Do P2P borrowers improve the quality of information disclosure? An analysis with text mining on loan descriptions

被引:0
|
作者
Chen, Yuan [1 ]
Feng, Ji [2 ]
Li, Xun [2 ]
Yu, Shijie [2 ]
机构
[1] Shanghai Univ Int Business & Econ, Sch Int Business, Shanghai, Peoples R China
[2] Wuhan Univ, Sch Econ & Management, Wuhan 430072, Hubei, Peoples R China
来源
INTERNATIONAL STUDIES OF ECONOMICS | 2025年 / 20卷 / 01期
基金
中国国家自然科学基金;
关键词
learning behavior; loan description; P2P lending; text mining; HERDING BEHAVIOR; SOFT INFORMATION; LENDING EVIDENCE; PEER; MARKET; UNCERTAINTY; PERSUASION; CREDIT; ERRORS; TRUST;
D O I
10.1002/ise3.91
中图分类号
F [经济];
学科分类号
02 ;
摘要
Most of peer-to-peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from , which is one of the largest P2P lending platforms in China. The loan description written by the borrower is an important way to disclose the borrower's information. We analyze changes in loan descriptions in multiple borrowings with text mining techniques and investigate whether a borrower has a learning behavior in writing loan descriptions. Empirical results show that after accumulating enough experience, borrowers can optimize the loan description to make it easier to obtain loans at lower interest rates.
引用
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页码:23 / 42
页数:20
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