The University of Southampton
University of Southampton Institutional Repository

Solving realistic portfolio optimization problems via metaheuristics: a survey and an example

Solving realistic portfolio optimization problems via metaheuristics: a survey and an example
Solving realistic portfolio optimization problems via metaheuristics: a survey and an example
Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality solutions to complex POPs in short computing times, which might be a desirable property of solving methods that support real-time decision making.
Portfolio optimization, SimILS, Metaheuristics, Simulation
22-30
Springer
Doering, Jana
c8b6e354-b54f-415b-8873-805cb1b7e8ea
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Fito, Angels
1fd5d8e8-9781-4d0b-8a6d-dcded432a342
Calvet, Laura
0c8e51bc-5ec3-469b-a8ab-cb2b1c760c33
León, R.
Muñoz-Torres, M.
Moneva, J.
Doering, Jana
c8b6e354-b54f-415b-8873-805cb1b7e8ea
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Fito, Angels
1fd5d8e8-9781-4d0b-8a6d-dcded432a342
Calvet, Laura
0c8e51bc-5ec3-469b-a8ab-cb2b1c760c33
León, R.
Muñoz-Torres, M.
Moneva, J.

Doering, Jana, Juan, Angel A., Kizys, Renatas, Fito, Angels and Calvet, Laura (2016) Solving realistic portfolio optimization problems via metaheuristics: a survey and an example. León, R., Muñoz-Torres, M. and Moneva, J. (eds.) In Modeling and Simulation in Engineering, Economics and Management. MS 2016. vol. 254, Springer. pp. 22-30 . (doi:10.1007/978-3-319-40506-3_3).

Record type: Conference or Workshop Item (Paper)

Abstract

Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality solutions to complex POPs in short computing times, which might be a desirable property of solving methods that support real-time decision making.

Text
KIZYS 2016 cright MSEEM Solving Realistic Portfolio Optimization Problems via Metaheuristics - Accepted Manuscript
Download (385kB)
Text
Doering2016_Chapter_SolvingRealisticPortfolioOptim - Version of Record
Restricted to Repository staff only
Request a copy

More information

e-pub ahead of print date: 26 June 2016
Keywords: Portfolio optimization, SimILS, Metaheuristics, Simulation

Identifiers

Local EPrints ID: 434502
URI: http://eprints.soton.ac.uk/id/eprint/434502
PURE UUID: 360cbe21-fa6e-4f44-b134-ff6c5da0ae31
ORCID for Renatas Kizys: ORCID iD orcid.org/0000-0001-9104-1809

Catalogue record

Date deposited: 25 Sep 2019 16:30
Last modified: 16 Mar 2024 04:41

Export record

Altmetrics

Contributors

Author: Jana Doering
Author: Angel A. Juan
Author: Renatas Kizys ORCID iD
Author: Angels Fito
Author: Laura Calvet
Editor: R. León
Editor: M. Muñoz-Torres
Editor: J. Moneva

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×