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The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review

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  • Dimitrios Billios

    (Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece)

  • Dimitra Seretidou

    (Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece)

  • Antonios Stavropoulos

    (Department of Applied Informatics, University of Macedonia, Egnatia 156, 546 36 Thessaloniki, Greece)

Abstract

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy.

Suggested Citation

  • Dimitrios Billios & Dimitra Seretidou & Antonios Stavropoulos, 2024. "The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review," JRFM, MDPI, vol. 17(10), pages 1-12, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:433-:d:1488076
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    References listed on IDEAS

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    1. Bruynseels, Liesbeth & Willekens, Marleen, 2012. "The effect of strategic and operating turnaround initiatives on audit reporting for distressed companies," Accounting, Organizations and Society, Elsevier, vol. 37(4), pages 223-241.
    2. Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    3. Daniela Bragoli & Camilla Ferretti & Piero Ganugi & Giovanni Marseguerra & Davide Mezzogori & Francesco Zammori, 2022. "Machine-learning models for bankruptcy prediction: do industrial variables matter?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 17(2), pages 156-177, April.
    4. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
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