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

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

doi:10.3390/su8050433

Cited by 72 publications

(57 citation statements)

References 43 publications

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“…SCF facilitates financing supports to the members of a supply chain, especially for SMEs, promoting their development and improving the sustainability of a supply chain [83,84]. However, there are a few articles in the literature exploring why the operational status and the transaction background of a supply chain can entitle SMEs new financing opportunities, based on a theoretical framework.…”

Section: Supply Chain Finance and Sustainabilitymentioning

confidence: 99%

Alleviating Financing Constraints of SMEs through Supply Chain

Yang

Chen

et al. 2019

Sustainability

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Small and medium-sized enterprises play a crucial role in sustaining economic development in both developed economies and developing economies, however, many of them suffer from chronic and structural difficulties in accessing external financing. In this paper, we develop a theoretical framework to illustrate how information structures work in the strategic interactions between banks and firms in a supply chain, and why the transaction information in a supply chain may serve to reduce information asymmetry and improve SMEs’ access to external financing. We find that under incomplete information, the transactions between SMEs and suppliers can serve as signals for banks, which may help banks access the private information of SMEs, thus reducing information asymmetry between them. To maximize profit, banks should dynamically adjust both their interest rate policies and risk management strategies when providing financing services to SMEs, according to the structure of the financial market. The improvement of the external financial environment for SMEs may benefit the entire supply chain, thus facilitating its sustainable development and the growth of SMEs. Our framework sheds light on how SMEs in a supply chain may enhance their survivability and facilitate their development through appropriate strategies to improve business performances and manage credit risks.

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Section: Supply Chain Finance and Sustainabilitymentioning

confidence: 99%

Alleviating Financing Constraints of SMEs through Supply Chain

Yang

Chen

et al. 2019

Sustainability

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“…Nevertheless, SCF cannot completely eliminate credit risk, which continues to be one of the major threats to FIs. Moreover, SCF has been promoted for almost 10 years and has experienced slow development in China because there is not as yet an appropriate SME credit risk evaluation index system, or an outstanding prediction model, which hinders SCF (for further details, see [13] Zhu et al, 2016)). SCF is concerned with the capital flows within a supply chain, an area that has often been neglected in the past.…”

Section: Definition Of Supply Chain Financementioning

confidence: 99%

Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains

2018

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Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.

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“…The *ST listed SME is facing the delisting risk because it suffers operating losses for two consecutive years. In this study, following Xiong et al [23], we present the benchmark for evaluating the credit risk of China's SME in SCF by 18 indexes, which also act as the independent variables of four ML models (see Table 1) [20,24]. As shown in Table 1 the 18 independent variables are grouped into five categories: liquidity, leverage, profitability, activity and non-finance.…”

Section: Data Preparationmentioning

confidence: 99%

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

You

Xie

Wang

et al. 2016

Entropy

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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.

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

Cited by 72 publications

References 43 publications

Alleviating Financing Constraints of SMEs through Supply Chain

Alleviating Financing Constraints of SMEs through Supply Chain

Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains

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

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