Credit Rating Prediction Through Supply Chains: A Machine Learning Approach
Production and Operations Management 31(4), 1613-1629
47 Pages Posted: 18 Nov 2021 Last revised: 10 Jul 2022
Date Written: October 27, 2021
As supply chain channels physical, financial, and information flows as well as associated risks, a firm’s supply chain information should be helpful in understanding and predicting its credit risks. Credit ratings as an approximate but important measure of corporate credit risks have been widely used by investors, creditors, and supply chain partners in their decision-making. This paper studies the role of supply chain information in predicting companies’ credit ratings. Using firm-level supplier-customer linkages and corporate credit rating data, we develop a machine learning framework with gradient boosted decision tree to examine whether and what supply chain features can significantly improve the prediction accuracy of credit ratings, and what types of supply chain links have higher information content that positively affects the predictability of the supply chain features. We construct a firm’s supply chain variables from its supplier and customer portfolios to be used in the machine learning models. We show that incorporating supply chain features can improve prediction accuracy over the benchmark credit rating model using only the focal firm’s features. Moreover, the informativeness of supply chain links in focal credit risk prediction depends on the focal firm’s industry sector, the relationship strength of such links, and the switching costs. Finally, we develop a focal credit rating prediction model with a high accuracy level using supply chain factors solely, which can potentially be applied to predict credit risks of small and medium-sized enterprises (SMEs) and private firms with no public financial information, as long as their supply chain information is available.
Keywords: credit risk, credit rating, supply chains, machine learning application
JEL Classification: E43, E51, G12, G14, G23, G24, G32, L11, L22
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