ã³ w. íë ð [- % ?û q > c è Å- ^ y.tmx b}nc è%" 7 ï vc ® ø [û q. ) ¬ Ò Ù ²¹...
TRANSCRIPT
- 3 -
|
|
CART |
|
±|
±|
2O#B;<���
|
2 1 2 2 2
3
2O1O(0H.7��$L
Manasse et al. (2003) 50
CART CART
2011
| SVM Leave-One-Out
SVM
2001 Salchenberger et al. (1992)Fletcher and Goss (1993) Sung et al. (1999)
- 4 -
2O2O$4�����$L
Dietrich and Kaplan (1982)Altman (1977) Wilcox (1973)
Altman Wilcox
Berger and DeYoung (1997) 1985 1994|
Martin (1977) West (1985) Kolari et al. (2002) Canbas et al. (2005)
NN Swicegood and Clark (2001)Multivariate Discriminant Analysis (MDA) Back Propagation Neural Network
(BPNN) BPNN 2
NN Tam (1991) Tam and Kiang (1992)Salchenberger et al. (1992) Bell (1997) Piramuthu et al. (1998)
2O3O3;< @��
|
|
99
|
| |
±|
| ±|
±|
- 5 -
3O;<�:������-7
±| ±|
±| ±|
CART CART |
±| ±|
±|
±| NN SVM 6 AIC
1998 | 99
6 | 2008| 99
3O1O ���
am bm
100 10 20 1xn 100 y
1998 | 99
1998
|
2008 | 99 |
| 2008
ε++=∑∑= =
)(20
1
100
1mnm
m nn bxaxy
- 6 -
3O2OF2(0
99 23
1 1
8
|
|
|
5 30
3O3OJ&+$4��(0H.-7
±|
25 1.5
welch T
10%
3O4OCART��(0H.-7
±| CART CARTGINI
- 7 -
GINI
0
CART GINI
3O5O/)���6=�E!
±| ±|
NN SVM R
3O6OF2(0'5K"8)
NN SVM
NN SVM
| |
2
4O���������$4>5
4O1O(0H.>5
CART4
9 11 18 21 1 2
( ) ( ) ( ){ } ( ){ }2
1111 ∑∑
==
−=−×=C
j
C
jtjptjptjpti
( ) �� ��������
���
jttjpC
::
��������������
����� ������
��������
)(
)(
,
,
,
xfOxfO
xaO
svmtsvm
nntnn
mtmr
=
=
+=∑ ε
- 8 -
CART 1 10 14 1 2
10 14 CART
CART
CART 2
2
4O2O/)���6=>5
CART NNAIC 5
CART SVM CART AIC
±|
CART ±|
5OD?�����$4>5
5O1O(0H.>5
1998 |
99 v43 v62 v65 v95
-1.30 58.7 59.1 45.3 v436 v95 |
|
v95
CART v12 v27 v61 v90 2
- 9 -
-3.39 27.2 54.1 0.952 v61
CART
v61
| |
| 3 v12 v27 v61 v90
63.8 72.2 54.1 -39.6
5O2O/)���6=>5
CART SVM AIC
CART NN AIC CART
±|
6O���������$4>5
6 | 2008 |
99
CART NNAIC
CART
SVM
- 10 -
7O(0'5K"�CI
7O1O����� �7�H.���(0
46
6 v95 v95
6 v62
1990
6 v65
6 v43
v62 v65 v43 6 16.5% 71.5% 32.0%
|
7O2OCART�H.���(0MD?���N
CART|
CART 3 CART 4
7 3 7 4 |
v61 v27 /| 7 v61
- 11 -
7 v61
|
v62 7 v27 /
|
v65 v12 / v90 |
7 | CART
2 v61|
v61 v12 v27 v90 |
CART
7O3OCART�H.���(0M������N
| CART 4 4 4| | v27 / |
| v61 v12 /
| CART v61
8O>5A*
2 1999 3CART NN
CART SVMCART
- 12 -
CART NN ±| ±|
±|
|
NN
| | NN| ± 1999 3 2009 3
|
CART| 1999 3 2009 3
| ± 1999 3 2009 3 |
9O>G
CART ±| ±|
±|
±|
1999 3 | 99±| ±|
±| ±|
|
2008 |
99 |
±| ±|
- 13 -
| |
±| v61v27 / v12 / v90
4 |
v61 |
v61 v27 /|
CARTCART
±|
%A19
Manasse, P., Roubini, N. and Schimmelpfenning, A. 2003. Predicting Sovereign Debt Crises. IMF Working paper : 1-40.
