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Complexity analysis of the stock market

Author

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  • Park, Joongwoo Brian
  • Won Lee, Jeong
  • Yang, Jae-Suk
  • Jo, Hang-Hyun
  • Moon, Hie-Tae

Abstract

We study the complexity of the stock market by constructing ε-machines of Standard and Poor's 500 index from February 1983 to April 2006 and by measuring the statistical complexities. It is found that both the statistical complexity and the number of causal states of constructed ε-machines have decreased for last 20 years and that the average memory length needed to predict the future optimally has become shorter. These results support that the information is delivered to the economic agents and applied to the market prices more rapidly in year 2006 than in year 1983.

Suggested Citation

  • Park, Joongwoo Brian & Won Lee, Jeong & Yang, Jae-Suk & Jo, Hang-Hyun & Moon, Hie-Tae, 2007. "Complexity analysis of the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 179-187.
    • Handle: RePEc:eee:phsmap:v:379:y:2007:i:1:p:179-187
    DOI: 10.1016/j.physa.2006.12.042
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    Citations

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    Cited by:

    1. Jizba, Petr & Korbel, Jan, 2014. "Multifractal diffusion entropy analysis: Optimal bin width of probability histograms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 438-458.
    2. Jinkyu Kim & Gunn Kim & Sungbae An & Young-Kyun Kwon & Sungroh Yoon, 2013. "Entropy-Based Analysis and Bioinformatics-Inspired Integration of Global Economic Information Transfer," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-10, January.
    3. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    4. Petr Jizba & Jan Korbel, 2014. "Multifractal Diffusion Entropy Analysis: Optimal Bin Width of Probability Histograms," Papers 1401.3316, arXiv.org, revised Mar 2014.

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