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Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods

Author

Listed:
  • Jing Xu

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

  • Ren Zhang

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China
    Collaborative Innovation Center on Meteorological Disaster Forecast, Warning and Assessment, Nanjing University of Information Science and Engineering, Nanjing 210044, China)

  • Yangjun Wang

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

  • Hengqian Yan

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

  • Quanhong Liu

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

  • Yutong Guo

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

  • Yongcun Ren

    (Institute of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China)

Abstract

The maritime silk road policy of China brings opportunities to companies relating to overseas investment. Despite the investment potentials, the risks cannot be ignored and have still not been well assessed. Considering the fact that ICRG comprehensive risk has certain subjectivity, it is not completely applicable to China’s overseas investment. Therefore, based on the data of the China Statistical Yearbook and International Statistical Yearbook, a new indictor is adopted to better capture the Chinese investment risk and to make our prediction more objective. In order to acquire the ability to predict the investment risk in the future which is essential to stakeholders, machine learning techniques are applied by training the ICRG data of the previous year and Outward Foreign Direct Investment (OFDI) data of the next year together. Finally, a relative reliable link has been built between the OFDI indicator in the next year and the left ICRG indicators in the last year with both the best precision score of 86% and recall score of 86% (KNN method). Additionally, the KNN method has a better performance than the other algorithms even for high-level risk, which is more concerning for stakeholders. The selected model cannot only be used to predict an objective and reasonable investment risk level, but can also be used to provide investment risk predictions and suggestions for stakeholders.

Suggested Citation

  • Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5780-:d:883965
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    References listed on IDEAS

    as
    1. Halid Kaplan & Kambiz Tehrani & Mo Jamshidi, 2021. "A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications," Energies, MDPI, vol. 14(20), pages 1-14, October.
    2. Brown, Christopher L. & Cavusgil, S. Tamer & Lord, A. Wayne, 2015. "Country-risk measurement and analysis: A new conceptualization and managerial tool," International Business Review, Elsevier, vol. 24(2), pages 246-265.
    3. Anam Javaid & Noman Arshed & Mubbasher Munir & Zahrahtul Amani Zakaria & Faten S. Alamri & Hamiden Abd El-Wahed Khalifa & Uzma Hanif, 2022. "Econometric Assessment of Institutional Quality in Mitigating Global Climate-Change Risk," Sustainability, MDPI, vol. 14(2), pages 1-13, January.
    4. Sungjin Lee & Soo Cho & Seo-Hoon Kim & Jonghun Kim & Suyong Chae & Hakgeun Jeong & Taeyeon Kim, 2020. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses," Energies, MDPI, vol. 14(1), pages 1-14, December.
    5. Busse, Matthias & Hefeker, Carsten, 2007. "Political risk, institutions and foreign direct investment," European Journal of Political Economy, Elsevier, vol. 23(2), pages 397-415, June.
    6. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    7. Mingzhu Tang & Qi Zhao & Steven X. Ding & Huawei Wu & Linlin Li & Wen Long & Bin Huang, 2020. "An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes," Energies, MDPI, vol. 13(4), pages 1-16, February.
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    Cited by:

    1. Hengbin Yin & Muhammad Mohsin & Luyao Zhang & Chong Qian & Yan Cai, 2022. "Accessing the Impact of FDI Goals on Risk Management Strategy and Management Performance in the Digital Era: A Case Study of SMEs in China," Sustainability, MDPI, vol. 14(22), pages 1-20, November.

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