A Hybrid Approach of Optimization and Sampling for Robust Portfolio Selection

Omar Rifki () and Hirotaka Ono ()
Additional contact information
Omar Rifki: Kyushu University
Hirotaka Ono: Kyushu University

A chapter in Operations Research Proceedings 2015, 2017, pp 317-324 from Springer

Abstract: Abstract Dealing with ill-defined optimization problems, where the actual values of the input parameters are unknown or not directly measurable, is generally not an easy task. In order to enhance the robustness of the final solutions, we propose in the current paper a hybrid metaheuristic approach that incorporates a sampling-based simulation module. Empirical application to the classical mean-variance portfolio optimization problem, which is known to be extremely sensitive to noises in asset means, is provided through a genetic algorithm solver. Results of the proposed approach are compared with that specified by the baseline worst-case scenario and the two approaches of stochastic programming and robust optimization.

Keywords: Portfolio Selection; Hybrid Genetic Algorithm; Simulation Module; Portfolio Optimization Problem; Portfolio Selection Model (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-42902-1_43

Ordering information: This item can be ordered from
http://www.springer.com/9783319429021

DOI: 10.1007/978-3-319-42902-1_43

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().