The model is aimed to discriminate the '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 .
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on 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. No SPV companies were included in the sample. The model shows in-sample Gini AR about 73\% and gives a reasonable and simple rule of mapping to external ratings. The model can be used to calculate implied credit rating for Russian companies which many of them don't have .
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29\% companies in the sample have credit ratings assigned compared to 34\% in the parent population.
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and simple rule of
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one