本文關鍵詞： 礦業(yè)海外開發(fā) 金融風險 變權 BP神經(jīng)網(wǎng)絡　出處：《江西理工大學》2014年碩士論文　論文類型：學位論文

【摘要】：本論文來源于國家社會科學基金項目“海外礦業(yè)投資經(jīng)營管理風險評估與預警系統(tǒng)研究”(編號:12CGL008)。本文以海外礦業(yè)投資金融風險評估與預警為研究主線,在整理國內外研究進展的基礎上,利用變權原理與BP神經(jīng)網(wǎng)絡方法對海外礦業(yè)投資金融風險進行評估與預警分析。本文的主要研究內容及結論如下:首先,闡述了選題背景以及研究的理論意義和實際意義。全面綜述了金融風險評估研究進展、風險預警國內外研究進展以及風險預警方法應用進展,并在此基礎上提出本文的研究方法和技術路線。其次,將金融風險按其產(chǎn)生原因不同分為匯率風險、利率風險和融資風險三個不同類別,并從這三個類別出發(fā)分析了金融風險的主要影響因素,包括匯率波動性、國際收支狀況、外匯儲備、國內生產(chǎn)總值、國內經(jīng)濟增長率、國內通脹率、世界經(jīng)濟增長率、財政收支狀況、銀行實際貸款利率和貸款償還期等。在此基礎上構建了海外礦業(yè)投資金融風險評價指標體系,利用專家調查法進行賦權,再根據(jù)指標數(shù)值制定指標分級規(guī)則以及評價等級集合。再次,利用變權原理建立了海外礦業(yè)投資金融風險評價模型,選取了我國企業(yè)進行海外礦業(yè)投資的8個主要投資國作為評價對象,并分析了各國的評價結果和極端指標。最后,結合BP神經(jīng)網(wǎng)絡原理建立了海外礦業(yè)投資金融風險預警模型,并以變權評價結果作為風險預警等級的期望輸出,應用BP神經(jīng)網(wǎng)絡模型對海外礦業(yè)投資的主要投資國進行預警分析,得出各國的風險預警程度。然后還針對不同的金融風險類別提出相應的風險防范與管控策略,為我國企業(yè)在海外進行礦業(yè)投資提供了規(guī)避各類金融風險的方法。研究結果表明:基于變權原理的海外礦業(yè)投資金融風險評價模型和基于BP神經(jīng)網(wǎng)絡的海外礦業(yè)投資金融風險預警模型能夠有效地進行金融風險的評價與預警,評價和預警結果與實際較為符合,因此,所建立的模型具有較好的理論與實際應用價值,建議我國企業(yè)在海外進行礦業(yè)投資時應考慮金融風險程度較低的國家。
[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.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F426.1;TP183;F832.48

【參考文獻】

相關期刊論文 前10條

1 王寧,劉黎明;金融風險預警系統(tǒng)設計及實現(xiàn)[J];北京工業(yè)大學學報(社會科學版);2001年02期

2 朱正福;李長福;何恩山;楊春華;;基于馬爾可夫鏈的動態(tài)故障樹分析方法[J];兵工學報;2008年09期

3 吳海霞,邢春華,孫嬋娟;運用信號分析法建立我國的金融風險預警系統(tǒng)[J];金融論壇;2004年06期

4 樓文高;喬龍;;基于神經(jīng)網(wǎng)絡的金融風險預警模型及其實證研究[J];金融論壇;2011年11期

5 陳嘉輝;;我國外貿企業(yè)面臨的利率風險及應對策略[J];國際商務財會;2011年06期

6 李馨;;模糊故障樹分析方法新探[J];電子產(chǎn)品可靠性與環(huán)境試驗;2007年01期

7 蔣祥林;王春峰;;基于貝葉斯原理的隨機波動率模型分析及其應用[J];系統(tǒng)工程;2005年10期

8 王海峰;;變權無參考視頻質量評價模型研究[J];計算機工程與應用;2012年17期

9 王志宏,劉志斌,彭世濟,張達賢;礦業(yè)投資風險評價方法[J];煤炭學報;1998年02期

10 胡燕京,高會麗,徐建鋒;金融風險預警——基于BP人工神經(jīng)網(wǎng)絡的一種分析[J];青島大學學報(工程技術版);2002年04期

，

本文編號：1468377