IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i2d10.1007_s13132-024-02084-8.html
   My bibliography  Save this article

RETRACTED ARTICLE: Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era

Author

Listed:
  • Huan Xu

    (Shandong University, School of Economics)

  • Li Yang

    (Southwest University of Political Science & Law, School of Economics)

Abstract

In the ever-evolving landscape of financial investment, the digital era has ushered in a new paradigm characterized by technological innovation and sustainability considerations. This research paper delves into the intersection of technology, sustainability, and financial risk prediction. With the rise of digital finance and automated investment mechanisms, including blockchain technology and social media-driven market sentiment analysis, discerning investors now focus on sustainability through environmental, social, and corporate governance (ESG) criteria. However, navigating this landscape is not without challenges, such as cybersecurity risks and privacy concerns. The paper addresses these issues by proposing a financial risk prediction model that leverages echo state networks (ESN) and differential evolutionary algorithms. By quantifying various risk indicators through data transformation and employing machine learning techniques, the model enhances the accuracy and robustness of risk identification. The research introduces an optimization methodology for multiple swarm differential planning algorithms, optimizing ESN networks for risk identification within financial investment data. Experimental results validate the efficacy of the proposed method, achieving accuracy levels near 90%. This study contributes valuable insights for the future of intelligent finance by demonstrating the superiority of the MPDE-ESN model in risk recognition. Future research directions include expanding the model’s generalization performance, addressing diverse financial risks, and integrating reinforcement learning for dynamic risk determination. Additionally, optimizing feature dimensions and identifying optimal features remain key areas of investigation in this digital age of financial innovation and sustainability.

Suggested Citation

  • Huan Xu & Li Yang, 2025. "RETRACTED ARTICLE: Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 7039-7060, June.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02084-8
    DOI: 10.1007/s13132-024-02084-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-02084-8
    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/s13132-024-02084-8?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. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    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. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning," Papers 2404.01337, arXiv.org.
    2. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    3. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    4. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    5. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    6. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    7. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    8. Jing Hao & Feng He & Feng Ma & Shibo Zhang & Xiaotao Zhang, 2025. "Machine learning vs deep learning in stock market investment: an international evidence," Annals of Operations Research, Springer, vol. 348(1), pages 93-115, May.
    9. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    10. Maarouf, Abdurahman & Feuerriegel, Stefan & Pröllochs, Nicolas, 2025. "A fused large language model for predicting startup success," European Journal of Operational Research, Elsevier, vol. 322(1), pages 198-214.
    11. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    12. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    13. David Alaminos & Ignacio Esteban & M. Belén Salas, 2023. "Neural networks for estimating Macro Asset Pricing model in football clubs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 57-75, April.
    14. Zografopoulos, Lazaros & Iannino, Maria Chiara & Psaradellis, Ioannis & Sermpinis, Georgios, 2025. "Industry return prediction via interpretable deep learning," European Journal of Operational Research, Elsevier, vol. 321(1), pages 257-268.
    15. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
    16. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    17. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    18. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    19. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    20. K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.

    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:spr:jknowl:v:16:y:2025:i:2:d:10.1007_s13132-024-02084-8. 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.