Accounting Institute, Xijing University, Xi'an, Shannxi, China.
PLoS One. 2020 Oct 2;15(10):e0239635. doi: 10.1371/journal.pone.0239635. eCollection 2020.
To evaluate the overseas investment risks of enterprises and expand the application and development of deep learning methods in risk assessment, 15 national clusters are utilized as samples to analyze and discuss the overseas investment risk indicators of enterprises. First, based on the indicator system of overseas investment risks, five major types of investment risks are identified. Second, the Deep Neural Network (DNN) is introduced; a risk evaluation model is constructed for enterprise overseas investment. Finally, the investment attractiveness index in the Fraser risk assessment learning label is adopted as the evaluation results of the model. According to the classification of risks, the model is trained and its performance is tested. The results show that the major source of overseas investment risks includes basic resources, political systems, economic and financial development, and environmental protection. The corresponding risk score is high. North American country clusters and Oceanian country clusters have lower investment risks, while the investment risks in Africa, Latin America, and Asia are affected by multiple factors of the specific cities. This is closely related to the resources and legal systems possessed by the country clusters. This is of great significance for enterprises to conduct risk assessment in overseas investment.
为了评估企业的海外投资风险，并扩大深度学习方法在风险评估中的应用和发展，利用 15 个国家集群作为样本，分析和讨论企业海外投资风险指标。首先，基于海外投资风险指标体系，确定了五大类投资风险。其次，引入深度神经网络（DNN）；为企业海外投资构建风险评估模型。最后，采用弗雷泽风险评估学习标签中的投资吸引力指数作为模型的评估结果。根据风险分类，对模型进行训练和性能测试。结果表明，海外投资风险的主要来源包括基本资源、政治制度、经济和金融发展以及环境保护。相应的风险得分较高。北美国家集群和大洋洲国家集群的投资风险较低，而非洲、拉丁美洲和亚洲的投资风险受到具体城市多种因素的影响。这与国家集群拥有的资源和法律制度密切相关。这对企业进行海外投资风险评估具有重要意义。
