Can Readability of Loan Description Affect Loan Success Rate and Loan Cost?: A Textual Analysis of P2P Loan Description

Abstract

Text is an important form of information transfer. In a P2P (Peer-to-Peer) loan market with severe information asymmetry, loan descriptive information voluntarily written by a borrower may generate significant influence on investors’ decision-making. As an important language feature, readability determines whether the audience can accurately identify textual contents. To discuss the influence of readability of loan description on borrowing behaviors, the data of a Chinese P2P lending platform Renrendai during 2013–2017 were used in this study. Readability index of loan description was constructed through the textual analysis method, followed by an empirical verification of the influences of readability of loan description on loan success rate and loan cost. Results indicate that the readability of loan description in China’s P2P loan market can generate incremental information, and the readability of loan description has evident nonlinear relations with loan success rate and loan interest rate. Moreover, the readability of loan description presents a “reverse U-shaped” relation with loan success rate and a “U-shaped” relation with loan interest rate. Demographic information (gender and educational background) of borrowers will not influence investors’ feedback behaviors toward the readability of loan description. Conclusions provide empirical evidence for standardizing loan descriptive information on P2P lending platforms.

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

“…e model results are shown in Table 13. We find that there is a significant positive correlation between the loan amount and borrowers' default; that is, the higher the borrower's total loan amount, the greater the possibility of default, which is consistent with the conclusion obtained by most scholars [30,47]. However, there is a significant negative correlation between the loan term and borrowers' default rate.…”

“…An example is whether the public expressed positive or negative emotions regarding hot events on Facebook. In addition, it can be used to analyze the product evaluation of an e-commerce platform, whether the user's attitude towards the product is that the product is excellent quality and reasonably price or whether the user recommends buying it; therefore, generally it is mainly to judge whether the emotion is positive or negative [47]. However, the loan description is not a comment on other events or items but a restatement of the loan situation.…”