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International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

IPMU 2012: Advances in Computational Intelligence pp 20–29Cite as

Evolving Fuzzy Modeling for Stock Market Forecasting

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Part of the book series: Communications in Computer and Information Science ((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.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Automation School of Electrical and Computer Engineering, University of Campinas, São Paulo, Brazil, 13.083-852

    Leandro Maciel & Fernando Gomide

  2. Department of Economic Theory Institute of Economics, University of Campinas, São Paulo, Brazil, 13.083-857

    Rosangela Ballini

Authors
  1. Leandro Maciel
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  2. Fernando Gomide
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  3. Rosangela Ballini
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco  & Benedetto Matarazzo  & 

  2. CNRS UMR 7606, DAPA, LIP6 8, Université Pierre et Marie Curie - Paris6, rue du Capitaine Scott, F-75015, Paris, France

    Bernadette Bouchon-Meunier

  3. Dip. Matematica e Informatica, Università di Perugia, 06123, Perugia, Italy

    Giulianella Coletti

  4. Department of Computer and Management Science, University of Trento, Via Inama 5, 38122, Trento, Italy

    Mario Fedrizzi

  5. Machine Intelligence Institute — IONA College,, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2012 Springer-Verlag Berlin Heidelberg

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Maciel, L., Gomide, F., Ballini, R. (2012). Evolving Fuzzy Modeling for Stock Market Forecasting. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-31724-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31723-1

  • Online ISBN: 978-3-642-31724-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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