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An Improved Deep-Learning-Based Financial Market Forecasting Model in the Digital Economy

Author

Listed:
  • Yang Dexiang

    (School of Finance, Central University of Finance and Economics, 39, South College Road, Beijing 100081, China)

  • Mu Shengdong

    (Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, China
    Chongqing Vocational College of Transportation Jiangjin, Chongqing 402200, China)

  • Yunjie Liu

    (Fudan Postdoctoral Fellowships in Applied Economic Studies, Fudan University, Shanghai 200433, China
    Guangxi Beibu Gulf Bank Postdoctoral Innovation and Practice Base, Nanning 530028, China)

  • Gu Jijian

    (Chongqing Vocational College of Transportation Jiangjin, Chongqing 402200, China)

  • Lien Chaolung

    (International College, Krirk University, Bangkok 10220, Thailand)

Abstract

The high-complexity, high-reward, and high-risk characteristics of financial markets make them an important and interesting study area. Elliott’s wave theory describes the changing models of financial markets categorically in terms of wave models and is an advanced feature representation of financial time series. Meanwhile, deep learning is a breakthrough technique for nonlinear intelligent models, which aims to discover advanced feature representations of data and thus obtain the intrinsic laws underlying the data. This study proposes an innovative combination of these two concepts to create a deep learning + Elliott wave principle (DL-EWP) model. This model achieves the prediction of future market movements by extracting and classifying Elliott wave models from financial time series. The model’s effectiveness is empirically validated by running it on financial data from three major markets and comparing the results with those of the SAE, MLP, BP network, PCA-BP, and SVD-BP models. Interestingly, the DL-EWP model based on deep confidence networks outperforms other models in terms of stability, convergence speed, and accuracy and has a higher forecasting performance. Thus, the DL-EWP model can improve the accuracy of financial forecasting models that incorporate Elliott’s wave theory.

Suggested Citation

  • Yang Dexiang & Mu Shengdong & Yunjie Liu & Gu Jijian & Lien Chaolung, 2023. "An Improved Deep-Learning-Based Financial Market Forecasting Model in the Digital Economy," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1466-:d:1100693
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    2. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    4. Andrey Yu. Nevela & Victor A. Lapshin, 2022. "Model Risk and Basic Approaches to its Estimation on Example of Market Risk Models," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 91-112, April.
    5. Treena Basu & Olaf Menzer & Joshua Ward & Indranil SenGupta, 2022. "A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series," Risks, MDPI, vol. 10(2), pages 1-16, February.
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