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Information Systems Frontiers
Published in: Information Systems Frontiers 3/2017

28-03-2017

Who can get money? Evidence from the Chinese peer-to-peer lending platform

Authors: Qizhi Tao, Yizhe Dong, Ziming Lin

Published in: Information Systems Frontiers | Issue 3/2017

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Abstract

This paper explores how borrowers’ financial and personal information, loan characteristics and lending models affect peer-to-peer (P2P) loan funding outcomes. Using a large sample of listings from one of the largest Chinese online P2P lending platforms, we find that those borrowers earning a higher income or who own a car are more likely to receive a loan, pay lower interest rates, and are less likely to default. The credit grade assigned by the lending platform may not represent the creditworthiness of potential borrowers. We also find that the unique offline process in the Chinese P2P online lending platform exerts significant influence on the lending decision. We discuss the implications of our results for the design of big data-based lending markets.
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Appendix
Available only for authorised users
Footnotes
1
In the UK, the peer-to-peer lending industry lent more than 2.2 billion GBP in 2015 and this number is more than twice that of the previous year, according to the figures released by the Peer-to-Peer Finance Association (P2PFA). In the US, loans originated by P2P lenders reached 20 billion USD by 2015, again double the amount of the previous year. In the Chinese P2P market in 2015, lending volume was about 982.304 billion CNY, a rise of 388% on the previous year’s figures. The number of P2P platforms now stands at 2595 as reported by WDZJ.COM.
 
2
The People’s Bank of China (the central bank) has started the collection of credit history of individuals and entities and calculating of credit scores, but the credit information is not shared with non-banking institutions, such as P2P lending platforms.
 
4
People’s Bank of China and 10 other Ministries and Commissions jointly issued “Guiding Opinions on Promoting the Healthy Development of Internet Finance” in 2015.
 
6
The smart-money is an intelligent lending feature that allows investors to automatically allocate their money across the listings by selecting their desired risk appetite and saving plan objectives. However, Renrendai stopped the smart-money lending mode at the end of 2012.
 
7
The Shanghai Branch of China Merchants’ Bank has been providing a custodian service for the risk reserve fund. of Renrendai.com and issues monthly report on its fund flows.
 
8
We divide China into six economic regions plus an extra category made up of four municipalities. The six regions are the centre and south (Henan, Hubei, Hunan, An’hui and Jiangxi), the east coast (Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan), the north (Inner Mongolia, Hebei and Shanxi), the northeast (Jilin, Liaoning and Heilongjiang), the southwest (Sichuan, Guizhou, Yunnan, Guangxi and Tibet), and the northwest (Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang). The four municipalities are Beijing, Shanghai, Tianjin, and Chongqing.
 
9
In the sample, we find there are no defaults on the loans which were originated by the offline authentication and third-party referral lending models. All default loans come from the loans which were funded based on the pure online lending process. Therefore, we examine the determinants of default probability based on the sample which only includes the loans made by the pure online lending process.
 
10
We also use Logit models to estimate the probability that a listing is funded successfully and a loan default is used as a robustness check. The Logit regression results are consistent with those from the Probit models.
 
