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Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması

Year 2017, Volume: 23 Issue: 4, 470 - 476, 18.08.2017

Can Berk Kalaycı Ökkeş Ertenlice Hasan Akyer Hakan Aygören

Abstract

Markowitz’in
ortaya koymuş olduğu ortalama-varyans portföy optimizasyonu, portföy yönetimi
problemine temel bir cevap vermiştir. Bu model, getirinin en büyüklenmesi ve
riskin en küçüklenmesi gibi iki çakışan amaç arasındaki en iyi ödünleşimi ile
bir etkin sınır aramaktadır. Bir etkin sınır belirleme probleminin NP-Zor
olduğu bilinmektedir. Problemin karmaşıklığı nedeniyle, giderek artan sayıda
araştırmacı bu problemi çözmek için genetik algoritmaları kullanmışlardır. Bu
çalışmada, mevcut literatürdeki genetik algoritmaların ortalama-varyans portföy
optimizasyonu uygulamaları incelenmiştir. Çalışılmış olan problemlerin ana
özellikleri ve önerilen genetik algoritma karakteristikleri özetlenmiştir.

Keywords

Portföy yönetimi ve optimizasyonu Ortalama-varyans modeli Evrimsel algoritmalar Genetik algoritma

References

  • Markowitz H. “Portfolio selection”. The Journal of Finance, 7(1), 77-91, 1952.
  • Markowitz H. Portfolio Selection: Efficient Diversification of Investments. Yale University Press, 1959.
  • Chang TJ, Meade N, Beasley JE, Sharaiha YM. “Heuristics for cardinality constrained portfolio optimisation”. Computers & Operations Research, 27(13), 1271-1302, 2000.
  • Xia Y, Liu B, Wang S, Lai KK. “A model for portfolio selection with order of expected returns”. Computers & Operations Research, 27(5), 409-422, 2000.
  • Chen W, Xu WJ, Yang L, Cai YM. “Genetic algorithm with an application to complex portfolio selection”. 4th International Conference on Natural Computation, (ICNC 2008), Jinan, China, 25-27 August 2008.
  • Lin CC, Liu YT. “Genetic algorithms for portfolio selection problems with minimum transaction lots”. European Journal of Operational Research, 185(1), 393-404, 2008.
  • Soleimani H, Golmakani HR, Salimi MH. “Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm”. Expert Systems with Applications, 36(3), 5058-5063, 2009.
  • Michael RG, David SJ. “Computers and intractability: A guide to the theory of np-completeness”. WH Free. Co., San Fr, 1979.
  • Bienstock D. “Computational study of a family of mixed-integer quadratic programming problems”. Mathematical programming, 74(2), 121-140, 1996.
  • Metaxiotis K, Liagkouras, K. “Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review”. Expert Systems with Applications, 39(14), 11685-11698, 2012.
  • Holland JH. Adaptation in Natural and Artificial Systems. Ann Arbor, MI, USA, The University of Michigan Press, 1975.
  • Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., 1989.
  • Shoaf J, Foster JA. “Efficient set ga for stock portfolios”. Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, USA, 04-09 May 1998.
  • Lai KK, Yu L, Wang S, Zhou C. “A double-stage genetic optimization algorithm for portfolio selection”. 13th International Conference on Neural Information Processing (ICONIP 2006), Hong Kong, China, 3-6 October 2006.
  • Moral-Escudero R, Ruiz-Torrubiano, R, Suarez, A. “Selection of optimal investment portfolios with cardinality constraints”. IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, Canada, 16-21 July 2006.
  • Rong X, Lu M, Deng L. “Multi-period model of portfolio investment and adjustment based on hybrid genetic algorithm”. Transactions of Tianjin University, 15(6), 415-422, 2009.
  • Guo Q, Li J, Zou C, Guo Y, Yan W. “A class of multi-period semi-variance portfolio for petroleum exploration and development”. International Journal of Systems Science, 43(10), 1883-1890, 2012.
  • Lu Z, Wang X. “Improved portfolio optimization with non-convex and non-concave cost using genetic algorithms”. International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC 2013), Shengyang, China, 20-22 December 2013.
  • hiangLin CY. “Applications of genetic algorithm to portfolio optimization with practical transaction constraints”. 9th Joint Conference on Information Sciences, (JCIS 2006), Kaohsiung, Taiwan, ,8-11 October 2006.
  • Aranha C, Iba, H. “The memetic tree-based genetic algorithm and its application to portfolio optimization”. Memetic Computing, 1(2), 139-151, 2009.
  • Branke J, Scheckenbach B, Stein M, Deb K, Schmeck H. “Portfolio optimization with an envelope-based multi-objective evolutionary algorithm”. European Journal of Operational Research, 199(3), 684-693, 2009.
  • Chang TJ, Yang SC, Chang, KJ. “Portfolio optimization problems in different risk measures using genetic algorithm”. Expert Systems with Applications, 36(7), 10529-10537, 2009.
