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The development of stock exchange simulation prediction modeling by a hybrid grey dynamic model

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Abstract

In this paper, we proposed a new GM(1, 1) dynamic model which is integrated with a nonlinear exponential smoothing (NES) model to improve the prediction accuracy. The proposed model is defined as the NESGM(1, 1) model and we developed the simulation prediction modeling of the weekly closing stock exchange index of Taiwan based on the proposed model. Furthermore, the original data are preprocessed by a standard normal distribution (SND) to enhance the predictive power of the NESGM(1, 1) model. We also discussed the predicted results compared with the ARIMA model. The experimental results show that the NESGM(1, 1) model has better predictive accuracy. However, as drawbacks, the training and testing times are increased.

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Authors and Affiliations

  1. Graduate School of Engineering, Kanagawa University, 221-8686, Yokohama, Japan

    Guo-Dong Li & Daisuke Yamaguchi

  2. Faculty of Engineering, Kanagawa University, 221-8686, Yokohama City, Japan

    Masatake Nagai

Authors
  1. Guo-Dong Li
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  2. Daisuke Yamaguchi
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  3. Masatake Nagai
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Correspondence to Guo-Dong Li.

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Li, GD., Yamaguchi, D. & Nagai, M. The development of stock exchange simulation prediction modeling by a hybrid grey dynamic model. Int J Adv Manuf Technol 36, 195–204 (2008). https://doi.org/10.1007/s00170-006-0819-5

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  • DOI: https://doi.org/10.1007/s00170-006-0819-5

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