Mean-VaR portfolio optimization: A nonparametric approach
•Present a new MOEA for complex portfolio optimization.•Six real-world trading constraints are considered.•Computational experiments are performed by using real market data.•Results show the effectiveness of the learning mechanism.•Proposed method yield improved performance relative to existing meth...
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| Vydáno v: | European journal of operational research Ročník 260; číslo 2; s. 751 - 766 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
16.07.2017
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| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line přístup: | Získat plný text |
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| Abstract | •Present a new MOEA for complex portfolio optimization.•Six real-world trading constraints are considered.•Computational experiments are performed by using real market data.•Results show the effectiveness of the learning mechanism.•Proposed method yield improved performance relative to existing methods.
Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice. |
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| AbstractList | •Present a new MOEA for complex portfolio optimization.•Six real-world trading constraints are considered.•Computational experiments are performed by using real market data.•Results show the effectiveness of the learning mechanism.•Proposed method yield improved performance relative to existing methods.
Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice. |
| Author | MacCarthy, Bart L. Qu, Rong Lwin, Khin T. |
| Author_xml | – sequence: 1 givenname: Khin T. surname: Lwin fullname: Lwin, Khin T. email: khin.lwin@anglia.ac.uk, khintheinlwin@gmail.com organization: Anglia Ruskin IT Research Institute, Faculty of Science and Technology, Anglia Ruskin University, Chelmsford, Essex CM11SQ, UK – sequence: 2 givenname: Rong orcidid: 0000-0001-8318-7509 surname: Qu fullname: Qu, Rong email: rxq@cs.nott.ac.uk organization: The Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, The University of Nottingham, Nottingham NG8 1BB, UK – sequence: 3 givenname: Bart L. surname: MacCarthy fullname: MacCarthy, Bart L. email: bart.maccarthy@nottingham.ac.uk organization: Nottingham University Business School, The University of Nottingham, Nottingham NG8 1BB, UK |
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| Keywords | Evolutionary computations Value at risk Multi-objective constrained portfolio optimization Nonparametric historical simulation |
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INFORMS, Hanover, MD |
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| Title | Mean-VaR portfolio optimization: A nonparametric approach |
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