The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models
Joint Authors
Guo, Haifeng
Peng, Ke
Xu, Xiaolei
Tao, Shuai
Wu, Zhen
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Guo, Haifeng& Peng, Ke& Xu, Xiaolei& Tao, Shuai& Wu, Zhen. 2020. The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models. Scientific Programming،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1209152
Modern Language Association (MLA)
Guo, Haifeng…[et al.]. The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models. Scientific Programming No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1209152
American Medical Association (AMA)
Guo, Haifeng& Peng, Ke& Xu, Xiaolei& Tao, Shuai& Wu, Zhen. The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1209152
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209152