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Complex dynamic behaviors of oriented percolation-based financial time series and Hang Seng index

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  • Niu, Hongli
  • Wang, Jun

Abstract

We develop a financial price model by the two-dimensional oriented (directed) percolation system. The oriented percolation model is a directed variant of ordinary (isotropic) percolation, and it is applied to describe the fluctuations of stock prices. In this work, we assume that the price fluctuations result from the participants’ investment attitudes toward the market, and we investigate the information spreading among the traders and the corresponding effect on the price fluctuations. We study the complex dynamic behaviors of return time series of the model by using the multiaspect chaos-exploring methods. And we also explore the corresponding behaviors of the actual market index (Hang Seng Index) for comparison. Further, we introduce the radial basic function (RBF) neural network to train and forecast the phase point of reconstructed phase space.

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  • Niu, Hongli & Wang, Jun, 2013. "Complex dynamic behaviors of oriented percolation-based financial time series and Hang Seng index," Chaos, Solitons & Fractals, Elsevier, vol. 52(C), pages 36-44.
    • Handle: RePEc:eee:chsofr:v:52:y:2013:i:c:p:36-44
    DOI: 10.1016/j.chaos.2013.03.009
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    References listed on IDEAS

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    1. Wen Fang & Jun Wang, 2012. "Statistical Properties And Multifractal Behaviors Of Market Returns By Ising Dynamic Systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(03), pages 1-14.
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    6. Xiao, Di & Wang, Jun, 2012. "Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4827-4838.
    7. Junhuan Zhang & Jun Wang & Jiguang Shao, 2010. "Finite-Range Contact Process On The Market Return Intervals Distributions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(05), pages 643-657.
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    Cited by:

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    2. Xing, Yani & Wang, Jun, 2019. "Statistical volatility duration and complexity of financial dynamics on Sierpinski gasket lattice percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 234-247.
    3. Chai, Soo H. & Lim, Joon S., 2016. "Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 118-126.
    4. Lahmiri, Salim & Bekiros, Stelios, 2018. "Chaos, randomness and multi-fractality in Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 28-34.
    5. Fang, Wen & Ke, Jinchuan & Wang, Jun & Feng, Ling, 2016. "Linking market interaction intensity of 3D Ising type financial model with market volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 531-542.
    6. Zhou, Liangqiang & Liu, Shanshan & Chen, Fangqi, 2017. "Subharmonic bifurcations and chaotic motions for a class of inverted pendulum system," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 270-277.
    7. Wang, Yiduan & Zheng, Shenzhou & Zhang, Wei & Wang, Jun & Wang, Guochao, 2018. "Modeling and complexity of stochastic interacting Lévy type financial price dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 498-511.

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