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Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

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  • You Zhu

    (College of Business Administration, Hunan University, Changsha 410082, China)

  • Chi Xie

    (College of Business Administration, Hunan University, Changsha 410082, China
    Center of Finance and Investment Management, Hunan University, Changsha 410082, China)

  • Bo Sun

    (Economics and Management School, Wuhan University, Wuhan 430072, China
    China Huarong Asset Management CO., LTD., Beijing 100033, China)

  • Gang-Jin Wang

    (College of Business Administration, Hunan University, Changsha 410082, China
    Center of Finance and Investment Management, Hunan University, Changsha 410082, China)

  • Xin-Guo Yan

    (College of Business Administration, Hunan University, Changsha 410082, China)

Abstract

Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China’s small and medium-sized enterprises (SMEs) for financial institutions (FIs) in the supply chain financing (SCF) by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs) in the period of 2012–2013 are chosen as the samples. The empirical results show that: (i) the “negative signal” prediction accuracy ratio of the ANN model is better than that of LR model; (ii) the two-stage hybrid model type I has a better performance of predicting “positive signals” than that of the ANN model; (iii) the two-stage hybrid model type II has a stronger ability both in aspects of predicting “positive signals” and “negative signals” than that of the two-stage hybrid model type I; and (iv) “negative signal” predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China’s FIs.

Suggested Citation

  • You Zhu & Chi Xie & Bo Sun & Gang-Jin Wang & Xin-Guo Yan, 2016. "Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models," Sustainability, MDPI, vol. 8(5), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:5:p:433-:d:69335
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    13. Mou, W.M. & Wong, W.-K. & McAleer, M.J., 2018. "Financial Credit Risk and Core Enterprise Supply Chains," Econometric Institute Research Papers EI2018-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Wang, Xinyi & Zeng, Deming & Dai, Haiwen & Zhu, You, 2020. "Making the right business decision: Forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
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    17. Jing Gu & Junyao Wang & Yang Yang & Zeshui Xu, 2019. "Credit Line Models for Supply Chain Enterprises with Channel Background and Soft Information," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
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