A hybrid approach based on double roulette wheel selection and quadratic programming for cardinality constrained portfolio optimization. (27th December 2021)

• Record Type: Journal Article
• Authors: Hu, Bo; Xiao, Hui; Yang, Nan; Jin, Hao; Wang, Lei
• Is Part Of: Concurrency and computation. Volume 34:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-27
• Subjects: clustering algorithms -- heuristic algorithm -- portfolio optimization -- quadratic programming; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6818
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The portfolio optimization problem with cardinality constraint is usually solved by exact algorithms, heuristic algorithms, or combinations of them. We decompose the cardinality constraint mean‐variance model, and determine the assets and proportions by double roulette wheel selection (DRWS) and quadratic programming (QP), respectively. Then the accuracy of the solution is improved by a local search after we obtain the preliminary solution by combining DRWS and QP. Experimental results show that the proposed algorithm achieves better accuracy and more efficiency than the algorithms in the literature. Therefore, we can see that the algorithm designed according to the characteristics of specific problems can improve the computational efficiency, but the algorithm needs to be adjusted for different problems.