Multi-objective evolutionary algorithms for financial portfolio design Online publication date: Sun, 17-Oct-2010
by Sudhansu Kumar Mishra, Ganapati Panda, Sukadev Meher, Ritanjali Majhi
International Journal of Computational Vision and Robotics (IJCVR), Vol. 1, No. 2, 2010
Abstract: Efficient portfolio design is a real challenge in the area of computational finance. Optimisation based on Markowitz (1959) two-objective mean-variance approach is computationally expensive for real financial world. Practical portfolio design introduces further complexity as it requires the optimisation of multiple return and risk measures. Some of these measures are non-linear and non-convex. Three well known multi-objective evolutionary algorithms, i.e., Pareto envelope-based selection algorithm, micro-genetic algorithm and multi-objective particle swarm optimisation are chosen and applied for solving the bi-objective portfolio optimisation problem which simultaneously maximise the return and minimise the associated risk. Performance comparison is obtained by carrying out using practical data. The results demonstrate that MOPSO outperforms the existing two methods for the considered test cases.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook