Portfolio optimization using Laplacian biogeography based optimization
- 17 Downloads
Portfolio optimization is defined as the most appropriate allocation of assets so as to maximize returns subject to minimum risk. This constrained nonlinear optimization problem is highly complex due to the presence of a number of local optimas. The objective of this paper is to illustrate the effectiveness of a well-tested and effective Laplacian biogeography based optimization and another variant called blended biogeography based optimization. As an illustration the model and solution methodology is implemented on data taken from Indian National Stock Exchange, Mumbai from 1st April, 2015 to 31st March, 2016. From the analysis of results, it is concluded that as compared to blended BBO, the recently proposed LX-BBO algorithm is an effective tool to solve this complex problem of portfolio optimization with better accuracy and reliability.
KeywordsPortfolio optimization Mean variance model LX-BBO
Funding was provided by Ministry of Human Resources, Govt. of India (Grant No. MHRD 02-23-200-429).
- 1.Aranha, C., Iba, H.: Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing. In: Proceedings of IEEE Congress on Evolutionary Computation, Singapore, pp. 196–203 25–28 Sept 2007 (2007)Google Scholar
- 4.Chen, A.H., Liang, Y.C., Liu, C.C.: An artificial bee colony algorithm for the cardinality-constrained portfolio optimization problems. In: Proceedings of IEEE World Congress on Evolutionary Computation, Brisbane, Australia, pp. 1–8. 10–15 June 2012 (2012)Google Scholar
- 7.Deep, K., Thakur, M.: A new crossover operator for real coded genetic algorithms. Appl. Math. Comput. 188, 895–912 (2007)Google Scholar
- 9.Garg, V., Deep, K.: Constrained Laplacian biogeography based optimization. Int. J. Syst. Assur. Eng. Manag. 8(2), 867–885 (2016)Google Scholar
- 13.Liu, S.T.: Solving portfolio optimization problem based on extension principle. In: Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Cordoba, Spain, 6096, pp. 164–174. 1–4 June 2010 (2010)Google Scholar
- 14.Ma, H., Simon, D.: Blended biogeography-based optimization for constrained optimization. Eng. Appl. Artif. Intell. 24(6), 517–525 (2010)Google Scholar
- 16.Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952)Google Scholar
- 19.Orito, Y., Hanada, Y., Shibata, S., and Yamamoto, H.: A new population initialization approach based on bordered hessian for portfolio optimization problems. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, England, pp. 1341–1346. 13–16 Oct 2013 (2013)Google Scholar
- 22.Tuba, M., Bacanin, N.: Upgraded Firefly Algorithm for Portfolio Optimization Problem. In: Proceedings of 16th International Conference of Computer Modelling and Simulation, Cambridge, England, pp. 112–117. 26–28 March 2014 (2014)Google Scholar