The Research Of China’s Financing Risk Early-warning Of Mining Listed Corporationsby BP ANN Model

Mining is the main body of our country’s mining enterprise, and it is also a basic industry of China’s national economy. The financing of mining is the first step of mining industry’s economic activity. How to get the capital and how to effectively use the capital is the key of enterprise’s capital operation. With the large scale development trend of international mining industries, the merger waves of mining industries are gradually expanding, and mining investment and financing activity research gradually becomes a hot issue. However, in practical operation, there are frequent failures in investment and financing activities. Therefore, it is necessary to study on the financing risk of mining industry. On the basis of the samples and data’s availability, the listed companies of mining industry are selected as research object. With the combination of normative analysis and empirical analysis, this thesis refers to mining economic theory, financing management theory, risk management theory and financing risk warning theory, etc. as guidance, and carries out graded early-warning analysis on listed company’s non-financing activities and financing activities. This thesis’designs the index system of financing risk early-warning in mining industry’s financing activity, and it uses MATLAB7.0 to carry out BP Artificial Neural Network financing risk early-warning research on 24 coal mining industries.The research contents mainly include five parts. (1) from the perspective of fixed samples, the division standards of domestic and foreign mining industries are gathered, and the new division standards of the value chain of the listed mining companies’plate is established. (2) from the perspective of mining industry’s financing activity and financing environment, the mining industry’s financing activity and financing mode are different in different stages, and the compositions and development degrees of domestic and foreign mining industries are different. (3)from the perspective of fixing financing risk early-warning index system, by means of the risk existing in mining industry’s non-financing activity and financing activity, the non-financing activity index is fixed. The financing activity’s innovativeness index system is designed from financing efficiency perspective. (4) from the perspective of financing risk early-warning idea design, in combination with risk management and financial affairs early warning design idea, on the basis of digital accuracy and model accuracy, the financing risk early-warning is selected in financing activity. The financing risk early-warning process of listed mining industry is designed. (5) from the perspective of application, the financing risk synthetically warning index (SWI),24 listed coal mining industries are selected, and MATLAB7.0 analysis software is used to carry out BP Artificial Neural Network financing risk early-warning research.The conclusions of this thesis are devided for four parts. (1) The determined conclusion of listed mining industry sample:during mine exploration stage, the enterprise takes the greatest risk, and there is very few financing mode. During mining stage, the enterprise takes the tiniest risk, and there are many financing modes. The foreign mining industry’s capital market allows enterprises in mine exploration stage and mining stage with different scale to go public. Mining industry’s capital market of our country only allows a few large scale mining industries to go public.(2) The conclusion of mining industry’s financing risk analysis:by the influence of medium level policy risk and micro resource and reserves risk, the mining industry’s financing risk is greatly influenced by non-financing activities. By the influence factors of financing scale, payment risk, and profitability risk, etc., the mining industry’s financing activity has certain risks, but the risk is not very huge. (3) The conclusion of financing risk early-warning application:the BP Artificial Neural Network which is used on 24 listed coal mining industries is of high accuracy and good applicability. The synthetical financing risk early-warning index (SWI) is in periodic fluctuation, and the reason is that coal is produced periodically. The listed coal mining’s financing risk is in relatively large yellow range, and the reason is that the factors of proportion of debt financing, the degree of received funds, and cost of debt financing, etc. have great influence. (4) The conclusion of financing risk countermeasure:establish financing risk awareness of mining industry, make scientific financing working plan, establish and improve mining industry’s financing credibility, and establish multi-level mining industry capital markets.