compared. In general, the main risk of lending to the network has the following two types: basic and particular risks. The basic risk and legal risk, credit risk and supervision risk; the specific risks include investment risk, the risk of information asymmetry. The credit risk is the most serious harm, many credit recognition model of lending platform or the use of the traditional commercial bank, and not according to the characteristics of the development of network lending is more suitable for the network by The credit risk identification model. Based on the loan pat loan data, combined with the credit risk existing recognition methods at home and abroad, set up the index system of credit risk identification. Based on SPSS and MATLAB operating software, using multinomial Logistic regression method, such as "E" in the mirror as the reference level, a number respectively. The establishment of A-F level regression equation, are significant factors for each grade. The neural network model was set up, the variables are normalized, and then determine the threshold and weight in the model, by changing the weights continuously, reached the training and simulation model. Finally, the two methods were compared and summary. According to this study, the above method can effectively recognize the credit risk of the borrower, according to the calculation results can effectively identify the borrower's credit risk level, and The two models have been verified the recognition ability of the network lending the borrower's credit risk. The follow-up study, some suggestions on how to improve the credit risk of the borrower's ability to identify risk identification is proposed. To strengthen the network lending industry, we should enrich the multi-level index authentication information, and strengthen the government's effective supervision of P2P network lending industry.

【學位授予單位】：吉林財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F724.6;F832.4

【參考文獻】

相關(guān)期刊論文 前10條

1 姚鰻玲;;互聯(lián)網(wǎng)金融背景下我國P2P網(wǎng)貸風險及對策探析[J];財經(jīng)界(學術(shù)版);2015年22期

2 張昭;朱峻萱;李安渝;;我國P2P網(wǎng)貸行業(yè)綜合評價體系研究[J];海南金融;2015年03期

3 傅彥銘;臧敦剛;戚名鈺;;P2P網(wǎng)絡(luò)貸款信用的風險評估[J];統(tǒng)計與決策;2014年21期

4 趙恒吉;;論新形勢下我國P2P網(wǎng)絡(luò)貸款平臺的風險與監(jiān)管[J];東方企業(yè)文化;2014年21期

5 李悅雷;郭陽;張維;;中國P2P小額貸款市場借貸成功率影響因素分析[J];金融研究;2013年07期

6 王會娟;廖理;;中國P2P網(wǎng)絡(luò)借貸平臺信用認證機制研究——來自“人人貸”的經(jīng)驗證據(jù)[J];中國工業(yè)經(jīng)濟;2014年04期

7 溫小霓;蔡瑞媛;;基于用戶行為的P2P網(wǎng)絡(luò)借貸信用體系構(gòu)建[J];西部金融;2014年02期

8 郭忠金;林海霞;;P2P網(wǎng)上借貸信用機制研究——以拍拍貸為例[J];現(xiàn)代管理科學;2013年05期

9 李鈞;;P2P借貸:性質(zhì)、風險與監(jiān)管[J];金融發(fā)展評論;2013年03期

10 奚尊夏;;P2P網(wǎng)絡(luò)借貸組織生存機理與框架設(shè)計研究——基于臺州案例[J];浙江金融;2012年08期

相關(guān)碩士學位論文 前6條

1 王夢佳;基于Logistic回歸模型的P2P網(wǎng)貸平臺借款人信用風險評估[D];北京外國語大學;2015年

2 高瑞瑤;我國P2P網(wǎng)絡(luò)借貸借款方風險分析與防范[D];中國社會科學院研究生院;2014年

3 劉峙廷;我國P2P網(wǎng)絡(luò)信貸風險評估研究[D];廣西大學;2013年

4 薛群群;國內(nèi)外P2P小額信貸企業(yè)運營模式研究及實例分析[D];中央民族大學;2013年

5 汪莉;基于Logistic回歸模型的中小企業(yè)信用評分研究[D];合肥工業(yè)大學;2008年

6 吳麗麗;基于Logistic回歸模型的商業(yè)銀行信用風險管理研究[D];哈爾濱工業(yè)大學;2007年

，

本文編號：1452353