Applying Particle Swarm Optimization: new solutions and cases for optimized portfolios
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. Acco...
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| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cham
Springer
2021
|
| Series: | International Series in Operations Research & Management Science
306 |
| Subjects: | |
| Summary: | 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. Markowitzs portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolios 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 Markowitzs 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 |
| Physical Description: | xii, 351 Seiten Diagramme |
| ISBN: | 9783030702816 |
Staff View
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| 505 | 8 | |a Part I: Applying Particle Swarm Optimization to Portfolio Optimization -- 1. Utility: Theories and Models -- 2. Portfolio Optimization -- 3. Behavioral Portfolio Theory -- 4. A Comparative Study on PSO with Other Metaheuristic Methods -- 5. Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems -- 6. Particle Swarm Optimization: The Foundation -- 7. The PSO Family: Application to the Portfolio Optimization Problem -- 8. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures -- 9. Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30 -- 10. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization -- Part II: Different Applications of PSO -- 11. Different Applications of PSO -- 12. Particle Swarm Optimization in Global Path Planning for Swarm of Robots -- 13. Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization -- 14. Solving Optimization Problem with Particle Swarm Optimization: Solving Hybrid Flow Shop Scheduling Problem with Particle Swarm Optimization Algorithm -- 15. Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation -- 16. An Overview of the Performance of PSO Algorithm in Renewable Energy Systems -- 17. Application of PSO in Distribution Power Systems: Operation and Planning Optimization | |
| 520 | |a 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. Markowitzs portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolios 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 Markowitzs 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 | ||
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| contents | Part I: Applying Particle Swarm Optimization to Portfolio Optimization -- 1. Utility: Theories and Models -- 2. Portfolio Optimization -- 3. Behavioral Portfolio Theory -- 4. A Comparative Study on PSO with Other Metaheuristic Methods -- 5. Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems -- 6. Particle Swarm Optimization: The Foundation -- 7. The PSO Family: Application to the Portfolio Optimization Problem -- 8. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures -- 9. Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30 -- 10. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization -- Part II: Different Applications of PSO -- 11. Different Applications of PSO -- 12. Particle Swarm Optimization in Global Path Planning for Swarm of Robots -- 13. Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization -- 14. Solving Optimization Problem with Particle Swarm Optimization: Solving Hybrid Flow Shop Scheduling Problem with Particle Swarm Optimization Algorithm -- 15. Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation -- 16. An Overview of the Performance of PSO Algorithm in Renewable Energy Systems -- 17. Application of PSO in Distribution Power Systems: Operation and Planning Optimization |
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| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
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| spelling | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios Burcu Adıgüzel Mercangö, editor Cham Springer 2021 xii, 351 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier International Series in Operations Research & Management Science 306 Part I: Applying Particle Swarm Optimization to Portfolio Optimization -- 1. Utility: Theories and Models -- 2. Portfolio Optimization -- 3. Behavioral Portfolio Theory -- 4. A Comparative Study on PSO with Other Metaheuristic Methods -- 5. Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems -- 6. Particle Swarm Optimization: The Foundation -- 7. The PSO Family: Application to the Portfolio Optimization Problem -- 8. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures -- 9. Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30 -- 10. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization -- Part II: Different Applications of PSO -- 11. Different Applications of PSO -- 12. Particle Swarm Optimization in Global Path Planning for Swarm of Robots -- 13. Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization -- 14. Solving Optimization Problem with Particle Swarm Optimization: Solving Hybrid Flow Shop Scheduling Problem with Particle Swarm Optimization Algorithm -- 15. Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation -- 16. An Overview of the Performance of PSO Algorithm in Renewable Energy Systems -- 17. Application of PSO in Distribution Power Systems: Operation and Planning Optimization 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. Markowitzs portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolios 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 Markowitzs 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 Operations research Decision making Management science Risk management Statistics Capital market Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd rswk-swf Portfolio Selection (DE-588)4046834-3 gnd rswk-swf Operations Research/Decision Theory / https://scigraph.springernature.com/ontologies/product-market-codes/521000 Operations Research, Management Science / https://scigraph.springernature.com/ontologies/product-market-codes/M26024 Risk Management / https://scigraph.springernature.com/ontologies/product-market-codes/612040 Statistics for Business, Management, Economics, Finance, Insurance / https://scigraph.springernature.com/ontologies/product-market-codes/S17010 Capital Markets / https://scigraph.springernature.com/ontologies/product-market-codes/616000 (DE-588)4143413-4 Aufsatzsammlung gnd-content Portfolio Selection (DE-588)4046834-3 s Partikel-Schwarm-Optimierung (DE-588)7658941-9 s DE-604 Mercangöz, Burcu Adıgüzel edt Erscheint auch als Druck-Ausgabe, Hardcover 978-3-030-70280-9 Erscheint auch als Druck-Ausgabe, Paperback 978-3-030-70283-0 Erscheint auch als Online-Ausgabe 9783030702816 International Series in Operations Research & Management Science 306 (DE-604)BV011630976 306 |
| spellingShingle | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios International Series in Operations Research & Management Science Part I: Applying Particle Swarm Optimization to Portfolio Optimization -- 1. Utility: Theories and Models -- 2. Portfolio Optimization -- 3. Behavioral Portfolio Theory -- 4. A Comparative Study on PSO with Other Metaheuristic Methods -- 5. Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems -- 6. Particle Swarm Optimization: The Foundation -- 7. The PSO Family: Application to the Portfolio Optimization Problem -- 8. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures -- 9. Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30 -- 10. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization -- Part II: Different Applications of PSO -- 11. Different Applications of PSO -- 12. Particle Swarm Optimization in Global Path Planning for Swarm of Robots -- 13. Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization -- 14. Solving Optimization Problem with Particle Swarm Optimization: Solving Hybrid Flow Shop Scheduling Problem with Particle Swarm Optimization Algorithm -- 15. Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation -- 16. An Overview of the Performance of PSO Algorithm in Renewable Energy Systems -- 17. Application of PSO in Distribution Power Systems: Operation and Planning Optimization Operations research Decision making Management science Risk management Statistics Capital market Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Portfolio Selection (DE-588)4046834-3 gnd |
| subject_GND | (DE-588)7658941-9 (DE-588)4046834-3 (DE-588)4143413-4 |
| title | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios |
| title_auth | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios |
| title_exact_search | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios |
| title_exact_search_txtP | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios |
| title_full | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios Burcu Adıgüzel Mercangö, editor |
| title_fullStr | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios Burcu Adıgüzel Mercangö, editor |
| title_full_unstemmed | Applying Particle Swarm Optimization new solutions and cases for optimized portfolios Burcu Adıgüzel Mercangö, editor |
| title_short | Applying Particle Swarm Optimization |
| title_sort | applying particle swarm optimization new solutions and cases for optimized portfolios |
| title_sub | new solutions and cases for optimized portfolios |
| topic | Operations research Decision making Management science Risk management Statistics Capital market Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Portfolio Selection (DE-588)4046834-3 gnd |
| topic_facet | Operations research Decision making Management science Risk management Statistics Capital market Partikel-Schwarm-Optimierung Portfolio Selection Aufsatzsammlung |
| volume_link | (DE-604)BV011630976 |
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