吉林大学学报(信息科学版) ›› 2019, Vol. 37 ›› Issue (4): 426-436.

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基于特征提取的网贷平台生存关键因素研究

于卓熙,温馨,李梦丽   

  1. 吉林财经大学管理科学与信息工程学院,长春130117
  • 出版日期:2019-07-24 发布日期:2019-12-16
  • 作者简介:于卓熙( 1970— ) ,女,沈阳人,吉林财经大学教授,主要从事回归分析、统计学习研究,( Tel) 86-13610728319( E-mial)yzx8170561@163. com。
  • 基金资助:
    国家社会科学基金资助项目( 16BTJ020)

Research on Key Survival Factors of Online Lending Platform Based on Feature Extraction#br#

YU Zhuoxi,WEN Xin ,LI Mengli   

  1. School of Management Science and Information Engineering,Jilin University of Finance and Economics,Changchun 130117,China
  • Online:2019-07-24 Published:2019-12-16

摘要: 针对P2P( Peer to Peer) 网贷平台出现“生命较短”的现象,分析了影响P2P 平台生存的关键因素,收集相关变量数据,应用装袋法、随机森林等数据挖掘技术对P2P 平台数据集进行分类,提取影响分类结果的重要变量; 应用生存分析中的Cox 比例风险模型和加速死亡模型进行实证研究,深度挖掘影响P2P 网贷平台“生存”或“死亡”的关键因素。结果表明,最重要的影响因素是用户资金是否进行银行存管。该结果可为投资者和监管者提供参考,为加快建立P2P 网贷平台资金的管理约束制度提供依据。

关键词: P2P 借贷, 随机森林, 装袋法, 生存分析

Abstract: In order to analyze the key factors affecting the survival of P2P ( Peer to Peer) platform for the phenomenon of“life is short”of the P2P online lending platform,we collected the relevant variable data. First,we apply the bagging method and random forest to classify the P2P platform dataset and extract the important variables that affect the classification results. Secondly,the Cox proportional hazard model and the accelerated death model in the survival analysis are used to conduct empirical research,and continue to explore the P2P online loan platform“survival”or the key factors of“death”. It indicates that the most important influencing factor is whether the user funds are deposited in the bank. This result provides reference for investors and regulators,and provides a basis for accelerating the construction of the management and restraint system for P2P online loan platform funds.

Key words: peer to peer( P2P) lending, random forest, bagging method, survival analysis

中图分类号: 

  • TP181