IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/2557865.html
   My bibliography  Save this article

A Novel Methodology for Credit Spread Prediction: Depth-Gated Recurrent Neural Network with Self-Attention Mechanism

Author

Listed:
  • Xiao Liu
  • Rongxi Zhou
  • Daifeng Qi
  • Yahui Xiong
  • Yiwen Zhang

Abstract

This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN’s prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.

Suggested Citation

  • Xiao Liu & Rongxi Zhou & Daifeng Qi & Yahui Xiong & Yiwen Zhang, 2022. "A Novel Methodology for Credit Spread Prediction: Depth-Gated Recurrent Neural Network with Self-Attention Mechanism," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, August.
    • Handle: RePEc:hin:jnlmpe:2557865
    DOI: 10.1155/2022/2557865
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/2557865.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/2557865.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/2557865?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
    ---><---

    More about this item

    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:hin:jnlmpe:2557865. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.