宋思远, 王洛锋, 张新生, 暴子旗. 大中型煤炭企业信用风险评估体系研究[J]. 中国矿业, 2022, 31(5): 34-41. DOI: 10.12075/j.issn.1004-4051.2022.05.013
    引用本文: 宋思远, 王洛锋, 张新生, 暴子旗. 大中型煤炭企业信用风险评估体系研究[J]. 中国矿业, 2022, 31(5): 34-41. DOI: 10.12075/j.issn.1004-4051.2022.05.013
    SONG Siyuan, WANG Luofeng, ZHANG Xinsheng, BAO Ziqi. Study on the credit risk assessment for large and medium-sized coal enterprises[J]. CHINA MINING MAGAZINE, 2022, 31(5): 34-41. DOI: 10.12075/j.issn.1004-4051.2022.05.013
    Citation: SONG Siyuan, WANG Luofeng, ZHANG Xinsheng, BAO Ziqi. Study on the credit risk assessment for large and medium-sized coal enterprises[J]. CHINA MINING MAGAZINE, 2022, 31(5): 34-41. DOI: 10.12075/j.issn.1004-4051.2022.05.013

    大中型煤炭企业信用风险评估体系研究

    Study on the credit risk assessment for large and medium-sized coal enterprises

    • 摘要: 大中型煤炭企业具有与市场关联度高、风险损失大等特点,一旦出现信用风险,对企业以及社会的影响都是巨大的。为了能够准确识别煤炭企业的信用风险,本文以上市煤炭企业为研究对象,提出基于Filter-Wrapper两阶段特征选择的大中型煤炭企业信用风险评估模型。首先针对大中型煤炭企业的特点,在通用指标选择上结合煤炭企业风险因素提出两个新指标:抗风险能力、煤炭及加工产品业务销售毛利率;然后使用Filter-Wrapper两阶段特征选择算法来筛选冗余特征,从而构建信用风险预测模型。实验表明所提出的模型与筛选前相比具有更高的预测准确性,同时对信用风险违约样本识别率也更高,验证了模型与所提指标的有效性,对大中型煤炭企业的信用风险识别具有重要意义。

       

      Abstract: Large and medium-sized coal enterprises have the characteristics of high market relevance and large risk losses.Once credit risk occurs, the impact on the company and society will be huge.To accurately identify the credit risk of coal enterprises, this paper takes listed coal enterprises as the research object, and proposes a credit risk assessment model for large and medium-sized coal enterprises based on Filter-Wrapper two-stage feature selection.First of all, according to the characteristics of large and medium-sized coal enterprises, two new indicators are proposed in combination with the risk factors of coal enterprises in the selection of general indicators:anti-risk ability, gross profit margin of coal and processed products business sales; then the Filter-Wrapper two-stage feature selection algorithm is used to filter redundant features to build a credit risk prediction model.The experimental results show that the proposed model has higher prediction accuracy than before screening, and higher recognition rate of credit risk default samples.It verifies the effectiveness of the model and the proposed indicators, which is of great significance to the credit risk identification of large and medium-sized coal enterprises.

       

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