FINANCIAL DISTRESS PREDICTION IN SLOVAKIA: AN APPLICATION OF THE CART ALGORITHM Cover Image

FINANCIAL DISTRESS PREDICTION IN SLOVAKIA: AN APPLICATION OF THE CART ALGORITHM
FINANCIAL DISTRESS PREDICTION IN SLOVAKIA: AN APPLICATION OF THE CART ALGORITHM

Author(s): Marek Durica, Jaroslav Frnda, Lucia Svabova
Subject(s): Business Economy / Management, Methodology and research technology, Accounting - Business Administration
Published by: Fundacja Centrum Badań Socjologicznych
Keywords: CART; financial distress; financial ratios; prediction model; prediction ability;

Summary/Abstract: The topic of predicting financial distress situation has been of interest to many economists and scientists from around the world for several years. As it turned out in practice, the application of existing prediction models to predict the financial difficulties of Slovak companies brings lower prediction accuracy, as these models were created in the conditions of another country. Therefore, the main aim of the article is to create the model for the prediction of the financial distress of the Slovak companies, based on the real conditions of Slovak economics. For this analysis, a dataset of the most important financial ratios that may affect the financial health of the Slovak companies was obtained from the Amadeus database, containing the data on more than 100,000 real companies operating in the Slovak economy in the period 2016 to 2018. For the creation of the models for the prediction of the financial distress of companies one year and two years in advance, the CART algorithm generating the binomial decision tree was used. The developed models achieve an overall accuracy of 87.3% and 91.9% and are very simple to the real application. The results from this prediction are important not only for companies themselves but also for all their stakeholders, as they could help the company to mitigate or eliminate the threat of financial distress and the other corporate risks related to such a situation in the company.

  • Issue Year: 14/2021
  • Issue No: 1
  • Page Range: 201-215
  • Page Count: 15
  • Language: English