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Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics

Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Str., 18534 Piraeus, Greece

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K. Metaxiotis

Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Str., 18534 Piraeus, Greece

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https://doi.org/10.1142/S0219622015300013Cited by:32 (Source: Crossref)

Abstract

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

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References

K. P. Anagnostopoulos and G. Mamanis , Computers & Operations Research 37 , 1285 ( 2010 ) . Crossref, Web of Science, Google Scholar
K. P. Anagnostopoulos and G. Mamanis , Expert Systems with Applications 38 , 14208 ( 2011 ) . Web of Science, Google Scholar
K. P. Anagnostopoulos and G. Mamanis , Computational Management Science 8 , 259 ( 2011 ) . Crossref, Google Scholar
K. Andriosopoulos and N. Nomikos , European Journal of Operational Research 234 , 571 ( 2014 ) . Crossref, Web of Science, Google Scholar
R. Armananzas and J. A. Lozano, A multiobjective approach to the portfolio optimization problem, 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 20052 (2005) pp. 1388–1395. Google Scholar
H. G. Beyer , S. Finck and T. Breuer , Swarm and Evolutionary Computation 17 , 74 ( 2014 ) . Crossref, Web of Science, Google Scholar
J. Branke et al. , European Journal of Operational Research 199 , 684 ( 2009 ) . Crossref, Web of Science, Google Scholar
T. J. Changet al., Computers & Operations Research 27(13), 1271 (2000). Crossref, Web of Science, Google Scholar
T. J. Chang , S. C. Yang and K. J. Chang , Expert Systems with Applications 36 , 10529 ( 2009 ) . Crossref, Web of Science, Google Scholar
Y. Chen and X. Wang , European Journal of Operational Research ( 2014 ) , http://dx.doi.org/10.1016/j.ejor.2014.07.034 . Google Scholar
S. C. Chiam, A. Al Mamun and Y. L. Low, A realistic approach to evolutionary multiobjective portfolio optimization, IEEE Congress on Evolutionary Computation, 2007. CEC 2007 (2007) pp. 25–28. Google Scholar
S. C. Chiam, K. C. Tan and A. Al Mamum, International Journal of Automation and Computing 5(1), 67 (2008). Crossref, Web of Science, Google Scholar
S. C. Chiam , K. C. Tan and A. Al Mamun , Expert Systems with Applications 36 , 10408 ( 2009 ) . Crossref, Web of Science, Google Scholar
S. C. Chiam , K. C. Tan and A. Al. Mamun , Expert Systems with Applications 36 , 3695 ( 2009 ) . Crossref, Web of Science, Google Scholar
M. Corazza , G. Fasano and R. Gusso , Applied Mathematics and Computation 224 , 611 ( 2013 ) . Crossref, Web of Science, Google Scholar
D. W. Corne, J. D. Knowles and M. J. Oates, The pareto envelope-based selection algorithm for multiobjective optimization, Proc. Parallel Problem Solving from Nature VI Conf.1917 (2000) pp. 839–848. Google Scholar
K. Deb , Computer Methods in Applied Mechanics and Engineering 186 , 311 ( 2000 ) . Crossref, Web of Science, Google Scholar
K. Deb and R. B. Agrawal, Complex Systems 9(2), 115 (1995). Google Scholar
K. Deb , Multiobjective Optimization using Evolutionary Algorithms ( Wiley , Chichester, UK , 2001 ) . Google Scholar
K. Debet al., IEEE Transactions on Evolutionary Computation 6(2), 182 (2002). Crossref, Web of Science, Google Scholar
K. Deb and S. Tiwari , European Journal of Operational Research 185 , 1062 ( 2008 ) . Crossref, Web of Science, Google Scholar
L. Di Gasperoet al., A hybrid solver for constrained portfolio selection problems: Preliminary report, Proc. Learning and Intelligent Optimization, LION (2007) pp. 44–58. Google Scholar
G. Di Tollo and A. Roli, International Journal of Operations Research 5(1), 13 (2008). Google Scholar
R. Drezewski, K. Obrocki and L. Siwik, Comparison of multi-agent co-operative co-evolutionary and evolutionary algorithms for multi-objective portfolio optimization, EvoWorkshops 20095484, LNCS (2009) pp. 223–232. Google Scholar
F. C. Duran, C. Cotta and A. J. Fernandez, Evolutionary Optimization for Multiobjective Portfolio Selection Under Markowitz's Model with Application to the Caracas Stock Exchange Nature-Inspired Algorithms for Optimisation, Studies in Computational Intelligence 193 (Springer, Berlin Heidelberg, 2009) pp. 489–509. Crossref, Google Scholar
