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A transformer-based model for default prediction in mid-cap corporate markets

A transformer-based model for default prediction in mid-cap corporate markets
A transformer-based model for default prediction in mid-cap corporate markets
In this paper, we study mid-cap companies, i.e. publicly traded companies with less than US$10 billion in market capitalisation. Using a large dataset of US mid-cap companies observed over 30 years, we look to predict the default probability term structure over the short to medium term and understand which data sources (i.e. fundamental, market or pricing data) contribute most to the default risk. Whereas existing methods typically require that data from different time periods are first aggregated and turned into cross-sectional features, we frame the problem as a multi-label panel data classification problem. To tackle it, we then employ transformer models, a state-of-the-art deep learning model emanating from the natural language processing domain. To make this approach suitable to the given credit risk setting, we use a loss function for multi-label classification, to deal with the term structure, and propose a multi-channel architecture with differential training that allows the model to use all input data efficiently. Our results show that the proposed deep learning architecture produces superior performance, resulting in a sizeable improvement in AUC (Area Under the receiver operating characteristic Curve) over traditional models. In order to interpret the model, we also demonstrate how to produce an importance ranking for the different data sources and their temporal relationships, using a Shapley approach for feature groups.
Deep learning, Default prediction, Mid-cap credit risk, OR in banking, Transformers
0377-2217
Korangi, Kameswara Rao
95d9b7d1-c299-4feb-b86c-350786434ae9
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Bravo, Cristián
d2e3a1d8-74fa-4300-ad91-26856eca161c
Korangi, Kameswara Rao
95d9b7d1-c299-4feb-b86c-350786434ae9
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Bravo, Cristián
d2e3a1d8-74fa-4300-ad91-26856eca161c

Korangi, Kameswara Rao, Mues, Christophe and Bravo, Cristián (2022) A transformer-based model for default prediction in mid-cap corporate markets. European Journal of Operational Research. (doi:10.1016/j.ejor.2022.10.032).

Record type: Article

Abstract

In this paper, we study mid-cap companies, i.e. publicly traded companies with less than US$10 billion in market capitalisation. Using a large dataset of US mid-cap companies observed over 30 years, we look to predict the default probability term structure over the short to medium term and understand which data sources (i.e. fundamental, market or pricing data) contribute most to the default risk. Whereas existing methods typically require that data from different time periods are first aggregated and turned into cross-sectional features, we frame the problem as a multi-label panel data classification problem. To tackle it, we then employ transformer models, a state-of-the-art deep learning model emanating from the natural language processing domain. To make this approach suitable to the given credit risk setting, we use a loss function for multi-label classification, to deal with the term structure, and propose a multi-channel architecture with differential training that allows the model to use all input data efficiently. Our results show that the proposed deep learning architecture produces superior performance, resulting in a sizeable improvement in AUC (Area Under the receiver operating characteristic Curve) over traditional models. In order to interpret the model, we also demonstrate how to produce an importance ranking for the different data sources and their temporal relationships, using a Shapley approach for feature groups.

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Accepted/In Press date: 19 October 2022
e-pub ahead of print date: 27 October 2022
Published date: 27 October 2022
Additional Information: Funding Information: This work was supported by the Economic and Social Research Council [Grant number ES/P000673/1 ]. The last author acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Discovery grant RGPIN-2020-07114 ]. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. Publisher Copyright: © 2022 The Author(s)
Keywords: Deep learning, Default prediction, Mid-cap credit risk, OR in banking, Transformers

Identifiers

Local EPrints ID: 471687
URI: http://eprints.soton.ac.uk/id/eprint/471687
ISSN: 0377-2217
PURE UUID: 1584b338-49e4-4e54-ac57-b0dd9b981066
ORCID for Kameswara Rao Korangi: ORCID iD orcid.org/0000-0001-6528-5092
ORCID for Christophe Mues: ORCID iD orcid.org/0000-0002-6289-5490

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Date deposited: 16 Nov 2022 17:42
Last modified: 17 Mar 2024 03:58

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Contributors

Author: Kameswara Rao Korangi ORCID iD
Author: Christophe Mues ORCID iD
Author: Cristián Bravo

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