TY - Jour A2 - Riguzzi，Fairizio Au - Guo，海峰Au - Peng，Ke Au - Xu，Xialei Au - 陶，帅奥 - 吴，Zhen Py - 2020 DA - 2020/11/29 Ti - 对等的预测分析-to-Peer Lending Platforms Default Risk Based on Comparative Models SP - 8816419 VL - 2020 AB - 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. SN - 1058-9244 UR - https://doi.org/10.1155/2020/8816419 DO - 10.1155/2020/8816419 JF - Scientific Programming PB - Hindawi KW - ER -