  • Li YF, Guo W. “The stock portfolios simulated annealing genetic algorithm based on raroc”. 2009 Chinese Control and Decision Conference (CCDC 2009), Guilin, China, 17-19 June 2009.
  • Loukeris N, Donelly D, Khuman A, Peng Y. “A numerical evaluation of meta-heuristic techniques in portfolio optimisation”. Operational Research, 9(1), 81-103, 2009.
  • Pai GAV, Michel T. “Evolutionary optimization of constrained k-means clustered assets for diversification in small portfolios”. IEEE Transactions on Evolutionary Computation, 13(5), 1030-1053, 2009.
  • Shaikh RA, Abbas, A. “Genetic algorithm and ms solver for portfolio optimization under exogenous influence”.2nd International Conference on Computer and Electrical Engineering (ICCEE 2009), Dubai, UAE, 28-30 December 2009.
  • Anagnostopoulos KP, Mamanis G. “A portfolio optimization model with three objectives and discrete variables”. Computers & Operations Research, 37(7), 1285-1297, 2010.
  • Ruiz-Torrubiano R, Suarez, A. “Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints”. IEEE Computational Intelligence Magazine, 5(2), 92-107, 2010.
  • Anagnostopoulos KP, Mamanis G. “The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms”. Expert Systems with Applications, 38(11), 14208-14217, 2011.
  • Anagnostopoulos K P, Mamanis, G. “Multiobjective evolutionary algorithms for complex portfolio optimization problems”. Computational Management Science, 8(3), 259-279, 2011.
  • Fu TC, Ng CM, Wong KW, Chung FL. “Models for portfolio management on enhancing periodic consideration and portfolio selection”. 7th International Conference on Natural Computation (ICNC 2011), Shangai, China, 26-28 July 2011.
  • Chen Y, Mabu S, Hirasawa K. “Genetic relation algorithm with guided mutation for the large-scale portfolio optimization”. Expert Systems with Applications, 38(4), 3353-3363, 2011.
  • Kremmel T, Kubalík J, Biffl S. “Software project portfolio optimization with advanced multiobjective evolutionary algorithms”. Applied Soft Computing, 11(1), 1416-1426, 2011.
  • Woodside-Oriakhi M, Lucas C, Beasley JE. “Heuristic algorithms for the cardinality constrained efficient frontier”. European Journal of Operational Research, 213(3), 538-550, 2011.
  • Sadjadi SJ, Gharakhani M, Safari E. “Robust optimization framework for cardinality constrained portfolio problem”. Applied Soft Computing, 12(1), 91-99, 2012.
  • Yi H, Yang J. “Multi-objective portfolio optimization based on fuzzy genetic algorithm”. 9th International Conference on Computational Intelligence and Security (CIS 2013), Leshan, China, 14-15 December 2013.
  • Ackora-Prah J, Gyamerah SA, Andam PS. “A heuristic crossover for portfolio selection”. Applied Mathematical Sciences, 8(65), 3215-3227, 2014.
  • Joglekar S. “Two-stage stock portfolio construction: Correlation clustering and genetic optimization”. 27th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2014), Pensacola Beach, USA, 21-23 May 2014.
  • Liagkouras K, Metaxiotis K. “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, 2014.
  • Lwin K, Qu R, Kendall G. “A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization”. Applied Soft Computing, 24, 757-772, 2014.
  • Adebiyi Ayodele A, Ayo Charles K. “Portfolio selection problem using generalized differential evolution 3”. Applied Mathematical Sciences, 9(41-44), 2069-2082, 2015.
  • Hadi AS, El Naggar AA, Abdel Bary MN. “New model and method for portfolios selection”. Applied Mathematical Sciences, 10(5-8), 263-288, 2016.
  • Mashayekhi Z, Omrani H. “An integrated multi-objective markowitz–dea cross-efficiency model with fuzzy returns for portfolio selection problem”. Applied Soft Computing, 38, 1-9, 2016.

A review on the current applications of genetic algorithms in mean-variance portfolio optimization

Year 2017, Volume: 23 Issue: 4, 470 - 476, 18.08.2017

Can Berk Kalaycı Ökkeş Ertenlice Hasan Akyer Hakan Aygören

Abstract

Mean-variance
portfolio optimization model, introduced by Markowitz, provides a fundamental
answer to the problem of portfolio management. This model seeks an efficient
frontier with the best trade-offs between two conflicting objectives of
maximizing return and minimizing risk. The problem of determining an efficient
frontier is known to be NP-hard. Due to the complexity of the problem, genetic
algorithms have been widely employed by a growing number of researchers to
solve this problem. In this study, a literature review of genetic algorithms
implementations on mean-variance portfolio optimization is examined from the
recent published literature. Main specifications of the problems studied and
the specifications of suggested genetic algorithms have been summarized.

