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

by You Zhu 1, Chi Xie 1,2,*, Bo Sun 3,4, Gang-Jin Wang 1,2 and Xin-Guo Yan 1
1
College of Business Administration, Hunan University, Changsha 410082, China
2
Center of Finance and Investment Management, Hunan University, Changsha 410082, China
3
Economics and Management School, Wuhan University, Wuhan 430072, China
4
China Huarong Asset Management CO., LTD., Beijing 100033, China
*
Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Ioppolo
Sustainability 2016, 8(5), 433; https://doi.org/10.3390/su8050433
Received: 18 February 2016 / Revised: 22 April 2016 / Accepted: 26 April 2016 / Published: 3 May 2016
(This article belongs to the Section Economic, Business and Management Aspects of Sustainability)
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. View Full-Text
Keywords: supply chain financing (SCF); credit risk; small and medium-sized enterprises (SMEs); core enterprises (CEs); financial institutions (FIs); logistic regression (LR); artificial neural network (ANN); two-stage hybrid model supply chain financing (SCF); credit risk; small and medium-sized enterprises (SMEs); core enterprises (CEs); financial institutions (FIs); logistic regression (LR); artificial neural network (ANN); two-stage hybrid model
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Zhu, Y.; Xie, C.; Sun, B.; Wang, G.-J.; Yan, X.-G. Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models. Sustainability 2016, 8, 433.

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