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Entropy 2016, 18(5), 195; doi:10.3390/e18050195

Predicting China’s SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods

1
College of Business Administration, Hunan University, Changsha 410082, China
2
Center of Finance and Investment Management, Hunan University, Changsha 410082, China
*
Author to whom correspondence should be addressed.
Academic Editor: J.A. Tenreiro Machado
Received: 18 April 2016 / Revised: 13 May 2016 / Accepted: 16 May 2016 / Published: 19 May 2016
(This article belongs to the Section Complexity)
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Abstract

We propose a new integrated ensemble machine learning (ML) method, i.e., RS-RAB (Random Subspace-Real AdaBoost), for predicting the credit risk of China’s small and medium-sized enterprise (SME) in supply chain finance (SCF). The sample of empirical analysis is comprised of two data sets on a quarterly basis during the period of 2012–2013: one includes 48 listed SMEs obtained from the SME Board of Shenzhen Stock Exchange; the other one consists of three listed core enterprises (CEs) and six listed CEs that are respectively collected from the Main Board of Shenzhen Stock Exchange and Shanghai Stock Exchange. The experimental results show that RS-RAB possesses an outstanding prediction performance and is very suitable for forecasting the credit risk of China’s SME in SCF by comparison with the other three ML methods. View Full-Text
Keywords: supply chain finance (SCF); credit risk; small and medium-sized enterprises (SMEs); machine learning method supply chain finance (SCF); credit risk; small and medium-sized enterprises (SMEs); machine learning method
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).

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Zhu, Y.; Xie, C.; Wang, G.-J.; Yan, X.-G. Predicting China’s SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Entropy 2016, 18, 195.

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