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      云南铜业综合财务分析与研究_综合财务指标

      • 2019-05-28 15:22:38
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      论文导读::基于SPSS16.0社会科学统计软件,在云南铜业财务指标的基础上,采用多指标综合评价的主成分分析法,对云南铜业的各项财务指标进行了综合,得出了综合财务指标,避免了人的主观随意性,从而能客观、合理地评价了云南铜业的财务状况和经营成果。
      论文关键词:云南铜业,综合财务指标,主成分分析

        一、引言
        随着我国证券市场的日益规范化,上市公司的财务状况和经营成果已成为各方面投资者和政府管理部门关心的主要问题。企业的财务状况和经营成果可通过会计报表及一系列财务指标来反映,但单一的指标只能反映企业经营管理活动中某个方面的情况综合财务指标,而我们更需要的是客观、合理地对上市公司某一时期的财务状况和经营成果做出综合评价。综合财务指标在现代财务分析中已被广泛采用,综合财务指标是将若干相关程度较高的财务指标根据一定的权数综合计算而得到的一个评估指标论文格式。在现行的财务工作中,财务指标评价体系中各指标的权重是依据主观或经验事先设定好的,这难免会给评价结果带来一定的主观性。本文应用主成分分析方法,依据财务指标内部结构关系来确定各指标权重,避免了人的主观随意性综合财务指标,从而能对云南铜业财务状况和经营成果进行比较客观、合理地综合评价。
        二、主成分分析的原理
        主成分分析是设法将原来众多具有一定相关性(比如P个指标),重新组合成一组新的互相无关的综合指标来代替原来的指标。通常数学上的处理就是将原来 P个指标作线性组合, 作为新的综合指标。最经典的做法就是用 F1(选取的第一个线性组合, 即第一个综合指标)的方差来表达, 即Var(F1)越大,表示 F1 包含的信息越多。因此在所有的线性组合中选取F1 应该是方差最大的, 故称F1为第一主成分。如果第一主成分不足以代表原来 P个指标的信息, 再考虑选取 F2 即选第二个线性组合,为了有效地反映原来信息, F1 已有的信息就不需要再出现在 F2中, 用数学语言表达就是要求Cov(F1, F2)=0, 则称 F2 为第二主成分, 依此类推可以构造出第三、第四, ……, 第 P个主成分论文格式。
        进行主成分分析主要步骤如下:
        1.根据研究问题选取指标与数据;
        2.进行指标数据标准化( SPSS 软件 Factor 过程自动执行) ;
        3.进行指标之间的相关性判定;
        4.确定主成分个数 m;
        5.确定主成分 Fi表达式;
        6.进行主成分 Fi命名;
        7.计算综合主成分值并进行评价与研究。
        三、对云南铜业的财务指标进行主成分分析
       ?。ㄒ唬┭∪≈副暧胧?br />  依据上述评价方法,我们选取云南铜业五年内的财务指标(见表1)。具体指标如下:每股收益(x1);净资产收益率(x2);流动比率(x3);速动比率(x4);资产负债比率(x5);净利润率(x6);总资产报酬率(x7);存货周转率(x8);总资产周转率(x9);固定资产周转率(x10)。在计算过程中,原始指标来源于巨潮资讯网。
        表1 云南铜业财务指标的原始数据
        

