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Forecasting of Bankruptcy: Evidence from Insurance Companies in Russia

Author

Listed:
  • Julia A. Tarasova

    (HSE University, Saint Petersburg 190121, Russian Federation)

  • Ekaterina S. Fevraleva

    (HSE University, Saint Petersburg 190121, Russian Federation)

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.

Suggested Citation

  • Julia A. Tarasova & Ekaterina S. Fevraleva, 2021. "Forecasting of Bankruptcy: Evidence from Insurance Companies in Russia," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 75-90, August.
    • Handle: RePEc:fru:finjrn:210406:p:75-90
    DOI: 10.31107/2075-1990-2021-4-75-90
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    References listed on IDEAS

    as
    1. Oz, Ibrahim Onur & Simga-Mugan, Can, 2018. "Bankruptcy prediction models' generalizability: Evidence from emerging market economies," Advances in accounting, Elsevier, vol. 41(C), pages 114-125.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. L. Lin & J. Piesse, 2004. "Identification of corporate distress in UK industrials: a conditional probability analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 14(2), pages 73-82.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    6. Hunter, John & Isachenkova, Natalia, 2001. "Failure risk: A comparative study of UK and Russian firms," Journal of Policy Modeling, Elsevier, vol. 23(5), pages 511-521, July.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    9. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    10. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    bankruptcy; insurance organizations; logit model; binary classification tree algorithm; random forest method;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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