网贷平台的利率究竟代表了什么?
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摘要
网贷市场的蓬勃发展为个人和小微企业提供了新的融资渠道,也为投资者提供了新的投资工具。与此同时,网贷平台风险的集中爆发,给投资者造成巨大损失,也带来区域性金融风险爆发的隐忧。研究网贷平台的风险,是中国网贷投资者和监管部门共同面临的重要课题,但以往文献对此关注较少。本文从利率这个重要变量出发,探讨利率与网贷平台风险之间的关系,考察了网贷平台提供给投资者的利率与网贷平台成为问题平台的概率之间的联系。研究发现,总体而言,利率越高,网贷平台成为问题平台的风险越高。进一步地,当利率处于行业同期较低水平时,利率与问题平台风险之间呈负相关;当利率处于行业中等水平时,利率和问题平台风险不存在显著相关;而当利率处于行业较高水平时,利率和问题平台风险之间呈正相关。本文揭示了利率与网贷平台风险之间的关系,有助于投资者和监管部门通过利率这一变量更好地监测网贷平台风险。
China has the largest P2 P lending market in the world. At the end of 2017, there were 2,249 P2 P platforms operating and outstanding P2 P loans amounting to approximately 1.04 trillion CNY(around 150 billion USD). The Chinese P2 P lending market provides an alternative funding source for many individuals and SMEs that are underserved by the traditional banking system. It also offers a new investment tool for households to allocate their wealth. However, along with its large size, the P2 P lending market in China is risky. As of the end of 2017, 65% of P2 P platforms that had ever operated had shut down. Many of these platforms had been found to engage in fraud, such as Ponzi schemes. Defunct platforms can incur huge losses for their investors and undermine the stability of the financial system and society.Understanding defunct platform risk is important for both P2 P investors and regulators. First, P2 P investors who understand this risk will make better investment decisions. The possibility of a platform going defunct is the primary risk facing P2 P investors. In contrast, the default of a single loan causes them little loss, given that nearly all P2 P platforms in China adopt a principal guarantee: when a borrower defaults on his/her loan, the platforms return the principal(or both principal and interest) of the investment to investors. Hence, P2 P investors suffer a loss only when the platforms go defunct. Second, understanding this risk can help regulators by improving their ability to intervene early, reducing the likelihood of systemic risk and negative effects on society.We examine the cross-sectional relation between interest rates offered to investors and defunct platform risk. We focus on interest rates for two reasons. First, they play a central role in P2 P lending: interest rates are highly related to the investment return of investors and the borrowing cost of borrowers. Many platforms mark the interest rates of their loans on the home page of their websites to attract more investors, while industry third parties often list the average interest rates of each platform for visitors' convenience. Second, interest rates can have complex relationships with defunct platform risk. On the one hand, low-risk platforms are motivated to set higher interest rates to signal their good financial situation and capability of offering principal guarantees. Hence, interest rates can be negatively related to defunct risk. On the other hand, platforms can have moral hazard problems such as engaging in Ponzi schemes. The existence of moral hazard implies that interest rates can be positively related to defunct risk(Karlan & Zinmman, 2009).We use a unique dataset for empirical testing. The dataset contains platform-week-level transaction data of 1,415 P2 P platforms, including average interest rates, average maturity, and the number of investors of P2 P loans transacted on a given platform in a given week. We hand-collect these platforms' basic information, including registered capital, whether the platform is defunct, the date it became defunct, and the platform's location. To the best of our knowledge, we are using the most comprehensive data available on the Chinese P2 P market.We perform Probit regressions with week fixed effects to examine the cross-sectional relationship between interest rates and defunct risk. We find that, in general, interest rates are positively related to defunct risk. We also show heterogenous effects across different interest rate levels. For platforms with interest rates in the lowest quartile of the industry, interest rates negatively correlate with defunct risk. For platforms in the second and third interest rate quartiles, interest rates are not significantly correlated to defunct risk. In the highest quartile of interest rates, interest rates are positively related to defunct risk.Our study contributes to the literature on P2 P lending. Earlier studies primarily look at how loan characteristics are related to the default risk of P2 P loans. Researchers find that the trust(Duarte et al., 2012), race(Pope & Sydnor, 2011), social networks(Lin et al., 2013), and linguistic features(Gao et al., 2018) of borrowers are related to default risk. Our research extends this literature by examining whether average interest rates are related to platform-level risk in P2 P lending.
