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Predicting systemic financial crises with recurrent neural networks

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  • Tölö, Eero

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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  • Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
  • Handle: RePEc:bof:bofrdp:2019_014
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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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