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Evolving Fuzzy Modeling for Stock Market Forecasting

  • Leandro Maciel
  • Fernando Gomide
  • Rosangela Ballini
Part of the Communications in Computer and Information Science book series (CCIS, volume 300)

Abstract

Stock market forecasting plays an important role in risk management, asset pricing and portfolio analysis. Stock prices involve non-linear dynamics and uncertainties due to their high volatility and noisy environments. Forecasting modeling with adaptive and high performance accuracy is a major requirement in this case. This paper addresses a new approach for stock market forecast within the framework of evolving fuzzy rule-based modeling, a form of adaptive fuzzy modeling. US and Brazilian stock market data are used to evaluate modeling characteristics and forecasting performance. The results show the high potential of evolving fuzzy models to describe stock market behavior accurately. The evolving modeling approach reveals the essential capability to detect structural changes arising from complex dynamics and instabilities like financial crisis.

Keywords

Evolving Fuzzy Systems Stock Price Forecasting Rule-Based Models Adaptive Systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leandro Maciel
    • 1
  • Fernando Gomide
    • 1
  • Rosangela Ballini
    • 2
  1. 1.Department of Computer Engineering and Automation School of Electrical and Computer EngineeringUniversity of CampinasSão PauloBrazil
  2. 2.Department of Economic Theory Institute of EconomicsUniversity of CampinasSão PauloBrazil

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