Prediction of Default of Small Companies in the Slovak Republic
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DOI: 10.2478/jec-2018-0010
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References listed on IDEAS
- Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
- David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
- Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
- J. Efrim Boritz & Duane B. Kennedy & Jerry Y. Sun, 2007. "Predicting Business Failures in Canada," Accounting Perspectives, John Wiley & Sons, vol. 6(2), pages 141-165, May.
- Maria Kovacova & Tomas Kliestik, 2017. "Logit and Probit application for the prediction of bankruptcy in Slovak companies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 12(4), pages 775-791, December.
- Katarina Zvarikova & Erika Spuchlakova & Gabriela Sopkova, 2017. "International Comparison Of The Relevant Variables In The Chosen Bankruptcy Models Used In The Risk Management," Oeconomia Copernicana, Institute of Economic Research, vol. 8(1), pages 145-157, March.
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Cited by:
- Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
- Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
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More about this item
Keywords
prediction of default; bankruptcy prediction models; financial distress; multivariate discriminant analysis;All these keywords.
JEL classification:
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
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