Predicting systemic financial crises with recurrent neural networks

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Title: Predicting systemic financial crises with recurrent neural networks
Author: Tölö, Eero
Organization: Bank of Finland
Series: Bank of Finland Research Discussion Papers
Series number: 14/2019
Year of publication: 2019
Publication date: 27.8.2019
Pages: 37
Subject (yso): pankkikriisit; neuroverkot; ennusteet
JEL: G21; C45; C52
Other keywords: Early Warning System; Banking Crises; Neural Networks; Validation
Abstract: We consider predicting systemic financial crises one to five years ahead using recurrent neural networks. The prediction performance is evaluated with the Jorda-Schularick-Taylor dataset, which includes the crisis dates and relevant macroeconomic series of 17 countries over the period 1870-2016. Previous literature has found simple neural network architectures to be useful in predicting systemic financial crises. We show that such predictions can be greatly improved by making use of recurrent neural network architectures, especially suited for dealing with time series input. The results remain robust after extensive sensitivity analysis.

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