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3 ( ) ( ) ( )

4 < > (1) (Index Model)10 (2) (Discriminant Analy sis Model) 10 (3) (Logit Analy sis Model)

5 (log it m ax im um lik e lih ood e s tim ator ) < > 39 < > 52

6 < > < 1> 23 < 2> 27 < 3> () 29 < 4> () 30 < 5> () 31 < 6> () 31 < 7> ( ) 32 < 8> ( ) 32 < 9> () 33 < 10> ()33 < 11> 34 < 12> 34 < 1> 39 < 2> 41 < 3> 42 < 4> 43 < 5> 44 < 6> 45 < 7> - 46 < 8> - 46 < 9> - 47 < 10> - 47 < 11> - 48 < 12> - 48 < 13> - 49 < 14> 50 < 15> 51 < > < 1> (G.W Newton )8

7 < > (corporate financial distress ),,.,.,.,.,,, 4 (). (logit maximum likelihood estimator ) (discriminant analy sis) (robustness). (normal distribution ).

8 ,,,,,,,,,,, 1,.,,,,,,, 1. 4.,,, 4, Concordant 86.9%. 82.4%, 83.5%.

9 ,

10 ,...,

11 2 4. 1, 2,

12 (corporate financial distress ).. 1),,. Dun & Bradstreet (reorganization). (economic failure), (insolvency ), (bankruptcy ) 3 2)...,, 1) E. I. Altman, Corporate Financial Distress(New York : John Wiley & Son s, 1983) 2) (2000),,, 7-4 -

13 . (technical insolvency ) (real insolvency ).....,, (bankruptcy insolvency )..,.,. (liquidation )

14 . (reorganization)...,.,,,. 2. (J.C. Van Horne) ,

15 ,,. 3.,. (G.W. Newton ),,, 4. 1.,,,,,.,,.. 2.,

16 . < 1> (G.W. N e w ton )

17 Beaver.,

18 . 2. (Univariate). (Multivariate). (Index Model), (Discriminant Analysis Model), (Logit Analy sis Model), (Probit Analysis Model). (1) (Index Model). Wall. Wall,,,,,, 7 100, 100. (2) (Discriminant Analy sis Model)

19 ....,,. (3) (Logit Analysis Model). (Robust ). (logit maximum likelyhood estimation )..,,. 50%, 10% 63.9%

20 3 1. W.H. Beaver (1966).,,, ) (profile analy sis) 6. 4) 6 (cut - off point ). 3. Altman (1968) Z- score. Z- model,,, 1) W.H. Beaver. "Financial Ratios as Predictor s of F ailure", in Empirical Research in Accounting : Selected Studies 1966, in Supplement to Journal of A ccounting R esearch (Jan, 1966) 4) 6:,,,,, {(++ - )/ }

"Financial Ratios as Predictor s of F ailure", in Empirical Research in Accounting :

21 , 5. Altman Z = 0.012X X X X X 5 X1 : / () X2 : / () X3 : / ( ) X4 : / ( ) X5 : / ( ). Z- s c ore Z < < Z < 2.99 Z > 2.99 Z > 2.99, Z < 1.81, 1.81 < Z < Altman (Multivariate Discriminant Analy sis). Z- score Altman ZET A (1993)

22 . Altman Z- modelohlson (1980) O- model. O- model 5) GNP,,,,. 1993Altman ZET A 2524, Begley, Ming, and W atts (1997) Altman Z- model Ohlson O- model Zmijew ski(1983) 6 ) ,573 F.,,,.,,., () 5) Ohlson 's (1980) model : O= log (total assets/ GNP price- level index )+6.03(total liabilities/ total as set s)- 1.43(w orking capital/ total asset s)+0.076(current liabilities/ current assets )- 1.72(1 if total liabilities > total asset s, else 0)- 2.37(net income/ total as set s)- 1.83(funds from operation/ total liabilities )+0.285(1 if net los s for last tw o year s, else 0) (net incomet - net incomet - 1)/ ( net incomet + net incomet - 1}) 6) M.E. Zmijew ski, "Predicting Corporate Bankrupty : An Empirical Comparison of the Extant Financial Distress Models", W ork ing P ap er(state Univer sity of New York at Buffalo, 1983)