Keywords

Portfolio management and optimization Mean-variance model Evolutionary algorithms Genetic algorithm

References

  • Markowitz H. “Portfolio selection”. The Journal of Finance, 7(1), 77-91, 1952.
  • Markowitz H. Portfolio Selection: Efficient Diversification of Investments. Yale University Press, 1959.
  • Chang TJ, Meade N, Beasley JE, Sharaiha YM. “Heuristics for cardinality constrained portfolio optimisation”. Computers & Operations Research, 27(13), 1271-1302, 2000.
  • Xia Y, Liu B, Wang S, Lai KK. “A model for portfolio selection with order of expected returns”. Computers & Operations Research, 27(5), 409-422, 2000.
  • Chen W, Xu WJ, Yang L, Cai YM. “Genetic algorithm with an application to complex portfolio selection”. 4th International Conference on Natural Computation, (ICNC 2008), Jinan, China, 25-27 August 2008.
  • Lin CC, Liu YT. “Genetic algorithms for portfolio selection problems with minimum transaction lots”. European Journal of Operational Research, 185(1), 393-404, 2008.
  • Soleimani H, Golmakani HR, Salimi MH. “Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm”. Expert Systems with Applications, 36(3), 5058-5063, 2009.
  • Michael RG, David SJ. “Computers and intractability: A guide to the theory of np-completeness”. WH Free. Co., San Fr, 1979.
  • Bienstock D. “Computational study of a family of mixed-integer quadratic programming problems”. Mathematical programming, 74(2), 121-140, 1996.
  • Metaxiotis K, Liagkouras, K. “Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review”. Expert Systems with Applications, 39(14), 11685-11698, 2012.
  • Holland JH. Adaptation in Natural and Artificial Systems. Ann Arbor, MI, USA, The University of Michigan Press, 1975.
  • Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., 1989.
  • Shoaf J, Foster JA. “Efficient set ga for stock portfolios”. Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, USA, 04-09 May 1998.
  • Lai KK, Yu L, Wang S, Zhou C. “A double-stage genetic optimization algorithm for portfolio selection”. 13th International Conference on Neural Information Processing (ICONIP 2006), Hong Kong, China, 3-6 October 2006.
  • Moral-Escudero R, Ruiz-Torrubiano, R, Suarez, A. “Selection of optimal investment portfolios with cardinality constraints”. IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, Canada, 16-21 July 2006.
  • Rong X, Lu M, Deng L. “Multi-period model of portfolio investment and adjustment based on hybrid genetic algorithm”. Transactions of Tianjin University, 15(6), 415-422, 2009.
  • Guo Q, Li J, Zou C, Guo Y, Yan W. “A class of multi-period semi-variance portfolio for petroleum exploration and development”. International Journal of Systems Science, 43(10), 1883-1890, 2012.
  • Lu Z, Wang X. “Improved portfolio optimization with non-convex and non-concave cost using genetic algorithms”. International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC 2013), Shengyang, China, 20-22 December 2013.
  • hiangLin CY. “Applications of genetic algorithm to portfolio optimization with practical transaction constraints”. 9th Joint Conference on Information Sciences, (JCIS 2006), Kaohsiung, Taiwan, ,8-11 October 2006.
  • Aranha C, Iba, H. “The memetic tree-based genetic algorithm and its application to portfolio optimization”. Memetic Computing, 1(2), 139-151, 2009.
  • Branke J, Scheckenbach B, Stein M, Deb K, Schmeck H. “Portfolio optimization with an envelope-based multi-objective evolutionary algorithm”. European Journal of Operational Research, 199(3), 684-693, 2009.
  • Chang TJ, Yang SC, Chang, KJ. “Portfolio optimization problems in different risk measures using genetic algorithm”. Expert Systems with Applications, 36(7), 10529-10537, 2009.
  • Li YF, Guo W. “The stock portfolios simulated annealing genetic algorithm based on raroc”. 2009 Chinese Control and Decision Conference (CCDC 2009), Guilin, China, 17-19 June 2009.
  • Loukeris N, Donelly D, Khuman A, Peng Y. “A numerical evaluation of meta-heuristic techniques in portfolio optimisation”. Operational Research, 9(1), 81-103, 2009.
  • Pai GAV, Michel T. “Evolutionary optimization of constrained k-means clustered assets for diversification in small portfolios”. IEEE Transactions on Evolutionary Computation, 13(5), 1030-1053, 2009.
  • Shaikh RA, Abbas, A. “Genetic algorithm and ms solver for portfolio optimization under exogenous influence”.2nd International Conference on Computer and Electrical Engineering (ICCEE 2009), Dubai, UAE, 28-30 December 2009.
  • Anagnostopoulos KP, Mamanis G. “A portfolio optimization model with three objectives and discrete variables”. Computers & Operations Research, 37(7), 1285-1297, 2010.
  • Ruiz-Torrubiano R, Suarez, A. “Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints”. IEEE Computational Intelligence Magazine, 5(2), 92-107, 2010.
  • Anagnostopoulos KP, Mamanis G. “The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms”. Expert Systems with Applications, 38(11), 14208-14217, 2011.
  • Anagnostopoulos K P, Mamanis, G. “Multiobjective evolutionary algorithms for complex portfolio optimization problems”. Computational Management Science, 8(3), 259-279, 2011.
  • Fu TC, Ng CM, Wong KW, Chung FL. “Models for portfolio management on enhancing periodic consideration and portfolio selection”. 7th International Conference on Natural Computation (ICNC 2011), Shangai, China, 26-28 July 2011.
  • Chen Y, Mabu S, Hirasawa K. “Genetic relation algorithm with guided mutation for the large-scale portfolio optimization”. Expert Systems with Applications, 38(4), 3353-3363, 2011.
  • Kremmel T, Kubalík J, Biffl S. “Software project portfolio optimization with advanced multiobjective evolutionary algorithms”. Applied Soft Computing, 11(1), 1416-1426, 2011.
  • Woodside-Oriakhi M, Lucas C, Beasley JE. “Heuristic algorithms for the cardinality constrained efficient frontier”. European Journal of Operational Research, 213(3), 538-550, 2011.
  • Sadjadi SJ, Gharakhani M, Safari E. “Robust optimization framework for cardinality constrained portfolio problem”. Applied Soft Computing, 12(1), 91-99, 2012.
  • Yi H, Yang J. “Multi-objective portfolio optimization based on fuzzy genetic algorithm”. 9th International Conference on Computational Intelligence and Security (CIS 2013), Leshan, China, 14-15 December 2013.
  • Ackora-Prah J, Gyamerah SA, Andam PS. “A heuristic crossover for portfolio selection”. Applied Mathematical Sciences, 8(65), 3215-3227, 2014.
  • Joglekar S. “Two-stage stock portfolio construction: Correlation clustering and genetic optimization”. 27th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2014), Pensacola Beach, USA, 21-23 May 2014.
  • Liagkouras K, Metaxiotis K. “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, 2014.
  • Lwin K, Qu R, Kendall G. “A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization”. Applied Soft Computing, 24, 757-772, 2014.
  • Adebiyi Ayodele A, Ayo Charles K. “Portfolio selection problem using generalized differential evolution 3”. Applied Mathematical Sciences, 9(41-44), 2069-2082, 2015.
  • Hadi AS, El Naggar AA, Abdel Bary MN. “New model and method for portfolios selection”. Applied Mathematical Sciences, 10(5-8), 263-288, 2016.
  • Mashayekhi Z, Omrani H. “An integrated multi-objective markowitz–dea cross-efficiency model with fuzzy returns for portfolio selection problem”. Applied Soft Computing, 38, 1-9, 2016.
There are 43 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Can Berk Kalaycı

