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有效因子综合偏好强度与CVaR整合优化模型
引用本文:黄东宾,周丹丹,汪涌. 有效因子综合偏好强度与CVaR整合优化模型[J]. 运筹与管理, 2019, 28(3): 24-30. DOI: 10.12005/orms.2019.0053
作者姓名:黄东宾  周丹丹  汪涌
作者单位:重庆邮电大学 经济管理学院,重庆 400065
基金项目:国家自然科学基金项目(71601026);重庆市基础前沿研究计划项目(cstc2017jcyjAX0359)
摘    要:本文从股票多维特征因子中选择有效因子,融合形成最大化有效因子综合偏好强度(IPS)的附加理性,构建并验证IPS-均值-CVaR投资组合优化模型。基于沪深300股2006~2015年数据分析显示:(1)有效因子IPS投资组合优越于单因子投资组合;(2)IPS方法相较于因子打分法,具有更优的多维数据整合功效;(3)IPS-均值-CVaR投资组合优化模型相对于均值-CVaR模型具有更优越的资产选择能力,也拓展了投资组合模型的多维数据处理能力和适用性。

关 键 词:多因子投资组合  熵权TOPSIS  均值-CVAR  偏好强度
收稿时间:2018-02-15

Strength of Preference on Effective Factors Integratedwith CVaR Portfolio Choice Model
HUANG Dong-bin,ZHOU Dan-dan,WANG Yong. Strength of Preference on Effective Factors Integratedwith CVaR Portfolio Choice Model[J]. Operations Research and Management Science, 2019, 28(3): 24-30. DOI: 10.12005/orms.2019.0053
Authors:HUANG Dong-bin  ZHOU Dan-dan  WANG Yong
Affiliation:School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
Abstract:This paper examines the selection of effective factors out of various dimensions of variables that characterize the common stocks of a market, which are then put together to form an additional rationality of maximizing the integrated preference strength(IPS)of effective factors for investment portfolios; in a subsequent manner, an IPS-Mean-CVaR model for multiple-factor portfolio selection is constructed and tested. With 10-year-data analysis of Husheng 300 stocks from 2006 to 2015, here we report three conclusions: (1)the IPS portfolio, constructed with the integrated strength of preference of effective factors outperforms that of every single effective factors; (2)the entropy-TOPSIS technique demonstrates better utility of factors integration than the commonly applied factors scoring technique; and (3)an IPS-Mean-CVaR model outperforms the classical Mean-CVaR model, and expands the capability of data analysis and its applicability for portfolio optimization.
Keywords:Multi-factor portfolio selection  Entropy-TOPSIS  Mean-CVaR  Strength of preference  
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