Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine

Hujun He (), Yichen Zhao, Hongxu Tian and Wei Li
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Hujun He: School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
Yichen Zhao: School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
Hongxu Tian: School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
Wei Li: School of Earth Science and Resources, Chang’an University, Xi’an 710054, China

Sustainability, 2022, vol. 15, issue 1, 1-14

Abstract: Analyzing the general method of establishing a support vector machine evaluation model, this paper discusses the application of this model in the risk assessment of overseas mining investment. Based on the analysis of the risk assessment index system of overseas mining investment, the related parameters of the optimal model were ascertained by training the sample data of 20 countries collected in 2015 and 2016, and the investment risk of 8 test samples was evaluated. All 8 samples were correctly identified, with an error rate of 0. South Africa’s mining investment risk in 2016 was assessed using the risk evaluation model for overseas mining investment based on a support vector machine, and it was rated as grade IV (general investment risk). The results show that the model can provide a new solution for the judgment and deconstruction of the risk of overseas mining investment.

Keywords: support vector machine; overseas investment; risk evaluation; training sample; South Africa (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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