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Predicting financial distress of agriculture companies in EU

Author

Listed:
  • Václav KLEPAC
  • David HAMPEL

    (Department of Statistics and Operation Analysis, Mendel University, Brno, Czech Republic)

Abstract

The objective of this paper is the prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in the EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models, there was chosen the Logistic regression, the Support vector machines method with the RBF ANOVA kernel, the Decision Trees and the Adaptive Boosting based on the decision trees to acquire the best results. From the results, it is obvious that with the increasing distance to the bankruptcy, there decreases the average accuracy of the financial distress prediction and there is a greater difference between the active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive Boosting offer a better accuracy for the distress prediction than the SVM and logit methods, what is comparable to the previous studies. From the total of 15 accounting variables, there were constructed classification trees by the Decision Trees with the inner feature selection method for the better visualization, what reduces the full data set only to 1 or 2 attributes: ROA and Long-term Debt to Total Assets Ratio in 2011, ROA and Current Ratio in 2012, ROA in 2013 for the discrimination of the distressed companies.

Suggested Citation

  • Václav KLEPAC & David HAMPEL, 2017. "Predicting financial distress of agriculture companies in EU," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(8), pages 347-355.
    • Handle: RePEc:caa:jnlage:v:63:y:2017:i:8:id:374-2015-agricecon
    DOI: 10.17221/374/2015-AGRICECON
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    References listed on IDEAS

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    Cited by:

    1. Mário Santiago Céu & Raquel Medeiros Gaspar, 2022. "Vegetative cycle and bankruptcy predictors of agricultural firms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(12), pages 445-454.
    2. Zheng, Linyi, 2024. "Big hands holding small hands: The role of new agricultural operating entities in farmland abandonment," Food Policy, Elsevier, vol. 123(C).
    3. Maria-Lenuţa Ciupac-Ulici & Daniela-Georgeta Beju & Ioan-Alin Nistor & Flaviu Pișcoran, 2023. "The impact of the Altman score on the energy sector companies," Journal of Financial Studies, Institute of Financial Studies, vol. 14(8), pages 45-56, June.
    4. repec:fst:rfsisf:v:8:y:2023:i:special-june_2023:p:45-56 is not listed on IDEAS
    5. Jindrich Spicka & Tomas Hlavsa & Katerina Soukupova & Marie Stolbova, 2019. "Approaches to estimation the farm-level economic viability and sustainability in agriculture: A literature review," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(6), pages 289-297.
    6. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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