Advertisement

Modified Moth Search Algorithm for Portfolio Optimization

  • Ivana Strumberger
  • Eva Tuba
  • Nebojsa Bacanin
  • Milan TubaEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)

Abstract

Portfolio optimization represents a widely researched problem in the field of finance and economy. Based on the available literature, numerous methods and heuristics were proposed for solving it and in this paper, a modified moth search algorithm for solving the portfolio selection task is introduced. The moth search algorithm is one of the most recent metaheuristics from the group of swarm intelligence algorithms. Since the portfolio optimization deals with simultaneous optimization of multiple conflicting objectives, modified moth search algorithm was adjusted for handling multi-objective optimization problems. Quality of the proposed algorithm was tested on the standard benchmark problems for portfolio optimization from the OR-library and comparison of the results obtained by the proposed method and the other methods from the literature proposed for the same model and data set was performed. According to these initial experiments, we conclude that the modified moth search algorithm can be successfully applied to this kind of problems.

Keywords

Portfolio optimization problem Moth search algorithm Metaheuristics Swarm intelligence Multi-objective optimization NP-h ard problems 

References

  1. 1.
    Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. Spec. Issue Comput. Intell. Metaheuristic Algorithms Appl. 2014(Article ID 721521), 16 (2014)Google Scholar
  2. 2.
    Tuba, E., Alihodzic, A., Tuba, M.: Multilevel image thresholding using elephant herding optimization algorithm. In: Proceedings of 14th International Conference on the Engineering of Modern Electric Systems (EMES), pp. 240–243 (2017)Google Scholar
  3. 3.
    Strumberger, I., Bacanin, N., Beko, M., Tomic, S., Tuba, M.: Static drone placement by elephant herding optimization algorithm. In: Proceedings of the 24th Telecommunications Forum (TELFOR) (2017)Google Scholar
  4. 4.
    Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control. 26(1), 33–42 (2017)CrossRefGoogle Scholar
  5. 5.
    Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R., Tuba, M.: Unmanned combat aerial vehicle path planning by brain storm optimization algorithm. Stud. Inform. Control. 27(1), 15–24 (2018)CrossRefGoogle Scholar
  6. 6.
    Eiteman, D.K., Stonehill, A.I., Moffett, M.H.: Multinational Business in Finance. Pearson Series in France, 13th edn. Pearson (2013)Google Scholar
  7. 7.
    di Tollo, G., Roli, A.: Metaheuristics for the portfolio selection problem. Int. J. Oper. Res. 15(1), 13–35 (2008)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Markowitz, H.: Portfolio selection. J. Financ. 7, 77–91 (1952)Google Scholar
  9. 9.
    Anagnostopoulos, K.P., Mamanis, G.: Multiobjective evolutionary algorithms for complex portfolio optimization problems. Comput. Manag. Sci. 8, 259–279 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Golmakani, H.R., Fazel, M.: Constrained portfolio selection using particle swarm optimization. Expert Syst. Appl. 38(1), 8327–8335 (2011)CrossRefGoogle Scholar
  11. 11.
    Zhu, H., Wang, Y., Wang, K., Chen, Y.: Particle swarm optimization (PSO) for the constrained portfolio optimization problem. Expert Syst. Appl. 38(1), 10161–10169 (2011)CrossRefGoogle Scholar
  12. 12.
    Sefiane, S., Benbouziane, M.: Portfolio selection using genetic algorithm. J. Appl. Financ. Bank. 4(2), 143–154 (2012)Google Scholar
  13. 13.
    Wang, G.-G.: Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput. 10(2), 151–160 (2016)CrossRefGoogle Scholar
  14. 14.
    Tuba, M., Bacanin, N.: Artificial bee colony algorithm hybridized with firefly metaheuristic for cardinality constrained mean-variance portfolio problem. Appl. Math. Inform. Sci. 8, 2831–2844 (2014)CrossRefGoogle Scholar
  15. 15.
    Bacanin, N., Tuba, M.: Fireworks algorithm applied to constrained portfolio optimization problem. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC 2015) (2015)Google Scholar
  16. 16.
    Tuba, M., Bacanin, N., Pelevic, B.: Framework for constrained portfolio selection by the firefly algorithm. Int. J. Math. Models Methods Appl. Sci. 7(10), 1888–896 (2014)Google Scholar
  17. 17.
    Bacanin, N., Tuba, M.: Artificial bee colony (ABC) algorithm for portfolio optimization problem. In: Proceedings of the 5th International Conference on Applied Economics, Business and Development (AEBD 2013), pp. 163–168 (2013)Google Scholar
  18. 18.
    Tuba, M., Bacanin, N., Pelevic, B.: Krill herd (KH) algorithm applied to the constrained portfolio selection problem. Int. J. Math. Comput. Simul. 8, 94–102 (2014)Google Scholar
  19. 19.
  20. 20.
    Chang, T., Meade, N., Beasley, J., Sharaiha, Y.: Heuristics for cardinality constrained portfolio optimization. Comput. Oper. Res. 27(13), 1271–1302 (2000)zbMATHCrossRefGoogle Scholar
  21. 21.
    Cura, T.: Particle swarm optimization approach to portfolio optimization. Nonlinear Anal. RealWorld Appl. 10(4), 2396–2406 (2008)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ivana Strumberger
    • 1
  • Eva Tuba
    • 1
  • Nebojsa Bacanin
    • 1
  • Milan Tuba
    • 1
    Email author
  1. 1.Faculty of Informatics and ComputingSingidunum UniversityBelgradeSerbia

Personalised recommendations