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The modelling of forecasting the bankruptcy in Romania

Author

Listed:
  • Onofrei, Mihaela
  • Lupu, Dan

Abstract

Bankruptcy prediction and the understanding of the causes for economic failure have a financial utility. The purpose of this study is to compare the predictive power, on the Romanian market, of the most popular bankruptcy models considering the firms listed on the BSE during 2007-2011. Using the principal component analysis, the best bankruptcy predictors of the established financial indicators were determined for Romanian companies.

Suggested Citation

  • Onofrei, Mihaela & Lupu, Dan, 2014. "The modelling of forecasting the bankruptcy in Romania," MPRA Paper 95511, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:95511
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    File URL: https://mpra.ub.uni-muenchen.de/95511/1/MPRA_paper_95511.pdf
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    References listed on IDEAS

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    Cited by:

    1. Roar Adland & Fredrik Bjerknes & Christian Herje, 2017. "Spatial efficiency in the bulk freight market," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(4), pages 413-425, May.
    2. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    3. Misankova Maria & Zvarikova Katarina & Kliestikova Jana, 2017. "Bankruptcy Practice in Countries of Visegrad Four," Economics and Culture, Sciendo, vol. 14(1), pages 108-118, June.
    4. 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.
    5. Alin Constantin RADASANU, 2015. "Cash-Flow Sustainable Growth Rate Models," Journal of Public Administration, Finance and Law, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 7(7), pages 62-70, June.

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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