Portfolio Selection of Health Care and Oil and Gas Sector by the Means of Genetic Algorithms Based on Population and Survival of the Fittest

Year 2017, Volume: 5 Issue: 1, 29 - 32, 31.03.2017

David Oyewola , D. Hakimi Y. Yahaya G. Bolarin M.d. Shehu

https://izlik.org/JA39EA24DP

Abstract

Portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market should make. The main purpose of this paper is to determine the optimal portfolio with regard to stock returns of companies, which are active in Health Care and Oil and Gas Sector of Nigerian Stock Exchange. For achieving this purpose, annual statistics of companies’ stocks spanning from 2010 – 2014 have been used. For analyzing statistics, information of companies stocks, the Genetic Algorithms and Particle Swarm Optimization (GAPSO) and Knapsack Problem have been used with the aim of increasing the total return, in order to form a financial portfolio.

Keywords

Genetic Algorithms , Survival of the fittest , Portfolio selection , Return , Knapsack Problem

References

• Bermúdez, J.D., Segura, J.V., Vercher, E. A multi- objective genetic algorithm for cardinality constrained fuzzy portfolio selection, Fuzzy Sets and Systems, 188, 2012, 16-26.
• Chien-Feng Huang, C.F. A hybrid stock selection model using genetic algorithms and support vector regression, Applied Soft Computing, 2012, 12(2), 807–818
• Ghodrati, H., & Zahiri, Z. A Monte Carlo simulation technique to determine the optimal portfolio. Management SCIENCE LETTERS, 4(3), 465-474, 2014.
• Guang He, G., Nan-jing Huang, N.J. A new particle swarm optimization algorithm with an application, Applied Mathematics and Computation (2014), 232, 521-528.
• Gujarati and Porter's Basic Econometrics McGraw Hill Irwin, 2009.
• Najafi Moghadam, A., Rahnama roodposhti, F., Farrokhi, M. Optimization of stock portfolio based on ant Colony & grey theory. International Research Journal of Applied and Basic Sciences, 8 (7): 780-788 (2014).
• Olowe O., Matthew O., & Fasina, Fagbeminiyi, Nigerian stock exchange and economic development, Knowledge Management, Information Management, Learning Management, No. 14 ~ 2011.
• Rupak Bhattacharyya, R., Ahmed Hossain, Sh., Kar, S. Fuzzy cross-entropy, mean, variance, skewness models for portfolio selection. Journal of King Saud University - Computer and Information Sciences, 26(1), 79–87,2014.
• Shadkam, E. FC Approach in Portfolio Selection of Tehran’s Stock Market, Journal of Asian Finance, Economics and Business, 2014, 1(2), 31-37.
• Agarwal A., Pirkul, H. and Jacob, V. Augmented Neural Networks for Task Scheduling. European Journal of Operational Research. 151(3) 481-502(2003).
• Agarwal, A., Jacob, V.S. and Pirkul, H. An Improved Augmented Neural-Networks Approach for Scheduling Problems. INFORMS Journal on Computing,2006, 18(1) 119-128.
• Agarwal, A., Colak, S., Jacob, V. and Pirkul, H. Heuristics and Augmented Neural Networks for Scheduling with Non-Identical Machines. European Journal of Operational Research,2006, 175(1) 296-317.
• Akpan, N. P , Etuk, E. H and Essi, I.D. A deterministic approach to a capital budgeting problem . Am. J. Sci. Ind. Res., 2011, 2(3): 456-460.
• Ali, Nadi Unal. A Genetic Algorithm for the Multiple Knapsack Problem in Dynamic Environment Proceedings of the World Congress on Engineering and Computer Science 2013 Vol II WCECS 2013, 23-25 October, 2013, San Francisco, USA.
• Balas, E. Facets of the knapsack polytope. Math. Program,1975.,8, 146–164.
• Brandstatter, B., Baumgartner. Particle swarm optimization–mass-spring system analogon. IEEE Trans. Magn. 38, 97–1000 (2002)
• Bellman, R. Comment on Dantzig's Paper on Discrete Variable Extremum Problems, Operations Research, Vol. 5, 1957, pp. 723 – 724
• Bellman, R. and S.E. Dreyfus. Applied Dynamic Programming, Princeton University Press (1962).
• Bellman, R. Dynamic Programming. Princeton, NJ: Princeton University Press(1957).
• Caprara, A., Pisinger, D. & Toth, P. Exact solution of the quadratic knapsack problem.INFORMS J.Comput.,11,1999, 125–137.
• Colak, S. and Agarwal, A. Non-greedy Heuristics and Augmented Neural Networks for the OpenShop Scheduling Problem. Naval Research Logistics. 52 (2005) 631-644.
• C. R. Reeves. Using Genetic Algorithms With Small Populations, In Proceedings of the Fifth International Conference on Genetic Algorithms, 1993, pp. 92-99.
• Dervis Karaboga. An Idea Based On Honey Bee Swarm For Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005
• Dervis Karaboga & Bahriye Basturk . A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,J Glob Optim(2007) 39:459–471
• Drezner, Z. & Hamacher, H. W. Facility Location: Application and Theory. New York: Springer(2002).Vol 6