A survey on financial applications of metaheuristics

Amparo Soler-Dominguez, Angel Alejandro Juan Perez, Renatas Kizys

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Abstract

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

Keywords

  • applied computing
  • operational research
  • decision analysis
  • law, social and behavioral science
  • economics
  • metaheuristics
  • finance
  • combinatorial optimization

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