摘要
复杂网络理论应用于股票市场的已有文献大多研究股票网络拓扑结构,较少与投资组合理论结合.从复杂网络理论视角,利用两种网络社团检测算法将股票网络进行社团划分,用分块矩阵刻画网络社团结构,据此构建新的投资组合模型.基于上证180指数成分股的实证研究发现,在给定的期望收益下,新投资组合风险比皮尔逊相关投资组合低,且有效前沿位于皮尔逊相关投资组合有效前沿的左侧.实证结果验证了新投资组合模型的有效性.
Most of the existing literature on the application of complex network theory to the stock market has studied the topology of stock network,but the combination with portfolio theory is scarce.In this paper,from the perspective of complex network theory,two kinds of community detection algorithms are used to divide the stock network into communities,and block matrix is used to describe the community structure of the stock network,so as to construct a new portfolio.Empirical analysis on the SSE 180 index shows that when the expected return is fixed,the risk of the new portfolio constructed in this paper is lower than that of Pearson portfolio,and the effective frontier is to the left of the effective frontier of Pearson portfolio.The effectiveness of the new portfolio model is verified by empirical evidence.
作者
徐维军
罗子俊
付志能
XU Wei-jun;LUO Zi-jun;FU Zhi-neng(School of Business Administration,South China University of'Technology,Guanigahou 51640,China;Guangzhou Financial Service Innovation and Risk Management Research Base,Guangzhou 510641,China)
出处
《数学的实践与认识》
北大核心
2020年第24期285-294,共10页
Mathematics in Practice and Theory
基金
国家自然科学基金面上项目(71771091)
国家自然科学基金国际(地区)合作与交流重点项目(71720107002)
广东省基础与应用基础研究基金(2019A1515011752)。
关键词
复杂网络
社团检测
分块矩阵
投资组合
complex network
community detection
block matrix
portfolio optimization
作者简介
通信作者:付志能。
