A great variety of complex real-life problems can be sufficiently solved by intelligent nature-inspired methods which can be considered part of artificial or computational intelligence. These newly introduced techniques have proven their important role on many successful implementations, mostly related to optimization problems. The basic reason for their success is that they imitate the way that real-life networks and other biological systems function and evolve in order to solve problems in different domains. Such systems can be found in the human brain (neurons), or can be observed in the natural world in the form of ant colonies, flocks of birds, as well as in other examples taken from the microcosm such as the human immune system. In this paper, we try to briefly present popular nature-inspired techniques, ant colony optimization and particle swarm optimization, and also to clarify the significance and appropriateness of nature-inspired intelligent approaches for solving complex financial optimization problems. A short discussion in included for a number of selected financial decision making applications, i.e. forecasting of financial distress, multi-stage portfolio optimization, credit scoring, investment decisions and capital investment planning.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Financial Distress Particle Swarm Optimization Approach Good Forecast Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Vasilios Vasiliadis
    • 1
  • Georgios Dounias
    • 1
  1. 1.Dept. of Financial Engineering and Management, Management & Decision Engineering LaboratoryUniversity of the AegeanChiosGreece

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