The Pricing Mechanism Of P2P Online Lending Platform And The Empirical Study

This paper studies the interest problem of P2P online lending platform formed by both lenders and borrowers, the first demonstrates the results of the bank resulting from competition policy using Hotelling model:in order to compete for customers, maximize the profit target, banks will gradually expand to other areas, resulting in smaller and smaller differences, the services provided by the homogenization more serious. P2P online lending platform provides a new financing channel for small and micro enterprises and individual borrowers, extending indirect financing. Then describe the characteristics of the online lending platform, cross-platform network externalities, borrowers and investors demand elasticity is different from the typical bilateral market. This article analyzes the P2P network lending platform in finding potential trading partners, trade negotiations, trade execution and breach of recourse action in four areas, the homogenization of money due to the different conditions of individual borrowers, there are different risks, online lending platform homogenization of its classification match, improve lending efficiency. This paper analyzes the theoretical part is also different models, the effects of different pricing mechanisms for lending rates. From the empirical point of view, the lack of market interest rates, we grab the software by writing data to obtain a loan for all loans and pat platform lot of transaction data, through the establishment of bivariate model Logistic and linear regression models to study how related to the question of how to improve the success rate and the lending interest rate formation, the conclusions show that the loan term, has a very important impact on the amount, interest rate, and the reputation of the borrower’s probability of success on the financing and financing costs. Their different conditions, the duration of the loan, different interest rates, credit ratings of different borrowers ultimately financing the probability of success is not the same. This shows that the online lending platform is very sensitive to borrower’s credit rating. Platform can be displayed on the borrowers credit description, the better the reputation of the borrower, the greater the likelihood of financial success, the lower borrowing rates. According to the results of theoretical models and empirical research, pointed out the current problems of online lending industry and propose policy recommendations.