Risk factor analysis combined with deep learning in the risk assessment of overseas investment of enterprises

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

Deep learning in economics: a systematic and critical review

From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.

Risk Assessment of Haier Group's Overseas Investment Under International Financial Reporting Standards

With the development of economic globalization and the policy guidance of International Financial Reporting Standards (IFRS), the overseas investment of Chinese enterprises has been greatly affected. To study the overseas investment risks of Chinese enterprises, this paper applies a risk analysis model to summarize and analyze the results of overseas investment of Haier from 2008 to 2020. This paper defines the risk analysis model as risk identification, risk assessment, and risk response, and studies overseas investment risks including political, economic, cultural, legal, cost, epidemic, and business risks. (1) The existence of various risks is identified in the company’s overseas investment projects; (2). The overseas investment risks of Haier are evaluated, through a comprehensive evaluation method combining qualitative and quantitative, financial data, and market response; (3). In view of the deficiencies of the existing risk prevention measures in the overseas investment of enterprises, some reasonable suggestions are purposed. The innovation of this paper are: This paper combines IFRS 9, IFRS 15, and IFRS 16, selects theories, and combines cases to evaluate the risks of overseas investment that are difficult to quantify, and analyzes the risks of overseas investment by Z-value models and market effects. This research method has pertinence and applicability to enterprises investing overseas.