本文關鍵詞：基于Logit模型的P2P網絡借貸平臺借款人信用風險影響因素研究　出處：《哈爾濱商業(yè)大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： P2P 網絡借貸 借款人信用風險 Logit

【摘要】：P2P(peer-to-peer)網絡借貸,是一種借助電子商務的專業(yè)網絡平臺,是個人與個人之間互為借貸的小額借貸交易。P2P網絡借貸由于具有門檻低、主要從事無抵押借貸、借款方式相對透明等特點,備受中小微企業(yè)和個人的追捧。特別是在美國2008年金融危機之后的幾年內,傳統(tǒng)金融機構融資低迷,但是,網絡借貸的發(fā)展浪潮持續(xù)增高。伴隨著互聯(lián)網金融以及互聯(lián)網金融產品的高速發(fā)展,P2P網絡借貸違約率即信用風險成為大家人以及社會所關注的焦點,高信用風險成為網絡借貸自身發(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,編寫宏程序,通過網頁固定抓取數(shù)據(jù),分別從國內最早的P2P網絡借貸平臺——拍拍貸網站,及目前發(fā)展最好的網站——人人貸網站上截取貸款數(shù)據(jù),選取了借款人個人特征信息(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)、平臺評價信息(信用等級、貸款規(guī)模、利率、貸款期限、每月還款額)和借款人投標信息(中標次數(shù)、流標次數(shù))四個方面作為信息數(shù)據(jù),并運用Logit模型對P2P網絡借貸借款人的信用風險影響因素進行了實證分析。研究結果顯示:(1)借款人職業(yè)與借款人信用風險存在著顯著的正相關關系。借款人職業(yè)越穩(wěn)定,其信用風險越大。(2)借款人借款記錄與借款人信用風險存在著顯著的正相關關系。借款人目的與借款人信用風險之間存在著顯著的負相關關系。借款人借款的信息越真實,借款動機可靠性越強,借款人信用風險越小。(3)借款人信用評級與借款人信用風險存在顯著的負相關關系。借款人貸款規(guī)模與借款人信用風險負相關,借款人利率和每月還款額與借款人信用風險正相關。(4)借款人中標次數(shù)與借款人信用風險存在著顯著的正相關關系;借款人流標次數(shù)與借款人信用風險存在著顯著的正相關關系。借款人在平臺中越活躍,其信用風險就越大。本研究結果,既可以為防范P2P網絡平臺信用風險提供新的思路,也可以為完善我國P2P網貸行業(yè)治理提供新的經驗證據(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.
【學位授予單位】：哈爾濱商業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F724.6;F832.4

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