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

Author

Listed:
  • Han He

    (School of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China)

  • Sicheng Li

    (School of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China)

  • Lin Hu

    (School of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China)

  • Nelson Duarte

    (School of Management and Technology, Porto Polytechnic, Center for Research and Innovation in Business Sciences and Information Systems, 4610-156 Felgueiras, Portugal)

  • Otilia Manta

    (Center for Financial and Monetary Research-Victor Slăvescu, Romanian Academy, 010071 Bucharest, Romania)

  • Xiao-Guang Yue

    (Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakon Patom 73170, Thailand)

Abstract
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.

Suggested Citation

  • Han He & Sicheng Li & Lin Hu & Nelson Duarte & Otilia Manta & Xiao-Guang Yue, 2019. "Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3525-:d:243353
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    References listed on IDEAS

    as
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    Cited by:

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    2. Xiao-Guang Yue & Xue-Feng Shao & Rita Yi Man Li & M. James C. Crabbe & Lili Mi & Siyan Hu & Julien S Baker & Liting Liu & Kechen Dong, 2020. "Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy," JRFM, MDPI, vol. 13(4), pages 1-26, April.
    3. Shilu Sun & Tiantian Li & Hong Ma & Rita Yi Man Li & Kostas Gouliamos & Jianming Zheng & Yan Han & Otilia Manta & Ubaldo Comite & Teresa Barros & Nelson Duarte & Xiao-Guang Yue, 2020. "Does Employee Quality Affect Corporate Social Responsibility? Evidence from China," Sustainability, MDPI, vol. 12(7), pages 1-19, March.
    4. Xiao-Guang Yue & Yan Han & Deimante Teresiene & Justina Merkyte & Wei Liu, 2020. "Sustainable Funds’ Performance Evaluation," Sustainability, MDPI, vol. 12(19), pages 1-20, September.
    5. Yu-Meng Luo & Wei Liu & Xiao-Guang Yue & Marc A. Rosen, 2020. "Sustainable Emergency Management Based on Intelligent Information Processing," Sustainability, MDPI, vol. 12(3), pages 1-4, February.
    6. Xiaolan Bao & Qiaosheng Luo & Sicheng Li & M. James C. Crabbe & XiaoGuang Yue, 2020. "Corporate Social Responsibility and Maturity Mismatch of Investment and Financing: Evidence from Polluting and Non-Polluting Companies," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    7. Lili Mi & Xiao-Guang Yue & Xue-Feng Shao & Yuanfei Kang & Yulong Liu, 2020. "Strategic Asset Seeking and Innovation Performance: The Role of Innovation Capabilities and Host Country Institutions," JRFM, MDPI, vol. 13(3), pages 1-22, March.
    8. Otilia Manta & Kostas Gouliamos & Jie Kong & Zhou Li & Nguyen Minh Ha & Rajendra Prasad Mohanty & Hongmei Yang & Ruihui Pu & Xiao-Guang Yue, 2020. "The Architecture of Financial Networks and Models of Financial Instruments According to the “Just Transition Mechanism” at the European Level," JRFM, MDPI, vol. 13(10), pages 1-24, October.

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