The Influence of Borrowers' Language Features on P2P Lending

Abstract

Under the background of the reshuffle of the P2P market in China, this paper investigates the influence of four borrower's language features on their funding and default rate based on language function theories. In our study, we use a logistic regression model and the empirical results show that: the more redundant the borrower's language expression is, the more open and objective the content is, and the more attention is paid to the punctuation details, the easier it is to obtain the loan successfully. When the borrower's description is more redundant and more attention is paid to the punctuation details, the probability of default would become lower. Taking the education level into consideration, we find that the negative relating effect between the description redundancy and the default rate would be lower with the increase of the borrower’s education level. Therefore, we can conclude that the four linguistic features of borrowers which are defined in this paper can alleviate the information asymmetry problem of P2P lending to some extent and the borrower's linguistic features can be included into the risk control system.