research-article

Multiobjective robustness for portfolio optimization in volatile environments

Authors:

Christopher D. ClackAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1507 - 1514

https://doi.org/10.1145/1389095.1389387

Published: 12 July 2008 Publication History

Abstract

Multiobjective methods are ideal for evolving a set of portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk, and an investor can choose her preferred point on the risk-return frontier. However, there are no guarantees that a low-risk solution will remain low-risk . if the environment changes, the relative positions of previously identified solutions may alter. A low-risk solution may become high-risk and vice versa.

The robustness of a Multiobjective Genetic Programming (MOGP) algorithm such as SPEA2 is vitally important in the context of the real-world problem of portfolio optimisation. We explore robustness in this context, providing new definitions and a statistical measure to quantify the robustness of solutions.

A new robustness measure is incorporated into a MOGP fitness function to bias evolution towards more robust solutions. This new system ("R-SPEA2") is compared against the original SPEA2 and we present our results.

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Cited By

Vivek YPrasad PMadhav VLal RRavi V(2024)Optimal Technical Indicator Based Trading Strategies Using Evolutionary Multi Objective Optimization AlgorithmsComputational Economics10.1007/s10614-024-10701-6Online publication date: 27-Sep-2024
https://doi.org/10.1007/s10614-024-10701-6
Ferreira FGandomi ACardoso R(2021)Artificial Intelligence Applied to Stock Market Trading: A ReviewIEEE Access10.1109/ACCESS.2021.30581339(30898-30917)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3058133
Quintana DDenysiuk RGarcia-Rodriguez SGaspar-Cunha A(2017)Portfolio Implementation Risk Management Using Evolutionary Multiobjective OptimizationApplied Sciences10.3390/app71010797:10(1079)Online publication date: 18-Oct-2017
https://doi.org/10.3390/app7101079
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Index Terms

Multiobjective robustness for portfolio optimization in volatile environments
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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July 12 - 16, 2008

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Vivek YPrasad PMadhav VLal RRavi V(2024)Optimal Technical Indicator Based Trading Strategies Using Evolutionary Multi Objective Optimization AlgorithmsComputational Economics10.1007/s10614-024-10701-6Online publication date: 27-Sep-2024
https://doi.org/10.1007/s10614-024-10701-6
Ferreira FGandomi ACardoso R(2021)Artificial Intelligence Applied to Stock Market Trading: A ReviewIEEE Access10.1109/ACCESS.2021.30581339(30898-30917)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3058133
Quintana DDenysiuk RGarcia-Rodriguez SGaspar-Cunha A(2017)Portfolio Implementation Risk Management Using Evolutionary Multiobjective OptimizationApplied Sciences10.3390/app71010797:10(1079)Online publication date: 18-Oct-2017
https://doi.org/10.3390/app7101079
Martínez-Jaramillo SCenteno TAlexandrova-Kabadjova BGarcía-Almanza A(2017)Evolutionary Computation in FinanceEncyclopedia of Machine Learning and Data Mining10.1007/978-1-4899-7687-1_88(435-444)Online publication date: 14-Apr-2017
https://doi.org/10.1007/978-1-4899-7687-1_88
Cervantes AQuintana DRecio G(2016)Efficient dynamic resampling for dominance-based multiobjective evolutionary optimizationEngineering Optimization10.1080/0305215X.2016.118772949:2(311-327)Online publication date: 22-Jun-2016
https://doi.org/10.1080/0305215X.2016.1187729
Martínez-Jaramillo SCenteno TAlexandrova-Kabadjova BGarcía-Almanza A(2016)Evolutionary Computation in FinanceEncyclopedia of Machine Learning and Data Mining10.1007/978-1-4899-7502-7_88-1(1-10)Online publication date: 12-Aug-2016
https://doi.org/10.1007/978-1-4899-7502-7_88-1
Wang WCaro SBennis FSoto RCrawford B(2015)Multi-Objective Robust Optimization Using a Postoptimality Sensitivity Analysis Technique: Application to a Wind Turbine DesignJournal of Mechanical Design10.1115/1.4028755137:1Online publication date: 1-Jan-2015
https://doi.org/10.1115/1.4028755
Azevedo CVon Zuben F(2013)Regularized hypervolume selection for robust portfolio optimization in dynamic environments2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557823(2146-2153)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557823
Arriaga-González JValenzuela-Rendón M(2013)Reinforcement Learning Method for Portfolio Optimal Frontier SearchProceedings of the 12th Mexican International Conference on Advances in Soft Computing and Its Applications - Volume 826610.1007/978-3-642-45111-9_22(245-255)Online publication date: 24-Nov-2013
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Aranha CAzevedo CIba H(2012)Money in treesInformation Sciences: an International Journal10.1016/j.ins.2011.05.023182:1(184-198)Online publication date: 1-Jan-2012
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