文章基本信息

标题：The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models
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作者：Haifeng Guo ; Ke Peng ; Xiaolei Xu 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2020
卷号：2020
页码：1-10
DOI：10.1155/2020/8816419
出版社：Hindawi Publishing Corporation
摘要：This paper examines the determinants of platform default risk using machine learning methods, including comprehensive models, and thus compares these models’ predictive abilities. To test platform’s default risk, this paper constructs three types of variables, which reflect a platform’s operating characteristics, customer feedback, and compliance capability. We find that the abnormal return tends to trigger default risk significantly. However, the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China. Empirical results indicate that the CART model outperforms the Random Forests model and Logit regression in predicting platform default risk. Our study sheds light on default risk prediction and thus can improve the government regulation ability.