A GRASP based solution approach to solve cardinality constrained portfolio optimization problems. (December 2015)

• Record Type: Journal Article
• Authors: Baykasoğlu, Adil; Yunusoglu, Mualla Gonca; Burcin Özsoydan, F.
• Is Part Of: Computers & industrial engineering. Volume 90(2015)
• Page Start: 339
• Page End: 351
• Publication Date: 2015-12
• Subjects: Portfolio optimization -- Meta-heuristics -- GRASP -- Quadratic programming; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.10.009
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A novel metaheuristic which combines GRASP with quadratic programming techniques. An algorithm which combines meta-heuristic and exact solution approaches. Novel solution generation techniques with several local search procedures. The proposed approach is applied to cardinality constrained portfolio optimization. Considerable improvements on the previously reported results. Abstract: In the current work, a solution methodology which combines a meta-heuristic algorithm with an exact solution approach is presented to solve cardinality constrained portfolio optimization (CCPO) problem. The proposed method is comprised of two levels, namely, stock selection and proportion determination. In stock selection level, a greedy randomized adaptive search procedure (GRASP) is developed. Once the stocks are selected the problem reduces to a quadratic programming problem. As GRASP ensures cardinality constraints by selecting predetermined number of stocks and quadratic programming model ensures the remaining problem constraints, no further constraint handling procedures are required. On the other hand, as the problem is decomposed into two sub-problems, total computational burden on the algorithm is considerably reduced. Furthermore, the performance of the proposed algorithm is evaluated by using benchmark data sets available in the OR Library. Computational results reveal that the proposed algorithm is competitive with the state of the art algorithms in the related literature.