摘要
以沪深A股林业上市企业为研究对象,利用聚类分析判定企业财务状态,然后对24个指标进行正态性检验,在此基础上通过wilcoxon秩和非参数检验选出最终的预警指标,其次通过主成分分析法计算得分,最后使用Logisitc回归模型进行财务预警研究。研究发现:聚类分析能够更有效地判定企业在同一时期内是否处于财务困境状态;通过主成分分析法能够更加有效地减少模型计算量,优化数据机集合;研究建立的Logisitc回归模型对林业企业财务预警预测能力表现较好,经检验准确率达80%以上;林业企业应当注意自身的债务结构和负债总额,优化偿债压力,保障企业能够可持续经营。
This paper took Shanghai and Shenzhen A-share listed forestry enterprises as the research object,and used cluster analysis to determine the financial status of enterprises,on this basis,the final early warning indicator was selected by wilcoxon rank sum nonparametric test,then it calculated the score by principal component analysis method,and finally the financial early warning research was carried out by using the logisitc regression model.The research found that:cluster analysis can more effectively determine whether the enterprise is in financial distress in the same period;the principal component analysis method can more effectively reduce the amount of model calculation and optimize the data set;The financial early warning and forecasting ability had performed well,with an accuracy rate of more than 80%after inspection;forestry enterprises should pay attention to their own debt structure and total debt,optimize debt repayment pressure,and ensure the sustainable operation of enterprises.
作者
王俊鹏
陆萍(指导)
WANG Jun-peng(College of Economics and Management,Nanjing Forestry University,Nanjing 210037,China)
出处
《中国林业经济》
2022年第4期135-139,共5页
China Forestry Economics
关键词
财务预警
主成分分析
林业上市公司
Logisitc回归
Financial early warning
Principal component analysis
Forestry listed companies
Logisitc regression
