Predicting financial distress of agriculture companies in EU

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting financial distress of agriculture companies in EU

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting financial distress of agriculture companies in EU

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA13-25897S" target="_blank" >GA13-25897S: Neholonomní vazby v optimálním řízení dynamických ekonomických systémů v zemědělství a přírodních zdrojích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Agricultural Economics

ISSN

0139-570X

e-ISSN

—

Svazek periodika

63

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

9

Strana od-do

347-355

Kód UT WoS článku

000410678400001

EID výsledku v databázi Scopus

2-s2.0-85027310060