Applying Particle Swarm Optimization
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This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz's portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio's decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for…mehr

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Produktbeschreibung
This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz's portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio's decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset.

The book explains PSO in detail and demonstrates how to implement Markowitz's portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.
Autorenporträt
Burcu Ad¿güzel Mercangöz is an Associate Professor of Operations Research at the Faculty of Transportation and Logistics, Istanbul University (Turkey). Her research interests include Quantitative Methods, Multicriteria Decision Analysis, Optimization Techniques, Optimization (Mathematics), Supply Chain Management, Transportation, Logistics, Information Systems, Heuristics, and Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization).