Predicting financial distress of agriculture companies in EU

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43908173" target="_blank" >RIV/62156489:43110/17:43908173 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.17221/374/2015-AGRICECON" target="_blank" >http://dx.doi.org/10.17221/374/2015-AGRICECON</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17221/374/2015-AGRICECON" target="_blank" >10.17221/374/2015-AGRICECON</a>

Result language
angličtina
Original language name
Predicting financial distress of agriculture companies in EU
Original language description
The objective of this paper is prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models we chose Logistic regression, Support vector machines method with RBF ANOVA kernel, Decision trees and Adaptive boosting based on decision trees to acquire the best results. From the results it is obvious that with the rising distance to the bankruptcy there drops average accuracy of financial distress prediction and there is a greater difference between active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive boosting offer better accuracy for distress prediction than SVM and logit methods, what is comparable to previous studies. From overall of 15 accounting variables, we construct classification trees by Decision trees with inner feature selection method for better vizualization, what reduce 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 discrimination of distressed companies.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GA13-25897S" target="_blank" >GA13-25897S: Non-holonomic constraints in optimal managing of dynamic economic systems in agriculture and natural resources</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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