Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Opt
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Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization Christos Konstantinou1 · Alexandros Tzanetos1 · Georgios Dounias1 Received: 8 February 2020 / Revised: 23 October 2020 / Accepted: 30 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The constraints and the vast solution space of operational research optimization problems make them hard to cope with. However, Computational Intelligence, and especially Nature-Inspired Algorithms, has been a useful tool to tackle hard and large space optimization problems. In this paper, a very consistent and effective hybrid optimization scheme to tackle cardinality constrained portfolio optimization problems is presented. This scheme consists of two nature-inspired algorithms, i.e. Sonar Inspired Optimization algorithm and Genetic Algorithm. Also, the incorporation of heuristic information, i.e. an expert’s knowledge, etc., to the overall performance of the hybrid scheme is tested and compared to previous studies. More specifically, under the framework of a financial portfolio optimization problem, the heuristic information-enhanced hybrid scheme manages to reach a new optimal solution. Additionally, a comparison of the proposed hybrid scheme with other hybrid schemes applied to the same problem with the same data is performed. Keywords Portfolio optimization · Sonar inspired optimization · Genetic algorithm · Hybrid algorithms · Nature-inspired algorithms
* Alexandros Tzanetos [email protected] Christos Konstantinou [email protected] Georgios Dounias [email protected] 1
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Str., 82132 Chios, Greece
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1 Introduction One of the most challenging optimization problems in Operations Research is the Portfolio Optimization problem, representing one of the most fundamental dilemmas for decision-makers. Due to the fact that the financial domain corresponds to a complex and ever-changing environment, the Cardinality Constraint Portfolio Optimization problem is usually an NP-hard problem (non-deterministic polynomial-time problem). Investors come across the problem of identifying the best combination of assets and the optimal allocation of the available capital on them, under non-linear objectives and binding restrictions. Classical methods have been implemented to cope with this problem, such as non-linear programming (Kocadağlı and Keskin 2015) and quadratic programming (Markowitz 1952). This two-fold formulation of portfolio optimization has been tackled in previous studies (Vassiliadis et al. 2012; Tzanetos et al. 2017, 2018a; Giannakouris et al. 2010) by hybrid schemes, containing Nature-Inspired Intelligent (NII) algorithms, which belong to Computational Intelligence (CI), one of the field of Artificial Intelligence. NII algorithms imitate the way natural systems w
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