Simple Fuzzy Score for Russian Public Companies Risk of Default

TL;DR

This paper presents a simple fuzzy scoring model to assess the default risk of Russian public companies using financial statement data, achieving high discrimination accuracy and enabling credit rating estimation for unrated firms.

Contribution

Introduces a novel fuzzy score model tailored for Russian companies that effectively predicts default risk and maps to external ratings.

Findings

Gini AR of about 73% in-sample

Model accurately distinguishes defaulted and non-defaulted companies

Enables credit rating estimation for unrated Russian firms

Abstract

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.