|
20 , 1 ,2011 6 ,23-38 | 13
1 1-21 Salchenberger, L., Mine, C. and Lash, N. 1992. Neural networks: A tool for predicting thrift
failures, Decision Sciences 23 : 899-916. Fletcher, D. and Goss, E. 1993. Application forecasting with neural networks an application using
bankruptcy data, Information and Management 24 : 159-167. Sung, T. K., Namsik, C. and Lee, G. 1999. Dynamics of Modeling in Data Mining: Interpretive
Approach to Bankruptcy Prediction. Journal of Management Information System (Summer) : 63-85. Dietrich, J.R. and Kaplan, R.S. 1982. Empirical analysis of the loan classification decision, The
Accounting Review 57 : 18-38. Altman, E.I., Haldeman, R.G., and Narayanan, P. 1977. ZETA ANALYSIS, a new model to
identify bankruptcy risk of corporations, Journal of Banking and Finance 1 : 29-54. Wilcox, J.W. 1973. A prediction of business failure using accounting data, empirical research in
accounting: Selected studies, Journal of Accounting Research (Suppl.) : 163-179. Berger, A.N. and DeYoung, R. 1997. Problem loans and cost efficiency in commercial banks,
- 14 -
Journal of Banking & Finance, vol. 21 (6) : 849-870. Martin, D. 1977. Early warning of bank failure: A logit regression approach, Journal of Banking
and Finance 1 : 249-276. West, R.C. 1985. A factor analytic approach to bank condition, Journal of Banking and Finance 9 :
253-266. Kolari, J., Glennon, D., Shin, H., and Caputo, M. 2002. Predicting large US commercial bank
failures, Journal of Economics and Business 54 (32 1) : 361-387. Canbas, S., Cabuk, A., and Kilic, S.B. 2005. Prediction of commercial bank failure via
multivariate statistical analysis of financial structure: The Turkish case, European Journal of Operational Research 166 : 528-546.
Swicegood, P. and Clark, J.A. 2001. Off-site monitoring for predicting bank under performance: A comparison of neural networks, discriminant analysis and professional human judgment, International Journal of Intelligent Systems in Accounting, Finance and Management 10 : 169-186.
Tam, K.Y. 1991. Neural network models and the prediction of bank bankruptcy, Omega 19 (5) : 429-445.
Tam, K.Y. and Kiang, M. 1992. Predicting bank failures: A neural network approach, Decision Sciences 23 : 926-947.
Salchenberger, L., Mine, C. and Lash, N. 1992. Neural networks: A tool for predicting thrift failures, Decision Sciences 23 : 899-916.
Bell, T.B. 1997. Neural nets or the logit model? A comparison of each model's ability to predict commercial bank failures, International Journal of Intelligent Systems in Accounting, Finance and Management 6 : 249-264.
Piramuthu, S., Ragavan, H. and Shaw, M.J. 1998. Using feature construction to improve the performance of the neural networks, Management Science 44 (3).
