Lenders’ Investment Decision-making Research On P2P Platform

With the development of web 2.0,P2P lending grew rapidly.P2 P network makes money lending become directly without the participation of traditional lending agencies.On this new lending market,one of the key question is how investors assess the borrowers’ credit risk and get effective investment.This paper use the real loan data from a P2 P platform,respectively,using logistic regression and random forest to predict the default probability of the loan,then combine the distance measurement model and the kernel regression to evaluate the return and risk of new demand for loans.Due to the amount limit to both the investors and the lenders on P2 P platform,this paper also solved a constrained portfolio problem.Data shows that compared with traditional credit rating,the method in this paper improves the loans’ yield prediction accuracy and can choose portfolios with higher return.