Distinctive features of student borrowers and suboptimal investor decision‐making: Evidence from the P2P lending market

Abstract

This study attempts to identify the characteristics and behavioral features of student borrowers, and to investigate how well individual investors incorporate the uniqueness of these borrowers in their funding decisions. We find that student borrowers are distinct from other types of borrowers, not only with respect to factors that existed prior to joining the market, but also in terms of information generated during loan requests, subsequent cancellation behavior, repayment performance, and even in the composition and predictive accuracy of loan repayment models. However, individual investors are not fully aware of the unique attributes of student borrowers in their funding decisions.

The Role of Marital Status in the Online Lending Market: Evidence from the Renrendai Platform

“…(2014) verify the existence of regional discrimination in China's P2P online lending market based on data from Renrendai and further point out that this regional discrimination is irrational. Kim and Kim (2020) show that student borrowers are significantly different from other types of borrowers but that individual investors do not discriminate against student borrowers in their financing decisions.…”

The Role of Social Media in Financial Risk Prediction: Evidence from China*

“…The research focus of borrower default risk detection is analyzing borrowers' attributes that may influence their default behaviors. Hard information on applicants' characteristics, such as credit ratings, income level, education level, and marital status, is useful (Emekter et al ., 2015; Kim and Kim, 2020), or soft information (Ge et al ., 2017; Chen et al ., 2018; Dorfleitner et al ., 2021) play a role on default behaviors. Machine‐learning approaches often outperform traditional statistical evaluation methods (Weiss et al ., 2010; Lin et al ., 2013; Gao et al ., 2021).…”

“…There are two types of P2P lending risk: the default risk of individual borrowers and the default risk of platforms (Suominen and Toivanen, 2016). The majority of existing studies have been designed to assess individual borrowers' default risk (Kaminski et al ., 2004; Weiss et al ., 2010; Lin et al ., 2013; Emekter et al ., 2015; Ge et al ., 2017; Chen et al ., 2018; Duan, 2019; Kim and Kim, 2020; Liu, 2020; Gao et al ., 2021); while other studies focused on identifying the role of detailed information in understanding platform risks or performances (Liu and Sun, 2018; Yang et al ., 2018, 2019; Gu et al ., 2019; Fu et al ., 2020).…”