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Multi-objective Basic Variable Neighborhood Search for Portfolio Selection

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Variable Neighborhood Search (ICVNS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12010))

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Abstract

The Portfolio Selection Problem looks for a set of assets with the best trade-off between return and risk, that is, with the maximum expected return and the minimum risk (e.g., the variance of returns). As these objectives are conflicting, it is a difficult multi-objective problem. Different models and algorithms have been proposed to obtain the (optimal) Pareto front. However, exact approaches take days for a large set of points to the Pareto front. Within this perspective, we develop a basic variable neighborhood search heuristic to solve the bi-objective portfolio selection problem. The proposed heuristic considers ten neighborhood structures that are mainly based on swap moves and has a local improvement based on averaging the proportions that are invested in consecutive assets. The proposed heuristic was experimentally compared with the Mean-Variance model of Markowitz, using benchmark instances from the OR-Library. The number of assets in these instances ranges from 31 to 225. According to the experimental results, the proposed heuristic performed well in the construction of different Pareto fronts.

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Acknowledgments

The authors would like to thank Intel, the National Counsel of Technological and Scientific Development (CNPq - grants 202006/2018-2 and 308312/2016-3), the State of Goiás Research Foundation (FAPEG), and the State of São Paulo Research Foundation (FAPESP - grant 2013/07375-0) for their financial support.

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Authors and Affiliations

  1. Institute of Mathematics and Technology, Federal University of Goiás, Campus Catalão, Catalão, GO, 75704-020, Brazil

    Thiago Alves de Queiroz

  2. Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, 13566-590, Brazil

    Leandro Resende Mundim & André Carlos Ponce de Leon Ferreira de Carvalho

Authors
  1. Thiago Alves de Queiroz
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  2. Leandro Resende Mundim
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  3. André Carlos Ponce de Leon Ferreira de Carvalho
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Correspondence to Thiago Alves de Queiroz .

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Editors and Affiliations

  1. Institut National de Statistique et d'Economie Appliquée (INSEA), Rabat, Morocco

    Rachid Benmansour

  2. University of Macedonia, Thessaloniki, Greece

    Angelo Sifaleras

  3. Khalifa University, Abu Dhabi, United Arab Emirates

    Nenad Mladenović

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de Queiroz, T.A., Mundim, L.R., de Carvalho, A.C.P.d.L.F. (2020). Multi-objective Basic Variable Neighborhood Search for Portfolio Selection. In: Benmansour, R., Sifaleras, A., Mladenović, N. (eds) Variable Neighborhood Search. ICVNS 2019. Lecture Notes in Computer Science(), vol 12010. Springer, Cham. https://doi.org/10.1007/978-3-030-44932-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-44932-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44931-5

  • Online ISBN: 978-3-030-44932-2

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