摘要:
近年来,收益途径评估方法中的折现现金流量法(以下简称DCF法)、折现现金流风险系数调整法和收入权益法是业界常用的评估方法,通过对DCF法中涉及的资源储量,矿山服务年限,矿产品价格,折现率等影响因素进行敏感性分析,有助于对矿业权价值做出更加客观、准确的评估,预见可能的风险,提高矿业权投资决策的准确性.本文将BP神经网络与局部敏感分析Garson算法相结合对煤炭资源矿业权评估进行敏感性分析.首先,构建矿业权评估指标体系;然后利用煤炭资源矿业权评估数据训练BP神经网络,获得网络阈值;最后,用敏感性分析Garson算法计算出影响煤炭矿业权评估指标的敏感系数.分析结果显示:本文所选取的影响煤炭资源矿业权评估的指标按敏感系数排序为:可采储量的敏感系数为26.39%、产品价格26.12%、矿山服务年限17.26%、投资规模14.65%、折现率9.97%、矿山年生产规模5.62%,可采储量对煤炭矿业权价值影响最大,矿山生产规模对煤炭矿业权价值影响低.%For years,the discounted cash flow method of income approach in the evaluation method (DCF method),discounted cash flow risk factor adjustment method and income rights method is commonly used to evaluate the mining industry.Through analysing factors in the reserves of DCF including the service life of the mine,the price of mineral products,the discount rate and so on,contribute to make the value of the mining rights more objective and accurate.In this paper,BP neural network and local sensitivity analysis Garson algorithm are combined to analyze the sensitivity of coal resources mining right evaluation.Firstly,establish the evaluation index system of mining rights;and utilization of coal resources mining rights assessment data to train the BP neural network,access network threshold;finally,calculate the impact sensitivity index of coal mining rights evaluation by using sensitivity analysis of Garson algorithm.The analysis results show that the evaluation index effect on coal resource mining rights:sensitive coefficient of recoverable reserves of 26.39%,price 26.12%,the service life of mine is 17.26%,the scale of investment 14.65%,the discount rate 9.97%,years of mine production scale 5.62%,recoverable reserves of the greatest impact on the coal mining right value effect of coal mine production scale,lowest value of mining rights.