- 15 -
1 1998 2009
��6%$��7
��/ 12�
����
���� %&!,() " )#' �� "%'!&,& %$$#$ +#+ ��� "'!&(% &(#( %(#) ��� "'!()+ &*#$ '#+ ��345 "(!*-) ')#' *)#$ ������� "&!(,* %,#+ )+#* .0� )-+ "(#) %*#,
��� "('+ " "-**#(
1
��'0$���1�� "(��199��
��������"&%*.�#
�����
��'0$
�
�
���
��,'-��� NN SVM
&"('0$
��'0$
&"('0$
��'0$
&"('0$
AIC AIC AIC AIC AIC AIC
CART
�����
��'0$
NN SVM
&"('0$
��'0$
&"('0$
��'0$
&"('0$
AIC AIC AIC AIC AIC AIC
+�!�/)%(
��'0$
&"('0$
16
2
|}p ';� ,8 |}p ';� ,8~u E���`q=5$*bK bK�d ~vw 1\@��_�BH _�l�G7~ss �=��KmQqbK bK�d ~vx ��+&@��_�BH _�l�G7~st �=��KS�qbK bK�d ~vy lZ@��_�BH _�l�G7~su �]q_�l bK�d ~vz ��K@��_�BH _�l�G7~sv ��K:�q_�l bK�d ~v{ ����@��_�BH _�l�G7~sw 6!c� ?�i� ~wr )���BH _�l�G7~sx Y.?2D�o ?�i� ~ws �+@��_�BH _�l�G7~sy C0F�l�-o ?�i� ~wt [f@��_�%�H _�l�G7~sz �9QRlQRo ?�i� ~wu 1\@��_�%�H _�l�G7~s{ k48� ?�i� ~wv ��+&@��_�%�H _�l�G7~tw nlqbK gL�^hAf ~ww lZ@��_�%�H _�l�G7~tx *>nlqbK gL�^hAf ~wx ��K@��_�%�H _�l�G7~ty �lqbK gL�^hAf ~wy ����@��_�%�H _�l�G7~tz n_l��� �N8 ~wz )���%�H _�l�G7~t{ "@�NqT�N �N8 ~w{ �+@��_�%�H _�l�G7~ur ��`q"@�N �N8 ~xr _�l%�H _�l�G7~us V�q"@`L �N8 ~xu nl%�H gL�^hAf~ut eXlq"@`L �N8 ~xv *>nl%�H gL�^hAf~uu E���`q"@`L �N8 ~xw n_H gL�^hAf~uv a <q"@U` �N8 ~xx n]H gL�^hAf~uw / `q"@`L �N8 ~xy ���=�]�qnl gL�^hAf~ux O��J`q"@`L �N8 ~xz ?2qnl gL�^hAf~uy nl�#� �N8 ~x{ WbKM��NH �N8~uz (j^h�#� �N8 ~yr WbKP3���NH �N8~u{ bl^h�#� �N8 ~ys "@U`qIl �N8~vr w�*qT�N �N8 ~yt ���^h�#� �N8~vs u�*qT�N �N8 ~yu �l�#� �N8~vv [f@��_�BH _�l�G7
3
��y (J� 2A ��y (J� 2A�| � ���� �HJ V-�;����,8 ��� e3mNRW �DQ�k��/�} � ���� �HJ(� V-�;����,8 ��� amXY��ZW �DQ�k��/�� �7f���l�q 0Od� ��{ amN^;��ZW �DQ�k��/�� �7f���uq 0Od� ��| '�wq�jGW �DQ�k��/�� �7f���l��J 0Od� ��} �~{]l�q��� V-�;����,8�� �7f���>Q J 0Od� ��~ �}{]l�q��� V-�;����,8�� >Q �����l�q 0Od� ��� �|{]l�q��� V-�;����,8�� >Q �����uq 0Od� ��� ��]l�q��� V-�;����,8�|{ >Q �����l��J 0Od� ��� �~]l�q��� V-�;����,8�}{ .�^W s\�pKA ��� [�l�q��� !Q$&�_�8�}| ���b:^qmXzmX s\�pKA ��� [�uq��� !Q$&�_�8�}} q������Izmq�Z s\�pKA ��� N7C#4l�qBrW¡�6¢ !Q$&�_�8�}~ IEFizmX s\�pKA ��{ N7C#4uqBrW¡�6¢ !Q$&�_�8�}� IEFi¡��X¢zmX s\�pKA ��| N7C#4h^C?BrW¡�6¢ !Q$&�_�8��} S)7fz�7f !Q�V@ ��} N7C#4l�qBrW¡|{6¢ !Q$&�_�8��~ 5%6x !Q�V@ ��~ N7C#4uqBrW¡|{6¢ !Q$&�_�8��| ������1Q��l� 0Od� ��� N7C#4h^C?BrW¡|{6¢ !Q$&�_�8��} �����l�q 0Od� ��� N7C#4��gNUJ !Q$&�_�8��� <9RW �DQ�k��/ ��� N7C#���gNUJ !Q$&�_�8��� P�mX"nMt �DQ�k��/ ��� !Q6J `!c�V@��� ��� �DQ�k��/ ��� *��v� `!c�V@��� L�+j�oT6J �DQ�k��/ ��� *��= J `!c�V@
