基于Logistic模型的林业上市公司财务预警

Financial Early-warning of Listed Forestry Companies Based on Logistic Model

从66家A股林业上市公司中选出45家作为研究样本,基于样本公司的财务数据,用K-S的方法对其进行正态性检验并选出预警模型需要的财务指标,最后采用Logistic模型对其进行财务预警研究。得出了以下结论:采用聚类分析的方法判别上市公司是否处于财务危机比传统方法更具有实际意义;运用主成分逻辑回归的方法可以优化变量集合、减少计算步骤,使模型易于结构分析;Logistic模型对林业上市公司预测效果较好,预测正确率高达80%;林业上市公司应致力于提高其企业营运能力,发掘森林资源的流动性资产价值。

By using the K-S method to test the normality of the data of 45 listed A-share forestry companies and selecting the required financial indicators,this paper used the Logistic model to conduct financial early warning research on 45 A-share listed forestry companies based on the financial data of the sample company.Finally it drew the following conclusions:using cluster analysis to determine whether a listed company is in financial crisis was more practical than traditional methods.The method of principal component logistic regression could optimize the set of variables,reduce the calculation steps,and make the model easy to structure analysis.The logistic model had a good forecasting effect on listed forestry companies,and the forecast accuracy rate was as high as 80%.Listed forestry companies should focus on improving the company's operational capabilities and discover the value of forest resources as current assets.