Advanced Technologies and Algorithms for Efficient Portfolio Selection
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Advanced Technologies and Algorithms for Efficient Portfolio Selection

Authors: Konstantinos Liagkouras, Konstantinos Metaxiotis

Abstract:

In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.

Keywords: Portfolio selection, optimization techniques, financial models, stochastics, heuristics.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108757

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[1] Armananzas R. and Lozano J. A., (2005) “A Multiobjective Approach to the Portfolio Optimization Problem,” in 2005 IEEE Congress on Evolutionary Computation (CEC’2005), vol. 2. Edinburgh, Scotland: IEEE Service Center, September 2005, pp. 1388–1395.
[2] Black, Fischer; Myron Scholes (1973). "The Pricing of Options and Corporate Liabilities". Journal of Political Economy 81 (3): 637–654.
[3] Chang, T.J., Meade, N., Beasley, J.E. and Sharaiha, Y.M. (2000), “Heuristics for cardinality constrained portfolio optimisation”, Computers and Operations Research, 27, (2000), 1271-1302.
[4] Chang, T.J., Yang S.C. & Chang K.J. (2009) Portfolio optimization problems in different risk measures using genetic algorithm, Expert Systems with applications, 36 (2009): 10529-10537.
[5] Crama, Y. & Schyns, M. (2003): Simulated annealing for complex portfolio selection problems, European Journal of Operational Research 150: 546-571.
[6] Dantzig, George B. (1949). "Programming of Interdependent Activities: II Mathematical Model". Econometrica 17 (3): 200–211.
[7] Deng, G.F & Lin W.T. (2010) Ant colony optimization for Markowitz mean-variance portfolio model, B.K. Panigrahi et al. (Eds): SEMCCO 2010, LNCS 6466, pp.238-245, Springer – Verlag Berlin Heidelberg 2010.
[8] Doerner K., Gutjahr W., Hartl R., Strauss C., Stummer C., (2004) Pareto Ant Colony Optimization: A Metaheuristic Approach to MUltiobjective Portfolio Selection, Annals of Operations Research 131, 79-99, 2004
[9] Ehrgott M., Klamroth K., and Schwehm C., (2004) “An MCDM approach to portfolio optimization,” European Journal of Operational Research, vol. 155, no. 3, pp. 752–770, June 2004.
[10] Fama, Eugene (1965). "The Behavior of Stock Market Prices". Journal of Business 38: 34–105.
[11] Fogel, L. J. (1966), Artificial Intelligence through Simulated Evolution, John Wiley, New York, NY.
[12] Golmakani, H. R. & Fazel, M. (2011) Constrained portfolio selection using particle swarm optimization, Expert Systems with Applications 38 (2011): 8327-8335.
[13] Konno H. and Yamazaki H.(1991) Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market, Management Science, v. 37, 5: 519-531.
[14] Liagkouras, K. & Metaxiotis, K. (2014) A new Probe Guided Mutation operator and its application for solving the cardinality constrained portfolio optimization problem, Expert Systems with Applications 41 (14), 6274-6290.
[15] Liagkouras, K and Metaxiotis, K. (2015) Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metric, International Journal of Information Technology & Decision Making, Vol. 14 (2015) / DOI: 10.1142/S0219622015300013.
[16] Metaxiotis, K. & Liagkouras, K. (2012) Multiobjective evolutionary algorithms for portfolio management: a comprehensive literature review, Expert Systems with Applications 39 (14), 11685-11698.
[17] Metaxiotis, K & Liagkouras, K., (2013) A fitness guided mutation operator for improved performance of MOEAs Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on, pp. 751-754, DOI: 10.1109/ICECS.2013.6815523.
[18] Maringer, D. & Kellerer, H., (2003) Optimization of cardinality constrained portfolios with a hybrid local search algorithm. OR Spectrum (2003) 25: 481-495
[19] Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1):77-91.
[20] Schaffer, J. D. (1985). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In Genetic algorithms and their applications: Proceedings of the first international conference on genetic algorithms (pp. 93–100). Hillsdale, NJ: Lawrence Erlbaum.
[21] Metaxiotis, K. & Liagkouras, K. (2014) An exploratory crossover operator for improving the performance of MOEAs, Advances in Applied and Pure Mathematics, pp. 158-162, ISBN: 978-1-61804-240-8.
[22] Liagkouras, K. & Metaxiotis, K. (2014) Application of Customer Relationship Management Systems in Business: Challenges and Opportunities, International Journal of Social, Education, Economics and Management Engineering Vol:8, No:6, 2014, World Academy of Science, Engineering and Technology.
[23] Liagkouras, K. & Metaxiotis, K. (2013) The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm, Journal of Software Engineering and Applications, 2013, 6, 22-29 doi:10.4236/jsea.2013.67B005.
[24] Metaxiotis, K. & Liagkouras, K. (2014) An Expert System Designed to Be Used with MOEAs for Efficient Portfolio Selection, International Journal of Computer, Information Science and Engineering Vol:8 No:2, 2014, World Academy of Science, Engineering and Technology.
[25] Liagkouras, K. & Metaxiotis, K. (2012) Modern Portfolio Management with the Support of Multiobjective Evolutionary Algorithms: An Analysis of Problem Formulation Issues, World Academy of Science, Engineering and Technology 69 2012, Paris, France 27 – 28 June 2012.
[26] Liagkouras, K. & Metaxiotis, K. (2013) An Elitist Polynomial Mutation Operator for Improved Performance of MOEAs in Computer Networks, pp. 1-5, Computer Communications and Networks (ICCCN), 2013 22nd International Conference on, DOI: 10.1109/ICCCN.2013.6614105.
[27] Liagkouras K. & Metaxiotis K. (2015) An experimental analysis of a new two-stage crossover operator for multiobjective optimization, Soft Computing, DOI: 10.1007/s00500-015-1810-6