Portfolio Optimization by Means of Meta Heuristic Algorithms
محورهای موضوعی : Multi-Criteria Decision Analysis and its Application in Financial ManagementMahmoud Rahmani 1 , Maryam Khalili Eraqi 2 , Hashem Nikoomaram 3
1 - Department of Management and Economics, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran .
2 - Department of Management and Economics, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Management and Economics, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Ant Colony Algorithm, Genetic Algorithm, Artificial Bee Colony, Portfolio optimization,
چکیده مقاله :
Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation
[1] Fabozzi, F., Kolm, P., Pachamanova, D., Focardi, S., Robust portfolio optimization and management, John Wiley and Sons, 2007.
[2] Karaboga, D., GorkemLi, B., A quick Artificial Bee colony (qABC) algorithm and its performance on optimization problems, Appl. Soft Compact, 2014, 23, P.227-238. Doi: 10.1016/j.asoc.2014.06.035.
[3] Markowitz, H., Portfolio Selection, Journal of Finance, 1952, 7(1), P.77-91, Doi:10.1002/nav.3800030110.
[4] Tollo, G., Roli, A., Meta heuristics for the Portfolio Selection Problem, International Journal of Operation Research, 2008, 15, P.13-35 , Doi:10.1.1.401.8706.
[5] Lin, C.C., Liu, T.Y., Genetic algorithms for portfolio selection problems with minimum transaction lost, European Journal of Operational Research and Applications, 2008, 185(1), P.393-404, Doi.org/10.1016/j.ejor. 2006.12.024.
[6] Arnone, S., Loraschi, A., Tettamanzi, A., A genetic approach to portfolio selection, Neural Network World, 1993, 6(93), P.597- 604.
[7] Fernandez, A., Gomez, S., Portfolio selection using neural networks, Computers and Operations Research, 2007, 34(4), P.1177-1191, Doi:10.1016/j.cor.2005.06.017
[8] Chan, T. J., Beasley, J. E., Medds, N., Sharaiha Y. M., Heuristic for cardinality constrained portfolio optimization, Computers and Operation Research, 2000, 27, P.1271-1302 , Doi:10.1016/S0305-0548(99)00074-X
[9] Eslami Bidgoli Gh., Tayebi Sani E., A novel Meta-Heuristic method for solving an extended Markowitz Mean–Variance portfolio selection model, Journal of Investment Knowledge, 2014, 3, P.122-101(In Persian)
[10] Kiani Harchegani, M., Nabavi Chashmi, S., Memarian, E., Optimizing Stock Portfolio with regard to Minimum Level of Total Risk using Genetic Algorithm, Journal of Investment Knowledge, 2013, 3, P.125-164.
[11] Wang, Z., Ouyang, R., Kong, X., A hybrid artificial bee colony algorithm for portfolio for portfolio optimization problem, Journal of Theoretical and Applied Information Technology, 2013, 4(4), P.8-16. Doi: 10.4156/ ijact.vol4.issue 4.2.
[12] Karaboga, D., An idea based on honey bee swarm for numerical optimization Technical report-tr06, Erciyes university, Engineering Faculty, Computer Engineering Department, 2005, 200, P.1-10.[13] Raei, R., Bahrani Jahromi, M., Portfolio optimization using a hybrid of fuzzy ANP, VIKOR and TOPSIS, Management Science Letters, 2012, 2, P. 2473–2484. Doi: 10.5267/j.msl.2012.07.019.
[14] Chen, W., Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem, Physca, 2015, 12, P.122- 139. Doi:10.1016/j.physa.2015.02.060.
[15] Karaboga, D., Basturk B., Artificial bee colony(ABC) optimization algorithm for solving constrained optimization problems. In: proceeding of the 12th international fuzzy systems association world congress on foundations of fuzzy logic and soft computing. Springer, Berlin, IFSA, 2007, 7, P.789-798, Doi:10.1007/978-3-540-72950-1_77
[16] Chang, T. J., Yang, S. C., Chang, K.J., Portfolio optimization Problems in different risk measures using Gentic algorithm, Expert, systems with Application, 2009, 36(1), P.1-52. Doi: 10.1016/j.eswa.2009.02.062.
[17] Tollo, G.D., Thomas, S., Birattari, M., A metaheuristic multi-criteria optimisation approach to portfolio selection, Journal of Applied Operational Research, 2014, 6(4), P. 222-242.
[18] Jarraya, B., Asset Allocation and Portfolio Optimization Problems with Meta heuristics: A Literature Survey, Business Excellence and Management, 2013, 3, P.38-56.
