Skip to content

A survey on financial applications of metaheuristics

Research output: Contribution to journalArticlepeer-review

  • Amparo Soler-Dominguez
  • Angel Alejandro Juan Perez
  • Renatas Kizys
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.
Original languageEnglish
Article number15
Number of pages23
JournalACM Computing Surveys (CSUR)
Volume50
Issue number1
Early online date1 Apr 2017
DOIs
Publication statusEarly online - 1 Apr 2017

Documents

  • KIZYS_2016_cright_CS_A Survey on Financial Applications of Metaheuristics

    Rights statement: ©ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, 15:1 (2017) http://doi.acm.org/10.1145/3054133.

    Accepted author manuscript (Post-print), 522 KB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 6631492