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Stock Price Prediction with Heavy-Tailed Distribution Time-Series Generation Based on WGAN-BiLSTM

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  • Ming Kang

    (University of Chinese Academy of Social Sciences)

Abstract

Accurate stock price prediction is essential for investing in the financial market. Aiming at the problem’s accuracy, the prediction model is restricted due to the lack of sufficient data samples for the stock price of newly listed companies. This paper proposes a novel approach to improve the generalization of Bidirectional Long Short-Term Memory Network (BiLSTM) with Wasserstein Generative Adversarial Network (WGAN) on stock time-series data augmentation. WGAN is used to learn the distribution rules of the real data, and the two-sample KS test and the area between the log–log plots are adopted to evaluate the extent of heavy-tailed distribution of the generated samples similar to that of real-world data. BiLSTM is employed to extract and predict data’s forward and reverse time information. With the stock price data, the prediction results of different algorithm models without and after augmented data are compared. Experimental results show that the accuracy of BiLSTM enhanced by WGAN is significantly improved, and the proposed WGAN-BiLSTM model is suitable for stock price prediction in few-shot settings.

Suggested Citation

  • Ming Kang, 2025. "Stock Price Prediction with Heavy-Tailed Distribution Time-Series Generation Based on WGAN-BiLSTM," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1775-1794, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10639-9
    DOI: 10.1007/s10614-024-10639-9
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    References listed on IDEAS

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    3. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
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