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Application of Fuzzy Cognitive Maps for Stock Market Modeling and Forecasting

  • Wojciech Froelich
  • Alicja Wakulicz-Deja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

In this paper, we investigated the possibility of discovering complex concepts for modeling and forecasting the stock market. We started from a short overview of existing domain knowledge and discussed the usefulness of well-known stock market indicators for predicting share prices. As opposed to other statistical or artificial intelligence approaches, we decided to concentrate on the modeling of cause and effect relationships among concepts within stock market behavior. After preliminary analysis, we made the case for the application of the resulting model for the forecasting stock market performance, using complex concepts that involve a mixture of diverse simple indicators and causal relationships. For the construction and evaluation of such complex concepts, we applied FCMs (fuzzy cognitive maps), a relatively easy approach that allows human interpretation of the results from the scheme. On the basis of the proposed formalism and the adapted evolutionary learning method, we have developed an FCM with the ability to support decisions relative to the stock exchange. We have verified the usefulness of the proposed approach using historical transactions of the Warsaw Stock Exchange.

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References

  1. 1.
    Elder, A.: Trading for a living: Psychology, Trading Tactics, Money Management. John Wiley & Sons, Chichester (1993)Google Scholar
  2. 2.
    Parikh, J.C.: Stochastic Processes and Financial Markets. Narosa Publishing House (2003)Google Scholar
  3. 3.
    Ehlers, J.: Cybernetic Analysis For Stock And Futures. John Willey & Sons, New York (2004)Google Scholar
  4. 4.
    Schwager, J.: Study Guide for Fundamental Analysis. John Wiley & Sons, Chichester (1995)Google Scholar
  5. 5.
    Pring, M.: Technical Analysis Explained. McGraw-Hill Company, New York (2004)Google Scholar
  6. 6.
    Vanstone, B., Tan, C.N.W.: A survey of the application of soft computing to investment and financial trading. In: Proceedings of the eighth Australian & New Zealand intelligent information systems conference (ANZIIS 2003), Sydney (2003)Google Scholar
  7. 7.
    Poole, D.: Learning, Bayesian Probability, Graphical Models, and Abduction. In: Flach, P., Kakas, A. (eds.) Abduction and Induction: essays on their relation and integration, Kluwer, Dordrecht (1998)Google Scholar
  8. 8.
    Shoham, Y.: Chronological Ignorance: Time, Nonmonotonicity, Necessity and Causal Theories, pp. 389–393. AAAI, Menlo Park (1986)Google Scholar
  9. 9.
    Sun, R.: Fuzzy Evidential Logic: a model of causality for commonsense reasoning. In: Proceedings of the Fourteenth Annual Conference of the Cognitive Scence Society (1992)Google Scholar
  10. 10.
    Pearl, J.: Causality, Models Reasoning and Inference. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  11. 11.
    Tollman, E.C.: Cognitive Maps in Rats and Men. The Psychological Review 55(3l), 189–208 (1948)CrossRefGoogle Scholar
  12. 12.
    Axelrod, R.: Structure of Decision–The Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)Google Scholar
  13. 13.
    Kosko, B.: Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24(l), 65–75 (1986)zbMATHCrossRefGoogle Scholar
  14. 14.
    Park, K.S., Kim, S.H.: Fuzzy cognitive maps considering time relationships. International Journal Human-Computer Studies 42, 157–168 (1995)CrossRefGoogle Scholar
  15. 15.
    Carvalho, J.P., Tome, J.A.B.: Rule Based Fuzzy Cognitive Maps - Fuzzy Causal Relations. In: Mohammadian, M. (ed.) Computational Intelligence for Modelling, Control and Automation: Evolutionary Computation & Fuzzy Logic for Intelligent Control, Knowledge Acquisition & Information Retrieval, IOS Press, Amsterdam (1999)Google Scholar
  16. 16.
    Stach, W., Kurgan, L.A., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems 153(3), 371–401 (2005)zbMATHMathSciNetGoogle Scholar
  17. 17.
    Huerga, A.V.: A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the 16th International Workshop on Qualitative Reasoning (2002)Google Scholar
  18. 18.
    Stach, W., Kurgan, L.A., Pedrycz, W., Reformat, M.: Higher-order Fuzzy Cognitive Maps. In: Proceedings of NAFIPS 2006 (International Conference of the North American Fuzzy Information Processing Society (2006)Google Scholar
  19. 19.
    Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M., Zopounidis, C.D.: Development of dynamic cognitive networks as complex systems approximators: validation in financial time series. Applied Soft Computing 5 (2005)Google Scholar
  20. 20.
    Froelich, W., Wakulicz-Deja, A.: Learning Fuzzy Cognitive Maps from the Web for the Stock Market Decision Support System. In: Proceedings of the 5th Atlantic Web Intelligence Conference - AWIC 2007, Fontainebleau, Advances In Soft Computing, vol. 43, pp. 106–111. Springer, Heidelberg (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wojciech Froelich
    • 1
  • Alicja Wakulicz-Deja
    • 1
  1. 1.Institute of Computer ScienceSilesian UniversitySosnowiecPoland

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