IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i4d10.1007_s10614-024-10811-1.html

MoF: A Background-Aware Multi-source Fusion Financial Trend Forecasting Mechanism

Author

Listed:
  • Fengting Mo

    (Guangdong University of Finance and Economics)

  • Shanshan Yan

    (Guangdong University of Finance and Economics)

  • Yinhao Xiao

    (Guangdong University of Finance and Economics)

Abstract

With the rapid growth of economic globalization and digital economics, accurately predicting stock price fluctuations has become crucial yet challenging due to high volatility and market noise. Existing forecasting methods, relying primarily on time-series data, technical indicators, and sentiment analysis, often fail to capture the semantic depth of background knowledge, particularly the influence of real-time events. To address this limitation, we propose MoF, a background-aware multi-source fusion mechanism for financial trend forecasting. MoF integrates stock price data with background knowledge on the impact of real-time events on stock trends, which includes key information from policy documents and stock commentaries, and subsequently leverages the MacBERT model to generate feature vectors for stock prediction. Our results show that MoF, by incorporating the influence of real-time events, improves accuracy and interpretability in stock trend forecasting, surpassing LSTM-based models with over 90% accuracy in predicting market fluctuations and providing reliable directional predictions.

Suggested Citation

  • Fengting Mo & Shanshan Yan & Yinhao Xiao, 2025. "MoF: A Background-Aware Multi-source Fusion Financial Trend Forecasting Mechanism," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3033-3062, October.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10811-1
    DOI: 10.1007/s10614-024-10811-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10811-1
    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/s10614-024-10811-1?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mohammad Rafiqul Islam & Nguyet Nguyen, 2020. "Comparison of Financial Models for Stock Price Prediction," JRFM, MDPI, vol. 13(8), pages 1-19, August.
    2. Li, Yelin & Bu, Hui & Li, Jiahong & Wu, Junjie, 2020. "The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1541-1562.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    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. Chopra, Ritika & Sharma, Gagan Deep & Pereira, Vijay, 2024. "Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction," Technovation, Elsevier, vol. 135(C).
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Si-Qi Chen & Chien-Hsiu Lin & Szu-Lang Liao, 2026. "Application of a Dual-Stream Hybrid Network for Exchange Rate Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 67(5), pages 3729-3775, May.
    4. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    5. Ushir HARRILALL & Yudhvir SEETHARAM, 2015. "Forecasting changes in the South African volatility index: A comparison of methods," EuroEconomica, Danubius University of Galati, issue 2(34), pages 51-70, November.
    6. Christos Katris, 2019. "Forecasting the Unemployment of Med Counties using Time Series and Neural Network models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(2), pages 1-3.
    7. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    8. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    9. Yasemin Deniz Akarım, 2013. "A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange," Journal of Economics and Behavioral Studies, AMH International, vol. 5(3), pages 164-172.
    10. Vogl, Markus & Kojić, Milena & Sharma, Abhishek & Stanisic, Nikola, 2025. "Decoding financial markets: Empirical DGPs as the key to model selection and forecasting excellence – A proof of concept," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 666(C).
    11. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    12. Trong‐Nghia Nguyen & Minh‐Ngoc Tran & Robert Kohn, 2022. "Recurrent conditional heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1031-1054, August.
    13. Hu, Michael Y. & Tsoukalas, Christos, 1999. "Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 407-422, November.
    14. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    15. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    16. Elfred John C. Abacan & Joseph Ryan G. Lansangan & Erniel B. Barrios, 2025. "Test for Change in Error Variance of Multiple Time Series," SAGE Open, , vol. 15(1), pages 21582440251, January.
    17. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    18. Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
    19. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    20. Wu, Yih-Jiuan, 1998. "Exchange rate forecasting: an application of radial basis function neural networks," ISU General Staff Papers 1998010108000013540, Iowa State University, Department of Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:66:y:2025:i:4:d:10.1007_s10614-024-10811-1. 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.