Linear and mixed integer programming for portfolio optimization [electronic resource]
- 作者： Mansini, Renata.
- 其他作者：
- 其他題名：
- EURO advanced tutorials on operational research,
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： EURO advanced tutorials on operational research,
- 主題： Portfolio management--Mathematical models , Linear programming , Integer programming , Economics/Management Science. , Operation Research/Decision Theory. , Finance/Investment/Banking. , Quantitative Finance. , Operations Research, Management Science.
- ISBN： 9783319184821 (electronic bk.) 、 9783319184814 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Portfolio optimization -- Linear models for portfolio optimization -- Portfolio optimization with transaction costs -- Portfolio optimization with other real features -- Rebalancing and index tracking -- Theoretical framework -- Computational issues.
- 摘要註: This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
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讀者標籤：
- 系統號： 005134609 | 機讀編目格式
館藏資訊
This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.