[19] Mamanis, G., Portfolio Optimization with Metaheuristics, Finance and Market, 2017, 2(2), Doi: http://dx.d oi.org/10.18686/fm.v2i2.1048.
[20] Miryekemami, S., Sadeh, E., Sabegh, Z., Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2017, 2(4), P.107-120.Doi: 10.22034/amfa.2017.536271.
[21] Darabi, R., Baghban, M., Application of Clayton Copula in Portfolio Optimization and its Comparison with Markowitz Mean-Variance Analysis, Advances in Mathematical Finance and Applications, 2018, 3(1), pp. 33-51. doi: 10.22034/amfa.2018.539133.
[22] Aouni, B., Multi-attribute portfolio selection: New Perspectives, INFOR: Information Systems and Operational Research, 2009, 47(1), P.1-4. Doi: https://doi.org/10.3138/infor.47.1.1.
[23] Abzari, M., Ketabi, S., Abbasi, A., Using linear programming and providing a functional model for investment optimization, Journal of Humanities and Social Sciences, Shiraz University, 2005, 22(2), P.43-57. (In Persian)
[24] Markowitz, Harry M., The Optimization of a Quadratic Function Subject to Linear Constraints, Naval Research Logistics Quarterly, 1956, 3, P.111–133.Doi: https://doi.org/10.1002/nav.3800030110 .
[25] Karaboga, D., Basturk, B., Artificial bee colony(ABC) optimization algorithm for solving constrained optimization problems, In: proceeding of the 12th international fuzzy systems association world congress on foundations of fuzzy logic and soft computing. Springer, Berlin, IFSA, 2007, 7, P.789-798, Doi:10.1007/978-3-540-72950-1_77.
[26] Karaboga, D., Akay, B., A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, 2009 , 214(1) , P.108-132, Doi:10.1016/j.amc.2009.03.090.
[27] Mala, D.J., Kamalapriya, M., Shobana, R., Mohan, V., A non-pheromone based intelligent swarm optimization technique in software test suit optimization. In :IAMA:2009 international conference on intelligent agent and multi – agent systems , IEEE Madras section ; IEEE computer society m Madras chapter ; Computer society of india Div II ; Council of science & industrial Research; Govtindia, Department of information Technology , 2009, 33, P.188-192, Doi:10.1109/IAMA.2009.5228055
[28] Lin C.J., Lee C., Efficient artificial bee colony algorithm for 3d protein folding simulation. In: 17th national conference on fuzzy theory and its applications, 2009, P.705-710, Doi:10.1016/j.ins.2010.07.015 .
[29] Akay, B., Karaboga D., A modified artificial bee colony algorithm for real- parameter optimization, Information Sciences, 2012, 192, P.120-142. Doi:10.1016/j.ins.2010.07.015 .
[30] Ruiz, V., Daz parra, O., Similarities between meta-heuristics algorithms and the science of life, Central European Journal in Operational Research, 2011, 19 ,445-466. Doi:10.1007/s10100-010-0135-x.
[31] Wu D., Yu W., Yin Z., Parameter estimation of rational models based on artificial bee colony algorithm. In: Proceedings of international conference on modeling, identification and control (ICMIC), 2011, 43, P. 219-224. Doi: 10.1109/ICMIC.2011.5973704 .
[32] John, H., Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992.
[33] G. Renner, Ekart, A., Genetic algorithms in computer aided design, Computer-Aided Design, 2003, 35, P.709-726. Doi:10.1016/S0010-4485(03)00003-4.
[34] Deng, Y., Genetic Algorithm for Financial portfolio selection, Master`s Thesis, University of Science & Technology Beijing, China, 2002.
[35] Coloni, A., Dorigo. M., Maniezzo, V., Distributed optimization by ant colony, in proceeding of European conference on Prtifical life (ecsla1), Elsevier publishing, Amsterdam ,1991.
[36] Marthins, P., Riberio, C., Metaheuristics for optimization problems in computer communications, Journal of Computer Communications, 2007, 30(4), P.656-669.Doi:10.1016/j.comcom.2006.08.027 .
[37] Eiben, A., Smit, E., Interoduction to Evolutionary, Springer Verlag, 2003.
[38] Dorigo, M., Birattari, M., Stutzle, T., Ant colony optimization-artificial ants as a computational intelligence technique, Belgium: Universit Libre de Bruxelles. IRIDIA Technical report Series, 2006, Doi: 10.1109/MCI.200 6.329691