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Computational ManagementPortfolio Insurance and Intelligent Algorithms

Computational Management: Portfolio Insurance and Intelligent Algorithms [Minimizing portfolio insurance (PI) costs is an investment strategy of great importance. In this chapter, by converting the classical minimum-cost PI (MCPI) problem to a multi-period MCPI (MPMCPI) problem, we define and investigate the MPMCPI under transaction costs (MPMCPITC) problem as a nonlinear programming (NLP) problem. The problem of MCPI gets more genuine in this way. Given the fact that such NLP problems are widely handled by intelligent algorithms, we are introducing a well-tuned approach that can solve the challenging MPMCPITC problem. In our portfolios’ applications, we use real-world data and, along with some of the best memetic meta-heuristic and commercial methods, we provide a solution to the MPMCPITC problem, and we compare their solutions to each other.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Computational ManagementPortfolio Insurance and Intelligent Algorithms

Part of the Modeling and Optimization in Science and Technologies Book Series (volume 18)
Editors: Patnaik, Srikanta; Tajeddini, Kayhan; Jain, Vipul
Computational Management — May 30, 2021

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References (44)

Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-72928-8
Pages
305 –323
DOI
10.1007/978-3-030-72929-5_14
Publisher site
See Chapter on Publisher Site

Abstract

[Minimizing portfolio insurance (PI) costs is an investment strategy of great importance. In this chapter, by converting the classical minimum-cost PI (MCPI) problem to a multi-period MCPI (MPMCPI) problem, we define and investigate the MPMCPI under transaction costs (MPMCPITC) problem as a nonlinear programming (NLP) problem. The problem of MCPI gets more genuine in this way. Given the fact that such NLP problems are widely handled by intelligent algorithms, we are introducing a well-tuned approach that can solve the challenging MPMCPITC problem. In our portfolios’ applications, we use real-world data and, along with some of the best memetic meta-heuristic and commercial methods, we provide a solution to the MPMCPITC problem, and we compare their solutions to each other.]

Published: May 30, 2021

Keywords: Portfolio selection; Multi-period portfolio insurance; Transaction costs; Nonlinear programming; Meta-heuristic optimization

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