2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-17.2
Paper Title SPARSE HIGH-ORDER PORTFOLIOS VIA PROXIMAL DCA AND SCA
Authors Jinxin Wang, The Chinese University of Hong Kong, Hong Kong SAR China; Zengde Deng, Cainiao Network, China; Taoli Zheng, Anthony Man-Cho So, The Chinese University of Hong Kong, Hong Kong SAR China
SessionSPTM-17: Sampling, Multirate Signal Processing and Digital Signal Processing 3
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we study the cardinality constrained mean-variance-skewness-kurtosis (MVSKC) model for sparse high order portfolio optimization. The MVSKC model is computationally challenging, as the objective function is non-convex and the cardinality constraint is discontinuous. Since the cardinality constraint has the difference-of-convex (DC) property, we transform it into a penalty term and then propose three algorithms, namely the proximal difference-of-convex algorithm (pDCA), pDCA with extrapolation (pDCAe), and the successive convex approximation (SCA), to handle the resulting penalized mean-variance-skewness-kurtosis (PMVSK) formulation. Moreover, we establish theoretical convergence results for pDCA and SCA. Numerical experiments on a real dataset demonstrate the superiority of our proposed methods in obtaining better objective values and sparser solutions efficiently.