本文在借鉴前人研究的基础上，阐述财务困境概念的界定及其形成原因，预
警指标的选取以及各预警模型的比较分析。并选取 66 家中小板企业作为研究样
本(财务困境和盈利企业各有 33 家)；在财务指标基础上，融合公司治理结构等
非财务因素，共选取 20 个指标；经过 T 检验筛选后，运用因子分析提取主因子；
最后建立逻辑回归预警模型，并对模型运行有效性进行回判检验。研究结果表明，
代表企业偿债能力、股利支付等 4 个因子的得分与财务困境概率呈显著相关，主
要体现在现金流量比率、资产经营现金流量回报率、高层管理者持股比例等组成
的这些指标上。检验结果显示，33 家盈利企业和 33 家财务困境企业的预测准确
率分别达到 78.8%和 93.9%，模型总的预测准确率为 86.35%。可见，该预警模型
有较好的预测效果，具有一定的应用价值
关键词：财务困境预警；财务指标；非财务指标；因子分析；逻辑回归模型Abstract
When the stock exchange has been established in 1990, the number of listed
companies has risen from 14 in 1991 to 2494 in 2012, in which increase 178 times.
With the competitive environment of market economy increasingly intense,
enterprises are faced with unprecedented challenges, the most prominent of which is
loss has increasing year by year. Therefore it is meaningful to establish an effective
financial early-warning model for enterprises themselves or stakeholders to monitor
the enterprise’s financial health and take precautions in a timely and effective manner.
On the basis of reference to previous studies, the definition and causes of
financial predicament was included, and the select principle of early-warning
indicators and a series of the classic techniques and methods were described. In this
dissertation, 66 training samples were selected, which were composed of 33 financial
predicament companies and 33 normal companies, and 16 financial indexes and 4
non-financial indexes were chosen. In virtue of statistic software of SPSS 19.0, firstly,
this dissertation make use of paired samples T-test to discover the significant indexes.
Secondly, The factor analysis was used to reduce indexes, avoid the multicollinearity
influence, and find out the main factors indicating companies in financial predicament
by virtue of the remarkable indexes. Finally, the dissertation set up logistic model
based on the host factors, and tested its effectiveness.The study showed that the main
factors on behalf of debt-paying ability, shareholdings and so on were significantly
correlated with financial predicament, which were composed of cash flow ratio, debt
ratio, managerial shares ratio, dividend payout ratio, etc. It’s showed there were only
2 financial predicament companies and 7 normal companies determined inconsistent
with the facts. And the total correct rate reached 86.35%, which meant predictive
model was appropriate.
Key words: Early-warning of Financial Predicament; Financial Indexes;
Non-financial Indexes; Factor Analysis; Logistic Regression Analysis
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