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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Vydané v:Applied sciences Ročník 7; číslo 10; s. 1079
Hlavní autori: Quintana, David, Denysiuk, Roman, Garcia-Rodriguez, Sandra, Gaspar-Cunha, António
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2017
Multidisciplinary digital publishing institute (MDPI)
Predmet:
Economic models
evolutionary computation
Machine Learning
Mathematics
multiobjective optimization
Optimization and Control
Portfolio Management
portfolio optimization
Quantitative Finance
robustness
Statistics
multiobjective optimization
robustness
portfolio optimization
evolutionary computation
ROBUST OPTIMIZATION
ISSN:2076-3417, 2076-3417
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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.
AbstractList 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.
Author Denysiuk, Roman
Garcia-Rodriguez, Sandra
Quintana, David
Gaspar-Cunha, António
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  surname: Gaspar-Cunha
  fullname: Gaspar-Cunha, António
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Keywords multiobjective optimization
robustness
portfolio optimization
evolutionary computation
ROBUST OPTIMIZATION
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SubjectTerms Economic models
evolutionary computation
Machine Learning
Mathematics
multiobjective optimization
Optimization and Control
Portfolio Management
portfolio optimization
Quantitative Finance
robustness
Statistics
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