Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/212448 
Authors: 
Year of Publication: 
2019
Series/Report no.: 
Bank of Finland Research Discussion Papers No. 14/2019
Publisher: 
Bank of Finland, Helsinki
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.
JEL: 
G21
C45
C52
Persistent Identifier of the first edition: 
ISBN: 
978-952-323-287-7
Document Type: 
Working Paper

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