The model is aimed to discriminate the %DIFDELCMD < {\guillemotleft}%%% good%DIFDELCMD < {\guillemotright} %%% and the%DIFDELCMD < {\guillemotleft}%%% bad%DIFDELCMD < {\guillemotright} %%%  companies in Russian corporate sector based on their financial statements data (Russian  Standards). The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36\% of total number of corporate bonds issuers. 25 companies have defaulted on their debt in 2008-2009 which represent around 30\% of default cases. 29\% companies in the sample have credit ratings assigned compared to 34\% in the parent population. No SPV companies were included in the sample. The model shows in-sample Gini AR about 73\% and gives a reasonable mapping to external ratings. The model can be used to calculate implied credit rating for Russian companies which many of them don't have one.
The model is aimed to discriminate the %DIFDELCMD < {\guillemotleft}%%% %DIFDELCMD < {\guillemotright} %%% %DIFDELCMD < {\guillemotleft}%%% %DIFDELCMD < {\guillemotright} %%% 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data (Russian Accounting Standards). The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36\% of total number of corporate bonds issuers. 25 companies have defaulted on their debt in 2008-2009 which represent around 30\% of default cases. 29\% companies in the sample have credit ratings assigned compared to 34\% in the parent population. No SPV companies were included in the sample. The model shows in-sample Gini AR about 73\% and gives a reasonable mapping to external ratings. The model can be used to calculate implied credit rating for Russian companies which many of them don't have one.
D
good
D
and the
D
bad
A
'good' and the 'bad'
A
Accounting