高收入者参与P2P网络借贷的动机:基于信息不对称的视角
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摘要
借助"人人贷"的交易数据,研究收入对借款人违约风险以及投资者行为的影响,探究高收入者在网贷市场中借贷的原因。结果显示,高收入者的违约概率反而更高,进一步研究发现,并非是借款人提供了虚假的收入信息,而是借款人隐瞒了征信信息,说明高收入者中高风险(信用较差)的借款人可能无法从正规金融机构获得贷款,因而选择在利率更高的网络借贷平台借贷。同时,高收入借款人的借贷利率更低、借款成功率更高。通过研究借款人的收入在借贷市场中的作用,可为P2P平台防范信用风险、建立合理的信用认证机制提供理论支持。
This paper uses the data of "renrendai" to study the impact of income on borrower default risk and investor behavior, and explores the reasons why high-income people borrow money in P2P lending. The results show that the probability of default for high-income people is higher. Further research finds that the borrower did not provide false income information, but the borrower concealed the credit information, indicating that borrowers with high-income and high-risk could not obtain loans from formal financial institutions, so they choose to lend on higher-interest P2P lending platforms. Borrowers with higher incomes have lower borrowing rates and higher borrowing success rates. This paper provides theoretical support for P2P platforms to prevent credit risk and establish a reasonable credit certification mechanism.
This paper uses the data of "renrendai" to study the impact of income on borrower default risk and investor behavior, and explores the reasons why high-income people borrow money in P2P lending. The results show that the probability of default for high-income people is higher. Further research finds that the borrower did not provide false income information, but the borrower concealed the credit information, indicating that borrowers with high-income and high-risk could not obtain loans from formal financial institutions, so they choose to lend on higher-interest P2P lending platforms. Borrowers with higher incomes have lower borrowing rates and higher borrowing success rates. This paper provides theoretical support for P2P platforms to prevent credit risk and establish a reasonable credit certification mechanism.
引文
[1]LIN M,PRABHALA N R,VISWANATHANS.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.
[2]GREINER M E,WANG H.Building consumer-to-consumer trust in E-finance marketplaces:An empirical analysis[J].International Journal of Electronic Commerce.201015(2):105-136.
[3]何平平,蒋银乔,胡荣才.网络借贷P2P:利差是否包含违约风险隐含信息?——来自人人贷交易数据的实证分析[J].金融经济学研究,2016(3):27-37.
[4]BAKLOUTI I.Determinants of microcredi repayment:The case of tunisian microfinance bank[J].African Development Review.201325(3):370-382.
[5]肖曼君,欧缘媛,李颖.我国P2P网络借贷信用风险影响因素研究——基于排序选择模型的实证分析[J].财经理论与实践,2015(1):2-6.
[6]DINH T H T,KLEIMEIER S.A credit scoring model for Vietnam’s retail banking market[J].International Review of Financial Analysis.2007,16(5):471-495.
[7]廖理,吉霖,张伟强.借贷市场能准确识别学历的价值吗?——来自P2P平台的经验证据[J].金融研究,2015(3):146-159.
[8]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.
[9]LARRIMORE L,JIANG L,LARRIMORE Jet al.Peer to peer lending:The relationship between language features,trustworthiness and persuasion success[J].Journal of Applied Communication Research.2011,39(1):19-37
[10]李悦雷,郭阳,张维.中国P2P小额贷款市场借贷成功率影响因素分析[J].金融研究,2013(7):126-138.
[11]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.
[12]李焰,高弋君,李珍妮,等.借款人描述性信息对投资人决策的影响——基于P2P网络借贷平台的分析[J].经济研究,2014(S1):143-155.
[13]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.
[14]胡金焱,李建文.“双创”背景的新型金融模式:解构P2P网络借贷[J].改革,2018(3):74-89.
[15]田秀娟,张智颖.P2P网络借贷职业身份的信贷歧视:基于投资者认知偏差视角[J]改革,2018(5):141-152.
(1)在研究借款人的违约风险和借款成功率时,借贷利率作为控制变量。
(2)由于还款中的借款人无法确定其未来是否会违约,因而本文在研究借款人违约风险时不包含这部分样本,仅包括借款状态为严重逾期、坏账和已还清的标的,共25 744个标的。
(1)本文只列出了原回归方程结果,未列出选择方程的回归结果。
[2]GREINER M E,WANG H.Building consumer-to-consumer trust in E-finance marketplaces:An empirical analysis[J].International Journal of Electronic Commerce.201015(2):105-136.
[3]何平平,蒋银乔,胡荣才.网络借贷P2P:利差是否包含违约风险隐含信息?——来自人人贷交易数据的实证分析[J].金融经济学研究,2016(3):27-37.
[4]BAKLOUTI I.Determinants of microcredi repayment:The case of tunisian microfinance bank[J].African Development Review.201325(3):370-382.
[5]肖曼君,欧缘媛,李颖.我国P2P网络借贷信用风险影响因素研究——基于排序选择模型的实证分析[J].财经理论与实践,2015(1):2-6.
[6]DINH T H T,KLEIMEIER S.A credit scoring model for Vietnam’s retail banking market[J].International Review of Financial Analysis.2007,16(5):471-495.
[7]廖理,吉霖,张伟强.借贷市场能准确识别学历的价值吗?——来自P2P平台的经验证据[J].金融研究,2015(3):146-159.
[8]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.
[9]LARRIMORE L,JIANG L,LARRIMORE Jet al.Peer to peer lending:The relationship between language features,trustworthiness and persuasion success[J].Journal of Applied Communication Research.2011,39(1):19-37
[10]李悦雷,郭阳,张维.中国P2P小额贷款市场借贷成功率影响因素分析[J].金融研究,2013(7):126-138.
[11]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.
[12]李焰,高弋君,李珍妮,等.借款人描述性信息对投资人决策的影响——基于P2P网络借贷平台的分析[J].经济研究,2014(S1):143-155.
[13]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.
[14]胡金焱,李建文.“双创”背景的新型金融模式:解构P2P网络借贷[J].改革,2018(3):74-89.
[15]田秀娟,张智颖.P2P网络借贷职业身份的信贷歧视:基于投资者认知偏差视角[J]改革,2018(5):141-152.
(1)在研究借款人的违约风险和借款成功率时,借贷利率作为控制变量。
(2)由于还款中的借款人无法确定其未来是否会违约,因而本文在研究借款人违约风险时不包含这部分样本,仅包括借款状态为严重逾期、坏账和已还清的标的,共25 744个标的。
(1)本文只列出了原回归方程结果,未列出选择方程的回归结果。