Neural nets or the logit model? A comparison of each model’s ability to predict commercial bank failures

1997 ◽  
Vol 6 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Timothy B. Bell
Keyword(s):  
Commercial Bank ◽  
Logit Model ◽  
Neural Nets ◽  
Bank Failures

Untangling Neural Nets

2004 ◽  
Vol 98 (2) ◽  
pp. 371-378 ◽  
Author(s):  
SCOTT DE MARCHI ◽  
CHRISTOPHER GELPI ◽  
JEFFREY D. GRYNAVISKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logit Model ◽  
Model Building ◽  
Neural Nets ◽  
Security Studies ◽  
Future Research ◽  
Linear Discriminant ◽  
Nonparametric Approach ◽  
Traditional Approaches

Beck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.

Predicting U.S. Bank Failures with MIDAS Logit Models

2018 ◽  
Vol 54 (6) ◽  
pp. 2575-2603 ◽  
Author(s):  
Francesco Audrino ◽  
Alexander Kostrov ◽  
Juan-Pablo Ortega
Keyword(s):  
Logit Model ◽  
Class Imbalance ◽  
The United States ◽  
Mixed Data ◽  
Bank Failures ◽  
Accuracy Evaluation ◽  
Data Sampling ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Long Term Forecasting

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.

scholarly journals Determinants of banking crisis: The case of Vietnam

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Luong Duy Quang
Keyword(s):  
Commercial Bank ◽  
Logit Model ◽  
Commercial Banks ◽  
Banking Sector ◽  
Banking Crisis ◽  
Large Shareholders ◽  
Using Data

This paper identifies determinants associated with probability of banking crisis in Vietnam. By using data sample of more than 30 commercial banks from 2005 to 2013, the results from our multivariate logit model indicate that banking crisis tends to erupt as non-performing loans, borrowings from government and State bank of Vietnam are high. Moreover, we also find that most risk in Vietnam’s banking sector from period 2005-2013 lies in private commercial bank which is highly manipulated by large shareholders.

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