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A Novel Hybrid Model by Integrating Gated Recurrent Unit Network with Weighted Error-Based Fuzzy Candlestick Model for Stock Market Forecasting

Author

Listed:
  • Yameng Zhang

    (University of Shanghai for Science and Technology)

  • Yan Song

    (University of Shanghai for Science and Technology)

  • Guoliang Wei

    (University of Shanghai for Science and Technology)

Abstract

Fuzzy candlestick models have been widely used to forecast the stock market due to their capability to handle ubiquitous nonlinearities and the knowledge of investors. However, such models take only partial historical data into account and make the prediction exclusively by the selected historical data without considering the estimation errors and also lack long-term sequence information. To address these problems, a hybrid model (WEF-GRU) combines the so-called weighted error-based fuzzy candlestick (WEF) model and the improved gated recurrent unit (GRU) network is designed to reflect the influence of historical data and investor sentiment on the predicted result adequately and properly. In this study, the WEF model is established to map the fuzzy inputs to rough output to extract effective features based on the experience and knowledge of investors. Meanwhile, the GRU network is employed to maintain the long-term sequence information according to technique indicators, and then the final predicted result is derived by fusing the outputs of the WEF model and the GRU model. Finally, experimental results on eight real-world stock data which contain daily data demonstrate that the proposed hybrid model outperforms the baseline models.

Suggested Citation

  • Yameng Zhang & Yan Song & Guoliang Wei, 2025. "A Novel Hybrid Model by Integrating Gated Recurrent Unit Network with Weighted Error-Based Fuzzy Candlestick Model for Stock Market Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1341-1371, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10599-0
    DOI: 10.1007/s10614-024-10599-0
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Li-Xin Wang, 2018. "Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction," Papers 1812.11226, arXiv.org, revised Aug 2019.
    3. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
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