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Decision tree based model of business failure prediction for Polish companies

Author

Listed:
  • Marek Durica

    (University of Zilina, Slovakia)

  • Jaroslav Frnda

    (University of Zilina, Slovakia)

  • Lucia Svabova

    (University of Zilina, Slovakia)

Abstract

Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database. Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we applied the CART and CHAID approach to create a model of predicting the financial difficulties of Polish companies. Findings & Value added: For the creation of the prediction model, a total of 37 financial and economic indicators of Polish companies were used. The resulting decision trees based prediction models for Polish companies reach a prediction power of more than 98%. The success of the classification for non-prosperous companies is more than 83%. The created decision tree-based prediction models are useful mainly for predicting the financial difficulties of Polish companies, but can also be used for companies in another country.

Suggested Citation

  • Marek Durica & Jaroslav Frnda & Lucia Svabova, 2019. "Decision tree based model of business failure prediction for Polish companies," Oeconomia Copernicana, Institute of Economic Research, vol. 10(3), pages 453-469, September.
    • Handle: RePEc:pes:ieroec:v:10:y:2019:i:3:p:453-469
    DOI: 10.24136/oc.2019.022
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    Citations

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    Cited by:

    1. Luminița RUS & Daniela ZĂPODEANU & Carmen SCORȚE & Sorina MOCIAR-COROIU, 2022. "Indicators And 3r-Type Measures In Overcoming Financial Difficulties Of Companies," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 2(2), pages 153-165, December.
    2. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    3. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    4. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    5. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    6. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.

    More about this item

    Keywords

    decision trees; prediction model; financial ratios; business failure; Polish companies;
    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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