Abstract
This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.
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S. C. Chiam received the B.Eng. degree (the first class honors) in electrical engineering from the National University of Singapore in 2005. He is currently a Ph.D. candidate at the Centre for Intelligent Control, National University of Singapore.
His research interests include evolutionary computation and neural networks, specifically in the application of evolutionary multi-objective optimization techniques in the field of computational finance, i.e., portfolio optimization and time series forecasting.
K. C. Tan received the B.Eng. degree (the first class honors) in electronics and electrical engineering and the Ph.D. degree from the University of Glasgow, Glasgow, Scotland, in 1994 and 1997, respectively. He is currently an associate professor in the Department of Electrical and Computer Engineering, National University of Singapore. He has authored or coauthored two books and more than 140 journal and conference publications. He is also an international program committee member for over 50 conferences and served in the organizing committee for over 15 international conferences, including the technical program co-chair for IEEE Congress on Evolutionary Computation in 2005, program chair for IEEE Conference on Cybernetics and Intelligent Systems in 2004 and 2006, general co-chair for IEEE Congress on Evolutionary Computation in 2007 in Singapore and IEEE Symposium on Computational Intelligence in Scheduling in 2007 in Hawaii. He is an associate editor for IEEE Transactions on Evolutionary Computation.
His research interests include computational intelligence, evolutionary computation, multi-objective optimization, and engineering design optimization.
A. Al Mamun graduated from the Indian Institute of Technology, Kharagpur, India in 1985. He received the Ph.D. degree from the National University of Singapore in 1997. In his professional career, he worked as a research engineer at the Data Storage Institute, Singapore, and as staff engineer at Maxtor Peripherals prior to joining the faculty of Department of Electrical and Computer Engineering, National University of Singapore. His research interests include precision servomechanism, mechatronics, intelligent control, and autonomous mobile robots.
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Chiam, S.C., Tan, K.C. & Al Mamum, A. Evolutionary multi-objective portfolio optimization in practical context. Int. J. Autom. Comput. 5, 67–80 (2008). https://doi.org/10.1007/s11633-008-0067-2
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DOI: https://doi.org/10.1007/s11633-008-0067-2