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

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.