英语原文共 23 页，剩余内容已隐藏，支付完成后下载完整资料
Abstract
This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loansrsquo; data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are
loan purpose, annual income, current housing situation, credit history and indebtedness.Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrowerrsquo;s debt level.Peer-to-peer (P2P) lending consists in individuals lending money to other individuals, without the inter mediation of a financial institution. P2P can be analyzed under several approaches. It can be considered as an example of financial disinter mediation;as another technological disruption provoked by Internet; as a case of collaborative economy, or even as a
platform to give loans to financially excluded people. Although no traditional bank is present in the process, there is an electronic lending platform that mediates between borrowers and lenders of loans, charging a fee for this service. Companies such as Prosper or Lending Club channel loans between individuals, whereas Kiva is focused on funding low-income people.P2P growth is remarkable, both in the number of loans and the number of investors, attracted by high returns expectations or socially responsible investment concerns.
The first research question of this paper aims at analyzing factors explaining default in P2P lending.P2P lending companies provide information on borrowersrsquo; characteristics and loan purpose. Hence, each loan is rated with a grade that tries to capture the risk of default and thus investors can make their choices. If the P2P lending site does its job well; the lower the grade,the higher the default risk is and, consequently, the higher the interest rate will be. This paper analyzes the relationship among the grade, the interest rate and the default, empirically. It also poses a series of hypotheses on the relationship between default and the information provided by P2P lending companies on aspects such as loan size, loan purpose and borrowerrsquo;s characteristics like annual income, indebtedness and credit history. The aim is to study the relevance of the information provided by the P2P lending site for lendersrsquo; decision making and for lowering information asymmetry. In other words, if lenders should be only focused on interest rates or whether they should analyze additional factors. The empirical study uses data from Lending
Club, the biggest US P2P lending company. The sample analyzed contains 24,449 loans.
Although there is available information on all the funded loans from 2008 to 2014, only loans funded until 2011 can be analyzed, because the status of later loans (defaulted or non-defaulted) is still unknown. This happens because the minimum maturity of Lending Club loans is 36 months. For example, the status of a loan funded in September 2012 with 36 months maturity, cannot be known until September 2015. Hypotheses have been tested by using uni-variate means tests and survival analysis.
It is not only interesting to know factors explaining P2P loan default, but also to accurately predict loan defaults. The second research question presents a mathematical model to assess the predictive capability of the factors analyzed. There are several statistical techniques for credit scoring and default prediction, such as discriminant analysis,logistic regression,neural networks or classification trees, among others. Logistic regression is the most widespread technique, because it combines a high predictive capability with accuracy percentages not statistically significant different from other more recent techniques. Classification techniques assign a 0 to defaulted loans and a 1 to non-defaulted loans. Explanation requires only cross validation whereas prediction requires inter-temporal validation. To do so, a primary sample is needed, called train sample, and to validate results, a test or holdout sample. The best outcome would be that the test sample will be gathered at a later time than the train sample, to ensure inter-temporal validation. This has been done in this paper.
To the best of our knowledge, this is the first study explaining defaults in the Lending Club platform, using a database large enough to extract a holdout sample. Until recently, this was not possible due to data availability on the loan status. Our results show that, the higher the interest rate, the higher the probability of default is. The grade assigned by the P2P lending company is the best default predictor. Loan characteristics such as loan purpose; borrower characteristics like annual income, current housing situation, credit history and borrower indebtedness are related to default. However, other common drivers in default studies, such as
loan amount or length of employment, have not a significant relationship with default within the data analyzed.
Hypothesis Development
It has been shown previously that it is important to study the relevance of the information provided by the P2P lending site for lowering information asymmetry, identifying the factors explaining P2P defaults. P2P lending platforms assign a grade to each loan, relying on third party information, like FICO score, used by
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