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Business failure prediction: a comparison of classification methods

Michael Doumpos, Zopounidis Konstantinos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/32CD41EE-3204-4B0F-8130-29E8D8954FF7-
Αναγνωριστικόhttp://link.springer.com/article/10.1007/BF02936387-
Αναγνωριστικόhttps://doi.org/10.1007/BF02936387-
Γλώσσαen-
Μέγεθος17 pagesen
ΤίτλοςBusiness failure prediction: a comparison of classification methodsen
ΔημιουργόςMichael Doumposen
ΔημιουργόςΔουμπος Μιχαληςel
ΔημιουργόςZopounidis Konstantinosen
ΔημιουργόςΖοπουνιδης Κωνσταντινοςel
ΕκδότηςSpringer Verlagen
ΠερίληψηBusiness failure prediction is one of the most essential problems in the field of finance. The research on developing business failure prediction models has been focused on building classification models to distinguish among failed and non—failed firms. Such models are of major importance to financial decision makers (credit managers, managers of firms, investors, etc.); they serve as early warning systems of the failure probability of a corporate entity. The significance of business failure prediction models has been a major motivation for researchers to develop efficient approaches for the development of such models. This paper considers several such approaches, including multicriteria decision aid (MCDA) techniques, linear programming and performs a thorough comparison to traditional statistical techniques such as linear discriminant analysis and logit analysis. The comparison is performed using a sample of 144 US firms for a period of up to five years prior to failure.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-18-
Ημερομηνία Δημοσίευσης2002-
Θεματική ΚατηγορίαBusiness failure prediction en
Θεματική ΚατηγορίαMulticriteria decision aiden
Θεματική ΚατηγορίαMultivariate statistical techniques en
Θεματική ΚατηγορίαComparisonen
Βιβλιογραφική ΑναφοράM. Doumpos and C. Zopounidis, "Business failure prediction: a comparison of classification methods," Operation. Res., vol. 2, no. 3, pp. 303-319, Sep. 2002. doi:10.1007/BF02936387en

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