有效因子综合偏好强度与CVaR整合优化模型

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。

Strength of Preference on Effective Factors Integratedwith CVaR Portfolio Choice Model

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.