On biologically inspired predictions of the global financial crisis

Peter Sarlin*

*Motsvarande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

12 Citeringar (Scopus)

Sammanfattning

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.

OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftNeural Computing and Applications
Volym24
Utgåva3-4
Sidor (från-till)663-673
Antal sidor11
ISSN0941-0643
DOI
StatusPublicerad - 03.2014
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

  • 511 Nationalekonomi

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