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Can Readability of Loan Description Affect Loan Success Rate and Loan Cost?: A Textual Analysis of P2P Loan Description
Author(s) -
Haitao Si,
Siqi Jiang,
Yizhuo Fang,
Muhammad Usman
Publication year - 2020
Publication title -
inžinerinė ekonomika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.303
H-Index - 29
eISSN - 2029-5839
pISSN - 1392-2785
DOI - 10.5755/j01.ee.31.3.25760
Subject(s) - loan , readability , participation loan , amortizing loan , non conforming loan , actuarial science , business , computer science , accounting , non performing loan , finance , programming language
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.

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