      年份

      x1

      x2

      x3

      x4

      x5

      x6

      x7

      x8

      x9

      x10

      2005

      0.30

      9.56

      1.03

      0.39

      81.91

      2.72

      1.83

      1.68

      0.67

      3.29

      2006

      -2.22

      -78.22

      1.01

      0.59

      79.58

      -11.28

      -11.42

      3.58

      1.01

      5.88

      2007

      0.73

      13.96

      1.21

      0.77

      75.17

      2.97

      4.20

      3.86

      1.41

      10.13

      2008

      1.51

      34.01

      1.20

      0.51

      75.13

      4.29

      10.35

      5.18

      2.41

      14.93

      2009

      0.62

      19.60

      1.07

      0.55

      69.22

      3.68

      6.34

      3.94

      1.72

      7.33

      (二)进行数据标准化
        将表1中的数据导入spaa软件中,即可得到标准化的数据
        表2 云南铜业财务指标的标准化数据
        

      年份

      x1

      x2

      x3

      x4

      x5

      x6

      x7

      x8

      x9

      x10

      2005

      0.08

      0.22

      -0.79

      -1.25

      1.17

      0.34

      -0.05

      -1.56

      -1.16

      -1.13

      2006

      -1.70

      -1.75

      -0.98

      0.20

      0.69

      -1.78

      -1.66

      -0.05

      -0.65

      -0.55

      2007

      0.38

      0.32

      1.10

      1.50

      -0.21

      0.38

      0.23

      0.17

      -0.05

      0.41

      2008

      0.93

      0.77

      1.04

      -0.39

      -0.22

      0.58

      0.98

      1.22

      1.44

      1.49

      2009

      0.30

      0.44

      -0.36

      -0.06

      -1.43

      0.49

      0.49

      0.23

      0.41

      -0.22

      (三)进行指标之间的相关性判定
        将表2中的数据导入到spss中,便可得到Communalities(变量共同度)、Correlation Matrix(相关系数矩阵)、Total VarianceExplained(总方差解释)、Component Matrix(主成分矩阵)。
        

      表3 Communalities


      Initial

      Extraction

      X1

      1.000

      0.991

      X2

      1.000

      0.992

      X3

      1.000

      0.796

      X4

      1.000

      0.529

      X5

      1.000

      0.504

      X6

      1.000

      0.970

      X7

      1.000

      0.999

      X8

      1.000

      0.923

      X9

      1.000

      0.864

      X10

      1.000

      0.882

      表3是变量共同度表,说明通过主成分分析综合财务指标,指标信息被提取的程度。从中可以看出,此次分析的效果较好,除了流动比率、资产负债比率指标分别被提取52.9%、50.4%的信息,其他指标的提取程度基本上都达到了达到80%以上论文格式。

      表4 Correlation Matrix

      Correlation


      X1

      X2

      X3

      X4

      X5

      X6

      X7

      X8

      X9

      X10


      X1

      1.000

      0.991

      0.721

      -0.063

      -0.452

      0.971

      0.990

      0.309

      0.614

      0.579

      X2

      0.991

      1.000

      0.633

      -0.120

      -0.458

      0.993

      0.983

      0.210

      0.538

      0.471

      X3

      0.721

      0.633

      1.000

      0.513

      -0.389

      0.580

      0.690

      0.662

      0.710

      0.875

      X4

      -0.063

      -0.120

      0.513

      1.000

      -0.368

      -0.119

      -0.082

      0.425

      0.164

      0.336

      X5

      -0.452

      -0.458

      -0.389

      -0.368

      1.000

      -0.435

      -0.545

      -0.625

      -0.676

      -0.450

      X6

      0.971

      0.993

      0.580

      -0.119

      -0.435

      1.000

      0.956

      0.111

      0.444

      0.374

      X7

      0.990

      0.983

      0.690

      -0.082

      -0.545

      0.956

      1.000

      0.378

      0.683

      0.602

      X8

      0.309

      0.210

      0.662

      0.425

      -0.625

      0.111

      0.378

      1.000

      0.919

      0.904

      X9

      0.614

      0.538

      0.710

      0.164

      -0.676

      0.444

      0.683

      0.919

      1.000

      0.922

      X10

      0.579

      0.471

      0.875

      0.336

      -0.450

      0.374

      0.602

      0.904

      0.922

      1.000

      表4是相关系数矩阵表,从中我们可以看出,大多数指标间的相关程度很高综合财务指标,存在大量的重叠信息。如x1与x4、x5、x8相关性较低之外,与其他六个指标的相关程度都很高。
       ?。ㄋ模┤范ㄖ鞒煞值母鍪?。
        通过上面分析,指标之间存在很强的相关性,因此我们要根据Total Variance Explained表来确定主成分,用较少的指标来反映大部分的信息。
        表5 Total Variance Explained
        

      Component

      Initial Eigenvalues

      Extraction Sums of Squared Loadings

      Total

      % of Variance

      Cumulative %

      Total

      % of Variance

      Cumulative %

      1

      6.178

      61.778

      61.778

      6.178

      61.778

      61.778

      2

      2.271

      22.713

      84.490

      2.271

      22.713

      84.490

      3

      0.829

      8.291

      92.781




      4

      0.722

      7.219

      100.000




      5

      2.456E-16

      2.456E-15

      100.000




      6

      5.892E-17

      5.892E-16

      100.000




      7

      -4.450E-17

      -4.450E-16

      100.000




      8

      -1.479E-16

      -1.479E-15

      100.000




      9

      -2.047E-16

      -2.047E-15

      100.000




      10

      -1.234E-15

      -1.234E-14

      100.000




      表5是总方差解释表,通过主成分分析综合财务指标,我们提取的2个主成分对总方差的解释程度为61.778%、22.713%,共解释总方差的84.490%,也就是说,此次分析具有84.490%的可信度。
       ?。ㄎ澹┤范ㄖ鞒煞值谋泶锸?br />  