China has the largest P2 P lending market in the world. At the end of 2017, there were 2,249 P2 P platforms operating and outstanding P2 P loans amounting to approximately 1.04 trillion CNY(around 150 billion USD). The Chinese P2 P lending market provides an alternative funding source for many individuals and SMEs that are underserved by the traditional banking system. It also offers a new investment tool for households to allocate their wealth. However, along with its large size, the P2 P lending market in China is risky. As of the end of 2017, 65% of P2 P platforms that had ever operated had shut down. Many of these platforms had been found to engage in fraud, such as Ponzi schemes. Defunct platforms can incur huge losses for their investors and undermine the stability of the financial system and society.Understanding defunct platform risk is important for both P2 P investors and regulators. First, P2 P investors who understand this risk will make better investment decisions. The possibility of a platform going defunct is the primary risk facing P2 P investors. In contrast, the default of a single loan causes them little loss, given that nearly all P2 P platforms in China adopt a principal guarantee: when a borrower defaults on his/her loan, the platforms return the principal(or both principal and interest) of the investment to investors. Hence, P2 P investors suffer a loss only when the platforms go defunct. Second, understanding this risk can help regulators by improving their ability to intervene early, reducing the likelihood of systemic risk and negative effects on society.We examine the cross-sectional relation between interest rates offered to investors and defunct platform risk. We focus on interest rates for two reasons. First, they play a central role in P2 P lending: interest rates are highly related to the investment return of investors and the borrowing cost of borrowers. Many platforms mark the interest rates of their loans on the home page of their websites to attract more investors, while industry third parties often list the average interest rates of each platform for visitors' convenience. Second, interest rates can have complex relationships with defunct platform risk. On the one hand, low-risk platforms are motivated to set higher interest rates to signal their good financial situation and capability of offering principal guarantees. Hence, interest rates can be negatively related to defunct risk. On the other hand, platforms can have moral hazard problems such as engaging in Ponzi schemes. The existence of moral hazard implies that interest rates can be positively related to defunct risk(Karlan & Zinmman, 2009).We use a unique dataset for empirical testing. The dataset contains platform-week-level transaction data of 1,415 P2 P platforms, including average interest rates, average maturity, and the number of investors of P2 P loans transacted on a given platform in a given week. We hand-collect these platforms' basic information, including registered capital, whether the platform is defunct, the date it became defunct, and the platform's location. To the best of our knowledge, we are using the most comprehensive data available on the Chinese P2 P market.We perform Probit regressions with week fixed effects to examine the cross-sectional relationship between interest rates and defunct risk. We find that, in general, interest rates are positively related to defunct risk. We also show heterogenous effects across different interest rate levels. For platforms with interest rates in the lowest quartile of the industry, interest rates negatively correlate with defunct risk. For platforms in the second and third interest rate quartiles, interest rates are not significantly correlated to defunct risk. In the highest quartile of interest rates, interest rates are positively related to defunct risk.Our study contributes to the literature on P2 P lending. Earlier studies primarily look at how loan characteristics are related to the default risk of P2 P loans. Researchers find that the trust(Duarte et al., 2012), race(Pope & Sydnor, 2011), social networks(Lin et al., 2013), and linguistic features(Gao et al., 2018) of borrowers are related to default risk. Our research extends this literature by examining whether average interest rates are related to platform-level risk in P2 P lending.
