Continuous-Time Smart Beta Strategy for Kernel Norm Regression in Sparse Networks
LI Aizhong1 ,REN Ruoen2 ,DONG Jichang3
Author information+
1. School of Public Finance & Economics, Shanxi University of Finance and Economics, Taiyuan
030006; 2. School of Economics and Management, Beihang University, Beijing 100191; 3. School of
Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
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
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