基于BP神经网络下的矿业上市公司融资风险预警研究

发布时间：2018-06-03 19:49

本文选题：矿业 + 矿业融资　；参考：《中国地质大学(北京)》2013年博士论文

【摘要】：矿业企业是我国企业的主体,矿业融资是矿业经济活动的第一步,如何取得资金、提高资金效率是矿业企业发展的关键。随着国际矿业企业向大规模化发展,企业间兼并浪潮日趋扩大,矿业企业需要大量资金。由于矿业投资回收期长、地质风险大等特点,矿业企业融资风险大、融资方式少和融资渠道有限,矿业企业融资困难,因此需要研究矿业企业融资风险。基于样本和数据的可获得性,选取矿业上市公司为研究对象。论文以矿业经济理论、融资管理理论、风险管理理论和财务风险预警理论等理论为指导,运用规范分析和实证分析相结合的方法,对矿业上市公司非融资活动和融资活动进行融资风险进行分级预警分析,设计采矿类融资活动中融资风险预警指标体系,运用MATLAB7.0对24家煤炭矿业进行BP神经网络融资风险预警研究。主要研究内容包括：(1)从确定样本角度,汇总国内外矿业上市公司划分标准,建立矿业上市公司板块价值链的新划分标准；(2)从矿业融资活动和融资环境角度分析,矿业企业不同阶段融资活动和融资方式不同,国内外矿业资本市场组成和发达程度不同；(3)从确定融资风险预警指标体系角度,通过矿业非融资活动和融资活动存在的风险,确定非融资活动指标,从融资效率角度设计融资活动创新性指标体系；(4)从融资风险预警思想设计角度,结合风险管理和财务预警设计思想,基于数字准确性和模型精确度,选取融资活动中的融资风险进行预警分析,并设计采矿类上市公司融资风险预警流程；(5)从融资风险预警应用角度,提出融资风险综合指数(SWI),选取24家煤炭矿业上市公司,应用MATLAB7.0分析软件对其进行BP神经网络融资风险预警的应用。研究相关结论包括：(1)矿业上市公司样本确定结论：探矿业阶段企业风险大,融资方式少,采矿阶段企业风险相对小,融资方式多,国外矿业资本市场允许不同规模的探矿阶段和矿业阶段企业上市,我国矿业资本市场只允许少量的大型采矿阶段企业上市；(2)矿业融资风险风险分析结论：受中观的政策风险和微观的资源和储量风险影响,矿业企业的融资风险受非融资活动影响程度大,其中包括融资规模、支付性风险、盈利性风险等因素影响,矿业融资活动存在一定风险,但总体风险不大；(3)融资风险预警应用结论：24家煤炭采矿类上市公司应用BP神经网络精度高适用性强,综合融资风险预警指数(SWI)呈周期性波动,原因是煤炭周期性生产,煤炭采矿类上市公司融资风险大,处于黄色预警区域,主要原因是债务融资比例、资金到位程度和债务融资成本等因素影响较大。
[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.
【学位授予单位】：中国地质大学(北京)
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TP183;F426.1;F406.7

【参考文献】