引文
陈霄、叶德珠,2016:《中国P2P网络借贷利率波动研究》,《国际金融研究》第1期。
高铭、江嘉骏、陈佳、刘玉珍,2017:《谁说女子不如儿郎?——P2P投资行为与过度自信》,《金融研究》第11期。
何启志、彭明生,2016:《基于互联网金融的网贷利率特征研究》,《金融研究》第10期。
胡金焱、宋唯实,2017:《P2P 借贷中投资者的理性意识与权衡行为——基于“人人贷”数据的实证分析》,《金融研究》第7期。
李焰、高弋君、李珍妮、才子豪、王冰婷、杨宇轩,2014:《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》,《经济研究》第S1期。
廖理、吉霖、张伟强,2015a:《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》,《金融研究》第3期。
廖理、吉霖、张伟强,2015b:《语言可信吗?借贷市场上语言的作用——来自P2P平台的证据》,《清华大学学报(自然科学版)》第4期。
廖理、李梦然、王正位,2014a:《中国互联网金融的地域歧视研究》,《数量经济技术经济研究》第5期。
廖理、李梦然、王正位,2014b:《聪明的投资者:非完全市场化利率与风险识别——来自P2P网络借贷的证据》,《经济研究》第7期。
廖理、张伟强,2017:《P2P网络借贷实证研究:一个文献综述》,《清华大学学报(哲学社会科学版)》第2期。
清华大学金融科技研究院课题组,2018:《网贷行业2018年问题平台报告》,《清华金融评论》第11期。
彭红枫、赵海燕、周洋,2016:《借款陈述会影响借款成本和借款成功率吗?——基于网络借贷陈述的文本分析》,《金融研究》第4期。
王正位、向佳、廖理、张伟强,2016:《互联网金融环境下投资者学习行为的经济学分析》,《数量经济技术经济研究》第3期。
向虹宇、廖理、王正位,2017:《注意力与P2P投资者投资决策——来自人人贷的证据》,《经济学报》第3期。
张海洋,2017:《信息披露监管与P2P借贷运营模式》,《经济学(季刊)》第1期。
张健华,2016:《互联网金融监管研究》,科学出版社。
Allen,F.,and G.R.Faulhaber,1989,“Signalling by Underpricing in the IPO Market”,Journal of Financial Economics,23(2),303—323.
Duarte,J.,S.Siegel,and L.Young,2012,“Trust and Credit:the Role of Appearance in Peer-to-peer Lending”,Review of Financial Studies,25(8),2455—2484.
Freedman,S.M.,and G.Z.Jin,2008,“Do Social Networks Solve Information Problems for Peer-to-Peer Lending?Evidence from Prosper.com”,Available at SSRN:https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1936057.
Freedman,S.M.,and G.Z.Jin,2011,“Learning by Doing with Asymmetric Information:Evidence from Prosper.com”,Available at NBER:http://www.nber.org/papers/w16855.
Gao,Q.,M.Lin,and R.W.Sias,2018,“Words Matter:The Role of Texts in Online Credit Markets”,SSRN working paper,Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2446114.
Karlan,D.,and J.Zinman,2009,“Observing Unobservables:Identifying Information Asymmetries with a Consumer Credit Field Experiment”,Econometrica,77(6),1993—2008.
Kuhnen,C.M.,and B.T.Melzer,2018,“Noncognitive Abilities and Financial Delinquency:The Role of Self-efficacy in Avoiding Financial Distress”,Journal of Finance,73(6),2837—2869.
Liao,L.,Z.Wang,J.Xiang,and J.Yang,2017,“Thinking Fast,Not Slow:Evidence from Peer-to-peer Lending”,Available at SSRN:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2830326.
Lin,M.,N.R.Prabhala,and S.Viswanathan,2013,“Judging Borrowers by the Company They Keep:Friendship Networks and Information Asymmetry in Online Peer-to-peer Lending”,Management Science,59(1),17—35.
Lin,M.,and S.Viswanathan,2015,“Home Bias in Online Investments:An Empirical Study of an Online Crowdfunding Market”,Management Science,62(5),1393—1414.
Pope,D.G.,and J.R.Sydnor,2011,“What’s in a Picture?Evidence of Discrimination from Prosper.com”,Journal of Human Resources,46(1),53—92.
Spence,M.,1973,“Job Market Signaling”,Quarterly Journal of Economics,87,355—374.
Wei,Z.,and M.Lin,2016,“Market Mechanisms in Online Peer-to-peer Lending”,Management Science,63(12),4236—4257.
Zhang,J.,and P.Liu,2012,“Rational Herding in Microloan Markets”,Management Science,58(5),892—912.
