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ДИФФЕРЕНЦИАЦИЯ БАНКОВСКИХ ДЕФОЛТОВ ПО ПРИЧИНАМИ ОТЗЫВА ЛИЦЕНЗИИ (Differentiation of Banking Defaults Due to License Cancellation)

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<b>Russian Abstract:</b> В данной работе рассматриваются различные причины отзыва лицензий у российских банков в период с января 2013 года по сентябрь 2020 года. За этот период 454 банки потеряли свои лицензии по экономическим причинам или по причине отмывания. С помощью моделей мультиномиальных логистических регрессий в настоящей работе проведён анализ факторов, влияющих на вероятность отзыва лицензии по одной из причин. На основе построенной модели проводится сравнение качества прогнозов по бинарной и мультиномиальной моделям <br><br><b>English Abstract:</b> This study analyzes different reasons of banking license withdrawals in Russian in the period from January 2013 to September 2020. During this period 454 banks lost their licenses due to economic reasons or laundry activity. Using multinomial logistic regression models we reveal the factors that affect the probability of banking license withdrawal due to one of aforementioned reasons. We also compare quality of forecast generated by our final multinomial model with results of binary version of same model.
Title: ДИФФЕРЕНЦИАЦИЯ БАНКОВСКИХ ДЕФОЛТОВ ПО ПРИЧИНАМИ ОТЗЫВА ЛИЦЕНЗИИ (Differentiation of Banking Defaults Due to License Cancellation)
Description:
<b>Russian Abstract:</b> В данной работе рассматриваются различные причины отзыва лицензий у российских банков в период с января 2013 года по сентябрь 2020 года.
За этот период 454 банки потеряли свои лицензии по экономическим причинам или по причине отмывания.
С помощью моделей мультиномиальных логистических регрессий в настоящей работе проведён анализ факторов, влияющих на вероятность отзыва лицензии по одной из причин.
На основе построенной модели проводится сравнение качества прогнозов по бинарной и мультиномиальной моделям <br><br><b>English Abstract:</b> This study analyzes different reasons of banking license withdrawals in Russian in the period from January 2013 to September 2020.
During this period 454 banks lost their licenses due to economic reasons or laundry activity.
Using multinomial logistic regression models we reveal the factors that affect the probability of banking license withdrawal due to one of aforementioned reasons.
We also compare quality of forecast generated by our final multinomial model with results of binary version of same model.

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