An ordinal classification framework for bank failure prediction: methodology and empirical evidence for US banksAn ordinal classification framework for bank failure prediction: methodology and empirical evidence for US banks Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2022-04-202020enInnovative applications of O.R.Bank failure prediction models usually combine financial attributes through binary classification approaches. In this study we extend this standard framework in three main directions. First, we explore the predictive power of attributes that describe the diversification of banking operations. Second, we consider the prediction of failure in a multi-period context. Finally, an enhanced ordinal classification framework is introduced, which considers multiple instances of failed banks prior to failure (up to three years prior to bankruptcy). Various ordinal models are developed using techniques from multiple criteria decision analysis, statistics, and machine learning. Moreover, ensemble models based on error-correcting output codes are examined. The analysis is based on a sample consisting of approximately 60,000 observations for banks in the United States over the period 2006–2015. The results show that diversification attributes improve the predictive power of bank failure prediction models, mainly for mid to long-term prediction horizons. Moreover, ordinal classification models provide a better description of the state of the banks prior to failure and are competitive to standard binary classification models.http://creativecommons.org/licenses/by/4.0/European Journal of Operational Research2822786–801 Manthoulis Georgios Μανθουλης Γεωργιος Doumpos Michail Δουμπος Μιχαηλ Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Galariotis, Emilios Elsevier OR in banking Bank failure prediction Ordinal classification Multiple criteria analysis