Literature
Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Lehmann, M., & Tiburtius, P. (2011). Online peer-to-peer lending- a literature review. Journal of Internet Banking and Commerce, 16(2), 1–18.
Belleflamme, P., Omrani, N., & Peitz, M. (2015). The economics of crowdfunding platforms. Information Economics and Policy, 33, 11–28.CrossRef
Berger, S. C., & Gleisner, F. (2009). Emergence of financial intermediaries in electronic markets: the case of online P2P lending. Business Research Journal, 2(1), 39–65.CrossRef
Carr, J. H., & Megbolugbe, I. F. (1993). The federal reserve bank of boston study on mortgage lending revisited. Journal of Housing Research, 4(2), 277–313.
Chen, Z., Li, B., Keung, G., Yin, H., Lin, C., & Wang, Y. (2009). How scalable could P2P live media streaming system be with the stringent time constraint? In IEEE International Conference on Communications (pp. 1–5).
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Colombo, M., Franzoni, C., & Rossi-Lamastra, C. (2015). Internal social capital and the attraction of early contributions in crowdfunding. Entrepreneurship Theory and Practice, 39(1), 75–100.CrossRef
Culkin, N., Ekaterina, M., & Andrew, D. (2016). Critical innovations in the UK peer-to-peer (P2P) alternative finance markets for small firm growth. The International Journal of Entrepreneurship and Innovation, 17(3), 192–202.CrossRef
Cumming, D., & Johan, S. (2016). Crowdfunding and entrepreneurial internationalization. In N. Dai, D. Siegel (Eds.), Entrepreneurial finance: Managerial and policy implications. Singapore: The World Scientific Publishers.
Cumming, D., & Zhang, Y. (2016). Alternative investments in emerging markets: a review and new trends. Emerging Markets Review, 29, 1–23.CrossRef
Diamond, D. W. (1984). Financial intermediation and delegated monitor. Review of Economic Studies, 51(3), 393–414.CrossRef
Dorfleitner, G., Priberny, C., Schuster, S., Stoiber, J., Weber, M., de Castro, I., & Kammler, J. (2016). Description-text related soft information in peer-to-peer lending–evidence from two leading European platforms. Journal of Banking & Finance, 64(C), 169–187.CrossRef
Duarte, J., Siegel, S., & Young, L. (2012). Trust and credit: the role of appearance in peer-to-peer lending. Review of Financial Studies, 25(8), 2455–2484.CrossRef
Fraser, S., Bhaumik, S., & Wright, M. (2015). What do we know about entrepreneurial finance and its relationship with growth? International Small Business Journal, 33(1), 70–88.CrossRef
Freedman, S., & Jin, G. Z. (2008). Do social networks solve information problems for peer-to-peer lending? Evidence from prosper.com. SSRN Electronic Journal, 8–43.
Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417–426.CrossRef
Herzenstein, M., & Andrews, R. L. (2008). The democratization of personal consumer loans? Determinants of success in online peer-to-peer loan auctions. Bulletin of the University of Delaware, 15(3), 274–277.
Hota, C., Upadhyaya, S., & Al-Karaki, J. N. (2015). Advances in secure knowledge management in the big data era. Information Systems Frontiers, 17(5), 983–986.CrossRef
Iyer, R., Khwaja, A. I., Luttmer, E. F. P., & Shue, K. (2009). Screening in new credit markets: can individual lenders infer borrower creditworthiness in peer-to-peer lending? Scholarly. Articles, 15242(rwp09-031).
Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2016). Screening peers softly: Inferring the quality of small borrowers. Management Science, 62(6), 1554–1577.CrossRef
Kankanhalli, A., Hahn, J., Tan, S., & Gao, G. (2016). Big data and analytics in healthcare: Introduction to the special section. Information Systems Frontiers, 18(2), 233–235.CrossRef
Klafft, M. (2008). Peer to peer lending: auctioning microcredits over the internet. Social Science Electronic Publishing.
Klievink, B. Romijn, B., Cunningham, S., & de Bruijn H. (2016). Big data in the public sector: uncertainties and readiness. Information Systems Frontiers, Forthcoming.
Ladd, H. F. (1982). Equal credit opportunity: women and mortgage credit. American Economic Review, 72(2), 166–170.
Lee, E., & Lee, B. (2012). Herding behavior in online p2p lending: an empirical investigation. Electronic Commerce Research and Applications, 11(5), 495–503.CrossRef
Lin, M., Prabhala, N., & Viswanathan, S. (2009). Social networks as signalling mechanisms: evidence from online peer-to-peer lending. WISE 2009.
Lin, M., Prabhala, R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: social networks and adverse selection in online peer-to-peer lending. Management Science, 59(1), 17–35.CrossRef
Liu, D., Brass, D., & Chen, D. (2015). Friendships in online peer-to-peer lending: pipes, prisms, and relational herding. MIS Quarterly, 39(3), 729–742.
Loureiro, Y., & Gonzalez, L. (2015). Competition against common sense: insights on peer-to-peer lending as a tool to allay financial exclusion. The International Journal of Bank Marketing, 33(5), 605–623.CrossRef
Mild, A., Waitz, M., & Wockl, J. (2015). How low can you go? — Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets. Journal of Business Research, 68(6), 1291–1305.CrossRef
Poblet, M., Garcia-Cuesta, E., & Casanovas, P. (2017). Crowdsourcing roles, methods and tools for data-intensive disaster management. Information Systems Frontiers, Forthcoming.
Pope, D. G., & Sydnor, J. R. (2011). What's in a picture? EVIDENCE of discrimination from prosper.com. Journal of Human Resources, 46(1), 53–92.CrossRef
Ross, S. L., & Yinger, J. (2004). The color of credit – mortgage discrimination, research methodology, and fair-lending enforcement. Journal of Economics, 81(2), 193–196.CrossRef
Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393–410.
Thuan, N. H., Antunes, P., & Johnstone. (2016). Factors influencing the decision to crowdsource: A systematic literature review. Information Systems Frontiers, 18(1), 47–68.CrossRef
Wang, H., Martina, G., & Jay, E. A. (2009). People-to-people lending: The emerging e-commerce transformation of a financial market. Value Creation in E-business Management, 36, 182–195.CrossRef
Weiss, G. N. F., Pelger, K., & Horsch, A. (2010). Mitigating adverse selection in p2p lending – Empirical evidence from prosper.com. Social Science Electronic Publishing.
Yuan, S. & Hsieh, C. (2016). An impactful crowdsourcing intermediary design – A case of a service imagery crowdsourcing system. Information Systems Frontiers, Forthcoming.
Metadata
Title
Who can get money? Evidence from the Chinese peer-to-peer lending platform
Authors
Qizhi Tao
Yizhe Dong
Ziming Lin
Publication date
28-03-2017
Publisher
Springer US
Published in
Information Systems Frontiers / Issue 3/2017
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-017-9751-5

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