Acta academica karviniensia 2011, 11(3):24-33 | DOI: 10.25142/aak.2011.043

SOLVING CARDINALITY CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM BY BINARY PARTICLE SWARM OPTIMIZATION ALGORITHM

Aleš Kresta
Ing. Aleš Kresta, Katedra financí, Ekonomická fakulta, VŠB-TU Ostrava, Sokolská tř. 33, 701 21 Ostrava, ales.kresta@vsb.cz

Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle swarm optimization algorithm and quadratic programming method to find an efficient frontier in portfolio optimization problem. Two datasets are utilized. First dataset consists of the stocks incorporated in the Dow Jones Industrial Average, second dataset contains stocks from the Standard & Poor's 500. The comparison of found efficient frontiers for different limitation on the number of stock held is made at the close of the paper.

Keywords: portfolio optimization, binary particle swarm optimization
JEL classification: C61, G11

Published: September 30, 2011  Show citation

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Kresta A. SOLVING CARDINALITY CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM BY BINARY PARTICLE SWARM OPTIMIZATION ALGORITHM. Acta academica karviniensia. 2011;11(3):24-33. doi: 10.25142/aak.2011.043.
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