稀疏网络下核范数回归的连续时间Smart Beta策略

李爱忠, 任若恩, 董纪昌

系统科学与数学 ›› 2021, Vol. 41 ›› Issue (7) : 1927-1937.

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系统科学与数学 ›› 2021, Vol. 41 ›› Issue (7) : 1927-1937. DOI: 10.12341/jssms20108

稀疏网络下核范数回归的连续时间Smart Beta策略

    李爱忠1,任若恩2,董纪昌3
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Continuous-Time Smart Beta Strategy for Kernel Norm Regression in Sparse Networks

    LI Aizhong1 ,REN Ruoen2 ,DONG Jichang3
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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.

Abstract

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

关键词

图网络结构,  / 最小一乘法,  / 链路预测,  / 连续时间,  / Smart Beta.

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李爱忠, 任若恩, 董纪昌. 稀疏网络下核范数回归的连续时间Smart Beta策略. 系统科学与数学, 2021, 41(7): 1927-1937. https://doi.org/10.12341/jssms20108
LI Aizhong, REN Ruoen, DONG Jichang. Continuous-Time Smart Beta Strategy for Kernel Norm Regression in Sparse Networks. Journal of Systems Science and Mathematical Sciences, 2021, 41(7): 1927-1937 https://doi.org/10.12341/jssms20108
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