基于Logistic模型的物流业上市公司财务预警

从物流业104家A股上市公司中选取了60家上市公司作为样本,分别用提取主成分构建逻辑回归模型和原指标构建逻辑回归模型的方法进行财务预警研究,得出了以下结论:用聚类分析方法判断上司公司是否处于财务困境能减少误差且更具有现实意义,方便投资者做出投资决策;主成分构建的逻辑回归模型和原指标构建的逻辑回归模型预测效果并无显著差异,构建的Logistic模型预测正确性都在80%左右,预测效果良好;在以后研究物流业上市公司财务预警时,必须考虑选取主营业务收入增长率、流动资产周转率、现金债务总额比和现金流量比率四个财务指标。

Financial Early-warning of Listed Logistics Companies Based on Logistics Model

Selecting 60 of the 104 A share listed logistics companies as the research samples, the paper carried on a financial earlywarning research of these companies by constructing two logistic regression models respectively through abstracting the principal components and based on the original indexes, and drew the following conclusions: using the clustering method to judge whether a listed company is in financial distress can reduce error and be informative for investment decision-makers; no significant difference exists between the two models established in terms of prediction effect, both having a fair prediction accuracy of around 80%; in future researches on the financial early-warning of listed companies in the logistics industry, considerations must be made of the four financial indexes which are the growth rate of the main business revenue, the turnover rate of current assets, the ratio of the total cash debt, and the cash flow.