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A Peer-reviewed scientific articles/A1 Journal article (refereed), original research
      
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Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity, Scandinavian Journal of Economics 1; January (2017). Ristolainen, Kim


Category A Peer-reviewed scientific articles
Sub-category A1 Journal article (refereed), original research
auki Internal authors
Ristolainen Kim / Research
All authors as text Ristolainen, Kim 
Number of authors
Status Published
Year of publication 2018 
Date 28.12.2017 
Name of article Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity 
Name of journal Scandinavian Journal of Economics
Volume of issue 120 
Number of issue 1; January 
Pages 31-62 
Abstract Studies of the early warning systems (EWSs) for banking crises usually rely on linear classifiers, estimated with international datasets. I construct an EWS based on an artificial neural network (ANN) model, and I also account for regional heterogeneity in order to improve the generalization ability of EWS models. All of the banking crises in my test set are then predictable at a 24-month horizon, using information from earlier crises. For some countries, estimation with a regional dataset significantly improves the predictions. The ANN outperforms the usual logit regression, assessed by the area under the receiver operating characteristics curve.
Free text descriptor in Finnish pankkikriisit; ennusteet; mallit; 
Free text descriptor in English artificial neural networks, banking crises, early warning system, financial stability 
ISSN / e-ISSN 0347-0520 
auki Internet addresses
Additional information First published: 28 December 2017