23 . 2. (1993) Logit.,, 3, 1.,, 3... (1995) 7),,, 1, 1, 1, 1 7,,, %, 82.35%. 7), 1995,

24 (1999).,,, (capital intensity ) 8),. 4., Logit.,,,,,.. 8)., D 'Aveni & Ilinitch (1992)

25 3 1 (stepwise method) k k, k 2 2kC2, 3kC3 kck =1. kc2 + kc3 + + kck = 2 k (forward selection ), (backw ard selection ), (maximum R- square selection )

26 R- square. R- square. k R- square. R- square R- square.. R- square. (T olerance Limit ) (VIF : Variance Inflation Factor )

27 (log it m ax im um lik elih ood e s tim ator ) (logit ).,. (efficiency ) (consistency ). (robustness).. y * i = ' x i + u i y * i >0 y i = 1, y i = 0. i( x i ) y i = 1, y i = 0. P ( y i = 1) (likelyhood function ), 9) 9) Amemiya(1978)

28 P r ob( y i = 1) = P r ob( u i > ' x i ) = 1 - F ( - ' x i ) (1) L = y i = 0 F ( - ' x i ) y i = 1 [ 1 - F ( - ' x i ) ] (2) (2) u i, u i (logistic) F (4). 10 ) F ( - ' X i ) = ex p ( - ' x i ) 1 + exp ( - ' x i ) = ex p ( ' x i ) (3) (maximum likelyhood estimator ), (3) (Fisher ' s scoring method). (3). k, kkk (3) P = exp ( - ' x k ) 1 + exp ( - ' x k ) = ex p ( - ' x k ). P. 10) u i F

29 ( )KIS - SMAT ) ( ) 1. (91 ) 2. (91 ) 3. (91 ) 4. (91 ).,,.,,, 4 11) KIS - SMAT

30 91. 12) , 11, 6, 7, 4. (PER), (PBR), < 1>..,,,.,,. 12)

31 < 1> S 1 / 100 S 2 / 100 S 3 / 100 S 4 / 100 S 5 (+)/ (+) 100 S 6 / 100 (S tability ) S 7 / 100 S 8 / 100 S 9 ( + )/ 100 S 10 / 100 (Profit - ability ) S 11 / 100 P 1 / 100 P2 / 100 P3 / 100 P4 / 100 P5 / 100 P6 / 100 P7 / 100 P8 / 100 P9 / 100 P 10 / 100 P 11 / 100 A 1 / 100 A 2 / 100 A 3 / 100 (A ctiv ity ) A 4 / 100 A 5 / 100 A 6 / 100 G1 ( / )- 1 G2 ( / )- 1 G3 ( / )- 1 G4 ( / )- 1 (Grow th) G5 ( / )- 1 G6 ( / )- 1 G7 ( / )- 1 V 1 / 100 V 2 / 100 (V alue - V 3 / 100 A dded) V 4 1 / F 1 (EPS) / F 2 (BPS) ( +)/ (F inancial F 3 (CPS) / Index ) F 4 (SPS) /

32 ,.,.,,.,,.,.,,,.,,..,

33 .,,, 4. (EPS : Earning Per Share), 1. EPS. (BPS : Book- value per share), ()..,,. 1. (CPS : Cashflow Per Share) 1.,. (SPS : Sales Per Share)

34 3 43.,,,.. 1. F -.. < 2>

35 < 2 > ,,,,,,,,,,, 1, 13< 7, 8 >.,

36 ,,,,, 1 7<9, 10 >.,,,,,,,, 9 <11, 12 >.,,,, 1, 5<13 > < 3>< 10> 4, (logit maximum likelihood estimates). 13) 13) SAS

37 < 3 > ( ) P aram et er S t an dard W al d P r > V ariable E s t im at e E rror Chi - S qu are Chi - S qu are S ** S S *** S ** P * P P ** A *** A *** G G ** V E E ** F * Concor dant : 86.9%, Disconcor dant : 12.9% * 10%, ** 5%, *** 1% < 3>1%,,, 5%,,,, 1 5, 10%,.,,,,, 1(+),,,, (- ). Concordant 86.9%. < 3>

