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Metaheuristic Approaches to Portfolio Optimization
Whole particles are neighbours of each other. Therefore, the neighbour with the best possible value in the swarm is taken in to consideration in order to calculate the best.
Published in Chapter:
Particle Swarm Algorithm: An Application on Portfolio Optimization
Burcu Adiguzel Mercangoz (Istanbul University, Turkey)
Copyright: © 2019 |Pages: 33
DOI: 10.4018/978-1-5225-8103-1.ch002
Abstract
Optimization is discovering an alternative with the most cost-effective or highest-achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. Portfolio optimization in finance depends on selecting assets from an opportunity set which yields highest expected return on each level of portfolio risk. Optimization algorithms based on natural events are called heuristic algorithms. The particle swarm optimization (PSO) is a population-based heuristic optimization technique. The technique is inspired by the ability of animals such as birds and fish to adapt to their environment by applying a “sharing of knowledge” approach, to find rich food sources and to avoid hunting. This chapter focuses on portfolio selection problems and shows how to manage financial portfolios using a particle swarm optimization (PSO) technique which is a heuristic algorithm. In order to better understand the subject, the technique has been evaluated in Istanbul Stock Exchange for three transportation sector stocks.
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