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  • Comparison of Predictive power of Econometric Models for Assessment of Creditworthiness of Telecommunication Companies from Emerging Economies

Comparison of Predictive power of Econometric Models for Assessment of Creditworthiness of Telecommunication Companies from Emerging Economies

Student: Lobanova Valeriya

Supervisor: Sergei Grishunin

Faculty: HSE Banking Institute

Educational Programme: Financial Analyst (Master)

Final Grade: 9

Year of Graduation: 2020

There has been an extensive work going on concerning assessment of credit risk. Many researches are focusing on analysing various statistical methods that would help to not only estimate but also predict the creditworthiness of companies from different industries by using financial and non-financial information of those companies. However, there is still not enough information about credit risk of companies from emerging economies because very often both financial and non-financial information is not publicly available. Apart from that, many researches are focusing on industries such as oil and gas or metallurgical; however, there is not a lot of information regarding credit risk of telecommunication industry in emerging economies. In fact, telecommunication industry is a very dynamic industry, which is known for a very rapid change in technology. Therefore, this research will focus on assessing the credit risk of telecommunication companies from emerging economies. The following research evaluates different statistical methods that help to estimate and predict the credit risk in a form of credit rating of telecommunication companies from emerging economies. The purpose is to identify which method would have the best performance in terms of its predictive power. In addition, the research analyses which variables, either financial or non-financial have the strongest contribution towards estimating the credit risk of telecommunication companies. The comparative analysis shows that model based on artificial intelligence have the highest accuracy in terms of predicting the credit rating of companies from telecommunication industry. On this basis, it is recommended to consider such models when estimating the credit risk. In addition, the research shows that both financial and non-financial information is necessary when estimating the credit risk. However, the most important thing is to choose such information based on industry specifics.

Full text (added May 20, 2020)

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