IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5780-d883965.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5780/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/16/5780/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "A New Framework for Assessment of Offshore Wind Farm Location," Energies, MDPI, vol. 15(18), pages 1-17, September.
    2. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    3. Akhtaruzzaman, Muhammad & Berg, Nathan & Lien, Donald, 2017. "Confucius Institutes and FDI flows from China to Africa," China Economic Review, Elsevier, vol. 44(C), pages 241-252.
    4. Jingyuan Hou & Zhonghai Cheng & Xinshu Gong, 2022. "The Effect of Exports and Two-Way Foreign Direct Investment between China and Pan-East Asian Countries," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    5. Hartwell, Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies : a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    6. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
    7. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    8. Stephane Helleringer & Chong You & Laurence Fleury & Laetitia Douillot & Insa Diouf & Cheikh Tidiane Ndiaye & Valerie Delaunay & Rene Vidal, 2019. "Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(9), pages 219-260.
    9. Rungmaitree, Pattamon & Boateng, Agyenim & Ahiabor, Frederick & Lu, Qinye, 2022. "Political risk, hedge fund strategies, and returns: Evidence from G7 countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    10. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    11. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    12. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    13. Azzimonti, Marina, 2019. "Does partisan conflict deter FDI inflows to the US?," Journal of International Economics, Elsevier, vol. 120(C), pages 162-178.
    14. Federico Carril-Caccia & Juliette Milgram-Baleix & Jordi Paniagua, 2019. "Foreign Direct Investment in oil-abundant countries: The role of institutions," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-23, April.
    15. Clegg, Jeremy & Lin, Hsin Mei & Voss, Hinrich & Yen, I-Fan & Shih, Yi Tien, 2016. "The OFDI patterns and firm performance of Chinese firms: The moderating effects of multinationality strategy and external factors," International Business Review, Elsevier, vol. 25(4), pages 971-985.
    16. Couttenier, Mathieu & Toubal, Farid, 2017. "Corruption for sales," Journal of Comparative Economics, Elsevier, vol. 45(1), pages 56-66.
    17. Thiemo Fetzer & Stephan Kyburz, 2024. "Cohesive Institutions and Political Violence," The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 133-150, January.
    18. Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," GLO Discussion Paper Series 1445, Global Labor Organization (GLO).
    19. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    20. Adewale Samuel Hassan, 2022. "Does Country Risk Influence Foreign Direct Investment Inflows? A Case of the Visegrád Four," Economies, MDPI, vol. 10(9), pages 1-22, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5780-:d:883965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.