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Detecting chaos and predicting in Dow Jones Index

Author

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  • Alves, P.R.L.
  • Duarte, L.G.S.
  • da Mota, L.A.C.P.

Abstract

A new theory for characterization of chaos is the basis for a chaos approach in Econophysics. Distinct periods of Dow Jone Index are the objects of study in the reconstruction scheme. They include the Economic Crashes of 1929 and 1987. The computational routines analyze the time series of stock market indices in the Algebraic Computational environment. The method developed distinguishes between chaos and randomness from real systems. This paper presents conclusive results about the dynamic characteristic of Dow Jones Index evolution.

Suggested Citation

  • Alves, P.R.L. & Duarte, L.G.S. & da Mota, L.A.C.P., 2018. "Detecting chaos and predicting in Dow Jones Index," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 232-238.
    • Handle: RePEc:eee:chsofr:v:110:y:2018:i:c:p:232-238
    DOI: 10.1016/j.chaos.2018.03.034
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    References listed on IDEAS

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    Cited by:

    1. Alves, P.R.L., 2022. "Quantifying chaos in stock markets before and during COVID-19 pandemic from the phase space reconstruction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 480-499.
    2. García, P., 2022. "A machine learning based control of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    3. Melike E. Bildirici & Bahri Sonustun, 2019. "Chaotic Behavior in Exchange Rate," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(1), pages 17-22, January.
    4. Alves, P.R.L., 2020. "Dynamic characteristic of Bitcoin cryptocurrency in the reconstruction scheme," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).

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