Sparse High-Order Portfolios via Proximal DCA and SCA

TL;DR

This paper introduces novel algorithms based on proximal DCA and SCA to efficiently solve complex high-order portfolio optimization problems with cardinality constraints, achieving sparse solutions with high utility.

Contribution

The paper proposes three new algorithms leveraging the DC property of the cardinality constraint for high-order portfolio optimization, with proven convergence and superior performance.

Findings

Algorithms outperform existing methods in utility and sparsity.

Proposed methods are computationally efficient.

Numerical experiments validate theoretical convergence and effectiveness.

Abstract

In this paper, we aim at solving the cardinality constrained high-order portfolio optimization, i.e., mean-variance-skewness-kurtosis model with cardinality constraint (MVSKC). Optimization for the MVSKC model is of great difficulty in two parts. One is that the objective function is non-convex, the other is the combinational nature of the cardinality constraint, leading to non-convexity as well dis-continuity. Based on the observation that cardinality constraint has the difference-of-convex (DC) property, we transform the cardinality constraint into a penalty term and then propose three algorithms including the proximal difference of convex algorithm (pDCA), pDCA with extrapolation (pDCAe) and the successive convex approximation (SCA) to handle the resulting penalized MVSK (PMVSK) formulation. Moreover, theoretical convergence results of these algorithms are established respectively. Numerical experiments on the real datasets demonstrate the superiority of our proposed methods in obtaining high utility and sparse solutions as well as efficiency in terms of time usage.