|
|
|
| Pricing Deviation and Passive Default Risk of Peer-to-Peer Borrowers: An Analysis Based on Transaction Data from Renrendai.com |
| FENG Sixian, NA Jinling
|
| School of Business, Nanjing Normal University; School of Economics, Nanjing University |
|
|
|
Abstract China's P2P (peer-to-peer) online lending industry has experienced numerous twists and turns in its development process, during which there have been several serious platform crises that have hindered the healthy development of the industry. Default risk has become an unavoidable problem, resulting in the loss of capital allocation efficiency in the industry. Previous studies have focused on active defaults, in which borrowers intentionally or strategically default despite having the ability to repay; passive defaults have been discussed less frequently. On many P2P platforms, borrowers can independently set borrowing rates in advance and make loan orders. Due to their relatively weak bargaining skills, borrowers are likely to produce pricing deviation, causing an unexpected rise of repayment pressure. When the pressure exceeds the borrower's ability, the borrower will have to default. This paper argues that even if there is no active default intention, excessive pricing deviation will often break the borrower's income balance and lead to passive default. We further propose that the borrower, as the party with more information, will try to reduce pricing deviation through information whitewashing and reverse his/her inferior position to obtain a larger bargaining surplus. We therefore study the characteristics of this behavior and investors' reaction to it.
In the theoretical part, based on behavior asset pricing theory and the pricing mechanism of Renrendai.com, this paper explores the reasons for pricing deviation. In an attempt to obtain a lower borrowing rate, borrowers may use unverifiable soft information; consequently, this paper discusses information whitewashing. Considering reputation cost and income balance, this paper also analyzes the relationship between pricing deviation and default risk.
In the empirical part, with data from Renrendai.com from 2014 to 2018, this paper studies the factors influencing the pricing deviation of peer-to-peer borrowers and the relationship between pricing deviation and passive default risk. Pricing deviation is obtained through a stochastic frontier approach by dividing interest rate into an efficient part and an inefficient part. The paper finds substantial pricing deviation among borrowers, which varies between different groups. Borrowers' whitewashing fails to reduce pricing bias and may even backfire. Furthermore, when the borrower's reputation cost is higher than the lending rate, defaults are mainly passive. Even if the borrower does not actively intend to default, the greater the pricing deviation, the tighter the borrower's remaining income, and the greater the likelihood that the number of overdue payments and the proportion of debt owed will increase, which leads to greater risk of passive default.
Based on these empirical results, this paper makes several suggestions. First, the rate pricing mechanism should be improved, which means that rate flexibility should be increased and P2P interest rate marketization should be promoted. Second, information disclosure should be strengthened and information standardization should be implemented. Finally, it is necessary to improve default risk control, understand borrowers' motivations, reduce the probability of passive default, and improve the efficiency of capital allocation.
Our study contributes to the literature in the following ways. First, the cost stochastic frontier (SFA) is used to construct the pricing deviation index. In contrast to the literature, this construction method comprehensively considers loan availability and the post-loan default risk of a loan order, which classifies the optimal interest rate and pricing deviation more objectively and reasonably. Second, focusing on the limits of platform information verification, this paper investigates the information whitewashing behavior of borrowers and the reaction of investors, and it reveals the information transmission and bargaining mechanism of both parties on the platform. Third, it outlines the reasons for borrowers' passive default, examines the relationship between pricing deviation and passive default, and enriches the risk control strategy of P2P platforms.