      表6 Component Matrix


      Component


      1

      2

      X1(每股收益)

      0.903

      -0.420

      X2(净资产收益率)

      0.855

      -0.511

      X3(流动比率)

      0.860

      0.236

      X4(速动比率)

      0.206

      0.698

      X5(资产负债比率)

      -0.666

      -0.247

      X6(净利润率)

      0.801

      -0.573

      X7(总资产报酬率)

      0.925

      -0.378

      X8(存货周转率)

      0.680

      0.678

      X9(总资产周转率)

      0.867

      0.335

      X10( 固定资产周转率)

      0.834

      0.432

      我们分别用y1和y2来表示第一主成分和第二主成分,根据主成分矩阵,我们便可以写出主成分的表达式。
        y1=0.903x1+0.855x2+0.860x3+0.206x4-0.666x5+0.801x6+0.925x7+0.680x8+0.867x9+0.834x10
        y2=-0.420x1-0.511x2+0.236x3+0.698x4-0.247x5-0.573x6-0.378x7+0.678x8+0.335x9+0.432x10
       ?。┙兄鞒煞址治霾⒍灾鞒煞纸忻?br />  表6是主成分矩阵表综合财务指标,从主成分的系数可以看出,我们提取的第一主成分在每股收益、总资产报酬率、净资产收益率、净利润率、总资产周转率、固定资产周转率六个方面的系数比较大,分别为0.903、0.925、0.855、0.801、0.867、0.834,这表明第一主成分是这6个指标的综合反映,主要说明公司的盈利能力、非流动资产的运营能力论文格式。尽管流动比率的系数0.860也比较高,但是速动比率的系数却为0.206综合财务指标,说明第一主成分不能充分反映公司的偿债能力。依此类推,第二主成分在速动比率和存货周转率的系数分别为0.698和0.678,主要说明公司的偿债能力和流动资产的运营能力。因此我们对第一主成分命名为“盈利能力和非流动资产的运营能力”,对第二主成分命名为“偿债能力和流动资产的运营能力”。
       ?。ㄆ撸┘扑阕酆现鞒煞种挡⒔衅兰塾胙芯?br />  将表2中的各指标数据带入主成分表达式中,我们就可以算得各主成分的得分。再根据每个主成分各自的方差贡献率为权数将2个主成分得分线性加权求和得到主成分的综合得分,即有y*=0.61778y1*+0.22713y2* 其中y1*、 y2* 、y*分别表示第一主成分得分、第二主成分得分、主成分综合得分。最后进行综合得分排名。
        表7 主成分得分和排名表
        

      年份

      y1*

      y2*

      y*

      排名

      2005

      -0.686

      -1.586

      -0.784

      5

      2006

      -1.344

      1.101

      -0.580

      4

      2007

      0.476

      0.440

      0.394

      2

      2008

      1.156

      0.217

      0.763

      1

      2009

      0.399

      -0.172

      0.207

      3

      从表7中我们可以看出综合财务指标,在盈利能力和非流动资产的运营能力方面,云南铜业在2008年最好,其次是2007年和2009年,其他两个年份比较差;在偿债能力和流动资产的运营能力方面,云南铜业在2006年、2007年、2008年比较好;在主成分得分综合排名方面,云南铜业在2008年最高综合财务指标,其次分别是2007年和2009年论文格式。实际上云南铜业在2005年和2006年财务状况不是很好,但是2007年10月中铝公司以现金出资,与云南省国资委正式签署合作协议,云铜集团由国有独资公司变更为有限责任公司。中铝公司将充分发挥自身资金、资源、深加工技术和行业影响力等方面优势,统筹考虑中铝公司和云铜集团的发展规划,实现战略协同,推动云南铜业的发展,现在正处于整合阶段。因此我们分析的结果与事实基本上相符。结论:采用主成分分析法得出的综合财务指标,可以客观合理的评价云南铜业的财务状况和经营成果。

      参考文献
      [1]叶宗裕.主成分综合评价方法存在的问题和改进[J]. 统计与信息论坛,2004
      [2]张文霖.主成分在spss中的操作应用[J]. 市场研究,2005
      [3]郭显光.如何用spss软件进行主成分分析[J]. 统计与信息论坛,1998

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