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

Jing Xu, Ren Zhang (), Yangjun Wang, Hengqian Yan, Quanhong Liu, Yutong Guo and Yongcun Ren
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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
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

Energies, 2022, vol. 15, issue 16, 1-15

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.

Keywords: investment risk prediction and assessment; machine learning; deep learning; international country risk guide; K-nearest neighbor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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