Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11633-008-0067-2
Title: Evolutionary multi-objective portfolio optimization in practical context
Authors: Chiam, S.C.
Tan, K.C. 
Al Mamum, A.
Keywords: Constraint handling
Evolutionary computation
Multi-objective optimization
Portfolio optimization
Preference-based multi-objective optimization
Issue Date: Jan-2008
Source: Chiam, S.C.,Tan, K.C.,Al Mamum, A. (2008-01). Evolutionary multi-objective portfolio optimization in practical context. International Journal of Automation and Computing 5 (1) : 67-80. ScholarBank@NUS Repository. https://doi.org/10.1007/s11633-008-0067-2
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. © 2008 Institute of Automation, Chinese Academy of Sciences.
Source Title: International Journal of Automation and Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/55933
ISSN: 14768186
DOI: 10.1007/s11633-008-0067-2
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