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Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends

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  • Doering, Jana
  • Kizys, Renatas
  • Juan, Angel A.
  • Fitó, Àngels
  • Polat, Onur

Abstract

Computational finance is an emerging application field of metaheuristic algorithms. In particular, these optimisation methods are becoming the solving approach alternative when dealing with realistic versions of several decision-making problems in finance, such as rich portfolio optimisation and risk management. This paper reviews the scientific literature on the use of metaheuristics for solving NP-hard versions of these optimisation problems and illustrates their capacity to provide high-quality solutions under scenarios considering realistic constraints. The paper contributes to the existing literature in three ways. Firstly, it reviews the literature on metaheuristic optimisation applications for portfolio and risk management in a systematic way. Secondly, it identifies the linkages between portfolio optimisation and risk management and presents a unified view and classification of both problems. Finally, it outlines the trends that have gradually become apparent in the literature and will dominate future research in order to further improve the state-of-the-art in this knowledge area.

Suggested Citation

  • Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
    • Handle: RePEc:eee:oprepe:v:6:y:2019:i:c:s2214716019300399
    DOI: 10.1016/j.orp.2019.100121
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    Cited by:

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    3. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 2020. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 22(5), pages 1009-1019, October.
    4. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 0. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 0, pages 1-11.
    5. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    6. Jelić, Marko & Batić, Marko & Krstić, Aleksandra & Bottarelli, Michele & Mainardi, Elena, 2023. "Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Martín Egozcue & Luis Fuentes García & Ričardas Zitikis, 2023. "The Slicing Method: Determining Insensitivity Regions of Probability Weighting Functions," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1369-1402, April.
    8. Christopher Bayliss & Marti Serra & Armando Nieto & Angel A. Juan, 2020. "Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities," Risks, MDPI, vol. 8(4), pages 1-14, December.

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