Financial application of neural networks: Two case studies in Greece

dc.contributor.authorΚουμανάκος, Ευάγγελοςel
dc.contributor.authorKotsiantis, S.en
dc.contributor.authorTzelepis, D.en
dc.contributor.authorTampakas, V.en
dc.date.accessioned2015-11-24T17:04:39Z
dc.date.available2015-11-24T17:04:39Z
dc.identifier.issn0302-9743-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11208
dc.rightsDefault Licence-
dc.subjectfraud detectionen
dc.subjectbankruptcy predictionen
dc.subjectbankruptcy predictionen
dc.subjectalgorithmen
dc.subjectbusinessen
dc.titleFinancial application of neural networks: Two case studies in Greeceen
heal.abstractIn the past few years, many researchers have used Artificial Neural Networks (ANNs) to analyze traditional classification and prediction problems in accounting and finance. This paper explores the efficacy of ANNs in detecting firms that issue fraudulent financial statements (FFS) and in predicting corporate bankruptcy. To this end, two experiments have been conducted using representative ANNs algorithms. During the first experiment, ANNs algorithms were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. During the second experiment, ANNs algorithms were trained using a data set of 150 failed and solvent Greek firms in the recent period 2003-2004. It was found that ANNs could enable experts to predict bankruptcies and fraudulent financial statements with satisfying accuracy.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.secondary<Go to ISI>://000241475200070-
heal.journalNameArtificial Neural Networks - Icann 2006, Pt 2en
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2006-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημώνel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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