| Paper ID | SPTM-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 |
| Session | SPTM-17: Sampling, Multirate Signal Processing and Digital Signal Processing 3 |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
