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A Study of Nature-Inspired Methods for Financial Trend Reversal Detection

  • Antonia Azzini
  • Matteo De Felice
  • Andrea G. B. Tettamanzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)

Abstract

This paper presents an application of two nature-inspired algorithms to the financial problem concerning the detection of turning points. Nature-Inspired methods are receiving a growing interest due to their ability to cope with complex tasks like classification, forecasting and anomaly detection problems. A swarm intelligence algorithm, Particle Swarm Optimization (PSO), and an artificial immune system one, the Negative Selection (NS), are applied to the problem of detection of turning points, modeled as an Anomaly Detection (AD) problem, and their performances are compared. Both methods are found to give interesting results with respect to an unpredictable behavior.

Keywords

Particle Swarm Optimization Negative Selection Particle Swarm Optimization Algorithm Anomaly Detection Artificial Immune System 
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

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Antonia Azzini
    • 1
  • Matteo De Felice
    • 2
    • 3
  • Andrea G. B. Tettamanzi
    • 1
  1. 1.Dipartimento di Tecnologie dell’InformazioneUniversità degli Studi di Milano 
  2. 2.ENEA (Italian Energy New Technology and Environment Agency) 
  3. 3.Dipartimento di Informatica e AutomazioneUniversità degli Studi “Roma Tre” 

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