國立虎尾科技大學 |

Applying Particle Swarm Optimization = New Solutions and Cases for Optimized Portfolios /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applying Particle Swarm Optimization/ edited by Burcu Adıgüzel Mercangöz.
其他題名:	New Solutions and Cases for Optimized Portfolios /
其他作者:	Mercangöz, Burcu Adıgüzel.
面頁冊數:	XII, 351 p. 85 illus., 10 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Capital Markets. -
電子資源:	https://doi.org/10.1007/978-3-030-70281-6
ISBN:	9783030702816

Applying Particle Swarm Optimization = New Solutions and Cases for Optimized Portfolios /
Applying Particle Swarm OptimizationNew Solutions and Cases for Optimized Portfolios /[electronic resource] :edited by Burcu Adıgüzel Mercangöz. - 1st ed. 2021. - XII, 351 p. 85 illus., 10 illus. in color.online resource. - International Series in Operations Research & Management Science,3062214-7934 ;. - International Series in Operations Research & Management Science,227.

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. 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.

ISBN: 9783030702816

Standard No.: 10.1007/978-3-030-70281-6doiSubjects--Topical Terms:

1106532
Capital Markets.

LC Class. No.: HD30.23

Dewey Class. No.: 658.40301

Applying Particle Swarm Optimization = New Solutions and Cases for Optimized Portfolios /
LDR:04074nam a22004215i 4500 001 1054192
003 DE-He213
005 20210915074206.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030702816 $9 978-3-030-70281-6
024 7 $a 10.1007/978-3-030-70281-6 $2 doi
035 $a 978-3-030-70281-6
050 4 $a HD30.23
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.40301 $2 23
245 1 0 $a Applying Particle Swarm Optimization $h [electronic resource] : $b New Solutions and Cases for Optimized Portfolios / $c edited by Burcu Adıgüzel Mercangöz.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XII, 351 p. 85 illus., 10 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a International Series in Operations Research & Management Science, $x 2214-7934 ; $v 306
505 0 $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. 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.
650 2 4 $a Capital Markets. $3 1106532
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 1211158
650 2 4 $a Risk Management. $3 569483
650 2 4 $a Operations Research, Management Science. $3 785065
650 1 4 $a Operations Research/Decision Theory. $3 669176
650 0 $a Capital market. $3 556702
650 0 $a Statistics . $3 1253516
650 0 $a Risk management. $3 559158
650 0 $a Management science. $3 719678
650 0 $a Decision making. $3 528319
650 0 $a Operations research. $3 573517
700 1 $a Mercangöz, Burcu Adıgüzel. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1359211
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030702809
776 0 8 $i Printed edition: $z 9783030702823
776 0 8 $i Printed edition: $z 9783030702830
830 0 $a International Series in Operations Research & Management Science, $x 0884-8289 ; $v 227 $3 1254441
856 4 0 $u https://doi.org/10.1007/978-3-030-70281-6
912 $a ZDB-2-BUM
912 $a ZDB-2-SXBM
950 $a Business and Management (SpringerNature-41169)
950 $a Business and Management (R0) (SpringerNature-43719)