On biologically inspired predictions of the global financial crisis

Peter Sarlin*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)

Abstract

Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The models are calibrated and evaluated in terms of usefulness for policymakers that incorporates preferences between type I and type II errors. Generally, model evaluations show that biologically inspired models outperform the statistical model. NG models are, however, shown not only to provide largest usefulness for policymakers as an early-warning model, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of an NN. For better generalization of data-driven models, we also advocate adopting to the early-warning literature a training scheme that includes validation data.

Original languageEnglish
Peer-reviewed scientific journalNeural Computing and Applications
Volume24
Issue number3-4
Pages (from-to)663-673
Number of pages11
ISSN0941-0643
DOIs
Publication statusPublished - 03.2014
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • Early-warning model
  • Genetic algorithms
  • Neural networks
  • Neuro-genetic model
  • Systemic financial crises

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