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作 者:万洁 郝俊 WAN Jie;HAO Jun(Institutes of Science and Development,Chinese Academy of Sciences,Beijing 100190;School of Public Policy and Management,University of Chinese Academy of Sciences,Beijing 100049;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190)
机构地区:[1]中国科学院科技战略咨询研究院,北京100190 [2]中国科学院大学公共管理学院,北京100049 [3]中国科学院大学经济与管理学院,北京100190
出 处:《系统科学与数学》2023年第5期1138-1156,共19页Journal of Systems Science and Mathematical Sciences
基 金:国家自然科学基金(72201265);博士后科学基金(2022M723105);中央高校基本科研业务费专项资金(E2E40803);中国科学院大学数字经济监测预测预警与政策仿真教育部哲学社会科学实验室(培育)基金资助课题。
摘 要:作为互联网消费金融的一种新型模式,P2P借贷(peer-to-peer lending)凭借其高回报与低门槛的特点,吸引了众多投资者与借款人的关注.如何有效地实现在P2P平台贷款的投资组合决策,为不同投资者提供最佳投资方案极为重要.文章将投资组合决策转化为一个给定时间下的收益最大化、风险最小化的优化问题,通过引入混合治愈模型来改进基于实例的投资组合优化模型,使用核函数来刻画历史贷款与待投贷款的相似性,在此基础上通过混合治愈模型筛选终止时间小于给定时间的待投贷款,构建基于实例的模型,对投资者的投资组合决策进行优化.通过美国Lending Club的数据对本文模型进行实证分析,结果表明,文章模型有助于实现资金的合理化分配,使投资者获得时间、收益、风险三方面的权衡的投资组合决策.As a new model of Internet consumer finance,P2P has attracted the attention of many investors and borrowers with its advantages of high return and low threshold.How to effectively implement the portfolio decision of lending on the P2P platform is extremely important to provide the best investment plan for different investors.This paper transforms portfolio decision-making into an optimization problem of maximizing returns and minimizing risks at a given time,improves the instance-based portfolio optimization model by introducing mixture cure model,and uses kernel function to portray the similarity between historical loans and pending loans.On this basis,the mixture cure model is used to screen pending loans whose termination time is less than a given time,and build an instance-based model to optimize investors' portfolio selection decision.The results of the empirical analysis of the proposed model with data from Lending Club in the United States show that the model is helpful to rationalize the allocation of funds and enables investors to obtain an investment portfolio decision with a trade-off of time,return and risk.
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