① 统计数据来自网贷之家网站,网址为:http://shuju.wdzj.com/。
② 统计数据来源同上。
③ 资料来源:北京市人民检察院网站,http://www.ajxxgk.jcy.cn/html/20161223/1/3900846.html。
(1)这里的标准差指对应子样本中的利率标准差,下同。
(2)廖理和张伟强(2017)对P2P网贷市场的文献进行了总结回顾,本文仅梳理回顾与本文研究内容紧密相关的文献。
(3)网贷市场贷款余额数据来自网贷之家,商业银行个人贷款数据来自中国人民银行网站,小微企业贷款余额数据来自银监会网站。
(4)以2017年12月为例,网贷市场平均利率为9.54%,同期商业银行一年期定期存款基准利率为1.50%。网贷市场利率数据来自网贷之家,商业银行个人存款余额和银行基准存款利率来自中国人民银行网站。
(5)网贷平台的担保进一步增加了网贷平台的风险。如果网贷平台缺乏风险管理能力和信用评估能力,借款人可能出现大面积集中违约。此时,网贷平台可能无力提供担保,最终会破产或者停止正常营业。
(6)根据银监会2016年8月公布的《网络借贷信息中介机构业务活动管理暂行办法》,禁止网贷平台提供担保或变相担保。但在本文研究的时间区间内,监管规则尚未完全落地,绝大多数网贷平台仍然提供担保。
(7)类似的信号发送也可能出现在其他金融市场。例如,Allen & Faulhaber(1989)指出,IPO中的抑价发行(underpricing)可能是好的公司向市场进行信号发送的结果。
(8)由于篇幅所限,此处省略了问题平台和正常平台的利率走势图。利率走势图的结果显示:问题平台的平均利率显著高于正常平台,且在问题平台倒闭之前,利率水平没有显著变化。
(9)网贷资讯门户网站通过各种方式搜集各个网贷平台的交易信息,然后向公众免费公布并及时更新各网贷平台的利率水平。具体案例参见:https://www.wdzj.com/dangan/。
(10)我们也使用网贷平台的成交金额代替投资者人数进行回归,结果是稳健的。
高铭、江嘉骏、陈佳、刘玉珍,2017:《谁说女子不如儿郎?——P2P投资行为与过度自信》,《金融研究》第11期。
何启志、彭明生,2016:《基于互联网金融的网贷利率特征研究》,《金融研究》第10期。
胡金焱、宋唯实,2017:《P2P 借贷中投资者的理性意识与权衡行为——基于“人人贷”数据的实证分析》,《金融研究》第7期。
李焰、高弋君、李珍妮、才子豪、王冰婷、杨宇轩,2014:《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》,《经济研究》第S1期。
廖理、吉霖、张伟强,2015a:《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》,《金融研究》第3期。
廖理、吉霖、张伟强,2015b:《语言可信吗?借贷市场上语言的作用——来自P2P平台的证据》,《清华大学学报(自然科学版)》第4期。
廖理、李梦然、王正位,2014a:《中国互联网金融的地域歧视研究》,《数量经济技术经济研究》第5期。
廖理、李梦然、王正位,2014b:《聪明的投资者:非完全市场化利率与风险识别——来自P2P网络借贷的证据》,《经济研究》第7期。
廖理、张伟强,2017:《P2P网络借贷实证研究:一个文献综述》,《清华大学学报(哲学社会科学版)》第2期。
清华大学金融科技研究院课题组,2018:《网贷行业2018年问题平台报告》,《清华金融评论》第11期。
彭红枫、赵海燕、周洋,2016:《借款陈述会影响借款成本和借款成功率吗?——基于网络借贷陈述的文本分析》,《金融研究》第4期。
王正位、向佳、廖理、张伟强,2016:《互联网金融环境下投资者学习行为的经济学分析》,《数量经济技术经济研究》第3期。
向虹宇、廖理、王正位,2017:《注意力与P2P投资者投资决策——来自人人贷的证据》,《经济学报》第3期。
张海洋,2017:《信息披露监管与P2P借贷运营模式》,《经济学(季刊)》第1期。
张健华,2016:《互联网金融监管研究》,科学出版社。
Allen,F.,and G.R.Faulhaber,1989,“Signalling by Underpricing in the IPO Market”,Journal of Financial Economics,23(2),303—323.
Duarte,J.,S.Siegel,and L.Young,2012,“Trust and Credit:the Role of Appearance in Peer-to-peer Lending”,Review of Financial Studies,25(8),2455—2484.
