Long-Short Portfolio Optimization Under Cardinality Constraints by Difference of Convex Functions Algorithm

• Published in: Journal of optimization theory and applications Vol. 161; no. 1; pp. 199 - 224
• Main Authors: Le Thi, Hoai An, Moeini, Mahdi
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.04.2014; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Applications of Mathematics; Asset acquisitions; Calculus of Variations and Optimal Control; Optimization; Computer Science; Convex analysis; Direct current; Engineering; Integer programming; Investments; Linear programming; Mathematical analysis; Mathematical models; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Portfolio management; Programming; Quadratic programming; Random variables; Short sales; Solvers; Statistical analysis; Stocks; Theory of Computation; Thresholds; Threshold constraints; Portfolio selection; DCA; Cardinality constraints; Complementarity constraints; DC programming; Mixed integer programming
• ISSN: 0022-3239; 1573-2878
• DOI: 10.1007/s10957-012-0197-0

In the matter of Portfolio selection, we consider an extended version of the Mean-Absolute Deviation (MAD) model, which includes discrete asset choice constraints (threshold and cardinality constraints) and one is allowed to sell assets short if it leads to a better risk-return tradeoff. Cardinality constraints limit the number of assets in the optimal portfolio and threshold constraints limit the amount of capital to be invested in (or sold short from) each asset and prevent very small investments in (or short selling from) any asset. The problem is formulated as a mixed 0–1 programming problem, which is known to be NP-hard. Attempting to use DC (Difference of Convex functions) programming and DCA (DC Algorithms), an efficient approach in non-convex programming framework, we reformulate the problem in terms of a DC program, and investigate a DCA scheme to solve it. Some computational results carried out on benchmark data sets show that DCA has a better performance in comparison to the standard solver IBM CPLEX.