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

Sustainability, 2019-07, Vol.11 (13), p.3525 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su11133525

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  • Title:
    Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm
  • Author: He, Han ; Li, Sicheng ; Hu, Lin ; Duarte, Nelson ; Manta, Otilia ; Yue, Xiao-Guang
  • Subjects: Accuracy ; Algorithms ; Banks ; Corporate debt ; Credit ratings ; Debt financing ; Default ; Economic development ; Economic growth ; Economics ; Guarantees ; Impact analysis ; Loans ; Model accuracy ; Municipalities ; Predictions ; Profitability ; Regional development ; Regression ; Risk analysis ; Risk factors ; Risk management ; Stockholders ; Training
  • Is Part Of: Sustainability, 2019-07, Vol.11 (13), p.3525
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su11133525
  • Source: Freely Accessible Journals
    AUTh Library subscriptions: ProQuest Central
    ROAD: Directory of Open Access Scholarly Resources
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