|
|
Received: 07 January 2019
Published: 30 March 2020
|
|
|
|
|
| [1] |
陈霄和叶德珠,2016,《定价效率、不确定性与借款利率——来自P2P网络借贷的经验证据》,《国际商务(对外经济贸易大学学报)》第5期,第113～122页。
|
| [2] |
方先明和高爽,2018,《上市公司管理层修正公告披露策略的市场反应》,《中国工业经济》第2期,第176～192页。
|
| [3] |
郭峰,2016,《网络昵称与P2P借贷的成功率和违约率》,《经济科学》第6期,第102～116页。
|
| [4] |
何启志和彭明生,2016,《基于互联网金融的网贷利率特征研究》,《金融研究》第10期,第95～110页。
|
| [5] |
李双杰、王林和范超,2007,《统一分布假设的随机前沿分析模型》,《数量经济技术经济研究》第4期,第84~91页。
|
| [6] |
廖理、吉霖和张伟强,2015,《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,第146～159页。
|
| [7] |
廖理、李梦然和王正位,2014,《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,第125～137页。
|
| [8] |
彭红枫、赵海燕和周洋,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,第158～173页。
|
| [9] |
苏治和胡迪,2014,《农户信贷违约都是主动违约吗？——非对称信息状态下的农户信贷违约机理》,《管理世界》第9期,第77～89页。
|
| [10] |
王会娟和廖理,2014,《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》,《中国工业经济》第4期,第136～147页。
|
| [11] |
王博、张晓玫和卢露,2017,《网络借贷是实现普惠金融的有效途径吗——来自“人人贷”的微观借贷证据》,《中国工业经济》第2期,第98～116页。
|
| [12] |
吴雨、李洁和尹志超,2018,《房价上涨对P2P网络借贷成本的影响分析——来自“人人贷”的经验证据》,《金融研究》第11期,第85～97页。
|
| [13] |
徐龙炳、陈历轶和邵天,2018,《上市公司为何更名:行为信号理论新解》,《财经研究》第8期,第74～87页。
|
| [14] |
叶德珠和陈霄,2017,《标点与字数会影响网络借贷吗——来自“人人贷”的经验证据》,《财贸经济》第5期,第65～79页。
|
| [15] |
郑志刚、刘思敏和黄继承,2018,《我国上市公司策略性股票更名:战略转型还是高位套现？》,《金融研究》第10期,第159～173页。
|
| [16] |
朱家祥、沈艳和邹欣,2018,《网络借贷:普惠？普骗？与监管科技》,《经济学(季刊)》第4期,第1599～1622页。
|
| [17] |
朱信凯和刘刚,2009,《二元金融体制与农户消费信贷选择——对合会的解释与分析》,《经济研究》第3期,第43～55页。
|
| [18] |
Aigner, Dennis, C. A. K. Lovell, and P. Schmidt. 1977. “Formulation and Estimation of Stochastic Frontier Production Function Models”, Journal of Econometrics, 6(1):21～37.
|
| [19] |
Baker, Malcolm, B. Mendal, and J. Wurgler. 2016. “Dividends as Reference Points: A Behavioral Signaling Approach” ,The Review of Financial Studies, 29(3):697～738.
|
| [20] |
Dorfleitner, Gregor, C. Priberny, S. Schuster, J. Stoiber, M. Weber, and I. De Castro. 2016. “Description-text Related Soft Information in Peer-to-Peer Lending -Evidence from Two Leading European Platforms”, Journal of Banking & Finance, 64(3):169～187.
|
| [21] |
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.
|
| [22] |
Emekter, Riza, Y. Tu, B. Jirasakuldech, and M. Lu. 2014. “Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer Lending”, Applied Economics, 47(1):54～70.
|
| [23] |
Freedman, Seth and G. Z. Jin. 2017. “The Information Value of Online Social Networks: Lessons from Peer-to-Peer Lending”, International Journal of Industrial Organization, 51(3):185～222.
|
| [24] |
Green, T. Clifton and R. Jame. 2013. “Company Name Fluency, Investor Recognition, and Firm Value”, Journal of Financial Economics, 109(3):813～834.
|
| [25] |
Greiner, Martina E. and H. Wang. 2010. “Building Consumer-to-Consumer Trust in E-Finance Marketplaces: An Empirical Analysis”, International Journal of Electronic Commerce, 15(2):105～136.
|
| [26] |
Lin, Mingfeng, N. Prabhala, and S. Viswanathan. 2013. “Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending”, Social Science Electronic Publishing, 59(1):17～35.
|
| [27] |
Michels, Jeremy. 2012. “Do Unverifiable Disclosures Matter？ Evidence from Peer-to-Peer Lending”, Accounting Review, 87(4):1385～1413.
|
| [28] |
Pope, Devin 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.
|
| [29] |
Ravina, Enrichetta. 2012. “Love & Loans: The Effect of Beauty and Personal Characteristics in Credit Markets”, SSRN Working Paper, No. 1101647.
|
| [30] |
Rona-Tas, Akos and A. Guseva. 2013. “Information and Consumer Credit in Central and Eastern Europe”, Journal of Comparative Economics, 41(2):420～435.
|
| [31] |
Stein, Jeremy C.. 2002. “Information Production and Capital Allocation: Decentralized versus Hierarchical Firms”, The Journal of Finance, 57(5):1891～1921.
|
| [32] |
Stiglitz, Joseph E and A. Weiss. 1981. “Credit Rationing in Markets with Imperfect Information”, American Economic Review, 71(3):393～410.
|
| [33] |
Wang, Hung J. and P. Schmidt. 2002. “One-step and Two-step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels”, Journal of Productivity Analysis, 18(2):129～144.
|
|
|
|