Ökkeş Ertenlice This is me

Hasan Akyer

Hakan Aygören

Publication Date August 18, 2017
Published in Issue Year 2017 Volume: 23 Issue: 4

Cite

APA Kalaycı, C. B., Ertenlice, Ö., Akyer, H., Aygören, H. (2017). Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(4), 470-476.
AMA Kalaycı CB, Ertenlice Ö, Akyer H, Aygören H. Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. August 2017;23(4):470-476.
Chicago Kalaycı, Can Berk, Ökkeş Ertenlice, Hasan Akyer, and Hakan Aygören. “Ortalama-Varyans portföy Optimizasyonunda Genetik Algoritma Uygulamaları üzerine Bir literatür araştırması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23, no. 4 (August 2017): 470-76.
EndNote Kalaycı CB, Ertenlice Ö, Akyer H, Aygören H (August 1, 2017) Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23 4 470–476.
IEEE C. B. Kalaycı, Ö. Ertenlice, H. Akyer, and H. Aygören, “Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 23, no. 4, pp. 470–476, 2017.
ISNAD Kalaycı, Can Berk et al. “Ortalama-Varyans portföy Optimizasyonunda Genetik Algoritma Uygulamaları üzerine Bir literatür araştırması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23/4 (August 2017), 470-476.
JAMA Kalaycı CB, Ertenlice Ö, Akyer H, Aygören H. Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23:470–476.
MLA Kalaycı, Can Berk et al. “Ortalama-Varyans portföy Optimizasyonunda Genetik Algoritma Uygulamaları üzerine Bir literatür araştırması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 23, no. 4, 2017, pp. 470-6.
Vancouver Kalaycı CB, Ertenlice Ö, Akyer H, Aygören H. Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23(4):470-6.

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