STUDY ON THE RESAMPLING TECHNIQUE FOR RISK MANAGEMENT IN THE INTERNATIONAL PORTFOLIO SELECTION BASED ON CHINESE INVESTORS

IF 1 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2013-08-12 DOI:10.1142/S0218488513400035
Mei Yu, Jiangze Bian, Haibin Xie, Qin Zhang, D. Ralescu
{"title":"STUDY ON THE RESAMPLING TECHNIQUE FOR RISK MANAGEMENT IN THE INTERNATIONAL PORTFOLIO SELECTION BASED ON CHINESE INVESTORS","authors":"Mei Yu, Jiangze Bian, Haibin Xie, Qin Zhang, D. Ralescu","doi":"10.1142/S0218488513400035","DOIUrl":null,"url":null,"abstract":"In this paper, we employ the resampling method to reduce the sample errors and increase the robustness of the classic mean variance model. By comparing the performances of the classic mean variance portfolio and the resampled portfolio, we show that the resampling method can enhance the investment efficiency. Through an empirical study of Chinese investors who invest in both Chinese market and other twelve major financial markets, we show that the resampling method helps to improve the performance of the mean variance model.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"9 1","pages":"35-49"},"PeriodicalIF":1.0000,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1142/S0218488513400035","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3

Abstract

In this paper, we employ the resampling method to reduce the sample errors and increase the robustness of the classic mean variance model. By comparing the performances of the classic mean variance portfolio and the resampled portfolio, we show that the resampling method can enhance the investment efficiency. Through an empirical study of Chinese investors who invest in both Chinese market and other twelve major financial markets, we show that the resampling method helps to improve the performance of the mean variance model.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于中国投资者的国际投资组合风险管理重抽样技术研究
本文采用重采样的方法来减小样本误差,提高经典均值方差模型的鲁棒性。通过对经典均值方差组合和重采样组合的表现进行比较,证明了重采样方法可以提高投资效率。通过对投资于中国市场和其他12个主要金融市场的中国投资者的实证研究,我们发现重抽样方法有助于提高均值方差模型的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.70
自引率
0.00%
发文量
48
审稿时长
13.5 months
期刊介绍: The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is a forum for research on various methodologies for the management of imprecise, vague, uncertain or incomplete information. The aim of the journal is to promote theoretical or methodological works dealing with all kinds of methods to represent and manipulate imperfectly described pieces of knowledge, excluding results on pure mathematics or simple applications of existing theoretical results. It is published bimonthly, with worldwide distribution to researchers, engineers, decision-makers, and educators.
期刊最新文献
A Structure-Enhanced Heterogeneous Graph Representation Learning with Attention-Supplemented Embedding Fusion Homogenous Ensembles of Neuro-Fuzzy Classifiers using Hyperparameter Tuning for Medical Data PSO Based Constraint Optimization of Intuitionistic Fuzzy Shortest Path Problem in an Undirected Network Model Predictive Control for Interval Type-2 Fuzzy Systems with Unknown Time-Varying Delay in States and Input Vector An OWA Based MCDM Framework for Analyzing Multidimensional Twitter Data: A Case Study on the Citizen-Government Engagement During COVID-19
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1