IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1912.09764.html
   My bibliography  Save this paper

An Artificial Intelligence approach to Shadow Rating

Author

Listed:
  • Angela Rita Provenzano
  • Daniele Trifir`o
  • Nicola Jean
  • Giacomo Le Pera
  • Maurizio Spadaccino
  • Luca Massaron
  • Claudio Nordio

Abstract

We analyse the effectiveness of modern deep learning techniques in predicting credit ratings over a universe of thousands of global corporate entities obligations when compared to most popular, traditional machine-learning approaches such as linear models and tree-based classifiers. Our results show a adequate accuracy over different rating classes when applying categorical embeddings to artificial neural networks (ANN) architectures.

Suggested Citation

  • Angela Rita Provenzano & Daniele Trifir`o & Nicola Jean & Giacomo Le Pera & Maurizio Spadaccino & Luca Massaron & Claudio Nordio, 2019. "An Artificial Intelligence approach to Shadow Rating," Papers 1912.09764, arXiv.org.
    • Handle: RePEc:arx:papers:1912.09764
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1912.09764
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1912.09764. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.