東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回上頁

切換: 標籤 | MARC模式 | ISBD

Advanced portfolio optimization = a ...

Cajas, Dany.

FindBook

Google Book

Amazon

博客來

Advanced portfolio optimization = a cutting-edge quantitative approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced portfolio optimization/ by Dany Cajas.
其他題名:	a cutting-edge quantitative approach /
作者:	Cajas, Dany.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xv, 503 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming.
Contained By:	Springer Nature eBook
標題:	Portfolio management - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-84304-4
ISBN:	9783031843044

Advanced portfolio optimization = a cutting-edge quantitative approach /
Cajas, Dany.

Advanced portfolio optimizationa cutting-edge quantitative approach /[electronic resource] :by Dany Cajas. - Cham :Springer Nature Switzerland :2025. - xv, 503 p. :ill. (some col.), digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming.

This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions. Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs. The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies.

ISBN: 9783031843044

Standard No.: 10.1007/978-3-031-84304-4doiSubjects--Topical Terms:

688629
Portfolio management
--Data processing.

LC Class. No.: HG4529.5

Dewey Class. No.: 332.60285

Advanced portfolio optimization = a cutting-edge quantitative approach /
LDR:03700nmm a2200349 a 4500 001 2410383
003 DE-He213
005 20250417130325.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031843044 $q (electronic bk.)
020 $a 9783031843037 $q (paper)
024 7 $a 10.1007/978-3-031-84304-4 $2 doi
035 $a 978-3-031-84304-4
040 $a GP $c GP
041 0 $a eng
050 4 $a HG4529.5
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 332.60285 $2 23
090 $a HG4529.5 $b .C139 2025
100 1 $a Cajas, Dany. $3 3784240
245 1 0 $a Advanced portfolio optimization $h [electronic resource] : $b a cutting-edge quantitative approach / $c by Dany Cajas.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xv, 503 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming.
520 $a This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions. Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs. The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies.
650 0 $a Portfolio management $x Data processing. $3 688629
650 0 $a Portfolio management $x Mathematical models. $3 647826
650 1 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Investment Appraisal. $3 2191345
650 2 4 $a Risk Management. $3 608953
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-84304-4
950 $a Mathematics and Statistics (SpringerNature-11649)