Zobrazit minimální záznam

dc.contributor.authorKlepáč, Václav
dc.contributor.authorHampel, David
dc.date.accessioned2022-01-15T01:02:13Z
dc.date.available2022-01-15T01:02:13Z
dc.date.issued2017
dc.identifier43908981
dc.identifier.issn0139-570X Sherpa/RoMEO, JCR
dc.identifier.urihttps://repozitar.mendelu.cz/xmlui/handle/20.500.12698/1438
dc.description.abstractThe 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.en
dc.format347-355
dc.publisherČeská akademie zemědělských věd
dc.relation.ispartofAgricultural Economics-Zemedelska ekonomika
dc.relation.urihttp://dx.doi.org/10.17221/374/2015-AGRICECON
dc.subjectagribusinessen
dc.subjectclassificationen
dc.subjectconstrainsen
dc.subjectdecision treeen
dc.subjectdefaulten
dc.subjectnonlinear techniquesen
dc.subjectsupport vector machinesen
dc.titlePredicting financial distress of agriculture companies in EUen
dc.typeJ_ČLÁNEK
dc.date.updated2022-01-15T01:02:13Z
dc.description.versionOA
local.identifier.doi10.17221/374/2015-AGRICECON
local.identifier.scopus2-s2.0-85027310060
local.identifier.wos000410678400001
local.number8
local.volume63
local.identifier.obd43908981
local.identifier.e-issn1805-9295
dc.project.IDGA13-25897S
dc.project.IDNeholonomní vazby v optimálním řízení dynamických ekonomických systémů v zemědělství a přírodních zdrojích
dc.identifier.orcidHampel, David 0000-0002-3865-5948
local.contributor.affiliationPEF


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Zobrazit minimální záznam