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An autocatalytic network model for stock markets

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  • Caetano, Marco Antonio Leonel
  • Yoneyama, Takashi

Abstract

The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.

Suggested Citation

  • Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2015. "An autocatalytic network model for stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 122-127.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:122-127
    DOI: 10.1016/j.physa.2014.10.052
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    References listed on IDEAS

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    Cited by:

    1. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.

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