IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i3d10.1007_s10614-024-10639-9.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10639-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10639-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhi Su & Heliang Xie & Lu Han, 2021. "Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1041-1058, April.
    2. 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.
    3. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    2. Pedro Reis & Ana Paula Serra & Jo~ao Gama, 2025. "The Role of Deep Learning in Financial Asset Management: A Systematic Review," Papers 2503.01591, arXiv.org.
    3. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    4. Mingchen Li & Kun Yang & Wencan Lin & Yunjie Wei & Shouyang Wang, 2024. "An interval constraint-based trading strategy with social sentiment for the stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.
    5. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    6. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    7. Daniel Philps & Tillman Weyde & Artur d'Avila Garcez & Roy Batchelor, 2018. "Continual Learning Augmented Investment Decisions," Papers 1812.02340, arXiv.org, revised Jan 2019.
    8. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    9. Linwei Li & Paul-Amaury Matt & Christian Heumann, 2022. "Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization," Papers 2204.12914, arXiv.org, revised May 2022.
    10. Sang Il Lee & Seong Joon Yoo, 2019. "Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets," Papers 1903.06478, arXiv.org, revised Sep 2019.
    11. Ying Wang & Bo Feng & Qing-Song Hua & Li Sun, 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    12. Hamid Nasiri & Mohammad Mehdi Ebadzadeh, 2022. "Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition," Papers 2212.14687, arXiv.org.
    13. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    14. Chien-Chih Wang, 2024. "T 2 -LSTM-Based AI System for Early Detection of Motor Failure in Chemical Plants," Mathematics, MDPI, vol. 12(17), pages 1-15, August.
    15. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    16. Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024. "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    17. Ruyi Tao & Kaiwei Liu & Xu Jing & Jiang Zhang, 2024. "Predicting Company Growth by Econophysics informed Machine Learning," Papers 2410.17587, arXiv.org.
    18. Xiaoxiao Liu & Wei Wang, 2025. "Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2057-2080, April.
    19. Yanbo Zhang & Mengkun Liang & Haiying Ou, 2024. "Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3243-3278, December.
    20. Divya Aggarwal & Sougata Banerjee, 2025. "Forecasting of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 339-355, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10639-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.