Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization
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...
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doaj-fe008e9f1c4349268054485d70a135242020-11-24T21:48:35ZengMDPI AGApplied Sciences2076-34172017-10-01710107910.3390/app7101079app7101079Portfolio Implementation Risk Management Using Evolutionary Multiobjective OptimizationDavid Quintana0Roman Denysiuk1Sandra Garcia-Rodriguez2António Gaspar-Cunha3Department of Computer Science, Universidad Carlos III de Madrid, Madrid 28911, SpainIPC—Institute of Polymers and Composites, University of Minho, Campus de Azurém, 4800-058 Guimarães, PortugalCEA, LIST, Data Analysis and System Intelligence Laboratory, 91191 Gif-sur-Yvette, FranceIPC—Institute of Polymers and Composites, University of Minho, Campus de Azurém, 4800-058 Guimarães, PortugalPortfolio 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.https://www.mdpi.com/2076-3417/7/10/1079evolutionary computationmultiobjective optimizationportfolio optimizationrobustness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Quintana Roman Denysiuk Sandra Garcia-Rodriguez António Gaspar-Cunha |
spellingShingle |
David Quintana Roman Denysiuk Sandra Garcia-Rodriguez António Gaspar-Cunha Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization Applied Sciences evolutionary computation multiobjective optimization portfolio optimization robustness |
author_facet |
David Quintana Roman Denysiuk Sandra Garcia-Rodriguez António Gaspar-Cunha |
author_sort |
David Quintana |
title |
Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization |
title_short |
Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization |
title_full |
Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization |
title_fullStr |
Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization |
title_full_unstemmed |
Portfolio Implementation Risk Management Using Evolutionary Multiobjective Optimization |
title_sort |
portfolio implementation risk management using evolutionary multiobjective optimization |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2017-10-01 |
description |
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. |
topic |
evolutionary computation multiobjective optimization portfolio optimization robustness |
url |
https://www.mdpi.com/2076-3417/7/10/1079 |
work_keys_str_mv |
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