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A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

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  • Zhiqiang Guo
  • Huaiqing Wang
  • Jie Yang
  • David J Miller

Abstract

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

Suggested Citation

  • Zhiqiang Guo & Huaiqing Wang & Jie Yang & David J Miller, 2015. "A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0122385
    DOI: 10.1371/journal.pone.0122385
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    References listed on IDEAS

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    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Zhiqiang Guo & Huaiqing Wang & Quan Liu & Jie Yang, 2014. "A Feature Fusion Based Forecasting Model for Financial Time Series," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
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    Cited by:

    1. Kavitha Ganesan & Udhayakumar Annamalai & Nagarajan Deivanayagampillai, 2019. "An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.
    2. Luca Di Persio & Matteo Garbelli & Kai Wallbaum, 2021. "Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis," Risks, MDPI, vol. 9(2), pages 1-16, February.

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