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

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

Research output: Contribution to journalArticlepeer-review

138 Downloads (Pure)

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.
Original languageEnglish
Article number100121
Number of pages19
JournalOperations Research Perspectives
Volume6
Early online date19 Aug 2019
DOIs
Publication statusEarly online - 19 Aug 2019

Keywords

  • Portfolio optimisation
  • Risk management
  • Combinatorial optimisation
  • Metaheuristics

Fingerprint

Dive into the research topics of 'Metaheuristics for rich portfolio optimisation and risk management: current state and future trends'. Together they form a unique fingerprint.

Cite this