海外矿业投资金融风险评估与预警研究

发布时间：2018-01-27 11:36

本文关键词： 矿业海外开发金融风险变权 BP神经网络　出处：《江西理工大学》2014年硕士论文　论文类型：学位论文

【摘要】：本论文来源于国家社会科学基金项目“海外矿业投资经营管理风险评估与预警系统研究”(编号:12CGL008)。本文以海外矿业投资金融风险评估与预警为研究主线,在整理国内外研究进展的基础上,利用变权原理与BP神经网络方法对海外矿业投资金融风险进行评估与预警分析。本文的主要研究内容及结论如下:首先,阐述了选题背景以及研究的理论意义和实际意义。全面综述了金融风险评估研究进展、风险预警国内外研究进展以及风险预警方法应用进展,并在此基础上提出本文的研究方法和技术路线。其次,将金融风险按其产生原因不同分为汇率风险、利率风险和融资风险三个不同类别,并从这三个类别出发分析了金融风险的主要影响因素,包括汇率波动性、国际收支状况、外汇储备、国内生产总值、国内经济增长率、国内通胀率、世界经济增长率、财政收支状况、银行实际贷款利率和贷款偿还期等。在此基础上构建了海外矿业投资金融风险评价指标体系,利用专家调查法进行赋权,再根据指标数值制定指标分级规则以及评价等级集合。再次,利用变权原理建立了海外矿业投资金融风险评价模型,选取了我国企业进行海外矿业投资的8个主要投资国作为评价对象,并分析了各国的评价结果和极端指标。最后,结合BP神经网络原理建立了海外矿业投资金融风险预警模型,并以变权评价结果作为风险预警等级的期望输出,应用BP神经网络模型对海外矿业投资的主要投资国进行预警分析,得出各国的风险预警程度。然后还针对不同的金融风险类别提出相应的风险防范与管控策略,为我国企业在海外进行矿业投资提供了规避各类金融风险的方法。研究结果表明:基于变权原理的海外矿业投资金融风险评价模型和基于BP神经网络的海外矿业投资金融风险预警模型能够有效地进行金融风险的评价与预警,评价和预警结果与实际较为符合,因此,所建立的模型具有较好的理论与实际应用价值,建议我国企业在海外进行矿业投资时应考虑金融风险程度较低的国家。
[Abstract]:This paper comes from the National Social Science Foundation project "overseas Mining Investment Management risk Assessment and early warning system Research" (No.: 12CGL008). The main line of this paper is financial risk assessment and early warning of overseas mining investment. On the basis of the research progress at home and abroad, using the variable weight principle and BP neural network method to assess and analyze the financial risk of overseas mining investment. The main contents and conclusions of this paper are as follows: first. This paper expounds the background of the topic and the theoretical and practical significance of the study. It comprehensively summarizes the research progress of financial risk assessment, the research progress of risk early warning at home and abroad and the application progress of risk warning method. On this basis, the research method and technical route are put forward. Secondly, the financial risk is divided into three different categories according to its causes: exchange rate risk, interest rate risk and financing risk. And from these three categories of financial risk analysis of the main factors, including exchange rate volatility, balance of payments, foreign exchange reserves, GDP, domestic economic growth rate, domestic inflation. Based on the world economic growth rate, financial income and expenditure situation, the actual loan interest rate and loan repayment period of the bank, the evaluation index system of overseas mining investment financial risk is constructed, and the expert investigation method is used to empower the foreign mining investment. Then according to the index value to establish the index classification rules and evaluation level set. Thirdly, using the variable weight principle to establish the overseas mining investment financial risk assessment model. Eight major investment countries of overseas mining investment by Chinese enterprises are selected as the evaluation object, and the evaluation results and extreme indicators of each country are analyzed. Finally. Combined with BP neural network principle, the financial risk early warning model of overseas mining investment is established, and the result of variable weight evaluation is taken as the expected output of risk warning level. The BP neural network model is used to analyze the main investment countries of overseas mining industry. The risk warning degree of each country is obtained. Then the corresponding risk prevention and control strategies are put forward according to different financial risk categories. It provides a method to avoid all kinds of financial risks for Chinese enterprises to invest in the mining industry overseas. The results show that:. The overseas mining investment financial risk assessment model based on variable weight principle and the overseas mining investment financial risk early warning model based on BP neural network can effectively carry out financial risk evaluation and early warning. The results of evaluation and early warning are in good agreement with practice. Therefore, the model has good theoretical and practical application value. It is suggested that Chinese enterprises should consider countries with low financial risk when they invest in mining industry overseas.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.1;TP183;F832.48

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