SOLVING PORTFOLIO SELECTION PROBLEM USING PARTICLE SWARM OPTIMIZATION WITH CARDINALITY AND BOUNDING CONSTRAINTS

The portfolio selection of assets for an investment by investors has remain a challenge in building appropriate portfolio of assets when investing hard earned money into different assets in order to maximize returns and minimize associated risk. Different models have been used to resolve the portfolio selection problem but with some limitations due to the complexity and instantaneity of the portfolio optimization model, however, particle swarm optimization (PSO) algorithm is a good alternative to meet the challenge. This study applied cardinality and bounding constraints to portfolio selection model using a meta-heuristic technique of particle swarm optimization. The implementation of the developed model was done with python programming language. The results of this study were compared with that of the genetic algorithms technique as found in extant literature. The results obtained with the model developed shows that particle swarm optimization approach gives a better result than genetic algorithm in solving portfolio selection problem