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Efficiency of Random Decision Forest Technique in Polish Companies’ Bankruptcy Prediction

  • Joanna Wyrobek
  • Krzysztof Kluza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

The purpose of the paper was to compare the accuracy of traditional bankruptcy prediction models with the Random Forest method. In particular, the paper verifies 2 research hypotheses (verification was based on the representative sample of Polish companies): [H1]: The Random Forest algorithm (trained on a representative set of companies) is more accurate than traditional bankruptcy prediction methods: logit and linear discriminant models, and [H2]: The Random Forest algorithm efficiently uses normalized financial statement data (there is no need to calculate financial ratios).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Corporate Finance DepartmentCracow University of EconomicsKrakówPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

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