M. Ehrgott , K. Klamroth and C. Schwehm , European Journal of Operational Research 155 , 752 ( 2004 ) . Crossref, Web of Science, Google Scholar
M. Erickson, A. Mayer and J. Horn, The niched pareto genetic algorithm 2 applied to the design of groundwater remediation systems, First International Conference on Evolutionary Multi-Criterion Optimization1993, Lecture Notes in Computer Science, eds. E. Zitzleret al. (2001) pp. 681–695. Google Scholar
E. Fernandez et al. , Information Sciences 228 , 131 ( 2013 ) . Crossref, Web of Science, Google Scholar
T. C. Fu , C. P. Chung and F. L. Chung , Computers and Mathematics with Applications 66 , 1743 ( 2013 ) . Crossref, Web of Science, Google Scholar
C. Fonseca and P. Fleming, Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization, Proc. 5th Int. Conf. Genetic Algorithms (1993) pp. 416–423. Google Scholar
V. Gabrel , C. Murat and A. Thiele , European Journal of Operational Research 235 , 471 ( 2014 ) . Crossref, Web of Science, Google Scholar
D. E. Goldberg and K. Deb, Foundations of Genetic Algorithms, ed. G. J. E. Rawlins (Morgan Kaufmann, Los Altos) pp. 69–93. Google Scholar
P. Gupta et al. , Information Sciences 229 , 1 ( 2013 ) . Crossref, Web of Science, Google Scholar
M. P. Hansen and A. Jaszkiewicz, Evaluating the quality of approximations to the nondominated set, Technical Report IMM-REP-1998-7, Institute of Mathematical Modeling, Technical University of Denmark (1998) . Google Scholar
G. Hariket al., Evolutionary Computing 7(3), 231 (1999). Crossref, Web of Science, Google Scholar
F. Hassanzadeh , H. Nemati and M. Sun , European Journal of Operational Research 238 , 41 ( 2014 ) . Crossref, Web of Science, Google Scholar
H. Kellerer and D. Maringer, Optimization of cardinality constrained portfolios with an hybrid local search algorithm, MIC'2001 — 4th Metaheuristics Int. Conf. (2001) pp. 585–589. Google Scholar
K. Khalili-Damghani et al. , Information Sciences 220 , 442 ( 2013 ) . Crossref, Web of Science, Google Scholar
J. Knowles and D. Corne, Approximating the non-dominated front using the Pareto Archieved Evolution Strategy, Department of Computer Science, University of Reading, UK (1999) . Google Scholar
J. Knowles, L. Thiele and E. Zitzler, A tutorial on the performance assessment of stochastic multiobjective optimizers, TIK Report 214, ETH Zurich: Computer Engineering and Networks Laboratory (TIK) (2006) . Google Scholar
G. Kouet al., International Journal of Information Technology & Decision Making 11(1), 197 (2012). Link, Web of Science, Google Scholar
G. Kou , Y. Peng and G. Wang , Information Sciences 275 , 1 ( 2014 ) . Crossref, Web of Science, Google Scholar
G. Kou and C. Lin , European Journal of Operational Research 235 , 225 ( 2014 ) . Crossref, Web of Science, Google Scholar
G. Kou , D. Ergu and J. Shang , European Journal of Operational Research 236 , 261 ( 2014 ) . Crossref, Web of Science, Google Scholar
M. Laumanns, E. Zitzler and L. Thiele, A unified model for multiobjective evolutionary algorithms with elitism, Proc. Congr. Evol. Comput.1 (2000) pp. 46–53. Google Scholar
J. Li and J. Xu , Information Sciences 220 , 507 ( 2013 ) . Crossref, Web of Science, Google Scholar
K. Liagkouras and K. Metaxiotis , Expert Systems with Applications 41 , 6274 ( 2014 ) . Crossref, Web of Science, Google Scholar
K. Liagkouras and K. Metaxiotis, Journal of Software Engineering and Applications 6(7), 22 (2013). Crossref, Google Scholar
K. Lwin , R. Qu and G. Kendall , Applied Soft Computing 24 , 757 ( 2014 ) . Crossref, Web of Science, Google Scholar
H. Markowitz, Journal of Finance 7(1), 77 (1952). Crossref, Web of Science, Google Scholar
K. Metaxiotis and K. Liagkouras, Expert Systems with Applications 39(14), 11685 (2012). Crossref, Web of Science, Google Scholar
J. Mira and J. R. Alvarez, Artificial intelligence and knowledge engineering applications: A bioinspired approach, First Int. Work-Conf. Interplay Between Natural and Artificial Computation, IWINAC 2005 (2005) pp. 385–394. Google Scholar
