Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/EC707282-3D0B-4679-8657-FE636EF510C8 | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.83773 | - |
| Language | en | - |
| Extent | 139 pages | en |
| Title | Assessing bankruptcy risk for financial institutions: methodological framework and predictive modelling | en |
| Creator | Manthoulis Georgios | en |
| Creator | Μανθουλης Γεωργιος | el |
| Contributor [Thesis Supervisor] | Zopounidis Konstantinos | en |
| Contributor [Thesis Supervisor] | Ζοπουνιδης Κωνσταντινος | el |
| Contributor [Committee Member] | Doumpos Michail | en |
| Contributor [Committee Member] | Δουμπος Μιχαηλ | el |
| Contributor [Committee Member] | Galariotis, Emilios | en |
| Contributor [Committee Member] | Chrysovalantis Gaganis | en |
| Contributor [Committee Member] | Pasiouras Fotios | en |
| Contributor [Committee Member] | Πασιουρας Φωτιος | el |
| Contributor [Committee Member] | Atsalakis Georgios | en |
| Contributor [Committee Member] | Ατσαλακης Γεωργιος | el |
| Contributor [Committee Member] | Kosmidou, Kyriaki | en |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Production Engineering and Management | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Μηχανικών Παραγωγής και Διοίκησης | el |
| Description | A 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 Summary | This 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 Item | Διδακτορική Διατριβή | el |
| Type of Item | Doctoral Dissertation | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2019-11-07 | - |
| Date of Publication | 2019 | - |
| Subject | OR in banking | en |
| Subject | Bank failure prediction | en |
| Bibliographic Citation | Georgios 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, 2019 | en |
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