Mean-VaR portfolio optimization: a nonparametric approach

Lwin, Khin T. and Qu, Rong and MacCarthy, Bart L. (2017) Mean-VaR portfolio optimization: a nonparametric approach. European Journal of Operational Research, 260 (2). pp. 751-766. ISSN 0377-2217

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Abstract

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 optimizing VaR 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 against 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. Experimental results shows 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.

Item Type: Article
Keywords: Evolutionary computations, Multi-objective Constrained Portfolio Optimization, Value at Risk, Nonparametric Historical Simulation
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1016/j.ejor.2017.01.005
Depositing User: Qu, Rong
Date Deposited: 01 Feb 2017 11:27
Last Modified: 18 Mar 2017 21:11
URI: http://eprints.nottingham.ac.uk/id/eprint/39884

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