本文選題：礦業(yè) + 礦業(yè)融資　； 參考：《中國地質(zhì)大學(xué)(北京)》2013年博士論文

【摘要】：礦業(yè)企業(yè)是我國企業(yè)的主體,礦業(yè)融資是礦業(yè)經(jīng)濟活動的第一步,如何取得資金、提高資金效率是礦業(yè)企業(yè)發(fā)展的關(guān)鍵。隨著國際礦業(yè)企業(yè)向大規(guī)�；l(fā)展,企業(yè)間兼并浪潮日趨擴大,礦業(yè)企業(yè)需要大量資金。由于礦業(yè)投資回收期長、地質(zhì)風(fēng)險大等特點,礦業(yè)企業(yè)融資風(fēng)險大、融資方式少和融資渠道有限,礦業(yè)企業(yè)融資困難,因此需要研究礦業(yè)企業(yè)融資風(fēng)險�；跇颖竞蛿�(shù)據(jù)的可獲得性,選取礦業(yè)上市公司為研究對象。論文以礦業(yè)經(jīng)濟理論、融資管理理論、風(fēng)險管理理論和財務(wù)風(fēng)險預(yù)警理論等理論為指導(dǎo),運用規(guī)范分析和實證分析相結(jié)合的方法,對礦業(yè)上市公司非融資活動和融資活動進行融資風(fēng)險進行分級預(yù)警分析,設(shè)計采礦類融資活動中融資風(fēng)險預(yù)警指標(biāo)體系,運用MATLAB7.0對24家煤炭礦業(yè)進行BP神經(jīng)網(wǎng)絡(luò)融資風(fēng)險預(yù)警研究。主要研究內(nèi)容包括：(1)從確定樣本角度,匯總國內(nèi)外礦業(yè)上市公司劃分標(biāo)準(zhǔn),建立礦業(yè)上市公司板塊價值鏈的新劃分標(biāo)準(zhǔn)；(2)從礦業(yè)融資活動和融資環(huán)境角度分析,礦業(yè)企業(yè)不同階段融資活動和融資方式不同,國內(nèi)外礦業(yè)資本市場組成和發(fā)達程度不同；(3)從確定融資風(fēng)險預(yù)警指標(biāo)體系角度,通過礦業(yè)非融資活動和融資活動存在的風(fēng)險,確定非融資活動指標(biāo),從融資效率角度設(shè)計融資活動創(chuàng)新性指標(biāo)體系；(4)從融資風(fēng)險預(yù)警思想設(shè)計角度,結(jié)合風(fēng)險管理和財務(wù)預(yù)警設(shè)計思想,基于數(shù)字準(zhǔn)確性和模型精確度,選取融資活動中的融資風(fēng)險進行預(yù)警分析,并設(shè)計采礦類上市公司融資風(fēng)險預(yù)警流程；(5)從融資風(fēng)險預(yù)警應(yīng)用角度,提出融資風(fēng)險綜合指數(shù)(SWI),選取24家煤炭礦業(yè)上市公司,應(yīng)用MATLAB7.0分析軟件對其進行BP神經(jīng)網(wǎng)絡(luò)融資風(fēng)險預(yù)警的應(yīng)用。研究相關(guān)結(jié)論包括：(1)礦業(yè)上市公司樣本確定結(jié)論：探礦業(yè)階段企業(yè)風(fēng)險大,融資方式少,采礦階段企業(yè)風(fēng)險相對小,融資方式多,國外礦業(yè)資本市場允許不同規(guī)模的探礦階段和礦業(yè)階段企業(yè)上市,我國礦業(yè)資本市場只允許少量的大型采礦階段企業(yè)上市；(2)礦業(yè)融資風(fēng)險風(fēng)險分析結(jié)論：受中觀的政策風(fēng)險和微觀的資源和儲量風(fēng)險影響,礦業(yè)企業(yè)的融資風(fēng)險受非融資活動影響程度大,其中包括融資規(guī)模、支付性風(fēng)險、盈利性風(fēng)險等因素影響,礦業(yè)融資活動存在一定風(fēng)險,但總體風(fēng)險不大；(3)融資風(fēng)險預(yù)警應(yīng)用結(jié)論：24家煤炭采礦類上市公司應(yīng)用BP神經(jīng)網(wǎng)絡(luò)精度高適用性強,綜合融資風(fēng)險預(yù)警指數(shù)(SWI)呈周期性波動,原因是煤炭周期性生產(chǎn),煤炭采礦類上市公司融資風(fēng)險大,處于黃色預(yù)警區(qū)域,主要原因是債務(wù)融資比例、資金到位程度和債務(wù)融資成本等因素影響較大。
[Abstract]:Mining enterprises are the main body of Chinese enterprises and mining financing is the first step of mining economic activities. How to obtain funds and improve capital efficiency is the key to the development of mining enterprises. With the large-scale development of international mining enterprises, the wave of mergers between enterprises is expanding day by day, and mining enterprises need a lot of capital. Due to the characteristics of long payback period of mining investment and large geological risk, mining enterprises have large financing risks, less financing methods and limited financing channels, and mining enterprises have difficulty in financing, so it is necessary to study the financing risks of mining enterprises. Based on the availability of samples and data, mining listed companies are selected as research objects. Under the guidance of mining economy theory, financing management theory, risk management theory and financial risk warning theory, the paper combines normative analysis with empirical analysis. The non-financing activities and financing activities of mining listed companies are analyzed and the index system of financing risk early-warning in mining financing activities is designed. Using MATLAB7.0 to carry on BP neural network financing risk early warning research to 24 coal mining industry. The main research contents include: (1) from the point of view of determining the sample, summarizing the classification standards of mining listed companies both at home and abroad, and establishing a new division standard of plate value chain for mining listed companies, the paper analyzes the mining financing activities and financing environment from the angle of mining financing activities and financing environment. Mining enterprises have different financing activities and financing methods in different stages. The composition and degree of development of mining capital markets at home and abroad are different. From the angle of determining the early warning index system of financing risks, the risks existing in mining non-financing activities and financing activities are analyzed. To determine the index of non-financing activities, to design the innovative index system of financing activities from the angle of financing efficiency. (4) from the point of view of early warning of financing risks, combining the ideas of risk management and financial early warning, based on the digital accuracy and model accuracy. Select financing risk in financing activity to carry on early warning analysis, and design mining listed company financing risk early warning process. From the angle of financing risk warning application, put forward the comprehensive index of financing risk, select 24 coal mining listed companies. The application of BP neural network financing risk early warning is carried out by MATLAB7.0 analysis software. The relevant conclusions of the study include: 1) the sample of mining listed companies determines the following conclusions: mining stage enterprises have large risks, few financing methods, relatively small mining stage enterprises risk, many financing methods, Foreign mining capital markets allow enterprises of different scales to be listed in the prospecting and mining stages. China's mining capital market only allows a small number of large mining stage enterprises to be listed on the market) the conclusion of the analysis of mining financing risk is that it is affected by the policy risk of meso scale and the risk of resources and reserves. The financing risk of mining enterprises is greatly affected by non-financing activities, including the scale of financing, the risk of payment, the risk of profitability, and so on. But the overall risk is not big. The application of financing risk early warning conclusion: 24 listed coal mining companies have high accuracy and high applicability using BP neural network. The comprehensive financing risk early warning index (SWI) fluctuates periodically because of the periodic production of coal. Coal mining listed companies are in the yellow early warning area because of the large financing risk, the proportion of debt financing, the degree of capital availability and the cost of debt financing.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)(北京)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TP183;F426.1;F406.7

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