تشخيص خطر الإفلاس باستخدام مميزات الإفصاح الحالي وفق النظام المحاسبي المالي

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.  )  (   )  (           30    SPSS   16           

The objective of this study is to search ability the accounting information of the financial accounting system in diagnostic biasness failure.

This study relies on the hypothec of a cash flow statement, is available within other statements, Based on data from the study, 30 statements for sample of companies Algerian, and using (AFD) method by SPSS program we reached the following result: There are 6

high-capacity ratios to distinguish between company failure and company good, 3 ratios

come from cash flow statement.

This result indicates the ability to use these ratios in the diagnosis of biasness failure.

                     1975                         SCF                                                                                               –             

       Beaver                     1       

Dun & Bradstreet,s

                                                                        2                                              

Schall and Haley

                          3                                      4 :   -        :          .         .   -          :          .            5        

                               6                                                                                            .                              1                       .                            .               . سﻼﻓﻻا ﻲﻧﻮﻧﺎﻗ ﺔﯿﻧﻮﻧﺎﻗ ﺔﯿﻔﺼﺗ ﻦﻣ ﺐﻠﻄﺑ بﺎﺤﺻا نﻮﯾﺪﻟا يدﺎﺼﺘﻗا رﺎﻤﺜﺘﺳﻻا ﺪﺋاﻮﻋ ﺪﺋﺎﻌﻟا ﻦﻣ ﻞﻗا بﻮﻠﻄﻤﻟا ﻲﻟﺎﻣ نﻮﻨﺋاد : ﻫﺎﺴﻣ نﻮﻤ : ضوﺮﻘﻟا ﺮﺜﻌﺗ ﺪﺋاﻮﻋ ﺺﻘﻧ ﻢﻬﺳﻻا نﻮﻨﺋاد : نﻮﻤﻫﺎﺴﻣ : داﺪﺳ ﻦﻋ ﻒﻗﻮﺗ ضوﺮﻘﻟا ﺪﺋاﻮﻌﻟا ﻒﻗﻮﺗ ﺮﺜ ﻌﺗ ﻞ ﺸ ﻓ

           Gordan      7                                                                                                                                                                                    .                                                                                                                             .      

                                                                    A_rgenti      Calpes           

Newly formed organization           poor  8          2  

Source: Agresti, Alan, (1996) An Introduction to categorical analysis, New York, Wiley.

                 1   13     

1 - 2 -               3 -  4 -       - - - - 5 -  6 -   7 -    8 -  9 -  10 -  11 - 12 - .     Young Organisation     14       

  3   

source : Agresti, Alan, (1996) An Introduction to categorical analysis, New York, Wiley.p

 1   18      1                     . 2                  . 3                    . 4              . 5          . 6                  . 7        . 8            . 9                                     . 10          . 11          . 12                 . 13         .

14         . 15                               . 16         . 17                 .   18             Mature Organization                  Collapse               4   

source : Agresti, Alan, (1996) An Introduction to categorical analysis, New York, Wiley.p

1 -                 2 -                                . 3 -         .  

4 -                            2.3.4                    . 5 -     .   6 -                  . 7 -      . 8 -             . 9 -                              . 10 -                 . 11 -       . 12 -                            .   13 -                        . 14 -                       . 15 -       5                       . 16 -                        .                       (AICPA)     :  

-                              . -                               . -                            . -                    . -            . -                    . -          -                     . -                            . -                   .     AFD             2003   2010                     5   4    36   20   16    

   1      ﺎﻬﺘﻔﺻ ) ( 01 ﺔﺴﻠﻔﻣ 8 02 ﺔﺴﻠﻔﻣ 7 03 ﺔﺴﻠﻔﻣ 2 04 ﺔﺴﻠﻔﻣ 3 05 ﺔﻤﻴﻠﺳ 2 06 ﺔﻤﻴﻠﺳ 2 07 ﺔﻤﻴﻠﺳ 6 08 ﺔﻤﻴﻠﺳ 3 09 ﺔﻤﻴﻠﺳ 3 09 36                     16   7    9   .

    2              x1       x2  /      x3  /     x4  /     x5  /     x6  /      x7  /     x8  /     x9  /     x10  /      x11  /     x12  /     x13  /      x14  /     x15  /     x16    /                   4   5      

  3            x8  /  x11  /  x12  /  x6  /  x5  /  x2  /     Exact F   0,000   0,05            3    

Variables Entered/Removeda,b,c,d

Step Entered Removed

Wilks' Lambda Statistic df1 df2 df3 Exact F Statistic df1 df2 Sig. 1 x8 .691 1 1 34.000 15.214 1 34.000 .000 2 x11 .322 2 1 34.000 34.682 2 33.000 .000 3 x12 .191 3 1 34.000 45.117 3 32.000 .000 4 x6 .153 4 1 34.000 42.764 4 31.000 .000 5 x7 .133 5 1 34.000 39.215 5 30.000 .000 6 x5 .115 6 1 34.000 37.033 6 29.000 .000 7 x2 .101 7 1 34.000 35.410 7 28.000 .000 8 x7 .106 6 1 34.000 40.907 6 29.000 .000

At each step, the variable that minimizes the overall Wilks' Lambda is entered. a. Maximum number of steps is 32. b. Minimum partial F to enter is 3.84. c. Maximum partial F to remove is 2.71.

d. F level, tolerance, or VIN insufficient for further computation.

                                         x8   23%               x5   18,2 %              x11   16,4 %              x12   2 8 %              x2   4 %              x6   0,7 %          Canonique              Bêta        

 .                      4     

Standardized Canonical Discriminant Function Coefficients Function 1 x2 1.479 x5 -.732-x6 .571 x8 1.982 x11 -1.868-x12 1.946            .                   14        87.5%             19        95 %          33        91,7  %   

   5     Classification Resultsb,c

Predicted Group Membership

Total ﺔﻤﻴﻠﺳ Original Count ﺳ ﺔﻤﻴﻠ 16 0 16 0 20 20 Ungrouped cases 0 46 46 % ﺔﻤﻴﻠﺳ 100.0 .0 100.0 .0 100.0 100.0 Ungrouped cases .0 100.0 100.0 Cross-validateda Count ﺔﻤﻴﻠﺳ 14 2 16 1 19 20 % ﺔﻤﻴﻠﺳ 87.5 12.5 100.0 5.0 95.0 100.0

a. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case.

b. 100.0% of original grouped cases correctly classified. c. 91.7% of cross-validated grouped cases correctly classified.

           Wilks' Lambda                Exact F    0,000      0,05        

                                                           

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8