P2P网贷违约人是否具有区域性特征——来自湖南省的例证
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摘要
借助网络爬虫技术手段获取"人人贷"平台上借款人的各项信息,提取两个样本:分为全国随机样本和湖南省随机样本,构建二元Logit回归模型,分析其中对违约率有显著影响的变量.研究表明,负债收入比、借款期限、学历、房产、房贷、描述指数对违约行为有负向影响,而借款利率、车产、认证个数对借款者违约行为有正向影响.同时,通过对两个样本最终回归模型的比较,发现湖南省违约人特征与全国随机样本中体现的违约人特征基本一致,但其中较为特殊的是,在湖南拥有房产和车产不能作为网络借款人履约能力提升的标志.
This paper obtains the information of the borrowers on the "renrendai" net loan platform with the help of the web crawler technology,and extracts two samples:the random samples of the whole country and the random samples of Hunan province.A binary Logit regression model is built to analyze the variables which have significant influence on the default rate.The study shows that the debt-to-income ratio,the maturity of the loan,the educational background,the property,the mortgage and the description index have a negative impact on the default behavior,while the interest rate of the loan,owning cars and the number of certification have a positive impact on the default behavior of the borrowers.At the same time,through the comparison of the final regression model of two samples,it is found that the characteristics of the defaulters in Hunan province is basically consistent with the random samples of the whole country,but especially,the ownership of real estate and car can not help to improve the performance of network borrowers in Hunan.
This paper obtains the information of the borrowers on the "renrendai" net loan platform with the help of the web crawler technology,and extracts two samples:the random samples of the whole country and the random samples of Hunan province.A binary Logit regression model is built to analyze the variables which have significant influence on the default rate.The study shows that the debt-to-income ratio,the maturity of the loan,the educational background,the property,the mortgage and the description index have a negative impact on the default behavior,while the interest rate of the loan,owning cars and the number of certification have a positive impact on the default behavior of the borrowers.At the same time,through the comparison of the final regression model of two samples,it is found that the characteristics of the defaulters in Hunan province is basically consistent with the random samples of the whole country,but especially,the ownership of real estate and car can not help to improve the performance of network borrowers in Hunan.
引文
[1]KUMAR S.Bank of one:empirical analysis of peer-to-peer financial marketplaces[C]//Reaching New Heights.Americas Conference on Information Systems,Keystone,Colorado,USA,2007:305.
[2]HERZENSTEIN M,SONENSHEIN S,DHOLAKIA U M.Tell me a good story and I may lend you money:the role of narratives in peer-to-peer lending decisions[J].Journal of Marketing Research,2011,48(SPL):138.
[3]POPE D G,SYDNOR J R.What’s in a picture?Evidence of discrimination from Prosper.com[J].Journal of Human Resources,2011,46(1):53-92.
[4]DUARTE J,SIEGEL S,YOUNG L.Trust and credit:the role of appearance in peer-to-peer lending[J].Review of Financial Studies,2012,25(8):2455-2484.
[5]NOWAK A,ROSS A,YENCHA C.Small business borrowing and peer-to-peer lending:evidence from lending Club[J].Contemporary Economic Policy,2018,36(2):318-336.
[6]缪莲英,陈金龙.P2P网络借贷中社会资本对借款者违约风险的影响---以Prosper为例[J].金融论坛,2014,19(3):9-15.
[7]王会娟,廖理.中国P2P网络借贷平台信用认证机制研究---来自“人人贷”的经验证据[J].中国工业经济,2014(4):136-147.
[8]廖理,李梦然,王正位.中国互联网金融的地域歧视研究[J].数量经济技术经济研究,2014,31(5):54-70.
[9]顾慧莹,姚铮.P2P网络借贷平台中借款人违约风险影响因素研究---以WDW为例[J].上海经济研究,2015(11):37-46.
[10]王重润,孔兵.P2P网络借贷违约行为影响因素实证研究---以“红岭创投”为例[J].农村金融研究,2016(2):28-33.
[11]于军.描述性信息与违约行为的关系研究---基于P2P平台的分析[J].当代经济管理,2017,39(5):86-92.
[12]IYER R,KHWAJA A I,LUTTER,E F P,et al.Screening in new credit markets:Can individual lenders infer borrower creditworthiness in peer-to-peer lending?[R].Combridge,MA:National Bureau of Economic Research,Working Paper 15242,2009.
[13]GREINER M E,WANG H.Building consumer-to-consumer trust in e-finance marketplaces[C]//Reaching New Heights.Americas Conference on Information Systems,Keystone,Colorado,USA,2007:211.
[14]DINH T H T,KLEIMEIER S.Credit scoring for vietnam’s retail banking market:implementation and implications for transactional versus relationship lending[D].Financial Economics,Limburg Institute of Maastricht University,2006.
[15]ROSLAN A,KARIM M.Determinants of microcredit repayment in malaysia:the case of agrobank[J].Pertanika Journal of Social Science&Humanities,2009,4(1):45-52.
[16]ARMINGER G,ENACHE D,BONNE T.Analyzing credit risk data:a comparison of logistic discrimination,classification tree analysis,and feedforward networks[J].Social Science Electronic Publishing,1997,12(2):293-310.
