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Assessing bankruptcy risk for financial institutions: methodological framework and predictive modelling

Manthoulis Georgios

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URIhttp://purl.tuc.gr/dl/dias/EC707282-3D0B-4679-8657-FE636EF510C8-
Identifierhttps://doi.org/10.26233/heallink.tuc.83773-
Languageen-
Extent139 pagesen
TitleAssessing bankruptcy risk for financial institutions: methodological framework and predictive modellingen
CreatorManthoulis Georgiosen
CreatorΜανθουλης Γεωργιοςel
Contributor [Thesis Supervisor]Zopounidis Konstantinosen
Contributor [Thesis Supervisor]Ζοπουνιδης Κωνσταντινοςel
Contributor [Committee Member]Doumpos Michailen
Contributor [Committee Member]Δουμπος Μιχαηλel
Contributor [Committee Member]Galariotis, Emiliosen
Contributor [Committee Member]Chrysovalantis Gaganisen
Contributor [Committee Member]Pasiouras Fotiosen
Contributor [Committee Member]Πασιουρας Φωτιοςel
Contributor [Committee Member]Atsalakis Georgiosen
Contributor [Committee Member]Ατσαλακης Γεωργιοςel
Contributor [Committee Member]Kosmidou, Kyriakien
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Production Engineering and Managementen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Μηχανικών Παραγωγής και Διοίκησηςel
DescriptionA dissertation submitted to the School of Production Engineering and Management at the Technical University of Crete in partial fulfilment of the requirements for the degree of Doctor of Philosophy.en
Content SummaryThis thesis is a comprehensive and complete research on bank failure prediction, as it examines various modeling aspects for obtaining improved results. The analysis is based on a comprehensive dataset of approximately 60,000 observations over an extensive period of nine years (2005-2014), and it examines different prediction horizons, for up to three years prior to failure. We explore whether the addition of variables related to the diversification of the banks’ activities, along with local effects, improves the predictability of the models. Seven popular and widely used machine-learning techniques are compared (logistic regression, support vector machines with linear and radial kernels, naïve Bayes, extreme gradient boosting, random forests and artificial neural networks) and three different classification performance metrics are calculated (AUROC, H-measure, and Kolmogorov-Smirnov metric). In order to ensure the robustness of the results, bootstrap testing is used. The results show that mid- and long-range predictions improve significantly with the addition of diversification variables. Local effects exist and further improve the results while support vector machines along with gradient boosting and random forests outperform the traditional models with the differences increasing over longer prediction horizons.en
Type of ItemΔιδακτορική Διατριβήel
Type of ItemDoctoral Dissertationen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2019-11-07-
Date of Publication2019-
SubjectOR in bankingen
SubjectBank failure predictionen
Bibliographic CitationGeorgios Manthoulis, "Assessing bankruptcy risk for financial institutions: methodological framework and predictive modelling", Doctoral Dissertation, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2019en

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