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Nonlinear analysis and prediction of soybean futures

Author

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  • Tao Yin
  • Yiming Wang

    (School of Economics, Peking University, Beijing, China)

Abstract

We use chaotic artificial neural network (CANN) technology to predict the price of the most widely traded agricultural futures - soybean futures. The nonlinear existence test results show that the time series of soybean futures have multifractal dynamics, long-range dependence, self similarity, and chaos characteristics. This also provides a basis for the construction of a CANN model. Compared with the artificial neural network (ANN) structure as our benchmark system, the predictability of CANN is much higher. The ANN is based on Gaussian kernel function and is only suitable for local approximation of nonstationary signals, so it cannot approach the global nonlinear chaotical hidden pattern. Improving the prediction accuracy of soybean futures prices is of great significance for investors, soybean producers, and decision makers.

Suggested Citation

  • Tao Yin & Yiming Wang, 2021. "Nonlinear analysis and prediction of soybean futures," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(5), pages 200-207.
    • Handle: RePEc:caa:jnlage:v:67:y:2021:i:5:id:480-2020-agricecon
    DOI: 10.17221/480/2020-AGRICECON
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    References listed on IDEAS

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    Cited by:

    1. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.

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