Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

Item Type Journal paper
Abstract

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

Authors Fauser, Daniel Valentin & Grüner, Andreas
Journal or Publication Title Credit and Capital Markets
Language English
Subjects finance
HSG Classification contribution to scientific community
HSG Profile Area SOF - System-wide Risk in the Financial System
Refereed Yes
Date 2020
Publisher Duncker & Humblot
Place of Publication Berlin
Volume 53
Number 4
Page Range 513-554
Number of Pages 42
ISSN 2199-1227
ISSN-Digital 2199-1235
Publisher DOI https://doi.org/10.3790/ccm.53.4.513
Depositing User Daniel Valentin Fauser
Date Deposited 01 Feb 2021 17:28
Last Modified 01 Feb 2021 17:28
URI: https://www.alexandria.unisg.ch/publications/262216

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Citation

Fauser, Daniel Valentin & Grüner, Andreas (2020) Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach. Credit and Capital Markets, 53 (4). 513-554. ISSN 2199-1227

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https://www.alexandria.unisg.ch/id/eprint/262216
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