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Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data

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Abstract

Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms’ default risk issues and verifies key factors that influence their risky behaviors.

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Notes

  1. Zopa (www.zopa.com), the first P2P lending platform, was established in 2005 in the United Kingdom and it has issued over £1365 million loans to more than 0.15 million borrowers. The first P2P lending platform developed in the US, Prosper (www.prosper.com), has accumulated more than 2 million users and has successfully made over $6 billion loans. China’s first P2P lending platform, PPDai (www.ppdai.com), was launched in 2007 and the company’s transacted loans reached 110 billion RMB by early 2014.

  2. Wangdaizhijia is a leading professional site dedicated to providing P2P industry information, analyses, and data.

  3. A trade organization of UK P2P lending platforms (https://www.lendingwokrs.co.uk/blog-post/lending-works-joins-p2pfa).

  4. https://www.finextra.com/newsarticle/30165/high-rate-of-defaults-hit-p2p-lending-sector.

  5. However, one can criticize classical moral hazard arguments, as they ignore another type of moral hazard problem that arises among bank borrowers, as previous models disregard bank loans [17]. Their models then argue that less competition (e.g., more monopoly power) leads to higher loan interest rates that lead to higher costs of debt for borrowers who are more likely to engage in riskier projects. As a result, banks are exposed to more risks, as their assets are now riskier. They thus argue that strong competition can reduce bank risks due to reduced levels of default probability. However, this theory is not relevant in the case of the Chinese P2P market; platforms act as the middlemen because the platforms themselves do not determine loan lending rates and loans are not their own assets. They glean no direct benefits from loans.

  6. https://www.yooli.com; http://www.touna.cn; https://www.tdw.cn; https://www.ppmoney.com; https://www.rrjc.com/index.jsp; https://www.mindai.com; https://www.xinrong.com/index.shtml; https://www.yyfaxgroup.com; https://www.33lend.com; https://www.xiaoying.com; https://www.lcfarm.com/index.html.

  7. On July 18th, 2015, the People's Bank of China, the Ministry of Industry and Information Technology, the Ministry of Public Security, the Ministry of Finance, the State Administration for Industry and Commerce, the Legislative Affairs Office of the State Council, the China Banking Regulatory Commission, the China Securities Regulatory Commission, the China Insurance Regulatory Commission, and the Cyberspace Administration Office jointly issued guidelines on promoting the healthy development of Internet finance.

  8. Wind is a leading data and information service company that provides comprehensive financial data on all listed companies. The Wind database is supported by many industrial and academic subscribers, and its data are used for professional and academic studies published in major Chinese and international journals.

  9. In China, the first online P2P lending business was created in 2007 and it has been dramatically developed from the end of 2012 as a part of the country’s unregulated shadow banking systems [45].

  10. According to yinghang.com, 238 new P2P platforms were established in the first half of 2016. Yinghang.com (www.yinhang.com) is the most popular search platform for financial products and is affiliated with its American parent company Bankrate (NYSE: RATE).

  11. Chinese Yuan.

  12. Macao Dollars.

  13. We also conduct a Variant-inflation factors (VIF) test and find no serious multicollinearity issues in our dataset (average score of 2.20).

  14. One might argue that this is also due to the fact that required information might not be shared across different platforms; thus, borrowers’ information can be misleading. While this is possible, the issue falls outside of the scope of this study. Future studies must examine the effectiveness of such information by considering interactions between different platforms.

  15. We thank anonymous reviewers who suggest this issue.

  16. Other competition measurements are also employed in previous literature as well as the numbers of platforms: e.g. indices reflecting monopoly power such as Tobin’s Q and Lerner Index [10, 66, 68, 79, 91], the concentration measurements such as 3-, 4-, or 5- firm concentration rate (i.e., C3, C4, and C5) [9, 66] and the Herfindahl-Hirschmann index (HHI) [18, 103], and so on. However, these data are not available for Chinese P2P platforms. Also, due to the property of cross-section data, it is not appropriate to use annual data in our model.

  17. The marginal effect \(\left( {\frac{{\partial {\text{Prob}}\left( {PDR_{i} = 1 | {\text{x}}} \right)}}{{\partial x_{j} }}} \right)\) is computed as \(\emptyset \left( {{\rm{x}^{\prime}\upbeta }} \right)*\beta_{j}\) and \(\Lambda (\rm {x}^{\prime}\upbeta )[1 - \Lambda (\rm{x}^{\prime}\upbeta )]*\beta _{j}\) for the Probit and Logit models, respectively [21].

  18. Thank you for this suggestion for the robustness check from the anonymous reviewer.

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  1. School of Management, Kyung Hee University, Obis Hall #506, Seoul, 02453, Korea

    Yeujun Yoon

  2. Peking University HSBC Business School, Office 758, Xili, Shenzhen, 518055, China

    Yu Li & Yan Feng

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Yoon, Y., Li, Y. & Feng, Y. Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data. Electron Commer Res 19, 131–158 (2019). https://doi.org/10.1007/s10660-018-9291-1

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