我国上市公司财务危机预警系统的构建

我国上市公司财务危机预警系统的构建——基于“熵权法”及“因子分析”模型仲恺农业工程学院蔡树钿、郝军章、陈丽摘要本文运用“熵权法”及“因子模型”分析方法，对我国上市公司中90家ST与90家非ST公司（ST与非ST公司根据同时期，同行业，规模相当的原则配对）的16个有代表性财务比率基础指标进行研究，建立上市公司财务危机预警系统。首先将180家公司分成估计组（45家ST与45家非ST）与测试组（45家ST与45家非ST），选取16个能全面反映公司财务状况的基础指标，通过熵权法筛选出10个包含信息量多，并能准确预警的指标，这10个指标通过KMO和巴特利球体效度检验，故建立“因子分析”模型对财务指标进行定量分析，求出估计组中每家公司的综合因子得分值，预警值和财务危机预警函数（即为ST与非ST的判别函数），最后将测试组中90家公司的数据回代到预警函数中检验其判别率，判别率达到81.11%，具有较高的的判别正确率，说明本文建立的上市公司财务危机预警系统对于上市公司财务危机的预测与防范起到一定的作用。关键词：财务预警系统；财务指标体系；熵权法；因子分析；预警函数目录TOC\o"1-5"\h\z1引言31.1建立财务危机预警的必要性31.2建立财务危机预警系统的意义31.3对于财务危机预警的研究状况42研究思路63样本、指标的选取73.1样本的选取73.2指标选取84基于“熵权法”筛选财务指标体系模型10“熵权法”的基本原理104.2本模型利用“熵权法”的基本原理104.3建模的思路104.4指标的正向化与标准化104.5用熵权法确定各指标的权重115KMO和巴特利球体效度检验13KMO和巴特利球体检验基本原理135.2效度检验通过的条件145.3KMO检验和Bartlett检验结果146基于“因子分析”模型分析146.1因子分析的基本原理146.2因子分析的数学模型156.3因子分析的求解及分析207财务预警模型的检验297.1检验结果297.2检验结果分析328结论、不足及展望338.1结论338.2不足及展望33参考文献34附录1相关数据表35附录2相关程序代码42#0.530.350.12—0.023.46.23—0.01—0.051.1—0.011.71.180.680.011.570.720.280.760.380.00012.081.180.410.020.841.450.710.710.590.00030.110.110.070.071.090.1—0.14—0.180.160.000.380.250.090.012.060.620.140.141.060.00070.370.290.13—0.011.120.370.170.110.29—0.00040.190.100.011.780.33—0.05—0.050.020.001.050.670.280.033.382.270.310.315.950.00120.190.180.140.083.930.540.060.060.30.001.250.50.20.030.920.490.570.530.270.00070.50.30.04—0.072.571.1—0.02—0.020.2—0.01640.890.720.52—0.110.780.530.20.20.63—0.00360.920.770.430.061.210.960.090.060.140.07850.720.560.360.021.190.30.570.570.62—0.00080.080.080.03—0.013.210.070.710.650.12—0.00011.131.091.020.010.560.360.320.17—0.130.00041.070.690.24—0.031.310.730.480.44—0.05—0.00060.570.340.18—0.063.181.230.220.23—0.07—0.00170.570.340.18—0.063.181.230.220.23—0.07—0.00170.580.450.170.011.510.280.260.219.940.02150.210.130.030.663.740.82—0.17—0.190.280.022.842.491.520.010.510.270.770.770.3—0.00341.551.330.740.341.391.030.410.412.810.000.730.720.380.040.540.31—0.54—0.520.080.000.770.070.02—0.220.190.15—0.08—0.080.47—0.0190.410.360.220.160.380.2—0.7—0.78—0.110.000.410.170.06—0.162.061.23—0.4—0.38—0.060.000.30.110.040.013.381.020.020.020.330.02340.50.390.080.010.560.20.110.110.2—0.00122.310.860.22—0.140.850.410.780.770.25—0.00141.130.60.180.020.980.510.520.510.070.00030.610.50.180.032.582.29—0.5—0.510.030.031.730.590.56—0.050.040.020.570.57—0.9—0.00080.460.440.020.211.560.380.510.50.280.00680.490.360.04—0.072.621.25—0.15—0.240.610.001.010.80.630.052.941.770.390.390.060.00141.020.760.160.021.010.690.320.320.160.00012.762.741.420.061.010.640.50.060.042.571.4.462.711.90.133.182.2.370.760.70.020.360.0.220.120.050.035.241.290.980.250.122.20.0.90.640.270.013.120.5.073.972.910.080.510.910.240.17-0.011.97-0.03071.20.980.460.021.911.9.157.945.020.041.271.861.61.60.092.431.282.121.530.740.040.611.411.030.580.016.581.190.560.330.072.232.221.840.760.010.24-0.00021.271.020.860.034.025.55.094.310.040.790.3.923.231.950.031.324.363.021.740.020.9410.730.370.042.751.1.380.960.330.041.36332.650.070.560.222.431.610.710.030.924.083.920.960.030.868.598.057.520.010.678.477.415.320.250.991.970.890.640.041.227.226.465.350.051.51.10.779.667.720.10.580.8.918.517.440.130.541.031.010.240.031.121.590.430.2300.20.181.010.570.190.031.176.876.233.40.041.571.1.511.160.5101.110.1.411.030.580.010.650.750.480.210.032.91.1.831.320.790.031.50.1.220.910.380.022.981.560.650.30.040.680.0.310.280.16-0.123.24

070013008372003010000016000160000000124500003077058932573.950.660.660.150.0008.310.310.350.00230.840.830.090.0021.570.570.040.660.250.250.320.00130.550.470.450.00220.450.420.080.0002.430.770.750.180.0011.980.360.350.06.380.381.160.170.90.90.4-0.0036.70.70.450.0006.480.520.520.210.0008.450.430.380.060.0004.990.590.590.250.0008.180.640.64-0.16.60.710.710.250.0003.850.850.110.0229.990.80.790.320.0211.670.830.821.010.00020.510.40.530.0007.960.470.470.80.0006.780.76-0.650.001.810.630.60.30.0006.460.870.790.380.0003.460.920.920.090.0001.620.850.850.320.0041.90.530.520.440.00770.890.890.560.03860.920.920.740.0007.470.890.860.160.0018.450.260.266.730.0013.320.26-0.750.0004.640.460.40.190.0011.870.870.580.04580.580.540.190.0001.450.430.380.060.00040.230.230.40.00140.430.370.030.0009.290.350.322.970.03770.460.31-0.440.0014.240.220.220.1-0.003513.5313.411.95>0.060.520.50.930.930.230.06096.375.364.420.021.691.550.850.850.010.0421.481.090.480.042.180.960.660.60.440.0072procfactordata=soceconM=prin〔priors=onep=0.8simplecorr;varx1-x10;run;procfactordata=soceconR=Vn=5scoreout=0951;varx1-x10;procprintdata=O951;varfactor1-factor5;run;MATLAB程序(2)估计组程序[ndata,text,alldata]=xlsread('tt.xls');i=0;while(iv=90)i=i+1;a=ndata(i,2);holdonifa==1plot(i,ndata(i,l),'g*');elseplot(i