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Appl. Sci. 2017, 7(10), 1079; https://doi.org/10.3390/app7101079

Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization

1
Department of Computer Science, Universidad Carlos III de Madrid, Madrid 28911, Spain
2
IPC—Institute of Polymers and Composites, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
3
CEA, LIST, Data Analysis and System Intelligence Laboratory, 91191 Gif-sur-Yvette, France
Current address: Department of Computer Science, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, 28911 Madrid, Spain.
*
Author to whom correspondence should be addressed.
Received: 11 September 2017 / Accepted: 12 October 2017 / Published: 18 October 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
View Full-Text   |   Download PDF [467 KB, uploaded 18 October 2017]   |  

Abstract

Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix. View Full-Text
Keywords: evolutionary computation; multiobjective optimization; portfolio optimization; robustness evolutionary computation; multiobjective optimization; portfolio optimization; robustness
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Quintana, D.; Denysiuk, R.; Garcia-Rodriguez, S.; Gaspar-Cunha, A. Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization. Appl. Sci. 2017, 7, 1079.

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