Next Article in Journal
Sustainable Load-Balancing Scheme for Inter-Sensor Convergence Processing of Routing Cooperation Topology
Previous Article in Journal
Research on Factors Affecting the Optimal Exploitation of Natural Gas Resources in China
Article Menu

Export Article

Open AccessArticle
Sustainability 2016, 8(5), 433; doi:10.3390/su8050433

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

1
,
1,2,* , 3,4
,
1,2
and
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
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)
View Full-Text   |   Download PDF [369 KB, uploaded 3 May 2016]   |  

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. 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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top