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Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

Author

Listed:
  • Mateusz Heba

    (Faculty of Economic Sciences, University of Warsaw)

  • Marcin Chlebus

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the prediction of companies bankruptcy. Some authors of scientific papers claim that the process of evaluation of the companies situation requires comparison of its characteristics defined as financial ratio with situation of whole sector in order to obtain reliable conclusions. In this paper, a hypothesis that usage of the industry benchmarks (transformation of raw financial ratios values into sectoral deciles groups numbers) improves results of bankruptcy prediction logistic regression model is verified. Based on empirical results for Polish market, it turns out that although models estimated on different types of data have similar discriminatory power, logistic regression using raw financial ratios obtained a bit better results than its industry equivalents defined as sectoral deciles groups numbers. It is worth emphasizing that empirical part of paper uses information about 109K companies what is the rarity in bankruptcy prediction papers – researchers usually use small datasets that include less than several hundred records.

Suggested Citation

  • Mateusz Heba & Marcin Chlebus, 2020. "Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model," Working Papers 2020-30, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2020-30
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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5805/
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    References listed on IDEAS

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    1. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    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. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. 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.
    6. Amendola, Alessandra & Restaino, Marialuisa & Sensini, Luca, 2015. "An analysis of the determinants of financial distress in Italy: A competing risks approach," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 33-41.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    bankruptcy prediction; financial ratios; industry financial ratios; sectoral financial ratios; logistic regression; financial econometrics;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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