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Novel Stock Market Prediction Using a Hybrid Model of Adptive Linear Combiner and Differential Evolution

  • Minakhi Rout
  • Babita Majhi
  • Ritanjali Majhi
  • G. Panda
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

The paper proposes a novel forecasting model for efficient prediction of small and long range predictions of stock indices particularly the DJIA and S&P500. The model employs an adaptive structure containing a linear combiner with adjustable weights implemented using differential evolution. The learning algorithm using DE is dealt in details. The key features of known stock time series are extracted and used as inputs to the model for training its parameters. Exhaustive simulation study indicates that the performance of the proposed model with test input is quite satisfactory and superior to those provided by previously reported GA and PSO based forecasting models.

Keywords

Stock market prediction hybrid model adaptive linear combiner differential evolution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Minakhi Rout
    • 1
  • Babita Majhi
    • 1
  • Ritanjali Majhi
    • 2
  • G. Panda
    • 3
  1. 1.Dept. of CSE/ITITER, Siksha O Anusandhan UniversityBhubaneswarIndia
  2. 2.School of ManagementNaitional Institute of TechnologyWarangalIndia
  3. 3.School of Electrical SciencesIndian Institute of TechnologyBhubaneswarIndia

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