A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem

K. Liagkouras () and K. Metaxiotis
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K. Liagkouras: University of Piraeus
K. Metaxiotis: University of Piraeus

Annals of Operations Research, 2018, vol. 267, issue 1, No 15, 319 pages

Abstract: Abstract This paper proposes a novel multiobjective evolutionary Algorithm (MOEA) for the solution of the cardinality constrained portfolio optimization problem (CCPOP). The proposed algorithm introduces an efficient encoding scheme specially designed for dealing with the difficulties of the CCPOP. Also, the proposed algorithm incorporates a new mutation and recombination operator tailor-made to work well with the new encoding scheme. Datasets from seven different stock markets are utilized for testing the efficiency of the proposed approach. In particular, the performance of the proposed efficiently encoded multiobjective portfolio optimization solver (EEMPOS) is assessed in comparison with two well-known MOEAs, namely NSGAII and MOEA/D. The experimental results indicate that the proposed EEMPOS outperforms the two other MOEAs for all examined performance metrics when is applied to the solution of the CCPOP for a fraction of time required by the other techniques.

Keywords: Multiobjective optimization; Evolutionary algorithms; Encoding scheme; Portfolio optimization; cardinality constrained (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-016-2377-z

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