摘要
大数据环境下,通过建立关联关系、网络关系可挖掘隐含在数据背后的深刻规律.文章通过图网络结构表征资产组合内部的稀疏与聚类关系,采用带网络结构和低秩稀疏的最小一乘策略有效盯住目标指数,深度发掘有效特征因子并优化连续时间资产组合,从而获得非完备市场下更优的Smart Beta性能和绩效.研究发现基于链路预测的稀疏网络结构能够更好地捕捉资产之间的非线性相依特性并实现有效的资产分类结果;稀疏分散回归和网络结构的特征提取方法能够深刻揭示资产潜含的内在特性;基于最小一乘法的核范数回归策略能够自适应地优化跟踪策略,从Alpha和Beta分离的角度有效地提升了投资组合的整体业绩,对非完备市场下资产配置优化和指数型投资组合管理具有重要的指导意义.
In the big data environment,by establishing association relationships and constructing network structures,the deep rules behind the data can be mined.This paper uses graph network structure to represent the sparse clustering relationship within the asset portfolio.The least-squares strategy with network structure and low rank sparseness is used to effectively target the index,in order to explore the effective feature factors and optimize the continuous-time asset portfolio.Finally,better Smart Beta performance was obtained.Studies have found that the sparse network structure based on link prediction can better capture the non-linear dependencies between assets and achieve effective asset classification results.Sparse decentralized regression and network structure feature extraction methods can profoundly reveal the inherent characteristics of assets.The robust regression strategy based on the least-squares method can adaptively optimize the tracking strategy,effectively solve the optimal investment decision problem in an incomplete market from the perspective of Alpha and Beta separation,and improve the overall performance of the investment portfolio.It has important guiding significance for index portfolio management.
作者
李爱忠
任若恩
董纪昌
LI Aizhong;REN Ruoen;DONG Jichang(School of Public Finance&Economics,Shanxi University of Finance and Economics,Taiyuan 030006;School of Economics and Management,Beihang University,Beijing 100191;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190)
出处
《系统科学与数学》
CSCD
北大核心
2021年第7期1927-1937,共11页
Journal of Systems Science and Mathematical Sciences
基金
国家社会科学基金(19BTJ026)资助课题