[17]LIN M.PRABHALA N R,VISWANATHAN S.Judging borrowers by the company they keep:friendship networks and information asymmetry in online peer-to-peer lending[J].Management Science,2013,59(1):17-35.
(1)数据来源:网贷之家共收录6606个平台信息
(2)数据来源:网贷之家.同时零壹财经给出的数据是截至2017年年底,正常运营的平台数目为1539.
(1)选择2015年数据的原因为:确认为坏账的时间.因为网贷期限大部分分布在1个月到36个月之间.如选择2017年数据,则无坏账呈现.
(1)在这里主要是指某人或某个家庭是否需要一辆车来完成家庭收入的需要,如跑运输的,或业务活动量大的工作等.
(1)购房借款者一般来说都倾向于走入收入或生活较稳定状态,风险较低,故而赋值与个人消费一样;而用途中描述为其他的说明借款人不方便说明借款用途,认定风险较高,所以赋值4.
(1)网贷之家《揭秘网贷圈老赖真实画像,有车产并不能降低违约率》2018-03-06,https://www.wdzj.com/hjzs/ptsj/20180306/588267-1.html
[2]HERZENSTEIN M,SONENSHEIN S,DHOLAKIA U M.Tell me a good story and I may lend you money:the role of narratives in peer-to-peer lending decisions[J].Journal of Marketing Research,2011,48(SPL):138.
[3]POPE D G,SYDNOR J R.What’s in a picture?Evidence of discrimination from Prosper.com[J].Journal of Human Resources,2011,46(1):53-92.
[4]DUARTE J,SIEGEL S,YOUNG L.Trust and credit:the role of appearance in peer-to-peer lending[J].Review of Financial Studies,2012,25(8):2455-2484.
[5]NOWAK A,ROSS A,YENCHA C.Small business borrowing and peer-to-peer lending:evidence from lending Club[J].Contemporary Economic Policy,2018,36(2):318-336.
[6]缪莲英,陈金龙.P2P网络借贷中社会资本对借款者违约风险的影响---以Prosper为例[J].金融论坛,2014,19(3):9-15.
[7]王会娟,廖理.中国P2P网络借贷平台信用认证机制研究---来自“人人贷”的经验证据[J].中国工业经济,2014(4):136-147.
[8]廖理,李梦然,王正位.中国互联网金融的地域歧视研究[J].数量经济技术经济研究,2014,31(5):54-70.
[9]顾慧莹,姚铮.P2P网络借贷平台中借款人违约风险影响因素研究---以WDW为例[J].上海经济研究,2015(11):37-46.
[10]王重润,孔兵.P2P网络借贷违约行为影响因素实证研究---以“红岭创投”为例[J].农村金融研究,2016(2):28-33.
[11]于军.描述性信息与违约行为的关系研究---基于P2P平台的分析[J].当代经济管理,2017,39(5):86-92.
[12]IYER R,KHWAJA A I,LUTTER,E F P,et al.Screening in new credit markets:Can individual lenders infer borrower creditworthiness in peer-to-peer lending?[R].Combridge,MA:National Bureau of Economic Research,Working Paper 15242,2009.
[13]GREINER M E,WANG H.Building consumer-to-consumer trust in e-finance marketplaces[C]//Reaching New Heights.Americas Conference on Information Systems,Keystone,Colorado,USA,2007:211.
[14]DINH T H T,KLEIMEIER S.Credit scoring for vietnam’s retail banking market:implementation and implications for transactional versus relationship lending[D].Financial Economics,Limburg Institute of Maastricht University,2006.
[15]ROSLAN A,KARIM M.Determinants of microcredit repayment in malaysia:the case of agrobank[J].Pertanika Journal of Social Science&Humanities,2009,4(1):45-52.
[16]ARMINGER G,ENACHE D,BONNE T.Analyzing credit risk data:a comparison of logistic discrimination,classification tree analysis,and feedforward networks[J].Social Science Electronic Publishing,1997,12(2):293-310.
[17]LIN M.PRABHALA N R,VISWANATHAN S.Judging borrowers by the company they keep:friendship networks and information asymmetry in online peer-to-peer lending[J].Management Science,2013,59(1):17-35.
(1)数据来源:网贷之家共收录6606个平台信息
(2)数据来源:网贷之家.同时零壹财经给出的数据是截至2017年年底,正常运营的平台数目为1539.
(1)选择2015年数据的原因为:确认为坏账的时间.因为网贷期限大部分分布在1个月到36个月之间.如选择2017年数据,则无坏账呈现.
(1)在这里主要是指某人或某个家庭是否需要一辆车来完成家庭收入的需要,如跑运输的,或业务活动量大的工作等.
(1)购房借款者一般来说都倾向于走入收入或生活较稳定状态,风险较低,故而赋值与个人消费一样;而用途中描述为其他的说明借款人不方便说明借款用途,认定风险较高,所以赋值4.
(1)网贷之家《揭秘网贷圈老赖真实画像,有车产并不能降低违约率》2018-03-06,https://www.wdzj.com/hjzs/ptsj/20180306/588267-1.html