發(fā)布時(shí)間：2018-01-21 19:20

  本文關(guān)鍵詞： P2P 風(fēng)險(xiǎn)識(shí)別 多項(xiàng)Logistic回歸模型 BP神經(jīng)網(wǎng)絡(luò)模型　出處：《吉林財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：自2007年P(guān)2P網(wǎng)絡(luò)借貸傳入中國(guó)以來(lái),經(jīng)歷了短暫的時(shí)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(jì)的快速發(fā)展,運(yùn)營(yíng)平臺(tái)的數(shù)量呈現(xiàn)出井噴式增長(zhǎng)。2007年至2015年之間,P2P網(wǎng)絡(luò)借貸行業(yè)運(yùn)營(yíng)平臺(tái)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(duì)的增加量遠(yuǎn)遠(yuǎn)超過(guò)過(guò)去任何一個(gè)時(shí)期。據(jù)統(tǒng)計(jì),平臺(tái)平均每個(gè)月的成交額為492.6億元,并且一直處于擴(kuò)張的狀態(tài)。但是,由于P2P網(wǎng)絡(luò)借貸平臺(tái)具有進(jìn)入門檻低、缺乏行業(yè)標(biāo)準(zhǔn)、監(jiān)管不嚴(yán)格等特點(diǎn),致使P2P網(wǎng)絡(luò)借貸平臺(tái)存在嚴(yán)重的信息不對(duì)稱問(wèn)題,從而P2P平臺(tái)會(huì)出現(xiàn)我們經(jīng)常聽到的“跑路”等現(xiàn)象。據(jù)統(tǒng)計(jì),截止到2015年6月,國(guó)內(nèi)出現(xiàn)信用問(wèn)題的P2P網(wǎng)絡(luò)借貸平臺(tái)達(dá)到550家之多。目前,國(guó)外的金融市場(chǎng)給P2P網(wǎng)絡(luò)借貸行業(yè)提供了一個(gè)健康的發(fā)展環(huán)境,此外,全面的監(jiān)管機(jī)制也促使了國(guó)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(shí)際的一定限制,由于P2P屬于新興起的行業(yè),風(fēng)險(xiǎn)控制能力和傳統(tǒng)的銀行是無(wú)法相比的。一般而言,網(wǎng)絡(luò)借貸主要風(fēng)險(xiǎn)有以下兩種:基本風(fēng)險(xiǎn)和特定風(fēng)險(xiǎn)�；撅L(fēng)險(xiǎn)有法律風(fēng)險(xiǎn)、信用風(fēng)險(xiǎn)和監(jiān)管風(fēng)險(xiǎn)等;特定風(fēng)險(xiǎn)則包括投資風(fēng)險(xiǎn)、信息不對(duì)稱風(fēng)險(xiǎn)等。而信用風(fēng)險(xiǎn)危害最為嚴(yán)重,很多借貸平臺(tái)還是采用的傳統(tǒng)商業(yè)銀行的信用識(shí)別模型,并沒有根據(jù)網(wǎng)絡(luò)借貸的特點(diǎn)開發(fā)出更適合網(wǎng)絡(luò)借貸的信用風(fēng)險(xiǎn)識(shí)別模型。本文基于“拍拍貸”的數(shù)據(jù),結(jié)合國(guó)內(nèi)外現(xiàn)有的信用風(fēng)險(xiǎn)識(shí)別方法,建立了信用風(fēng)險(xiǎn)識(shí)別的指標(biāo)體系。在SPSS和MATLAB操作軟件的基礎(chǔ)上,應(yīng)用多項(xiàng)Logistic回歸方法,以魔鏡等級(jí)“E”為參照等級(jí),分別建立A-F等級(jí)的多項(xiàng)回歸方程,得到每個(gè)等級(jí)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(jìn)行歸一化處理,然后確定模型中的閾值和權(quán)值,通過(guò)不斷的改變權(quán)值,達(dá)到了對(duì)模型訓(xùn)練及仿真的目的。最后對(duì)兩種方法進(jìn)行了對(duì)比和總結(jié)。根據(jù)本次研究,以上方法都可以對(duì)借款人信用風(fēng)險(xiǎn)進(jìn)行有效的識(shí)別,根據(jù)計(jì)算結(jié)果可以有效的識(shí)別借款人的信用風(fēng)險(xiǎn)等級(jí),并且驗(yàn)證了兩個(gè)模型對(duì)網(wǎng)絡(luò)借貸借款人信用風(fēng)險(xiǎn)的識(shí)別能力。后續(xù)的研究中,對(duì)如何提升借款人信用風(fēng)險(xiǎn)的識(shí)別能力提出了建議。為加強(qiáng)網(wǎng)絡(luò)借貸行業(yè)的風(fēng)險(xiǎn)識(shí)別能力,應(yīng)豐富多層次的信息認(rèn)證指標(biāo),并加強(qiáng)政府機(jī)構(gòu)對(duì)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(xué)位授予單位】：吉林財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;F832.4

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