Freedman,S.M.,and G.Z.Jin,2008,“Do Social Networks Solve Information Problems for Peer-to-Peer Lending?Evidence from Prosper.com”,Available at SSRN:https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 1936057.
Freedman,S.M.,and G.Z.Jin,2011,“Learning by Doing with Asymmetric Information:Evidence from Prosper.com”,Available at NBER:http://www.nber.org/papers/w16855.
Gao,Q.,M.Lin,and R.W.Sias,2018,“Words Matter:The Role of Texts in Online Credit Markets”,SSRN working paper,Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2446114.
Karlan,D.,and J.Zinman,2009,“Observing Unobservables:Identifying Information Asymmetries with a Consumer Credit Field Experiment”,Econometrica,77(6),1993—2008.
Kuhnen,C.M.,and B.T.Melzer,2018,“Noncognitive Abilities and Financial Delinquency:The Role of Self-efficacy in Avoiding Financial Distress”,Journal of Finance,73(6),2837—2869.
Liao,L.,Z.Wang,J.Xiang,and J.Yang,2017,“Thinking Fast,Not Slow:Evidence from Peer-to-peer Lending”,Available at SSRN:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2830326.
Lin,M.,N.R.Prabhala,and S.Viswanathan,2013,“Judging Borrowers by the Company They Keep:Friendship Networks and Information Asymmetry in Online Peer-to-peer Lending”,Management Science,59(1),17—35.
Lin,M.,and S.Viswanathan,2015,“Home Bias in Online Investments:An Empirical Study of an Online Crowdfunding Market”,Management Science,62(5),1393—1414.
Pope,D.G.,and J.R.Sydnor,2011,“What’s in a Picture?Evidence of Discrimination from Prosper.com”,Journal of Human Resources,46(1),53—92.
Spence,M.,1973,“Job Market Signaling”,Quarterly Journal of Economics,87,355—374.
Wei,Z.,and M.Lin,2016,“Market Mechanisms in Online Peer-to-peer Lending”,Management Science,63(12),4236—4257.
Zhang,J.,and P.Liu,2012,“Rational Herding in Microloan Markets”,Management Science,58(5),892—912.
① 统计数据来自网贷之家网站,网址为:http://shuju.wdzj.com/。
② 统计数据来源同上。
③ 资料来源:北京市人民检察院网站,http://www.ajxxgk.jcy.cn/html/20161223/1/3900846.html。
(1)这里的标准差指对应子样本中的利率标准差,下同。
(2)廖理和张伟强(2017)对P2P网贷市场的文献进行了总结回顾,本文仅梳理回顾与本文研究内容紧密相关的文献。
(3)网贷市场贷款余额数据来自网贷之家,商业银行个人贷款数据来自中国人民银行网站,小微企业贷款余额数据来自银监会网站。
(4)以2017年12月为例,网贷市场平均利率为9.54%,同期商业银行一年期定期存款基准利率为1.50%。网贷市场利率数据来自网贷之家,商业银行个人存款余额和银行基准存款利率来自中国人民银行网站。
(5)网贷平台的担保进一步增加了网贷平台的风险。如果网贷平台缺乏风险管理能力和信用评估能力,借款人可能出现大面积集中违约。此时,网贷平台可能无力提供担保,最终会破产或者停止正常营业。
(6)根据银监会2016年8月公布的《网络借贷信息中介机构业务活动管理暂行办法》,禁止网贷平台提供担保或变相担保。但在本文研究的时间区间内,监管规则尚未完全落地,绝大多数网贷平台仍然提供担保。
(7)类似的信号发送也可能出现在其他金融市场。例如,Allen & Faulhaber(1989)指出,IPO中的抑价发行(underpricing)可能是好的公司向市场进行信号发送的结果。
(8)由于篇幅所限,此处省略了问题平台和正常平台的利率走势图。利率走势图的结果显示:问题平台的平均利率显著高于正常平台,且在问题平台倒闭之前,利率水平没有显著变化。
(9)网贷资讯门户网站通过各种方式搜集各个网贷平台的交易信息,然后向公众免费公布并及时更新各网贷平台的利率水平。具体案例参见:https://www.wdzj.com/dangan/。
(10)我们也使用网贷平台的成交金额代替投资者人数进行回归,结果是稳健的。