Predicting U.S. Bank Failures with MIDAS Logit Models

Item Type Journal paper
Abstract

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.

Authors Audrino, Francesco; Kostrov, Alexander & Ortega, Juan-Pablo
Journal or Publication Title Journal of Financial and Quantitative Analysis
Language English
Subjects economics
finance
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date December 2019
Publisher Graduate School of Business Administration
Volume 54
Number 6
Page Range 2575-2603
ISSN 0022-1090
Publisher DOI 10.1017/S0022109018001308
Contact Email Address alexander.kostrov@unisg.ch
Depositing User Alexander Kostrov
Date Deposited 17 Oct 2019 18:01
Last Modified 05 Feb 2020 01:23
URI: https://www.alexandria.unisg.ch/publications/258150

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Citation

Audrino, Francesco; Kostrov, Alexander & Ortega, Juan-Pablo (2019) Predicting U.S. Bank Failures with MIDAS Logit Models. Journal of Financial and Quantitative Analysis, 54 (6). 2575-2603. ISSN 0022-1090

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https://www.alexandria.unisg.ch/id/eprint/258150
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