17
4
�� �� ���� ��� ����
��� ����� ���� ���� �
��� �� �� ��� ������ ����� ����� ������ ���� � ��� ������� ����� ���� ������� � ��� ���� ������ � �� ���� �� ��� � �� ����� ������� � � ���� ��� ���� ���� ���� ��� ����� ����� ���� �� ����� ���� ��� ������� ���� ��� ��������� ���� �� ������� ����� ��� ��� ������� ��� ��� ������� � ��� � ��� ������� ����� ���� ��� ����� ���� ���� ��� ��� � ��� ��� � ��
CART
2 CART
v61<=22.65
v90<=0.2071 0.01737n=9
v27<0.01166 v12<-0.01325
v48>=0.06453 0.01185n=10
0.004454n=23
0.009976n=9
v14<0.3123 v61<16.26
0.004586n=11
0.008544n=9v52<-0.00669 0.005506
n=9
0.001129n=13
0.003453n=21
v61<=22.65
v90<=0.2071 0.01737n=9
v27<0.01166 v12<-0.01325
v48>=0.06453 0.01185n=10
0.004454n=23
0.009976n=9
v14<0.3123 v61<16.26
0.004586n=11
0.008544n=9v52<-0.00669 0.005506
n=9
0.001129n=13
0.003453n=21
18
5
�� 619�� ���� #%$ ����.5;:1;- ����� ����� ��!� "� ����� �
����� 37;89:4/2 ������� ����" ������ (*& ����" " ����� �$#') ����� �����"� ����� �$#') 37;89:4/2 �����"" ��� � �$#') (*& �! ��"! ���"� �
��1;- ����� ����� ������� �!!� ������ 37;89:4/2 ������� �!!� ������ (*& ������" �!!! �$#') ����� �����!! �! � �$#') 37;89:4/2 ������� �!"� �$#') (*& ������� �"�� ��� + ����� ������� �" � ��� + 37;89:4/2 ������� �" ! ��� + (*& ������� �"!� �
0,2:1;- ����� ����� ������� �!"� ������ 37;89:4/2 �����"� �!�! ������ (*& ������� �!�� �$#') ����� ������! �!"� �$#') 37;89:4/2 �����"" �"�� �$#') (*& �����!� �! � ��� + ����� ������� ���" ��� + 37;89:4/2 ������ ���� ��� + (*& ������� ���� �
6
�� ��� �� ��
�� �������� �������� ���� ����
��� ��������� �������� ����� ���� ��� �������� �������� ���� ���� ��� �������� �������� ���� ���� ��� �������� �������� ���� ���
19
7 CART
�� ��� �� ��
�� �������� �������� ���� ����
��� �������� �������� ���� ���� ��� �������� �������� ���� ���� ��� �������� �������� ���� ���� ��� ��������� �������� ����� ����
3
CART
P(Xv90=1)=0.46 P(Xv12=1)=0.45
P(Xv61=1)=0.22 P(Xv27=1 Xv61=1)=0.56
P(Xv27=2 Xv61=1)=0.35
4 |
CART
P(Xv90=1)=0.48
P(Xv12=1 Xv61=1)=0.35
P(Xv12=2 Xv61=1)=0.51 P(Xv61=1)=0.21