A Mean–Variance–Skewness Portfolio Optimization Model
Authors: Kostas Metaxiotis
Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2576964Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 632
 P. Chunhachinda, K. Dandapani, S. Hamid, A.J. Prakash, Portfolio selection and skewness: evidence from international stock markets, J. Bank. Finance 21 (2) (1997) 143–167.
 T. Lai, Portfolio selection with skewness: a multiple-objective approach, Rev. Quant. Finance Account. 1 (1991) 293–305.
 S.C. Liu, S.Y. Wang, W.H. Qiu, A mean–variance–skewness model for portfolio selection with transaction costs, Int. J. Syst. Sci. 34 (4) (2003) 255–262.
 H. Konno, H. Shirakawa and H. Yamazaki, “A mean–absolute deviation–skewness portfolio optimization model”. Annals of Operations Research, (1993), 45 (1), 205–220.
 K. Liagkouras and K. Metaxiotis “A new Probe Guided Mutation Operator and its application for solving the Cardinality Constrained Portfolio Optimization Problem”. Expert Systems with Applications, Elsevier, (2014) 41 (14), 6274-6290.
 K. Metaxiotis, and K. Liagkouras, "Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review". Expert Systems with Applications, 2012, Elsevier, 39 (14): 11685-1169.
 K. Liagkouras and K. Metaxiotis, “Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics”, International Journal of Information Technology & Decision Making, (2015) 14 (03), 535-564, World Scientific.
 K. Liagkouras and K. Metaxiotis, (2018) A new Efficiently Encoded Multiobjective Algorithm for the Solution of the Cardinality Constrained Portfolio Optimization Problem, Annals of Operations Research, Springer, vol. 267, issue 1–2, pp 281–319.
 K. Liagkouras and K. Metaxiotis, “Enhancing the performance of MOEAs: An experimental presentation of a new Fitness Guided Mutation Operator”, Journal of Experimental & Theoretical Artificial Intelligence, (2017) vol. 29, no. 1, 91 – 131. Taylor & Francis.
 K. Liagkouras and K. Metaxiotis, “Multi-period Mean Variance Fuzzy Portfolio Optimization Model with transaction costs”, Engineering Applications of Artificial Intelligence, (2018) 67C, pp. 260-269.
 K. Liagkouras and K. Metaxiotis, “Examining the Effect of different Configuration Issues of the Multiobjective Evolutionary Algorithms on the Efficient Frontier Formulation for the Constrained Portfolio Optimization Problem”, Journal of the Operational Research Society, (2018) vol. 69 (3), Taylor & Francis.
 K. Liagkouras and K. Metaxiotis, “Handling the complexities of the Multi-constrained Portfolio Optimization Problem with the support of a Novel MOEA”, Journal of the Operational Research Society, (2017) Taylor & Francis, http://www.tandfonline.com/doi/abs/10.1057/s41274-017-0209-4
 K. Liagkouras and K. Metaxiotis, “Multi-period mean–variance fuzzy portfolio optimization model with transaction costs”, vol. 67, January 2018, Pages 260-269.
 K. Liagkouras and K. Metaxiotis, “Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem”, Annals of Operations Research, (2018) pp 1–19, https://link.springer.com/article/10.1007/s10479-018-2876-1.
 K. Liagkouras “A new three-dimensional encoding multiobjective evolutionary algorithm with application to the portfolio optimization problem”, Knowledge-Based Systems, (2018) https://doi.org/10.1016/j.knosys.2018.08.025.
 K. Liagkouras and K. Metaxiotis, "A new Two Stage Crossover Operator and its application for solving the Cardinality Constrained Portfolio Selection Problem", “Multiple Criteria Decision Making in Finance, Insurance and Investment”, Springer, 2015, edited by Minwir Al-Shammari and Hatem Masri DOI 10.1007/978-3-319-21158-9_8.
 K. Metaxiotis and K. Liagkouras, "An exploratory crossover operator for improving the performance of MOEAs", Advances in Applied and Pure Mathematics, 2014, pp. 158-162, ISBN: 978-1-61804-240-8.
 K. Metaxiotis and K. Liagkouras, "The Current State of ERP Systems in Banking Sector", International Research Journal of Electronics & Computer Engineering, (2017) vol. 3 (1), pp.23-26.
 K. Metaxiotis and K. Liagkouras, "The solution of the 0-1 multi-objective knapsack problem with the assistance of multi-objective evolutionary algorithms based on decomposition: A comparative study", 2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, 2015 WCSE 2015-IPCE.
 K. Deb, and S. Tiwari, S., “Omni-Optimizer: A Generic Evolutionary Algorithm for Single and Multi-Objective Optimization,” European Journal of Operational Research, 2008, vol. 185.
 D. Dhaliwal, O. Zhen Li, A. Tsang, and Y.G. Yang, “Corporate social responsibility disclosure and the cost of equity capital: The roles of stakeholder orientation and financial transparency”, J. Account. Public Policy 33 (2014) 328–355.
 A. Di Giuli and L. Kostovetsky, “Are red or blue companies more likely to go green? Politics and corporate social responsibility”, Journal of Financial Economics 111(2014)158–180.
 S.M. Gasser, M. Rammerstorfer, K. Weinmayer, “Markowitz revisited: Social portfolio engineering”, European Journal of Operational Research, 2017 258(3), pp. 1181-1190.
 H.M. Henke, “The effect of social screening on bond mutual fund performance”, Journal of Banking & Finance, Volume 67, June 2016, Pages 69-84.