The Borrowing Cost of Peer-to-peer Lending in China: An Empirical Study University of Amsterdam Economics MSc Track Industrial Organization, Regulation and Competition Policy Master Thesis Name: Zhang Yiming Student number: 10652108 Student email: [email protected] Supervisor: He Simin Master thesis The borrowing cost of peer-to-peer lending in China: an empirical study Zhang Yiming Abstract This paper studies the borrowing costs in the peer-to-peer (P2P) lending market in China. Different modes of P2P platforms are identified, and the fee schedules vary among them. The distinction between the actual borrowing costs and the interest rates posted online is emphasized, because large differences are observed. The main reason is that there is not a well-established national credit scoring system available in China, and P2P platforms have to conduct most parts of credit grading and provide some kinds of safeguard from lenders. Data from two P2P platforms is analyzed and the results show that hard information (credit grade, financial conditions, etc.) is given more weight while the effect soft information (gender, loan purpose, etc.) is rather ambiguous in the determination of borrowing costs. Key words: P2P, credit grade, borrowing costs I would like to thank my supervisor, He Simin, from University of Amsterdam, for her comments and assistance. 1 Master thesis The borrowing cost of peer-to-peer lending in China: an empirical study Zhang Yiming Table of Contents 1 Introduction ..................................................................................................................... 3 2 Literature review ............................................................................................................. 4 3 Background: P2P lending in China ............................................................................... 8 3.1. Market overview ....................................................................................................... 8 3.2. Operation modes of P2P platforms in China ......................................................... 8 3.2.1. Basic P2P modes in Western countries ........................................................... 8 3.2.2. P2P modes in China ........................................................................................ 11 3.2.3. More suitable modes in China ....................................................................... 13 4. Data and Methodology .................................................................................................. 15 4.1. Data sample ............................................................................................................. 15 4.2. Methodology ........................................................................................................... 18 5. Analysis ........................................................................................................................... 20 5.1. Summary statistics ................................................................................................. 20 5.2. Regression analysis ................................................................................................. 24 5.2.1. Anaylsis of the pooled sample ........................................................................ 25 5.2.2. Anaylsis of Renrendai .................................................................................... 27 5.2.3. Anaylsis of Kaikaidai...................................................................................... 29 5.2.4. Anaylsis of Kaikaidai’s default rate .............................................................. 31 5.3. Discussion ................................................................................................................ 32 6. Conclusion ...................................................................................................................... 34 7. Reference ........................................................................................................................ 36 2 Master thesis The borrowing cost of peer-to-peer lending in China: an empirical study Zhang Yiming 1 Introduction Peer-to-peer (P2P) lending has emerged as one of the most notable financial innovations in recent years. In this new mode of finance, borrowers post their loan requests on P2P platforms and lenders can choose to fund them after judging their information disclosed online. Those who are in urgent need of money would find P2P lending extremely convenient: on the British platform Zopa1, for example, borrowers can receive loans within two working days if everything goes smoothly. With the help of P2P platforms, lenders and borrowers can match each other without traditional financial intermediaries such as banks. Not only does it provide extra source of finance for borrowers, but it also creates a new way of investment for lenders. A major P2P platform in the US, Lending Club, is planning its process of going public recently. This news has made people dream of the prosperity of this new area. In the wake of the global financial crisis, as banks are extremely careful about making loans, peer-to-peer lending has seen explosive growth as an easier way to acquire small loans. This is just the case in China. Hundreds of platforms have been launched which provide liquidity to those who are turned down by banks. Now that the financial markets in China are not as mature as in western countries, it would be interesting to see how P2P lending, which is only ten years from its birth, has developed in China. This paper looks into the Chinese P2P lending market, with an emphasis on the financing costs of borrowers. Different modes of P2P platforms are identified based on what kind of safeguard they provide to lenders and how they approach borrowers. Those platforms tend to have varied fee schedules. An interesting phenomenon is observed that the actual borrowing costs of peer-to-peer lending are much higher than the interest rates posted online, which suggests that the P2P platforms in China are acting more than information intermediaries. Data manually collected from two P2P platforms is analyzed and it turns out that the determination of borrowing costs is different from the determination of interest rates. Furthermore, it is the hard information that exerts more influence on the borrowing costs, and the role of soft information is still not clearly understood by P2P platforms in China. The structure of the paper is as follows: the next section reviews relevant literature on P2P lending; section 3 introduces the basic information about P2P lending in China and distinguishes several different operation modes of P2P platforms; section 4 introduces the manually collected dataset; section 5 analyzes the data, with the emphasis on the distinctions between the two platforms as well as the difference between borrowing costs and interest rates. Finally, section 6 concludes. 1 www.zopa.com 3 Master thesis The borrowing cost of peer-to-peer lending in China: an empirical study Zhang Yiming 2 Literature review When it comes to an innovative financial mode such as peer-to-peer lending, one of the first question to ask is: what role does it play in the current financial system? Berger and Gleisner (2009) studied the self-formed lending groups on the platform Prosper1. They found that the group leaders act as financial intermediaries by filtering out potential borrowers with lower credit scores, so that they reduce the information asymmetry prevalent in the electronic marketplace. Recommending loan listing by group leaders significantly increases the average borrowers' credit conditions and reduces the interest rate spread. Weiss et al. (2010) presented empirical evidence that P2P platforms are successful in limiting adverse selection from bad borrowers. Their results show that P2P lending platforms can act as direct financial intermediaries and reduce information asymmetry via the screening of potential borrowers. Khwaja et al. (2013) found that lenders on the online peer-to-peer lending market can effectively use soft information, and such information is more important in screening out borrowers with lower credit scores. Higher dependence on soft information enables peer-to-peer markets to complement traditional sources of finance and improve access to credit. When the role of peer-to-peer lending is clear, the focus of mainstream research turns to the determinants of P2P lending, e.g., what affects the funding rate (probability of being fully funded), interest rate, and default rate of a certain loan listing? The financial conditions of borrowers are among the most important determinants. Herzenstein et al. (2008) found that borrowers with a credit rating of AA or A (rating with the lowest risk assigned by the platform Prosper, based on individual credit history) are almost ten times as likely to be funded for a loan as borrowers with HR rating (representing the highest risk). Lin et al. (2009) provided further evidence that borrowers with lower credit grades are less likely to get the loan successfully. Furthermore, Klafft (2008) showed that the borrower’s credit rating and debt-to-income ratio have the greatest impact on the interest rate of the loan, and this is supported by Collier and Hampshire (2010). In addition, Puro et al. (2010) discovered that the loan amount requested have a negative impact on the funding rate and interest rate of a listing. But the story does not end here, researchers keep looking for other factors that may