Stocks’ Trading System Based on the Particle Swarm Optimization Algorithm

  • Jovita Nenortaite
  • Rimvydas Simutis
Conference paper

DOI: 10.1007/978-3-540-25944-2_109

Volume 3039 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Nenortaite J., Simutis R. (2004) Stocks’ Trading System Based on the Particle Swarm Optimization Algorithm. In: Bubak M., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3039. Springer, Berlin, Heidelberg

Abstract

One of the central problems in financial markets is to make the profitable stocks trading decisions using historical stocks’ market data. This paper presents the decision-making method which is based on the application of neural networks (NN) and swarm intelligence technologies and is used to generate one-step ahead investment decisions. In brief, the analysis of historical stocks prices variations is made using “single layer” NN, and subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select ”global best” NN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different number of NN, moving time intervals and commission fees. The experimental results presented in the paper show that the application of our proposed method lets to achieve better results than the average of the market.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jovita Nenortaite
    • 1
  • Rimvydas Simutis
    • 1
  1. 1.Kaunas Faculty of HumanitiesVilnius UniversityKaunasLithuania