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Stock index prediction based on wavelet transform and FCD‐MLGRU

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  • Xiaojun Li
  • Pan Tang

Abstract

With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT‐FCD‐MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.

Suggested Citation

  • Xiaojun Li & Pan Tang, 2020. "Stock index prediction based on wavelet transform and FCD‐MLGRU," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1229-1237, December.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:8:p:1229-1237
    DOI: 10.1002/for.2682
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    References listed on IDEAS

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    1. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
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    2. Deng, Shangkun & Huang, Xiaoru & Zhu, Yingke & Su, Zhihao & Fu, Zhe & Shimada, Tatsuro, 2023. "Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. Ghosh, Indranil & Jana, Rabin K. & David, Roubaud & Grebinevych, Oksana & Wanke, Peter & Tan, Yong, 2024. "Modelling financial stress during the COVID-19 pandemic: Prediction and deeper insights," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 680-698.
    4. Pan Tang & Cheng Tang & Keren Wang, 2024. "Stock movement prediction: A multi‐input LSTM approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1199-1211, August.
    5. Shangkun Deng & Yingke Zhu & Xiaoru Huang & Shuangyang Duan & Zhe Fu, 2022. "High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method," Future Internet, MDPI, vol. 14(6), pages 1-21, June.
    6. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    7. Nathan Zavanelli, 2023. "Wavelet Analysis for Time Series Financial Signals via Element Analysis," Papers 2301.13255, arXiv.org.
    8. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    9. Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
    10. Anurag Kulshrestha & Abhishek Yadav & Himanshu Sharma & Shikha Suman, 2024. "A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2685-2704, November.
    11. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.

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