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An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion

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  • Wang, Delu
  • Tong, Xian
  • Wang, Yadong

Abstract

Outward foreign direct investment in mineral resource-based enterprises (OFDI-MREs) is usually a substantial long-term investment. However, as it is affected by many uncertain factors, the investment process is full of risks. In order to reduce or lessen the investment risk of enterprises and improve the scientific approach to decision-making, it is of great significance to construct an efficient early risk warning system. In this paper, a novel method which combines the coefficient of variation method, system clustering and multi-classifier fusion to early-warn the risk of OFDI-MREs is proposed. The validity of the model is verified by using 173 sample data from 42 MREs in China. The main results are as follows: First, a hierarchically-structured risk warning indicator system with 20 indicators in three dimensions is obtained with indicator reduction; Second, the risks facing OFDI-MREs is classified into four levels based on the rate of return on equity, earnings per share, and capital accumulation rate, and most of the OFDI-MREs are at high risk; Third, the proposed multi-class fusion technology based on self-organizing data mining had higher accuracy and stability than the four widely used single-classifier models (logit regression, support vector machine, neural network, Decision Tree) and the six commonly used multi-classifier fusion methods (such as majority voting, the Bayesian method, and genetic algorithm). Accordingly, some targeted policy implications are put forward in terms of institutional distance, enterprise resource and competency foundation, which may help MREs to reduce the OFDI risks and enhance their risk prevention capabilities.

Suggested Citation

  • Wang, Delu & Tong, Xian & Wang, Yadong, 2020. "An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion," Resources Policy, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:jrpoli:v:66:y:2020:i:c:s0301420719304982
    DOI: 10.1016/j.resourpol.2020.101593
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    2. Hujun He & Yichen Zhao & Hongxu Tian & Wei Li, 2022. "Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    3. Asoke K Nandi & Kuldeep Kaur Randhawa & Hong Siang Chua & Manjeevan Seera & Chee Peng Lim, 2022. "Credit card fraud detection using a hierarchical behavior-knowledge space model," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
    4. Qiong Xu & Xin Li & Fei Guo, 2023. "Digital transformation and environmental performance: Evidence from Chinese resource‐based enterprises," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1816-1840, July.
    5. Zhao, Yanping & Chen, Qing & de Haan, Jakob, 2023. "Does central bank independence matter for the location choices of Chinese firms’ foreign investments?," International Business Review, Elsevier, vol. 32(4).
    6. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).

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