Template-Type: ReDIF-Article 1.0
Author-Name: Julia A. Tarasova
Author-Email: yutarasova@hse.ru
Author-Workplace-Name: HSE University, Saint Petersburg 190121, Russian Federation
Author-Name: Ekaterina S. Fevraleva
Author-Email: esfevraleva@gmail.com
Author-Workplace-Name: HSE University, Saint Petersburg 190121, Russian Federation
Title: Forecasting of Bankruptcy: Evidence from Insurance Companies in Russia
Abstract: This work is devoted to creating a model which could predict bankruptcy of Russian insurance companies. The aim of the study is to build a model based on panel data; its final version should have a good predictive power. Said topic is relevant because the number of revoked licenses has changed a lot
over the past few years — this situation may influence both insurance organizations and the population in a negative way. The paper reflects the main characteristics of bankruptcy as well as analyzes the bankruptcy prediction models which have been made by various authors since the 20th century. In the
practical part of the study, an econometric analysis of the collected data was carried out and a logit model was built. The model’s predictive power was tested on a sample of insurers. In addition, a random forest algorithm and a binary classification tree algorithm were used. As a result, it was discovered that the volume of insurance premiums to net profit ratio, which could be calculated only for insurers, and financial stability coefficients influence insurance companies’ bankruptcy the most. Further research can be expanded by including new, more sophisticated methods, such as neural networks or boosting.
Classification-JEL: C51, G22
Keywords: bankruptcy, insurance organizations, logit model, binary classification tree algorithm, random forest method
Journal: Finansovyj žhurnal — Financial Journal
Pages: 75-90
Issue: 4
Year: 2021
Month: August
DOI: 10.31107/2075-1990-2021-4-75-90
File-URL: https://www.finjournal-nifi.ru/images/FILES/Journal/Archive/2021/4/statii/06_4_2021_v13.pdf
File-Format: Application/pdf 
Handle: RePEc:fru:finjrn:210406:p:75-90