本文關(guān)鍵詞：基于Logit模型的P2P網(wǎng)絡(luò)借貸平臺(tái)借款人信用風(fēng)險(xiǎn)影響因素研究　出處：《哈爾濱商業(yè)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

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【摘要】：P2P(peer-to-peer)網(wǎng)絡(luò)借貸,是一種借助電子商務(wù)的專(zhuān)業(yè)網(wǎng)絡(luò)平臺(tái),是個(gè)人與個(gè)人之間互為借貸的小額借貸交易。P2P網(wǎng)絡(luò)借貸由于具有門(mén)檻低、主要從事無(wú)抵押借貸、借款方式相對(duì)透明等特點(diǎn),備受中小微企業(yè)和個(gè)人的追捧。特別是在美國(guó)2008年金融危機(jī)之后的幾年內(nèi),傳統(tǒng)金融機(jī)構(gòu)融資低迷,但是,網(wǎng)絡(luò)借貸的發(fā)展浪潮持續(xù)增高。伴隨著互聯(lián)網(wǎng)金融以及互聯(lián)網(wǎng)金融產(chǎn)品的高速發(fā)展,P2P網(wǎng)絡(luò)借貸違約率即信用風(fēng)險(xiǎn)成為大家人以及社會(huì)所關(guān)注的焦點(diǎn),高信用風(fēng)險(xiǎn)成為網(wǎng)絡(luò)借貸自身發(fā)展的最大瓶頸。本文采用排序選擇模型,基于excel VBA數(shù)據(jù)挖掘技術(shù),編寫(xiě)宏程序,通過(guò)網(wǎng)頁(yè)固定抓取數(shù)據(jù),分別從國(guó)內(nèi)最早的P2P網(wǎng)絡(luò)借貸平臺(tái)——拍拍貸網(wǎng)站,及目前發(fā)展最好的網(wǎng)站——人人貸網(wǎng)站上截取貸款數(shù)據(jù),選取了借款人個(gè)人特征信息(年齡、性別、借款人職業(yè))、借款人交易特征信息(歷史借款記錄、借款目的)、平臺(tái)評(píng)價(jià)信息(信用等級(jí)、貸款規(guī)模、利率、貸款期限、每月還款額)和借款人投標(biāo)信息(中標(biāo)次數(shù)、流標(biāo)次數(shù))四個(gè)方面作為信息數(shù)據(jù),并運(yùn)用Logit模型對(duì)P2P網(wǎng)絡(luò)借貸借款人的信用風(fēng)險(xiǎn)影響因素進(jìn)行了實(shí)證分析。研究結(jié)果顯示:(1)借款人職業(yè)與借款人信用風(fēng)險(xiǎn)存在著顯著的正相關(guān)關(guān)系。借款人職業(yè)越穩(wěn)定,其信用風(fēng)險(xiǎn)越大。(2)借款人借款記錄與借款人信用風(fēng)險(xiǎn)存在著顯著的正相關(guān)關(guān)系。借款人目的與借款人信用風(fēng)險(xiǎn)之間存在著顯著的負(fù)相關(guān)關(guān)系。借款人借款的信息越真實(shí),借款動(dòng)機(jī)可靠性越強(qiáng),借款人信用風(fēng)險(xiǎn)越小。(3)借款人信用評(píng)級(jí)與借款人信用風(fēng)險(xiǎn)存在顯著的負(fù)相關(guān)關(guān)系。借款人貸款規(guī)模與借款人信用風(fēng)險(xiǎn)負(fù)相關(guān),借款人利率和每月還款額與借款人信用風(fēng)險(xiǎn)正相關(guān)。(4)借款人中標(biāo)次數(shù)與借款人信用風(fēng)險(xiǎn)存在著顯著的正相關(guān)關(guān)系;借款人流標(biāo)次數(shù)與借款人信用風(fēng)險(xiǎn)存在著顯著的正相關(guān)關(guān)系。借款人在平臺(tái)中越活躍,其信用風(fēng)險(xiǎn)就越大。本研究結(jié)果,既可以為防范P2P網(wǎng)絡(luò)平臺(tái)信用風(fēng)險(xiǎn)提供新的思路,也可以為完善我國(guó)P2P網(wǎng)貸行業(yè)治理提供新的經(jīng)驗(yàn)證據(jù)。
[Abstract]:P2Ppeer-to-peer) online lending is a professional network platform with the aid of electronic commerce. Peer-to-Peer network lending is a small loan transaction between individuals and individuals. Because of its low threshold, mainly engaged in unsecured lending, borrowing methods are relatively transparent and so on. Especially in the years following the 2008 financial crisis in the United States, the financing of traditional financial institutions was depressed, but. With the rapid development of Internet finance and Internet financial products, P2P network loan default rate, that is, credit risk, has become the focus of people and society. High credit risk has become the biggest bottleneck in the development of network lending. This paper adopts the sorting selection model, based on excel VBA data mining technology, compiles macro programs, and grabs data through web pages. From the earliest domestic P2P network lending platform-PPDAI website, and the best developed website-peer-to-peer lending website to intercept loan data, selected the borrower's personal characteristics information (age, gender). Borrower occupation, borrower transaction information (historical loan record, loan purpose, platform evaluation information (credit rating, loan size, interest rate, loan maturity). The monthly repayment amount) and the information of the borrower's bid (the number of winning bids, the number of the flow mark) are taken as the information data. Logit model is used to analyze the influencing factors of credit risk of P2P network loan borrowers. The results show that: 1). There is a significant positive correlation between the borrower's occupation and the borrower's credit risk, and the more stable the borrower's occupation. The greater the credit risk, the greater the credit risk.). There is a significant positive correlation between the borrower's loan record and the borrower's credit risk. There is a significant negative correlation between the borrower's purpose and the borrower's credit risk. The stronger the reliability of borrowing motivation, the smaller the borrower's credit risk.) there is a significant negative correlation between the borrower's credit rating and the borrower's credit risk, and the scale of the borrower's loan is negatively correlated with the borrower's credit risk. The borrower's interest rate and monthly repayment amount are positively correlated with the borrower's credit risk. (4) there is a significant positive correlation between the number of times the borrower wins the bid and the borrower's credit risk. There is a significant positive correlation between the number of logovers and the credit risk of the borrower. The more active the borrower in the platform, the greater the credit risk. It can not only provide new ideas for preventing the credit risk of P2P network platform, but also provide new empirical evidence for perfecting the governance of P2P network loan industry in China.
【學(xué)位授予單位】：哈爾濱商業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F724.6;F832.4

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