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

You Zhu,Bo Sun,Xin-Guo Yan,Chi Xie,Gang-Jin Wang

doi:10.3390/su8050433

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.

Highlights

IntroductionCapital shortages and globalization generate complex and dynamic supply chains
Intense market competition, capital shortages and globalization generate complex and dynamic supply chains
This system is used to evaluate the credit risks from different points of view, which consist of small and medium-sized enterprises (SMEs)’ financial and non-financial conditions and contain core enterprises (CEs)’ financial and non-financial conditions, the operational status of the entire supply chain, and the transactional relationship between SMEs and CEs; (2) we demonstrate that the SME credit risk prediction performance of the type-III two-stage hybrid model is better than that of the logistic regression (LR) and artificial neural network (ANN) models and that of the type-I and type-II two-stage hybrid models in supply chain financing (SCF)

Summary

Introduction

Capital shortages and globalization generate complex and dynamic supply chains. The focus of supply chain management today is on the design and optimization of cash flow [1]. Chain financing (SCF) has increasingly become a hot topic in supply chain management and a growing product category of financial institutions (FIs). In China, SCF is experiencing a rapid development stage and numerous FIs have begun to focus on developing and designing new SCF services and products to solve the financing issues facing SMEs (e.g., 1 + N SCF of the Pingan Bank). SCF is a type of channel for financing, which manages, plans and controls all cash flows across supply chain members to improve the turnover efficiency of working capital [2]. SCF has been promoted for almost ten years and has experienced slow development in China because we do not have an appropriate SME credit risk evaluation index system or an outstanding prediction model, which hinder SCF

Methods

Results

Conclusion