38 .,. E ( L og it) = S S V F 1 P r () = e ( E ( L og it) ) P r () = 1 - P r () < 4 > P ( ) = 75.8%, P ( ) = 73.6% 50% 50%. < 4> % 73.6%. < 5> < 6>.,

39 ,, 1., (+), 1(- ). Concordant 86.1%, 72.5% 83.5%. < 5 > ( ) P aram et er S tan dard W ald P r > V ari ab le E s tim at e E rror Ch i - S qu are Ch i - S qu are S S *** P P *** A *** V V E E *** Concor dant : 86.1%, Disconcor dant : 13.9% * 10%, ** 5%, *** 1% < 6 > P ( ) = 72.5%, P ( ) = 83.5%

40 < 7> < 8>.,,,. (+) (- ). Concordant 82.1% 82.4% (P ( )) 58.2% (P ( )). < 7 > ( ) P aram e ter S t an dard W ald P r > V ari ab le E s t im at e E rror Ch i - S qu are Ch i - S qu are S S * P P *** P *** A A G *** G Concor dant : 82.1%, Disconcor dant : 17.9% * 10%, ** 5%, *** 1% < 8 > P ( ) = 82.4%, P ( ) = 58.2%

41 < 9> < 10>.,,. (+) (- ). Concordant 83.9% 79.1% 73.6%. < 9 > ( ) P aram ete r S tan dard W ald P r > V ari ab le E s t im at e E rror Chi - S qu are Chi - S qu are S *** S *** P *** G *** F V E E Concor dant : 83.9%, Disconcor dant : 16.1% * 10%, ** 5%, *** 1% < 10 > P ( ) = 79.1%, P ( ) = 73.6% < 11>

42 Concordant 86.9% 82.1%. 82.4% 83.5%. < 11> (Con c ordan t ) P ( ) P ( ) 86.9% 75.8% 73.6% < 12 > (2 ) (2 ) (2 ) (2 ) (3 ) (2 ) (2 ) 7(4) * 6(3) * 3(2) * 3(2) * 1 (2 ) 2(1) * 1(1) * 22(13) * : ( )

43 < 12>. 4, 3, 2, 2, 1, 1. 43,,,,,,

44 , ().,.,,, 4., (logit maximum likelihood estimator ) (discriminant analy sis) (robustness). (normal distribution )

45 .,,,. 1.,,,,,,,,,,, 1,.,,,,,,, 1. 4.,,, 4, Concordant 86.9%. 82.4%, 83.5%

46 .,.,.,., IMF

47 < > < 1> LG 1997 LG LG

48 1998 LG

49 < 2 > V ariable M e an S td D ev M in im um M ax im um A A A A A A G G G G G G G F F F F P P P P P P P P P P P S S S S S S S S S S S V V V V

50 < 3 > V ariable M e an S td D ev M inim um M ax im um A A A A A A G G G G G G G F F F F P P P P P P P P P P P S S S S S S S S S S S V V V V

51 < 4 > V ariable M e an S td D ev M inim um M ax im um A A A A A A G G G G G G G F F F F P P P P P P P P P P P S S S S S S S S S S S V V V V

52 < 5 > V ariable M e an S t d D e v M inim um M ax im um A A A A A A G G G G G G G F F F F P P P P P P P P P P P S S S S S S S S S S S V V V V

53 < 6 > V ariable M e an S td D ev M inim um M ax im um A A A A A A G G G G G G G F F F F P P P P P P P P P P P S S S S S S S S S S S V V V V

54 < 7 > - F P rob > F T ol eran c e V IF In t erc ept S S S S S P P P A E A G G V V F F - Value : 7.24 R- Square : < 8 > - P aram et er S t an dard V ari an c e V ari abl e e s t im at e e rror T P rob > T T oleran c e Inf l at ion In t e rc ept S S E S S P P P E A A E G G E V E E F E F - Value : R- Square :

55 < 9 > - P aram et e r S t an dard V ari an c e V ari abl e E s tim at e E rror F P rob > F T ole ran c e Inf l at ion In t e rc ept S S P P E A 5-5.4E E V V V 4 2E E F - Value : R- Square : < 10 > - P aram e ter S t an dard V arian c e v ari abl e e s tim at e e rror T P rob > T T oleran c e Inf l at ion In t e rc ept S S P P E A 5-5.9E E V V E E F - Value : R- Square :

