Extending the logit model with Midas aggregation: The case of US bank failures

Item Type Monograph (Working Paper)
Abstract We propose a new approach based on a generalization of the classic logit model to improve prediction accuracy in US bank failures. We introduce mixed-data sampling (Midas) aggregation to construct financial predictors in a logistic regression. This allows relaxing the limitation of conventional annual aggregation in financial studies. Moreover, we suggest an algorithm to reweight observations in the log-likelihood function to mitigate the class-imbalance problem, that is, when one class of observations is severely undersampled. We also address the issue of the classification accuracy evaluation when imbalance of the classes is present. When applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies more bank failure cases than the reference logit model introduced in the literature, in particular for long-term forecasting horizons. This improvement has a strong significant impact both in statistical and economic terms. Some of the largest recent bank failures in the US that were previously misclassified are now correctly predicted.
Authors Audrino, Francesco; Kostrov, Alexander & Ortega, Juan-Pablo
Language English
Subjects economics
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Date 19 January 2018
Contact Email Address francesco.audrino@unisg.ch
Depositing User Prof. Ph.D Francesco Audrino
Date Deposited 12 Mar 2018 15:09
Last Modified 20 Jul 2022 17:34
URI: https://www.alexandria.unisg.ch/publications/253878



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Audrino, Francesco; Kostrov, Alexander & Ortega, Juan-Pablo: Extending the logit model with Midas aggregation: The case of US bank failures. , 2018,


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