Credit Rating Prediction Through Supply Chains: A Machine Learning Approach

Forthcoming in Production and Operations Management

47 Pages Posted: 18 Nov 2021

See all articles by Jing Wu

Jing Wu

The Chinese University of Hong Kong (CUHK) - CUHK Business School

Zhaocheng Zhang

University of Cambridge, Faculty of Economics, Students

Sean Zhou

The Chinese University of Hong Kong (CUHK) - Department of Decision Sciences & Managerial Economics

Date Written: October 27, 2021

Abstract

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

Suggested Citation

Wu, Jing and Zhang, Zhaocheng and Zhou, Sean, Credit Rating Prediction Through Supply Chains: A Machine Learning Approach (October 27, 2021). Forthcoming in Production and Operations Management, Available at SSRN: https://ssrn.com/abstract=3951507

Jing Wu (Contact Author)

The Chinese University of Hong Kong (CUHK) - CUHK Business School ( email )

Cheng Yu Tung Building
12 Chak Cheung Street
Shatin, N.T.
Hong Kong

HOME PAGE: http://www.jingwulab.org

Zhaocheng Zhang

University of Cambridge, Faculty of Economics, Students ( email )

Cambridge
United Kingdom

Sean Zhou

The Chinese University of Hong Kong (CUHK) - Department of Decision Sciences & Managerial Economics ( email )

Shatin, N.T.
Hong Kong

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