56 < 11> - P aram et er S t an dard V arian c e V ari abl e E s t im at e E rror F P rob > F T oleran c e In f lati on In t e rc ept S S E E S P P P E E A E E A E E G G V F - Value : 9.16 R- Square : < 12 > - P aram et er S tan dard V arian c e V ari abl e E s tim at e E rror T P rob > T T oleran c e Inf l at ion In terc ept S S P 4-1.7E E P P E A E E A 5-9.8E E G G F - Value : R- Square :

57 < 13 > - P aram e ter S t an dard V arian c e V ari abl e E s t im at e E rror F P rob > F T ole ran c e Inf l at ion In t e rc ept S S P E E G M 4-1.3E E V 4 3E E F - value : R- Square :

58 < 14 > D D U D U D D D U D U D D D U D U D D D U D U D : D = UD =

59 < 14 > 1%- 10 % %- 10% %- 10 % %- 10% %- 10 % 1%- 10% %- 10 % %- 10%

60 < > Altm an, E. I.(1968), "Financial Ratios, Discriminant Analy sis and the Pr ediction of Corpor ate Bankruptcy," Journal of Finance, September. Altm an, E. I.(1980), "Comm ercial Bank Lending Pr ocess, Credit Scoring and Cost s of Err or s in Lending," Journal of Financial and Quantitativ e Analy sis, November, Altm an, E. I.(1984), "T he Succes s of Bu siness F ailure Prediction Models," Journal of Banking and Finance 8, Amemiya, T.(1978), "On a T wo- Step Estimation of a Multivariate Logit Model," Journal of Econometrics 8(1), Deakin, E.(1972), "A Discriminant Ananly sis of Pr edict or s of Bu siness F ailure," Journal of A ccounting Research 10, No.1. Dietrich, J.R. and Kaplan, R.S.(1982), "Empirical Ananly sis of the Commercial Loan Classification Decision," Accounting Review, January, Edmister, R. O.(1972), "An Empirical T est of Financial Ratio Analy sis of Snakk - Bu sines s F ailur e Pr ediction," Journal of Financial and Quantitativ e Analy sis, March, F ost er, G.(1986), Financial Stat ement Analy sis, New Jer sey : Pr entice- Hall. Goldberger, A. S.(1964), Economic T heory, New York : Wiley. Libby, Robert (1975), "A ccounting Ratio and the Prediction of F ailure: Some Behavior al Evidence," Journal of A ccounting Research, Spring, O 'har a, T. F., Hosm er, D.W., Lem eshow, S. and Hartz, S. C.(1982), A

61 Comparison of Discriminant Function and Maximum Likelihood E stimates of Logisitic Coefficient for Categorical Data, Univ er sity of Massachusett s, Amher st, Mass. Ohlson, J. A.(1980), "Financial Ratio and the Pr obablistic Pr ediction of Bankruptcy," Jour anl of Accounting Resear ch, Spring, Orgler, Y. E.(1970), "A Cr edit Scoring Model for Comm ercial Loan s," Journal of Money, Credit and Banking, Nov ember, Pr egibon, D.(1981), "Logistic Regr ession Diagnostics," Annal of Statistics 9(4), Pr ess, S. J. and Wilson S.(1978), "Choosing Betw een Logistic Regres sion and Discriminant Analy sis," Journal of Am erican Statistical A ssociation, W. H. Beav er (1966), "Financial Ratios as Pr ediction of F ailur e," Journal of Accounting Research. M. E. Zmiew ski, "Pr edicting Bankruptcy : An Empirical comparison of the Extant Financial Distr ess." SAS In stitut e Inc.(1990), SAS/ ST AT User ' s Guide(Ver sion 6. F ourth Edition ).,, (1995),,,. (1995),,. (1982),, ( )., (1998),,

62 , 4 1., (1999),, 25., (1995),,,., (1999),,, 28 4., (1999),, 5 1. (2000),,, 7., SAS, 1997,.,,, 2000.,,, 1997., SAS,,

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