S. K. Mishra , G. Panda and R. Majhi , Swarm and Evolutionary Computation 16 , 38 ( 2014 ) . Crossref, Web of Science, Google Scholar
R. Moral-Escudero, R. Ruiz-Torrubiano and A. Suarez, Selection of optimal investment portfolios with cardinality constraints, Proc. 2006 IEEE Congress on Evolutionary Computation (2006) pp. 2382–2388. Google Scholar
S. Mousavi , A. Esfahanipour and M. H. Fazel Zarandi , Knowledge-Based Systems 66 , 68 ( 2014 ) . Crossref, Web of Science, Google Scholar
C. S. Ong , J. J. Huang and G. H. Tzeng , Applied Mathematics and Computation 169 , 1195 ( 2005 ) . Crossref, Web of Science, Google Scholar
B. Rebiasz , Computers & Industrial Engineering 64 , 1019 ( 2013 ) . Crossref, Web of Science, Google Scholar
A. Schaerf, Computational Economics 20(3), 177 (2002). Crossref, Google Scholar
B. Scherer and R. Douglas , Introduction to Modern Portfolio Optimization with NUOPT and S-Plus ( Springer , 2005 ) . Crossref, Google Scholar
F. Schlottmann and D. Seese , Computational Statistics & Data Analysis 47 , 373 ( 2004 ) . Crossref, Web of Science, Google Scholar
N. Shukla et al. , International Journal of Production Economics 141 , 146 ( 2013 ) . Crossref, Web of Science, Google Scholar
K. Smimou , European Journal of Operational Research 234 , 546 ( 2014 ) . Crossref, Web of Science, Google Scholar
F. Streichert, H. Ulmer and A. Zell, Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem, Proc. Congress on Evolutionary Computation (CEC 2004) (2004) pp. 932–939. Google Scholar
F. Streichert, H. Ulmer and A. Zell, Comparing discrete and continuous genotypes on the constrained portfolio selection problem, Proc. Genetic and Evolutionary Computation Conf. Genetic and Evolutionary Computation–GECCO 20043103, Lecture Notes in Computer Science, eds. K. Debet al. (2004) pp. 1239–1250. Google Scholar
F. Streichert and M. Tanaka-Yamawaki, The effect of local search on the constrained portfolio selection problem, Proc. IEEE Congress on Evolutionary Computation (2006) pp. 2368–2374. Google Scholar
K. Suksonghong , K. Boonlong and K. L. Goh , Electrical Power and Energy Systems 58 , 150 ( 2014 ) . Crossref, Web of Science, Google Scholar
D. A. V. Veldhuizen, Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations, Ph.D. thesis, Graduate School of Engineering, Air Force Institute of Technology, Air University (1999) . Google Scholar
D. A. V. Veldhuizen and G. B. Lamont, Multiobjective evolutionary algorithm research: A history and analysis, Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH (1998) . Google Scholar
J. P. Vazhayil and R. Balasubramanian , Electrical Power and Energy Systems 55 , 13 ( 2014 ) . Crossref, Web of Science, Google Scholar
G. A. Vijayalakshmi Pai and M. Thierry, Metaheuristic multi-objective optimization of constrained futures portfolios for effective risk management, Swarm and Evolutionary Computation, in press, http://dx.doi.org/10.1016/j.swevo.2014.08.002 . Google Scholar
F. Wang , P. L. H. Yu and D. W. Cheung , Expert Systems with Applications 41 , 3016 ( 2014 ) . Crossref, Web of Science, Google Scholar
M. Woodside-Oriakhi , C. Lucas and J. E. Beasley , Omega 41 , 406 ( 2013 ) . Crossref, Web of Science, Google Scholar
P. Zhang and W. G. Zhang , Fuzzy Sets and Systems 255 , 74 ( 2014 ) . Crossref, Web of Science, Google Scholar
E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications, Ph.D. dissertation, Swiss Federal Inst. Technology (ETH), Zurich, Switzerland (1999) . Google Scholar
E. Zitzler, K. Deb and L. Thiele, Evolutionary Computation 8(2), 173 (2000). Crossref, Web of Science, Google Scholar
E. Zitzler and S. Kunzli, Indicator-based selection in multiobjective search, Conf. Parallel Problem Solving from Nature (PPSN VIII)3242, LNCS (Springer, 2004) pp. 832–842. Google Scholar
E. Zitzler, M. Laumanns and L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm, Computer Engineering and Networks Laboratory (TIK), Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH), Zurich (2001) . Google Scholar
E. Zitzler and L. Thiele, IEEE Transactions on Evolutionary Computation 3(4), 257 (1999). Crossref, Web of Science, Google Scholar
E. Zitzleret al., IEEE Transactions on Evolutionary Computation 7(2), 117 (2003). Crossref, Web of Science, Google Scholar