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Open AccessArticle

Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm

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School of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
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School of Management and Technology, Porto Polytechnic, Center for Research and Innovation in Business Sciences and Information Systems, 4610-156 Felgueiras, Portugal
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Center for Financial and Monetary Research-Victor Slăvescu, Romanian Academy, 010071 Bucharest, Romania
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Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakon Patom 73170, Thailand
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Authors to whom correspondence should be addressed.
Sustainability 2019, 11(13), 3525; https://doi.org/10.3390/su11133525
Received: 30 May 2019 / Revised: 21 June 2019 / Accepted: 22 June 2019 / Published: 27 June 2019
In order to investigate the factors influencing the sustainable guarantee network and its differences in different spatial and temporal scales, logistic regression algorithm is used to analyze the data of listed companies in 31 provinces, municipalities and autonomous regions in China from 2008 to 2017 (excluding Hong Kong, Macau and Taiwan). The study finds that, overall, companies with better profitability, poor solvency, poor operational capability and higher levels of economic development are more likely to join the guarantee network. On the temporal scale, solvency and regional economic development exert increasing higher impact on the companies’ accession to the guarantee network, and operational capacity has increasingly smaller impact. On the spatial scale, the less close link between company executives and companies in the western region suggests higher possibility to join the guarantee network. The predictive accuracy test results of the logistic regression algorithm show that the training model of the western sample enterprises has the highest prediction accuracy when predicting enterprise behavior of joining the guarantee network, while the accuracy is the lowest in the central region. When forecasting enterprises’ failure to join the guarantee network, the training model of the central sample enterprise has the highest accuracy, while the accuracy is the lowest in the eastern region. This paper discusses the internal and external factors influencing the guarantee network risk from the perspective of spatial and temporal differences of the guarantee network, and discriminates the prediction accuracy of the training model, which means certain guiding significance for listed company management, bank and government to identify and control the guarantee network risk. View Full-Text
Keywords: guarantee network; risk factors; temporal-spatial difference; logistic regression algorithm guarantee network; risk factors; temporal-spatial difference; logistic regression algorithm
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He, H.; Li, S.; Hu, L.; Duarte, N.; Manta, O.; Yue, X.-G. Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm. Sustainability 2019, 11, 3525.

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