Evolutionary Multiobjective Optimization Approach for Evolving Ensemble of Intelligent Paradigms for Stock Market Modeling

  • Ajith Abraham
  • Crina Grosan
  • Sang Yong Han
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3789)


The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. As evident from the empirical results, none of the five considered techniques could find an optimal solution for all the four performance measures. Further the results obtained by these five techniques are combined using an ensemble and two well known Evolutionary Multiobjective Optimization (EMO) algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Pareto Archive Evolution Strategy (PAES)algorithms in order to obtain an optimal ensemble combination which could also optimize the four different performance measures (objectives). We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that the resulting ensemble obtain the best results.


Root Mean Square Error Multiobjective Optimization Mean Absolute Percentage Error Stock Index Multiobjective Optimization Problem 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ajith Abraham
    • 1
  • Crina Grosan
    • 2
  • Sang Yong Han
    • 1
  • Alexander Gelbukh
    • 3
  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  2. 2.Department of Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania
  3. 3.Centro de Investigación en Computación (CIC)Instituto Politécnico Nacional (IPN)Mexico

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