The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models,Scientific Programming

当前位置： X-MOL 学术 › Sci. Program. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models
Scientific Programming ( IF 1.672 ) Pub Date : 2020-11-29 , DOI: 10.1155/2020/8816419
Haifeng Guo ₁ , Ke Peng ₁ , Xiaolei Xu ₂ , Shuai Tao ₃ , Zhen Wu ₂

Affiliation

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.

更新日期：2020-12-01

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南

全部期刊列表>>